BEACON Researchers at Work: Plasmid evolution is the key to fighting antibiotic resistance

Posted on May 2, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Idaho graduate student Julie Hughes.

We are in the midst of a medical crisis. Even though we have more antibiotics on the market than ever before, our ability to effectively combat antibiotic-resistant pathogens is constantly decreasing. While many people know that bacteria are becoming increasingly resistant to antibiotics, it is not common knowledge just how they are adapting so rapidly. One of the key weapons in the bacterial arsenal is the plasmid. Plasmids are DNA molecules that can be transferred between different bacteria through a process known as horizontal gene transfer. Most plasmids can code for a variety of traits, including the ability to degrade organic compounds, virulence, and, of course, resistance to antibiotics. Moreover, one plasmid may contain multiple such genes, providing its bacterial host with a full range of armor against various types of antibiotics at once. Therefore, if we want to combat bacterial pathogens, we need to learn more about plasmids and how they interact with their bacterial hosts over evolutionary time. We need to get to know the enemy and start looking for chinks in its armor for antibiotic use to be successful in the future.

Fortunately, a bacterial population won’t necessarily maintain plasmids forever. Just as wearing armor will protect a knight during battle, plasmids are useful to their hosts under certain environmental conditions (like in the presence of antibiotics, or an organic compound that could be used in metabolism). After the battle, a knight will shed his armor because it is no longer needed and it only serves to slow him down. Likewise, the cost of producing plasmid-encoded protein products and replicating the plasmid can retard host growth. Plasmid-free cells would therefore have a competitive edge over plasmid-bearing cells, and so eventually only plasmid-free cells will persist in the population in the absence of selection for plasmid maintenance. Additionally, to be maintained in a population, plasmid and host proteins must interact properly to make sure that copies of the plasmid are made before cell division and to ensure that each daughter cell gets at least one of those copies. Otherwise a plasmid may be lost from a population over time – that is, it may have poor stability.

When a plasmid is first introduced into a new host, it may exhibit poor stability. If selective pressures mandate plasmid maintenance for survival or competitive success over evolutionary time, either the plasmid, the host, or both can adapt to each other to improve plasmid stability or reduce the cost of plasmid-carriage. However, it is not known how quickly plasmid-host adaptation occurs or what evolutionary dynamics are involved in this process. The goals of my research are to address these two questions.

I am currently working with a mini-replicon, pMS0506. This plasmid was originally constructed from a natural plasmid that was isolated from Bordetella pertussis, the causative agent of whooping cough and exactly the type of host we don’t want plasmids giving antibiotic resistance to. The host I work with is much more benign, as Shewanella oneidensis MR-1 was originally isolated from lake sediment, and is well-loved by many for its ability to reduce uranium to more stable forms. It was previously shown that after long-term evolution of pMS0506 in this host (over 1000 generations, or 100 days of growth), our plasmid improved its stability in strain MR-1 through mutations in the gene that codes for the replication initiation protein, TrfA1. The tradeoff of adapting to this new host was that it could no longer replicate in the human pathogen Pseudomonas aeruginosa. This is called a host range shift.

My research involves determining the tempo and evolutionary mechanisms of pMS0506 evolution. While we knew that plasmid adaptation occurred within 1,000 generations we now know that these populations exhibit improved stability within 200 generations (only 20 days!). Keep in mind that this is when the population reached high stability – individual plasmids mutated well before then. It’s quite possible that even during the course of patient medication, plasmids are adapting to their bacterial hosts, which may mean that bacteria will remain resistant to certain antibiotics even after medication is completed. The longer a plasmid persists, the more likely it is that it will find its way to new hosts, including the next bug that’s going to make you sick…and so on.

