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halocantik
Sep 11, 2021
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Catatan Kaki
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halocantik
Aug 25, 2021
Swarm Cognition in Honey Bees
Swarm Cognition in Honey Bees
Kevin M. Passino
Dept. Electrical and Computer Eng.
Ohio State University
2015 Neil Avenue, Columbus, OH 43210, USA [emailprotected]
Thomas D. Seeley
Dept. Neurobiology and Behavior Cornell University
Ithaca, NY, 14853, USA
[emailprotected]
P. Kirk Visscher Dept. Entomology
University of California Riverside Riverside, CA 92521, USA [emailprotected]
September 27, 2006
Abstract: We synthesize findings from neuroscience, psychology, and behavioral biology to show that some key features of cognition in neuron-based brains of vertebrates are also present in bee- based swarms of honey bees. We present our ideas in the context of the cognitive task of nest-site selection by honey bee swarms. After reviewing the mechanisms of distributed evidence gathering and processing that are the basis of decision-making in bee swarms, we point out numerous sim- ilarities in the functional organization of vertebrate brains and honey bee swarms. These include the existence of interconnected subunits, parallel processing of information, a spatially distributed memory, layered processing of information, lateral inhibition, and mechanisms of focusing attention on critical stimuli. We also review the performance of simulated swarms in standard psychological tests of decision making: tests of discrimination ability and assessments of distractor effects. We conclude that relating swarm cognition to knowledge from cognitive neuroscience will be fruitful for broadly understanding the mechanisms of group cognition in social species.
1 Introduction
The study of group decision making sometimes uses a “collective intelligence” or “superorganism” perspective where the group is viewed as a single decision-maker (Holldobler and Wilson 1990; Levine et al. 1993; Franks 1989; Seeley 1989 1995; Hinsz et al. 1997; Laughlin 1999; Camazine
et al. 2001; Surowiecki 2004; Conradt and Roper 2005). Moreover, it has been recognized for some time that massively parallel inter-animal communications and actions could form a basis for complex group cognition processes that are functionally equivalent to ones in neuron-based brains (Markl 1985; Bonner 1988). The primary goal of this paper is to show explicit examples of how several of the key elements underlying cognition in neuron-based brains of vertebrates are also found in bee-based swarms of honey bees.
We focus on swarm cognition in the context of the group decision-making process whereby a swarm of bees selects its new nest site. Honey bee swarms are formed in the spring through a process of colony fission wherein the mother queen and about half the workers in a colony leave their nest and form a cluster on a nearby branch (the biology of swarming is reviewed in Winston 1987). Scout bees fly from the cluster to search the countryside for potential dwelling places, usually cavities in trees. Discovered nest sites of sufficient quality are reported on the cluster via the scouts’ waggle dances, which recruit other bees to evaluate the sites. Higher quality sites evoke stronger dancing and hence more recruits. When, via recruitment, 10-20 bees are assembled at a candidate nest site a quorum threshold is reached which triggers choice of the site. The scouts from the chosen site return to the cluster, initiate lift off, and the swarm flies as a group to the new nest. The study here of what we call “swarm cognition” during this nest-site selection task uses the model in (Passino and Seeley 2006) (others are in (Britton et al. 2002; Myerscough 2003)). This model was validated using the experiments in (Seeley and Buhrman 1999; Camazine et al. 1999; Seeley and Buhrman 2001; Seeley 2003; Seeley and Visscher 2003 2004b). Overviews of the biology of nest-site selection are given in (Seeley and Visscher 2004a; Seeley et al. 2006).
Beginning at the “cognition unit-level” (analogous to the cellular neuron-level), we build a detailed explanation of the nest-site selection process to identify the key elements and functional organization of swarm cognition. We show that the swarm has identifiable elements that correspond to neurons, action potentials, inter-neuron communications, lateral inhibition, short-term memory, neural images, and layers of processing (Kandel et al. 2000). We identify functional similarities to the networks of neurons that perform certain attention, perception, and choice functions in solitary animals (Gazzaniga et al. 1998; Kandel et al. 2000). Via simulations we show that the swarm’s short-term memory (what we call “group memory”) is on average a representation of the relative quality of the discovered nest sites that leads to good choice performance.
For nest-site selection the swarm benefits by quickly choosing the best discovered nest site. Time pressure arises due to energy costs and weather risks to the exposed cluster. Also, the quality of the nest affects hive fitness. Hence, we take the view that the nest-site selection task is a type of “reaction-time test” studied in psychology (Luce 1986). Such tests have received significant attention in mathematical and cognitive psychology where diffusion (Ratcliff 1978), accumulator (Usher and McClelland 2001), and other mathematical models (Busemeyer and Townsend 1993) have been used for behavioral-level representations of perception-selection reaction-time tests for humans and animals (see overviews and applications in (Luce 1986; Busemeyer and Townsend 1993; Ratcliff et al. 1999; Roe et al. 2001; Ratcliff and Smith 2004; Smith 2000)). Such models have been used for studies of choice speed-accuracy trade-offs, ones studied also for social animals (Franks et al. 2003; Passino and Seeley 2006; Marshall et al. 2005) and human groups (Karau and Kelly 1992). Here, we show that our model of swarm nest-site selection (Passino and Seeley 2006) shares im- portant features with the above-listed models for human decision making behavior, but also key differences. Moreover, we use methods from (Gazzaniga et al. 1998; Treisman and Gelade 1980; Ratcliff 1978; Roe et al. 2001) to show that for simulated choice tests swarms exhibit several of the same properties, and choice error characteristics, as humans and animals with neuron-based
cognition. Two categories of tests are administered in simulation: discrimination and distraction. First, we illustrate that the swarm has discrimination abilities that allow it to distinguish between two relatively close quality nest-sites. This ability is especially strong when site quality differences are large. Discrimination abilities are amplified for low quality site comparisons and are achieved in spite of significant assessment noise at the level of individual bees. Second, we show that the swarm can effectively eliminate from consideration many low quality distractor sites. However, if the distractors’ qualities are high enough the swarm can make many errors since the best site is essentially hidden in a field of adequate-quality sites that compete for the swarm’s attention. Sup- porting evidence for our conclusions on swarm choice behavior is provided in the supplementary on-line information in the Appendix. Moreover, we discuss implications for group decision making in other species.
2
Process Dynamics of Nest-site Selection
The model from (Passino and Seeley 2006) is summarized via the flow-diagram in Figure 1. Consider a sequence of “expeditions” indexed by k = 0, 1, 2... by B “scout” bees that take on different roles in the nest-site selection process (we use B = 100). When an “explorer” bee finds a candidate nest site it evaluates its attributes in order to form a quality assessment. We denote the quality of nest j as N j ∈ [0, 1] with “1” representing a perfect site. We let the position of bee i be θi, and J(θ) denote the “landscape” of nest quality, with θ = [0, 0]⊤ the position of the cluster. We have J(θi) = N j if bee i is at site j, but bee i has assessment noise wi(k), and a quality threshold ǫt = 0.2 below which it will ignore a site. Hence, bee i’s assessment of a site is Si(k) = J(θi(k)) + wi(k), if J(θi(k)) + wi(k) > ǫt, and zero otherwise. Here, wi(k) is uniformly distributed on (−0.1, 0.1) to represent up to a ±10% error in scouts’ nest-site quality assessment. Any bee that finds an
above-threshold nest site dances for it, and hence becomes “committed” to that site. Bees die with a small probability pd = 0.0016 on each expedition so that less than 10% die over the whole process.
An unsuccessful explorer returns to the cluster and seeks to observe a dance. The expedition
j
that bee i first discovers site j is k
i
and if the sensed quality of the site is above the quality
threshold, this is a successful explorer and it returns to the cluster and dances with a strength
Lij(ki ) = γSi(ki ) waggle runs where γ = 150. After dancing, this committed bee returns to the
j j
site, and then back to the cluster, possibly several times; however, each time it returns it dances
ǫs = 15 fewer waggle runs than the initial time. The sequence of waggle runs produced by bee i over the whole process is Li and the total number of waggle runs produced on the cluster for all sites at step k is Lt(k). We call a sequence of dances by one bee for one site, from the time of the initial dance to when the dance strength goes to zero, a “dance decay series.” After a committed scout’s dance strength has decayed to zero it rests, and rejoins the process (by seeking to observe a
dance) at each step with a pr(obability)pm = 0.25. Bees that seek to observe will end up exploring
t
with probability p (k) = exp
− 1 L2(k)
where σ = 4000, representing that when there is not much
e 2 σ2
dancing on the cluster (small Lt(k)), then there will be more exploring, and vice versa. There are Br(k) resters, Bo(k) bees that seek to observe, Bu(k) = Bo(k) + Br(k) uncommitted bees, and Bc(k) committed bees. With probability 1 − pe(k) observer bees will observe dances, and with
probability p (k) = �
Li(k)
will be recruited by the ith committed dancing bee. Bees recruited
i Bc(k) Li(k)
i
=1
to site j visit and dance for it according to their own assessment as described above.
Swarm cluster Candidate nest sites
Figure 1: Nest-site selection process.
Even though at any particular time the swarm of bees is spatially distributed across the envi- ronment (explorers), candidate nest sites, and the cluster, it is best to think of the process dynamics in terms of the simultaneous activities at all of these places. So, a key part of the process occurs simultaneously with the activities on the cluster via the nest-site quality assessments and quorum sensing at each candidate nest site where the bees sense the number of other bees at the site they are visiting. When the quorum threshold ǫq is reached, bees return to the cluster, they produce piping signals that elicit heating, and then the entire swarm lifts off flies to the chosen site. Each expedition is assumed to take 30 min, and the maximum amount of time to decide is set at 32 hrs, so there are up to 64 expeditions. Due to the time pressure to decide, the possibility of close-quality alternatives, and randomness, there can be simultaneous quorum achievement at two or more sites resulting in a “split decision.” In this case, the process is restarted by having the swarm reform the cluster. Also, the process can fail to come to agreement before 64 expeditions are completed; this is called a “no-decision failure.” These failures can arise if a site of sufficient quality is not discovered early enough, one that will generate a recruitment rate that will assemble the required ǫq bees at a nest site.
2.1 Distributed Evidence Gathering and Feedback
Within the decision-making process, the evidence of the quality of a site takes two forms: dances on the cluster and bees at the sites. First, bees that are committed to a site perform a series of dances for it on the cluster to advertise it to the observers. The number of bees recruited to each site occurs in proportion to the number of waggle runs for each site at each step. If a recruit assesses
the site to be of similar quality to what its recruiter found, it will perform a similar dance decay series. A positive feedback is created since the number of recruiters and their recruited bees will grow exponentially (assuming an infinite pool of recruits). There are, however, also two types of negative feedback, one induced by dance decay rate ǫs and the other by scout deaths as defined by pd. Since the probability of death on an expedition is low, its impact on the dynamics is small. However, the impact of the dance decay rate impact is large. Since decay rate is the same for different quality sites, the dancing for poor sites will quickly fade; hence there are few recruits to poor sites. This ensures that not too much time or too many assessors will be dedicated to poor sites.
The second form of evidence of a site’s quality is the number of bees assembled at the site. Assuming that the site is not independently discovered by many bees, if several bees are at a site it is due to recruitment to the site which only occurs if the site is of adequate quality. And, if there are many bees at the site, this could only occur if all those bees had judged it to be of good quality. The numbers of bees visiting each site changes dynamically as new sites are discovered and evidence is gathered by bees. Recruitment at the cluster is driven by the number of bees visiting the sites, yet the recruitment also impacts the number of bees visiting the sites. This tight coupling between activities at the cluster and candidate sites demands a broad perspective on the spatially distributed dynamics of the process, one where equal attention is given to all locations where the bees congregate, not just the cluster. Indeed, concurrent quorum sensing activities occur at all the candidate nest sites and when one or more quorum thresholds are reached, this signals that the process is complete; without the quorum sensing the process will not terminate.
2.2 Mechanisms of Selection
The ability of the swarm to discriminate between sites of different quality depends on an interplay between positive and negative feedback. We illustrate this via a simple example. Assuming no assessment noise, if two bees are dancing on the cluster for two different above-threshold sites j
and j′ with qualities N j > N j′ , then the difference in the number of recruits for the two cases (and
′
hence strength of positive feedback) is proportional in a nonlinear way to the difference N j − N j .
45
As an example, first consider two low quality sites N
j
= 0.4 and N
j
′ = 0.2 so N
j
− N
j
′ = 0.2. A bee that dances for site j
′
does so with γN
j
′ = 150(0.2) = 30 waggle runs in the first visit back to the site, and then 15 fewer, or 15 waggle runs on the second visit. Then, the dance decay series ends. The total number of runs in this case is 45. In contrast, a bee that dances for site j has a dance strength sequence of 60, 45, 30, 15, 0 for a total of 150 waggle runs in its dance decay series. The percentage increase when quality increases from 0.2 to 0.4 is
105
× 100 = 233%. Hence,
540
there are
many
more recruits for the better site compared to the inferior site. Now, if there are two relatively high quality sites N
j
= 1 and N
j
′ = 0.8, again with N
j
− N
j
′ = 0.2, the total numbers of waggle runs per dance decay series for qualities of 0.8 and 1 are 540 and 825 respectively. The percentage increase when quality increases from 0.8 to 1 is
285
× 100 = 53%, much smaller than the above case. Hence, when site qualities are relatively low, a small difference in quality leads to
a big percentage difference in the number of dances and the positive feedback gain on recruitment rate to that site. So, the swarm has a a very good ability to discriminate between nest-site qualities when they are relatively low quality sites When site qualities are higher, the same size difference in quality leads to a lower percentage difference in the number of dances and hence recruits. Hence, the swarm’s site discrimination abilities for high quality sites is less than for low quality sites.
At the same time, it is important to highlight that the evidence gathering dynamics have three
features that make it difficult for a swarm to always succeed at picking the best nest site. First, each bee has a noisy assessment of the quality of any site, hence all evidence is noisy. The noise is amplified by the nonlinear relationship between differential site quality and the number of recruits, which helps discriminate between sites that differ in quality. However, the quality assessment noise of each individual bee is filtered out at the group level to a significant extent by:
1. Averaging the effects of multiple dancing bees on the cluster: Some bees will assess quality low, and some high, but the relative mean number of dances per site will closely represent the relative site quality.
2. Quorum threshold effect: The proportioning of dancing on the cluster results in a propor- tioning of bees assessing each site in accordance with its relative quality. Since the number allocated to a site must be above the quorum threshold before it is chosen this ensures that averages are taken over a sufficient number of error-prone bees so that the swarm does not make mistakes.
The second feature that conspires against the swarm making the best choice is the simple observation that evidence of the quality of a site, in the form of returning bees that dance, arrives at the cluster asynchronously. Evidence arrives randomly in time depending on the time a site is discovered and the bee returns to dance for it, and the times of occurrence of subsequent dances by recruits. Even high quality discoveries near the very end of the process will be unlikely to impact the swarm’s choice since many of the scouts are apt to be committed to other sites and hence not available for recruitment. It is possible that a relatively low quality site discovered early in the process will be chosen, especially if no high quality site is found, since then there is enough time so that enough bees can be assembled at the poor site to achieve the quorum threshold.
The third feature that negatively impacts accurate choice is the presence of “distractor” nest sites, ones of sufficient quality to be evaluated, but that should not be chosen since they are of inferior quality. Strong negative feedback due to dance decay generally enables the swarm to consider a distractor site only briefly. However, if there are many distractors the strength of the positive feedback for the best site is attenuated, perhaps even low enough that a distractor is chosen. Essentially, the swarm is too busy paying attention to many inferior sites to allow it to focus on the assessment and choice of the best site. Noise can amplify this distraction effect, but the averaging discussed above will reduce its effect at the group level.
2.3 Search-Select Phases and Dynamic Internal Coupling
Nest-site selection has a search phase and a selection phase. Even though these two phases are always interleaved and simultaneous, and hence blend together, it is useful to characterize their general features. The search phase is characterized by a high value of Be(k), relatively low value of Bc(k), and a small value of Lt(k) since there is not much dancing on the cluster since not many bees are visiting sites. The select phase is characterized by the exact opposite of this situation so that Lt(k) is higher, pe(k) is lower, not as many bees explore, and this raises the positive feedback on recruitment so that a quorum threshold ǫq can be quickly reached and a crescendo can occur resulting in agreement at time Ta.
The feedback processes discussed above are modified by internal coupling (cross-inhibition) between variables associated with different candidate nest sites. Recruitment to one site means
that the recruits will not be recruited to other sites until they possibly dance for the site they are recruited to, rest, and re-enter the process as a new recruit rather than an explorer. Hence, if the total strength of dancing on the cluster and number of visitors to one site goes up, bees are inhibited from visiting other sites, and the number of visitors at other sites may even decrease (since if bees finish dancing for a poor site they will be likely to be recruited to the better one).
The size of the pool of uncommitted scouts and the phase of the process (search vs. select) both impact the type of internal coupling. If there is a large amount of dancing on the dance floor, then independent of the number of bees producing the dances, the probability of exploring will go down, the probability to follow dances will go up, which then strengthens the potential for positive feedback due to recruitment. If there are many distractors, then there are generally fewer uncommitted scouts, but still a relatively low amount of total dancing, so that exploration can stay high when not enough good sites of sufficient quality have been found. If a large number of uncommitted bees is available, then recruiters can achieve their maximum recruitment rate which will tend to focus the swarm on a single great site and inhibit consideration of relatively low quality sites. Hence, in the beginning of the process (during the search phase) there is a type of flexibility in that many alternatives are often considered, but their consideration is coupled in complex ways depending on discovery times and qualities. The nature of the coupling changes as the phase switches from search to selection. Near the end of the process, the coupling is such that there is a strong tendency of the swarm not to consider new evidence. Normally, the relatively low quality alternatives have become significantly less visited due to dance decay, and the best one gets many more recruits and the crescendo occurs. Hence, the cross-inhibition helps to avoid having oscillations between different alternative choices, at least when there are clear quality differences.
2.4 Dynamics Create a Speed-Accuracy Trade-off
There is a trade-off between speed and accuracy of choice (Passino and Seeley 2006) that is a direct consequence of the dynamics. Generally, error rate reduction typically costs more time Ta or more dances, or both. The time when agreement is reached, Ta, is a random variable that is affected by the pattern of times of discovery of sites, their qualities, and indeed all aspects of the dynamics
�
described above. Let Lt denote the total amount of dances that have been performed by time
Ta. The agreement time is in a sense controlled by the swarm: the swarm will reach agreement when it has evaluated enough alternatives of sufficient quality to make it likely that it picks the
�
best one. Natural selection will favor reducing Lt so that the swarm invests less time and energy
in scouting and dancing to achieve a decision. Also, it will favor reducing Ta so that the swarm can lift off, fly to the new nest, and quickly establish its new home. Generally, choice errors are made
�
in trying to minimize Ta and Lt. The main mechanism that tries to make the decision occur
quickly is the positive feedback aided by cross-inhibition that forces discrimination and a quick transition from the search to select phases. Correspondingly, the negative feedback tends to slow the process. The balance between the two, mediated by coupling and randomness in assessments and discovery times, leads to the agreement time.
Specific scenarios serve to highlight how the dynamics affect the speed-accuracy trade-off. First, if there are no sites discovered for a period of time, or only low quality sites are discovered, then the positive feedback is not strong enough to completely switch from a search to a select phase, which is good since the swarm needs more time to find a sufficiently high quality site. There can also be an increase in Ta due to the presence of similar quality sites that require more “deliberation time” (coupled dynamic interactions that inhibit each other from achieving a sufficient recruitment
�
rate and hence a quorum), which normally costs more dances (i.e., an increased Lt). Such
extra deliberation time can even result from the need to distinguish between inferior distractors of
�
different quality. When luckily a great nest site is found quickly, both Ta and Lt can be relatively
low and the error rate can be low.
3
Features of Swarm Cognition
j j
If N
j
∈ [0, 1] is the quality of nest site j, its relative quality is N
j
/ � N
j
. For nest site j, let d
be the distance from the hive to site j and let φj be the angle to site j for a coordinate system
with origin at the cluster and the x axis pointing to an appropriate reference point, the one used
�
i
in bee dance communication on the cluster. Let
�
Lij(k) denote the total number of waggle runs
for site j at step k; hence “ i” denotes the sum over all bees dancing with (dj, φj). The total
� �
number of waggle runs for site j up to T is denoted by Lij(k). Its mean over many nest-
� � ij a k i
site selection processes is E[ k i L (k)]. This expectation is a measure of the total amount of
signaling (advertisement) for site j. For plotting purposes we will consider the relative mean total
�
amount of dancing for site j, which is E[ k
� ij i L
�
(k)]/(
�
j E[ k
� ij i L
(k)]). Let B(j, k) be the
number of bees that visit site j at step k. Then, maxk B(j, k) denotes the maximum number of bees
that visited site j up to Ta. Its mean over many nest-site selection processes is E[maxk B(j, k)]. This expectation is a measure of the maximum number of bees that a site can “muster” before the agreement is reached when its value reaches quorum for some j (the chosen site). For plotting
�
purposes, we use the relative mean E[maxk B(j, k)]/( j E[maxk B(j, k)]) for each site j.
3.1 Interconnected Cognition Units
An individual scout bee is the fundamental unit of cognition (analogous to a neuron). The unit can change its role and location. It can sense, act, and communicate with other units. The swarm’s sensory process is the sum of the sensory processes of all the scout bees. The swarm’s spatial “field of view” (Kandel et al. 2000) is dynamically modulated by scout bee choices (e.g., during exploration or site assessment) to appropriately span many square kilometers and possibly many candidate nest sites.
Dances by bee i of strength Lij for site j are analogous to action potentials (Kandel et al. 2000). For this, there is an assessment of quality relative to threshold ǫt before such dancing is activated (analogous to activation thresholds (Kandel et al. 2000) in neurons). The dances play a special role in that they act as signals between different units. If we view bees as nodes and bee-to-bee dance communications as directed arcs, a time-varying “random graph” can describe the dynamic interconnection topology of the inter-bee communications on the swarm cluster. The form of the graph is driven by where bees dance on the cluster and which bees happen to be present and observe this dancing. If another graph is used to represent the union of all communications until the agreement time, there would be arcs clustered around bees that discovered relatively high quality sites, and arcs pointing from the bee that first discovered each site on a path (of recruiting recruits) to the last bee recruited for that site. Hence, while it is not a fixed network of communicating units as is often the case for neural networks, the interconnection topology for bee cognition units has a structure with some predictable properties.
3.2 Group Memory
The group of bees committed to nest site j is a “population” of units (analogous to a population of neurons (Kandel et al. 2000)) that can accumulate quality evidence for the site. The set of such groups for each discovered site serves as a short-term “group memory” for the swarm. The group memory is an internal model of the pattern of nest-site qualities currently in the swarm’s field of view (analogous to a “neural image” (Kandel et al. 2000)). The group memory is spatially distributed across the cluster and the candidate sites with the distribution defined by the current locations of the committed scouts in the populations for each site j:
1. Group memory at the cluster: Group memory on the cluster can be decoded from the cluster surface where dances are performed. For each dancing bee, the value of (dj, φj) can be found. Then the populations of bees can be formed for each nest site j. The dancing on the cluster at any step k < Ta is a representation of the cluster’s current estimate of the nest-site quality
� �
i
landscape. Mathematically, when
j
L
ij
(k) > 0
� Lij(k)
� �i
j
i Lij (k)
is the relative total amount of waggle runs for site j at step k, the cluster’s internal model of
j
N
j
/ � N
j
at time k. If a site has not been found it will not be in the representation. The
representation becomes more accurate as more bees visit the site (the averaging filters the noise due to individual assessment errors).
2. Group memory at the candidate nest sites: The component of group memory distributed across the nest-sites is composed of the number of bees at each candidate site B(j, k). We call B(j, k) the current “swarm preference” level for each nest site j. When swarm preference for site j reaches the quorum threshold, site j is chosen.
For examples of the dynamical time evolution of the cluster internal model estimate and swarm preferences see (Passino and Seeley 2006) or for corresponding experimental data see (Seeley et al. 2006).
The swarm’s distributed group memory is distinct from the internal neural-based memory of each individual bee. What is known by the swarm is actually far more than the sum of what is known by the individual bees since the swarm’s knowledge includes what is in the bees’ brains and information coded in the locations of the bees and their actions. No bee can know all the locations and activities for all other bees. But this information is coded at the swarm level and, as discussed below, is explicitly used in swarm decision making.
3.3 Layered Early/Late Processing at the Cluster and Nest Sites
Individual bees can sense group level variables and memory, but with noise, and this provides for “layered” processing of information via “parallel and converging paths” as found in neural systems (Kandel et al. 2000; Gazzaniga et al. 1998). There are three forms of sampling of group memory variables:
1. Allocation to exploration vs. recruitment: By sensing delays in finding dancers, bees can estimate the total number of dancing bees, i.e., a current group memory value. They can
use such a value to make decisions about whether to explore or get recruited. The individual decisions can be error-prone (e.g., by being unlucky and not seeing any dancers even though there are many), but on average the group of bees that use this cue can make a correct decision. Indeed, as the sizes of groups of committed scouts increase, as is the case for relatively high quality sites, the variance on the groups’ sample will decrease. Hence, group memory is in a sense more accurate for better sites.
2. Proportionate recruitment: Even though a bee cannot measure the sizes of the individual groups dancing for the candidate sites, they can be influenced directly by these values in that they are proportionally recruited according to the proportion of dancing for each site. Hence cluster-based group memory is used by the swarm to allocate the cognition units to nest sites. Again, the key is that while the individual cannot estimate the proportions of bees dancing for each site, the group of observers can. Moreover, they will do it more accurately for higher quality sites.
3. Nest-site bee assembly: At each nest site the assembly of bees provides a group memory of the number of bees that have assessed (or is assessing) its quality. Via the simultaneous and self-referential process of quorum sensing, the bees take actions based on this group-level memory by deciding when the process should be terminated.
Thus there is a layering of processing in the swarm, with bees playing roles in the early processing of sensory signals and dancing, and roles in late processing where the overall level of dance activity, proportions of committed bees, or numbers of bees assembled at sites are used.
3.4 Functional Relationships to Attention, Perception, and Choice
Key functionalities of neuron-based attention, perception, and choice processes (Gazzaniga et al. 1998) are found in swarm decision making. For attentional systems, the group memory sets the overall relative level of attention paid to site j at step k. More attention will be paid to higher quality sites when they are found. Distractor nest sites will enter into swarm’s field of view, but they will be purged from the memory by low average quality assessments and dance decay. There is a coupling between late and early attentional processes where via recruitment, swarm resources are directed to the more interesting (higher quality) points in the spatial field of view of the swarm. Distractors require swarm resources and hence can degrade the quality of the focusing process. Focusing dynamics are slowed by a cluttered field of close quality sites. Cross-inhibition between site variables affects focusing dynamics.
Relative to neural perception and choice systems, there are receptive fields (Kandel et al. 2000) set by individual bees. “Lateral inhibition” (Kandel et al. 2000) is closely related to what we call cross-inhibition above and it defines coupling in the process due to: (i) individual bees not comparing dances, (ii) abandonment via dance decay of relatively poor sites, (iii) the finite size of the pool of potential recruits, and (iv) since sites winning recruits inhibit other sites from getting recruits. Cross inhibition is driven by the distance between alternatives as in neural systems (Kandel et al. 2000), but “distance” in this case is not, for instance, physical distances between nest sites, but is defined via (i) relative site quality, (ii) closeness in times of discovery, and (iii) the random interconnectedness of all site variables via the dance floor. There is a type of “two-point discrimination” process (see above discussion) that is due to “on-center, off-surround” feature found in neural perceptual systems (Kandel et al. 2000; Gazzaniga et al. 1998). There is a process of
“feature detection” (Kandel et al. 2000) that occurs in what can be viewed as the later processing by the swarm. The “feature” that is detected in this late processing is the “best-of-N” discovered sites. There is a type of “winner-take-all” process that emerges via the quorum threshold achievement, agreement, and lift-off and it is analogous to ones in neural systems (Gazzaniga et al. 1998).
3.5 Simulation: Group Memory Quality and Impact on Choice Performance
For the model in (Passino and Seeley 2006) we use 1000 simulation runs where sites 1-6 have qualities of 0.5, 0.6, 0.7, 0.8, 0.9, 1, respectively (“case 2” from (Passino and Seeley 2006)). This is a representative, yet challenging quality landscape since, for instance, the sites of low quality will distract the swarm, and it will be difficult to discriminate between the sites of close quality.
Consider the top-right plot in Figure 2. This shows that group memory as measured by the relative mean total amount of dancing for site j (cluster-portion, white bars) and relative mean maximum number of bees visit site j (nest-site portion, light gray bars) is skewed per the relative site quality (black bars). The skewing is seen as you scan from site 1 (low quality), to site 6 (high quality), where the white and light gray bars increase more than relative nest-site quality. The swarm’s group memory (both at the cluster and nests) views relatively low quality sites as worse than they are (see site 1). The swarm’s group memory views relatively high site qualities (site 6) as better than they are. The swarm’s group memory is used to make choices. The skew represents that the swarm forms good memory for the task at hand; the skew helps the swarm discriminate between sites. This shows that on average the swarm develops a useful group memory of the quality landscape.
Next, consider the top- and bottom-left plots. The probability distribution of Ta shows that it never decides too fast, or too slow (there were zero no-decision failures). The distribution is “heavy- tailed” which means that long agreement times are not highly unlikely. Early decisions are reached via quick discovery of a great site, and few or no other good discoveries, so that a maximum level of positive feedback is achieved and the quorum is reached quickly. Longer Ta times result from slow
discoveries and long deliberation times in trying to discriminate between sites. The distribution for
�
Lt has similar characteristics and is also somewhat heavy-tailed. Finally, the bottom-right plot
shows that almost no bees dance for two or more sites which illustrates that the swarm choice has little dependence on bees switching from dancing for one site to another (i.e., choice is a group-level phenomenon).
4
Swarm Choice Tests
Viewing the swarm as a superorganism, in this section we evaluate its choice performance. We use the values of the behavioral parameters validated in (Passino and Seeley 2006), and consider performance for several types of nest-site quality landscapes. For each landscape quality pattern we use 100 nest-site selection processes that terminate with a single site chosen.
4.1 Discrimination
To test discrimination, let all sites have zero quality, except sites 5 and 6, which both start out at a quality of N 6 = N 5 = 0.75 and differentially move to 0.5 and 1. The results are shown in Figure 3.
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Figure 2: Simulation results, case 2 quality landscape. Top-left: Number of times terminated at Ta with vertical lines the indicated quantiles (e.g., 2.5% of the cases terminated with a Ta less than the left-most vertical bar) with the mean indicated by the gray-dotted line. Bottom-left: Similar to
�
top-left, but number of times terminated at Ta with Lt. Top-right: horizontal is site no., vertical:
black (relative site quality, N j/ � N j), white (relative mean total amount of dancing for site j,
�
E[ k
� ij i L
�
(k)]/(
�
j E[�k
� j
i Lij(k)])), light gray (relative mean maximum number of bees visit
site j, E[maxk B(j, k)]/( j E[maxk B(j, k)])), dark gray (proportion times chosen). Bottom-right:
quantile plot of number of bees that dance for zero, one, two, etc. sites.
Before interpreting the results, note that besides the horizontal axes, labeling of all plots in the remainder of this paper is the same.
To interpret the results in Figure 3 note that ideally, once the two sites have different qualities the swarm should always choose the best one. However, the results show that many errors are committed by the swarm, especially for low values of differential quality. The swarm can, however, amplify the quality differences (at a rate of increase higher than the relative quality of the two sites) and often make the correct choice, with choice performance increasing as the differential site quality increases. The slope of the curves in the top-right plot are positively correlated with the level of discrimination ability. The slope of the choice percentage curve for the best site (site 6) is about (1-0.5)/0.4=5/4; we will compare other values to this one (e.g., in the Appendix). When
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Figure 3: Discrimination effect, linear differential quality. Top-left: middle line in each box is the median value of Ta, boxes with notches that do not overlap represent that the medians of the two groups differ at the 5% significance level, whiskers (dashed lines) represent 1.5 times the interquartile range, and outliers are designated with a “+”. Top-right: percentage of times each nest site is chosen (black lines), with site 1 designated by ⊲ (“tR” represents “triangle right”, a right-pointing triangle), site 2 by △ (“tU” represents “triangle up”), site 3 by ◊ (“dia”), site 4 by
□ (“sq”), site 5 by ◦, and site 6 by ∗. The gray lines with markers show the relative site qualities,
j
t
N
j
/ � N
j
. Bottom-left: left-vertical axis and the black lines show the mean � L (solid line,
dots), and its standard deviation (dash-dot line, + marker), while gray lines and right-vertical axis show the number of split decision (×) and no-decision (◦) cases that occur for the 100 nest site selection processes that terminate with a single choice. Bottom-right: left-vertical axis and the black lines show the mean number of bees out of the 100 total that visit 0 sites (designated with
⊲), 1 site (△), 2 sites (◊), 3 sites (□), 4 sites (◦), 5 sites (∗), and 6 sites (×), and right-vertical axis shows via the gray lines the mean number Bc of committed scouts (×) and mean number of explorers Be (+) at the agreement time Ta.
the differential site quality is close to zero the swarm “generalizes” and treats the two sites as having the same quality. Note that there is uniformly distributed noise in individual bee quality assessments on (−0.1, 0.1), yet the swarm can often discriminate (due to filtering with multiple bee assessments) two sites with less then a 0.1 quality difference. There is little change in the median
�
Ta or mean Lt for a change in differential quality. For low values of differential quality there are
many split decision cases due to the build-up of bees at each of the sites being similar due to the close quality sites. The number of no-decision cases is higher for lower differential quality simply because in that case there are no obviously superior sites for the swarm to pick. The number of bees that visit no sites is around half the bees; these bees explore or rest. Of the bees that visit sites, most visit only one. Of the bees that do visit sites, the relative mean number of bees visiting 2 sites is about 6/40=15% for all cases, which demonstrates that there is some coupling between site variables via dance decrease to zero and then reentry to the process via recruitment to a different site. The mean number of bees that are committed to sites and explore is relatively constant.
4.2 Distraction
a
To test distraction, let N
6
= 1, N
1
= 0, a distractor quality variable D = N
5
= N
4
= N
3
= N
2
, and consider D ∈ [0, 1]. See Figure 4. The key feature is that the swarm his very effective at evaluating then discarding from consideration distractors so long as they are of a quality D < 0.4 since for this range the swarm always makes the correct choice. As the distractor quality D gets closer to one, more errors are made. In fact, at about D = 0.55 the percentage of times the correct site is chosen is about 80% as in (Seeley and Buhrman 2001). The percent of correct choices quickly decreases for D > 0.55 where by about D = 0.8 the percentage of times the swarm picks the best site is close to that for the distractors, even though the best site is markedly better. The median value of T decreases significantly representing that the errors are made fast, yet the value of the
�
mean of Lt increases since the swarm tries to discriminate between many distractors and the best
site and this requires many dancers. For high distractor quality there are many split decisions and the relatively high coupling in the process decreases and this is why the bees lock in on incorrect decisions. The bees are busy discriminating between sites, but this reduces the coupling and hence the number of correct decisions in this case. Indeed, the number of explorers decreases significantly for high distractor quality since bees quickly find and recruit to all the sites and this leads to a reduction in cross-inhibition between site variables so that there is no clear winner.
In the Appendix we study the impact of the number of distractors via a Treisman-type test (Treisman and Gelade 1980), several interactions between discrimination and distraction effects, context-dependent effects, the effect of individual bee assessment noise magnitude, and cognition mechanism adaptation.
5 Discussion
This synthesis paper provides evidence that bee-based swarms have a cognition process that shares key features with neuron-based brains, both at the level of the neuron/bee and at the level of the brain/swarm.
5.1 Relations to Neuroscience and Psychology
In Section 3, we noted numerous similarities between swarms and brains in their functional orga- nization for information processing. This entailed showing close relationships to key ideas from neuroscience (Kandel et al. 2000; Gazzaniga et al. 1998), including analogs to: early sensory pro- cessing (field of view, receptive fields), neurons (bees), activation levels (quality thresholds), action
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Figure 4: Distractor effect, four distractors (see Figure 3 caption for axes explanation).
potentials (dances), neuron populations (groups of dancing bees), neural network structure and communications (bee-to-bee communications on a random topology), neural image and short-term memory (spatially distributed internal model, group memory), lateral inhibition (cross-inhibition), late processing based on group memory (for explore/get recruited allocation, proportional alloca- tion of recruits, and quorum sensing), and parallel and converging paths (simultaneous assessment of multiple sites, yet late processing for agreement on the best-of-N). The study of group memory accuracy in Section 3.5 showed that group memory on average provides the swarm with a represen- tation of the relative nest-site qualities under consideration that enhances its choice performance. Identification and evaluation of these cognition elements allowed us to relate swarm cognition to well-studied attention-perception-choice tasks from cognitive neuroscience (Gazzaniga et al. 1998). We explained at a conceptual level the surprising similarities to attentional systems with a view of the swarm as eliminating distractors from consideration, and simultaneously focusing on the best sites. Similarly, for perception-choice tasks we explained how the swarm considers its field of view, develops a representation of its problem domain, and then uses that to choose.
The series of behavioral tests we administer in simulation for the swarm borrowed from ideas in neuroscience and psychology (Gazzaniga et al. 1998; Treisman and Gelade 1980; Ratcliff 1978; Roe et al. 2001). First, we performed discrimination tests and showed that in spite of significant
individual assessment noise the swarm was able to discriminate between close quality sites. In the Appendix we show that the ability to discriminate depends not only on the differential quality, but the absolute quality. In particular, discrimination performance improves for lower quality sites. Next, we studied the impact of distractor sites. First, we showed that multiple distractors can be effectively eliminated from consideration provided their quality is relatively low. However, when distractors are of higher quality, fast but erroneous choices are made. It is as if the best site is “hidden” by the set of distractors. In the Appendix we administer a version of a Treisman feature search test (Treisman and Gelade 1980). This shows that choice performance only degrades slightly if relatively low quality distractors are added to the task of finding the best quality site. The original idea that this provides evidence of parallel processing in the brain (Treisman and Gelade 1980) clearly also holds for the swarm. Moreover, we show that if the high quality site is removed, the swarm takes much longer to decide which site to choose. It was proposed that this was due to a switch in humans to a sequential search mode (see discussion in (Treisman and Gelade 1980; Gazzaniga et al. 1998)). Here, however, the delay is clearly induced by the dynamics of the process that leads to deliberation. This provides an alternative way to interpret the delays that occur in reaction time tests and this may be useful to infer mental dynamics and structure in other species.
To gain more insights into discrimination and distraction effects we report additional studies in the Appendix. First, we show that distractors can attenuate the ability of the swarm to discrim- inate. Second, we show how discrimination mechanisms try to overcome the negative impacts of distractors. Third, we study whether swarms exhibit “irrational” choice behavior commonly found in human decision making (Luce and Suppes 1965; Huber et al. 1982; Tversky 1972; Simonson 1989; Simonson and Tversky 1992) in the presence of “context-dependent effects” (i.e., certain patterns of choice alternatives that can conspire to mislead the decision-maker). Context-dependence has also been studied for human group decision making (Steiner 1966; Laughlin and Ellis 1986; Laugh- lin 1999; Kerr and Tindale 2004; Hastie and Kameda 2005) as summarized in (Hinsz et al. 1997). We use an approach like in (Ratcliff et al. 1999; Ratcliff and Smith 2004; Roe et al. 2001; Busemeyer and Townsend 1993) where humans are subjected to reaction-time tests. For a very wide variety of nest-site quality patterns, our simulations show that the swarm cannot be tricked into misordering its choice percentages in relation to the nest-site quality pattern. This implies that violations of strong stochastic transitivity (Luce and Suppes 1965), the similarity effect (Tversky 1972), and the comparison effect (Simonson 1989; Simonson and Tversky 1992) will not occur. Finally, we show that for a special nest-site quality pattern (where a discrimination-distraction interaction is induced) the attraction effect (Huber et al. 1982) can occur. However, this leads to improved choice performance.
Diffusion, accumulator, and other models of reaction-time tests (Ratcliff 1978; Luce 1986; Rat- cliff et al. 1999; Usher and McClelland 2001; Ratcliff and Smith 2004; Roe et al. 2001; Busemeyer and Townsend 1993; Smith 2000) have a number of similarities to our model of nest-site selection (Passino and Seeley 2006). For instance, they have representations of the time evolution of pref- erence based on probabilistic arrival of evidence regarding multiple alternatives. There is internal coupling between the dynamical evolution of the preferences and preference thresholds that trigger choice. Moreover, a range of speed-accuracy trade-offs have been discovered with these models as also found by Passino and Seeley (2006). In spite of these broad similarities, direct use of these models to represent the nest-site selection process is not possible since: (i) unique nonlinearities exist in the swarm (e.g., due to dance decay and explorer allocation); (ii) swarm cognition is a dis- tributed process which leads to a significantly different “information structure” (i.e., when and how
variables interact with each other); (iii) cross-inhibition is present in the swarm but primarily via the indirect path of dance decay and re-entry of bees to the recruitment process; and (iv) there are spatially-distributed feedback paths in the swarm that couple the process of evidence gathering on the cluster with preference dynamics at the discovered nests (e.g., as preference for a site increases the swarm gathers more evidence on that site via dispatching more assessors).
Recent work from the diffusion model literature, however, does resonate with our approach. Smith and Ratcliff (2004) describe their initiatives to use the diffusion models from psychology to model neural-level signals in a reaction test for monkeys. This innovative work shows that the well-studied behavioral-level diffusion models can actually model the system at the neural level. Their descriptions of the dynamics of decision making for monkeys are strikingly similar to our description of the nest-site selection dynamics in (Passino and Seeley 2006) and Section 2. The model in (Passino and Seeley 2006) essentially starts at the other end of the spectrum, the “neural- level” for the swarm, to create a model. This is possible due the unique feature that honey bee swarms can be “dissected” and studied while functioning largely intact (Seeley 1995). This paper expands on the work in (Passino and Seeley 2006) by identifying the elements of cognition and adding a detailed behavioral-level assessment of swarm choice performance. So, while we have proceeded in the bottom-up direction, and the work in (Smith and Ratcliff 2004) rests on a top- down method, we have met in the middle with models of reaction time tests. It is an important direction to determine if the paths, functions, and dynamics in swarm cognition could give useful hints for the search for neural circuit structures and dynamics.
