BEACON's Elena Litchman and Ben Kerr honored by President Obama

Posted on September 26, 2011 by Danielle Whittaker

Ben Kerr

Elena Litchman

BEACON faculty members Elena Litchman (Zoology, MSU) and Benjamin Kerr (Biology, UW) are on the list of recipients of the Presidential Early Career Awards for Scientists and Engineers, announced today by the White House.

President Obama today named 94 researchers as recipients of the Presidential Early Career Awards for Scientists and Engineers, the highest honor bestowed by the United States government on science and engineering professionals in the early stages of their independent research careers.

The Presidential early career awards embody the high priority the Obama Administration places on producing outstanding scientists and engineers to advance the Nation’s goals, tackle grand challenges, and contribute to the American economy.  Sixteen Federal departments and agencies join together annually to nominate the most meritorious scientists and engineers whose early accomplishments show the greatest promise for assuring America’s preeminence in science and engineering and contributing to the awarding agencies’ missions.

“It is inspiring to see the innovative work being done by these scientists and engineers as they ramp up their careers—careers that I know will be not only personally rewarding but also invaluable to the Nation,” President Obama said. “That so many of them are also devoting time to mentoring and other forms of community service speaks volumes about their potential for leadership, not only as scientists but as model citizens.”

The awards, established by President Clinton in 1996, are coordinated by the Office of Science and Technology Policy within the Executive Office of the President. Awardees are selected for their pursuit of innovative research at the frontiers of science and technology and their commitment to community service as demonstrated through scientific leadership, public education, or community outreach.

See the whole list of recipients here.

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BEACON Researchers at Work: Spatial patterning in microbial communities

Posted on September 26, 2011 by Danielle Whittaker

This week’s blog post is by Fred Hutchinson Cancer Research Center postdoc Babak Momeni.

Synthetic communities may help us understand the biology of natural microbial communities.

Microbial communities in nature are abundant, with amazing diversity and huge impact on life around us. Our understanding of microbial communities, despite their importance, is limited. We know little about their structure: what their constituents are, and why they are composed the way they are. As for the functions of the communities, their activities and influence on their surroundings, some of the outcomes, for example nitrogen fixation in soil communities, are known, but we know little about the underlying mechanisms leading to such outcomes.

As part of human’s drive to “tame” the nature, almost an obsession, we would like to understand microbial communities better. How do we control microbial communities? Can we use them as miniature plants to do our dirty job for us in waste management? How do we stop them from hurting us when they are not welcome, for example in our lungs and gums? How can we make sure we stay on good terms with the massive microbial communities we are carrying around? Of course, in studying microbial communities, like many other topics in biology, the complexity is daunting. Considering the network of so many populations interconnected through multitude of diverse interactions, how do we even start to decipher the order underneath the complexity?

We thought (and “we” means my lab-mates and I from the Shou Lab at the Fred Hutchinson Cancer Research Center) it would be a good starting point to study systems we know better. That means working with mathematical models, abstract yet fully controllable, or model systems, not fully understood, but biologically relevant and at least studied extensively.  Being from an engineering background, I followed the habit of making stuff from components: what if we made communities from scratch? The idea is simple: to reduce complexity, we tried to take some of the complexity out of the picture. We built communities with only a few populations and known interactions, with the hope that understanding these simplified communities will be an entry point into the world of more complex communities.

The particular problem I am studying is about spatial patterning within microbial communities. Quite often some order has been observed in spatial distribution of populations in communities of microbes. The order may come from gradients in environmental factors, for example oxygen concentration, from specific division of labor, for example surface adhesion of some of the community members, or from interactions among populations. We focused on the latter and looked into how different community interactions may affect the spatial patterning of populations.

The interactions we chose to study first were competition for shared resources and cooperation – a mutually beneficial interaction between the two partners. Competition is ubiquitous in all microbial communities, and cooperation is thought to be one of the main drivers of complexity in life. In an attempt to avoid unnecessary complexities, we chose to look at these interactions at the ecological level, regardless of what their molecular mechanisms were.

We first used individual-based simulations, giving “cyber-cells” a set of rules to live by, and followed the development of communities under either competition or cooperation interactions. The advantage of these simulations was that it gave us an initial intuition about how interactions affected community patterning. In addition these simulations offers a precise picture of developments within the community, a complete life history including all the details of the environment for all individuals.  However, the precision and controllability of these simulations comes at a cost: the abstract model we have full control on may be out of touch with reality.