Another aspect of my work involves the dynamics of evolution. I am interested in questions about how many different mutations may arise in the population, how do they interact with one another (if at all), etc. It was once thought that beneficial mutations would occur so rarely in a population that you would only ever have one mutant type affecting a given phenotype in a population at a given time in an asexual population. Now it is clear that in large populations with high mutation rates, it is possible to have more than one variant present at once. Since they are asexual, bacteria don’t recombine their genes all that often, and so two different beneficial mutations on two different cells are likely to stay in different cells. These cells will then compete with each other (and the ancestral types as well) until one of them eventually “wins,” or outcompetes the other types. This process, called clonal interference, has some important implications for evolutionary dynamics. If a given mutant (let’s call it Mutant A) was competing only with the ancestor, it would be relatively easy to dominate the populations. If you add another strong competitor into the mix (named Mutant B), A doesn’t have to just outdo the ancestor, but B as well! Since B has a beneficial mutation he’s no pushover, so he’s hard to beat, so they have to duke it out for a little while. In the meantime Mutant C comes along and joins the fun, making it even hard for A or B to take over the population quickly. This will continue until one of the best mutations is generated and gains dominance, eventually outcompeting everyone else. One of the consequences of clonal interference, therefore, is that it takes longer for a mutation to fix in the population (that is, outcompete everyone else). Another consequence is that it’s usually the best mutants that eventually dominate the population. It turns out that this appears to be exactly what happened in our case of plasmid evolution.

We see many different variants of this gene (some have a point mutation, others a deletion, or a duplication, some are in-frame mutations, some are frame-shift mutations….you name it, we’ve got it!). When I randomly chose ten clones from each of five evolved lineages from each of 11 time points (ancestral population through generation 1,000, in increments of 100 generations), we see up to nine different trfA genotypes present simultaneously, all within the first 400 basepairs of the gene. Talk about diversity! When we used Roche 454 pyrosequencing of whole plasmids with up to 1200X coverage we see over 100 trfA genotypes throughout the 1,000 generations of evolution in one of our lineages. What’s more, it looks like four of five populations were dominated by the same 129-basepair deletion by generation 1,000. It’s too early to be sure, but I wouldn’t be surprised if this particular mutation was just a slight bit bet

ter in some way than many of the other competitors, either that or it got lucky four out of five times.

So what does all that mean? First of all, like I mentioned before, these things adapt fast! So we need to find fast-acting solutions to our antibiotic crisis. On the other hand, we might want certain bacteria to hold on to certain plasmids for a while (think bioremediation!). If we learn more about the tempo and dynamics of plasmid evolution maybe we could use that knowledge to coerce bacteria to do what we wanted them to do. To keep us from getting too cocky, though, the knowledge of how many variants of one gene region can be present in a given population should warn us that bacteria (and plasmids!) have options, and they might not always chose the option that we think is best. In general, though, the more we know about plasmids and how (and when!) they evolve, the better chances we’ll have of combating the spread of antibiotic resistance among pathogenic bacteria.

For more information about Julie’s work, contact her at nich5271 at vandals.uidaho.edu.

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BEACON Researchers at Work: The Role of Environment in the Evolution of Cooperation

Posted on April 25, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU graduate student Brian Connelly.

Cooperation is something that most people take for granted.  It’s woven into just about every part of our lives.  Our societies have even developed a wide variety of measures to make sure we’re cooperating, such as punishing those that don’t.  This level of cooperation isn’t reserved to humans.  Cooperation plays a vital role in nearly all forms of life, from our primate cousins to ants and termites, and all the way down to simple microorganisms such as bacteria.  There’s even an astounding amount of cooperation going on within our bodies.  Amazingly, of the ten trillion or so cells in the human body, over 90% of those are bacterial cells made up of thousands of different species.

While it’s easy to find examples of cooperation in nature, understanding how cooperation got its roots, how it evolved, and how it is maintained are very tricky questions, especially when viewing evolution as “survival of the fittest”.  If the goal is to outcompete everyone, why would one want to pay some costs to help others?  This is a question evolutionary biologists have been asking since Darwin, who wrote “If it could be proved that any part of the structure of any one species had been formed for the exclusive good of another species, it would annihilate my theory, for such could not have been produced through natural selection”.

Over the years, a lot has been learned about cooperation.  Most of this knowledge has come from studying cooperation using mathematical and computational models or by studying organisms in lab environments.  The problem with these methods, though, is that they only examine cooperation in contexts that don’t necessarily match real world situations.