5.2 Relations to Behavioral Ecology
The speed-accuracy trade-off and irrationality have also been studied in the field of behavioral ecology. For instance, in hoarding gray jays simultaneous choice errors decrease as the rate of avail- ability of choices decreases, since then choice errors are costly (Waite 2001; Waite and Field 2000; Waite 2002). Honey bees and gray jays have been shown to exhibit context-dependent decision making (Shafir et al. 2002). In these studies errors (“irrationality”) seem to arise due to sensory noise, cognitive processing limitations, and physical constraints (which all cause choice errors in nest-site selection also). Consideration of the work in (Shafir et al. 2002) on honey bees also natu- rally leads us to wonder if, for the same task and species, context-dependent effects are amplified or attenuated when comparing cases of social and solitary choice.
Our analysis of the adaptive “tuning” of the processes underlying swarm cognition in the Ap- pendix allows us to study the cognitive ecology (Dukas 1998) of the honey bee swarm. We showed that several behavioral parameters underlying swarm cognition (e.g., ǫq, γ as studied in the Ap- pendix, and ǫs in (Passino and Seeley 2006)) have values evidently shaped by natural selection to balance speed and accuracy of choice. Moreover, we showed that the accuracy of the group mem- ory is insensitive to bee-level assessment errors, and is also the result of natural selection balancing choice speed and accuracy. The swarm exploits the information from multiple assessor bees, aver- ages this information, and holds it in a group-level memory. Simultaneously, in late processing it uses this information to efficiently allocate assessors and terminate the process via quorum sensing. Our results demonstrate that the key component of swarm cognition, group memory, is a robust component that mediates early and late processing.
5.3 Implications for Other Species?
It seems likely that elements and functionalities of cognition can be identified in other social animal species and human groups. For instance, ants performing nest-site selection (Marshall et al. 2005; Mallon et al. 2001; Pratt et al. 2002; Franks et al. 2002 2003; Pratt 2005; Pratt et al. 2005) use quorum sensing in a way similar to honey bees. Hence, the number of tandem runs and quorum levels for these ants may also correspond to types of group memory. Similarly, consensus decision making in other species is likely to contain group memory (Conradt and Roper 2005). In this paper, the physical elements of group memory were built from the biotic environment (groups of bees performing activities at locations). In other species or decision-making processes the abiotic environment could also be exploited for group memory. For instance, it is plausible to view the network of pheromone trails laid by several species of ants as a type of abiotic group memory. Trails are built in a distributed fashion by individuals, used to guide foraging activities of the group, and forgetting occurs via trail evaporation (Holldobler and Wilson 1990). Other forms of “stigmergy” (Camazine et al. 2001) may be related to group memory.
With respect to human group decision making, note that dynamics and evolutionary adaptation are key current directions identified in (Kerr and Tindale 2004). To address these challenges, we suggest that experiments be designed where human groups perform a time-constrained choice task (that perhaps also involves searching for choice alternatives). Then, data representing time-histories of individual decision making and individual-to-individual communications should be gathered and represented with a mathematical model (unlike in (Kerr 1982; Abelson 1964) and perhaps via extending the models in (Ratcliff 1978; Usher and McClelland 2001; Roe et al. 2001)). Simulations can then be used to evaluate implied group choice dynamics, performance, and adaptation in the context of speed-accuracy trade-offs. This could lead to the identification of features of human group cognition mechanisms (e.g., group memory). Such discoveries could significantly deepen our understanding of human collective intelligence and enable us to more effectively structure groups to enhance their performance in business, economics, law, and politics (Steiner 1966; Laughlin and Ellis 1986; Laughlin 1999; Kerr and Tindale 2004; Hastie and Kameda 2005; Surowiecki 2004).
Acknowledgements: The authors would like to thank B. Moore, P. Laughlin, R. Ratclliff, and
T.A. Waite for their inputs. The research reported here was supported in part by the U.S. National Science Foundation (grant no. IBN02-10541 to TDS).
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Appendix: Supplementary On-Line Information
Alternative Quantifications of Group Memory
In this section we reconsider how to quantify group memory. Let L¯j (k) be the mean number of dances for site j at time k (mean taken across all bees that are dancing with (dj, φj ), and zero if no bees are dancing for it). Then,
¯
∗
Lj = max
{ }
¯
L
j
(k) : 0 ≤ k ≤ T
a
is the maximum mean number of dances for site j before Ta. Let E[L¯∗] be the mean taken over
j ¯∗ � ¯∗
many nest-site selection processes, and then the relative mean for site j is E[Lj ]/( j E[Lj ]). This
relative mean is an alternative measure of group memory at the cluster. It represents the maximum
number of waggle runs that a site can muster.
� �
Let k B(j, k) be the total number of bees that visit site j up to Ta and E[ k B(j, k)] is
the mean total number of bees that visit site j over many nest-site selection processes. It is an alternative measure of group memory at the nest sites. It represents the total amount of bees that visit a nest, and hence is a measure of the amount of signaling via quorum sens-
ing at the site (notice the parallel with the cluster measure used earlier). The relative mean is
� �
E[ k B(j, k)]/(
�
j E[
k B(j, k)]) for each j.
Consider the top-right plot in Figure 5. This shows that group memory as measured by the relative mean maximum amount of dancing for site j (cluster-portion, white bars) and relative mean total number of bees visit site j (nest-site portion, light gray bars) closely matches the relative site quality (black bars) (but there is no skewing as found in Section 3.5). These measures are not the ones that are the basis for choice (right bars); if they were then poorer choice performance would result. However, they do demonstrate that the swarm holds valid group memory when it is measured via alternative metrics.
Discrimination Amplification
Let all sites have zero quality, except sites 5 and 6, which both start out at a quality of N 6 = N 5 =
0.65 and differentially move to 0.4 and 0.9. The results in Figure 6 show that discrimination ability goes up relative to Figure 3, indicated by increased slope on the percentage of times the correct site (site 6) is chosen as they move away from each other (in the initial part, the slope is approximately (1-0.5)/0.3=5/3, compared to 5/4 when the sites start at 0.75). Hence, discrimination is better
when site qualities are lower since mistakes are more costly. There is a slight decrease in the median
�
value of Ta and mean value of Lt since in this case the quality of site 5 is much lower so that it
is not nearly as viable of a candidate so it is easier to choose the best site (i.e., without as much deliberation). Notice that compared to Figure 3 there is more coupling in the process here due to more bees abandoning the low quality site and switching to the high quality site (here, for some values of differential quality about 11/40=27.5% dance for two sites).
Effect of Number of Distractors: Treisman Test
Let N 6 = 1 and N 5 = 0.55, then successively add sites 1, 2, 3, and 4 as additional distractors of quality 0.55. So, there are a total of 2, 3, 4, and 5 distractors. This is a Treisman feature search
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At T :MeanB =51.808;MeanB =18.634;Mean p =0.36807
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Figure 5: Simulation results, 1000 runs, case 2 quality landscape. Top-left: Number of times terminated at T
a
with vertical lines the indicated quantiles (e.g., 2.5% of the cases terminated with a T less than the left-most vertical bar) with the mean indicated by the gray-dotted line. Bottom-
�
left: Similar to top-left, but number of times terminated at Ta with Lt. Top-right: horizontal is
site no., vertical: black (relative site quality, N j/ � N j), white (relative mean maximum amount
¯∗ � ¯∗ j
of dancing for site j, E[Lj ]/( j E[Lj ])), light gray (relative mean total number of bees visit site
�
j, E[
�
k B(j, k)]/(
�
j E[
k Q(j, k)]), dark gray (proportion times chosen). Bottom-right: quantile
plot of number of bees that dance for zero, one, two, etc. sites.
test (Treisman and Gelade 1980) with one “target” (the best site) that should be chosen, and a variable number of distractors. The results in Figure 7 show that as more distractors of such a low quality are added there is little effect on the percent of correct choices relative to when there are
�
only two distractors; however, this comes at the cost of an increased mean Lt for the swarm to
try to resolve the differences. Treisman and others took this as evidence of early parallel neural processing of alternatives, early enough that it was not at the level of consciousness.
The main result, however, comes from comparing to the case where everything is the same but we let N 6 = 0, that is, when the target is removed. In standard feature search tests human subjects are asked to decide if the target is there or not, something we cannot request from the swarm. Instead, we let the swarm come to a decision for this case and compare to the last case.
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Figure 6: Amplified discrimination effect (see Figure 3 caption for axes explanation).
The results in Figure 8 show that as expected, with two distractors each is chosen 50% of the time, 3 are each chosen about 33% of the time, 4 are chosen around 25% of the time, and 5 about 20% of
�
the time. The mean Lt increases with more distractors but the median Ta goes up, then comes
down since it becomes easier to make the errors that occur with 5 distractors. Also, comparing Figures 7 and 8, the median Ta values are significantly higher for the case when there is no target compared to when the target is present. An analogous result is obtained in tests for humans, and Treisman hypothesized that humans switched to a “sequential search mode” where by a process of elimination they decided that the target was not present (Treisman and Gelade 1980; Gazzaniga et al. 1998). For the swarm such a mode switch is not possible. The swarm simply takes longer to decide due to the internal dynamics of the decision-making process being slowed by a more lengthy evaluation of the evidence gathered.
Discrimination-Distraction Interactions
Earlier tests were primarily designed to illustrate isolated swarm discrimination abilities and dis- tractor effects. In other nest-site quality landscapes, however, both effects are present and interact with each other as we show in this section.
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A Distractor Can Attenuate Discrimination
First, we show that distraction can attenuate discrimination. To illustrate this, let all sites have zero quality, except let site 4 have a quality of 0.5 and let sites 5 and 6 both start out at a quality of 0.75 and differentially move to 0.5 and 1. We consider site 4 to be a distractor since it should not be chosen. Notice that in Figure 9 the region of “generalization” (i.e., quality range where the swarm treats qualities as similar) (Gazzaniga et al. 1998) grows relative to Figure 3 and the slope of the line representing correct choices is about 1 (lower than the cases in Sections 4.1 and 5.3) so that discrimination is attenuated by the relatively low quality distractor.
Discrimination Tries to Overcome Distraction
Let quality of site 5 be at 0.75, let the quality of site 4 vary as N 4 = D ∈ [0, 1] and consider site 4 to be a distractor for the range D ∈ [0, 0.75] since it should not be chosen for that range of values, but it is the best site for D ∈ (0.75, 1] when you can view site 5 as the distractor (this is case of nonlinearly decreasing differential quality). Of course, you could view the basic task as one of
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Figure 8: Effect of number of distractors, without perfect site present (see Figure 3 caption for axes explanation).
a
discriminating between the two sites. The results are in Figure 10. First, note that discrimination level is asymmetric in the sense that the swarm is better at discriminating when D ∈ [0, 0.75], but discrimination is not as good for D ∈ (0.75, 1] (mistakes are not as costly in that region). There is a region of generalization around 0.75. It is interesting that the median T values are relatively high
�
until the quality of the sites approach each other, and then the amount of dancing Lt increases in
order to discriminate between the sites, but then decreases as the sites move apart again. Also, the median Ta value decreases significantly in the range D ∈ [0.8, 1] (since it is easier to make a quick but incorrect decision) and note that there are fewer outliers. There are many no-decision cases when site 4 has a low quality; this is due to the difficulties of finding the single relatively good site
5. Overall, this shows that distraction tends to have an effect on choice performance degradation that discrimination tries to overcome.
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Figure 9: Distraction can attenuate discrimination (see Figure 3 caption for axes explanation).
Context Dependence
Transitivity, Similarity, and Comparison Effects
We tested a broad range of nest-site quality landscapes to see if the swarm would ever misorder the percentages of choices for sites in comparison to the order of relative nest-site qualities. Our tests included all the simulation results shown in the discrimination and distraction tests above, the general class of landscapes described at the beginning of the next section, the cases considered in (Passino and Seeley 2006), and many specific landscapes many not reported here. We were never able to “trick” the swarm: the percentage choice order is always given by the ordering of the relative qualities of the nest sites (to see this, review all plots of choice performance in this paper). Hence, the swarm never violates “strong stochastic transitivity” (Luce and Suppes 1965) which can lead to choice errrors. Moreover, the “similarity” (Tversky 1972) and “comparison” effects (Simonson 1989; Simonson and Tversky 1992) discussed in (Roe et al. 2001), both which represent choice performance degradations, are not exhibited for the swarm.
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Figure 10: Nonlinear differential quality (see Figure 3 caption for axes explanation).
Attraction Effect
Let sites 5 and 6 have qualities 0.7 and 0.75, respectively, then take site 4 and let its quality vary as N 4 = D ∈ [0, 1] to see its effect on the ability of the swarm to discriminate between sites 5 and 6. This test is designed to evaluate if the swarm exhibits the “attraction effect” (Huber et al. 1982) even though there is only a single quality dimension that is being compared. Let P(i|i, j, ℓ, ...) represent the probability of choosing site i when sites i, j, ℓ, ... are available. The
attraction effect is present if for some i, j, and ℓ, P(i|i, j) < P(i|i, j, ℓ) so that the probability of choosing i goes up with the introduction of ℓ (Roe et al. 2001). If the attraction effect is present,
the “regularity principle” (i.e., that a preference cannot go up by adding dominated alternatives) of decision making is violated. Let i = 6, j = 5, and ℓ = 4 and notice that the attraction effect arises in the region around D = 0.3 to D = 0.5 in Figure 11. Even though the differential quality between the two best sites is constant across that region, a range of distractor qualities around 0.4 amplifies the discrimination to a great enough extent that the best site is chosen more often than if the distractor is not present (the D = 0 case). Essentially, the distractor acts more strongly on site 5, which assists the swarm in its discrimination task for sites 5 and 6. In terms of the underlying mechanisms and dynamics, it seems that with an appropriate quality level a distractor site can capture recruits and then “dump” them at a point in the process where it is unlikely that they will
go explore and hence likely that they will then get recruited. When they are recruited they will be preferentially recruited by bees advertising for the best site. This surge in recruits for the best site then makes it more likely that it is chosen.
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Figure 11: Attraction effect (see Figure 3 caption for axes explanation).
Overall, then we get the counter-intuitive result that the distractor improves choice performance. In other words, the attraction effect corresponds to improved choice performance. Considering the above results, this means that distraction can improve or degrade discrimination. Clearly, the challenges presented by an arbitrary nest-site quality landscape are complex and interconnected.
Adaptive Tuning of Swarm Cognition Processes
To gain more insights into the mechanisms of swarm decision making we study their adaptation. First, we study the effect of the amount of individual bee nest-site assessment noise on the swarm’s choice performance and speed-accuracy trade-off. Second, we study the adaptive tuning of the parameters of the individual bees’ decision-making. This helps to further validate the model we use, and serves to show how speed accuracy trade-offs emerge from the adaptive tuning of the swarm’s cognition process.
To study the adaptive tuning of individual bee-level behavioral parameters we change (“pseudo- mutate”) their values from experimentally-determined ones and determine average time/energy costs and choice performance. We use six sites with qualities uniformly distributed on [ǫt, 1], where ǫt = 0.2 is the threshold quality. We order the randomly generated qualities so that site 1 is the lowest quality, site 2 is the second best, and so on, and make site 6 the best site. Each nest-site quality landscape generated this way is highly likely to produce interacting distraction and discrimination effects and hence generally more challenging choice tests than in (Passino and Seeley 2006). We consider seven values of each behavioral parameter, and for each of these, 1000 nest-site selection processes, each for a randomly generated landscape. Performance is characterized by statistics of the 1000 runs for each parameter.
Effect of Bee Assessment Noise Magnitude
The effect of varying the magnitude w of the bee assessment noise wi ∈ [−w, w] is shown in Figure 12. If w increases, the choice error rate does not degrade or improve much over the case where w = 0.1, the value from (Passino and Seeley 2006). It is impossible for the individual bees to have w = 0 and there is a small choice performance degradation when w increases to w = 0.1. For w > 0.1 the choice performance slightly increases (and most importantly, does not decrease), but
�
the swarm needs a higher median value of Ta and mean Lt to reach agreement. This is due to a
slowing of the decision process due to resolving the differences due to noise. For increasing noise magnitudes, the number of split decisions goes down (since it becomes more unlikely there will be simultaneous agreement) and the number of no-decision cases generally goes up (due to too much confusion caused by the noise). Also, an increasing noise magnitude results in more cross-inhibition as seen in the mean number of bees that visit two sites. This occurs since the noise perturbs the system away from a quick decision and thereby avoids “locking” onto a low quality site. Noise results in more deliberation so that on average better sites will be found (i.e., deliberation allows more time for search and consideration).
From a swarm cognition perspective, since group memory is more accurate when more bees are committed to a site, and choices are made based on group memory, the swarm effectively filters individual level assessment errors especially for the chosen site (even for unrealistically large quality assessment error magnitudes such as w = 0.5). However, natural selection seems to have favored a reduction in individual bee site assessment noise magnitude because that leads to shorter agreement times and less dancing, without much degradation in choice accuracy.
Effect of Bee Assessment Noise Magnitude on Discrimination
The results in Section 5.3 for the effects of noise magnitude are for a very general class of nest-site quality landscapes. The effect can be amplified for specific and common landscapes. For instance, it seems likely that the swarm will often face discrimination problems for relatively close quality sites. To study the effect of noise magnitude on discrimination we take the landscape from Figure 3 and specifically for the case where all site qualities are zero, except N 6 = 0.76 and N 5 = 0.74, a differential quality of only 0.02, which by Figure 3 results in around 50% of the time the swarm choosing each of the two sites. That is, the sites are in the region of generalization and it is difficult for the swarm to discriminate between the two since their quality is so close. Figure 13 shows that by increasing the amount of individual assessment noise, choice performance for the best site can increase somewhat as the noise magnitude increases. Notice that in this case we are performing
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1000 simulations for each parameter value so that at w = 0.1 there is about a 5% difference in the choice rates for the two sites (the increased number of simulations gives an accurate estimate of the choice percentages for low differential quality values). In the region above w > 0.3 there is about a 10% difference in the rates of choice for the two sites; hence, the noise has increased the
ability of the swarm discriminate between these close-quality sites. This increase in performance
�
comes, however, at the expense of a higher median Ta and mean Lt, and an increased number
of no-decision cases. These results further confirm the conclusions reached for the general class of landscapes.
Effect of Quorum Threshold Size
Figure 14 shows that small ǫq values result in fast decisions (upper-left plot) and relatively few dances (bottom-left plot), but relatively frequent errors (upper-right plot) since only a few bees evaluate the chosen site. High ǫq values result in slower decisions, more dancing, and relatively low error rates since many bees evaluate the chosen site. The experimentally-determined quorum threshold value (in the range of 10-20 (Seeley and Visscher 2003)) is the adaptive result of balancing
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the trade-off between keeping the median Ta and mean Lt values low and the percent of correct
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Figure 13: Effect of w, N 6 = 0.76 and N 5 = 0.74 case.
choices high. This range of ǫq also keeps (bottom-right plot) enough bees involved in the process by visiting sites and evaluating them, enough explorers in the role of searching for sites, yet relatively few bees that visit two sites since that can lead to degraded performance in some cases.
Effect of Initial Dance Strength and Model Validation
For the effect of variation on γ see Figure 15. This shows that the value of 150 waggle runs for the initial dance strength from an excellent site found in experiments (Seeley 2003) is the result of a
�
trade off between keeping the median Ta and mean Lt values low (high γ values) and the percent
of correct choices high (low γ values), while at the same time avoiding split and no-decision cases. This provides a more complete verification that the values we used in (Passino and Seeley 2006) (and here) are in the range settled on by evolution since the class of quality landscapes considered here is considerably broader. Similar results are found for ǫs and σ. The results here also help to verify the model in (Passino and Seeley 2006) since ǫq, ǫs, and γ (a parameter not studied in (Passino and Seeley 2006)) are the ones found in experiments (Seeley and Buhrman 1999 2001; Seeley 2003; Seeley and Visscher 2003 2004b 2004a).
Overall, from a swarm cognition perspective, the results here show that individual-level bee
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10 20 30
e
q
0 0
10 20 30
e
q
Figure 14: Effect of ǫq (see Figure 3 caption for axes explanation).
behavioral parameters related to early (γ and ǫs) and late (ǫq) processing have values that are the result of balancing a swarm-level choice speed and accuracy trade-off.
Effects of Other Behavioral Parameters
Results for considering the tendency to seek to observe dances, pm ∈ [0, 1] show that this parameter has little effect on most variables. Increasing it does, however, increase the number of split decisions and decrease the number of no-decision cases since there is an increase in coupling in the process
that leads to build-up for similar sites to be closer, and helps to ensure that some site will have enough bees to reach a quorum. If site qualities are generated on [0.2, 1] but we consider ǫt ∈ [0, 0.4], there is little effect on the choice performance since higher values of ǫt simply eliminate inferior alternatives that the swarm is already quite capable of eliminating. Simulations show that all low values of pd have no significant impact on choice performance.
30 0.6
25 0.5
20 0.4
15 0.3
10 0.2
5 0.1
Marker(site): *(6),o(5),sq(4),dia(3),tU(2),tR(1)
100 120 140 160 180 200
0100 120 140 160 180 200
x 104
Split(x),no dec(o)(right)
Marker(#sites):x(6)*(5)o(4)sq(3)dia(2)tU(1)tR(0)
3.0455
2.4364
1.8273
1.2182
0.6091
1396
1116.8
837.6
558.4
279.2
52 50
42 40
31 30
21 20
10 10
0 0
100 120 140 160 180 200
g
0 0
100 120 140 160 180 200
g
Figure 15: Effect of γ (see Figure 3 caption for axes explanation).
Reference
https://cloverhoney.web.id/
https://cloverhoney.web.id/clover-honey-madu-hdi/
https://cloverhoney.web.id/propoelix/
https://cloverhoney.web.id/royal-jelly-hdi/
https://cloverhoney.web.id/clover-honey-harga/
https://cloverhoney.web.id/propoelix-harga/
https://cloverhoney.web.id/hdi-propoelix-adalah/
https://cloverhoney.web.id/manfaat-propoelix/
https://cloverhoney.web.id/madu-hdi-harga/
https://cloverhoney.web.id/propoelix-plus/
https://cloverhoney.web.id/madu-hdi-manfaatnya/
https://cloverhoney.web.id/clover-honey-manfaatnya/
halocantik
Aug 25, 2021
Honey in the Management of Infections
NICHOLAS NAMIAS
ABSTRACT
Background: Honey, a natural product of bees of the genera Apis and Meliponinae, has been recognized for medicinal properties since antiquity. Honey has demonstrated antimicrobial properties. These effects are variably ascribed to the pH, hydrogen peroxide content, osmotic effect, and as yet unidentified compounds putatively described as inhibines.
Materials and Methods
: This review will explore the use of honey in necrotizing soft tis- sue infections, postsurgical wound infections, wounds other than postsurgical infections,
Hel- icobacter pylori
of the stomach and duodenum, and burns. Throughout, the
in vitro
evidence that exists and the explanations that can be offered for the purported benefits of honey will be reviewed. Most of the reports are either uncontrolled case series or
in vitro
observations. As such, detailed critique of statistical methods will not be undertaken.
Conclusion: The purpose of this paper is not to debunk honey therapy as a myth, but to stimulate thought among surgeons interested in surgical infection and perhaps serve as the nidus for future research. The use of honey should be considered when more conventional therapies have failed.
And your Lord revealed to the bee: Make hives in the mountains and in the trees and in what they build. Then eat of all the fruits and walk in the ways of your Lord submissively. There comes forth from their bellies a beverage of many colors, in which there is healing for mankind. Verily in this is a sign for those who give thought.
—The Koran, Surah Al-Nahal, verse 68 & 69
H
ONEY HAS BEEN RECOGNIZED for medicinal properties since antiquity. It is mentioned
for healing purposes in the Bible, the Koran, and the Torah. It is mentioned in the Edwin
Smith Papyrus dating from the 17th century B.C., and is again referred to by Hippocrates and Democritus in ancient Greece, Galen in an- cient Rome, and Avicenna in medieval times. In the past century there have been sporadic re- ports of its use in the treatment of various wounds and infections, which will be reviewed here.
HONEY AS A SUBSTANCE
Honey is a natural product of bees of the gen- era Apis and Meliponinae. The bees collect nec- tar from flowering vegetation. The nectar is
De Witt Daughtry Family Department of Surgery, University of Miami School of Medicine, Miami, Florida.
219
subjected to enzymatic processing in vivo in both the collecting bee and in a processing bee inside the hive. The processing bee then de- posits the nectar into a wax cell in the hive, where due to relative warmth and fanning by bees, the water content is reduced by evapora- tion to 17%. The sugars in the nectar are con- verted enzymatically into glucose and fructose. Glucose oxidase then converts the glucose into gluconic acid and hydrogen peroxide. The an- timicrobial effects of honey are variably as- cribed to the pH, the hydrogen peroxide con- tent, the osmotic effect, and as yet unidentified compounds putatively described as inhibines. Various researchers have neutralized the hy- drogen peroxide with catalase in vitro in order to exclude the activity of hydrogen peroxide, with varying results. For the bee’s purposes, the antimicrobial effect is very useful; honey can feed a hive through a long winter, and like- wise, has a shelf life of many years for human consumption. Commercial processing involves heating of the honey to inactivate enzymes that may facilitate crystallization of the honey, making it less attractive commercially. Honey can be purchased commercially in both un- processed and processed states.
The use of honey as an anti-infective agent
was limited until recently to wounds, includ- ing burns, pressure ulcers, other ulcers of the skin, and traumatic or surgical wounds [1–4]. With the recognition in recent years that pep- tic ulcer disease is in large part an infectious disease (Helicobacter pylori), there has been at- tention to the use of honey in its eradication [5–10], as application to the gastric and duo- denal mucosa would be both simple and pleas- ant for the patient. This review will explore the use of honey in necrotizing soft tissue infec- tions, post-surgical wound infections, wounds other than post–surgical infections, Helicobacter pylori of the stomach and duodenum, and burns, including in vitro evidence and possible explanations for the purported benefits of honey. Most of the reports are either uncon- trolled case series or in vitro observations. As such, a detailed critique of statistical methods will not be undertaken. The purpose of this pa- per is not to debunk honey therapy as a myth, but to stimulate thought among surgeons in-
terested in surgical infection and perhaps serve as the nidus for future research.
NECROTIZING SOFT TISSUE INFECTIONS
Spencer E. Efem of the University Teaching Hospital in Calabar, Nigeria, has published a series of papers on the antimicrobial and wound healing effects of honey. He first pub- lished a series of 59 patients with wounds and nonhealing ulcers, 80% of which had failed to heal with conventional therapy for periods of one month to two years [11]. He showed that wounds which initially cultured positive for a variety of organisms were sterile at one week, and that 58 of the wounds went on to heal rapidly, with separation of eschar, diminished edema, and rapid reepithelialization. His method was to apply 15–30 mL of unprocessed honey to the wound daily, after cleaning the wound with normal saline. One ulcer was due to a mycobacterial infection and did not re- spond to honey. Although Efem did not pro- vide data to support the following impressions, he described the effects of honey to be “de- bridement of wounds by a chemical or enzy- matic action; absorption of oedema fluids around wounds; inactivation of bacteria; de- odorization of offensive wounds; promotion of granulation tissue formation and epithelializa- tion; and improvement of nutrition.” Efem noted the low pH (3.6) and hygroscopic (os- motic) effects of honey and their probable role in its antibacterial effect, but he also noted the effect of inhibine, a previously described ther- molabile bactericidal substance. As mentioned earlier, hydrogen peroxide is produced by the action of glucose oxidase, and Efem considered the “inhibine” to be hydrogen peroxide, al- though there is not universal agreement on this [12,13]. In 1993, Efem published his experience with twenty consecutive cases of Fournier’s gangrene managed with systemic antibiotics (amoxicillin/clavulanic acid and metronida- zole) and topical unprocessed honey [14]. He compared these patients to 21 similar cases managed by other physicians in the same insti- tution, in which the standard approach of sur-
gical debridement and systemic antibiotics was used. The patients treated with honey had their wounds cleaned with saline upon pre- sentation, then dressed with topical un- processed honey or packed with gauze soaked in honey, with the wounds inspected and the honey reapplied daily after cleansing with nor- mal saline. At seven days after the start of treat- ment all wounds were swabbed and found to be sterile, after having grown the usual ex- pected mix of organisms recovered by a surface swab upon initial presentation. Although not analyzed statistically, there were more opera- tions and re-operations required in the ortho- dox group, although the length of stay was shorter, on average, by 0.5 weeks in this group (Table 1). In the group treated with honey, foul odor, edema, and discharge resolved within 1 week of the commencement of therapy, and all necrotic tissues had separated. Efem con- cluded that honey is superior to standard ther- apy and that it may revolutionize the treatment of this disease. Later reports from other authors show that some have indeed adopted honey as an adjunct in the treatment of Fournier’s gan- grene. Hejase et al. reported on a series of 38 patients with Fournier’s gangrene, all of whom had surgical debridement and systemic antibi- otics followed by topical application of un- processed honey on gauze pads three times a day, with one death in the series. They provided neither data for the effects of honey nor con- trols in their series, but presented the cases as a series. They credited honey with local cleans- ing and improved healing of the wounds [15].
INFECTED SURGICAL WOUNDS
Support for the use of honey in the treatment of infected surgical wounds is anecdotal, but interesting nonetheless. In both reported series (two patients and nine patients, respectively), honey was used as a salvage maneuver, and therefore there were no controls.
Armon [16] reported on the use of locally produced honey for the treatment of infected wounds at his center in Tanzania. The first was a 20-cm sacral pressure ulcer to the level of bone. The treatment described was application of a “thin layer” of “pure honey” three times a day, followed by a dry dressing. Armon stated that the wound was suitable for surgical clo- sure by day 9, but other complications pre- cluded surgery and the wound went on to heal nonoperatively in 70 days. The second was an infected laparotomy wound after hysterec- tomy, with pus emanating from the wound and the vagina. The patient had been referred to him for lack of response to partial opening of the wound and several courses of antibiotics. In addition to removing the surgical sutures to allow for drainage, he treated the wound with honey and reported that the wound was gran- ulating by the tenth day and healed by the four- teenth day, without the use of any antibiotics. It is not clear what portion of the good outcome was due to the application of honey, and what part was due to the application of the basic sur- gical technique of adequate drainage.
Vardi et al. reported on a series of nine in-
fants with infected surgical wounds treated
TABLE 1. HONEY VS. ORTHODOX THERAPY OF FOURNIER’S GANGRENE, AFTER EFEM [14]
No. of Length of
patients No. of operations No. of re-operations stay Deaths
Orthodox treatment 21 21 19 delayed 4.0 weeks 3
primary closure, 2 flap
reconstruction of scrotum
Honey treatment 20 1 (delayed 0 4.5 weeks 0
primary closure)
with honey [17]. This series developed from one patient in whom honey was used as a sal- vage therapy for a sternal wound infection with Pseudomonas aeruginosa and mediastinitis with Staphylococcus aureus. After this patient did well, they created a standard protocol wherein if a patient had failed conventional treatment of 14 days of intravenous antibiotics and wound cleansing with chlorhexidine solution and fusidic acid ointment, honey therapy was begun. Unprocessed, non-pasteurized, non-ir- radiated, commercial honey was applied twice daily after cleaning the wound with normal saline. Six of the patients had systemic antibi- otics discontinued at the commencement of honey therapy; three continued to receive sys- temic antibiotics. All wounds were closed by day 21 of the twice-daily application of fresh unprocessed honey. The authors commented on the theoretical risk of introduction of spores of Clostridium botulinum and resulting infection. They pointed out that this is a risk known only for the ingestion of non-pasteurized honey by neonates due to the relatively non-acidic milieu of their stomachs, but that no case of clostridial infection of a wound from honey has ever been reported. Although this case series is promis- ing, the lack of appropriate controls makes it impossible to determine if the good outcomes were the result of the benefits of honey, the detriments of standard therapy, or just good fortune.
HELICOBACTER PYLORI
Ali et al. reported in 1991 that natural honey had an inhibitory effect on Helicobacter pylori in vitro, at solutions of both 10% and 20% honey,
and proposed that clinical studies on the treat- ment of H. pylori infection be undertaken [5]. Al Somal et al. performed in vitro experiments to determine what concentrations of honey would be inhibitory for H. pylori, what the ac- tive component of the honey is, and whether it was merely an osmotic effect that inhibits H. pylori. They found that Manuka honey from New Zealand, at concentrations as low as 5% v/v, completely inhibit the growth of H. pylori, and that 2.5% v/v partially inhibits the growth of H. pylori. The authors also found that non- Manuka honey, and an artificially prepared so- lution mimicking the physical properties of honey, had no inhibitory effect on H. pylori. The authors stated that although the active prop- erty in Manuka honey has not been identified, they know it is a hydrophilic molecule of a weight of 500 Daltons that is stable at a pH of
1. They proposed clinical trials, and the possi-
bility that an extract of the Manuka tree or Manuka honey could be used in the eradica- tion of H. pylori [6]. Although no such large- scale trial has been undertaken, McGovern et al. reported on a small series of volunteers with Helicobacter pylori infection by 14C urea breath tests, treated with Manuka honey or Manuka honey and omeprazole. After two weeks of treatment, all 12 of the patients remained pos- itive for H. pylori by 14C urea breath test. The authors concluded that, if Manuka honey is ef- fective against dyspepsia, it is not due to erad- ication of H. pylori [9].
Osato et al. revisited the topic in 1999; they compared Manuka honey to honeys obtained commercially from Texas and Iowa, and to an artificially prepared solution mimicking honey (Table 2). They found that at concentrations
.15% v/v, all honeys and the artificial solu-
TABLE 2. H. PYLORI ISOLATES INHIBITED BY VARIOUS SOLUTIONS, AFTER OSATO ET AL. [7]
% inhibited
5% v/v
10% v/v
$15% v/v
U.S. honey
33%
78%
100%
U.S. honey 1 catalase
33%
78%
100%
Manuka honey
60%
100%
100%
Manuka honey 1 catalase
60%
100%
100%
Glucose
Not reported
Not reported
100%
Fructose
Not reported
Not reported
100%
Glucose/fructose
Not reported
Not reported
100%
tion inhibited growth of all H. pylori isolates tested. Additionally, when catalase was added to the honeys concentrated .15% v/v, the hon- eys retained their ability to inhibit all H. pylori isolates; therefore, the anti–Helicobacter pylori activity was interpreted to be due to the os- motic effect, as opposed to hydrogen peroxide content. At the lowest concentration tested, 5% v/v, the Manuka honey inhibited 60% of the isolates tested, whereas the U.S. honeys inhib- ited only 33% of the Helicobacter pylori isolates tested. This difference was not statistically significant. The authors concluded that non- oxidant effects are important in bacterial killing, and that paramount among these ef- fects is the osmotic effect. They also concluded that since 15% v/v honey was needed to inhibit all Helicobacter pylori, that honey would not be a feasible treatment for Helicobacter pylori, as it would probably not be possible to maintain this concentration at the gastric mucosa [7]. In fairness, they probably should have concluded that the Manuka honey deserved further in- vestigation for its non-oxidant, non-osmotic killing property, due to the intriguing, if not statistically significant finding of differences in
H. pylori inhibition at 5% v/v concentrations.
Finally, Booth suggested in a letter to the ed- itor that if there is so much interest in the role of honey eradicating Helicobacter pylori, and He- licobacter pylori has been postulated to have a role in the pathogenesis of gastric lymphoma, that there should be interest in the use of honey as a possible cure for a form of gastric cancer [8].
BURNS
The use of alternative treatments for com- mon ailments is particularly attractive in de- veloping countries. Subrahmanyam has con- ducted a series of clinical trials on the use of honey and other alternative treatments for burn wounds in India. He compared honey to silver sulfadiazine in two randomized trials. The second trial differed from the first in that histological specimens were taken to corrobo- rate clinical impressions. In the first trial, 104 patients with superficial burns , 40% total body surface area were randomized in two
groups, to receive topical therapy with either silver sulfadiazine or unprocessed honey. The wounds treated with honey had earlier eradi- cation of bacteria and shorter time to closure, with 45 of the 52 patients achieving wound clo- sure by the fifteenth day as opposed to only five of the silver sulfadiazine-treated patients achieving wound closure by the fifteenth day [18]. Subrahmanyam revisited this subject in 1998, this time also obtaining histological spec- imens [19]. In addition to reporting the subjec- tive benefits in the honey-treated burns, he also reported that 100% of the honey-treated wounds were closed by day 21 as opposed to only 84% of the conventionally treated burns (p , 0.001). The histological specimens essen- tially corroborated his clinical findings in terms of the presence of granulation, inflam- mation, and epithelialization. Additionally, in the silver sulfadiazine-treated group, four pa- tients whose burns were assessed initially as superficial and not in need of operation, con- verted to full thickness and required excision and grafting. Subrahmanyam interpreted this as a bacteriological failure of silver sulfadi- azine. He did not consider the possibility of a failure of randomization. In other papers, Subrahmanyam compared honey to potato peels [20], amniotic membranes [21], and Op- Site® polyurethane film [22]; honey was su- perior in each study. However, honey is not always the answer. Subrahmanyam found in his most recent study that early excision and grafting, the modern standard of care, was su- perior to honey in the treatment of burns [23]. He performed a prospective, randomized trial with 25 patients in each arm, randomized to early excision and grafting or expectant man- agement with topical unprocessed honey ap- plied on alternate days, with delayed grafting after the separation of slough. The only ad- vantage seen in the honey group was that they required less blood transfusion (21% of blood volume vs. 35% of blood volume). There were three deaths, all from sepsis, in the honey group versus one death, from status asthmaticus, in the excision group. Ninety- two percent of the excision patients had a good functional and cosmetic outcome, whereas only 55% of the honey-treated group had a good outcome.
EVIDENCE FOR THE ANTIMICROBIAL PROPERTIES OF HONEY
The text of this section is summarized in Table 3. In 1984 Obaseiki-Ebor and Afonya, from the University of Benin in Nigeria, re- ported on the anti-candidal effects of a distil- late of honey in vitro [24,25]. They showed that 72 isolates of Candida albicans were all suscep- tible to the HY-1 fraction of honey distillate, whereas 10% of the isolates were variably re- sistant to nystatin, miconazole nitrate, or clo- trimazole. Minimal inhibitory concentrations (MIC) were determined for this compound as well as for commercial antifungals as v/v%. The MIC 90 for HY-1 was 2 v/v%, as compared to mycostatin suspension with an MIC 90 of 0.5 v/v%. They did not elaborate on the chemical nature of the distillate or on the mechanism of action. They also did not comment on the os- motic activity of the solutions, but a 2 v/v% so- lution of a distillate of honey is not likely to have as great an osmotic effect as honey.
Willix et al. of the University of Waikato in
New Zealand reported on the antibacterial ac- tivity of Manuka honey as opposed to other
honeys [26]. They stated that the antibacterial effects of honey are due in large part to hy- drogen peroxide derived from an enzymatic system intrinsic to unprocessed honeys. How- ever, they cited a systematic review of com- mercially available honeys in New Zealand by Allen et al. [27], using an assay that controlled for the osmotic effects of honey and negated the effect of hydrogen peroxide by adding cata- lase to the assay. They found that the antibac- terial effect of honey (tested against Staphylo- coccus aureus) varied widely among honeys, comparable to a range of between 2% and 58% w/v of phenol, in an almost Gaussian distrib- ution. They proposed that an unidentified fac- tor in a local honey, Manuka honey, was responsible for this effect. Descriptions of the chemical nature or proposed mechanism of ac- tion of this factor have not been published. Manuka honey is a variety of honey that comes only from New Zealand, from bees fed on the nectar of the Manuka bush, Leptospermum sco- parium. Similar antibacterial activity has also been found in honey from bees fed on the nectar of Leptospermum polygalifolium, which is found in the wilds in Australia. Willix et al.
TABLE 3. SUMMARY OF FINDINGS OF VARIOUS STUDIES ON THE ANTIMICROBIAL PROPERTIES OF HONEY
Author Principal findings
Obaseiki-Ebor 1. 72 isolates of Candida albicans were susceptible to the HY-1 fraction of honey et al. [24] distillate, whereas 10% were variably resistant to pharmacologic antifungals
2. MIC90 for HY-1 fraction 2 v/v%, MIC90 for mycostatin, 0.5 v/v%
Allen et al. [27] 1. Antibacterial effect of various honeys was comparable to phenol 2% to 58%
w/v in a Gaussian distribution.
Cooper et al. [3] 1. Non-Manuka honey at 25% w/v, with catalase, had no antibacterial effect
against Staphylococcus aureus.
2. Manuka honey at the same concentration, with catalase, had no loss of antibacterial activity.
3. Compared sugar solutions to honey
a. Lowest concentration of sugar with antibacterial activity against S. aureus is 29% v/v
b. MIC for Manuka honey 2–3 v/v%
c. MIC for non-Manuka honey 3–4 v/v%
d. Concluded that non-osmotic effect must be responsible for antibacterial effect.
Efem [28] 1. Tested honey vs. sugar solutions against clinical microbiology isolates
2. Honey effect in vitro against broad range of organisms, including fungi
3. Sugar effective only against Streptococcus pyogenes, but was not tested against anaerobes or fungi
Waldhan et al. [29] 1. Honey vs. sugar syrup against 21 bacteria and 2 fungi
2. At full strength, no difference in bacteriostatic effect, but honey more bactericidal
3.
At lesser dilutions, honey more bacteriostatic and bactericidal at all concentrations.
