Jay D. Evans, USDA-ARS Bee Research Laboratory, Beltsville, MD
Beekeepers, inspectors, and researchers have a shared interest in checking bees and hives for clues related to bee health and disease. These checks take many forms, from lifting fall supers prior to feeding decisions to carrying out sticky board or jar tests for estimating varroa populations. Most decisions for managing beehives are, appropriately, made in the field using these and other traits presented by the bees themselves. Nevertheless, some tests are best carried out on hive samples that have been gathered and brought to a home or laboratory where different tools can be used. In the laboratory, genetic tools are becoming the norm for everything from disease diagnosis and research into bee behavior and nutrition to validation of promising bee stock.
Diagnostic genetic tools are available for each of the major honey bee pests and pathogens and these tools offer new avenues for screening bees and colonies to predict ailments and causes of colony declines. In addition, the sequencing of the honey bee genome (Honey Bee Genome Sequencing Consortium, 2006) and associated efforts to define the genetic and protein makeup of bees have generated informative genetic tags for bee proteins involved with development, immunity, physiology, and behavior. These resources for bees and their disease agents can be exploited in order to improve bee breeding schemes, manage diseases or bee nutrition, and regulate the movement of viruses, bacteria, fungi, and other infectious agents.
Interest in such ‘forensic’ tools has surged in the past several years, triggered by enigmatic colony losses that have defied typical explanations. Currently, molecular diagnostics are a routine part of national surveys (Genersch et al., 2010) and research efforts (de Miranda, Fries, 2008; Johnson et al., 2009; vanEngelsdorp et al., 2009) aimed at understanding disease risk factors in the field.
Two recent national disease surveys provide a hint of the power of genetic tools for identifying the major honey bee pathogens and predicting their impacts on bees. Genersch and colleagues (2010) describe extensive methods and target sequences for a national survey carried out in Germany for several years. Their major findings (a connection between mite and virus levels and winter losses), were consistent with a biological cause of colony losses. Similarly, vanEngelsdorp and colleagues (2009) used genetic markers to show higher microbial loads in collapsed colonies from a U.S. survey than in healthier controls. This survey exploited markers for a group of well known and newly recognized viruses (Evans, 2006; Cox-Foster et al., 2007).
Building a better toolkit
A useful toolkit for understanding bee health faces four major hurdles in order to become a lasting component for bee diagnostics or research:
1) Tools must be robust and reproducible. This stage weeds out many promising tools. Diagnostics that can not be reproduced, ideally via a new technique and by an independent team of researchers, will soon be forgotten. This is a sobering fact of science. While credit is given for proposing novel diagnostics, true validation is more precious and most fleeting.
2) Tools must have a purpose and predictive value. Scientists, or companies, can develop a very precise and accurate diagnostic for a genetic trait or something like the exact degree of darkness on a bee’s backside that in the end offers little insight into bee biology, breeding, or management. Research notebooks, including ours, are riddled with brilliant tests that have not yet found, and likely will never find, their niche as a useful genetic tool. Still, these tools are often made public in hopes that someone with more insights or other experiences will find them a niche. Few will actually fulfill that promise. On the plus side, there is an abundance of new tools right now and chances are great that gems will emerge for understanding bee health or giving great fundamental insights into what it means to be a social insect, or an insect at all. These gems, and the toolkits built around them, are what keep scientists going.
3) Tools must be adopted and further tested across the community. Imitation is flattery, and new tools have a greater impact if they are tested and then adopted in many places, after being proven reliable in different parts of the world. Scientists, while we try to think independently on the bigger questions, are eager to adopt a technique that has worked elsewhere. This tendency, in fact, unites scientists and beekeepers. While it is useful to keep a critical mind, imitation can push some really useful behaviors into the community. Maybe 10% of a scientist’s work (o.k. at least THIS scientist’s work) actually gets imitated in this way.