My research focuses on understanding the different ways in which the environment can affect the evolution of cooperation.  Peter and Rosemary Grant summed this up nicely when they wrote, mimicking a famous quote by Theodosius Dobzhansky, “Nothing in evolutionary biology makes sense except in the light of ecology.”

The benefits of understanding how cooperation is maintained are huge.  For billions of years, life existed only as single-celled organisms.  At some point, cells began cooperating with each other, and our first multicellular ancestors emerged.  Cooperation among bacteria also plays a large role in diseases like cholera, which killed over 100,000 people in 2010.  A substantial factor in the spread of cholera is quorum sensing, a cooperative process that bacteria use to coordinate behaviors.  By understanding how cooperation works in infections like Cholera, treatments can potentially be designed to disrupt cooperation, and perhaps lessen the strength of the infection or limit its spread.  Further, by understanding how the environment affects this behavior, researchers will have a better idea of how their results in laboratory environments will translate to natural environments like the body.

In simulations of cooperative behaviors, cooperators exist in patches which are constantly invaded by cheaters, or those that take advantage of the cooperation without themselves contributing.

My background is in computer science, so to start understanding how the environment can affect cooperation, I’ve used computational models of cooperation in Avida and SEEDS, an open source package I’ve co-developed.  My initial models looked at the role that environmental disturbance plays in cooperation and demonstrated that cooperation increases as environmental conditions worsen.

Some of my other work examined the effect that the amount of resource present in the environment has on cooperation.  We found that the more resource an individual had, the more likely they were to cooperate, since the costs relative to their wealth decreased.  This only occurred after a certain point, though.  Below this point, it the benefits provided by cooperation just didn’t outweigh the costs, so no cooperation occurred.

Another study looked at how the number of social interactions one has affects a population’s ability to maintain cooperation and diversity.  Here we found that as the number of interactions go up, at one point populations quickly lose the ability to maintain diversity.  Although these results were targeted at a small system, I still wonder if they could tell us anything about the direction our increasingly-connected society is heading.

One of the really outstanding aspects of both BEACON and Michigan State University is the opportunity for collaboration.  I’m extremely fortunate to have an advisor, Dr.  Philip McKinley, who personifies this spirit of collaboration.  One such collaboration that he initiated was a meeting with Dr. Chris Waters, a fairly new faculty member in the Department of Microbiology and Molecular Genetics.  This was at a point where I’d finished some of my initial computational work on cooperation and had become familiar with how cooperative behaviors were being studied using microorganisms.  Meeting with Chris was really exciting for me, since I’d known about some of his earlier work with quorum sensing in bacteria.

Plates of Vibrio cholerae used to measure cooperation in different resource environments

What I didn’t expect to happen was that Chris offered me the opportunity to start asking the same kinds of questions about how environment affects cooperation in his lab – using real bacteria!  Now, I’ve always been the kind of person who gets excited about learning and trying new things, so I was thrilled.  Still, my microbiology background was nonexistent, and pretty much the only thing I remembered about biology (which I hadn’t taken since my freshman year of high school) was how to draw the stages of mitosis. Fortunately, Chris was really helpful at getting me started, and with the help of other people in the lab, I was able to perform some initial experiments. I’m now at a point where I’m performing some pretty complex (although maybe just to me) experiments that I designed based on what I’d learned.  I’ve seen first hand that what I do in the wet lab improves and inspires my computational work, and that the computational work can also improve and inspire the wet lab work.  I’m hoping that this sets the pace for the rest of my career.  I don’t know if I’ll ever not feel at least a little like an outsider in a microbiology lab, but I know I want to continue approaching problems from multiple perspectives.  Great collaborations really make that possible.

There’s an enormous amount of exciting research going on within BEACON, but I’m equally excited about the possibilities for outreach and

education.  Because evolution usually takes place on very long time scales, it can be extremely hard to demonstrate processes such as selection in a way that’s seen and understood within a few minutes.  When this is accomplished, though, evolution moves away from being just a vague concept to people and becomes a whole lot more approachable.  Sometimes, this means stripping away the notions of what life is based on our limited set of examples on earth and looking to alternate worlds.