MIC 5 minimal inhibitory concentrations.
tested Manuka and non-Manuka honey against a variety of wound-infecting species of bacte- ria. They found that the relative sensitivities of various organisms varied between the Manuka honey and other honeys, but that overall both types of honey can completely inhibit bacter- ial growth at concentrations below 11% v/v. Manuka honey, with catalase added to neu- tralize hydrogen peroxide, could still inhibit completely the growth of Staphylococcus aureus at a concentration of 1.8% v/v. The sugar con- tent of the two honeys was the same, so they ascribed the different relative antibacterial ef- fects of the honeys to a different, unknown ac- tivity in Manuka honey. Another comparison of Manuka and non-Manuka honey was un- dertaken in 1999 [2], this time against Staphy- lococcus aureus isolates from clinical wound infections, at various dilutions and with the ad- dition of catalase to inactivate hydrogen per- oxide. The non-Manuka honey at a 25% v/v di- lution, in the presence of catalase, had no detectable antibacterial activity, whereas the Manuka honey under these conditions had no loss of antibacterial activity in the presence of catalase. The authors noted also that the low- est concentration of sugar that has antibacter- ial activity against S. aureus is 29% v/v, and that the MIC values for Manuka honey (2–3% v/v) and non-Manuka honey (3–4% v/v) are well below the concentration at which osmo- larity could be credited with the antibacterial activity.
Efem addressed the question of the osmotic
effect of honey in 1992 by testing in vitro the antibacterial effect of honey and the effect of a sugar syrup with physical properties similar to honey [28]. He used a wide variety of bacterial and fungal isolates from clinical infections (Streptococcus pyogenes, Enterococcus faecalis, Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Pseudomonas spp., Pseudomonas aeruginosa, Bacteroides fragilis, Clos- tridium welchii, Clostridium tetani, Clostridium oedematiens) and incubated them on appropri- ate culture media with wells of the honey or sugar cut into the media. Zones of inhibition were measured. Honey was inhibitory against all bacteria tested except Pseudomonas aerugi- nosa and Clostridium oedematiens. The sugar syrup was ineffective against any of the bacte-
ria tested, with the exception of moderate ac- tivity against Streptococcus pyogenes (the anaer- obes were not tested against the sugar syrup). The fungi tested were all uniformly suppressed by honey at 100% concentration, but, when di- luted to 50% and 20%, the honey lost efficacy against the fungi. The fungi were not tested with sugar solution.
In 1998, Wahdan et al. compared the antimi- crobial activity of honey and a sugar syrup with the same sugar content as honey against 21 bac- teria and 2 fungi [29]. They found that there was no difference in bacteriostatic activity be- tween full-strength honey and sugar syrup, but that the honey was statistically significantly more bactericidal. At dilute concentrations, the honey was always more bactericidal and bac- teriostatic. Because of these differences when concentration was controlled for, the authors invoked some other properties of honey as at least partially responsible for its antimicrobial activity. They also point out multiple refer- ences from the apiary literature describing “in- hibines,” which are suspected to be hydrogen peroxide and phenolic acids, among which caf- feic and ferulic acids were identified in honey for the first time in their laboratory.
In conclusion, honey has been shown to be
clinically useful in various settings involving soft tissue infections and non-healing wounds, and there appear to be some properties of honey that are controlling infection other than via the strictly osmotic effect. The caveat is that all of the data are generated from small stud- ies, generally without rigorous statistical analy- sis. It is unlikely that the large studies with elaborate monitoring of protocol and profes- sional statistical analysis will ever be done, as the expense of such studies is unlikely to ever be rewarded with the proceeds of honey sales to make such research financially feasible. The applicability of in vitro studies of antibacterial effects is unknown in vivo, but the clinical evi- dence suggests that honey may be useful in certain circumstances. Its use should be con- sidered when more conventional therapies have failed. The usefulness in the management of Helicobacter pylori is less compelling, and in light of the other effective and safe treatments available, is probably not worth further inves- tigation.
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Aug 25, 2021
Thelytoky in the honey bee
Review article
Thelytoky in the honey bee
Frances GOUDIE, Benjamin P. OLDROYD
Behavior and Genetics of Social Insects Laboratory A12, University of Sydney, Sydney, NSW 2006, Australia Received 24 July 2013 – Revised 11 November 2013 – Accepted 2 December 2013
Abstract – Thelytoky, the asexual production of females, is rare in honey bees. However, it is ubiquitous in workers of the Cape honey bee Apis mellifera capensis. Thelytoky allows some workers to be reincarnated into the queen phenotype, and thereby selects for reproductive competition among workers. Thelytoky also acts as an exaptation for the emergence of reproductive parasites, the most extreme example of which is an entirely clonal ‘cancerous’ lineage of workers (the Clone) that lethally parasitises colonies of another subspecies Apis mellifera scutellata. The Clone is an enigma because thelytoky results in the accumulation of homozygosity at any loci that are free to recombine, yet the Clone retains considerable heterozygosity. The Clone pays a cost for its thelytoky: the selective removal of homozygous offspring at each generation. We propose that workers, queens and Clones have differing abilities to endure the costs and benefits of sex and asexuality, accounting for the heterogeneous distribution of reproductive strategies across the A. mellifera capensis population. We further suggest that multiple factors must fall into place for thelytoky to emerge as an effective reproductive strategy in a honey bee population, and that geographic isolation resulting in genetic drift and founder effects may have enabled thelytoky to emerge in A. mellifera capensis. Finally, we consider the honey bee in the broader context of haplodiploid Hymenoptera, and argue that constraints on the evolution of sex in non-haplodiploid taxa may make sexual reproduction an evolutionary ‘one-way street’.
Apis mellifera / Apis mellifera capensis / asexual / thelytoky / reproductive parasitism
1. INTRODUCTION
In the typical image of a honey bee (Apis mellifera) colony, there is a queen reigning over her worker force of daughters with an iron… wing? The queen, and only the queen, lays eggs. If she chooses to fertilise an egg with stored sperm, it develops as a diploid daughter, a future queen or worker. Alternatively, if the queen lays an unfertilised egg, it develops into a haploid male, a drone that will eventually fly out and attempt to mate with virgin queens of other colonies.
This image might come close to approximating a particularly well-behaved colony of the European
Corresponding author: F. Goudie, [emailprotected] Manuscript editor: Stan Schneider
honey bee. However, in reality, like the human suburbs of the 1950s, even the best-behaved honey bee colonies can have nefarious goings on beneath the surface. Here, we review one of the most fascinating ways in which reality differs from outward appearance: the asexual production of diploid females via thelytokous parthenogenesis. We discuss the physiological, evolutionary and social consequences of thelytoky in the subspecies in which is best characterised, the Cape honey bee Apis mellifera capensis (hereafter, Capensis). We further discuss the possibility of thelytoky in other honey bee species and subspecies, and explore how thelytoky may have evolved in honey bees.
1.1. Thelytoky
Honey bees are haplodiploid. Diploid females are normally produced sexually, from fertilised
eggs, while haploid males develop from unfertil- ized eggs via arrhenotokous parthenogenesis. Both queens and workers are capable of laying unfertilised, male-destined eggs, although in most circumstances workers rarely utilise this ability (Visscher 1989; Winston 1991; but see Barron et al. 2001). Thelytoky is an alternative develop- mental pathway for the unfertilised egg, which results in the production of a diploid female offspring.
Thelytoky, the asexual production of females, is rare among animal taxa, where sexual repro- duction predominates (White 1984; Suomalainen et al. 1987). Examples of notable thelytokous animals include the anciently asexual bdelloid rotifer, which has gone without sex for millions of years (Mark Welch et al. 2004; Gladyshev and Meselson 2008) and the Amazon molly (Poecilia formosa), in which females must mate with males of another species before they can reproduce thelytokously. This odd behaviour causes local extinctions as molly females ‘steal all the men’ (Tiedemann et al. 2005; Heubel et al. 2009).
Thelytoky has evolved at least 255 times in populations of normally arrhenotokous haplodiploids (Normark 2003; Engelstadter 2008). Many transitions from arrhenotoky to thelytoky are driven by maternally transmitted endobacteria, such as Wolbachia, Rickettsia and Cardinium (Zchori-Fein et al. 2001; Huigens and Stouthamer 2003; Hagimori et al. 2006; Engelstadter 2008). One mechanism by which these bacteria drive their own propagation is by inducing female-producing parthenogensis to reduce or eliminate the production of males (a genetic dead end for the bacteria) by their host. However, there are a rapidly increasing number of examples of genetically determined thelytoky being identified in haplodiploids. In particular, the ‘molecular natural history’ movement (Keller 2007) is revealing a fascinating array of novel reproductive systems that are based on genetically determined thelytoky. While the ants have thus far yielded the greatest diversity of unusual reproduction systems based on thelytoky (e.g. Pearcy et al. 2004; Ravary and Jaisson 2004; Fournier et al. 2005; Gruber et al. 2010), the bees, and particularly the honey bees,
are beginning to show that they can be equally weird (Sumner and Keller 2008).
2. APIS MELLIFERA CAPENSIS
2.1. Thelytoky in Capensis
Thelytoky in bees was first identified in Capensis (Onions 1912). In this South African subspecies of honey bee, thelytoky is almost ubiquitous in workers (Verma and Ruttner 1983). When Capensis workers lay unfertilised eggs, the eggs usually develop into diploid female offspring via automictic thelytoky with central fusion (Verma and Ruttner 1983; Figure 1). In automictic thelytoky, the reduc- tional division of Meiosis II occurs as normal, resulting in four haploid nuclei. Diploidy is then restored by one of several mechanisms, each with a different genetic outcome (Pearcy et al. 2006). In Capensis, diploidy is restored by central fusion; the fusion of two non- homologous pronuclei as if one of the nuclei acted as a sperm. In the absence of meiotic recombination between a locus and the centro- mere, central fusion results in clonal reproduc- tion so that the genotype of the daughter is identical to the genotype of the mother. However, when recombination occurs, hetero- zygosity can be lost, so that the daughter will be homozygous for one of her mother's alleles (Suomalainen et al. 1987).
If a Capensis worker produces a daughter queen via thelytoky, she is genetically reincarnated in the form of a queen with no frog kissing required. This is no doubt why Capensis workers target their egg laying around existing queen cells, in places where queen cells are likely to be built and during periods of queen rearing (Figure 2). Around 40–60 % of queens produced during swarming events are the daughters of workers (Jordan et al. 2008; Allsopp et al. 2010). Thelytokously-produced Capensis queens go on to mate and reproduce sexually (Beekman et al. 2011). Capensis workers also utilise thelytoky to raise a replace- ment queen whenever they are queenless and broodless (Holmes et al. 2010).
Without recombination With recombination
iii
i
Mother
Meiosis I
Meiosis II
Central fusion
ndomisation of leles during combination
ii
Maintenance of
Daughter
chance of loss of
heterozygosity (AB) heterozygosity
Figure 1 Automixis with central fusion. Meiosis occurs as normal resulting in four haploid pronuclei (i). Pronuclei occupying the central position fuse for form the diploid zygote (ii). As this fusion is central, the pronuclei involved are descended from the two different homologous chromosomes (iii). In the absence of recombination, heterozygosity in the mother will be maintained in the daughter. When recombination occurs, alleles are randomised among the four pronuclei and as a result there is a 1/3 chance that heterozygosity will be lost in the offspring. This is allocation of alleles to the central pronuclei is an example of sampling without replacement. If one of the central pronuclei carries an A allele, there is a 1/3 chance that the other central pronucleus will carry the second A allele, and a 2/3 chance that it will carry one of the two B alleles. If you do not believe us (many readers will not) try writing ‘A ‘on two bits of paper and ‘B’ on two other bits. Draw one piece of paper at random: this is the first central pronucleus. Let us pretend it is an A. Now, what is the probability that the second pronucleus you draw will also be an A?
Thelytoky dramatically increases the repro- ductive potential of the honey bee worker, resulting in competition between workers and worker patrilines (lineages of full-sister workers, sharing a father) (Moritz et al. 1996; Figure 3). This tendency has selected for traits related to reproduction and reproductive com- petition in Capensis workers (Greeff and Villet 1993). Capensis workers often have a well- developed spermatheca (a sperm storage organ found in queens), which is absent in workers of other honey bee subspecies (Hepburn and Crewe 1991; Phiancharoen et al. 2010). Furthermore, the Capensis worker has an
average of 10–20 ovarioles per ovary (Ruttner 1977; Hepburn and Crewe 1990; Allsopp et al. 2003; Goudie et al. 2012a). In contrast, workers in arrhenotokous honey bee populations typi- cally have far fewer ovarioles (Amdam et al. 2004; Oldroyd and Beekman 2008). Ovariole number in worker patrilines is heritable and highly variable (Goudie et al. 2012a). This suggests that certain patrilines dominate repro- duction in Capensis.
The Capensis worker does not always limit herself to competing with her sisters over the production of new queens. Capensis workers are able to act as non-natal reproductive
Figure 2 Worker laid eggs in a queenless Capensis colony. i Egg laying workers focus around holes in the comb where queen cells are most likely to be built. ii Worker laid eggs on the outside of an existing mature queen cell. Photos by B Oldroyd.
parasites, entering foreign colonies and laying eggs that may be raised as queens. Non-natal workers are responsible for the production of between 0.5 and 46 % of new queens (Jordan et al. 2008; Allsopp et al. 2010; Holmes et al. 2010; Moritz et al. 2011). Variation in the degree of parasitism experienced by different colonies suggests that parasitism may be assisted by beekeeping methods (Dietemann et al. 2006a; Härtel et al. 2006). However, Holmes et al. (2010) observed rates of parasit- ism that were independent of apiary layout and distance between colonies. Furthermore, Neumann et al. (2001) found that Capensis workers disperse significantly more than other subspecies of A. mellifera and are more likely to parasitize queenless colonies. Whether or not
movement of workers between colonies is an active process, as it seems to be in bumble bees (Blacher et al. 2013) and stingless bee queens (Wenseleers et al. 2011), remains open to question. However, it appears that once a non-natal worker enters a nest, she targets queen cells for oviposition. In colonies with high rates of parasitic queen production (38 %), only 6.9 % of the workers were non- natal (Jordan et al. 2008). Thus, the reproduc- tive output of non-natal workers is disproportionally high, as is seen in colonies of the Asian species Apis florea (Nanork et al. 2005; Chapman et al. 2009) and Apis cerana (Nanork et al. 2007). This suggests the exis- tence of specialised parasitic genotypes within the Capensis population.
0.75
0.325
Arrhenotoky Thelytoky
Figure 3 The relatedness (r) of a focal worker (red circle) to other individuals in a honey bee colony. Females are represented by circles and males by squares. The queen wears the crown, however, all females have the potential to be raised as a queen (although subfamilies differ in their likelihood of doing so). In a colony in which workers reproduce arrhenotokously, the focal worker is more closely related to the son of her mother (r = 0.25) than the son of her half sister (r=0.125) and so selection favours policing behaviour to suppress the reproductive efforts of other workers. In a colony in which workers reproduce thelytokously, the focal worker can produce daughters that are related to her by unity (r=1). She can use this ability to produce daughters that might become queens, resulting in her effective genetic reincarnation as a queen. She is equally related to her sisters as she is to the thelytokous daughters of her sisters. In the same way, the queen is equally related to her daughters as she is to the thelytokous daughters of her daughters, and so it makes no difference to the colony as a whole if the new queen is produced sexually by the original queen or thelytokously by a worker. Therefore, selection for policing behavior is relaxed relative to arrhenotokous populations.
2.2. The Capensis Clone
By liberating the worker from reliance on a sexual queen, thelytoky has enabled the emer- gence of entirely asexual lineages of social parasites. On at least three occasions, two historic and one current, parasitic lineages have emerged as specialised reproductive parasites of the strictly arrhenotokous subspecies Apis mellifera scutellata (hereafter Scutellata; Martin et al. 2002).
While Capensis is confined to the southern- most tip of South Africa, Scutellata occupies the rest of the southern and most of central Africa (Hepburn and Radloff 1998; Figure 4). In 1990, a beekeeper moved approximately 200 commer- cial Capensis colonies across the stable hybrid zone that separates the two subspecies (Beekman et al. 2008; Allsopp and Crewe 1993) and into Scutellata range (Allsopp and Crewe 1993; Neumann and Moritz 2002). From here, Capensis workers drifted into (or perhaps
invaded) the local Scutellata colonies, com- menced laying and produced thelytokous daughters. One of these daughters founded a thelytokous lineage of clonal workers that has infested commercial Scutellata colonies ever since (Kryger 2001; Baudry et al. 2004; Oldroyd et al. 2011). Over the past 23 years, the Clone has been responsible for what became known as the ‘Capensis Calamity’ (Allsopp 1992; Neumann and Moritz 2002). While new beekeeping practices have reduced rates of transmission, the Clone lineage remains highly virulent and is still responsible for the loss of hundreds of commercial Scutellata colonies each year (Cobey 1999).
The invasion of Scutellata colonies by the Clone appears to be largely dependent on apicultural practices (Moritz 2002; Neumann and Hepburn 2002; Dietemann et al. 2006a). Clone infestation is observed at only low levels in the wild Scutellata population, and only when the wild colonies are in contact with domestic
Figure 4 Map of South Africa (after Oldroyd et al. 2011), showing (I) the natural range of Capensis, (II) the stabile hybrid zone between Capensis and Scutellata and (III) the South African distribution of Scutellata, over much of which the Clone can now be found.
colonies (Härtel et al. 2006). Clones have diffi- culty invading Scutellata colonies without assis- tance (Moritz et al. 2008). However, once a Clone has successfully established in a host colony, the colony's downfall is all but inevitable.
When Clones enter a Scutellata colony, they activate their ovaries and produce queen-like mandibular gland secretions despite the presence of the host queen (Härtel et al. 2011). Clones thus establish themselves as pseudoqueens, and are tended to by host workers as if they were the rightful Scutellata queen of the colony (Figure 5). The host queen is soon lost as a result of lethal fighting (Moritz et al. 2003) and pheromonal competition (Dietemann et al. 2006b; Moritz et al. 2004). The presence of reproductively active pseudoqueens may suppress the development of later Clone offspring, resulting in the establish- ment of dominance hierarchies (Härtel et al. 2011), with only a small number of Clones reaching reproductive dominance within the host
colony (Martin et al. 2002). However, despite the suppression of reproduction in many Clone offspring, they rarely engage in work such as foraging or brood care (Martin et al. 2002).
Clone larvae manipulate host nurse workers, eliciting greater levels of feeding, with food that is more similar in composition to that of royal jelly, than the fare normally provided to mere workers (Calis et al. 2002). The resultant Clones have more queen-like characteristics than normal workers, including shorter developmental time, higher weight, larger spermatheca and larger number of ovarioles, while worker characteristics such as pollen combs and pollen baskets on their hind legs are suppressed (Wirtz and Beetsma 1972; Calis et al. 2002). Thus, the host colony is soon over run with Clone pseudoqueens and their offspring, which only adds to the burden of useless reproductive workers already afflicting the host colony. With time, the number of host workers dwindles and the colony inevitably
Figure 5 Reproductive Clones in a host Scutellata colony. i Darker-bodied Clones (circled in red) are tended to by host workers as if they were host queens. ii In the later stages of invasion, Clones lay dozens of eggs in host brood cells that should only hold one. Photos by B Oldroyd.
declines and collapses (Allsopp and Crewe 1993; Hepburn and Allsopp 1994).
2.3. A social cancer
The analogy of the honeybee colony as a ‘super organism’ is well established and compelling (e.g. Wheeler 1911; Seeley 1989; Moritz and
Southwick 1992; Moritz and Fuchs 1998; Amdam and Seehuus 2006; Hölldobler and Wilson 2008; Johnson and Linksvayer 2010; Seeley 2010; Page 2013). The queen can be compared to the gonads of a multicellular organ-
ism, supported by the somatic cells, a role played by workers. Somatic cells do not reproduce themselves, instead they make up the larger whole that enables the gametic cells (drones and virgin queens) to survive and propagate into the next generation. Multicellularity has been able to evolve because the cells that make up the multicellular organism are identical, having prop- agated from a single zygote. Similarly, worker bees forgo direct reproduction in favour of supporting the reproductive efforts of their queen, and through their work, allowing the colony to survive and send forth reproductive swarms. Like
the cells of a multicellular organism, the individ- uals of a honey bee colony are related and so kin selection theory provides an explanation for how individuals could evolve to sacrifice direct repro- duction in favour of propagating their genes through the reproductive success of related individuals (Hamilton 1964).
Cancer occurs in a multicellular organism when mutations in somatic cells result in cellular replication without restraint (Weinberg 1998). Similarly, in the honey bee colony, cheater workers regularly emerge that abandon reproductive self-restraint and reproduce at the expense of the colony (Barron et al. 2001; Beekman and Oldroyd 2008b; Châline et al. 2002; Holmes et al. 2013; Montague and Oldroyd 1998; Oldroyd et al. 1994). In the Capensis population, cheating occurs when daughters of the colony lay eggs in queen cells (Jordan et al. 2008; Allsopp et al. 2010; Holmes et al. 2010; Moritz et al. 2011). Thus, these workers can be compared to cancerous cells in a multicellular organism. This is taken a step further, when reproductive parasites invade non-natal colonies and begin competing over reproduction. We might view non-natal repro- ductive parasitism in the Capensis population as a kind of transmissible cancer. This is not without precedent in multicellular organisms. (See for example, the contagious facial tumours of the Tasmanian Devil Sarcophilus harrisii and the sexually transmitted cancer of domes- tic dog Canis lupus familiaris; Siddle and Kaufman 2013.)
The Clone is an extreme example of this phenomenon, a self-propagating ‘cancerous’ line- age that reproduces outside any constraint imposed by the colony, while taking full advantage of the resources it provides (Oldroyd 2002). The Clone goes further than most cancers of multicellular organisms, for it is sometimes able to survive the destruction of its host and transfer to another.
Moritz et al. (2008) regard the Clone as parasite with high virulence and low transmis- sibility, resulting from shortsighted within-host selection (Levin 1996). Under this model, the most virulent parasitic genotype outcompetes less virulent genotypes during the infection
phase of the invasion, before horizontal trans- mission occurs, resulting in a selection of a lineage with high virulence but low transmis- sion (Bull 1994).
As predicted by a scenario of ‘short-sighted evolution’, rates of horizontal Clone transmis- sion were not only undetectably small in a source-sink experimental setup without apicul- tural intervention, they were much lower than rates of transmission of Capensis workers taken from the endemic Capensis range (Moritz et al. 2008).
The Clone emerged after the movement of over 200 Capensis colonies into the Scutellata range (see above and Allsopp and Crewe 1993). Assuming that each colony comprised maybe 20,000 workers, made up of at least 20 patrilines (Palmer and Oldroyd 2000), the truckload of colonies comprised approximately 4 million worker genotypes and at least 4,000 patrilines. From these genotypes, a single Clonal lineage emerged, one selected for high virulence within Scutellata host colonies. Clonal reproduction then enabled this lineage to endure for generations, with its virulent genotype unchanged by sexual recombination.
2.4. Maintenance of heterozygosity in the Clone
Thelytoky in Capensis (automixis with central fusion, see above) carries the inherent feature of loss of heterozygosity. Specifically, wherever recombination exchanges genetic material be- tween chromosomes, there is a 1/3 chance that a locus that is heterozygous in the mother will become homozygous in offspring (Pearcy et al. 2006; Oldroyd et al. 2008; Engelstadter et al. 2010) (Figure 1). Therefore, ongoing generations of thelytoky should result in population-wide homozygosity at all loci that are free to recombine (Goudie et al. 2012b). Yet empirical studies have revealed levels of heterozygosity in the Clone that are remarkably high (Baudry et al. 2004; Neumann et al. 2011; Oldroyd et al. 2011).
Historically, the unexpectedly high levels of heterozygosity observed in the Clone were attrib- uted to a reduction in meiotic recombination
(Moritz and Haberl 1994; Baudry et al. 2004). However, Goudie et al. (2012a) demonstrate that a reduction in recombination is insufficient to ex- plain current levels of heterozygosity. Loss of heterozygosity in a thelytokous lineage is cumula- tive. Heterozygous mothers produce homozygous daughters at 1/3 the rate of recombination, while homozygous mothers produce homozygous off- spring exclusively (Engelstadter et al. 2010; Goudie et al. 2012b). Therefore, for any realistic level of recombination (whether reduced or not), homozygosity will inevitably accumulate. After 20 years of exclusive thelytokous reproduction, reduction in recombination cannot explain the maintenance of heterozygosity in the Clone at any but the most centromeric loci where recombi- nation is exceedingly rare (Goudie et al. 2012b).
Maintenance of heterozygosity in the Clone can instead be explained by selection against homozy- gous recombinants at genes that are subject to heterozygote advantage (Oldroyd et al. 2011; Goudie et al. 2012b). A key example of the effects of this kind of selection is the maintenance of heterozygosity at the complementary sex- determining locus (csd). Honey bees must be heterozygous at the csd for the female phenotype to be expressed, while haploid males are hemizy- gous. Homozygosity at the csd results in the production of a diploid male. Diploid males are inviable because they are eaten at early larval stages (Woyke 1963; Beye et al. 2003). Therefore, the csd locus is homozygous lethal, and any Clone offspring in which recombination results in homozygosity at this locus will be lost to cannibalism, thus main- taining heterozygosity at the csd in perpetuity.
The csd is a locus with known heterozygous advantage (overdominance). However, heterozy- gosity is observed throughout the Clone genome at loci that are unlinked to csd and presumed to be selectively neutral. How? There is now strong evidence that heterozygosity is maintained throughout much of the Clone's genome by selection acting on overdominant loci (Goudie et al. 2012b, 2014). These putative overdominant loci are theorised to be in linkage disequilibrium with the marker loci that are observed to be heterozy- gous. In support of this theory, the frequency of homozygosity is significantly higher in Clone eggs
than in it is in larvae and pupae, at both the csd and a range of neutral markers unlinked to the csd (Goudie et al. 2012b). These analyses show that recombination occurs at normal or near-normal rates in the Clone, resulting in the production of homozygotes, including diploid males. However, these recombinants are rapidly removed from the population, permanently retaining heterozygosity in the surviving Clones (Goudie et al 2012a, 2014). Goudie et al. (2014) mapped patterns of zygosity along chromosomes III and IV in the Clone to determine the evolutionary outcome of recombina- tion and selection. Loss of heterozygosity in a Clonal lineage is non-reversible, and a single recombination event will result in loss of heterozy- gosity at all markers located in a telomeric direction from the point of chiasmata, unless a second, concurrent recombination event results in restora- tion of heterozygosity (Figure 6). Yet in the Clone, complete loss of heterozygosity occurs in restricted regions, with subsequent restoration of heterozy- gosity in telomeric regions (Figure 7). This pattern of hetero/homo-zygosity along chromosomes sug- gests that overdominant genes located in the telomeric regions maintain heterozygosity, and indeed this pattern is observed at the csd on chromosome III. Goudie et al. (2014) therefore suggested that there are at least three overdominant genes maintaining heterozygosity on chromosome IV, and four genes (including the csd) that maintain
heterozygosity on chromosome III.
While low rates of recombination were once thought to maintain heterozygosity during thelytoky, growing evidence suggests that usu- ally high rates of recombination may in fact shape the evolution of the Capensis genome under thelytoky. The honey bee genome is characterised by very high rates of recombina- tion (4 times higher than most other taxa and 20 times higher than in humans) (Beye et al. 2006; Solignac et al. 2007). Furthermore, the honey bee has low levels of positive interference (Solignac et al. 2004), i.e. one recombination event does not suppress the probability of a second recombination event occurring nearby. Therefore, high rates of double recombination events within relatively small genetic distances are not unexpected in a honey bee genome. It is
Figure 6 i A single recombination event (orange X) will result in loss of heterozygosity at all telomeric markers (b, c, d). If a locus d (here linked to the marker c) is selectively overdominant, such a recombinant genotype will be selected against. ii Loss of heterozygosity at the marker B will only be observed if it is accompanied by a second, concurrent ‘rescue’ recombination event (blue X) which restores heterozygosity at locus D which is assumed to be under overdominant selection for heterozygosity.
these double-recombination events that are required to generate the genotypes that allow selection to maintain heterozygosity at isolated overdominant loci under selection, while het- erozygosity is lost along the majority of the chromosome (above, Goudie et al. 2013).
High rates of recombination and positive inter- ference in the honey bee has recently been linked to a high rate of gene conversion without crossover (non-crossover) events (Bessoltane et al. 2012). In fact, recombination events are more frequent in the honey bee genome than crossover events. Allelic gene conversion (the replacement of one allele with another at the same locus) can result in loss of heterozygosity during thelytoky. However, non- allelic gene conversion (the replacement of an allele with another from a different locus) could in fact increase genetic diversity, even in a clonal population. It is thus possible that gene conversion may counter loss of heterozygosity during thelytoky. The degree to which gene conversion occurs in Capensis remains to be established, as does the impact that gene conversion may have on the already-documented selective processes that retain heterozygosity in the Clone (Goudie et al. 2012b, 2013).
2.5. Clone drones
Recent evidence has shown that Clones do not reproduce exclusively via thelytoky, as had previ-
ously been assumed (Lattorff et al. 2005). Haploid male eggs were detected in the brood of Clone workers, at a frequency of one in eight (Goudie et al. 2012b). In larvae of the Clones, the frequency of haploid males dropped fivefold relative to eggs, suggesting strong selection against Clone males, though this selective removal may have arisen as a result of haploid male eggs being laid in worker cells. Preliminary evidence now suggests that a few Clone drones survive to maturity. A single adult haploid male carrying Clone alleles at all loci tested (n=9) was detected among 78 black drones collected from Clone-infested Scutellata colonies (Goudie et al., unpublished data). Thus, despite the apparent low frequency of adult Clone drones, our singular example (thus far) shows that some haploid males produced by the Clone lineage are able to reach maturity. It is therefore possible that Clone drones mate with Scutellata queens, resulting in introgression of Clone alleles in to the Scutellata population. This further raises the possibility of contagious parthenogenesis (Engelstadter et al. 2010) in which the rare production of males by clonal lineages leads to the transmission of alleles conferring asexuality into otherwise sexual populations.
2.6. The genetics of thelytoky in Capensis
Thelytoky in Capensis is thought to be con- trolled by a single recessive locus termed thelytoky
Figure 7 The pattern of zygosity along chromosomes III and IV, of the Clone incorporating a descriptive model for the maintenance of heterozygosity (Goudie et al 2013). Heterozygosity is maintained in blue regions by linkage to a heterozygosity-maintaining factor (HMF): the centromere, the csd, or putative genes under overdominant selection (a, b, c). As we move down the chromosome towards the telomere, heterozygosity is lost in purple regions as a result of a recombination event at points telomeric to a HMF, but restored by a second, concurrent recombination which occurred before the next HMF (e.g. heterozygosity is lost after the overdominant gene A on chromosome III, but restored before the gene B). In the yellow region of chromosome IV heterozygosity declines gradually, suggesting either incomplete selection, or the escape from selection by some sublineages due to double recombination events that were undetected in this analysis.
(th) (Lattorff et al. 2005). However, backcross experiments suggest that while th plays a major role in determining the thelytoky phenotype, the genetic basis of thelytoky may be a little more complex than is currently appreciated (Oldroyd et al., unpublished data). Furthermore, the frequent production of haploid eggs by Clones (with the putative genotype th,th) suggests that the th locus may not have complete expressivity (Goudie et al.
2012b). Alternatively, errors in thelytoky may be frequent in this lineage.
Under the single gene model for thelytoky, it has been proposed that differential splicing of the transcription factor CP2, a homolog for the Drosophila transcription factor gemini, results in the development of the thelytokous pheno- type (Jarosch et al. 2011). Splice forms of CP2 in Capensis are more similar to those of sexual
queens then arrhenotokous workers in other subspecies. Jarosch et al. (2011) suggest that thelytoky in Capensis may be determined by the lack of a short splice enhancer motif. Knockdown of this motif in arrhenotokous workers results in rapid ovary activation, which is one of a number of features that characterise the highly reproductive Capensis worker phe- notype.
/
2.7. Capensis and sex
Capensis, and in particular the Clone, pro- vides a valuable model with which to investi- gate the genetic and evolutionary consequences of thelytokous parthenogenesis, providing unique insights into the evolutionarily tradeoff between sex and asexuality that drives the distribution of reproductive strategies among animal taxa.
Sexual reproduction is the predominant form of reproduction among multicellular organisms (White 1984; Suomalainen et al. 1987), yet the near ubiquity of sexual reproduction remains an enduring evolutionary mystery. Many potential benefits of sex have been proposed and inves- tigated (see Otto and Gerstein 2006; Engelstadter 2008). These seek to deal with the fundamental question of how an allele imparting sexual reproduction could outcom- pete an allele causing asexual reproduction when sexuality reduces the reproductive poten- tial of a population by a factor of 2, as a consequence of the production of males that do not themselves reproduce (Maynard Smith 1978). In Capensis, we observe three unique female reproductive phenotypes, the queen, the worker and the Clone. Each of these utilise the same underlying genotype, however, the interplay of life history with the costs and benefits of sex and asexuality has resulted in the evolution of
distinctly different reproductive strategies.
2.8. The queen
At first, it appears perplexing that the Capensis queen forgoes thelytoky. Thelytokous reproduc- tion would allow a Capensis queen to produce
daughter queens that are related to her by unity (r
=1). Like certain thelytokous ant species, she could perhaps continue to employ sexual repro- duction to produce workers (Cataglyphis cursor, Pearcy et al. 2004; Wasmania auropunctata, Fournier et al. 2005; Vellonhovia emeryi, Kobayashi et al. 2008), gaining the best of both evolutionary worlds: effective genetic immortality in her reproductive offspring and genetic variabil- ity with its associated benefits (Jones et al. 2004; Mattila and Seeley 2007; Oldroyd and Fewell 2007; Seeley and Tarpy 2007) in her workers. Thelytoky is a very real evolutionary option for a Capensis queen. A virgin Capensis queen can reproduce both thelytokously and arrhenotokous when induced to start laying by double narcosis with CO2 (Allsopp and Crewe 1993; Oldroyd et al. 2008). Yet, despite the potential benefits, there is no evidence that mated Capensis queens ever lay thelytokous eggs (Jordan et al. 2008; Holmes et al. 2010; Moritz et al. 2011), providing strong evidence that for the Capensis queen, the costs of thelytoky outweigh the costs of sex.
A honey bee colony reproduces via the production of drones and swarms. The queen leaves the colony heading a swarm comprised of about half the workers, leaving behind a small number of queen cells containing her queen-destined daughters. One of these daugh- ters will inherit the original colony, while one or two others may head secondary swarm that has a lower survival than the first (Hepburn and Radloff 1998; Seeley 2010). Thus, queens trust their reproductive futures in a tiny number of daughter queens. Any reduction in larval via- bility associated with thelytoky may therefore have a substantial impact on a queen's fitness (her larva may be usurped by that of a worker), which is compounded by the absence of the many benefits associated with sexual reproduc- tion (Otto and Gerstein 2006).
A queen that produces daughter queens asexu- ally and daughter workers sexually would come into direct conflict with her worker daughters. She would share twice as many alleles with her own thelytokously-produced queen daughter (r=1) than she would with the thelytokously-produced daugh- ters of her sexually produced worker (r=0.5;
Figure 8). A queen that produces both worker and queen offspring thelytokously eliminates this com- petition, but in the process, massively reduces the genetic diversity of her work force and so potentially the fitness of the colony she relies on to raise her reproductive offspring (Figure 8). However, a sexual queen is equally related to her sexual daughter as she is to her thelytokously- produced granddaughter (Figure 8). Therefore, provided her workers are working (and not just breeding, Hillesheim et al. 1989), a queen is predicted to be indifferent to the production of new queens by natal workers (Greeff 1996; Beekman and Oldroyd 2008a).
2.9. The typical Capensis worker
Thelytoky massively increases the reproduc- tive potential of a Capensis worker, enabling her to produce diploid daughters and to compete with her mother and fellow workers over the production of new queens. Not only does thelytoky increase the reproductive potential of the Capensis worker, it fundamentally alters the kin structure of a Capensis colony relative to that of an arrhenotokous colony, eliminating, or greatly reducing, the selective pressures that normally drive workers to suppress the repro- ductive proclivities of their worker sisters (Greeff and Villet 1993; Moritz et al. 1999). Honey bee queens are extremely polyandrous (Palmer and Oldroyd 2000) and as a result workers within a colony are mainly half sisters. Thus, in an arrhenotokous colony, a worker is more closely related to the sons of her mother (r
=0.25) than to the sons of a fellow worker (r= 0.125). While a worker might benefit from producing her own sons (r=0.5), collectively workers prefer to raise the sons of their mother (Ratnieks 1988). As a result, worker policing has evolved, where workers eat eggs that have not been laid by the queen (Ratnieks and Visscher 1989). In contrast, Capensis workers can benefit immensely from personal reproduc- tion, while the queen and other workers are largely indifferent to it, provided it does not unduly reduce colony productivity (Beekman et al. 2002, 2009; Greeff and Villet 1993; Moritz
et al. 1999; Pirk et al. 2002). While the Capensis worker still benefits more from pro- ducing her own offspring than raising the offspring of another, she is indifferent to whether the offspring of other females are produced by workers or queens. Thus, instead of policing, directed worker competition is expected to evolve, and is observed (Jordan et al. 2008; Moritz et al. 1996, 2011).
As described above, thelytoky incurs a cost in Capensis workers; a 1/3 loss of heterozygos- ity per generation for any locus that is free to recombine. So, for example, 1/3 of eggs laid by Capensis workers should be inviable diploid males. However, the reproductive Capensis worker takes advantage of reproductive oppor- tunities that are otherwise unavailable. Unlike the Capensis queen, sex is not an evolutionary option for the worker, while thelytoky provides a worker with a window of opportunity to be genetically reincarnated to the queen phenotype. A worker's thelytokously-produced daughter queen subsequently reproduces sexually, and so the cost of thelytoky in the worker is only paid over a single generation; loss of heterozy- gosity does not compound once the worker is reincarnated as a queen.
The thelytokous worker has everything to gain and little to lose though thelytokous parthenogenesis, particularly when frequency- dependent selective forces maintain ‘cheater’ parasitic lineages at low levels that do not jeopardise the stability of the eusocial colony (Hillesheim et al. 1989).
2.10. The Clone
The introduction of highly reproductive Capensis workers to Scutellata colonies enables the emergence of asexual lineages that are completely liberated from reliance on a sexual queen for their vicarious reproduction. However, for thelytoky to endure over evolutionary time, a cost must be paid. Maintenance of heterozygosity by selection requires the removal of homozygous recombinant offspring each and every generation. To be specific, heterozygosity will be maintained at a locus provided that the number of homozygotes
a b c
0.5
Figure 8 The three evolutionary options for Capensis queens. The queen in each pedigree is circled in red, all values refer to her relatedness (r) to each individual. a A queen that produces workers sexually (ii) and new queens thelytokously (ii) maximises her relatedness to her queen daughters while maintaining genetic diversity in her worker force. However, she will come into conflict with her daughters. She is twice as related to her own queen daughter (ii) as she is to a worker-produced queen (iii). b A queen that produces both worker (i) and queen (ii) daughters thelytokously avoids competition. She is equally and maximally related to her own queen daughter (ii) as she is to a worker-produced queen (iii). However, she has much reduced genetic diversity in her colony, which may suffer from reduced disease resistance and less efficient task allocation. c A queen that produces both worker (i) and queen (ii) daughters sexually also avoids conflict with her female offspring, while maintaining genetic diversity in the colony In this scenario, which is what we also see in reality, the queen only shares half her alleles with her queen daughters. However, when we take into account the evolutionary alternatives (a and b), we see that this strategy maximises queen fitness.
being removed by selection is equal to or greater than the number being produced by recombination (Goudie et al. 2012b). Therefore, for a thelytokous lineage such as the Clone to endure, the benefits of thelytoky must outweigh the per-generational cost in reduced viability, which is necessary to maintain the integrity of the clonal genome. We (Goudie et al. 2012b) proposed that the parasitic life history of the Clone does indeed make it ideally suited to enduring this cost.
During an invasion, Clone workers lay a massive number of eggs. Eventually, brood cells that should only hold one egg become packed with dozens of Clone progeny (Figure 5). Only a tiny proportion of these eggs can ever be expected to hatch, let alone emerge as an adult, survive colony collapse and continue the invasive cycle (Martin et al. 2002; Neumann and Hepburn 2002). And for a Clone, the production of these eggs is cheap; she is waited on, wing and tarsis, by her hosts, taking no part in non-reproductive tasks, such as foraging and brood care. Any of her offspring that emerge
are abandoned to the care of their hosts, from whom Clones elicit level of attention normally reserved for royalty (Beekman et al. 2000; Allsopp et al. 2003). A Clone can therefore dedicate her life to producing thelytokous eggs, in the hope that some will reach maturity. High rates of reproduc- tion, low maternal investment and concordantly high mortality are an inherent part of the Clone's parasitic reproductive strategy—when the vast majority of eggs cannot be raised to maturity, it hardly matters if many of them are inviable.
2.11. Of queens, workers and Clones
While thelytoky imposes high costs, it allows the Clone to exploit a new niche that would otherwise be unavailable, that of social parasitism. Parasitism is both the means by which the Clone benefits from going without sex, and the means in which it is able to endure the costs of thelytoky. Workers from the sexual Capensis population, in contrast, play the odds, giving thelytoky a go
because they have no other avenue for direct reproduction, while still being relatively assured of the indirect benefits of a eusocial existence. In the queen, we see the more standard outcome to the evolutionary tradeoff between sex and asexu- ality, the costs of thelytoky may be too high a price when the queen's reproductive future, and that of the colony, is vested in a small number of potentially reproductive daughters.
Using Capensis and its Clone as a model, we suggest that the costs and benefits of sex and asexuality should be considered in a more conditionally than is often the case. The specific life history of a population, and the outcomes of the mode of thelytoky it employs, must be examined to account for where costs and benefits are imposed, and where they can be endured.