4) Diagnostic tests must be cost effective and ‘portable’ to different labs. This last stage is one in which genetic techniques excel and will continue to do so. Many genetic toolkits revolve around the polymerase chain reaction (PCR), a decades-old technique (Nobel Prize already bagged) that imitates the cell’s replicative machinery by making a measurable amount of DNA for a specific marker by ‘priming’ replication of that, and only that, region of the genome. As a tool, PCR is quick, cheap, sensitive, and specific, MOST of the time. It often fails for identifying novel or rogue targets like unstudied viruses, driving constant efforts to tag those targets by more tedious sequencing efforts (e.g., Cox-Foster et al., 2007). Nevertheless, most would-be targets of interest for bee health can be studied with robust and sensitive PCR assays. Since nearly every University, government lab, or small tech outfit has the machinery needed to enact PCR, this technique also leads the way currently in terms of portability. Victories are temporary in science and completely novel diagnostics are in the background, but PCR has some years left in it as a key part of any genetic toolkit.
One Lab’s toolkit for Disease Diagnostics
With help from the CAP program and with insights from many colleagues and especially my coworkers Judy Chen and Dawn Lopez, I have pieced together a modest genetic toolkit for diagnosing honey bee disease and addressing some of the many bee health and regulatory issues. After much taxpayer support, all of the actual diagnostics we use have inched past stage ’1’ above, most have passed stage ‘2’ and a few are knocking on the doors of stage ‘4’. The routine we use is illustrated in Figure 1 and in more detail at the Bee Health website Protocol for Honey Bee PCR Diagnostics . We stick more or less to the same script whether samples are generated in laboratory experiments (Evans et al., 2009), field experiments, or field crises. The hope is that a cycle of diagnostics will lead to new insights into a general health issue of bees or a local collapse of bee colonies.
Genetic markers have had great impacts on honey bee research and on the discovery of potential disease agents. It is hoped that these insights will now lead to more cost-effective screens that can be used to assess management and regulatory practices and speed the selection and maintenance of desired bee stock. All of the technical pieces are in place to do this in a big way, and in fact genetic insights into bee behaviors including the switch from nurse bees to foragers (Whitfield et al., 2006) and specific tendencies (Hunt et al., 2007) are well established, as are indicators of honey bee immunity and stress (e.g., Johnson et al., 2007). On the pathogen side, genetic markers gave unique insights into the distinctiveness of Nosema ceranae from Nosema apis, and helped map the great recent spread of N. ceranae (Klee et al., 2007). Genetic sequences for N. ceranae also allowed for the first field diagnostic test for this species (Aronstein, 2010). Genetic signals are now known for each of the major honey bee pests, and diagnostics based on these signals are ready to complement other measures of honey bee health and disease.
Cox-Foster DL, Conlan S, Holmes EC, et al. (2007) A metagenomic survey of microbes in honey bee colony collapse disorder. Science 318, 283-287.
Aronstein (2010) Detect Nosema Parasite in Time to Save Bee Colonies, American Bee Journal, 150 (1), pp. 63-65.
Evans JD (2006) Beepath: An ordered quantitative-PCR array for exploring honey bee immunity and disease, Journal of Invertebrate Pathology, 93 (2), pp. 135-139.
Evans JD, Chen YP, Di Prisco G, Pettis J, Williams V (2009) Bee cups: Single-use cages for honey bee experiments. Journal of Apicultural Research 48, 300-302.
Genersch E, Von Der Ohe W, Kaatz H, et al. (2010) The German bee monitoring project: A long term study to understand periodically high winter losses of honey bee colonies. Apidologie 41, 332-352.
Honey Bee Genome Sequencing Consortium (2006) Insights into social insects from the genome of the honeybee Apis mellifera. Nature 443, 931-949.
Hunt, G. J., G. V. Amdam, et al. (2007). Behavioral genomics of honeybee foraging and nest defense. Naturwissenschaften 94(4): 247-267.
Johnson RM, Evans JD, Robinson GE, Berenbaum MR (2009) Changes in transcript abundance relating to colony collapse disorder in honey bees (Apis mellifera). Proceedings of the National Academy of Sciences of the United States of America 106, 14790-14795.
Klee, J., A. M. Besana, et al. (2007). "Widespread dispersal of the microsporidian Nosema ceranae, an emergent pathogen of the western honey bee, Apis mellifera." Journal of Invertebrate Pathology 96(1): 1-10.
vanEngelsdorp D, Evans JD, Saegerman C, et al. (2009) Colony collapse disorder: a descriptive study. PLoS ONE 4 (8).
Whitfield, C. W., Y. Ben-Shahar, et al. (2006). Genomic dissection of behavioral maturation in the honey bee. Proceedings of the National Academy of Sciences of the United States of America 103(44): 16068-16075.