Biolume project. Rendering by Adam Brown

One unique opportunity that being a part of this community has afforded me is a collaboration with BEACON’s artist in residence, Adam Brown, for his Biolume project.  In this project, glowing, sensing, noisy, and evolving robotic units will be attached to the walls and interact with each other and with people who walk by.  Once I found out that Adam was planning to create large populations of these Biolumes, I was immediately excited by the possibility of evolving behaviors on these robots in a way that visitors could observe and, most importantly, affect!  I can’t think of a better way for people to learn about topics like natural selection than to participate in the process of selection, and define which behaviors are beneficial in the environment and which ones should quickly lead to extinction.

For more information about Brian’s research, you can contact him at bdc at msu dot edu.

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Evolution podcast by BEACON faculty member Randall Hayes

Posted on April 21, 2011 by Danielle Whittaker

Check out NC A&T BEACONite Randall Hayes’ weekly evolution podcast, Variation Selection Inheritance. You can access it at http://variationselectioninheritance.podbean.com/ or you can subscribe on iTunes.

This week’s topic: the similarities between graduate school and marathons. As Randall notes, “They both take a long time, and hydration is essential for both.”

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BEACON Researchers at Work: Using digital evolution to understand host-parasite co-evolution

Posted on April 18, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by MSU graduate student Luis Zaman. Enjoy!

We’ve all been stuck in stand-still traffic on the highway. Slowly people start exiting to use a newly found alternate route. Unfortunately, this detour starts backing up as everyone else on the road decides to merge over four lanes of traffic. Soon, the alternate route is as backed up as the highway. In this situation, if everyone has the same distance to travel, the person who finds the least-used detour would arrive quickest. We call this situation “negative frequency-dependence,” where a frequently-used detour is worse than a rare one.

Negative frequency-dependence is also important in evolutionary biology, where rare types of individuals may be more successful than common types. The flu virus is a prevalent example: we build up immunity to the types of viruses we have encountered in the past, yet every year we are at risk of getting a new strain of the flu. The rare types of flu become successful (that is, they successfully infect many people) in a particular year because the general population does not have a built-up immunity to them.

I study what is known as “host-parasite coevolution,” or the reciprocal adaptation of hosts to their parasites and the co-adaptation of parasites to their hosts. In this form of antagonistic coevolution, negative frequency-dependent selection often maintains diversity. Think back to the congested highway: if the least traveled detour is best, then many different detours will probably be used at any one time – that is, there will be diversity of detours. Using a computer program called Avida in which digital organisms can evolve in different digital environments, we are able to study the effects of host-parasite coevolution in great detail, and with a level of control impossible in typical biological systems. In our newest paper (co-authored with Suhas Devangam, an undergraduate researcher working in the Devolab, Dave Bryson, and Dr. Charles Ofria), we explore how host-parasite coevolution maintains diversity in these digital organisms. Specifically we look at how host diversity is affected by the presence of parasites and the presence of mutations. Because Avida allows us to completely turn off mutations (which are an important part of biological evolution), we can take experiments where hosts and parasites are coevolving, and flip a switch to disallow any new variation. The following graph shows how, whether we leave the mutation switch on (a), or switch it off (b), host diversity is much greater in the presence of parasites.

A host that can escape infection will proliferate, but as it becomes more common, parasites will have increasing pressure to adapt to it. Hosts escape parasites by mutating into new species or types. Diversity is maintained when these types fluctuate back and forth as parasites target the most common hosts. That is, negative frequency-dependent selection imposed by the parasites maintains host diversity. This is true even after we stop mutations just as we would expect in our highway analogy: even if we were able to somehow stop any new detours from being found, drivers would still use a diverse set of alternate routes.

Now we know that coevolution with parasites increases diversity, and even maintains it in the absence of new mutations. But what are the effects of these diverse communities on the whole coevolutionary process? To begin answering this question, we need to “retrain our brains” to think in network contexts (as expressed by Ben Kerr at University of Washington). There are diverse communities of hosts and parasites interacting in complex ways, and coevolution is not strictly operating on any one interaction. Rather, coevolution affects the entire network. To get a glimpse into these complex networks, my collaborator, Miguel Fortuna (currently a post-doc in Simon Levin’s lab), turned data from digital host-parasite interactions over coevolutionary time into a movie.