3. THE EVOLUTION OF THELYTOKY IN APIS
3.1. Thelytoky in Apis more broadly
Capensis appears to be the only honey bee in which thelytoky is ubiquitous. However, Mackensen (1943) reported that approximately one percent of eggs produced by virgin queens of the Italian (Apis mellifera ligustica) and Caucasian (Apis mellifera caucasica) subspecies were female, the result of thelytokous partheno- genesis. (Mackensen's experimental queens had been exposed to double CO2 narcosis, which induces oviposition in honey bee queens, normal- ly resulting in the production of arrhenotokous males.) The low frequency of thelytokous repro- duction may well be the result of errors in arrhenotokous parthenogenesis. However, the regularity with which thelytokous offspring was observed by Mackensen (1943) suggests that thelytoky is a threshold character that can be released with relatively small genomic, and perhaps environmental, changes.
This conjecture is supported by the frequency with which thelytokous reproductive systems are being identified in another taxa of eusocial Hymenoptera. Thelytoky is relatively common in ants (Rabeling and Kronauer 2013) and the number of known thelytokous ant species has
dramatically increased over the last few years, as more species are investigated with molecular techniques. Importantly, thelytoky in ants ap- pears to be associated with invasive life histo- ries (Rabeling and Kronauer 2013).
While not a honey bee, the solitary little carpenter bee Ceratina dellatorreana has been reported to reproduce thelytokously. As with many ants, thelytoky in the C. dellatorreana was observed in an invasive population, where it is hypothesised to have facilitated its intro- duction (Daly 1966). However, no further information is available on C. dellatorreana.
It appears very possible that more thelytokous bees are waiting to be discovered. Even well- studied populations in which both males and females are present can be producing thelytokous females that are not detectable until we look for them explicitly, usually with molecular techniques.
3.2. Thelytoky in A. cerana
Evidence is currently accumulating that indicates that another species of honey bee, the Asiatic honey bee A. cerana, reproduces thelytokously (Holmes, unpublished data).
In recent years, an invasive population of A. cerana was identified in Queensland, Australia (Koetz 2013). This population was probably founded by a single reproductive swarm, and as such the population has limited genetic diversity. We hypothesise that thelytoky may have enabled the successful establishment of this invasive population. A disturbing possibility is that inter- specific matings between A. cerana males and A. mellifera queens may induce thelytoky in A. mellifera queens (unpublished data).
3.3. The evolution of thelytoky in Capensis
Thelytoky may have evolved to be ubiquitous in Capensis during periods of the Pleistocene in which rising sea levels isolated the Cape Peninsula from the rest of the African continent (Ruttner 1977). It was once feared that the world's only known thelytokous bee would be overrun by the more aggressive and widespread Scutellata subspecies (a.k.a. the African killer bee; Ruttner
1977). This fear is ironic in hindsight, given that we now know that Scutellata comes off second best when bought into contact with Capensis. In reality, a stable hybrid zone that neither subspecies is able to cross without human intervention (Beekman et al. 2008) now separates the two populations. Hybrid or mixed colonies of Capensis and Scutellata are assumed to suffer from reduced fitness (Beekman et al. 2008, 2012), though evidence for this hypothesis is currently lacking. Scutellata drones and virgin queens may outcompete Capensis at mating leks. However, even low frequencies of Capensis genotypes within a mixed subspecies colony is expected to result in a misallocation of resources by easily duped Scutellata workers, leading to the produc- tion of more reproductive Capensis workers, and a breakdown in regulation of worker reproduction (Beekman et al. 2008).
It has been hypothesised that thelytoky in Capensis originally evolved in response to high rates of queen loss on the windy and often inclement Cape Peninsular (Tribe 1983). Thelytoky does indeed provide Capensis with the means to produce a new queen when arrhenotokous subspecies might fail. However, given the global range of the honey bee, it seems highly unlikely that any environmental conditions experienced by Capensis are so unique that they alone have driven the evolution of such a distinctly divergent reproductive strategy.
We suggest that the evolution of thelytoky in Capensis was facilitated by genetic drift in a small, isolated population. While queen replace- ment may have played a role in selecting for thelytoky, a range of other factors would have been required for thelytoky to become ubiqui- tous in the population. Even assuming that thelytoky occurs at low frequency in otherwise arrhenotokous honey bee populations, workers laying thelytokous eggs in drone cells run into a genetic dead end, because these eggs will never develop to queens. Thus, thelytoky would not be selected for until such time as it co-occurs with a heritable behavioural variant in which workers target their reproductive efforts to queen cells. Furthermore, thelytoky emerging from a background of worker arrhenotoky does
so in an environment of intense worker policing (Ratnieks 1988). Selection to reduce worker policing will not occur until after thelytoky has become ubiquitous. Thus, policing acts as an evolutionary barrier, reducing or eliminating any immediate payoff from thelytoky.
If thelytoky is to reach a high frequency in a honey bee population, multiple factors must fall into place concurrently: a thelytokous mode of worker reproduction, targeting of worker repro- duction to queen cells, and the relaxation of worker policing. In a large, outbred population the suppression of worker reproduction by worker policing may significantly reduce variance in both the mode and target of worker's reproductive efforts. Thus, thelytoky is unlikely to emerge. However, in a small, isolated population, faced with additional pressures such as a high rate of queen loss (above), genetic drift and founder effects may have resulted in the necessary combination of factors falling into place for thelytoky to reach a stable point in the population.
4. CONCLUSION
The honey bee has played an important role in driving and informing evolutionary theory, a role that shows no sign of ending soon. Here, we have shown that the honey bee, and particularly the Capensis subspecies, has much to contribute to questions concerning the evolution of sex and asexuality. In Capensis, sex and asex co-exist, distributed among castes and lineages that share the same genetic background. Differing life histories results in divergent outcomes when the costs and benefits of sex and asexuality come into conflict. We suggest that the broader question of why sex evolved from ancestral asex, and how it has been maintained, should be addressed with an eye for more conditional costs and benefits.
We further propose that there may be broader implications to the insights provided by bees, ants and the haplodiploid Hymenoptera in general. In these species, a form of asexual reproduction, arrhenotoky, is ancestral. While arrhenotokous species still require sex for the production of females, this reproductive system is hypothesised to predispose haplodiploids to the evolution of true,
thelytokous, asexuality (Engelstadter 2008; Rabeling and Kronauer 2013). In non- arrhenotokous species, the transition from sex back to asexuality is not as easy (Engelstadter 2008) and so the evolution of sex may be, in most circum- stances, a one-way street. The question of “why is sex always better then asex?” then becomes “why is sex ever better then asex?” Sexual reproduction may evolve in a species during a period in which environmental conditions are such that the evolu- tionary tradeoff between sex and asex is similar to that faced by the Capensis queen, i.e. the need to invest maximally in a limited number of offspring. However, having taken this route, they cannot simply switch back to asex when conditions change. And so it is possible that there are many potential Clones waiting to emerge, but for these species asexuality is not a realistic evolutionary option, despite the benefits it may confer.
Parthénogenèse thélytoque chez l'abeille
Apis mellifera / Apis mellifera capensis / reproduction asexuée / thélytocie / parasitisme reproducteur
Thelytökie bei Honigbienen
Apis mellifera / Apis mellifera capensis / asexuale Reproduction / Thelytökie / reproduktiver Parasitismus
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Honey bee pathology: current threats to honey bees and beekeeping
Honey bee pathology: current threats to honey bees and beekeeping
Abstract Managed honey bees are the most important commercial pollinators of those crops which depend on animal pollination for reproduction and which account for 35% of the global food production. Hence, they are vital for an economic, sustainable agriculture and for food security. In addition, honey bees also pollinate a variety of wild flowers and, therefore, contribute to the biodiversity of many ecosystems. Honey and other hive products are, at least economically and ecologically rather, by-products of beekeeping. Due to this outstanding role of honey bees, severe and inexplicable honey bee colony losses, which have been reported recently to be steadily increasing, have attracted much attention and stimulated many research activities. Although the phenomenon “decline of honey bees” is far from being finally solved, consensus exists that pests and pathogens are the single most important cause of otherwise inexplicable colony losses. This review will focus on selected bee pathogens and parasites which have been demonstrated to be involved in colony losses in different regions of the world and which, therefore, are considered current threats to honey bees and beekeeping.
Keywords Honey bees . Varroa . Virus . Nosema . European Foulbrood . Colony losses
Introduction
Honey bees and honey bee health have become a major topic recently due to the important role honey bees play in
pollination and food production. Although it is often suggested that mankind will not survive for long once honeybees are gone, this is rather exaggerating the role of honey bees for human nutrition since primary food production, and especially our staple foods, is independent of animal (insects, birds, bats) pollination. Our staple foods (e.g. wheat, corn, rice, potatoes) are wind- or passively self- pollinated or are vegetatively propagated, meaning that their production does not increase with animal pollinators (Ghazoul 2005a; Klein et al. 2007; Richards 2001). Yet, the production of many fruits, vegetables and stimulant crops contributing to a healthy diet depends on animal pollination. Therefore, thinking beyond caloric intake, for a balanced and nutritionally valuable human diet, animal pollination is essential (Steffan-Dewenter et al. 2005), leading us back to the unquestionable importance of honey bees for food production. To roughly put it into figures: Crops which are
independent of animal pollination account for ∼65% of global food production, leaving as much as ∼35% depending
on pollinating animals (Klein et al. 2007). Of commercial pollination, 90% is performed by managed honey bees (Apis mellifera): Therefore, although mankind will not die if honey bees go extinct, they are still the most important commercial pollinators worldwide, and the human diet would be greatly impoverished if honey bee populations decline or disappear (Steffan-Dewenter et al. 2005).
Due to this link between honey bees and global food security, the decline of managed honey bees and the loss of wild pollinators are of increasing concern. Although there is an ongoing discussion whether or not we are really facing a
“global pollinator crisis,” there is no question that many
E. Genersch (*)
Institute for Bee Research, Friedrich-Engels-Str. 32,
16540 Hohen Neuendorf, Germany
e-mail: [emailprotected]
solitary and social bees are declining (Allsopp et al. 2008; Ghazoul 2005a, b; Steffan-Dewenter et al. 2005). A recent metastudy revealed that although the global number of managed honey bee colonies increased by 45% over the last
five decades, there is a marked decrease of such colonies in Europe and North America at the same time (Aizen and Harder 2009). Since crop pollination in North America and Europe is highly and increasingly dependent on honey bees (Aizen et al. 2008), this development is alarming, although not all countries are equally affected. In Europe, for instance, Austria, Germany, Sweden and Switzerland are facing a critical decrease in the number of managed honey bee colonies, while other European countries like Greece, Italy, Portugal, and Spain even report a considerable increase (vanEngelsdorp and Meixner 2010).
Many factors may account for the rise and fall in the apicultural sector, and socio-economic factors for sure do not play an underpart. The decline in the number of honey bee colonies observed during the 1990s in Europe can be related to the political and economic upheaval in Eastern Europe caused by the Soviet collapse (Aizen and Harder 2009). In many countries of the Soviet bloc, honey had served as a second currency, and, thus, many people had been motivated to keep bees. When the economic system changed in the early 1990s, honey lost its relevance, and people who had kept their bees for economic reasons gave up beekeeping or reduced the number of managed bee hives (vanEngelsdorp and Meixner 2010). The influence of the profitability of beekeeping on the managed colony pop- ulations can also be observed in the USA where changes in the honey price and the beekeepers' income from renting colonies for pollination are significantly related to changes in national colony numbers (Sumner and Boriss 2006; vanEngelsdorp and Meixner 2010). These economic links imply that sustainably increasing the economic benefit for beekeepers or the profitability of beekeeping operations should contribute to a lasting stabilization or even an increase in the number of managed bee hives in those countries currently facing a decline in honey bee populations.
However, although the long-term (positive and negative) development of colony numbers over the last five decades
may have been influenced by economic factors, in recent years we are confronted by a steady decline in honey bee populations and/or catastrophic winter losses in some regions of the world which elude this explanation (Genersch et al. 2010; vanEngelsdorp et al. 2007, 2008, 2009). Honey bees are susceptible to a variety of diseases and environmental threats, some of which have increased significantly over the last 5 to 10 years. While it is impossible to identify a single factor which on its own can account for all colony losses in all regions of the world over a given time period, it is clear that several biological and environmental factors acting alone or in combination have the potential to cause premature colony mortality by adversely affecting colony health and lifespan. Among these factors, certain honey bee diseases and parasites have been shown to play a significant role in increased honey bee colony mortality and in the described colony losses. The ectoparasitic mite Varroa destructor as well as the bee pathogenic viruses acute bee paralysis virus (ABPV) and deformed wing virus (DWV) are implicated in winter losses in Germany (Genersch et al. 2010); Israeli acute paralysis virus (IAPV) has been identified as a marker of dramatic colony losses termed colony collapse disorder (CCD) in the USA (Cox-Foster et al. 2007; vanEngelsdorp et al. 2009); the microsporidium Nosema ceranae is causing severe colony mortality in Spain (Higes et al. 2006, 2008a); Melissococcus plutonius, the etiolog- ical agent of European Foulbrood, is of increasing concern in Switzerland and the UK (Roetschi et al. 2008; Wilkins et al. 2007); Paenibacillus larvae, causing American Foulbrood, is causing economic losses to beekeepers worldwide (Genersch 2008). This review will focus on these honey bee pathogens and their corresponding diseases (Table 1) because of their role in honey bee collapse (Ratnieks and Carreck 2010) and their impact on beekeeping rather than giving a general overview on bee pathology.
Table 1 Honey bee pathogens shown to be involved in severe colony losses in different regions of the world
Pathogen Region Reference Viruses
ABPV Germany (Genersch et al. 2010)
DWV Germany (Genersch et al. 2010)
IAPV USA (Cox-Foster et al. 2007) Bacteria
Melissococcus plutonius Switzerland, UK (Roetschi et al. 2008; Wilkins et al. 2007)
Fungi
Nosema ceranae Spain (Higes et al. 2008a) Metazoan parasites
Varroa destructor Germany, Canada and elsewhere (Genersch et al. 2010)
(Guzmán-Novoa et al.
2010
)
Parasites: V. destructor
The ectoparasitic mite V. destructor impairs both brood and adult bees causing a non-uniform disease pattern called varroosis or parasitic mite syndrome and including a specific form of brood damage termed “snotty brood” (Shimanuki et al. 1994). The symptoms of varroosis are dependent on the rate of mite infestation of a given colony and on viral infections vectored to individual bees by the parasitizing mites (see below). Infested colonies in temper- ate climates will eventually die within around 2 years after the initial infestation if left untreated (Boecking and Genersch 2008). Therefore, varroa control strategies have had to become an integral part of the beekeeping practice in order to keep infestation levels below the damage threshold (Delaplane and Hood 1999) for reducing colony losses caused by this parasite. Still, varroosis inflicts much greater damage and higher economic costs than all other known bee diseases.
V. destructor belongs to the genus Varroa which is currently represented by at least four species of obligate ectoparasitic mites of honey bees: (1) Varroa jacobsoni Oudemans (Anderson and Trueman 2000; Oudemans 1904), which is a natural ectoparasitic mite of the Eastern honey bee Apis cerana; (2) Varroa underwoodi, which is also a parasite of A. cerana (Delfinado-Baker and Aggarwal 1987); (3) Varroa rindereri, which was found parasitizing Apis koschevnikovi in Borneo (De Guzman and Delfinado-Baker 1996); and (4) V. destructor, which was erroneously classified as V. jacobsoni until it turned out to be a separate species (Anderson 2000; Anderson and Trueman 2000) [Cave: Due to this classification problem, V. destructor is called V. jacobsoni in many pre-2000 articles!]. The original host of V. destructor is A. cerana. Several mitochondrial haplotypes of V. destructor exist, but only two of them, the Korean type and the Japanese/Thailand type, are able to reproduce on A. mellifera. Microsatellite analyses found almost no poly- morphism within these two haplotypes, indicating a quasi clonal population structure (Solignac et al. 2003, 2005) and suggesting that most likely two independent switches from the original host A. cerana to the new host A. mellifera, at two different times and from two different populations in Asia, occurred. From there on, V. destructor started to conquer the Western honey bee around the world in the second half of the past century. Today, V. destructor can be found worldwide wherever A. mellifera colonies are kept, and it is hardly possible to find a mite-free colony any longer. The only exception is Australia, which still considers V. destructor an exotic bee mite since it has not become established there so far (AQIS, Australian Government: http://www.daff.gov.au/aqis/quarantine/ pests-diseases/honeybees, visited Jan 4, 2010).
The ectoparasite V. destructor is intimately linked to its host, the honey bee. All life stages of the mites take place on bees and/or in the colony, and no free-living, bee- independent stages do exist.
The biology and reproduction of V. destructor has been recently reviewed (Rosenkranz et al. 2010) and, therefore, will not be detailed here. The pathology of V. destructor at the individual insect level is in particular determined by
(1) the feeding activities of the mites (i.e. injuring the cuticle of pupae and adults, sucking substantial amounts of haemolymph) and (2) vectored viruses. The loss of haemolymph during the honey bee pupal development significantly reduces the size and the weight of the hatching bee (De Jong et al. 1982; Duay et al. 2003). For drones, it has been demonstrated that such a reduced weight led to decreased flight performance and sperm production (Duay et al. 2002). Parasitized foragers showed a reduced capability of non-associative learning, and their orientation and homing ability was impaired, i.e. infested bees needed longer time to return or even did not return at all to the colony (Kralj et al. 2007; Kralj and Fuchs 2006).
Recent studies revealed that mite infestation during pupal development might also have an effect on the immune capacity of the parasitized pupae and even on the adult bees. Injection bioassays performed with adult bees and Escherichia coli provided correlative evidence for a partially impaired immune response towards microbial challenge in adult bees which suffered as pupae from mite parasitism (Yang and Cox-Foster 2005). However, when transcript levels for genes encoding antimicrobial peptides (abaecin and defensin) in pupae which differed in the number of parasitizing mites were analyzed, a differential regulation of these immune effectors in relation to the level of parasitization could be demonstrated (Gregory et al. 2005). Only pupae with low mite abundances showed the proposed decrease in immune response, while heavily parasitized pupae showed increased transcript levels for abaecin and defensin. Accordingly, a recent analysis of honey bee immune-gene activity in V. destructor parasitized pupae using microarray analyses (Evans 2006) did not reveal any decrease in transcript abundance of immune pathway members found on this array (Navajas et al. 2008). These three different studies (Gregory et al. 2005; Navajas et al. 2008; Yang and Cox-Foster 2005) show that V. destructor mites have impacts on the bee immune response and, thereby, most likely on the susceptibility of honey bees towards various pathogens, but we are still far from understanding the interplay between the parasites and their hosts' immune system.
In addition to these direct effects of V. destructor on the performance and health of individual bees, there are also indirect effects caused by viruses vectored through the mite.
So far, 18 different viruses have been isolated from honey bees (Chen and Siede 2007) and for Kashmir bee virus (KBV), sacbrood virus, acute bee paralysis virus, Israel acute paralysis virus and deformed wing virus, it has been proven that they can be vectored by V. destructor [see references in Boecking and Genersch (2008)]. In the absence of V. destructor, these viruses cause covert infections (Hails et al. 2007); therefore, they have been considered a minor problem to honey bee health until the arrival of the mite changed the picture (Allen et al. 1986; Bailey and Ball 1991; Ball 1983, 1989, 1996; Bowen- Walker et al. 1999). While feeding on covertly infected bees, the mites obviously acquire viral particles which can then be vectorially transmitted to the next parasitized bee. The virulence of the aforementioned viruses had been tested and confirmed in the laboratory by injection bio- assays using pupae or adult bees (Bailey 1964, 1967; Bailey and Ball 1991). Hence, it is not surprising that these viruses, when “injected” into the haemocoel of pupae and adult bees in the course the mites' feeding activities, induce overt disease outbreaks and exhibit all their potential virulence. In addition, evidence exists that V. destructor can cause activation of endogenous virus infection also leading to overt disease outbreaks as a consequence of the immune suppression in pupae and adult bees which are/ were parasitized during their ontogenetic development (Yang and Cox-Foster 2005).
Although it has long been known that the haemophagous honey bee mite V. destructor is able to induce colony losses especially in combination with virus infections (Ball 1983, 1989; Ball and Allen 1988; Delaplane and Hood 1999; Fries et al. 1994; Hung et al. 1995; Martin 2001; Todd et al. 2007), the mite did not come into focus when inexplicable overwintering losses and seasonal losses were reported from different regions in the world in the recent past. One explanation used to exculpate V. destructor was that the mite has been around now for nearly 40 years and has spread around the world during this period, but increased and inexplicable colony losses—like CCD in the USA (vanEngelsdorp et al. 2007, 2008, 2009)—have been reported only recently. Therefore, many efforts have been made to identify the cause of these losses which was expected to be newly introduced or to have emerged recently. One of these efforts has been a long-term bee- monitoring program initiated in Germany in the winter 2004/2005. Analysis of the first period of 4 years revealed that, at least in Germany and over the study period, V. destructor still has been the main cause of winter losses (Genersch et al. 2010), confirming that no other pathogen has a comparable impact on beekeeping. In addition, two honey bee pathogenic viruses known to be associated with
V. destructor, DWV and ABPV, were significantly related to the observed winter losses (Genersch et al. 2010).
Viral pathogens: DWV, ABPV and IAPV
The existence of honey bee viruses has been known since 1963 when chronic bee paralysis virus (CBPV) and ABPV were first isolated (Bailey et al. 1963). Most of the 18 known honey bee viruses may exist and even co-exist in honey bee individuals or colonies without causing any symptomatic infection and, hence, without causing any obvious problems for apiculture (Gauthier et al. 2007; Tentcheva et al. 2004). However, in the wake of V. destructor, two viruses became of increasing concern: DWV (Iflaviridae) and ABPV (Dicistroviridae).
Soon after V. destructor arrived in the A. mellifera population of the Western World, emerging bees with deformed or atrophied wings were increasingly observed. As the occurrence of these deformed wings was clearly related to mite infestation of the developing pupae, these deformities were first considered a consequence of the haemolymph deprivation of pupae by the parasitizing mites (De Jong et al. 1982; Koch and Ritter 1991; Marcangeli et al. 1992). However, since 1989, evidence has been accumulating that the deformed wing symptom was rather related to the simultaneous infection of infested bees by a virus which was then called deformed wing virus (Bailey and Ball 1991; Ball 1989, 1993).
Like all other bee viruses, DWV is a rather benign virus mainly causing covert, symptomless infections (Hails et al. 2007), as long as it is transmitted vertically (through drones and queens) or horizontally (through larval food) (de Miranda and Fries 2008; Yue and Genersch 2005; Yue et al. 2006, 2007). Vectorial transmission of DWV to pupae through V. destructor is a prerequisite for the manifestation of overt DWV infections characterized by deformed wings, shortened and bloated abdomen and miscolouring (Ball and Allen 1988; Bowen-Walker et al. 1999; Yang and Cox- Foster 2007; Yue and Genersch 2005). Bees with deformed wings are not viable and die within less than 67 h after emergence (Yang and Cox-Foster 2007). Hence, overt infections induced by the mite acting as virus vector can cause considerable damage to colonies. The degree of virus-induced damage is related to the proportion of overtly DWV-infected and, hence, non-viable bees. This again is related to the varying proportion of mites which actually act as virus vector in the colony (Yue and Genersch 2005). In addition, recent studies have shown that replication of the virus in the mites prior to transmission and a high enough DWV titre in the mites are necessary preconditions for the induction of an overt DWV infection in the developing bee (Gisder et al. 2009; Yue and Genersch 2005). These results indicate that the mite can act not only as mechanical but also as biological vector for DWV and that it is the latter function which is related to overt DWV infections. Therefore, the more mites in a colony transmit the virus
and the more of these mites support replication of the virus prior to transmission, the higher the chances that develop- ing pupae will develop a fatal DWV infection and that the colony will eventually collapse [for a recent detailed review: de Miranda and Genersch (2010)]. Furthermore, the above-discussed manipulation of the bees' immune system by parasitizing mites also seems to play a role in the development of overt DWV infections. Recent studies suggested that V. destructor might actively contribute to the activation of endogenous DWV infections by immuno- suppressing the host (Navajas et al. 2008; Yang and Cox- Foster 2005).
In conclusion, in association with V. destructor, DWV can be considered an emerging viral disease of honey bees with detrimental effects not only for individual bees but also for entire colonies. The negative impact of DWV on winter survival of honey bee colonies has been confirmed recently by a 4-year bee-monitoring program in Germany. The detection of DWV viral sequences in the brains of otherwise symptomless honey bee workers, which had been shown to be of diagnostic relevance (Yue and Genersch 2005; Yue et al. 2007), was significantly related to winter mortality of the respective colonies (Genersch et al. 2010). ABPV and IAPV are closely related members of the family Dicistroviridae. ABPV had been discovered inad- vertently during laboratory work on CBPV (Bailey et al. 1963). In contrast to CBPV, which causes natural outbreaks of bee paralysis [for a recent review: Ribière et al. (2010)], ABPV can be found at similar concentrations in naturally paralyzed (caused by CBPV) and apparently healthy bees (Bailey et al. 1963). However, in injection bioassays, ABPV turned out to be highly virulent causing death of injected adult bees within 3–5 days (Bailey et al. 1963). This contrasted sharply to the observations in the field at that time, where ABPV had null or low impact on infected bees and colonies suggesting that ABPV in contrast to CBPV caused covert infections (presence of the virus in the absence of disease symptoms). ABPV has a geographical distribution similar to that of A. mellifera and, therefore, has been isolated from healthy adult bees from most regions of the world (Allen and Ball 1996; Anderson 1988; Bailey 1965, 1975; Bailey et al. 1981; Hung et al. 1996a; Tentcheva et al. 2004). The apparent harmlessness of ABPV infections dramatically changed with the advent of
V. destructor in Europe. In severely mite-infested colonies, brood and adult bees were obviously dying from ABPV infection (Ball 1983, 1985; Ball and Allen 1988; Bekesi et al. 1999; Berenyi et al. 2006; Hung et al. 1996c; Nordström et al. 1999; Ritter et al. 1984). Considering the extreme virulence of ABPV when injected into the bee haemo- lymph, it is not surprising that this virus started to cause problems when V. destructor entered the stage, became established as ABPV vector and began to inject the virus
into pupae and adult bees (Ball 1989). For ABPV, V. destructor acts solely as mechanical vector (Ball 1989; Tentcheva et al. 2004; Wiegers 1988); in contrast to vectorial DWV transmission, there is no evidence that V. destructor supports or allows ABPV replication and hence can act as biological vector of ABPV.
Since ABPV can frequently be detected in apparently healthy as well as in dead bees and, accordingly, likewise in bees from healthy and collapsing colonies (Bekesi et al. 1999; Berenyi et al. 2006; Tentcheva et al. 2004), it is difficult to assess the impact of ABPV on colony mortality. A recent 2-year study on ABPV and winter losses in Germany by Siede et al. (2008) demonstrated a significant relation between pre-winter ABPV infection and winter mortality for the 2005/2006 season but not for the 2004/ 2005 season. A much broader German study performed over 4 years and involving more than 1,200 colonies from more than 120 apiaries, however, revealed a highly significant relation between ABPV infection in autumn and colony collapse in the following winter season (Genersch et al. 2010).
In summary, while ABPV had originally been consid- ered an economically irrelevant viral pathogen of honey bees, it developed an alarming virulence in association with
V. destructor. Similar to DWV, it now adds to the damage inflicted to honey bee colonies by V. destructor parasitism and became a key player in the parasitic mite syndrome (Hung et al. 1995, 1996b; Shimanuki et al. 1994). The exact role of the mite in the increase in ABPV virulence still remains elusive.
IAPV has recently been identified when the homogenate of a single dead bee collected in the course of studies related to severe colony mortality in Israel was inoculated into healthy-looking bee larvae (Maori et al. 2007). Subsequent studies revealed that IAPV has been around for quite some time (Chen and Evans 2007; Palacios et al. 2008) and may have been mistaken for KBV in past studies due to its close genetic relationship with KBV, which makes it difficult to discriminate between these two viruses via conventional reverse transcription polymerase chain reaction (PCR) protocols (de Miranda et al. 2010). IAPV, KBV and ABPV form a complex of genetically and biologically related viruses within the family Dicistroviridae. IAPV is extremely virulent when injected into pupae or adult bees (Maori et al. 2007). Hence, it can be assumed that V. destructor plays a role in the virulence of IAPV as it does for DWV and ABPV. So far, little is known about the transmission and pathomechanisms of IAPV since it came into the focus of bee virologists only quite recently in the context of colony collapse disorder, a condition described mainly in the USA and leading to severe colony losses (vanEngelsdorp et al. 2007, 2009). However, the potential virulence of IAPV for bees and colonies is unquestioned as
it has been identified as a marker or secondary agent of CCD (Cox-Foster and VanEngelsdorp 2009; Cox-Foster et al. 2007), and anti-viral treatment using IAPV-specific RNAi was able to silence IAPV and to reduce the symptoms of CCD (Maori et al. 2009). These results suggest that IAPV is at least in part responsible for the described symptoms and colony mortality in the course of CCD. IAPV is prevalent in the Middle East, Australia and the USA but less frequently found in Europe (de Miranda et al. 2010). This could explain why IAPV is implicated in colony losses in the USA (Cox-Foster et al. 2007) but so far not in Europe (Genersch et al. 2010).
Bacterial pathogens: M. plutonius and P. larvae
Only two bacterial pathogens are known from honey bees, and both are pathogenic for honey bee larvae but not for adult bees: M. plutonius, causing European Foulbrood (EFB) (Bailey 1956, 1983) and P. larvae, causing American Foulbrood (AFB) (Genersch et al. 2006). AFB is not implicated in any inexplicable colony losses since this brood disease is easily diagnosed and, as a notifiable disease, well controlled by the authorities. However, it is not at all a rare disease but occurs rather frequently (about 5–10% of the colonies in Germany are infected although not yet clinically AFB diseased; own unpublished data from nearly 10 years of monitoring P. larvae incidence) and causes considerable economic losses to beekeepers all over the world. Therefore, it can be considered one of the major threats to honey bee health. Despite its relevance for apiculture, AFB will not be covered in this review since this disease and its etiological agent, P. larvae, have been reviewed in great detail recently (Ashiralieva and Genersch 2006; Genersch 2007, 2008, 2010).
M. plutonius, the causative agent of EFB, is a Gram- positive, lanceolate coccus with a close phylogenetic relationship to the genus Enterococcus (Cai and Collins 1994). The vegetative form is occurring singly, in pairs or in chains of varying length. The identification of M. plutonius as causative agent of EFB has long been hampered by the fact that M. plutonius is very fastidious and, therefore, hardly culturable from diseased larvae. It took quite some time until the original hypothesis of White (1912) that EFB is caused by a unique organism named Bacillus pluton had been substantiated (Bailey 1983). The classification of M. plutonius has not been easy and, hence, it can be found in the literature as B. pluton (White 1912), Streptococcus pluton (Bailey 1957), Melissococcus pluton (Bailey and Collins 1982; Cai and Collins 1994) and finally
M. plutonius (Truper and dé Clari 1998).
Infection of larvae occurs when larvae ingest food contaminated with M. plutonius. Larvae are susceptible at
any stage before cell capping, but their susceptibility decreases with increasing age. Bacteria proliferate in the larval midgut assimilating much of the larval food. It is assumed that infected larvae die from starvation (Bailey 1983) and are then decomposed by secondary invaders like Paenibacillus alvei and Enterococcus faecalis, two sapro- phytic bacteria frequently found associated with EFB. Dead larvae are not found in their normal coiled position at the cell bottom, but instead they are twisted around the walls or stretched out in the cell (Fig. 1). Usually, larvae die when they are 4 or 5 days old, but infected larvae may also survive and pupate after discharging bacterially contami- nated faeces which is deposited on the walls of the brood cells (Bailey and Ball 1991). These faecal remains are infective, indicating that the durable encapsulated stages of
M. plutonius are also infectious. Surviving infected larvae produce pupae and adults of subnormal weight (Bailey 1960). Nothing is described about the role of these infected (?) adults in the intra-colonial spread of the disease and the persistence of the pathogen within the bee population [see Forsgren (2010) for a recent review].
Many aspects of the pathogenesis, transmission and control of M. plutonius are poorly understood and still remain elusive. A literature survey will reveal that most of the work concerning EFB has been conducted decades ago, and molecular work so far mostly concentrated on developing and applying PCR techniques for the detection of M. plutonius (Djordjevic et al. 1998; Forsgren et al. 2005; Govan et al. 1998; Roetschi et al. 2008). This was for sure influenced by the fact that for a long time EFB did not create much of a problem in apiculture since many infected and diseased colonies spontaneously recovered from the disease (Bailey and Ball 1991). This situation changed at least in some regions of Europe, like Switzerland and the
Fig. 1 Honey bee larvae succumbed to EFB (picture taken by Pia Aumeier)
UK, where EFB has become a major problem for apiculture recently (Roetschi et al. 2008; Wilkins et al. 2007). In Switzerland, the infection and re-infection rates have been dramatically increasing since 2002, and the situation now is nearly out of control with 796 official outbreaks in 2009 (Fig. 2). Sanitizing measures formerly successfully applied seem to be ineffective now. There is an urge to find a way to effectively combat the disease and to prevent further spread of M. plutonius. But the lack of knowledge about this pathogen is hampering the development of a sustain- able cure or control method against EFB at the time being. The situation exemplifies that understanding a disease is an imperative prerequisite for developing a cure.
Fungal pathogens: Nosema spp.
Nosema spp. belongs to the phylum Microsporidia which comprises more than 160 genera and almost 1,300 species isolated from insects and other invertebrates but also known from vertebrates including humans (Becnel and Andreadis 1999; Canning and Lom 1986; Weber et al. 1994). Microsporidia are obligate intracellular, fungal parasites which exist outside the host cell only as metabolically inactive spores. These environmental spores are the infectious form of all microsporidia driving disease trans- mission between individuals. Infection of the host cell involves germination of the spores leading to mechanical injection of the extruded polar tube into the host cell followed by the transmission of the sporoplasm through the polar tube into the host cell's cytoplasm (Bigliardi and Sacchi 2001; Franzen 2005). Two species of this phylum, Nosema apis and N. ceranae, are pathogenic for adult
Fig. 2 Official data on the development of diagnosed EFB outbreaks in Switzerland between 1991 and 2009 (Source: Bundesamt für Veterinärwesen BVET, Switzerland)
honeybees. The original assumption was that N. apis specifically infects the European honey bee, A. mellifera, causing nosemosis (Zander 1909), and N. ceranae is a specific pathogen of the Asian honey bee, A. cerana (Fries et al. 1996). Recently, it became evident that N. ceranae is also widespread in the A. mellifera population throughout the world (Chen et al. 2008; Giersch et al. 2009; Higes et al. 2006; Huang et al. 2007; Invernizzi et al. 2009; Klee et al. 2007; Paxton et al. 2007).
Adult bees become infected by ingesting Nosema spores which are present in faeces but can also be found in pollen (Higes et al. 2008b). Spores germinate in the midgut and infect the cells of the midgut epithelium where they vigorously proliferate to produce new environmental spores which are released into the gut lumen (Fries 2010). Nosemosis, i.e. the clinical outbreak of Nosema infection caused by N. apis, is characterized mainly by dysentery, whereas N. ceranae is described to cause death of individuals and colonies not preceded by any visible symptoms (Higes et al. 2008a; Martin-Hernandez et al. 2007). N. apis infection is restricted to the midgut epithelium (Fries 1988), whereas N. ceranae has also been detected in other bee tissues like malpighian tubules and hypopharyngeal glands although so far only via highly sensitive, molecular methods (Chen et al. 2009).
Reports on the impact of N. ceranae infections on honey bee health and colony survival are contradictory. In Spain,
N. ceranae causes an unusual form of nosemosis eventually leading to colony collapse (Higes et al. 2008a; Martin- Hernandez et al. 2007). Accordingly, in laboratory infection assays, Spanish N. ceranae isolates were found to be highly virulent (Higes et al. 2007). However, this extreme in vitro virulence of N. ceranae could not be confirmed by others (Mayack and Naug 2009; Paxton et al. 2007), which might be due to different isolates used. Likewise, the worldwide distribution of N. ceranae (Chauzat et al. 2007; Chen et al. 2008; Cox-Foster et al. 2007; Fries et al. 2006; Higes et al. 2006; Invernizzi et al. 2009; Klee et al. 2007; Paxton et al. 2007; Tapaszti et al. 2009; Williams et al. 2008), which is not inevitably accompanied by the symptoms described by Higes et al. (2008a), also suggest that N. ceranae killing honey bee colonies might be a regional problem rather than a global phenomenon. The observed virulence of N. ceranae in Spain has been explained by the better adaptation to elevated temperatures of N. ceranae compared to N. apis (Fenoy et al. 2009; Martin-Hernandez et al. 2009). However, it has also been reported that N. ceranae spores were susceptible to freezing in laboratory experiments (Fries 2010) and that the virulence of N. ceranae might be influenced by climatic conditions (Gisder et al. 2010). This is an especially intriguing thought since changes in disease prevalence and pathogen virulence due to climatic change is a hot topic nowadays.
Summary
Alarmingly increasing honey bee colony losses have been frequently reported in the media over the past few years and attracted much attention in non-scientific and scientific communities. From recent surveys of honey bee losses in North America and Europe, it became evident that pests and pathogens could be identified as the single most important cause of these colony losses so far (Genersch et al. 2010; vanEngelsdorp et al. 2008; vanEngelsdorp and Meixner 2009). We here introduced several bee pathogens which are thought to be involved in such honey bee colony losses. These examples show that, even within Europe, diverse pathogens are involved in the presumed “inexplicable” colony losses. Therefore, although the decline in managed honey bees equally seems to be a problem in the USA, Europe and Japan (Oldroyd 2007) despite great differences in beekeeping practice (Ratnieks and Carreck 2010), the factors responsible for colony losses differ from continent to continent and from region to region. We should be prepared that we will not find a globally valid solution to honey bee decline but that we will have a panel of possible factors, all of them asking for a specific solution to address the problem properly. If we are to explain unusual colony losses and if we are to find the cause for these losses, then we need to move from the mere detection of bee pathogens in individuals and colonies to molecular beepathology focussing on host– (vector–)pathogen interactions with equal emphasis on the pathogen (the vector) and the host. Only then will we understand the diseases and the pathogens of honey bees which in turn will enable us to develop adequate control measures.
I will close this review with a quotation from George F. White, who identified Bacillus larvae [later reclassified as
P. larvae (Genersch et al. 2006)], the etiological agent of American foulbrood, a quotation that did not lose its timeliness over the past hundred years:
In order to combat a disease to the best advantage, it is clear that its cause must be known, as well as the means by which the infection is transmitted and the environ- mental conditions which are favourable for the breaking out of an epidemic (White 1906).
Acknowledgements Own work presented in this review was supported by the EU (according to regulation 797/2004) as well as by grants from the Ministries of Agriculture of Brandenburg, Sachsen- Anhalt, Sachsen and Thüringen, and the Senate of Berlin, Germany.
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Aug 25, 2021
Processing of Honey: A Review
International Journal of Food Properties
PROCESSING OF HONEY: A REVIEW
R. Subramanian, H. Umesh Hebbar and N.K. Rastogi
Department of Food Engineering, Central Food Technological Research Institute, Mysore, India
Thermal processing of honey eliminates the microorganisms responsible for spoilage. Microwave heating, infrared heating, ultrasound processing, and membrane processing have been explored as alternatives to conventional heat processing. Microwave heating pro- vides a rapid method for achieving the desired level of yeast reduction with reduced thermal damage. Infrared heating is not as rapid as microwave heating but desired results are achieved in a relatively shorter duration (3 to 4 minutes) compared to the conventional method. Membrane processing is an athermal process and very effective in the complete removal of yeast cells from honey. Microfiltration and ultrafiltration could be employed to produce enzyme-enriched honey besides clarified honey.
Keywords: Diastase activity, Honey, Hydroxymethylfurfural (HMF), Infrared heating, Membrane processing, Microwave heating, Ultrasound treatment, Yeast count.
INTRODUCTION
Honey, a natural biological product evolved from nectar and of great benefit to human beings both as medicine and food, is consumed in every country of the world in some form. Honey contains glucose, fructose, and water, in addition to small quantities of proteins, minerals, organic acids, and vitamins.[1] It is liked for its characteristic flavor, sweetness, and texture.
Honey extracted from combs and apiaries contains pollens, beeswax, and other undesirable materials, besides yeast, that are to be removed for better product quality and shelf life. Hence, honey is processed before packing in bottles or other containers. The type of equipment used and steps followed in processing, however, depends upon the scale of operation. Two important stages of honey processing are filtration and heating. The separation of pollens, beeswax, and other materials are normally done through strain- ing and pressure filtration. Heat or thermal processing of honey eliminates the microor- ganisms responsible for spoilage and reduces the moisture content to a level that retards the fermentation process. The general process flow diagram for conventional honey pro- cessing is provided in Fig. 1. The physical separation of undesirable, suspended matter is done before thermally processing honey.
Received 8 May 2006; accepted 30 August 2006.
Address correspondence to R. Subramanian, Department of Food Engineering, Central Food Technological Research Institute, Mysore 570020, India. E-mail: [emailprotected]
127
FILTRATION
STRAINING
Suspended Particles
Fine Particles
Moisture reduction
Yeast count reduction
Figure 1 Conventional method of processing honey.
Straining
The straining operation to remove suspended solids (including large wax particles) is carried out either manually or by mechanical means. The method and the equipment used for straining depend on the size of the operation. In small-scale operations, straining is done using cloth or nylon bags, which are frequently cleaned to remove the suspended particles. In large-scale operations, the straining operation is combined with the pre- heating (up to 40°C) operation in a jacketed tank fitted with a stirrer.[2]
Filtration
The strained honey is further processed using pressure filters. Typically a polypro- pylene micro filter of 80 mm is used as a filter medium. The honey temperature is main- tained between 50–55°C, which prevents the melting of the beeswax.[2] Large-scale processors subject honey to coarse filtration, centrifugal clarification, fine filtration, and blending, prior to filling.
THERMAL PROCESSING OF HONEY
The major problem faced by honey producers in tropical countries is its rapid deteri- oration in quality due to fermentation. Honey generally contains osmophilic (sugar–tolerant) yeast in greater or lesser amount and ferment, if the moisture content is high enough and
storage temperature is favorable. Lockhead[3] had reported that a raw honey sample con- taining more than 20% moisture readily undergoes fermentation irrespective of the initial yeast count. Moisture influences the rate of fermentation, granulation, and honey flavor. Reduction of moisture content below 17% is considered to be a safe level for retarding yeast activity. However, the chances of granulation increase with decrease in moisture content of honey. The unprocessed honey tends to ferment within a few days of storage at ambient temperature because of its high moisture content and yeast count. To prevent fer- mentation, honey is heat processed before storage. Heat processing of honey eliminates the microorganisms responsible for spoilage and reduces the moisture content to a level that retards the fermentation process.
Honey has high viscosity (1.36 N·s/m2 at 25°C and 21.5% moisture)[4] that pose problems in handling and processing. Viscosity of honey is influenced by several factors. As honey is heated, it initially undergoes a very rapid decrease in viscosity up to 30°C, beyond which the change in viscosity is much slower. Besides temperature and moisture content, variations in viscosity are attributed to the composition of individual sugars and to non-sugar and colloidal material. Mossel et al.[5] successfully described the sugar con- centration dependence of the viscosity of honey samples with varying moisture contents with a model that was originally developed to describe the viscosity data of various sugar mixtures. Newton’s law of viscosity could adequately describe the flow behavior of honey samples, and the temperature dependency of viscosity was found to follow the Arrehenius model.[6] Yanniotis et al.[7] also reported the Newtonian behavior of honey after studying the effect of moisture content on its viscosity at different temperatures. Generally, honey is a Newtonian fluid but non-Newtonian behaviour has also been reported owing to the presence of colloids (possibly proteins) and the polysaccharide dextran.[8] Recently, Escobedo et al.[9] reported that honey samples at the 12th week of storage showed a strong tendency to behaving as a non-Newtonian fluid owing to the presence of crystals that changed the flow behavior. The authors rightly cautioned that such a change in flow behavior in honey may be due to the nature of the test. Sopade et al.[10] studied the pump- ing characteristics of Australian honey and proposed an equation for the prediction of pressure loss in a pipeline of typical size at any temperature and flow rate. Bhandari et al.[11] correlated the glass transition temperature (Tg) with critical viscosity and reported that the critical viscosity is reached at a temperature 10 to 20°C above the Tg.
Crystallization is an undesirable property in handling, processing, and marketing, except for certain purpose such as in the production of creamed honey. Glucose is the principal component that crystallizes in honey as it exists in a supersaturated state. Bhandari et al.[8] summarized some of the methods proposed to stop crystallization of honey: storage at freezing temperature (-40°C), heat treatment to dissolve crystals and crystal nuclei, removal of air bubbles, dust and pollen particles by filtration, filling at higher temperatures (>45°C) to avoid air bubbles incorporation during filling, addition of inhibitors such as isobutyric and sorbic acid, and adjusting the glucose to fructose ratios or the water content. Ultrasound processing has also been reported for preventing crys- tallization in honey, and the various attempts made are discussed under a separate head- ing. Heating is a common method to control the crystallization. It helps to melt invisible crystals present in honey. After melting all the crystals and nuclei, even the most crystal- lisable honey can remain liquid for many months. Presence of air bubbles in the packag- ing containers can provoke nucleation and crystallization of honey. Filling at higher temperatures eliminates air bubbles and avoids air incorporation during packing due to low viscosity.[8]
Although, honey is thermally processed to eliminate yeast, it could result in product quality deterioration. Uncontrolled heating alters the parameters such as hydroxymethyl- furfural (HMF) content and enzyme activity unfavorably. The initial HMF content in dif- ferent honey types varies drastically and it depends upon the climatic condition of the region besides other factors. Excessive amount of HMF has been considered evidence of overheating, implying a darkening of color and a loss of freshness of honey. The starch– digesting enzyme(s) of honey are also used as an indicator of honey quality because of their sensitivity to heat. The major enzymes present in honey are invertase, amylase (dia- stase), and glucose oxidase along with minute quantities of catalase and acid phos- phatase.[1] The enzyme content in honey is measured as diastase activity and expressed in terms of diastase number (DN). The European Regional Standard for honey[12] specifies a minimum DN of 8 in processed honey.
CONVENTIONAL PROCESSING OF HONEY
The conventional process involves preheating to 40°C, straining, filtering /clarification, and indirect heating of filtered honey at 60–65°C for 25–30 minutes in a tubular heat exchanger followed by rapid cooling in order to protect its natural color, flavor, enzyme content, and other biological substances.[2] Studies have shown that heating honey at 63, 65, and 68°C for 35, 25, and 7.5 minutes, respectively can destroy the yeast cells completely.[13] There is a lack of literature about the application of high-temperature short-time (HTST) heating treatment on honey. Tosi et al.[14] reported that a mild HTST treatment condition, typically heating at 80°C for 60 seconds in the transient stage and 30 seconds in the isothermal stage, destroyed all microorganisms responsible for quality dam- age without spoiling honey. Temperature beyond 80°C through 140°C, at very short times, did not seem to cause deleterious effects on honey as measured by HMF and diastase activity. White et al.[15] reported the effect of storage and processing temperatures on honey quality. Samples with high, intermediate and low diastase activity were stored at different temperatures (20–70°C). The half-life time of diastase and invertase activity, estimated using the empirical equations indicated the direct relationship with temperature. Singh and Bath[16] studied the relationship between heating and HMF formation in different types of honey. Heating temperature and time showed significant effect on HMF formation. They observed a large difference in HMF formation between different types of honey and used second order polynomials to effectively predict the HMF formation in different types of honey. Effects of thermal treatment on HMF content in honey was also studied by Tosi et al.[17] and reported that the kinetics of HMF formation did not depend on the initial HMF concentration in honey. They also reported that during thermal processing, the time- temperature combination is very crucial for maintaining the HMF level below the maxi- mum permissible limit. Gupta et al.[18] studied the influence of different treatments and storage conditions on some physicochemical characteristics and sensory qualities of Indian honey. The color of the honey was significantly affected by the storage temperature and period with maximum deterioration at a storage temperature of 40°C. However, gran- ulation was completely eliminated in honey stored at 40°C due to melting. Evaluation of honey samples stored for six months showed comparatively higher overall sensory score for unheated honey stored at 5°C (Table 1). Visquert et al.[19] investigated the effect of thermal processing on honey quality during storage. Honey was processed at 35 to 85°C for 1–672 hours, depending on the chosen processing temperature and evaluated with respect to HMF content, acidity, electrical conductivity, and moisture content. Thermal
Table 1 The effect of different treatment on the sensory characteristics of honey after six months of storage.[18]
Treatment
Color
Consistency
Taste
Aroma
Overall qualities
Room temperature (7–30°C)
Unheated
18.8
16.7
17.3
17.8
17.8
Unheated (KMS)
17.3
16.5
17.6
17.1
18.2
Heated
15.8
17.2
16.1
16.7
15.6
Temperature 5°C Unheated
19.3
15.4
19.2
18.9
19.6
Unheated (KMS)
18.6
15.0
18.6
17.5
18.4
Heated
16.0
16.2
16.5
17.6
15.5
Temperature 40°C Unheated
14.4
19.1
13.8
14.0
1.37
Unheated (KMS)
14.0
18.7
14.2
13.5
1.39
Heated
14.2
19.0
13.9
14.2
1.38
processing increased the HMF content of honey considerably. However, acidity, electrical conductivity, and moisture content were unaffected by thermal processing and during sub- sequent storage.
Central Bee Research and Training Institute, Pune had developed a honey process- ing plant to handle Indian varieties of honey, which have very high moisture content.[20] The plant having the capacity to process ~40 kg/h, could reduce the moisture content by
~8–10%. The constructional features of the plant include cartridge type micro filters, pro- cessing tanks with helical coil heat exchangers, and falling-film evaporator.
In recent times the demand for better quality honey is on the rise as honey is being consumed now for its health benefits. So, efforts are being made to look for alternatives to conventional thermal process, which can produce better quality honey. Application of microwave, ultrasound, and infrared heating of honey has been reported and claims have been made on the improved product quality. Microwave and infrared heating have gained popularity in food processing over conventional heating owing to their inherent advantage of rapidity and better-quality product. Pressure-driven membrane processes are remark- ably simple allowing ambient temperature operation and requiring less energy. Membrane technology has the potential in replacing or complementing some of the traditional meth- ods of processing, as well as in developing new products.
MICROWAVE HEAT PROCESSING OF HONEY
The application of microwave heating is well known in the food industry, particu- larly for tempering, blanching, drying, and pasteurization of food material. Microwave heating is greatly affected by the presence of water in foods, as water is the major absorber of microwave energy in food, consequently, the higher the moisture content, the better the resultant heating. In contrast to conventional heating, microwaves penetrate the material, interact with it, and generate heat leading to its rapid heating. Materials containing polar molecules, such as water, are rapidly heated when exposed to microwave radiation due to molecular friction generated by dipolar rotation in the presence of an alternating electric field. It is also reported that dissolved sugars are the main microwave susceptors in high carbohydrate foods and syrups.[21] Since honey contains a substantial amount of water
(18–24%), as well as large amounts of dissolved sugars (70–80%), microwave radiation could be effectively used for heating honey.
The processed honey containing yeast cells could be safely stored at room tempera- ture provided the count is apparently insufficient to initiate fermentation. Ghazali et al.[22] studied the effect of microwave processing of starfruit honey for its storage stability. Their study showed that the fairly short time taken to reach the required processing temperature ensured little change in chemical properties. Honey was heated to 71°C using a micro- wave oven and stored at two different storage conditions, room temperature (28 ± 2°C) and 4°C for 16 weeks. The physicochemical properties of unheated and heated honey were measured before and during storage (Table 2). Spoilage was noticed in unheated honey (control), irrespective of the storage temperature. Heated samples were more resistant to spoilage. The spoilage of honey was attributed to the yeast count in honey, which was much higher (1.02 ´ 105 cfu/g) in the unheated honey compared to heated honey sample (5.90 ´ 102 cfu/g). No appreciable variation in yeast count was noticed during storage of heated honey. Granulation was not observed in honey samples that were heated before storing. Storing of unheated honey at room temperature was also free from granulation in agreement with the general observation related to dextrose-to-water ratio and storage tem- perature. Darkening of honey color was observed during storage in both heated and unheated honey from light golden color to golden brown. However, the honey stored at 4°C was considerably lighter in color than honey stored at room temperature. Heated honey was darker than the unheated honey, whatever the storage temperature. Heating did not alter the moisture content of honey (20.8%). Also, no noticeable change in ash, nitro- gen contents, pH, and acidity was observed in microwave heated honey sample. Heat pro- cessing of honey led to a 37.5% loss in diastase activity. Storage at 4°C had no effect on subsequent activity. However, room temperature storage led to further loss (33%) in activ- ity. Heating did not show any effect on the sugar contents. However, the concentration of glucose, maltose, and sucrose changed during storage depending on the storage tempera- ture and whether the sample had been heated or not.
Bath and Singh[23] studied the effect of microwave heating on HMF formation and
browning in two types of honey (Helianthus annuus and Eucalyptus lanceolatus). The for- mation of HMF and browning increased with microwave power levels as well as with heating duration, the former showing greater effect. Both the types of honey differed sig- nificantly with respect to HMF formation and browning under similar microwave heating conditions, which was attributed to the difference in chemical composition of honey.
Table 2
The physicochemical properties of unheated and microwave heated starfruit honey before and after storage for 16 weeks.
[22]
Storage temperature
Property Unheated Heated 4°C Room temperature
Moisture (%)
20.80
20.80
21.00 (22.00)
20.80 (21.20)
Diastase number
4.00
2.51
2.32 (3.28)
1.69 (2.58)
Fructose (%)
32.10
32.12
32.10 (32.07)
30.08 (30.12)
Glucose (%)
30.14
30.10
30.02 (24.75)
26.85 (24.90)
Maltose (%)
2.31
2.30
2.42 (5.33)
5.47 (7.47)
Sucrose (%)
5.96
6.01
5.92 (5.48)
1.67 (0.00)
Yeast (cfu/g)
1.02 ´ 105
5.90 ´ 102
4.00 ´ 102
3.45 ´ 102
Values indicated in the parentheses are for unheated honey.
Hebbar et al.[24] conducted studies on microwave heating in a micro-convective oven (2450 MHz, maximum power of 800 W). Experiments were carried out at different power levels (PL) ranging from 10 to 100 (175–800 W) and for different heating periods from 15 to 90 seconds. The extent of change in properties (HMF, diastase activity, mois- ture content, and yeast count) mainly depended on the power level (power intensity) and duration of heating. The changes in these properties were prominent in samples that were heated at higher power levels and for longer durations. The peak temperature attained by the sample depended on the power level used as well as duration of heating.
The reduction in yeast count was observed to the extent of commercially acceptable level (< 500 cfu/mL) at power levels of 10, 30, and 50 when the samples were heated for more than 45 seconds. At higher power levels of 70 and 100, heating duration of 30 and 15 seconds, respectively, was sufficient to achieve the same level of reduction in yeast count. A semi-log plot of yeast count reduction ratio (ratio of yeast count at any given time to ini- tial count) with time at different power levels is shown in Fig. 2a. The reduction in yeast count was rapid, generally during the first 20 to 30 seconds, and the rate of yeast reduction was directly related to the input power intensity. The reduction of yeast count is attributed to the rapid increase in sample temperature due to microwave exposure, leading to the rupture of yeast cell walls.
The increase in HMF value was gradual with heating duration at power levels of 10, 30, 50, and 70. But HMF level increased sharply in the samples heated for a longer dura- tion at the maximum power level of 100. However, these values were far below the maxi- mum permissible statutory level of 40 mg/kg of honey.[4] Variation of HMF with duration of heating at different power levels is shown in Fig. 2b. The trends in the variation of HMF values of the samples clearly depicted the sensitivity of honey to the period of heating and temperature (power level).
Heating affects the enzyme activity and the diastase activity showed a decline with heating under all conditions employed. The reduction in diastase activity with heating duration at different power levels is depicted in Fig. 3a. Long heating periods of 60 to 90 seconds duration at power levels of 30, 50, and 70 reduced the diastase activity of honey by ~50% of its original value. At a power level of 100, heating above 45 seconds resulted in reduction of the diastase activity to a level lower than the minimum permissible DN of 8.[12] Heating also led to a reduction in moisture content above 9% at power levels of 50,
70, and 100 when the samples were heated for 60 seconds. Fig. 3b indicates the reduction in moisture content with heating duration at different power levels. Larger reduction in moisture content was not observed at lower power levels. The final moisture content in most of the samples was in the range of 19.8 to 21.2%, which is below the acceptable level (22%) for commercial processed honey.
Though different combinations of time and microwave power level could be used to achieve the commercially acceptable level of yeast reduction in honey, it is equally impor- tant to take the peak temperature attained by the sample into consideration. Heating honey above 90°C results in caramelization of sugar.[25] Also it seems that this has a direct bear- ing on the increase in HMF value and loss in diastase activity. Therefore, it is beneficial to achieve the desired yeast reduction by choosing any suitable combination of power level and duration that will maintain the temperature of honey well below 85–90°C.
Among the various selected combinations, higher power level and shorter duration seems to be better than lower power level and longer duration. At power level 100 (power intensity 16 W/g), heating for 15 seconds resulted in substantial reduction in yeast count (450 cfu/mL), lower HMF value (3.8 mg/kg) and higher retention of
Time (s)
0 20 40 60 80 100
-1
-2
-3
-4
-5
(a)
300
250
200
150
100
50
0 20 40 60 80 100
Time (s)
(b)
Figure 2 (a) Reduction in yeast count and (b) Increase in HMF with time at different power levels (PL) of microwave heating.[24]
70
60
50
40
30
20
10
0 20 40 60 80 100
Tim e (s)
(a)
14
12
10
8
6
4
2
0 20 40 60 80 100
Time (s)
(b)
Figure 3 (a) Reduction in diastase activity and (b) Reduction in moisture content with time at different power levels (PL) of microwave heating.[24]
diastase activity (DN 12). Also, the desired reduction in yeast count was achieved with least undesired changes in the sample at a much lower processing temperature (54°C).
INFRARED HEAT PROCESSING OF HONEY
Infrared heating of food is gaining popularity due to its transient response, significant energy savings over other thermal processes and ease of construction of hybrid systems with convective and conductive heating sources.[26] Infrared heaters provide high rates of energy input to the material surface and the radiant heat flux penetrates the material to a depth, which depends on the nature of the material and the wavelength of the incident radiation.[27] Sugar and water are the two major constituents of honey and both have good absorption bands in the thermal radiation region.[26] The above factors could be successfully utilized for efficient processing of honey.
Hebbar et al.[24] conducted studies on infrared heating in a near infrared (NIR) batch oven (locally fabricated) fitted with lamps (1.0 kW, peak wavelength 1.1–1.2 mm). In these experiments, the samples were heated continuously for 2, 3, 4, 5, and 8 minutes. HMF, diastase activity, moisture content and yeast count in these samples were analysed (Table 3). In all the cases, heating caused substantial reduction in yeast count. Heating for 5 minutes resulted in a product temperature of 85°C, HMF increase of 220%, and 37% drop in enzyme activity. When the samples were heated for 8 minutes, no viable colony forming units of yeast were noticed. However, the diastase activity drastically reduced in these samples, clearly indicating excessive heating of honey that also showed up in a very high product temperature (110°C). A heating period of 3 to 4 minutes was adequate to obtain a commercially acceptable product, which met all the statutory requirements of quality in terms of HMF (£40 mg/kg), diastase activity (DN ³ 8), moisture content, and yeast count.
ULTRASOUND PROCESSING FOR LIQUEFYING CRYSTALLIZED HONEY
Granulation or crystallization is a natural tendency of honey and this common phenomenon is a serious problem that affects marketing of honey. Crystallization of honey is a complex process controlled by a number of factors acting simultaneously and sometimes in a contradictory fashion. Concentration and super-saturation of the major constituents (levulose, dextrose, and sucrose) and the minor ones (proteins and dextrins), the presence of colloidal particles (nuclei), and temperature with its varying and contradictory effects are some of the more important factors involved.[28] Traditionally,
Table 3 Continuous heating of honey with infrared radiation.[24]
Time
Temperature
Moisture
Yeast count
HMF
Enzyme
S. No.
(min)
(°C)
(%)
(cfu/mL)
(mg/kg)
activity (DN)
Control
21.8
7000
2.0
16.6
1
2
47
20.2
500
3.2
13.8
2
3
61
19.8
300
3.6
12.4
3
4
74
19.8
200
4.6
11.6
4
5
85
19.2
150
6.5
10.5
5
8
110
18.2
Nil
7.9
Traces
honey is heated to temperatures of 77°C to kill yeasts and to delay crystallization.[29] Heating affects the delicate flavors of honey, therefore alternate methods by means other than heating such as ultrasonic, freezing, and chemical inhibitor treatments were attempted for preventing crystallization in honey.[28] Ultrasonic waves are sound waves with a higher frequency than the maximum that can be sensed by the human ear. These waves when transmitted through liquid medium cause mechanical and thermal changes in the material through which they pass, and also induce changes in unicellular organisms.[30]
Kaloyereas[31] reported first in 1955 that high frequency sound waves (9 kHz) eliminated the existing crystals and retarded further crystallization in honey. Ultrasound processing destroyed most of the yeast cells that were present in the honey, and those that survived had lost their ability to grow. No crystals were observed in ultrasound treated honey and inhibited granulation for a period (15 months at 16°C) comparable to heating the honey.[28] One disadvantage of this method was that exposure times of 15 to 30 min- utes were required with cost implications.
Liebl[29] proposed an improved method for preventing the granulation by exposing the honey to ultrasound waves of a much higher frequency (18 kHz) that drastically reduced the liquefaction time to less than 30 seconds. This patented process was designed to work at lower processing temperature (33°C) facilitating greater retention of aroma and flavor along with huge savings on cost of energy compared to the conventional processing involving heating and cooling steps. Studies were carried out at a considerably higher scale (liquefaction of ~1500 kg of honey/h) to demonstrate the claims on the cost effectiveness of the process.
Thrasyvoulou et al.[30] studied the effects of ultrasonic waves on the quality of honey focusing on some of the chemical characteristics. Crystallized honey samples (100 g each) were liquefied by ultrasonic waves at 23 kHz and compared with conven- tionally heated (water bath heating; 60°C for 30 minutes) and untreated samples. The complete liquefaction of honey (5 samples each of blossom honey and honeydew honey) required 18 to 25 minutes of ultrasound processing. Accordingly the energy required for liquefaction varied from 0.1056–0.1466 kWh, and the maximum temper- ature attained by the samples from 76–82°C. The variation in the time required for liq- uefaction was attributed to the original granulated condition and the nature of samples.
The combined effect of temperature and processing time resulted in increase in HMF level. The average increase in HMF content was significantly low (86%) in samples liquefied by sonication compared to samples liquefied by heating (129%). Ultrasonic energy negatively affected the distaste activity of samples. The average decrease of dia- stase activity was 16% after ultrasonic treatment and 23% after heat treatment. The influ- ence of factors other than sonication or heat and typical behavior of individual samples could also affect diastase activity. Moisture content, electrical conductivity, and pH were not significantly affected by the treatments. The ultrasonic and heat treated samples were stored at 25 ± 4°C and there was no significant difference in their recrystallization time. The ultrasound treated samples remained in the liquefied state for 344 ± 39 days and heat treated samples for 282 ± 86 days.[30]
Liquefaction by ultrasonic treatment affect the honey quality to a lesser extent com- pared to heat treatment. It may be desirable to undertake studies on the effect of ultrasonic treatment on a wide range of frequencies and also by eliminating the associated thermal effect due to temperature rise.
MEMBRANE PROCESSING OF HONEY
Membrane processing is an athermal process and an alternate approach to the conventional process. It is difficult to completely destroy the microorganisms present in honey by traditional thermal processing methods practiced by the industries. Besides, thermal processing results in reduction in enzymatic activity. Anticipated benefits of membrane processing of honey are no cloudiness or sedimentation/granulation in the product, reduced viscosity, commercially sterile product and consistent quality characteristics.[32]
The applications of ultrafiltered honey in gel formulations, cosmetics and pharma- ceutical preparations besides its use as sweetener in tea/coffee and fruit beverages have been reviewed by National Honey Board.[32] Itoh et al.[33] observed that poor rising of sponge cakes (castilla) and sediment formation in fruit juices (lemon, grape, and apple), when honey is used as an ingredient, was due to the water-soluble proteins (enzymes) present in the honey. Further, they showed that ultrafiltration (UF) of honey completely eliminated these problems. Besides, UF membrane with a molecular weight cutoff (MWCO) of 10 000 could completely remove the microorganisms present in honey, rendering it as a microbiologically safe ingredient.
But the ultrafiltered honey is devoid of desirable enzymes and proteins, and hence, cannot be regarded for applications related to health foods. The major enzymes present in honey are amylase or diastase (a-amylase), invertase (a-glucosidase), and glucose oxidase. Diastase and invertase are nutritionally important enzymes present in honey. Dia- stase hydrolyses carbohydrates for easy digestibility while invertase hydrolyses sucrose and maltose.[1] Glucose oxidase is another important enzyme in honey that catalyses glucose to form gluconic acid and hydrogen peroxide.[1] Generally, honey is well known for antimicrobial activity against a number of microorganisms, probably due to the presence of high levels of tetracyclines, phenolic compounds, and hydrogen peroxides.[34] Sato and Miyata[35] attributed the antimicrobial property of honey predominantly due to hydrogen peroxide, which allows its use in the treatment of wounds and gastrointestinal diseases such as dyspepsia, bacterial gastroenteritis, gastric, and duodenal ulcers.
White and Kushnir[36] reported the approximate molecular weights for major honey enzymes as 24 000 Da for amylase and 51 000 Da for a-glucosidase. Bergner and Diemair[37] reported that glucose oxidase exhibits the highest molecular weight (>100 000 Da) among the enzymes present in honey. The enzyme content in honey is measured as dia- stase activity, which includes only amylases and not other enzymes such as invertase and glucose oxidase. It is also preferable to measure the amylase content owing to its lower molecular weight for the estimation of enzyme retention/rejection by the membranes.
The honey processors practicing membrane technology do not divulge technical information as they are regarded as trade secretes directly linked with their commercial success. There are very few reports available on membrane processing of honey in the lit- erature. Itoh et al.[38] used 7000, 30 000, and 80 000 MWCO membranes in honey pro- cessing and reported that bacteria and protein could be eliminated in honey using UF membranes. Itoh et al.[39] also assessed the performance of UF process for honey with var- ious processing parameters in a cross-flow membrane apparatus. The authors showed that the total permeation flux, as well as permeation rate of sugar increased with increase in operating temperature and increased dilution of honey at constant feed flow velocity and applied pressure. Permeate flux also increased with increase in MWCO of the membrane (10 000, 30 000, and 150 000). The authors also measured the water activity at various
sugar concentrations in honey for 4 different types of honey, which suggested that the sugar concentration shall be greater than 35.5% to maintain the water activity below 0.94 so as to inhibit multiplication of microorganisms. Protein content in the permeate decreased with decrease in MWCO of the membrane (500 000, 150 000, and 30 000) and the permeate of 30 000 MWCO membrane did not contain any protein.
Barhate et al.[40] examined the rejection of enzymes in honey (50% diluted with water) with various MWCO UF membranes and the effectiveness of these membranes in eliminating yeast cells. Besides, attempts were made using membrane technology to pro- duce a honey that is free of microorganisms and suspended matter, but containing a signif- icant concentration of enzymes.
UF membranes (20 000, 25 000, 50 000, and 100 000 MWCO) completely removed yeasts (Table 4). The sugar content in the feed was 220 mg/mL and did not change during the UF process. The conventional heat treatment process used for honey is effective for the inactivation of yeast but not for the other heat resistant organisms. It is reported that microfiltration (MF) membrane with a pore size of 200 nm would remove the viable microorganisms completely and as such could be used for sterilization.[41] Therefore, UF membranes would be useful in eliminating other microorganisms besides effectively removing the yeast cells present in the honey.
There was no diastase activity found in the permeates of UF membranes (Table 4). The rejection of lower molecular weight enzymes gave a clear indication that enzymes of higher molecular weights were also rejected by these membranes. These results imply that there are reasons other than pore size of the membrane for the rejection. A secondary layer formed on the upstream side of the membranes seems to be responsible for the complete rejection of enzymes. Secondary layer or in other words dynamic membrane formation is a process in which an active layer is formed on the membrane surface due to adsorption and other deposition phenomenon of the substances contained in the feed being processed. Such layers may also influence the rejection of other solutes present in the feed and can be used advantageously in the process.[42]
Table 4 Rejection and permeate flux of different MF/UF membranes.[40]
Membrane
pore size/MWCO
Sample
Enzyme activity (DN)
Yeast (cfu/mL)
Rejection
of enzymes (%)
Av. flux (kg/m2·h)
MF membranes
25 nm
Feed
5.3
Permeate
1.9
ND
66
ND
100 nm
Feed
5.5
Permeate
2.6
ND
54
1.49
450 nm
Feed
4.8
Permeate
3.6
ND
26
1.77
UF membranes
20 000
Feed
4.9
375
Permeate
0.0
0100
ND
25 000
Feed
4.9
500
Permeate
0.0
0100
0.90
50 000
Feed
5.0
600
Permeate
0.0
0100
1.00
100 000
Feed
5.5
325
Permeate
0.0
0100
1.15
ND, Not Determined.
Permeate flux is an important factor that determine the economics of the membrane processes. In a situation like this, permeate flux is also influenced by the dynamic active layer besides the flow resistance offered by the porous membrane, which is primarily con- trolled by the pore size. The dynamic layer offers benefits for the overall processing of honey since the permeate flux can be improved significantly by adopting higher MWCO membranes without making any compromise in the rejection requirements of the process. As expected MF membranes gave greater permeate flux and lower rejection com- pared to UF membranes (Table 4). The rejection of enzymes was between 26 to 66% depending on the pore size of the membrane. The rejection decreased as the pore size
increased.
Enzyme Enriched Honey
The enzymes present in honey, is the special reason for its wide spread use in nutri- tion, therapeutic, and health related applications.[35] During the UF process, these enzymes are retained in the retentate fraction limiting the use of ultrafiltered honey (with zero dia- stase activity) in the above applications. A different processing strategy for the production of enzyme enriched honey is possible using a combination of MF and UF membranes, which is outlined in Fig. 4. The use of MF membrane (pore size 100 nm) in the process will ensure elimination of yeast as well as other microorganisms while retaining the nutri- tional factors in the product stream (permeate) at a higher permeation rate through the sys- tem. An appropriate UF membrane (20 000 MWCO) will fractionate the microfiltered honey into two fractions namely enzyme enriched honey and regular ultrafiltered honey. By adopting this method, it is possible to enrich enzymes in honey to the extent of 2.2 fold. Higher enrichment of enzymes can be achieved by altering the volume concentration ratio (VCR) in the membrane process. Such an enriched product with enzymes may find use in very special applications related to health. These studies were conducted in batch mode with stirred membrane cells, and the performance data obtained could form a basis for conducting pilot scale process assessment before commercialization.
Concentration of Membrane Processed Honey
Honey is very viscous and is required to dilute with water before membrane pro- cessing. In the case of UF and MF, there is no change in the water content during the pro- cess. Though water removal is an expensive process step, the added water from the membrane-processed honey is to be removed to obtain the original consistency. Vacuum concentration at low temperatures is desirable to minimize the thermal damage to the product. Despite the loss in the evaporation step (Table 5), there is an overall enrichment of enzymes in the UF retentate fraction in the total process (Fig. 4).
CONCLUSIONS
Microwave heating can be effectively used for thermal processing of honey, as it provides a rapid heating to achieve the desired results for long-term storage. Infrared heat- ing is not as rapid as microwave heating but the desired results are obtained in a relatively shorter period of 3 to 4 minutes offering advantages over the conventional method. Fur- ther studies in the area of microwave and infrared heating of honey are needed to establish the relationship between various processing conditions and honey quality in continuous
Feed
(Diluted honey at 1:1) (DN=4.8)
Filtered enzyme enriched honey
(DN=21.0)
Ultrafiltered honey (DN=0)
Figure 4 Schematic diagram of membrane processing of honey.[40]
Table 5 Quality variations during concentration of membrane processed honey.[40] Operating conditions
Batch size (g)
Temperature (°C)
Absolute pressure (mbar)
Loss in diastase activity (%)
Increase in HMF content (%)
250
50
50
20.8
5.2
400
57
98
27.0
9.0
flow systems to reach the industrial application level. Ultrasound processing destroys most of the yeast cells present in the honey, besides eliminating the existing crystals and retarding further crystallization in honey. Although this approach was first reported in 1955, projected benefits have not been realized even after 5 decades.
The UF membranes completely reject enzymes and totally eliminate yeast cells in honey. Although scientific data is scanty, UF membranes are in commercial use to pro- duce clarified honey. A combination of MF and UF membranes in the process provides a scope to produce enzyme-enriched honey besides the regular clarified honey.
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43.
44. https://cloverhoney.web.id/
45. https://cloverhoney.web.id/clover-honey-madu-hdi/
46. https://cloverhoney.web.id/propoelix/
47. https://cloverhoney.web.id/royal-jelly-hdi/
48. https://cloverhoney.web.id/clover-honey-harga/
49. https://cloverhoney.web.id/propoelix-harga/
50. https://cloverhoney.web.id/hdi-propoelix-adalah/
51. https://cloverhoney.web.id/manfaat-propoelix/
52. https://cloverhoney.web.id/madu-hdi-harga/
53. https://cloverhoney.web.id/propoelix-plus/
54. https://cloverhoney.web.id/madu-hdi-manfaatnya/
55. https://cloverhoney.web.id/clover-honey-manfaatnya/
56. .
halocantik
Aug 25, 2021
After: American Journal of the College of Nutrition, 2008, 27: 677-689
Honey for Nutrition and Health: a Review
Stefan Bogdanov, PhD, Tomislav Jurendic, Robert Sieber, PhD, Peter Gallmann, PhD1
Swiss Bee Research Centre, Agroscope Liebefeld-Posieux Research Station ALP, Berne, Switzerland
Key words: honey, nutrition, composition, glycemic index
Due to the variation of botanical origin honey differs in appearance, sensory perception and composition. The main nutritional and health relevant components are carbohydrates, mainly fructose and glucose but also about 25 different oligosaccharides. Although honey is a high carbohydrate food, its glycemic index varies within a wide range from 32 to 85, depending on the botanical source. It contains small amounts of proteins, enzymes, amino acids, minerals, trace elements, vitamins, aroma compounds and polyphenols. The review covers the composition, the nutritional contribution of its components, its physiological and nutritional effects. It shows that honey has a variety of positive nutritional and health effects, if consumed at higher doses of 50 to 80 g per intake.
1 Adress reprint requests to: Peter Gallmann, PhD, Swiss Bee Research Centre, Agroscope Liebefeld-Posieux Research Station ALP, CH-3003 Bern, Switzerland
Abbreviations: CHO = carbohydrate, GI = glycemic index, GL = glycemic load, ORAC = oxygen radical absorbance capacity; PGE = prostaglandin E; PGF = prostaglandin F, RDI
= recommended daily intake
Key teaching points:
· About 95% of the honey dry matter is composed of carbohydrates, mainly fructose and glucose. 5-10 % of the total carbohydrates are oligosaccharides, in total about 25 different di- and trisaccharides.
· The Glycemic Index of honey varies from 32 to 85, depending on the botanical source which is lower than sucrose (60 to 110). Fructose-rich honeys such as acacia honey have a low GI.
· Besides, honey contains small amounts of proteins, enzymes, amino acids, minerals, trace elements, vitamins, aroma compounds and polyphenols.
· Honey has been shown to possess antimicrobial, antiviral, antiparasitory, anti- inflammatory, antioxidant, antimutagenic and antitumor effects.
· Due to its high carbohydrate content and functional properties honey is an excellent source of energy for athletes.
· Most of the health promoting properties of honey are only achieved by application of rather high doses of honey such as 50 to 80 g per intake.
INTRODUCTION
As the only available natural sweetener honey was an important food for Homo sapiens from his very beginnings. Indeed, the relation between bees and man started as early as Stone Age [1]. In order to reach the sweet honey, man was ready to risk his life (Figure 1). The first written reference to honey, a Sumerian tablet writing, dating back to 2100-2000 BC, mentions honey’s use as a drug and an ointment [2]. In most ancient cultures honey has been used for both nutritional and medical purposes [2-5]. According to the bible, King Solomon has said: “Eat honey my son, because it is good” (Old Testament, proverb 24:13). The belief that honey is a nutrient, a drug and an ointment has been carried into our days. For a long time in human history it was an important carbohydrate source and the only largely available sweetener until industrial sugar production began to replace it after 1800 [2]. In the long human tradition honey has been used not only as a nutrient but also as a medicine [3]. An alternative medicine branch, called apitherapy, has developed in recent years, offering treatments based on honey and the other bee products against many diseases. The knowledge on this subject is compiled in various books [e.g.
6,7] or on relevant web pages such as www.apitherapy.com, www.apitherapy.org. The major use of honey in healing today is its application in the treatment of wounds, burns and infections which is not a subject of this review since it is reviewed elsewhere [8].
At present the annual world honey production is about 1.2 million tons, which is less than 1% of the total sugar production. The consumption of honey differs strongly from country to country. The major honey exporting countries China and Argentina have small annual consumption rates of 0.1 to 0.2 kg per capita. Honey consumption is higher in developed countries, where the home production does not always cover the market demand. In the European Union, which is both a major honey importer and producer, the annual consumption per capita varies from medium (0.3-0.4 kg) in Italy, France, Great Britain, Denmark and Portugal to high (1-1.8 kg) in Germany, Austria, Switzerland, Portugal, Hungary and Greece, while in countries such as USA, Canada and Australia the average per capita consumption is 0.6 to 0.8 kg/year [see http://www.apiservices.com/].
Different surveys on nutritional and health aspects of honey have been compiled [8- 13]. However, as they are not complete and comprehensive, we undertook the task to review all the available relevant sources on this topic.
COMPOSITION
Table 1 The overall composition of honey is shown in Table 1. The carbohydrates are the main constituents, comprising about 95% of the honey dry weight. Beyond carbohydrates, honey contains numerous compounds such as organic acids, proteins, amino acids, minerals, polyphenols, vitamins and aroma compounds.
Summarising the data shown in Table 1 it can be concluded that the contribution of honey to the recommended daily intake is small. However, its importance with respect to nutrition lies in the manifold physiological effects [16]. It should be noted that the composition of honey depends greatly on the botanical origin [17], a fact that has been seldom considered in the nutritional and physiological studies.
Table 2
Carbohydrates
The main sugars are the monosaccharides fructose and glucose. Additionally, about 25 different oligosacharides have been detected [18,19]. The principal oligosaccharides in blossom honey are the disaccharides sucrose, maltose, trehalose and turanose, as well as some nutritionally relevant ones such as panose, 1-kestose, 6-kestose and palatinose. Compared to blossom honey honeydew honey contains higher amounts of the oligosaccharides melezitose and raffinose. In the process of digestion after honey intake the principal carbohydrates fructose and glucose are quickly transported into the blood and can be utilized for energy requirements by the human body. A daily dose of 20 g honey will cover about 3% of the required daily energy (Table 2).
Proteins, enzymes and amino acids
Honey contains roughly 0.5% proteins, mainly enzymes and free amino acids. The contribution of that fraction to human protein intake is marginal (Table 2).
The three main honey enzymes are diastase (amylase), decomposing starch or glycogen into smaller sugar units, invertase (sucrase, α-glucosidase), decomposing
sucrose into fructose and glucose, as well as glucose oxidase, producing hydrogen peroxide and gluconic acid from glucose.
Table 3
Vitamins, minerals and trace compounds
The amount of vitamins and minerals is small and the contribution of honey to the recommended daily intake (RDI) of the different trace substances is marginal (Table 2). It is known that different unifloral honeys contain varying amounts of minerals and trace elements [26]. From the nutritional point of view chromium, manganese and selenium are important, especially for 1 to 15 years old children. The elements sulphur, boron, cobalt, fluoride, iodide, molybdenum and silicon can be important in human nutrition too, although there are no RDI values proposed for these elements (Table 3).
Honey contains 0.3-25 mg/kg choline and 0.06 to 5 mg/kg acetylcholine [12]. Choline is essential for cardiovascular and brain function as well as for cellular membrane composition and repair, while acetylcholine acts as a neurotransmitter.
Aroma compounds, taste-building compounds and polyphenols
There is a wide variety of honeys with different tastes and colours, depending on their botanical origin [29]. The sugars are the main taste-building compounds.
Generally, honey with a high fructose content (e.g. acacia) are sweeter compared to those with high glucose concentration (e.g. rape). The honey aroma depends also on the quantity and type of acids and amino acids present. In the past decades extensive research on aroma compounds has been carried out and more than 500 different volatile compounds were identified in different types of honey. Indeed, most aroma building compounds vary in the different types of honey depending on its botanical origin [30]. Honey flavour is an important quality for its application in food industry and also a selection criterion for the consumer’s choice.
Polyphenols are another important group of compounds with respect to the appearance and the functional properties of honey. 56 to 500 mg/kg total polyphenols were found in different honey types [31,32]. Polyphenols in honey are mainly flavonoids (e.g. quercetin, luteolin, kaempferol, apigenin, chrysin, galangin), phenolic acids and phenolic acid derivatives [33]. These are compounds known to have antioxidant properties. The main polyphenols are the flavonoids, their content can vary between 60 and 460 μg/100 g of honey and was higher in samples produced during a dry season with high temperatures [34].
Contaminants and toxic compounds
The same as any other natural food, honey can be contaminated by the environment, e.g. by heavy metals, pesticides, antibiotics etc. [35]. Generally, the contamination levels found in Europe do not present a health hazard. The main problem in recent years was the contamination by antibiotics, used against the bee brood diseases, but at present this problem seems to be under control. In the European Union antibiotics are not allowed for that purpose, and thus honey containing antibiotics is also not permitted to be traded on the market.
A few plants used by bees are known to produce nectar containing toxic substances. Diterpenoids and pyrrazolidine alkaloids are two main toxin groups relevant in nectar. Some plants of the Ericaceae family belonging to the sub-family Rhododendron, e.g. Rhododendron ponticum contain toxic polyhydroxylated cyclic hydrocarbons or diterpenoids [36]. The substances of the other toxin group, the pyrrazolidine alkaloids, found in different honey types and the potential intoxication by these substances is reviewed [37]. Cases of honey poisoning have been reported rarely in the literature and have concerned individuals from the following regions: Caucasus, Turkey, New Zealand, Australia, Japan, Nepal, South Africa, and also some countries in North and South America. Observed symptoms of such honey poisoning are vomiting, headache, stomach ache, unconsciousness, delirium, nausea and sight weakness. In general the poisonous plants are known to the local beekeepers and honey, which can possibly contain poisonous substances, is not marketed. To minimise risks of honey born poisoning in countries where plants with poisonous nectar are growing tourists are advised to buy honey in shops and not on the road and from individual beekeepers.