In this movie, unique host and parasite species or types are represented as spheres. Green spheres are host types, and red spheres are parasite types. The size of a sphere represents the abundance of that particular type in the community. A link between spheres represents a parasitic interaction where the width of the green links depicts how popular a parasite is to the host, while the width of the red links depicts how popular a host is to the parasite. You can see that we begin with one type of parasite, and one type of host, but over time, both hosts and parasites diversify, and the interactions become very complex.

How coevolution shapes these networks, and how these networks shape the paths that coevolution takes are fascinating questions. The majority of my current work focuses on understanding host-parasite coevolution in communities with many interactions, and I hope my next post will shed some light on how coevolution in this context proceeds. For now, I wish you the best of luck at finding the detour least traveled.

For more information, contact Luis at luis.zaman at gmail dot com.

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Postdoctoral opportunities in the Ellington lab

Posted on April 12, 2011 by Danielle Whittaker

The Ellington lab at the University of Texas at Austin has several post-doctoral positions available to work on DNA circuitry (ala Winfree and Pierce), in particular the development of methods for executing and analyzing computations with DNA molecules in vitro and in vivo.  The prime requirement for these positions are proven track records of productivity and innovation.

For more information, see Andy Ellington’s lab page and his page at the Institute for Cellular and Molecular Biology. You can contact him at ellingtonlab at gmail dot com.

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BEACON Researchers at Work: Facial recognition software improved by evolutionary computing

Posted on April 11, 2011 by Danielle Whittaker

Here is the second installment of the new BEACON Researchers at Work series, from North Carolina A&T graduate student Joseph Shelton.

Biometric security systems use biometric identification to determine whether or not an individual is allowed access to a resource or location by scanning a biometric modality. A biometric modality is a trait that is unique for most individuals, it is universal for all individuals, such as fingerprints, irises, voice and the human face, and it should be practical to use. BSS has a significant advantage over a conventional security system, which uses a string of characters as a password, because a biometric modality is more difficult to copy.

My interest in this research was spurred by action movies that involved hacking a system that scanned any part of the human body. Whenever a biometric system needed to be spoofed, the burglar/ex-CIA/teen genius always went through a big deal trying to replicate somebody’s biometric signature, and it seemed like biometric systems were much cooler and more futuristic then a regular “type-in-a-password” system. Admittedly, a guilty part of me wanted to know if it was really possible to break a system using the same methods as in the movies. So when I was told about a biometrics research team, I wanted to join and learn how biometric systems truly functioned. Before I could even begin to understand how to break a BSS and how it works, I had to gain an understanding of the modules of a system, meaning I had to study the subtopics of components involved, and at times, there were sub-subtopics to be studied. Through studying, I answered a lot of my own questions, and realized that in some areas, biometrics isn’t as flashy as movies makes it out to be, but in most areas, theatrics do not do the field of biometrics justice.

A biometric security system (BSS) is composed of 4 modules: the sensor, Feature Extraction, comparison/matching and database storage. The sensor is where biometric modalities, are read into the system. Feature extraction is the process of converting a modality into a form that can be compared in the comparison module, and the modality is compared to previously enrolled subjects in a database. My research focuses on the feature extraction module, and it uses the face, or a facial image, as the modality to extract features from. The goal of GEFE is to increase the probability that an individual will be correctly identified as well as reduce the number of features that need to be looked at.

An important concept of GEFE is Genetic and Evolutionary Computation (GEC). A GEC is a technique that is used to seek out an optimal/near-optimal solution to a problem by simulating the evolutionary process. For any problem (e.g. What two numbers added together would give the greatest value?) a population of possible solutions are created and given a fitness value. For example, the solution 12,45 would be given the fitness 12+45 = 57. Just like in evolution, parents are selected within the population of solutions, offspring are created, and the offspring replace unfit individuals in the population. This process is repeated until either an optimal solution is obtained, or a number of generations/evaluations have run. There are different forms of GEC that select parents and create offspring in unique ways, but the overall function is to find the best solution to a problem.