Table 4
Glycemic index and fructose
The impact of carbohydrates on human health is discussed controversially, especially the understanding of how the carbohydrates of a given food affect the blood glucose level. Today, the dietary significance of carbohydrates is often indicated in terms of the glycemic index (GI). Carbohydrates with a low GI induce a small increase of glucose in blood, while those with a high GI induce a high blood glucose level. The only comprehensive data on honey GI are the one presented in Table 4, based mainly on data of different Australian honeys [38,39]. There is a
significant negative correlation between fructose content and GI, probably due to the different fructose/glucose ratios of the honey types tested. It is known that unifloral honeys have varying fructose content and fructose/glucose ratios [17]. Some honeys, e.g. acacia and yellow box, with relatively high concentration of fructose, have a lower GI than other honey types (Table 4). There was no significant correlation between GI and the other honey sugars. The GI values of 4 honeys found in one study varied between 69 and 74 [40], while in another one the value of a honey unidentified botanical origin was found to be 35 [41]. As the GI concept claims to predict the role of carbohydrates in the development of obesity [42], low GI honeys might be a valuable alternative to high GI sweeteners. In order to take into consideration the quantity of ingested food, a new term, the glycemic load, was introduced. It is calculated as follows: the GI value is multiplied by the carbohydrate content in a given portion and divided by 100. Values lower than 10 are considered low, between 10 and 20 are intermediate and above 20 belong to the category high. For an assumed honey portion of 25 g the glycemic load of most honey types is low and some types are in the intermediate range (Table 4).
The GI concept was developed to provide a numeric classification of carbohydrate foods, assuming that such data are useful in situations where the glucose tolerance is impaired. Therefore, food with a low GI should provide benefits with respect to diabetes and to the reduction of coronary heart disease [43]. The consumption of honey types with a low GI, e.g. acacia honey might have beneficial physiological effects and could be used by diabetes patients. An intake of 50 g honey of unspecified type by healthy people and diabetes patients led to smaller increases of blood insulin and glucose than the consumption of the same amounts of glucose or of a sugar mixture resembling to honey [44,45]. It was shown that consumption of honey has a favourable effect on diabetes patients, causing a significant decrease of plasma glucose [46-48]. Honey was well tolerated by patients with diabetes of unspecified type [49] and by diabetes type-2 patients [50-52]. According to recent studies, long term consumption of food with a high GI is a significant risk factor for type-2 diabetes patients [53]. However, the GI concept for the general population is still an object of discussions [54].
Fructose is the main sugar in most honey types (Table 1). A surplus consumption of fructose in today’s American diet, mainly in the form of high-fructose corn syrup, is suspected to be one of the main causes for overweight problems [55]. By reviewing
clinical studies these authors found that fructose ingestion causes a rise of de-novo lipogenesis, which has an unfavourable effect on energy regulation and on body weight. In rat feeding experiments the hypertriglyceridemic effect observed after intake of fructose does not take place after feeding of honey [56]. Compared to rats fed with fructose, honey-fed rats had higher plasma a-tocopherol levels, higher a- tocopherol/triacylglycerol ratios, lower plasma NOx concentrations and a lower susceptibility of the heart to lipid peroxidation. These data suggest a potential nutritional benefit of substituting fructose by honey in the ingested diets.
Ingestion of both honey (2 g/kg body weight) and fructose prevented the ethanol- induced transformation of erythrocytes in mice. In humans faster recovery from ethanol intoxication after honey administration has been reported while a higher ethanol elimination rate has also been confirmed [58,59].
Table 5
Table 6
DIFFERENT PHYSIOLOGICAL EFFECTS
Antimicrobial, antiviral and antiparasitic activity
Honey inhibits the growth of micro-organisms and fungi. The antibacterial effect of honey, mostly against gram-positive bacteria, is well documented [60-63]. Both bacteriostatic and bactericidal effects have been reported for many strains, many of them pathogenic (Table 5). Further, it was reported that honey has also been shown to inhibit Rubella virus in vitro [64], three species of the Leishmania parasite [65] and Echinococcus [66].
The antimicrobial effect of honey is due to different substances and depends on the botanical origin of honey [60-63]. The low water activity of honey inhibits bacterial growth. Honey glucose oxidase produces the antibacterial agent hydrogen peroxide [67], but the peroxide production capacity depends also on honey catalase activity [68]. There are also other non-peroxide antibacterial substances with different chemical origin, e.g. aromatic acids [69], unknown compounds with different chemical properties [63] and phenolics and flavonoids [70,71]. The low honey pH can also be responsible for the antibacterial activity [72].
Contrary to the non-peroxide activity, the peroxide one can be destroyed by heat, light and storage [63] (Table 6). These different factors had a bigger effect on the antibacterial activity of blossom honey than on honeydew honey. Thus, for optimum antibacterial activity, honey should be stored in a cool, dark place and be consumed when fresh.
Table 7
Antioxidant effects
The term “oxidative stress” describes the lack of equilibrium between the production of free radicals and the antioxidant protective activity in a given organism. Protection against oxidation is thought to prevent some chronic diseases [73]. The oxidative modification of the lipoproteins is considered to be an important factor for the pathogenesis of arteriosclerosis [74]. Honey has been found to contain significant antioxidant activity including glucose oxidase, catalase, ascorbic acid, flavonoids, phenolic acids, carotenoid derivatives, organic acids, Maillard reaction products, amino acids and proteins [31,75-84]. The antioxidative activity of honey polyphenols can be measured in vitro by comparing the oxygen radical absorbance capacity (ORAC) with the total phenolics concentration (Table 7). There is a significant correlation between the antioxidant activity, the phenolic content of honey and the inhibition of the in vitro lipoprotein oxidation of human serum [85]. Furthermore, in a lipid peroxidation model system buckwheat honey showed a similar antioxidant activity as 1 mM α-tocopherol [83]. The influence of honey ingestion on the antioxidative capacity of plasma was tested in two studies [86,87]. In the first one, the trial persons were given maize syrup or buckwheat honeys with a different antioxidant capacity in a dose of 1.5 g/kg body weight. In comparison to the sugar control, honey caused an increase of both the antioxidant and the reducing serum capacity. In the second study humans received a diet supplemented with a daily honey serving of 1.2 g/kg body weight. Honey increased the body antioxidant agents: blood vitamin C concentration by 47%, β-carotene by 3%, uric acid by 12%, and glutathione reductase by 7% [87]. It should be borne in mind that the antioxidant activity depends on the botanical origin of honey and varies to a great extent in honeys from different botanical sources [31,77,78,88-90].
The impact of heat and storage time on the antioxidant capacity of clover and buckwheat honey was analysed recently [91]. While processing of clover honey did not significantly influence its antioxidant capacity, storage during 6 months reduced it by about 30%. After a given storage period the antioxidant capacity of processed and raw honeys was similar. In another study both antioxidant activity and brown pigment formation increased upon heat treatment and storage [92].
Antimutagenic and antitumor activity
Mutagenic substances act directly or indirectly by promoting mutations of the genetic structure. During the roasting and frying of food heterocyclic amines are formed, e.g. Trp-p-1 (3-Amino-1,4-dimethyl-5H-pyridol [4,3-b] indole). The antimutagenic activity of honeys from seven different floral sources (acacia, buckwheat, fireweed, soybean, tupelo and Christmas berry) against Trp-p-1 was tested by the Ames assay and compared to a sugar analogue as well as to individually tested simple sugars [93]. All honeys exhibited a significant inhibition of Trp-p-1 mutagenicity. Glucose and fructose were found to have a similar antimutagenic activity as honey. Nigerose, another sugar, present in honey [18,19] has an immunoprotective activity [94]. The anti-metastatic effect of honey and its possible mode of anti-tumor action was studied by the application of honey in spontaneous mammary carcinoma in methylcholanthrene-induced fibrosarcoma of CBA mice and in anaplastic colon adenocarcinoma of Y59 rats [95]. A statistically significant anti-metastatic effect was achieved by oral application of honey. These findings indicate that honey activates the immune system and honey ingestion may be advantageous with respect to cancer and metastasis prevention. In addition, it is postulated that honey given orally before tumour cell inoculation may have a decreased effect on tumour spreading. In another study of the same group the effect of honey on tumour growth, metastasising activity and induction of apoptosis and necrosis in murine tumour models (mammary and colon carcinoma) was investigated [96]. A pronounced antimetastatic effect was observed when honey was applied before tumour-cell inoculation (per oral 2 g kg-1 for mice or 1 g kg-1 for rats, once a day for 10 consecutive days).
In another study the anti-tumour effect of honey against bladder cancer was examined in vitro and in vivo in mice [97]. According to these results honey is an effective agent for inhibiting the growth of different bladder cancer cell lines (T24, RT4, 253J and MBT-2) in vitro. It is also effective when administered intralesionally or orally in the MBT-2 bladder cancer implantation mice models.
Anti-inflammatory effects
Anti-inflammatory effects of honey in humans were studied by Al Waili and Boni [98] after ingestion of 70 g honey. The mean plasma concentration of thromboxane B(2) was reduced by 7%, 34%, and 35%, that of PGE(2) by 14%, 10%, and 19% at 1, 2,
and 3 hours, respectively, after honey ingestion. The level of PGF(2a) was decreased by 31% at 2 hours and by 14% at 3 hours after honey ingestion. At day 15, plasma concentrations of thromboxane B(2), PGE(2) and PGF(2a) decreased by 48%, 63% and 50%, respectively. The ingestion of honey decreased inflammation in an experimental model of inflammatory bowel disease in rats [99]. Honey administration is as effective as prednisolone treatment in an inflammatory model of colitis. The postulated mechanism of action is by preventing the formation of free radicals released from the inflamed tissues. The reduction of inflammation could be due to the antibacterial effect of honey or to a direct antiinflammatory effect. The latter hypothesis was supported in animal studies, where antiinflammatory effects of honey were observed in wounds with no bacterial infection [100].
Various physiological effects
The effect of honey on the antibody production against thymus-dependent antigen in sheep red blood cells and thymus-independent antigen (Escherichia coli) in mice was studied [101]. Oral honey intake stimulates antibody production during primary and secondary immune responses against thymus-dependent and thymus- independent antigens.
In animal experiments honey showed an immunosuppressive activity [102]. This might explain why it has been hypothesised, that ingestion of honey can relieve pollen hypersensitivity.
In a study humans received a diet supplemented with a daily honey consumption of
1.2 g/kg body weight [87]. The effects observed in blood serum were an increase of monocytes (50 %), iron (20%), copper (33%), a slight increase of lymphocyte and eosinophil percentages, zinc, magnesium, hemoglobin and packed cell volume and a reduction of: ferritin (11%), immunoglobulin E (34%), aspartate transaminase (22%), alanine transaminase (18%), lactic acid dehydrogenase (41%), creatine kinase (33%) and fasting sugar (5%).
NUTRITION AND HEALTH EFFECTS
Oral health
There is much debate whether honey is harmful to teeth. Some reports show a cariogenic effect of honey [103-106] or a much less cariogenic effect than sucrose
[107]. Due to its antibacterial activity honey ingestion inhibits the growth of bacteria, causing caries [108,109] and might induce a carioprotective effect [110,111]. It was shown that Manuka honey, a very potent antimicrobial honey, has a positive effect against dental plaque development and gingivitis [112] and can be used instead of refined sugar in the manufacture of candy [109].
According to electron microscope studies the ingestion of honey causes no erosion of tooth enamel as observed after drinking fruit juice [113]. Ten minutes after consumption of fruit juice tooth erosion was observed, while 30 minutes after honey ingestion the erosion was only very weak. This effect can be explained only partially by the calcium, phosphorous and fluoride levels of honey and other colloidal honey components might also play a role.
Summarising the different findings, it can be concluded that honey is probably not as cariogenic as other sugars and in some cases it can be carioprotective. But to be on the safe side, it is advised to clean the teeth after consumption of honey.
Gastroenterology
According to the Muslim holy book “The Holy Hadith”, dating back to the 8th century AD prophet Mohamed recommended honey against diarrhoea [114]. Also, the Roman physician Celsus (ca. 25 AD) used honey as a cure for diarrhoea [115]. The application of honey for prevention and treatments of gastro-intestinal disorders such as peptic ulcers, gastritis, gastroenteritis has been reported in various books and publications from Eastern Europe [6,7,116-120] and from Arab countries [121].
Honey is a potent inhibitor of the causing agent of peptic ulcers and gastritis, Helicobacter pylori [122-124]. In rats honey acted against gastric ulcers experimentally induced by indomethacin and alcohol [125-128]. Honey is not involved in prostaglandin production, but it has a stimulatory effect on the sensory nerves in the stomach that respond to capsaicin [125,129]. A second mechanism of action has been proposed, postulating that this effect is due to the antioxidant properties of honey. Honey intake in rats prevented indomethacin-induced gastric lesions in rats by reducing the ulcer index, microvascular permeability, and myeloperoxidase activity of the stomach [130]. In addition, honey was found to maintain the level of non-protein sulfhydryl compounds (e.g. glutathione) in gastric tissue subjected to factors inducing ulceration [125,129,131,132]. Ingestion of dandelion honey reduced gastric juice acidity by 56% [133]. The gastric emptying of
saccharides after ingestion of honey was slower than that after ingestion of a mixture of glucose and fructose [134].
Other important effects of honey on human digestion have been linked to oligosaccharides. These honey constituents have prebiotic effects, similar to that of fructo-oligosaccharides [135,136]. The oligosaccharide panose was the most active oligosaccharide. The oligosaccharides cause an increase of bifidobacteria and lactobacilli and exert the prebiotic effect in a synergistic mode of action [137].
According to an invitro study on five bifidobacteria strains honey has a growth promoting effect similar to that of fructose and glucose oligosaccharides [138]. Unifloral honeys of sour-wood, alfalfa and sage origin stimulated the growth of five human intestinal bifidobacteria [139]. In another study honey increased both in vivo (small and large intestines of rats) and in vitro the building of Lactobacillus acidophilus and Lactobacillus plantarum, while sucrose had no effect [140].
In clinical studies with infants and children honey shortens the duration of bacterial diarrhoea and did not prolong the duration of non-bacterial diarrhoea [141].
In certain cases, consumption of relatively large amounts of honey (50 to 100 g) can lead to a mild laxative effect in individuals with insufficient absorption of honey fructose [142,143]. Fructose alone is less readily absorbed in the intestinal tract than fructose together with glucose [144]. The mild laxative properties of honey are used for the treatment of constipation in Eastern Europe [6].
Supplementation of honey in concentrations of 2, 4, 6 and 8 g/100 g protein fed to rats, improved protein and lipid digestibility [145].
Cardiovascular health
The effects of ingestion of 75 g of natural honey compared to the same amount of artificial honey (fructose plus glucose) or glucose on plasma glucose, plasma insulin, cholesterol, triglycerides (TG), blood lipids, C-reactive proteins and homocysteine, most of them being risk factors for cardiovascular diseases, were studied in humans [47]. Elevation of insulin and C-reactive protein was significantly higher after glucose intake than after honey consumption. Glucose reduced cholesterol and low-density lipoprotein-cholesterol (LDL-C). Artificial honey slightly decreased cholesterol and LDL-C and elevated TG. Honey reduced cholesterol, LDL-C, and TG and slightly elevated high-density lipoprotein-cholesterol (HDL-C). In patients with hypertriglyceridemia, artificial honey increased TG, while honey decreased TG. In
patients with hyperlipidemia, artificial honey increased LDL-C, while honey decreased LDL-C. In diabetic patients, honey compared with dextrose caused a significantly lower rise of plasma glucose [47].
Honey can contain nitric oxide (NO) metabolites which are known indicators for cardiovascular disease risk. Increased levels of nitric oxides in honey might have a protecting function in cardiovascular diseases. Total nitrite concentration in different biological fluids from humans, including saliva, plasma, and urine was measured after ingestion of 80 g of honey [146,147]. Salivary, plasma and urinary NO metabolite concentrations showed a tendency to increase. Different honey types contained various concentrations of NO metabolites, darker or fresh honeys containing more NO metabolites than light or stored honey. After heating, NO metabolites decreased in all honey types.
Compared to fructose-fed rats, honey-fed rats had a higher plasma a-tocopherol level, and a higher a-tocopherol/triacylglycerol ratio, as well as lower plasma nitrate levels and lower susceptibility of the heart to lipid peroxidation [56].
Infants
The application of honey in infant nutrition used to be a common recommendation during the last centuries and there are some interesting observations. Infants on a diet with honey had better blood formation and a higher weight gain than when a diet without honey was applied [148]. Honey was better tolerated by babies than sucrose
[149] and compared to a water based placebo significantly reduced the crying phases of infants [150]. Infants had a higher weight increase when fed by honey than by sucrose, and showed less throw up than the sucrose controls [151]. When infants were fed on honey rather than on sucrose an increase of haemoglobin content, a better skin colour and no digestion problems were encountered [152,153]. Infants on honey diet had a better weight increase and were less susceptible to diseases than infants fed normally or when given blood building agents [148].
The positive effects of honey in infant diet are attributed to effects on the digestion process. One possible cause is the well established effect of oligosaccharides on B. bifidus [154], see also section Gastroenterology. When fed on a mixture of honey and milk infants showed a regularly steady weight gain and had an acidophilic micro- organism flora rich in B. bifidus [155]. Another experiment with honey and milk showed that infants were suffering less frequently from diarrhoea, and their blood
contained more haemoglobin compared to those on a diet based on sucrose sweetened milk [152]. Honey fed infants had an improved calcium uptake, and lighter and thinner faeces [156].
However, there is a health concern for infants regarding the presence of Clostridium (Cl.) botulinum in honey. Since the presence of this bacterium in natural foods is ubiquitous and honey is a non sterilized packaged food from natural origin the risk of a low contamination level cannot be excluded. Spores of this bacterium can survive in honey, but they cannot build toxin. Thus, in the stomach of infants younger than one year the bacteria spores from honey can survive and theoretically build the toxin, while children older than 12 months can ingest honey without any risk. In some cases, infant botulism has been attributed to ingestion of honey [157-160]. In Germany one case of infant botulism per year is reported [160]. As a result of the reported infant botulism cases some honey packers (e.g. the British Honey Importers and Packers Association) place a warning on the honey label that “honey should not be given to infants under 12 months of age”. Recently, a scientific committee of the EU examined the hazard of Cl. botulinum in honey [161]. It has concluded that microbiological examinations of honey are necessary for controlling the spore concentration in honey, as the incidence of Cl. botulinum is relatively low and sporadic and as such tests will not prevent infant botulism. In the EU countries the health authorities have not issued a regulation for placing a warning label on honey jars.
Athletic performance
The physiological action of gel and powdered forms of honey as a carbohydrate source for athlete performance was studied recently under controlled conditions by Kreider and coworkers [162-165]. Honey increased significantly the heart frequency and the blood glucose level during the performance [162]. It did not promote physical or psychological signs of hypoglycaemia in fasted athletes [163], or during resistance training [164]. In another trial the effect of low and high GI carbohydrate gels and honey were tested on a 64 km cycling performance [162,165]. Both high (glucose) and low GI (honey) gels increased cycling performance and the effect of honey was slightly better than the one of glucose. According to the above studies honey is well tolerated and can be an effective carbohydrate source for athletic performance.
Different health enhancing effects
A positive effect of honey on hepatitis A patients was found after ingestion of clover and rape honey, causing a decrease of the alanine aminotranferase activity (by 9 to 13 times) and a decrease of bilirubin production by 2.1 to 2.6 times [133].
Honey has a supportive effect on patients who have undergone a cancer radiation therapy by reducing the incidence of radiation mucositis. Patients with head and neck cancer treated with radiation therapy were given honey. There was a significant reduction in the symptomatic grade 3/4 mucositis among honey-treated patients compared to the controls; i.e. 20% versus 75%. The compliance of the honey-treated group of patients was better than the controls. 55% of the patients treated with honey showed no change or a positive gain in body weight compared to the controls, the majority of which lost weight [166]. Honey was administered to chemotherapy patients with neutropenia and was found to reduce the need for colony-stimulating factors [167]. Febrile neutropenia is a serious side effect of chemotherapy.
Allergy
Honey allergy seems relatively uncommon; allergies reported can involve reactions varying from cough to anaphylaxis [145]. In this study it was reported that patients allergic to pollen are rarely allergic to honey, although there is one reported case of combined honey pollen allergy [168]. The incidence of honey allergy, reported in a group of 173 food allergy patients was 2.3% [cited in 169]. In this study the honey allergy is explained by the presence of components of bee origin.
CONCLUSION
Due to variation of botanical origin honey differs in appearance, sensory perception and composition. It contains mainly carbohydrates. The glycemic index of honey varies from 32 to 87, depending on botanical origin and on fructose content. The main nutrition- and health relevant components are the carbohydrates, which make it an excellent energy source especially for children and sportsmen. Besides its main components, the carbohydrates fructose and glucose, honey contains also a great number of other constituents in small and trace amounts, producing numerous nutritional and biological effects: antimicrobial, antioxidant, antiviral, antiparasitic, antiinflammatory, antimutagenic, anticancer and immunosuppressive activities.
Different nutritional studies have confirmed various effects after honey ingestion, e.g.
enhanced gastroenterological and cardiovascular health. Besides, honey showed physiological effects on blood health indicators as well as effects on hepatitis A and radiation mucositis patients. However, it should be pointed out that most of these studies were based on relatively high honey intakes of 50 to 80 g. Honey compositions, and also its different biological effects, depend to a great extent on the botanical origin of honey. This fact was often not considered in the reviewed studies.
1 Figure 1: Prehistoric man gathering honey
2 A rock painting, made around 6000 BC. La Arana shekter, Bicorp, Eastern Spain.
3
4
5
6 Table 1: Honey composition (data in g/100 g) [14,15]
7
Blossom honey
Honeydew honey
average
min. - max.
average
min. - max.
Water
17.2
15-20
16.3
15-20
Monosaccharides
fructose
38.2
30-45
31.8
28-40
glucose
31.3
24-40
26.1
19-32
Disaccharides
sucrose
0.7
0.1-4.8
0.5
0.1-4.7
others
5.0
2-8
4.0
1-6
Trisaccharides
melezitose
<0.1
4.0
0.3-22.0
erlose
0.8
0.5-6
1.0
0.1-6
others
0.5
0.5-1
3.0
0.1-6
Undetermined oligosaccharides
3.1
10.1
Total sugars
79.7
80.5
Minerals
0.2
0.1-0.5
0.9
0.6-2.0
Amino acids, proteins
0.3
0.2-0.4
0.6
0.4-0.7
Acids
0.5
0.2-0.8
1.1
0.8-1.5
pH-value
3.9
3.5-4.5
5.2
4.5-6.5
8
9
10
11
1 Table 2: Honey nutrients (values compiled after different authors [14,20-27] and
2 recommended daily intake [28])
3
Ingredient
Amount in 100 g
Recommended Daily Intake1
1-4
years old
4-15
years old
After 15 years old
Energy
kcal
Carbohydrates
kcal
300
1000-1100
1400-2700
2400-3100
Proteins
g
0.5
13-14
17-46
44-59
Fats
g
0-
-
-
Minerals
mg
Sodium (Na)
1.6-17
300
410-550
550
Calcium (Ca)
3-31
600
700-1200
1000-1200
Potassium (K)
40-3500
1000
1400-1900
2000
Magnesium (Mg)
0.7-13
80
120-310
300-400
Phosphorus (P)
2-15
500
600-1250
700-1250
Zinc (Zn)
0.05-2
3
5-9.5
7-10
Copper (Cu)
0.02-0.6
0.5-1
0.5-1
0.5-1
Iron (Fe)
0.03-4
8
8-15
10-15
Manganese (Mn)
0.02-2
1-1.5
1.5-5
2-5
Chromium (Cr)
0.01-0.3
0.02-0.06
0.02-0.1
0.03-1.5
Selenium (Se)
0.002-0.01
0.001-0.004
0.001-0.006
0.003-0.007
Vitamins
mg
Phyllochinon (K)
ca. 0.025
15
20-50
60-70
Thiamin (B1)
0.00-0.01
0.6
0.8-1.4
1-1.3
Riboflavin (B2)
0.01-0.02
0.7
0.9-1.6
1.2-1.5
Pyridoxin (B6)
0.01-0.32
0.4
0.5-1.4
1.2-1.6
Niacin2
0.10-0.20
7
10-18
13-17
Panthothenic acid
0.02-0.11
4
4-6
6
Ascorbic acid (C)
2.2-2.5
60
70-100
100
4 *-only major components considered
5 1 after the German Nutrition Society [28]
6 2 Niacin equivalents: 1 mg nicotinamide = 1 mg niacin = 60 mg tryptophan (= niacin-precursor)
7
1
2 Table 3: Other trace elements in honey [14,20-27]
3
Element
mg/100 g
Element
mg/100 g
Aluminium (Al)
0.01-2.4
Lead (Pb)*
0.001-0.03
Arsenic (As)
0.014-0.026
Lithium (Li)
0.225-1.56
Barium (Ba)
0.01-0.08
Molybdenum (Mo)
0-0.004
Boron (B)
0.05-0.3
Nickel (Ni)
0-0.051
Bromine (Br)
0.4-1.3
Rubidium (Rb)
0.040-3.5
Cadmium (Cd)*
0-0.001
Silicon (Si)
0.05-24
Chlorine (Cl)
0.4-56
Strontium (Sr)
0.04-0.35
Cobalt (Co)
0.1-0.35
Sulfur (S)
0.7-26
Floride (F)
0.4-1.34
Vanadium (V)
0-0.013
Iodide (I)
10-100
Zirconium
0.05-0.08
4 *- elements regarded as toxic, can be partially of man-made origin
5
6
7
8 Table 4: Glycemic index (GI) and glycemic load (GL) for a serving (25 g) of honey 9 [38,39]
10
honey
origin
Fructose
g/100 g
GI
AC
g/serving
GL (per
serving)
Acacia (black locust)*
Romania
43
32
21
7
Yellow box
Australia
46
35±4
18
6
Stringy bark
Australia
52
44±4
21
9
Red gum
Australia
35
46±3
18
8
Iron bark
Australia
34
48±3
15
7
Yapunya
Australia
42
52±5
17
9
Pure Australia
Australia
58±6
21
12
Commercial blend
Australia
38
62±3
18
11
Salvation June
Australia
32
64±5
15
10
Commercial blend
Australia
28
72±6
13
9
Honey of unspecified origin
Canada
87±8
21
18
average
55
55±5
18
10
Sucrose (mean of 10 studies)
68±5
Glucose
100
11
12 AC = available carbohydrate
1 Table 5: List of bacteria that were found to be sensitive to honey [60,61]
2
Pathogen
Infection caused
Bacillus anthracis
anthrax
Corynebacterium diphtheriae
diphtheria
Escherichia coli
diarrhoea, septicaemia, urinary infections, wound infections
Haemophilus influenzae
ear infections, meningitus, respiratory infections, sinusitis
Klebsiella pneumoniae
pneumonia
Mycobacterium tuberculosis
tuberculosis
Proteus sp.
septicaemia, urinary infections
Pseudomonas aeruginosa
urinary infections, wound infections
Salmonella sp.
diarrhoea
Salmonella cholerae-suis
septicaemia
Salmonella typhi
typhoid
Salmonella typhimurium
wound infections
Serrata marcescens
septicaemia, wound infections
Shigella sp.
dysentery
Staphylococcus aureus
abscesses., boils, carbuncles, impetigo, wound infections
Streptococcus faecalis
urinary infections
Streptococcus mutans
dental carries
Streptococcus pneumoniae
ear infections, meningitis, pneumonia, sinusitis
Streptococcus pyogenes
ear infections, impetigo, puerperal fever, rheumatic fever, scarlet fever, sore throat, wound infections
Vibrio choleriae
cholera
Actinomyces pyogenes, Klebsiella pneumoniae, Nocardia asteroids, Staphylococcus aureus, Streptococcus agal., dysgal., uber
mastitis
Epidermophyton floccosum, Microsporum canis, M.. gypseum, Trichophyton rubrum, T. tonsurans, T. mentagrophytes var. ?
tinea
diff. Escherichia coli, Salmonella, Shigella, Vibrio, Helicobacter pylori
peptic ulcer
1 Table 6: Effect of heat, light and storage time on the antibacterial activity of honey.
2 The antibacterial activity is expressed in % of the untreated controls [63] 3
Non-peroxide activity
Peroxide activity
Storage: 15 months rt
light
dark
light
dark
Blossom honey
76
86
19
48
Honeydew honey
78
80
63
70
Heat: 15 min 70oC
Blossom honey
86
8
Honeydew honey
94
78
4
5 rt = room temperature 15-20oC 6
7
8
9 Table 7. Antioxidative activity (ORAC) and total phenol content of different unifloral
10 honeys [32]
11
Honey type
ORAC
μmol TE/g
total phenolics GAE mg/kg
Buckwheat Illinois
16.95 ± 0.76
796 ±3 2
Buckwheat
9.81 ± 0.34
nd
Buckwheat New York
9.75 ± 0.48
456 ± 55
Buckwheat
9.34 ± 0.57
nd
Buckwheat
9.17 ± 0.63
nd
Buckwheat
7.47 ± 0.27
nd
Soy (2000)
9.49 ± 0.29
nd
Soy (1996)
8.34 ± 0.51
269 ± 22
Hawaiian Christmas berry
8.87 ± 0.33
250 ± 56
Clover (January 2000)
6.53 ± 0.70
nd
Clover (July 2000)
6.05 ± 1.00
128 ± 11
Tupelo
6.48 ± 0.37
183 ± 9
Fireweed
3.09 ± 0.27
62 ± 6
Acacia
3.00 ± 0.16
46 ± 2
12 ORAC = Oxygen radical absorbance capacity,
13 TE = Trolox equivalent, GAE = gallic acid equivalent, nd = not determined
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ANTIBACTERIAL EFFECT OF HONEY*
ANTIBACTERIAL EFFECT OF HONEY*
J.H. DUSTMANN F.R.G.
It is known that honey strongly inhibits the growth of microorganisms. Already in 1892, the Dutch scientist Van KETEL demonstrated that honey has bactericidal effects. A great number of research reports have subsequently confirmed his findings (PLACHY, DOLD, PRICA, DUISBERG, LAVIE, BUCHNER). Responsible for these antiseptic properties are both the high sugar content and, above all, the various bacteriostatic constituents. WHITE and his colleagues in the United States take the great merit of having analysed the thermolabile and light-sensitive components of this inhibitory substance (classed with the inhibine of Dold): WHITE demonstrated that the antibacterial effects of inhibine result from the accumulation of hydrogen peroxide (H2O2) which is enzyme-produced – by a natural glucose oxidase system in honey, and as a by–product of glucose oxidase activity in honey or sugar dilutions.
The hydrogen peroxide which is forming is partially responsible, alongside of other components, for the antibacterial effect of honey.
What relation exists between the inhibine values recorded during chemical assays (following hydrogen peroxide accumulation/minute) and the results of bioassays – inhibition of the bacterial growth not on nutrient agar plates containing honeys (produced in FRG) but in dialysed honey solutions (in test tubes)?
How great is the antibacterial effect of the enzyme prepared from the secretion of the hypopharingeal glands? Which is the effect of light or of other inhibitory factors on the inhibine activity?
Our investigations were precisely intended for answering these questions.
Method and technique
2.5 g of honey was diluted, using phosphate – 0.4 M and pH 6.5 – as substrate; then it was dialysed in dialysis dishes with running water, for removing sugars. Then the solution was sterilized by filtering and several dilutions were prepared in 10 test tubes. In each test tube, a drop of bacterial suspension and 0.2 ml glucose 10 M was pipetted. All tubes were kept in an incubator at 37 oC, for 14 hours. The level of dilution up to which the honey inhibine had inhibited bacterial growth was established by the turbidity of the solution (depending on the rate of bacterial growth).
The inhibine effect was assayed on the following bacteria: Staphylococcus aureus, Streptococcus spp., Salmonella pullorum, S. gallinarum, Pseudomonas aeruginosa, Bacillus subtilis, Escherichia coli, Sarcina lutea, and Proteus vulgaris.
We used the White method modified by us.
Results
Leaving aside a few characteristic exceptions, the following relation was found to occur in general: when the peroxide content of honey is high, its bactericidal effect is very great. On the contrary, when the peroxide content is low, the inhibitory effect on the bacterial growth is slight or absent (Table 1).
It results that of the bacteria assayed by us Staphylococus aureus and Sarcina lutea were the most sensitive, while Streptococcus spp., Salmonella spp., Pseudomonas, and Proteus were less sensitive to the inhibitory action of inhibine.
Honeys with high inhibine values, e.g. of Centaurea, have antibacterial effect even in very low concentrations: starting from the initial concentration of 625 mg (the first test tube) we made several dilutions; the 1 : 128 dilution (only 2.45 mg honey/ml – from which sugars had been removed) was found to inhibit bacterial growth and even destroy them: the solution remained clear for several days.
Great peroxide accumulation and an antibacterial effect similar to that of such honeys with strong inhibitory action also be obtained by using the homogenate of one hypopharingeal gland (yellow greenish glands from honey bees of older age). As is but natural, the inhibine values depend on the age and physiological condition of bees. The highest value obtained up to now from one pair of glands was of 32.7 µg H2O2/g/min.
* Report delivered at the 3rd APIMONDIA-sponsored International Apitherapy Symposium, Portorož, 1978.
Table 1
Inhibitory effect of various honey types on Staphylococcus (test-tube assay), for different peroxide values
Honey type
Peroxide accumulation µg H2O2/g/min
1 : 1
312.5
mg/ml
1 : 2
156.2
1 : 4
78.1
1 : 8
39.1
1 : 16
19.5
1 : 32
9.8
1 : 64
4.9
1 : 128
2.4
1 : 256
1.2
Centaurea
10.4
00
00
00
0+
+
Decidous tree honeydew
7.52
00
00
0+
+
+
+
Spruce + fir tree
4.3
00
00
00
+
+
+
Spruce + fir tree
2.5
00
00
0+
+
+
+
Spruce + fir tree
2.4
00
00
0+
+
+
+
Rape
1.5
00
00
+
+
+
+
+
Heather + mixed
1.7
00
0+
+
+
+
+
+
Acacia
0.4
0+
+
+
+
+
+
+
+
Rape
1.1
+
+
+
+
+
+
+
+
+
Glucose oxidase (1µg) (Boehringer)
>25
00
00
00
00
0Heated glucose
0+
+
+
+
+
+
+
+
+
Glucose 0.1 M
-
+
+
+
+
+
+
+
+
+
0 = clear solution, no bacterial growth; + = turbid solution, with bacterial growth
Comparatively we point out the effect of the glucose-oxidase obtained from yeasts, now available in the market (Boehringer, No. 154222). This enzyme, with 2.44 ng*/ml concentration, lyophilized and specifically purified has a great inhibitory effect on staphylococci.
A very high peroxide accumulation was also recorded in beebread – the pollen-honey mixture to which bees add different secretions: 2 g of bee bread produced a maximum amount of 47.6 µg H2O2/g/minute, with a corresponding inhibitory action on bacterial growth (Staphylococcus).
Which is the factor which makes honeys to differ in this respect? Theoretically, the following relation should be true: the more secretion bees add to the nectar or honeydew – hence the gland enzyme glucose oxidase, the higher the peroxide accumulation and the greater the antibacterial effect of honey. But this relation is not always true, because of various natural inhibitory factors existing in honey.
1. Firstly, catalase: it is a natural constituent of a number of honeys; it originates from pollen and from nectar; it splits the H2O2 produced by glucose oxidase, with water and oxigen resulting.
Especially in Ericaceae and Rosaceae honeys (Prunus, Malus, and other fruit trees) we found a great catalase activity, while the inhibitory effect was small as it was but natural.
In general, with great catalase activity the inhibine value is relatively low, while in honeys devoid of catalase activity (Centaurea, sweet chestnut, etc.) inhibine values are very light (Table 2).
Table 2
Catalase and inhibine activity (H2O2 accumulation) in Ericaceae honey (1-6) and in fruit-tree honey (7). 8-11 = control honeys
Honey
Catalase a)
Inhibine b)
1. Calluna vulgaris
46.1
111.8
2. Erica tetralix
104.0
2.7
3. Vaccinium myrtillus
198.0
127.5
4. Erica cinerea
119.0
187.2
5. Rhododendron spp.
241.0
15.3
6. Erica vagans
54.0
10.6
7. Prunus spp.
193.0
99.5
8. Trifolium repens
0380.0
9. Centaurea cyanus
0624.0
10. Castanea sativa
0545.0
11. Pinus silvestris
0662.5
a) Kfx103; b) µg H2O2/g honey/hour
Definition of the Kf indice: the Kf indice measuring the catalase activity is calculated as follows:
1
Kf =
t
(1 n
Xo D
)
X W
, where t = time; Xo = the substrate at to (the beginning of the reaction); X = the substrate after t; D = the dilution
factor; W = weight of honey (in g).
2. Vitamin C (ascorbic acid) or other reducing substances can easily destroy the peroxide (H2O2) produced. There may also be other unknown substances which could inactivate glucose oxidase (no investigation in this respect has been made yet).
* nanogram
3. Another factor which can affect the peroxide value and consequently the inhibine value too, which under natural conditions in the bee colony has an importance but may become very important for the antibacterial effect of honey during its extraction, processing, and storage is the direct light. This matter has been investigated by WHITE and myself.
In an earlier report I have demonstrated the effect of direct sunlight and of the fluorescent light on the glucose oxidase activity, hence on the natural properties of honey. The table below shows that the light- sensitivity of honey depends on the source of the honey (See Figure 1 and Table 3).
Fig. 1 – Inhibitory effect of various honeys (exposed and not to light) on Staphylococcus
❏Full inhibitory effect of honey not exposed to light
■ Full inhibitory effect after exposure to sunlight (15 min)
❏Reduced inhibitory effect after exposure to sunlight (15 min)
1. Centaurea 10.4/2.1 µg H2O2/g/min
2. Centaurea 6.1/0.9 µg H2O2/g/min
3. Calluna 2.0/1.6 µg H2O2/g/min
4. Brassica 1.5/0.2 µg H2O2/g/min
5. Decidous tree honeydew 7.5/7.7 µg H2O2/g/min
6. Spruce + fir tree 6.1/5.6 µg H2O2/g/min 1 : 1 - 1 : 64 = dilution
Table 3
Reduction of peroxide accumulation in various honeys after exposure of 5 mm layer to sunlight (7 x 404 lux) for 10 minutes
Honey
pH value
Peroxide accumulation
Reduction %
Not exposed
Exposed
Pine, Pinusb
4.9
418.7
113.3
1.3
Decidous tree “honeydew” b
5.2
378.0
368.9
2.4
Fir, Abies
5.1
270.0
258.9
4.1
Spruce, Picea b
5.0
168.5
147.1
12.7
Sweet chestnut, Castanea b
5.1
510.0
432.0
15.3
Heather, Calluna
4.5
132.2
103.2
22.0
Lime, Tilia
4.4
188.0
143.9
23.5
Dandelion, Taraxacum
4.3
243.7
186.2
23.6
Germander, Teucrium
3.9
38.5
19.9
48.3
Cornflower, Centaurea
4.3
624.5
308.5
50.6
Fruit-trees, Prunus, Malus
4.6
35.5
13.7
61.0
Rape, Brassica
4.6
73.3
25.1
65.7
Acacia, Robinia
4.5
23.5
7.4
68.5
Blackberry, Vaccinium
4.3
127.4
27.6
78.3
White clover, Trifolium
4.1
332.5
44.1
86.7
White clover, Trifolium
4.0
182.0
9.8
94.6
a = accumulation of µg H2O2 • g-1 hour-1; b = mostly honeydew flow
Honeydew honeys which have a higher pH (usually over 5), are less sensitive than the relatively acid floral honeys. Honeys of the same colour and with the same pH value have however been found to be sometimes very differently sensitive to light. It is assumed that certain substances exist in some honeys which increase their sensitivity to light (their chemical nature is not known). The effect of light may be so strong as to destroy the enzyme which produces peroxide, with no inhibine effect existing any more, even in the 500 g jar when exposed to sunlight for a longer time in the shop window: after keeping a jar with a honey mixture of Brassica, Trifolium and Potentilla in the sunlight for 48 hours, no trace of enzyme activity was recorded to exist anywhere in the honey mass. Investigation of different honey samples showed that honeys are 15 % less sensitive to the light of an incandescent lamp (3000 lux) than to the fluorescent light of the same intensity. As the honey packed in white glass jars is often exposed to fluorescent light in shop windows for several months, it is very likely that much of this honey has none of its praised natural qualities any more when the consumer buys it. There is also the effect of heat which inactivates the enzyme as well, as reported by WHITE.
All these findings show that the secretion – including glucose oxidase, of the hypopharingeal glands of bees plays a part of overriding importance in the antibacterial effect of honey.
The antibacterial effect conditioned by other factors than the enzyme activity and sugar content in honey is very small. It was found that in acetone extracts of floral and honeydew honeys the antibacterial effect was a small fraction only of the antibacterial effect resulting from peroxide accumulation (often less than 1/50). In conclusion we point out once again that the antibacterial effect of honey varies substantially depending on the honey type. As shown above, with honeys with high inhibine values very small amounts of honey are sufficient to provide for an antibacterial effect. Greater care should be taken with respect to the sensitivity of the natural honey to light and other factors which negatively affect its quality.
By our studies we wish to provide in the future for the control of certain important pathogen germs by means of honeys with high inhibine values.
LI TE R A T U RE
BUCHNER, R: Südwestdeutsch. Imker 18, 240 (1966)
DOLD, H., H. DU, S.T. DZIAC: Z. Hyg. Infektionskrankh, 120 155 (1937)
DUISBERG, H., B. WARNECKE: Z. Lebensm. Untersuchung und Forsch. 124, 265 (1964) DUSTMANN, J.H.: Z. Bienenforsch., 9, 66 (1967)
DUSTMANN, J.H.: Z. Lebensm. Unters.-Forsch. 134, 20 (1967)
DUSTMANN, J.H.: Z. Lebensm. Unters.-Forsch. 145. 294 91971)
DUSTMANN, J.H.: Z. Lebensm. Unters.-Forsch. 148. 263 (1972)
VAN KETEL, B.A.: Feestnummer der Berichsten van de Nederlandische Maatschappij ter Bevordering der Pharmacie 67/96 (1892) LAVIE, P.: C.R. Acad. Sci. 256, 1856 (1963)
PLACHY, E.: Zentr. Bakteriol, Parasitenk, Abt. 106, 401 (1944)
PRICA, M.Z. Hyg. Infektionskrankh. 120, 437 (1938)
SCHEPARTZ, A. I., M.H. SUBERS: J. Apicultural Res. 5, 37 (1966)
WHITE, J.W., Jr., M.H. SUBERS, A.I. SCHEPARTZ: Apicultural Res. 2, 93 (1963) WHITE, J.W., Jr. M.H. SUBERS, A.I. SCHEPARTZ: Biochim. Biophys. Acta 72, 57 (1963) WHITE, J.W., Jr., M.H. SUBERS: J. Apicultural Res. 3, 45 (1964)
WHITE, J.W., Jr., M.H. SUBERS: J. Food Sci 29, 819 (1964)
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https://cloverhoney.web.id/manfaat-propoelix/
https://cloverhoney.web.id/madu-hdi-harga/
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halocantik
Aug 25, 2021
Honey: An immunomodulator in wound healing
PERSPECTIVE ARTICLE
Honey: An immunomodulator in wound healing
Juraj Majtan, PhD1,2
1. Institute of Zoology, Slovak Academy of Sciences, and
2. Department of Microbiology, Faculty of Medicine, Slovak Medical University, Bratislava, Slovakia
Reprint requests:
Dr. J. Majtan, Institute of Zoology, Slovak Academy of Sciences, Dubravska cesta 9, 845 06 Bratislava, Slovakia.
Tel: +421 2 59302647;
Fax: +421 2 59302646;
Email: [emailprotected]
Manuscript received: April 5, 2013 Accepted in final form: August 28, 2013
DOI:10.1111/wrr.12117
ABSTRACT
Honey is a popular natural product that is used in the treatment of burns and a broad spectrum of injuries, in particular chronic wounds. The antibacterial potential of honey has been considered the exclusive criterion for its wound healing properties. The antibacterial activity of honey has recently been fully characterized in medical- grade honeys. Recently, the multifunctional immunomodulatory properties of honey have attracted much attention. The aim of this review is to provide closer insight into the potential immunomodulatory effects of honey in wound healing. Honey and its components are able to either stimulate or inhibit the release of certain cytokines (tumor necrosis factor-α, interleukin-1β, interleukin-6) from human monocytes and macrophages, depending on wound condition. Similarly, honey seems to either reduce or activate the production of reactive oxygen species from neutrophils, also depending on the wound microenvironment. The honey-induced activation of both types of immune cells could promote debridement of a wound and speed up the repair process. Similarly, human keratinocytes, fibroblasts, and endothelial cell responses (e.g., cell migration and proliferation, collagen matrix production, chemotaxis) are positively affected in the presence of honey; thus, honey may accelerate reepithelization and wound closure. The immunomodulatory activity of honey is highly complex because of the involvement of multiple quantitatively variable com- pounds among honeys of different origins. The identification of these individual compounds and their contributions to wound healing is crucial for a better under- standing of the mechanisms behind honey-mediated healing of chronic wounds.
Honey has been used as a traditional medicine for centuries by different cultures for the treatment of various disorders including burns and chronic wounds. Honey offers broad spectrum antimicrobial properties and promotes rapid wound healing.1 The antibacterial potential of honey has been con- sidered the exclusive criterion for its wound healing proper- ties. Therefore, the antibacterial activity of honey from different floral sources has been intensively studied over the past few decades. Recently, defensin1, one of the major anti- bacterial factors in honey, was shown to be a regular but quantitatively variable component of each honey.2 One reason for the varying contents of defensin1 in different honeys seems to be constitutive but variable defensin1 expression in
immunomodulatory effects that can positively affect the wound healing process. Therefore, the antibacterial potential of honey may not be the sole criterion for selecting medical- grade honeys.
It has been assumed that the antibacterial action of honey has its main impact on the healing process of chronic wounds. Honey eliminates pathogens from wounds and provides an appropriate moist environment for proper wound healing. As the direct antimicrobial effects of honey were fully character- ized in vitro, research has also focused on identifying the substances responsible for its immunomodulatory effects.7,8
individual honeybees in bee populations.3 It has also been
found that some types of honey derived from specific floral sources become more potent than others because of the pres- ence of phytochemicals with antibacterial properties.4–6 These potent natural honeys, such as manuka (Medihoney, Comvita NZ Ltd., Te Puke, New Zealand) and RS honey (Bfactory Health Products B.V., Rhenen, The Netherlands) (honey with unknown origin used as a source for Revamil), are currently being used as medical-grade honeys in clinical applications. Medical-grade honey is being incorporated into sterile devices that are applied topically to wounds. However, honeys may also contain bee- or plant-derived substance(s) with
COX-2 Cyclooxygenase-2
IL Interleukin
LPS Lipopolysaccharide
MM6 Mono Mac 6
MMP-9 Matrix metalloproteinase 9
MRJP1 Major royal jelly protein 1
mRNA Messenger ribonucleic acid
MW Molecular weight
NO Nitric oxide
ROS Reactive oxygen species
TNF-α Tumor necrosis factor-α
Table 1. Immumodulatory compounds of various honey samples and their biological functions involved in honey-induced wound healing
Specific factor(s)
Honey
Immunomodulatory activity
Reference
Arabinogalactans
Kanuka honey
Monocytes activation
Gannabathula et al.18
261 MW component
Jungle honey
Neutrophils activation
Fukuda et al.7
5.8 kDa component
Manuka honey
Monocytes activation
Tonks et al.17
MRJP1
Acacia honey
Macrophages activation
Majtan et al.20
MRJP1
Acacia honey
Keratinocytes activation
Majtan et al.8
Apigenin, Kaempferol
Honeydew honey
MMP-9 inhibition
Majtan et al.55
MMP-9, matrix metalloproteinase 9; MRJP1, major royal jelly protein 1; MW, molecular weight.
Some promising candidates with immunomodulatory proper- ties have been identified in honey (Table 1), but further research is necessary to prove these immunomodulatory properties.
The aim of this work is to review the immunomodulatory effects of natural honey on immune and cutaneous cells that participate in the wound healing process and to elucidate the different mechanisms of honey-induced immunomodulation.
HONEY AND CYTOKINE PRODUCTION
Besides providing a structural barrier, the skin contains several types of immune cells that can be activated by skin damage. One of the most important groups of immune cells involved in wound healing are macrophages, which exhibit different immunological functions in the skin, including phagocytosis and antigen presentation. Tissue macrophages are cells derived from peripheral blood monocytes. In injured tissue, monocytes migrate through the vessel wall; they release enzymes that fragment extracellular matrix proteins, creating space for monocytes to migrate to the wound bed. Macrophages can be activated either classically (by lipopoly- saccharide [LPS] and interferon-γ) or alternatively (by interleukin [IL]-4 and IL-13).9,10 LPS-stimulated mac- rophages are capable of synthesizing and secreting inflamma- tory mediators, including tumor necrosis factor-α (TNF-α), nitric oxide (NO), and IL-6. IL-4-activated macrophages play important roles in wound healing and angiogenesis.10
In addition to the above-mentioned properties, macrophages produce many other cytokines and growth factors that stimu- late new capillary growth, collagen synthesis, and fibrosis.11
In recent years, several groups have examined honey and/or its individual components in order to elucidate its wound healing properties. Macrophages/monocytes are a suitable model for monitoring the immunomodulatory activity of novel potential immunomodulators. Tonks and coworkers suggested that the wound healing effect of honey may be partly related to the release of proinflammatory cytokines from surrounding cells, mainly monocytes and mac- rophages.12,13 An immunomodulatory effect was showed by cytokine release from the monocytic cell line Mono Mac 6 (MM6) and human peripheral monocytes after incubation with 1% (w/v) honey. Several natural honeys were used in this study, including manuka and jelly bush honey. All types of honey induced or stimulated the release of TNF-α, IL-1β, and IL-6 from MM6 cells and peripheral blood monocytes when
compared with the syrup control (artificial honey) and untreated cells. The MM6 cells treated with jelly bush honey showed a significantly higher above-mentioned cytokines release than cells treated with manuka or the other natural honeys. The authors of the study also claimed that the con- centration of endotoxins in all natural honeys (from 56 to 690 pg/mL) is negligible, and that stimulation of MM6 cells is independent of endotoxins. However, it is important to note that MM6 cells are very sensitive to endotoxins,14 and it is very likely that the endotoxin content of honey could be responsible for its stimulatory effect. Endotoxins possess special characteristics. They are, to a large extent, heat stable, and their activity can be abrogated by the antibiotic poly- myxin B.15 It has been shown that MM6 cells responded to an endotoxin with a detection limit as low as 3.1 pg/mL,16 and that robust release of IL-6 occurred when they were stimu- lated with 100 pg/mL endotoxin.
In a recent study, Timm et al. (2008) investigated the effect of four different honeys including manuka honey on the release of important proinflammatory cytokine (IL-6) from MM6 cells.14 Similar to previous studies,12,13 natural honeys induced maximal release of IL-6 after 18 hours of treatment. They reported that the substances in honey responsible for its immunomodulatory activity are (1) heat stable; (2) retained in the high molecular weight (MW) fraction (>20 kDa); and that
(3) their activity was abrogated when the honey was incubated with polymyxin B, an inhibitor of endotoxin activity. All of these characteristics are in concordance with the properties of endotoxins. In contrast to these findings, Tonks et al. demon- strated that heat treatment caused a significant reduction in the ability of honey to stimulate cytokine production in MM6 cells.17 Moreover, the cytokine-stimulatory effect of honey was assessed in the presence of polymyxin B. Similarly, the ability of New Zealand honeys to release TNF-α from the monocytic cell lines THP-1 and U937 has recently been char- acterized.18 The immunomodulatory activity of all the honeys was associated with a high MW (>30 kDa) component that was partially heat labile and inhibitable with polymyxin B.18 A number of peptides and proteins from natural sources are known for their nonspecific immunostimulatory responses.19 Peptide and protein immunomodulators, in general, generate a physiological response in target cells via their specific receptors. Glycosylated proteins are known to induce TNF-α secretion from macrophages, and this cytokine is known to induce wound repair mechanisms. We have previously shown that a natural acacia honey is able to stimulate TNF-α secre- tion from murine macrophages, whereas deproteinized honey
has no effect on the release of TNF-α.20 This suggests that the protein content of honey, primarily the 55 kDa glycoprotein major royal jelly protein 1 (MRJP1), which is the dominant protein in royal jelly21 as well as in honey,22 might be respon- sible for the immunomodulatory effects of honey. Our previ- ous results also showed that the production of TNF-α from murine macrophages is actually increased after limited pro- teolytic digestion. We found that the N-terminal region of recombinant MRJP1 elicited marked release of TNF-α. The endotoxin content of acacia honey or of native and recombi- nant MRJP1 samples was not determined in our study. It is very likely that samples of purified MRJP1 contain endotox- ins at a sufficient level to stimulate the release of TNF-α from murine macrophages. Therefore, we can assume that endo- toxins in honey may play an important role in the activation of monocytes and/or macrophages depending on the individual honey. On the other hand, it has been reported that MRJP1, at concentration of 25 μg/mL, increased the level of TNF-α messenger ribonucleic acid (mRNA) expression twofold in primary cultures of epidermal keratinocytes.8 Similarly, an upward trend in mRNA expression of IL-1β and transforming growth factor-β was observed following treatment with MRJP1 in human keratinocytes.
Another promising immunostimulatory protein identified in honey belongs to the group of type II arabinogalactan proteins, with an MW of about 110 kDa. Type II arabinogalactan proteins from a range of sources have been shown to have immunomodulatory properties.23 They are able to stimulate the release of TNF-α from monocytic cell lines THP-1 and U937.18 Although honey is a natural product and rich in various phytochemical and bee-derived compounds that may possess immunomodulatory activities, some researchers have postu- lated that the immunomodulatory effects of honey could be because of its endotoxin content.14 Sterilization of honey using gamma irradiation effectively eliminates bacterial spores and vegetative forms of any bacteria present; however, bacterial endotoxins may still remain present. Bacterial endo- toxins (LPSs), major components of the outer membrane of Gram-negative bacteria, are complex glycolipids composed of a hydrophilic polysaccharide moiety and a hydrophobic domain known as lipid A. Endotoxins activate macrophages to produce proinflammatory cytokines. The production of these cytokines is tightly regulated as excessive production leads to amplified inflammatory responses and devastating
illness characteristic of severe septic shock.24
HONEY AND REACTIVE OXYGEN SPECIES (ROSs)
Many studies suggest that honey rapidly eradicates infection with no adverse effects, reduces inflammation, swelling, pain, and odor, and also stimulates the wound healing process.25–29 Research supporting positive clinical observations has mainly focused on the anti-inflammatory and antioxidant properties of honey.
Chronic wounds are considered to be highly oxidizing environments owing to the release of ROS from infiltrating neutrophils and macrophages. ROSs are thought to possess certain beneficial antimicrobial properties against invading bacteria;30 prolonged exposure to elevated levels of ROS causes cell damage and may inhibit the healing of both acute and chronic wounds.
Therefore, one way to interrupt chronic inflammatory cycle is to remove ROS with antioxidants, and honey is known to contain antioxidants that scavenge free radicals.31,32 Various components of honey contribute to its antioxidant properties, including flavonoids, phenolic acids, catalase, peroxidise, ascorbic acid, and carotenoids, and products of the Maillard reaction.33 The quantity of these components varies according to the floral and geographical origin of each type of honey.34–37 Several studies have shown that phenolic compounds in honey are partially responsible for its antibac- terial and antioxidant activities.36,38–40 It has been shown that ROSs mediate TNF-α-induced cytotoxicity, which can be blocked by specific free radical scavengers (e.g., flavo- noids).41,42 In fact, Habtermariam43 demonstrated that pheno- lic compounds, such as caffeic acid, effectively inhibit TNF- α-induced cytotoxicity in L929 cells. In a very recent study,44 a honey methanol extract and a honey ethyl acetate extract were tested in vitro for their effect on NO production in the endotoxin- and IFN-γ-stimulated murine macrophage cell line RAW264.7. It was shown that both honey extracts were capable of inhibiting NO production in the macrophages. The concentration of NO was inhibited in a dose-dependent manner in the presence of the honey extracts. The honey ethyl acetate extract exhibited greater activity than the honey methanol extract. However, the methanol extract contained a higher concentration of phenolic compounds, where the majority of the phenolics were ellagic, gallic, and ferulic acids, myricetin, chlorogenic acid, and caffeic acid. Simi- larly, Woo et al.45 found that chrysin, a natural flavonoid found in many plant extracts, honey, and propolis46,47 inhib- ited cyclooxygenase-2 (COX-2) gene expression in LPS- stimulated cultured macrophages, and this effect was mediated through inhibition of the binding activity of nuclear factor IL-6. The fact that nuclear factor IL-6 is negatively regulated by chrysin is important because this transcription factor plays a critical role in the regulation of a variety of genes involved in inflammatory responses.
Another study, by Ahmad et al., supports the hypothesis that honey exhibits its anti-inflammatory activity through inhibition of activated macrophages.48 They found that honey treatment of rodent macrophages activated by bovine throm- bin resulted in effective suppression of oxidative respiratory bursts. Interestingly, all honey samples from different origins showed effective suppression.
Taken together, these findings are contradictory, and it is difficult to distinguish which molecule(s) in honey is fully responsible for its immunomodulatory effect. It is important to carry out further detailed research in order to explain the immunomodulatory effect of honey on macrophages/ monocytes.
Persistent neutrophil infiltration and release of ROS by neutrophils contribute to the pathophysiology of chronic wounds. A decrease in neutrophil superoxide production by honeys has recently been reported.31,49,50 An antioxidant activ- ity of honeys was attributed to inhibition of ROS formation, either by inhibiting the respiratory burst of neutrophils or by direct ROS scavenging.32 Interestingly, a dose-dependent reduction in human neutrophils’ superoxide production by honeys did not correlate with the levels of known honey- based phenolic compounds, which are well-known free radical scavengers.50 This observation indicates that the anti- oxidant activity of honey is likely caused by inhibition of neutrophils’ respiratory burst.
Figure 1. The immunomodulatory action of honey on immune and cutaneous cells involved in wound healing. Honey is able to either stimulate or inhibit the release of certain factors (cytokines, MMP-9, ROS) from immune and cutaneous cells depending on wound condition. Honey induces secretion of proinflammatory cytokines and MMP-9 during the inflamma- tory and proliferative wound healing phase, respectively. On the other hand, when the wound inflammation is uncontrolled, honey abrogates prolonged wound inflammation and reduces the elevated levels of proinflammatory cytokines, ROS, and MMP-9. IL, interleukin; MMP-9, matrix metalloproteinase-9; ROS, reactive oxygen species; TNF-α, tumor necrosis factor α.
In a very recent study, a compound with an MW of 261 Da isolated from jungle honey was found to elicit chemotactic activity in neutrophils.7 The authors of this study also inves- tigated the mechanism of the antitumor activity of jungle honey, which seemed to be related to the production of ROS by activated neutrophils. The jungle honey was injected into tumor tissues in mice, and many neutrophils infiltrated necrotic areas in the tumor and produced ROS. The incidence and mean weight of the tumors decreased in jungle honey- injected mice.
Taking these results together, honey seems to either reduce or activate the production of ROS from neutrophils, depend- ing upon the microenvironment (Figure 1).
ANTI-INFLAMMATORY ACTIONS OF HONEY
Reduced inflammation observed in the clinic following the application of honey is supported by histological evidence of reduced numbers of inflammatory cells present in wound tissue.51 Inflammation is a nonspecific response of mammalian tissue to a variety of hostile agents.52 There are many mediators of inflammation, such as endotoxins, some cytokines, and NO. Therefore, the inhibition of inflammatory mediators is one of the important steps in controlling inflammation.
Honey exhibits potent multiple anti-inflammatory effects. Clinically, there have been numerous observations reported of honey reducing edema and exudate, minimizing scarring and having a soothing effect when applied to inflamed wounds and burns (reviewed in Molan53). The anti-inflammatory effect of honey may be explained by several mechanisms of action: (1) inhibition of the classical complement pathway;31
(2) inhibition of ROS formation;31 (3) inhibition of leukocyte infiltration;50 and (4) inhibition of COX-2 and inducible NO synthase expression.54 Finally, the inhibition of matrix metalloproteinase 9 (MMP-9), a major protease responsible for the degradation of matrix and cell growth-promoting agents in chronic wound fluids, in human keratinocytes has been reported very recently55 and represents another novel anti-inflammatory mechanism of honey action.
In a very recent study, we found that acacia honey at a concentration of 1% (w/v) significantly enhanced the expres- sion of MMP-9 mRNA in primary cultures of human keratinocytes.8 Furthermore, incubation of human skin frag- ments with honey for 24 hours was associated with increased expression of MMP-9 protein in the epidermis near the base- ment membrane. Subsequently, we also found a decrease in the relative amount of collagen type IV in the basement membrane and around the blood vessels following incubation of the skin with honey for 24 hours. These results appear contradictory to the results presented in our very recent study55 where honey inhibited TNF-α-induced MMP-9 expression. Therefore, we assume that honey can act as an immunomodulator with both proinflammatory and anti-inflammatory properties (Figure 1). We speculate that honey stimulates the production of inflam- matory cytokines and MMP-9 from keratinocytes when a low level of an inflammatory/stimulatory mediator is present. On the other hand, if an environment is infected and inflammation is in progress, honey suppresses the production of inflamma- tory cytokines and MMP-9. This hypothesis is very promising and could result in new therapeutic advantages for the treat- ment of skin inflammation in the future.
To date, the components including phenolic compounds and flavonoids responsible for the anti-inflammatory honey in vitro activities have been partially identified. However, it is not clear whether these components within honey exhibit anti-inflammatory activities in vivo.
CONCLUSION
Honey, at medical-grade level, is a high-quality wound care product, as supported by the sheer number of papers in the recent scientific literature. It has been found to be particularly effective where standard wound care is limited or unsuccess- ful. However, some wound-care professionals are still skep- tical about the benefits of honey in wound care. As the antibacterial action of honey is well characterized, there is a need to fully elucidate the compounds/mechanisms respon- sible for honey’s immunomodulatory and anti-inflammatory properties in order to support a positive clinical outcome of using honey in wound management.
ACKNOWLEDGMENTS
Source of Funding: This work was supported by the Slovak Research and Development Agency under contract no. APVV-0115-11.
Conflict of Interest: Authors have no conflict of interest to disclose.
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halocantik
Aug 25, 2021
How honey kills bacteria
The FASEB Journal
•
Research Communication
How honey kills bacteria
Paulus H. S. Kwakman,* Anje A. te Velde,† Leonie de Boer,* Dave Speijer,‡ Christina M. J. E. Vandenbroucke-Grauls,*,§ and Sebastian A. J. Zaat*,1
*Department of Medical Microbiology, Center for Infection and Immunity Amsterdam, †Laboratory of Experimental Gastroenterology and Hepatology, and ‡Department of Medical Biochemistry, Academic Medical Center, University of Amsterdam, Amsterdam; and §Department of Medical Microbiology and Infectious Diseases, Vrije Universiteit Medical Center, Amsterdam,
The Netherlands
ABSTRACT With the rise in prevalence of antibiotic- resistant bacteria,
honey
is increasingly valued for its antibacterial activity. To characterize all bactericidal factors in a medical-grade honey, we used a novel approach of successive neutralization of individual honey bactericidal factors. All bacteria tested, including
Bacillus subtilis
, methicillin-resistant
Staphylococcus au-
reus
, extended-spectrum J3-lactamase producing
Esche-
richia coli
, ciprofloxacin-resistant
Pseudomonas aerugi-
nosa
, and vancomycin-resistant
Enterococcus faecium
, were killed by 10 –20% (v/v) honey, whereas >40% (v/v) of a honey-equivalent sugar solution was required for similar activity. Honey accumulated up to 5.62 ±
0.54 mM H2O2 and contained 0.25 ± 0.01 mM methyl-
glyoxal (MGO). After enzymatic neutralization of these two compounds, honey retained substantial activity. Using
B. subtilis
for activity-guided isolation of the additional antimicrobial factors, we discovered bee defensin-1 in honey. After combined neutralization of H
2
O
2
, MGO, and bee defensin-1, 20% honey had only minimal activity left, and subsequent adjustment of the pH of this honey from 3.3 to 7.0 reduced the activity to that of sugar alone. Activity against all other bacteria tested depended on sugar, H
2
O
2
, MGO, and bee defensin-1. Thus, we fully characterized the antibacterial activity of medical-grade honey.—Kwak- man, P. H. S., te Velde, A. A., de Boer, L., Speijer, D., Vandenbroucke-Grauls, C. M. J. E., Zaat, S. A. J. How honey kills bacteria.
FASEB J.
24, 2576 –2582 (2010).
www.fasebj.org
Key Words: antibacterial agents · drug resistance · isolation and purification · methicillin-resistant Staphylococcus aureus
· peptides
Honey has been renowned for its wound-healing properties since ancient times (1). At least part of its positive influence is attributed to antibacterial proper- ties (2, 3). With the advent of antibiotics, clinical application of honey was abandoned in modern West-
The potent in vitro activity of honey against antibiotic- resistant bacteria (6, 7) and its successful application in treatment of chronic wound infections not re- sponding to antibiotic therapy (3) have attracted considerable attention (8 –10).
The broad spectrum antibacterial activity of honey is multifactorial in nature. Hydrogen peroxide and high osmolarity— honey consists of ~80% (w/v) of sugars— are the only well-characterized antibacterial factors in
honey (11). Recently, high concentrations of the anti- bacterial compound methylglyoxal (MGO) were found specifically in Manuka honey, derived from the Manuka tree (Leptospermum scoparium) (12, 13). Until now, no honey has ever been fully characterized, which ham- pers clinical application of honey.
Recently, we determined that Revamil medical-grade honey, produced under standardized conditions in greenhouses, has potent, reproducible bactericidal ac- tivity (14). In the current study, we identified all bactericidal factors in the honey used as source for this product and assessed their contribution to honey bac- tericidal activity.
To accomplish this, we used a novel approach of successive neutralization of individual honey bacteri- cidal factors combined with activity-guided identifica- tion of unknown factors.
MATERIALS AND METHODS
Honey
Unprocessed Revamil source (RS) honey was kindly provided by Bfactory Health Products (Rhenen, The Netherlands). RS honey has a density of 1.4 kg/L and contains 333 g/kg glucose, 385 g/kg fructose, 73 g/kg sucrose, and 62 g/kg maltose. To study the contribution of the sugars to the bactericidal activity of honey, a solution with a sugar compo- sition identical to that of the honey was prepared.
ern medicine, although in many cultures, it is still used
(4). These days, however, abundant use of antibiotics has resulted in widespread resistance. With the devel- opment of novel antibiotics lagging behind (5), alter- native antimicrobial strategies are urgently needed.
1 Correspondence: Department of Medical Microbiology, Academic Medical Center, Meibergdreef 15, 1105 AZ Amster- dam, The Netherlands, E-mail: [emailprotected]
doi: 10.1096/fj.09-150789
Microorganisms
Bactericidal activity of honey was assessed against the labora- tory strains Bacillus subtilis ATCC6633, Staphylococcus aureus 42D, Escherichia coli ML-35p (15), and Pseudomonas aeruginosa PAO-1 (ATCC 15692), and against clinical isolates of methi-
cillin-resistant S. aureus (MRSA), vancomycin-resistant Entero- coccus faecium (VREF), extended-spectrum [3-lactamase-pro- ducing E. coli (E. coli ESBL) and ciprofloxacin-resistant
P. aeruginosa (CRPA).
aliquots of undiluted and 10-fold serially diluted incubations were plated on blood agar. Bacterial survival was quantified after overnight incubation at 37°C. The detection level of this assay is 100 CFU/ml.
To assess the contribution of H2O2 to the bactericidal activity of honey, bovine liver catalase (Sigma) was added to a final concentration of 600 U/ml. A catalase stock solution was prepared according to the manufacturers’ instructions in 50 mM phosphate buffer (pH 7.0). The addition of 0.25% (v/v) of this catalase stock solution reduced the amount of H2O2 to undetectable levels at all honey concentrations tested and did
Determination of H O
concentration in honey
not affect bacterial viability.
2 2 Sodium polyanetholsulfonate (SPS) (Sigma) was added to neutralize cationic bactericidal components (19) at a final
Hydrogen peroxide concentrations in honey were deter-
mined quantitatively using a modification of a method de- scribed previously (16). Undiluted and 10-fold diluted sam-
ples of honey (40 µl) were mixed in wells of microtiter plates with 135 µl reagent, consisting of 50 µg/ml O-dianisidine (Sigma, St. Louis, MO, USA) and 20 µg/ml horseradish peroxidase type IV (Sigma) in 10 mM phosphate buffer (pH
6.5). O-dianisidine and peroxidase solutions were freshly prepared from a 1 mg/ml stock in demineralized water and from a 10 mg/ml stock in 10 mM phosphate buffer (pH 6.5), respectively. After 5-min incubations at room temperature,
reactions were stopped by addition of 120 µl6MH SO , and
concentration of 0.025% (w/v). The incubation buffer did not affect the pH of the concentrations of honey used in our experiments.A1M NaOH solution was used to titrate honey solutions to pH 7.0.
Agar diffusion assay
To assess antibacterial activity of fractionated honey, an agar diffusion assay was used (20). In brief, a B. subtilis inoculum suspension was prepared as described for the liquid bacteri- cidal assay. Bacteria (107 CFU) were mixed with 20 ml
2 4 nutrient-poor agar [0.03% (w/v) TSB in 10 mM sodium
absorption at 540 nm was measured. Hydrogen peroxide concentrations were calculated using a calibration curve of 2-fold serial dilutions of H2O2 ranging from 2200 to 2.1 µM.
MGO neutralization assay
Reduced glutathione (Sigma) was added to diluted honey to a final concentration of 15 mM, and conversion of MGO to S-d-lactoyl-glutathione (SLG) was initiated by addition of 0.5 U/ml glyoxalase I (Sigma). The amount of MGO converted was determined using the extinction coefficient of SLG of
3.37 mM-1 at 240 nm (17). Thus, we determined that up to 10 mM of exogenous MGO added to 40% honey was com-
pletely converted, and that undiluted RS honey contained
0.25 0.01 mM of MGO.
Antibee defensin-1 polyclonal antibody
An affinity-purified polyclonal antibee defensin-1 antibody was purchased from Eurogentec (Seraing, Belgium). The N-terminal part of bee defensin-1 is hydrophobic and con- tains 3 disulfide bonds, whereas the hydrophilic C-terminal region lacks cysteine residues (18). Therefore, rabbits were immunized with a synthetic peptide corresponding to the C terminus of bee defensin-1 (CRKTSFKDLWDKRF), and anti- bodies were subsequently affinity-purified using this peptide coupled to AF-Amino Toyopearl 650 M resin (Toso, Tokyo, Japan).
Liquid bactericidal assay
Bactericidal activity of honey was quantified in 100-µl volume liquid tests, in polypropylene microtiter plates (Costar Corn- ing, New York, NY, USA). For each experiment, a 50% (v/v)
stock solution of honey was freshly prepared in incubation buffer containing 10 mM phosphate buffer (pH 7.0) supple- mented with 0.03% (w/v) trypticase soy broth (TSB; BD Difco, Detroit, MI, USA). Bacteria from logarithmic phase cultures in TSB were washed twice with incubation buffer and suspended at a final concentration of 1 X 106 CFU/ml, based
on optical density. Plates were incubated at 37°C on a rotary
shaker at 150 rpm. At indicated time points, duplicate 10-µl
phosphate buffer (pH 7.0) with 1% low EEO agarose (Sigma)] of 45°C, and immediately poured into 10- X 10-cm culture plates. Wells of 1 mm diameter were punched into the agarose, and 2.5-µl samples were added to the wells and allowed to diffuse into the agarose for 3 h at 37°C. Subse- quently, the agarose was overlaid with 20 ml of double- strength nutrient agarose [6% TSB and 1% Bacto-agar (BD Difco), 45°C], and plates were incubated overnight at 37°C. Clear zones around the wells indicated antibacterial activity.
Ultrafiltration of honey components
Fifteen milliliters of 20% honey was centrifuged in a 5-kDa molecular weight cutoff Amicon Ultra-15 tube (Millipore, Bedford, MA, USA) at 4000 g for 45 min at room tempera- ture. The <5-kDa filtrate was collected, and the >5-kDa reten- tate was subsequently washed 3 times in the filter tube with 15 ml of demineralized water and concentrated to 0.4 ml.
Bacterial overlay assay
Native cationic proteins were separated by acid urea polyacryl- amide gel electrophoresis (AU-PAGE) (21). Gels were either stained with PAGE-Blue (Fermentas, St. Leon-Rot, Germany) or washed 3 X 8 min in 10 mM phosphate buffer (pH 7.0) for a bacterial overlay assay. After washing, the gel was incubated for 3 h on B. subtilis-inoculated nutrient-poor agarose (see
Agar Diffusion Assay). After removal of the gel, the agarose was overlaid with double-strength nutrient agarose and treated as described for the agar diffusion assay.
Immunoblotting
Proteins were separated by tris-tricine SDS-PAGE, as de- scribed previously (22), and transferred onto nitrocellulose membranes (Schleicher and Schuell, Keene, NH, USA). Membranes were subsequently blocked with 5% nonfat dry milk (Bio-Rad, Veenendaal, The Netherlands) plus 0.5 M NaCl and 0.5% (v/v) Tween-20 in 10 mM Tris-HCl, pH 7.5 (rinse buffer), for 1 h. Blocked membranes were incubated with affinity-purified antibee defensin-1 antibody at 1.4
µg/ml in rinse buffer for 2 h. After incubation with primary antibody, membranes were washed 2X for 15 min in rinse buffer, incubated with horseradish peroxidase-labeled goat-
anti-rabbit secondary antibody (Jackson ImmunoResearch West Grove, PA, USA) at 0.4 µg/ml in rinse buffer for 1 h, and washed again for 10 min. in rinse buffer and 5 min in PBS, respectively. The membrane was developed using a DAB liquid substrate kit (Sigma).
Purification of antibacterial peptide from honey
An amount of >5-kDa honey retentate equivalent to 13 ml of honey was dissolved in loading buffer (3M urea in 5% acetic acid with methyl green as tracking dye) and loaded on a preparative acid-urea PAGE, as described previously (21) with
slight modifications. A cylindrical gel (3.7 cm diameter, 6 cm height) in a model 491 Prep Cell (Bio-Rad) was prepared, prerun at reversed polarity for3h at 150V in 5% acetic acid at 4°C, and protein was electrophoresed at 40 mA with reversed polarity. Protein was eluted in 5% acetic acid at 0.5 ml/min and collected in fractions of 2 ml. Fractions were assessed for protein composition by tris-tricine SDS-PAGE and for antibacterial activity by bacterial overlay assay. Frac- tions containing purified antibacterial protein were pooled, concentrated, dialyzed against 0.01% acetic acid in a 3.5-kDa molecular weight cutoff MINI Slide-A-Lyzer tube (Pierce, Rockford, IL, USA), freeze-dried, and dissolved in deminer- alized water.
Protein identification by V8 digestion with subsequent mass analysis
Duplicate fractions (estimated to contain ~2 µg of protein each) were adjusted to 50 mM sodium phosphate (pH 7.9)
and 5% (v/v) acetonitrile. Approximately 0.5 µg of endopro- teinase Glu-C (Fluka) was added per fraction and incubated at 25°C overnight. The resulting peptide mixtures were purified and concentrated with the aid of C18 ziptips (Milli- pore) and eluted in 10 µl 90% (v/v) acetonitrile and 1% (v/v) formic acid. The samples were checked for the presence of nonautodigest peptides with a reflectron MALDI-TOF mass spectrometer (MALDI; Waters, Milford, MA, USA). Next,
samples were analyzed with ESI-tandem mass spectrometry (MS/MS). Data were acquired with a QT of 1 (Waters) coupled to an Ultimate nano-LC system (LC Packings Di- onex, Sunnyvale, CA, USA). One microliter of peptide mix- ture was diluted in 10 µl of 0.1% TFA. The peptides of both
samples were separated on a nanoanalytical column (75 µm
i.d. X 15 cm C18 PepMap; LC Packings Dionex) using a standard gradient of acetonitrile in 0.1% formic acid. The
flow of 300 nl/min was directly electrosprayed in the QT of 1 operating in data-dependent MS and MS/MS mode. The resulting MS/MS spectra were analyzed with Mascot software (Matrix Science, Boston, MA, USA). In both fractions, a doubly charged ion (VTCDLLSFKGQVND, mass 1537.8) with a sequence corresponding to the mature N terminus of bee defensin-1 could be identified (MOWSE scores >73).
RESULTS
Hydrogen peroxide is produced by the Apis mellifera (honeybee) glucose oxidase enzyme on dilution of honey. RS honey diluted to 40 to 20% accumulated high levels of H2O2 24 h after dilution, with a maximum of
5.62 0.54 mM H2O2 formed in 30% honey (Fig. 1A). The addition of catalase reduced H2O2 to negligible
Figure 1. Contribution of H2O2, sugars, and MGO to the bactericidal activity of honey after 24 h. A) Mean se hydrogen peroxide accumulation in different concentrations of honey, without catalase (squares) or with catalase added (asterisks).
B) Bactericidal activity against indicated laboratory strains (top row) and against clinical isolates of vancomycin-resistant
E. faecium (VREF), methicillin-resistant S. aureus (MRSA), extended-spectrum [3-lactamase-producing E. coli (E. coli ESBL), and ciprofloxacin-resistant P. aeruginosa (CRPA) (bottom row). Bacteria were exposed to various concentrations of honey (squares), honey with catalase added (asterisks), or to honey-equivalent sugar solutions (circles). C) Killing of B. subtilis by honey in incubation buffer without addition (squares), with catalase (asterisk), with glyoxalase (small solid circles), or with catalase and glyoxalase I (inverted triangles), added to neutralize H2O2 and MGO, respectively, or by a honey-equivalent sugar solution
(circles). Data are mean se log-transformed bacterial concentration (CFU/ml).
levels (Fig. 1A) and markedly reduced the bactericidal activity against all bacteria tested, except B. subtilis (Fig. 1B). However, H2O2-neutralized honey exerted stron- ger bactericidal activity than equivalent sugar solutions (Fig. 1B). This indicates that H2O2 is important for the bactericidal activity of honey, but that additional factors must also be present. As B. subtilis was the most susceptible bacterium for nonperoxide bactericidal activity, we used it for identification of additional bactericidal factors.
The honey bactericidal compound MGO can be converted into S-lactoylglutathione (SLG) by glyoxalase I, and this product can be measured spectrophoto- metrically. RS honey contained 0.25 0.01 mM MGO. We aimed to apply glyoxalase I to neutralize the bactericidal activity of MGO in honey. This required that SLG, the reaction product of MGO, would be nonbactericidal. Indeed, the activity of up to 20 mM MGO was neutralized by conversion into SLG (Supple- mental Fig. 1), indicating that SLG up to high concen- trations did not kill the bacteria. Neutralization of MGO or H2O2 alone did not alter bactericidal activity of RS honey, but simultaneous neutralization of MGO and H2O2 in 10% honey reduced the killing of B. subtilis by 4-logs (Fig. 1C). At higher concentrations of honey, the bactericidal activity was not affected by neutralization of H2O2 and MGO (Fig. 1C), indicating that still more factors were involved.
As a first step to characterize the unknown bacteri- cidal factors, we size-fractionated honey by ultrafiltra- tion with a 5-kDa molecular weight cutoff membrane. Unfractionated honey produced a small zone of com- plete bacterial growth inhibition and a larger zone with partial growth inhibition in an agar diffusion assay with
B. subtilis (Fig. 2A). After ultrafiltration, the factors that caused complete and partial bacterial growth inhibition were separated and were present in the >5-kDa reten-
tate and the <5-kDa filtrate, respectively (Fig. 2A).
Ion exchange chromatography of the retentate indi-
cated a cationic nature of the antibacterial factors. Indeed, the polyanionic compound SPS abolished the antibacterial activity of the retentate (Fig. 2B). More- over, pepsin treatment also abolished this activity (Fig. 2B). Together, this implies that cationic antibacterial proteins were present.
We separated cationic proteins in the retentate using a native acid-urea PAGE gel, and allowed the separated components to diffuse from this gel into a B. subtilis- inoculated agar to identify antibacterial proteins. This yielded a single zone of bacterial growth inhibition that corresponded to a protein band in a Coomassie-stained gel run in parallel (Fig. 2C). This protein was purified from a larger amount of retentate using preparative acid-urea PAGE (Fig. 2D), and identified by peptide mass analysis as bee defensin-1.
To specifically assess the contribution of bee defen- sin-1 to the bactericidal activity of honey, an antibee defensin-1 antibody was raised (Fig. 2E). Like SPS, this antibody negated all bactericidal activity of the >5-kDa
retentate against B. subtilis (Fig. 3A). The <5-kDa
filtrate had only minor bactericidal activity (Fig. 3A), but this was not due to cationic compounds, since SPS
failed to neutralize this activity (Fig. 3A). Thus, bee defensin-1 was the only cationic bactericidal compound present in RS honey.
Next, we assessed the contribution of bee defensin-1 to the bactericidal activity of nonfractionated honey
Figure 2. Identification of bee defensin-1 in honey. A) Honey was fractionated by ultrafiltration using a 5-kDa molecular weight cutoff filter tube; antibacterial activity of 2.5 µl of 80% honey, and equivalent amounts of the <5-kDa filtrate and >5-kDa retentate, were tested in an agar diffusion assay. B) Retentate equivalent to 7.5 µl of undiluted honey was tested for the presence of cationic and proteinaceous antibacterial components. Activity of cationic components was neutralized by
adding SPS, and protein was digested with pepsin, followed by 5-min inactivation at 100°C. As control, incubation for 5 min at 100°C without pepsin was performed. Activity in retentate (ret.)
was compared with that of 0.2 µg hen egg white lysozyme (lys.). C) To identify cationic antibacterial proteins in retentate, amounts of this fraction equivalent to 750 µl honey, and 3 µg lysozyme as a reference, were run in duplicate sets on a single native acid-urea PAGE gel. One half
of the gel was Coomassie-stained (left); other was used for a bacterial overlay assay with B. subtilis
(right). D) Silverstained tris-tricine SDS-PAGE of different amounts of lysozyme and preparative acid-urea PAGE-purified bee defensin-1, separated by an empty lane. E) Retentate separated on tris-tricine SDS-PAGE, blotted to nitrocellulose, stained with either Ponceau S (Pon. S, left) or immunostained with antibee defensin-1 (right).
Figure 3. Roles of bee defensin-1 and pH in bactericidal activity of honey against B. subtilis. A) Contribution to bacte- ricidal activity of cationic components in general and of bee defensin-1 specifically was tested by neutralization with SPS or with antibee defensin-1 antibody (C-bd), respectively, at con- centrations of retentate equivalent to 20% honey (open bars) and 40% honey (solid bars); ctrl. indicates survival without
neutralization. B) To assess the contribution of bee defen- sin-1 to bactericidal activity of unfractionated honey, B. subtilis was incubated in various concentrations of honey in incuba- tion buffer (squares), or with catalase and glyoxalase I added either without (triangles) or with SPS (diamonds), or in a honey-equivalent sugar solution (circles). C) To assess the contribution of the low pH to the bactericidal activity of honey, B. subtilis was incubated in various concentrations of honey in incubation buffer (squares), or with catalase, glyox- alase I, and SPS added either without (triangles) or with neutralization to pH 7 (diamonds), or in a honey-equivalent sugar solution (circles). After 24 h, numbers of surviving bacteria were determined. Data are mean se log-trans- formed bacterial concentration (CFU/ml).
against B. subtilis. As previously observed, >20% honey retained bactericidal activity when H2O2 and MGO were neutralized. Additional neutralization of bee de- fensin-1 strongly reduced the bactericidal activity of 20% honey but did not affect the activity of 30 and 40% honey (Fig. 3B). So, bee defensin-1 contributed to the bactericidal activity of honey, but still other bactericidal factors were involved.