The Local Binary Pattern algorithm (LBP), a texture analysis algorithm proposed by Timo Ojala, can be used for feature extraction. It is used to “describe” the textures that are displayed within an image, the idea being that different textures have different patterns that represent them. In order to identify images, the patterns, or features, must be extracted and converted in a way that can be used in comparisons. The traditional way of doing this is to segment an entire image into non-overlapping regions that are all uniform sized. Within each region, center pixels (pixels that are surrounded by a number of neighboring pixels) must be sought out. If the image is gray scale, then the pixels will have an intensity value between 0 and 255, 0 being all black and 255 being all white. Each center pixel has a pattern that can be interpreted by comparing the values of the center pixel to the neighboring pixels. If the difference between one neighbor and the center is negative, then a 0 will be chosen as a bit, and if the difference is positive, then 1 would be chosen as the bit. In our research, we focus on 8 neighbors, so when the 8 bits are obtained from the pixel comparisons, they are concatenated to form a pattern string.

We then make a histogram for the region that keeps count of the possible patterns that can be extracted. There are initially 256 patterns that can be created, but to reduce this number, only uniform patterns are considered. A uniform pattern is a bit string that has two or fewer changes between bit values when compared in sequential, circular order. The uniform pattern 00111110 has a change between the second and third position and the seventh and eighth position, while the pattern 11100110 is considered non-uniform because it has a total of four changes: between the third and forth position, the fifth and sixth position, the seventh and eighth position, and the eighth and first position. There are 58 possible uniform 8-bit patterns, so the number of bins in the histogram is 59, the 59th bin being a bin that keeps count of the non-uniform patterns. The histograms for all the regions are concatenated together to form a feature vector. Then we compare this feature vector to a database of other feature vectors using a Manhattan distance measure, and the distance determines how identical two feature vectors are.

The GEFE technique combines LBP and GEC to optimize feature extraction. The GEFE method is basically evolving the regions that features are to be extracted from. GEFE differs from the traditional LBP method in that regions are allowed to overlap, the entire region doesn’t have to be segmented, and the regions don’t have to be uniform. A genetic Feature Extractor (FE) holds sets of region parameters and the fitness. The parameters include the (X,Y) coordinates of each region (the center of the region), the widths and heights of each region and a masking value for each. The masking value determines whether or not features are extracted from that region by turning regions on or off. The purpose is to reduce the number of features objectively, without any human bias. The fitness is just the number of incorrect matches that the FE obtains on a biometric system added to the percent of regions that are turned on.

Different ways of implementing the GEFE method have been evolved with different types of GECs and have been tested on facial images from the Facial Recognition Grand Challenge (FRGC) dataset. In every test, the genetic FE beats out the traditional method, in terms of recognition accuracy and the percent of features used.

My biggest interest in GEFE is the evolutionary aspect, which can be thought of as a form of Artificial Intelligence when it is applied to computing. Before joining the research team, the concept of computational evolution was foreign to me. The basic idea was simple enough, but understanding the particulars was more difficult. The computer is not always deterministic, so one of the more interesting studies is observing the results of using different types of GECs multiple times. The different areas of research with biometrics seem boundless, with subjects branching off into different subjects, but that’s one reason why the field is appealing to me.

For more information, please contact Joseph Shelton at jashelt1 at ncat dot edu.

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BEACON Researchers at Work: Tropical crickets hitchhike their way to rapid evolution

Posted on April 4, 2011 by Danielle Whittaker

This week we are introducing a new feature on the BEACON Blog: BEACON Researchers at Work! Please enjoy the first post from Michigan State University postdoc Robin Tinghitella.

Robin Tinghitella on KauaiWhat would happen if all the lions suddenly lost their manes, or all of the peacocks suddenly lost their tails? Equally as strange, but perhaps not as obvious to the casual observer, is the recent (and rapid!) evolutionary loss of song in Polynesian field crickets on the Hawaiian island of Kauai.