Honey has a low pH, mainly because of the conver- sion of glucose into hydrogen peroxide and gluconic acid by glucose oxidase. This low pH might also con- tribute to the bactericidal activity of honey (23). Titra- tion of the pH of 40 –10% RS honey from 3.4 –3.5 to 7.0, combined with neutralization of H2O2, MGO and bee defensin-1, reduced the bactericidal activity of honey to a level identical to that of a honey-equivalent sugar solution (Fig. 3C). Thus, with this experiment, we
succeeded in identifying all bactericidal factors in RS honey responsible for killing of B. subtilis.
The contribution of the identified bactericidal fac- tors to activity against antibiotic-susceptible and -resis- tant strains of various species was tested with honey diluted to 20%, since this killed the entire inocula of all bacteria tested independent of sugar (Fig. 1). Simulta- neous neutralization of H2O2, MGO and bee defensin-1 negated all activity (Fig. 4), showing that these were the major factors responsible for broad spectrum bacteri- cidal activity of honey.
We studied the contribution of the honey bacteri- cidal factors in more detail by neutralizing the factors individually or combined. Neutralization of H2O2 alone strongly reduced the bactericidal activity against all bacteria tested except B. subtilis (Fig. 4). Neutralization of MGO alone strongly reduced killing of E. coli and
P. aeruginosa strains (Fig. 4). Neutralization of bee defensin-1 alone reduced killing of VREF, but not of the other bacteria tested (Fig. 4). When compared to neutralization of MGO alone, the additional neutraliza- tion of bee defensin-1 reduced killing of all bacteria tested, except E. coli ESBL (Fig. 4). In summary, H2O2, MGO, and bee defensin-1 differentially contributed to the activity of honey against specific bacteria, and their combined presence was required for the broad-spec- trum activity.
DISCUSSION
All bacterial species tested were susceptible to different combinations of bactericidal factors in honey, indicat- ing that these bacteria were killed via distinct mecha- nisms. This clearly demonstrates the importance of the multifactorial nature of honey for its potent, broad- spectrum bactericidal activity.
Some factors had overlapping activity. For instance, the activity of bee defensin-1 against most bacteria was only revealed after neutralization of MGO. This clearly demonstrates the importance of neutralizing known bactericidal factors in honey to reveal the presence of additional factors. Similarly, the contribution of the low pH for activity of honey against B. subtilis was only revealed when H2O2, MGO, and bee defensin-1 were simultaneously neutralized.
In other situations, bactericidal activity depended on the combined presence of different factors. Thus, the activity of honey against E. coli and P. aeruginosa was markedly reduced by neutralization of either H2O2 or MGO. Alternatively, the activity of certain bactericidal factors likely is more potent in the context of honey than as pure substances. This is most clearly illustrated by the activity of MGO. When tested in a buffer, >0.3 mM MGO was required for activity against B. subtilis (Supplemental Fig. 1). In contrast, as little as 0.05 mM MGO, the concentration in 20% RS honey, was suffi- cient to substantially contribute to the bactericidal activity. This suggests that the presence of the other bactericidal factors in honey enhanced the effect of
Figure 4. Effect of neutralization of H2O2, MGO, and bee defensin-1 on bactericidal activity of honey. Hydrogen peroxide, MGO, and bee defensin-1 were neutralized in 20% honey by adding catalase (cat.), glyoxalase I (gly I) and SPS, respectively. Bactericidal activity was tested against indicated laboratory strains (left 4 panels) and against clinical isolates of VREF, MRSA,
E. coli ESBL, and CRPA (right 4 panels). A sugar solution equivalent to 20% honey was used as a reference. After 24 h, numbers of surviving bacteria were determined. Data are mean se log-transformed bacterial concentration (CFU/ml).
MGO. It is not possible to quantify the contribution of the different factors to honey bactericidal activity since, as we have shown, these factors may have redundant activity, be mutually dependent, or have additive or synergistic activity depending on the bacterial species targeted.
We have demonstrated for the first time that honey contains an antimicrobial peptide, bee defensin-1, and that this peptide substantially contributes to the bacte- ricidal activity. Bee defensin-1 was previously isolated from royal jelly (24), the major food source for bee queen larvae (and then referred to as “royalisin”), and was identified in honeybee hemolymph (18). Royal jelly is produced by young worker bees and contains their hypopharyngeal and mandibular gland secretions (25, 26). Bee defensin-1 mRNA has been identified in the hypopharyngeal gland of young worker bees (18), suggesting this gland is involved in production of bee defensin-1 found in royal jelly (24). When worker bees age, they become the major producers of honey. Major differences develop in morphology and protein expres- sion of their hypopharyngeal glands (27, 28), e.g., several important carbohydrate-metabolizing enzymes, including glucose oxidase are expressed (29). The bees add the secretion from their hypopharyngeal glands to the collected nectar. The carbohydrate-metabolizing enzymes then convert sucrose to glucose and fructose, and glucose oxidase converts the glucose to hydrogen peroxide and gluconic acid. These latter compounds presumably are involved in prevention of microbial spoilage of unripe honey (11). Since we have found bee defensin-1 in honey, this suggests that after the transi- tion in hypopharyngeal gland function of the worker bees with age, the gland still produces bee defensin-1. This peptide, therefore, likely contributes to protection of both royal jelly and honey against microbial spoilage. It remains to be established whether bee defensin-1 is also present in other honeys. In Manuka honey, no evidence was found for the presence of antimicrobial peptides (30). For several other honeys, proteins were reported to contribute to the antibacterial activity (31, 32), but their identity remains unknown. Using our antibee defensin-1 antibody, we aim to assess the role of
bee defensin-1 for the antibacterial activity of other honeys.
Previous studies regarding the effect of low pH to antibacterial activity of honey have yielded conflicting results (11). In our study, the contribution of the low pH for activity against B. subtilis was only revealed on inactivation of all other bactericidal factors. So, in other studies, which did not employ an approach of neutral- ization of bactericidal factors in honey, the contribu- tion of the low pH of honey may easily have been overlooked.
Much effort has been put into identification of phenolic antibacterial components in honey (11). Sev- eral of these compounds have been isolated from honey, but as they were tested at concentrations far exceeding those in honey, no conclusions can be drawn regarding their contribution to honey bactericidal ac- tivity (11). Our data do not show a role of phenolic compounds in RS honey bactericidal activity.
Our approach of selectively neutralizing individual bactericidal factors present in a medical-grade honey allowed us to unravel the multifactorial bactericidal activity of a honey for the first time. We presently use the same approach to assess the contribution of these factors to activity of other honeys, and simultaneously to screen for novel bactericidal factors. Such honeys, or isolated components thereof, may serve as novel agents to prevent or treat infections, in particular those caused by antibiotic-resistant bacteria.
The authors thank Jorn Blom and Sadira Thomas for their help with purification of bee defensin-1; Henk Dekker for expert nano ESI-ms/ms experiments; and Ton Bisseling, Ben Berkhout, Mark van Passel, and Brendan McMorran for critically reviewing the manuscript.
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C. M. J. E., Schultz, M. J., and Zaat, S. A. J. (2008) Medical-grade honey kills antibiotic-resistant bacteria in vitro and eradicates skin colonization. Clin Infect Dis. 46, 1677–1682
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of the division of labor in adult honeybees (Apis-Mellifera L). 1. Effect of methoprene on corpora allata and hypopharyngeal gland, and its alpha-glucosidase activity. Appl. Entomol. Zool. 24, 66 –77
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fication and quantitative levels of antibacterial components of some New Zealand honeys. Food Chem. 70, 427– 435
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32. Gallardo-Chacon, J. J., Casellies, M., Izquierdo-Pulido, M., and Rius, N. (2008) Inhibitory activity of monofloral and multifloral honeys against bacterial pathogens. J. Apicul. Res. 47, 131–136
33. https://cloverhoney.web.id/
34. https://cloverhoney.web.id/clover-honey-madu-hdi/
35. https://cloverhoney.web.id/propoelix/
36. https://cloverhoney.web.id/royal-jelly-hdi/
37. https://cloverhoney.web.id/clover-honey-harga/
38. https://cloverhoney.web.id/propoelix-harga/
39. https://cloverhoney.web.id/hdi-propoelix-adalah/
40. https://cloverhoney.web.id/manfaat-propoelix/
41. https://cloverhoney.web.id/madu-hdi-harga/
42. https://cloverhoney.web.id/propoelix-plus/
43. https://cloverhoney.web.id/madu-hdi-manfaatnya/
44. https://cloverhoney.web.id/clover-honey-manfaatnya/
45.
Received for publication November 18, 2009.
Accepted for publication February 4, 2010.
halocantik
Aug 25, 2021
Microorganisms in honey
Microorganisms in honey
Abstract
Knowledge of the moisture and temperature conditions influencing growth of microorganisms in honey has long been used to control the spoilage of honey. However, the need for additional microbiological data on honey will increase as new technologies for, and uses of honey develop. Microorganisms in honey may influence quality or safety. Due to the natural properties of honey and control measures in the honey industry, honey is a product with minimal types and levels of microbes. Microbes of concern in post-harvest handling are those that are commonly found in honey (i.e., yeasts and spore-forming bacteria), those that indicate the sanitary or commercial quality of honey (i.e., coliforms and yeasts), and those that under certain conditions could cause human illness.
Primary sources of microbial contamination are likely to include pollen, the digestive tracts of honey bees, dust, air, earth and nectar, sources which are very difficult to control. The same secondary (after-harvest) sources that influence any food product are also sources of contamination for honey. These include air, food handlers, cross-contamination, equipment and buildings. Secondary sources of contamination are controlled by good manufacturing practices.
The microbes of concern in honey are primarily yeasts and spore-forming bacteria. Total plate counts from honey samples can vary from zero to tens of thousands per gram for no apparent reason. Most samples of honey contain detectable levels of yeasts. Although yeast counts in many honey samples are below 100 colony forming units per gram (cfug), yeasts can grow in honey to very high numbers. Standard industry practices control yeast growth. Bacterial spores, particularly those in theBacillusgenus, are regularly found in honey. The spores ofC. botulinumare found in a fraction of the honey samples tested — normally at low levels. No vegetative forms of disease-causing bacterial species have been found in honey. Bacteria do not replicate in honey and as such high numbers of vegetative bacteria could indicate recent contamination from a secondary source. Certain vegetative microbes can survive in honey, at cool temperatures, for several years. However, honey has anti-microbial properties that discourage the growth or persistence of many microorganisms. Typically, honey can be expected to contain low numbers and a limited variety of microbes.
A routine microbiological examination of honey might include several different assays. A standard plate count provides general information. Specialized tests, such as a count of yeasts and an assay for bacterial spore-formers, may also be useful. An indicator of sanitary quality as provided by coliform counts might be included. Additional tests, to explain unusually high counts or address a certain problem, may be needed. The use of honey in products that receive no or limited heat treatment may require additional tests. More information on the source and control of microbes in honey is needed to answer the concerns currently facing the industry.
halocantik
Aug 18, 2021
Dietary Guidelines Framework
Customizing the Dietary Guidelines Framework
The Dietary Guidelines approach of providing a framework–not prescriptive details–ensures that its recommendations can “meet people where they are,” from personal preferences to cultural foodways, and including budgetary considerations. The examples below are a sample of the range of options in each food group—to be eaten in nutrient- dense forms. Additional examples are listed under Table A3-2 in Appendix 3.
Vegetables
• Dark-Green Vegetables: All fresh, frozen, and canned dark- green leafy vegetables and broccoli, cooked or raw: for example, amaranth leaves, bok choy, broccoli, chamnamul, chard, collards, kale, mustard greens, poke greens, romaine lettuce, spinach, taro leaves, turnip greens, and watercress.
• Red and Orange Vegetables: All fresh, frozen, and canned red and orange vegetables or juice, cooked or raw: for example, calabaza, carrots, red or orange bell peppers, sweet potatoes, tomatoes, 100% tomato juice, and winter squash.
• Beans, Peas, Lentils: All cooked from dry or canned beans, peas, chickpeas, and lentils: for example, black beans, black-eyed peas, bayo beans, chickpeas (garbanzo beans), edamame, kidney beans, lentils, lima beans, mung beans, pigeon peas, pinto beans, and split peas. Does not include green beans or green peas.
• Starchy Vegetables: All fresh, frozen, and canned starchy vegetables: for example, breadfruit, burdock root, cassava, corn, jicama, lotus root, lima beans, plantains, white potatoes, salsify, taro root (dasheen or yautia), water chestnuts, yam, and yucca.
• Other Vegetables: All other fresh, frozen, and canned vegetables, cooked or raw: for example, asparagus, avocado, bamboo shoots, beets, bitter melon, Brussels sprouts, cabbage (green, red, napa, savoy), cactus pads (nopales), cauliflower, celery, chayote (mirliton), cucumber, eggplant, green beans, kohlrabi, luffa, mushrooms, okra, onions, radish, rutabaga, seaweed, snow peas, summer squash, tomatillos, and turnips.
Fruits
• All fresh, frozen, canned, and dried fruits and 100% fruit juices: for example, apples, Asian pears, bananas, berries (e.g., blackberries, blueberries, currants, huckleberries, kiwifruit, mulberries, raspberries, and strawberries); citrus fruit (e.g., calamondin, grapefruit, lemons, limes, oranges, and pomelos); cherries, dates, figs, grapes, guava, jackfruit, lychee, mangoes, melons (e.g., cantaloupe, casaba, honeydew, and watermelon); nectarines, papaya, peaches, pears, persimmons, pineapple, plums, pomegranates, raisins, rhubarb, sapote, and soursop.
Dietary Guidelines for Americans, 2020-2025 | Chapter 1: Nutrition and Health Across the Lifespan | Page 28
Figure 1-5 Customizing the Dietary Guidelines Framework (continued)
cereals and crackers, corn grits, cream of rice, cream of wheat, barley (pearled), masa, pasta, and white rice. Refined- grain choices should be enriched.
Protein Foods
• Meats, Poultry, Eggs: Meats include beef, goat, lamb, pork, and game meat (e.g., bison, moose, elk, deer). Poultry includes chicken, Cornish hens, duck, game birds (e.g.,
ostrich, pheasant, and quail), goose, and turkey. Organ meats include chitterlings, giblets, gizzard, liver, sweetbreads, tongue, and tripe. Eggs include chicken eggs and other birds’ eggs. Meats and poultry should be lean or low-fat.
• Seafood: Seafood examples that are lower in methylmercury include: anchovy, black sea bass, catfish, clams, cod, crab, crawfish, flounder, haddock, hake, herring, lobster, mullet, oyster, perch, pollock, salmon, sardine, scallop, shrimp, sole, squid, tilapia, freshwater trout, light tuna, and whiting.
• Nuts, Seeds, Soy Products: Nuts and seeds include all nuts (tree nuts and peanuts), nut butters, seeds (e.g., chia, flax, pumpkin, sesame, and sunflower), and seed butters (e.g., sesame or tahini and sunflower). Soy includes tofu, tempeh, and products made from soy flour, soy protein isolate, and soy concentrate. Nuts should be unsalted.
Page 29 | Dietary Guidelines for Americans, 2020-2025 | Chapter 1: Nutrition and Health Across the Lifespan
halocantik
Aug 16, 2021
Psychology has struggled for a century to make sense of the mindne
Psychology has struggled for a century to make sense of the mindne
Crucial details of the Little Albert experiment remain unclear or in dispute, such as who the child was, whether he had any neurological conditions and why the boy was removed from the experiment, possibly by his mother, before the researchers could attempt to reverse his learned fears. Also uncertain is whether he experienced any long-term effects of his experience.
Although experimental psychology originated in Germany in 1879, Watson’s notorious study foreshadowed a messy, contentious approach to the “science of us” that has played out over the last 100 years. Warring scientific tribes armed with clashing assumptions about how people think and behave have struggled for dominance in psychology and other social sciences. Some have achieved great influence and popularity, at least for a while. Others have toiled in relative obscurity. Competing tribes have rarely joined forces to develop or integrate theories about how we think or why we do what we do; such efforts don’t attract much attention.
But Watson, who had a second career as a successful advertising executive, knew how to grab the spotlight. He pioneered a field dubbed behaviorism, the study of people’s external reactions to specific sensations and situations. Only behavior counted in Watson’s science. Unobservable thoughts didn’t concern him.
Even as behaviorism took center stage — Watson wrote a best-selling book on how to raise children based on conditioning principles — some psychologists addressed mental life. American psychologist Edward Tolman concluded that rats learned the spatial layout of mazes by constructing a “cognitive map” of their surroundings (SN: 3/29/47, p. 199). Beginning in the 1910s, Gestalt psychologists studied how we perceive wholes differently than the sum of their parts, such as, depending on your perspective, seeing either a goblet or the profiles of two faces in the foreground of a drawing (SN: 5/18/29, p. 306).
And starting at the turn of the 20th century, Sigmund Freud, the founder of psychoanalysis, exerted a major influence on the treatment of psychological ailments through his writings on topics such as unconscious conflicts, neuroses and psychoses (SN: 7/9/27, p. 21). Freudian clinicians guided the drafting of the American Psychiatric Association’s first official classification system for mental disorders. Later editions of the psychiatric “bible” dropped Freudian concepts as unscientific — he had based his ideas on analyses of himself and his patients, not on lab studies.
Shortly after Freud’s intellectual star rose, so did that of Harvard University psychologist B.F. Skinner, who could trace his academic lineage back to Watson’s behaviorism. By placing rats and pigeons in conditioning chambers known as Skinner boxes, Skinner studied how the timing and rate of rewards or punishments affect animals’ ability to learn new behaviors. He found, for instance, that regular rewards speed up learning, whereas intermittent rewards produce behavior that’s hard to extinguish in the lab. He also stirred up controversy by calling free will an illusion and imagining a utopian society in which communities doled out rewards to produce well-behaved citizens.
Skinner’s ideas, and behaviorism in general, lost favor by the late 1960s (SN: 9/11/71, p. 166). Scientists began to entertain the idea that computations, or statistical calculations, in the brain might enable thinking.
At the same time, some psychologists suspected that human judgments relied on faulty mental shortcuts rather than computer-like data crunching. Research on allegedly rampant flaws in how people make decisions individually and in social situations shot to prominence in the 1970s and remains popular today. In the last few decades, an opposing line of research has reported that instead, people render good judgments by using simple rules of thumb tailored to relevant situations.
Starting in the 1990s, the science of us branched out in new directions. Progress has been made in studying how emotional problems develop over decades, how people in non-Western cultures think and why deaths linked to despair have steadily risen in the United States. Scientific attention has also been redirected to finding new, more precise ways to define mental disorders.
No unified theory of mind and behavior unites these projects. For now, as social psychologists William Swann of the University of Texas at Austin and Jolanda Jetten of the University of Queensland in Australia wrote in 2017, perhaps scientists should broaden their perspectives to “witness the numerous striking and ingenious ways that the human spirit asserts itself.”
Revolution and rationality
Today’s focus on studying people’s thoughts and feelings as well as their behaviors can be traced to a “cognitive revolution” that began in the mid-20th century.
The rise of increasingly powerful computers motivated the idea that complex programs in the brain guide “information processing” so that we can make sense of the world. These neural programs, or sets of formal rules, provide frameworks for remembering what we’ve done, learning a native language and performing other mental feats, a new breed of cognitive and computer scientists argued (SN: 11/26/88, p. 345).
Economists adapted the cognitive science approach to their own needs. They were already convinced that individuals calculate costs and benefits of every transaction in the most self-serving ways possible — or should do so but can’t due to human mental limitations. Financial theorists bought into the latter argument and began creating cost-benefit formulas for investing money that are far too complex for anyone to think up, much less calculate, on their own. Economist Harry Markowitz won the Nobel Memorial Prize in Economic Sciences in 1990 for his set of mathematical rules, introduced in 1952, to allocate an investor’s money to different assets, with more cash going to better and safer bets.
But in the 1970s, psychologists began conducting studies documenting that people rarely think according to rational rules of logic beloved by economists. Psychologists Daniel Kahneman of Princeton University, who received the Nobel Memorial Prize in Economic Sciences in 2002, and Amos Tversky of Stanford University founded that area of research, at first called heuristics (meaning mental shortcuts) and biases. of the most infamous psychology experiments ever conducted involved a carefully planned form of child abuse. The study rested on a simple scheme that would never get approved or funded today. In 1920, two researchers reported that they had repeatedly startled an unsuspecting infant, who came to be known as Little Albert, to see if he could be conditioned like Pavlov’s dogs.
Psychologist John Watson of Johns Hopkins University and his graduate student Rosalie Rayner viewed their laboratory fearfest as a step toward strengthening a branch of natural science able to predict and control the behavior of people and other animals.
At first, the 9-month-old boy, identified as Albert B., sat placidly when the researchers placed a white rat in front of him. In tests two months later, one researcher presented the rodent, and just as the child brought his hand to pet it, the other scientist stood behind Albert and clanged a metal rod with a hammer. Their goal: to see if a child could be conditioned to associate an emotionally neutral white rat with a scary noise, just as Russian physiologist Ivan Pavlov had trained dogs to associate the meaningless clicks of a metronome with the joy of being fed.
Pavlov’s dogs slobbered at the mere sound of a metronome. Likewise, Little Albert eventually cried and recoiled at the mere sight of a white rat. The boy’s conditioned fear wasn’t confined to rodents. He got upset when presented with other furry things — a rabbit, a dog, a fur coat and a Santa Claus mask with a fuzzy beard.
halocantik
Aug 16, 2021
Probiotics help laboratory corals survive deadly heat stress
Probiotics help laboratory corals survive deadly heat stress
Laboratory experiments show that good bacteria can help make coral reefs more resilient to climate change
Warming oceans threaten to turn coral reefs from a kaleidoscope of colors to fields of bleached debris. To stop this damage, some scientists are studying a surprising ointment: probiotics.
Doses of coral with a mix of beneficial bacteria prevented death in a simulated heatwave in an aquarium, researchers report in Science Advances Aug. 13. In comparison, nearly half of the corals fed the benign saline solution did not survive the same conditions. This study provides evidence for the concept that probiotics may help some corals survive heat stress.
"The results are very promising," said Blake Ushidjima, a microbiologist at the University of North Carolina Wilmington, who was not involved in the study. The work legitimizes the use of probiotics as coral remedies, he says, “but we're only scratching the surface. We don't understand how many of these beneficial microbes are at work. "
Corals are not individual units, but coalitions of cooperative actors. Photosynthetic algae, which use solar energy to supply their host animals, coral polyps, with energy are the key step. Corals are also home to many bacteria, many of which obtain their hosts by circulating nutrients or fighting off pathogens. In short, the so-called "holobion" corals, corals and their microbial partners form the basis of one of the most diverse ecosystems on earth.
A worsening heatwave at sea is testing the integrity of a healthy holobion (SN: 4/10/18). Under heat stress, coral algae secrete toxic chemicals that cause polyps to excrete. This process, known as bleaching, can kill corals (SN: 18/10/16). For example, heatwave bleaching wiped out 29% of shallow coral on the northern Great Barrier Reef in 2016. Bacterial communities were also displaced by heat stress, which reduced the benefits of some bacteria.
"In general we see a breakdown of the symbiotic relationship and all the microorganisms start fighting back," said Raquel Peixoto, a marine ecologist at King Abdullah University of Science and Technology in Tuvalu, Saudi Arabia. He and his colleagues have shown in laboratory experiments that treating corals with a carefully prepared probiotic cocktail can reduce coral bleaching. That's fine, he said, "but we want to see if we can protect them from dying."
In the latest setting, the researchers simulated ocean heatwaves in 10 aquariums, each with four fragments of the hard coral Mussismilia hispida, which raised the temperature to 30°C for 10 days before dropping back to 26°C. Half of the corals were sprayed with six strains. M. hispida bacteria every three days during the heat wave and every five days thereafter until the other half received salt. Over 75 days, Peixoto and his colleagues measured coral health and changes in globion metabolic activity, along with turning genes on and off.
The corals in both groups were bleached, but the probiotic treatment was ultimately successful. While 40% of salt-treated corals succumbed to the heat, all bacteria-treated corals survived. "It was surprising and very exciting," Peixoto said. Probiotics appear to aid coral repair by causing genetic and metabolic changes in the host that are linked to suppressing inflammation and allowing damaged cells to repair, the researchers found.
"Climate change affects corals faster than they can adapt to," but their microbial partners may respond more quickly to change, said Kimberly Richie, a marine biologist at the University of South Carolina Beaufort who was not involved in the study. Such changes, which probiotic treatments might make, "could buy more time for corals," he says.
Peisho and his colleagues plan to leave the aquarium and begin experiments in mid-August to see if probiotics can help wild corals. However, some scientists are skeptical about the ultimate benefits of additional bacteria, especially for large reefs with hundreds of coral species. "Probiotics are hot right now," says Ty Roach, a molecular ecologist at the Hawaiian Institute of Marine Biology in Kaneohe. "I can think of scenarios where that would be a good approach ... but I don't think they will save the reef."
For example, applying probiotics to large reefs with hundreds of coral species seems a logical challenge, says Roach. And there can be unintended consequences. "What is good for one coral may not be good for another coral or organism," Roach said. "For an ecosystem as complex as coral reefs, I can't imagine doing this on a large scale without unforeseen harmful effects."
Peixoto says the probiotics used here are carefully screened and don't use strains that are known to be life-threatening. In general, probiotics "are not going to be a panacea," he says. The only thing that will save coral reefs is to reduce carbon dioxide emissions to reduce global warming. "But we still need recovery and rehabilitation to cope with the reality we have now," he says, and probiotics hold promise.
halocantik
Aug 16, 2021
Measuring the mass of a black hole is not easy. New technology can change that
An active powered black hole is surrounded by a disk of hot gas and dust that vibrates like a bonfire. Astronomers have discovered that tracking changes in these vibrations can reveal something that is extremely difficult to measure: the weight of the hippopotamus.
"This is a new way to weigh black holes," said astronomer Colin Burke of the University of Illinois at Urbana-Champagne. Additionally, the method can be applied to any astrophysical object with an accretion disk and could even help locate elusive medium-sized black holes, researchers reported to Science on Aug. 13.
It is not easy to measure the mass of a black hole. On the one hand, dark giants are known for their difficult visibility. But sometimes black holes appear when they eat. As gas and dust fall into the black hole, matter is placed in a disk that warms to temperatures hot to white and, in some cases, can shade all the stars in the galaxy together.
Measuring the diameter of a black hole can reveal its mass using Einstein's theory of general relativity. But only Horizon telescopes worldwide have made this type of measurement and so far only for black holes (SN: 22/04/19). Other black holes are weighed by observing their effect on the surrounding matter, but these require a lot of data and won't work for every supermassive black hole.
Looking for another route, Burke and his colleagues turned to accretion disks. Astronomers aren't sure how the disk flashes in a black hole, but small changes in the light seem to combine to illuminate or darken the entire disk over a period of time. Previous research has shown that the time it takes the disk to fade, flash, and fade again is related to the mass of the central black hole. But these claims are contradictory and do not cover the full spectrum of the black hole's mass, Burke said.
So he and his colleagues gathered observations of 67 black holes of known mass that were actively feeding. Hippos cover sizes from 10,000 to 10,000 billion solar masses. In this tiniest black hole, the vibrations change hourly from hour to week. Supermassive black holes with masses between 100 million and 10 billion solar masses flash more slowly every few hundred days.
"This gives us an indication that if this association holds for both small supermassive black holes and large black holes, it may be a universal characteristic," Burke said.
Out of curiosity, the team also examined white dwarfs, the dense bodies of sun-like stars that are among the smallest objects for successive accretion disks. These white dwarfs follow the same relationship between vibrational velocity and mass.
The black holes analyzed do not cover the entire range of possible masses. Famous black holes, which have masses of about 100 to 100,000 times the sun, are rare. There are several potential candidates, but only one is confirmed (SN: 09/02/20). In the future, the relationship between disc vibrations and the mass of black holes could tell astronomers what disc vibrations to look for to get this medium-sized beast out of its hiding place when found, Burke said.
Astrophysicist Vivien Baldassare of the University of Washington at Pullman studies black holes in dwarf galaxies that may retain some of the properties of old black holes in the early universe. One of the biggest challenges in his work was measuring the mass of black holes. "The results of this very interesting study ... will have a major impact on my research and I look forward to many more," he said.
This method offers a simpler way to weigh black holes than previous techniques, Burke said -- but not necessarily faster. A more massive black hole, for example, takes hundreds of days and maybe even years of observation to reveal its mass.
Upcoming observatories are already planning to record the data. The Vera C. Rubin Observatory is expected to observe the entire sky every night starting in 2022 or 2023 (SN: 1/10/20). After the telescope has run long enough, the observations needed to weigh the black hole will be "omitted from the Rubin Observatory data free of charge," Burke said. "We've built it. We can do that too. "
When it comes to space, you must have had a black hole, aka a black hole, right? Among all space objects, black holes are one of the most feared because they are considered to bring destruction and destroy everything. The effect is very damaging.
Then, what will happen if one day we meet a black hole and are sucked into it? Are we going to live, die, or just disappear? Reported from various sources, here are some theories that hope to answer these questions!
1. To clarify, first of all, let's get to know what a black hole really is
What Happens If You Get Into a Black Hole? Astronomers Sayaskamathematician.com
According to NASA's statement from its website, a black hole is a place where gravity pulls so hard that even light cannot escape. The gravity is very strong because the material is compressed into a very small space.
Since light is completely incapable of surviving this gravitational pull, it is no wonder that it is completely black in color. From here we know the origin of the name, right?
Some of them are as small as an atom, but have a mass as heavy as a mountain on Earth. Reporting from interesting engineering , Stellar black holes (with a mass more than 20 times our sun) are formed when large stars disintegrate. This process will create a curvature of space and time.
Meanwhile, supermassive black holes (with masses ranging from more than 1 million times our sun) are thought to have formed when the galaxy they inhabit formed. Reporting from phys.org , V616 Mon is located about 3 thousand light-years from Earth and has a mass of 9-13 times heavier than the Sun in our solar system. The researchers know its location because it is located in a binary system with a star half the mass of the Sun.
Only a black hole can make its binary partner feel buzzing so fast. Astronomers cannot see black holes. They only know it's there as a result of the spinning "gravity dance." Spooky too, huh!
Reporting from phys.org , V616 Mon is located about 3 thousand light-years from Earth and has a mass of 9-13 times heavier than the Sun in our solar system. The researchers know its location because it is located in a binary system with a star half the mass of the Sun.
Only a black hole can make its binary partner feel buzzing so fast. Astronomers cannot see black holes. They only know it's there as a result of the spinning "gravity dance." Spooky too, huh!
halocantik
Aug 11, 2021
Bahan Kimia Berpotensi Beracun
Bahan Kimia Berpotensi Beracun yang Disebut PFAS Umum dalam Kosmetik, Temuan Studi
Berbagai macam kosmetik yang tersedia di Amerika Serikat dan Kanada mengandung bahan kimia beracun tingkat tinggi, menimbulkan pertanyaan tentang transparansi perusahaan dan peraturan federal, serta pentingnya pendidikan konsumen.
Untukpenelitian, yang diterbitkan pada Juni 2021 diEnvironmental Science and TechnologyLetters, para ilmuwan menguji 231 produk kosmetik - termasuk concealer, riasan mata, alas bedak, warna bibir, dan maskara - untuk fluor, penanda zat polifluoroalkil dan perfluoroalkil (PFAS) .Secara keseluruhan, 52 persen dari produk ini memiliki kadar fluor yang tinggi, menunjukkan bahwa kosmetik tersebut kemungkinan mengandung PFAS tingkat tinggi.PFAS telah dikaitkan dengan banyak masalah kesehatan termasuktekanan darah tinggi,obesitas,diabetes, infertilitas, dankankertertentu.
Beberapa jenis riasan diuji lebih tinggi untuk tingkat PFAS daripada yang lain.Tes laboratorium menemukan kadar fluor yang tinggi dalam 82 persen maskara tahan air, 63 persen alas bedak, dan 62 persen lipstik cair.
Hasil studi juga menunjukkan bahwa mungkin tidak ada cara bagi konsumen untuk mengetahui apakah mereka berisiko terpapar.Secara keseluruhan, hanya 8 persen dari kosmetik yang diidentifikasi mengandung PFAS dalam tes laboratorium yang memiliki bahan yang mengandung bahan kimia ini yang tercantum sebagai bahan pada label.
“Hasil ini sangat memprihatinkan ketika Anda mempertimbangkan risiko paparan konsumen yang dikombinasikan dengan ukuran dan skala industri bernilai miliaran dolar yang menyediakan produk ini kepada jutaan konsumen setiap hari,” kata penulis studi senior
Graham Peaslee, PhD
, seorang profesor fisika di Universitas Notre Dame di Indiana.
Orang yang tanpa disadari menerapkan PFAS ke wajah mereka saat mereka memakai riasan memiliki potensi bahan kimia ini untuk diserap melalui kulit atau saluran air mata, atau melalui inhalasi atau konsumsi, tergantung pada bagaimana produk diterapkan, kata Dr. Peaslee.
"PFAS adalah bahan kimia yang persisten - ketika masuk ke aliran darah, ia tetap di sana dan terakumulasi," tambah Peaslee.“Ada juga risiko tambahan pencemaran lingkungan yang terkait dengan pembuatan dan pembuangan produk ini, yang dapat mempengaruhi lebih banyak orang.”
Potensi Risiko Kesehatan dari Bahan Kimia PFAS
PFAS adalah bahan kimia buatan manusia yang telah ditemukan di berbagai produk konsumen selama beberapa dekade, termasuk peralatan masak antilengket, kain tahan api, dan pembungkus makanan cepat saji.Selain produk konsumen, orang dapat terpapar PFAS dari udara, debu dalam ruangan, serta makanan dan air,menurut Pusat Pengendalian dan Pencegahan Penyakit.
Seperti penelitian terbaru, penelitian sebelumnya tentang kemungkinan risiko kesehatan dari PFAS belum dapat membuktikan secara pasti bahwa bahan kimia secara langsung menyebabkan masalah medis tertentu.Namun penelitian sebelumnya menunjukkan bahwa paparan PFAS tingkat tinggi tertentu dapat menyebabkan berbagai masalah kesehatan, menurut CDC.Ini termasuk:
· Kolesterol Tinggi
· Tekanan darah tinggi
· Preeklamsia(peningkatan tekanan darah selama kehamilan)
· Berat badan lahir rendah
· Penurunan respons vaksin pada anak-anak
· Kanker tertentu
Beberapa penelitian juga menunjukkan paparan PFAS dapat dikaitkan dengan peningkatan risiko masalah kesehatan kronis seperti obesitas, diabetes, dan asma.
Satustudi, yang diterbitkan pada Februari 2019 diThe Lancet Planetary Health, misalnya, mengaitkan paparan PFAS prenatal dengan gangguan fungsi paru-paru pada anak-anak.Studilain, yang diterbitkan dalamDiabetes Carepada Juli 2019, mengaitkan paparan PFAS dengan peningkatanrisiko diabetes.Dan sebuahpenelitian yang diterbitkan pada Mei 2020 diToxicological and Environmental Chemistrymengaitkan paparan PFAS dengan peningkatan risiko obesitas, diabetes, dan penyakit hati tertentu.
Undang-undang Baru yang Diusulkan Akan Melarang PFAS di AS
Konsumen yang ingin menghindari PFAS dalam kosmetik mereka mungkin tidak dapat melakukannya, kata Peaslee.Itu karena penelitian ini tidak mengidentifikasi merek makeup utama yang sepenuhnya bebas dari PFAS.
Menanggapi penelitian Peaslee,"No PFAS in Cosmetics Act"diperkenalkan di DPR dan Senat AS pada 15 Juni,menurut siaran pers.Undang-undang ini akan mengarahkan Food and Drug Administration (FDA) AS untuk mengusulkan aturan yang melarang penambahan PFAS yang disengaja ke kosmetik.
Seperti yang terjadi sekarang, label bisa menipu, kataXindi Hu, ScD, peneliti kesehatan lingkungan di Mathematica.
Produk yang mengklaim bebas bahan kimia, organik, bersih, atau alami mungkin masih mengandung PFAS, dan tidak ada cara yang sangat mudah bagi konsumen untuk mengetahui apakah itu masalahnya, kata Dr. Hu.Sementara peraturan di Amerika Serikat menetapkan kriteria makanan yang akan diberi label organik, misalnya, tidak ada peraturan seperti itu untuk kosmetik.
“Saya tidak yakin apakah ada merek yang bebas PFAS, tetapi mudah-mudahan karena masalah ini mendapat lebih banyak perhatian, produsen yang yakin dengan proses pembuatannya dapat melaporkannya sendiri,” kata Hu.“Namun, pengawasan regulasi yang diperlukan masih diperlukan.”
Cara Membatasi Paparan PFAS Dari Kosmetik
Pergi tanpa riasan adalah satu-satunya cara pasti untuk menghindari paparan PFAS dalam kosmetik, tetapi konsumen dapat mencoba mengurangi risiko mereka dengan membatasi berapa banyak riasan yang mereka gunakan dan seberapa sering mereka menggunakannya, kata Hu.Hari-hari tanpa riasan mungkin bisa membantu.
“Mereka juga dapat mempertimbangkan untuk menghapus riasan segera setelah mereka tiba di rumah,” saran Hu."Dan untuk lipstik, adalah ide yang baik untuk menyekanya sebelum orang minum atau makan untuk membantu mengurangi paparan melalui konsumsi."
Hasil studi juga menyarankan bahwa konsumen harus mencoba untuk menghindari produk berlabel tahan air, tahan lama, atau tahan aus, kata Peaslee.
Konsumen juga dapat beralih keEWG's Healthy Living, sebuah aplikasi yang dirilis oleh Environmental Working Group (EWG), sebuah kelompok penelitian dan advokasi, untuk memberikan informasi keamanan tentang kosmetik kepada konsumen, kataLeonardo Trasande, MD, direktur Center for the Investigation of Bahaya Lingkungan di Universitas New York di Kota New York.
Aplikasi gratis ini jugatersedia secara online.Ini menggunakan basis data bahan yang dibangun dari informasi pelabelan produk serta toksikologi independen dan laporan peraturan untuk membuat skor bahaya (0–10), dengan skor yang lebih rendah menunjukkan produk yang lebih aman dengan lebih sedikit bahan yang terkait dengan masalah kesehatan.Konsumen dapat memindai barcode produk atau mengetik nama merek atau produk tertentu untuk mendapatkan peringkat keamanan.
halocantik
Aug 11, 2021
Basket Ball
Before a preseason game on September 1, 2016, San Francisco 49ers quarterbackColin Kaepernickknelt during the national anthem to call attention to issues of racial inequality and police brutality — a protest that continues to stir intense debate. For decades, American athletes have used their platforms for protests. Here are some of the more notable examples.
1. 1995: Mahmoud Abdul-Rauf’s National Anthem Stance
During the 1995–96 NBA season, the Denver Nuggets’ star refused to stand for the national anthem, declaring it would be a violation of his Muslim faith. Mahmoud Abdul-Rauf told reporters the American flag was “a symbol of oppression, of tyranny.” The NBA suspended him for one game before reaching a compromise — Abdul-Rauf would stand and pray during the anthem. But Abdul-Rauf paid a price for his stance: Denver traded him to Sacramento after the season, and despite his prolific scoring, he was out of the NBA by age 29.
2. 1961: Bill Russell, Celtics Boycott Game in Kentucky
When he and four of his Black teammates on the Boston Celtics were refused service in a restaurant in Lexington, Kentucky, Bill Russell told coach Red Auerbach they wouldn’t play in an exhibition game in the city. Two members of theSt. Louis Hawks, Boston’s opponent, joined them in the boycott. Auerbach didn’t exactly stick up for his players, telling the Associated Press, “The Negro boys got real emotional. They said they’d like to go home. We talked for two hours, and I couldn’t change their minds.”
Russell and his teammates flew back to Boston, where the star declared: “Negroes are in a fight for their rights, a fight for survival in a changing world. I am with these Negroes.” A day later, Celtics owner Walter A. Brown told theBoston Globethe Celtics wouldn’t play games in the South again, adding, “I’m not so hungry for money that I’d arrange games that might embarrass my players.” At the time, the league consisted of only nine teams.
August 2003 was the hottest August ever recorded in the northern hemisphere and broke all previous records for heat-related deaths. France was the worst hit, with almost 15,000 victims, followed by Germany, where approximately 7,000 people died. Thousands also died in Spain and Italy. A majority of the victims were elderly, very young, or chronically ill.
When a person experiences extreme heat, their bodies can struggle to cool themselves — which can prove especially dangerous in the very old, very young or already ill. If a person’s internal body temperature reaches 104 degrees Fahrenheit, the organs can began to fail and the person can eventually die. The Washington, D.C.-based Earth Policy Institute estimates that more people die every year from heat than floods, tornadoes and hurricanes combined.
In addition to directly causing deaths, the extreme heat also caused massive fires. In Portugal, 10 percent of the country’s forests were destroyed and 18 people were killed in the fires. The heat also caused glacial melt, flash floods and avalanches in Switzerland.
Scientists project that, because of global warming, the earth’s average temperature will continue to rise, reaching 42.44 degrees Fahrenheit by the end of the century, a gain of 2.5 degrees. The only way to stop the rise in global temperatures and extreme weather catastrophes is to reduce levels of the carbon-dioxide emissions that contribute to global warming.
3. 1965: AFL Moves All-Star Game After Players Protest
The AFL All-Star Game in New Orleans was a nightmare for the Black All-Stars. Taxis refused to pick up Black players, nightclubs on Bourbon Street were segregated, and a bouncer pulled a gun on tackle Ernie Ladd. After that, the players refused to play. New Orleans Mayor Victor Schiro told the Associated Press the protest would “aggravate the very condition they are seeking, in time, to eliminate.”
But AFL commissionerJoe Fossdidn’t blame the players for withdrawing, and he quickly relocated the game to Houston. The protest accelerated the desegregation of New Orleans, as business owners feared the financial losses from missing out on big sports events.
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