As a graduate student I studied the Polynesian field cricket, whose scientific name is Teleogryllus oceanicus. They live in grassy areas in coastal parts of Australia, and on Pacific Islands, including Hawaii. Between 1999 and 2003, a puzzling thing happened in the fields of Kauai. Imagine walking your dog in your neighborhood at night and noticing that over the course of the last few years the level of cricket noise has gone from almost deafening to near silence. What happened? Did they leave? Move to another neighborhood? That’s what we thought, but we were wrong, and the real story is much more interesting.

When you hear chirping crickets, it’s always the males making the noise. Females can’t sing, but they pay attention to males’ songs and use them to find males – they also use the information in the song to decide who to mate with, just like female peahens pay attention to the “sexy” eye spots on a peacock’s tail feathers. So, cricket songs are really critical for mating. It turns out that in Hawaii, female crickets aren’t the only ones paying attention to this cricket’s song. A fly that only encounters this cricket in Hawaii also uses the song to find a host. The fly has ears that are specially attuned to the crickets’ song. Pregnant females locate calling male crickets and spray maggots on them, which will burrow into the cricket and basically eat him from the inside out. So, singing helps you find a mate, but it’s very risky! What’s a guy to do? Here’s what evolution did: a rare mutation occurred, called ‘flatwing’, that eliminates the structures on the wing that males use to produce the song. The crickets were still there, but they had lost the ability to sing. In fly-free areas (Australia and islands outside of Hawaii) we’d expect a silent flatwing male – who would be less attractive to females – would get fewer chances to mate, and would leave fewer offspring. On fly-infested islands, mutant flatwings have the advantage – the flies can’t find them, while their singing counterparts get attacked by flies and eaten by maggots (yum).  The silent guys, meanwhile, survive to mate another day. If you traveled to Kauai today you would find that more than 95% of the males there are silent flatwings.

Fly (Ormia ochracea) on the cricket (picture by M. Read)

In my most recent paper, published with co-authors Marlene Zuk, Maxine Beveridge, and Leigh Simmons, we used genetic tools called microsatellites to answer the question of how the crickets got to Hawaii from Australia in the first place. We were also interested how long they’ve co-existed with the fly. It’s an important question, because the crickets were introduced to Hawaii by humans and we know that rapid evolution is common when new populations are founded. New populations are typically made up of very few individuals.  Think about it – how many crickets do you think might make it to a small Pacific Island from Australia with the vast open ocean in between? In addition, the environment in a new location may be very different from that of the source population (for instance, it may contain novel selection pressures, like the fly in this case).  Both of these can contribute to rapid evolutionary change. When a new colony is started by a few members of the original population, this small population size means that the colony may have reduced genetic variation (relative to the original population) and a non-random sample of the genes in the original population. It’s called a founder effect. Check out this website to learn more about founder effects and population bottlenecks.

Polynesian field crickets are not great flyers, and there are records of them in Hawaii as early as 1877. One intriguing hypothesis we had was that the crickets may have been introduced to Hawaii by Polynesian settlers who traveled around the Pacific on canoes settling on oceanic islands during the Polynesian Expansion. Polynesian folklore suggests that the calls of crickets represent the cries of dead ancestors, so Polynesians may even have purposefully moved the crickets along with them!

To answer our question, we first had to collect DNA samples from 19 populations across the crickets’ range (including eight in Australia, eight Pacific islands outside of Hawaii, and three Hawaiian Islands). We extracted DNA from the femur muscles of 5-25 crickets from each of those locations and “looked” for microsatellites, which are little pieces of repetitive DNA. Microsatellites mutate quickly, so they are a handy tool for researchers who are trying to understand relationships between groups of organisms that only became separated relatively recently – like hundreds to thousands of years ago. In a population there may be many alleles (versions) of a single microsatellite locus (location on a chromosome). We can use microsatellites to infer information about things like which populations are most closely related (another way to think about that is to ask which population the original founding Kauai crickets came from?) by looking at the proportion of shared alleles between any two populations. Lots of shared alleles means the populations are probably pretty closely related.

So, what did we find? It looks like Hawaiian T. oceanicus are most closely related to those from the Society Islands and the Cook Islands. Intriguingly, both locations are consistent with the crickets traveling with Polynesian settlers. During the Polynesian Expansion, humans colonized Hawaii at least twice and scholars agree that one of those trips started near the Societies/Cooks. Whether they hitched a ride as stowaways or were invited along with the intrepid explorers, we’ll never know, but it appears that these island-hopping crickets were introduced to Hawaii by humans, where they later encountered a deadly fly, setting the stage for the story of “how the cricket lost its song.”

Now, you’re probably asking yourself some of the very same questions that I did: Why can’t they call? Without song, how do females find mates? Why would a female mate with a male who can’t sing? All fascinating questions – but these are topics for another post!

For more information on Robin’s work, please contact her at hibbsr at msu dot edu.

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Predicting Plasmid Promiscuity Could Help Fight Antibiotic Resistance

Posted on March 28, 2011 by Danielle Whittaker

A recent paper by BEACON researcher Eva Top and colleagues in the Journal of Bacteriology was highlighted as one of the five “Journal Highlights” in the December 2010 issue of Microbe magazine, the news magazine of the American Society for Microbiology. From the original article:

Bacterial plasmids spread antibiotic resistance, virulence, and many other traits. Eva Top and colleagues of the University of Idaho, Moscow, show that the range of bacteria in which plasmids have resided over evolutionary time can be inferred based entirely on information from plasmid DNA. “Using a so-called genomic signature, we found that plasmids known to have narrow host ranges have signatures that are similar only to a set of closely related hosts,” says Top. “In contrast, promiscuous plasmids, known to replicate in diverse proteobacteria, have genomic signatures that are either similar to those of a wide range of bacterial chromosomes, or that are different from all bacterial chromosomes sequenced to date. This strongly suggests that the DNA sequence of a plasmid is like a book that tells stories about where-in which bacteria-the plasmid has previously resided.” The research is important, she says, because it could help predict which additional bacteria might be serving as hosts for resistance- or virulence-carrying plasmids. “Our genomic signature tool can provide insight into the promiscuity and potential reservoirs of plasmids and other mobile genetic elements in the horizontal gene pool,” she concludes.

(H. Suzuki, H. Yano. C. J. Brown, and E. M. Top. 2010. Predicting plasmid promiscuity based on genomic signature. J. Bacteriol. 192: 6045-6055.)

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Inclusive fitness theory and eusociality

Posted on March 24, 2011 by Danielle Whittaker

Tom Getty and 136 colleagues have published a Brief Communication Arising in Nature that clarifies the role of inclusive fitness theory in guiding our understanding of the evolution of social behavior (Abbot, P. et al. 2011).  Most biologists think that fundamental questions about the evolution of altruism were settled by W.D. Hamilton’s formal development of the concepts of kin selection and inclusive fitness.   A recent Comment in Nature (Oksaha, S. 2010. Nature 467, 653-655) explains why some biologists continue to question the importance of kin selection in the evolution of social interactions.  The root of the problem is that there are different ways to conceptualize and model the evolution of social interactions and many of us do not fully understand and appreciate the alternatives to our favorite approach.  Getty and colleagues clarify the conceptual issues and use examples to illustrate how the inclusive fitness approach has helped to predict and explain otherwise baffling phenomena, including the evolution of eusociality.

Tom Getty is a professor at MSU in the Department of Zoology and at the Kellogg Biological Station.  He is currently working on two BEACON research projects focused on sexual selection and the evolution of biodiversity, and on three BEACON education projects at the K-12 and undergrad levels.

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Selection for Evolvability

Posted on March 22, 2011 by Danielle Whittaker

In a new Science paper, BEACON faculty Jeff Barrick, Rich Lenski and their co-authors show that greater evolutionary potential can sometimes overcome a short-term fitness disadvantage.  Taking advantage of the ‘frozen fossil record’ from a long-term evolution experiment with bacteria, they demonstrate that the eventual winners in one population were less fit than the eventual losers, but the winners prevailed because they produced more beneficial mutations.

Carl Zimmer and Tina Saey wrote about these findings in the New York Times and Science News, respectively. Science Podcast interviewed Jeff and Rich, and the NSF taped Rich discussing the research.

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