To ensure that conclusions from the mathematical model were realistic, we constructed communities of engineered yeast populations.  In competitive communities, two different strains tagged with different fluorescent proteins were competing for all the resources in the environment. To implement cooperation, we used strains of yeast each requiring a metabolite that the partner overproduced. Neither of these cooperative populations could grow on its own without supplementing their required metabolite, but when together, they complemented each other and formed a community that could grow. These engineered communities were a step toward more realistic communities, yet they enabled us to engineer desired interactions in the communities to see the effect of specific interactions.

One point we learned was that interaction-driven patterning may quickly, within a few generations, influence communities. One immediate implication of this observation is in studying evolution of microbes. We have learned a lot by tracking the fate of populations in experimental evolution in well-mixed tubes. Is evolution trajectory different in a spatially structured environment, with microbes actively influencing their environment through spatial arrangement of populations? If it is, how?

The next step would be to investigate how our observations in simplified systems hold when we move to more complex communities. How does adding more populations and/or other interaction types affect spatial patterns of communities? Can we extend our observations to natural communities? In addition, we would like to understand how the spatial  patterning driven by interactions affect the function and evolution of communities. The goal is to uncover basics of how communities develop and function from simplified communities and eventually integrate the gathered knowledge to understand more complex communities.

For more information about Babak’s work, you can contact him at bmomeni at fhcrc dot org.

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BEACON research featured on Scientific American blog

Posted on September 19, 2011 by Danielle Whittaker

NESCent‘s Robin Smith was a guest speaker at BEACON’s annual Congress in August. While she was here, Robin learned about all the great interdisciplinary BEACON projects involving Xiaobo Tan’s robotic fish.

In landlocked East Lansing, Michigan, you’re unlikely to swim with dolphins. But you can swim with robotic fish, thanks to a team of scientists who are developing underwater robots that swim in schools to monitor water quality.

A surprisingly lifelike robotic fish swims in an aquarium on the Michigan State University campus, its tail fin flexing back and forth.

The demo is part of a three-day meeting at BEACON, a National Science Foundation-funded think tank dedicated to research at the intersection of biology, engineering and computer science.

Read her whole post here!

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BEACON Researchers at Work: Effects of rising temperatures on marine phytoplankton

Posted on September 19, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU graduate student Mridul Thomas.

Diatoms, one of the most important & prettiest groups of phytoplankton

Every day, a staggering quantity of carbon is drawn out of the atmosphere into the oceans as a result of the silent actions of massive numbers of photosynthetic microbes. These microbes, called phytoplankton, incorporate around one hundred million tonnes of carbon per day into their cells, almost as much as all land plants put together. This community plays such a large role in the global cycle of carbon that fertilizing the oceans to boost their numbers is increasingly discussed as a potential solution to climate change (there’s serious problems with that idea, though). The flipside of this vision is that changes in ocean conditions that lower phytoplankton growth may lead to faster carbon accumulation in the atmosphere and more rapid climate change. And we are driving changes in the oceans, with measurable changes in acidity and temperature having taken place during our lifetimes.

As a student of oceanography, I’m ultimately interested in understanding how the biological, physical and chemical pieces of this story will unfold. It’s a fascinatingly complex system of interconnected parts, with human economic policy playing a major role. My own research concerns a modest chunk of this: I’m trying to understand the effects of changing temperatures on the ecology & evolution of phytoplankton species.

Work that I am currently doing with other members of the Klausmeier-Litchman lab at MSU’s Kellogg Biological Station shows that phytoplankton taken from around the world grow fastest near the average temperature that they experience in their part of the oceans. This is a sign that they’ve adapted to their environmental conditions in the past. As temperatures rise, they will no longer be well-adapted to local conditions, prompting both ecological and evolutionary changes. These evolutionary changes are the focus of a BEACON-funded project I am working on.

Adapting to higher temperatures is a little different from adapting to other environmental stresses, due to some interesting peculiarities of thermal physiology. These flow from the effects of temperature on biochemistry. In organisms, reactions are controlled by the action of enzymes, which change shape as part of the process. Higher temperatures increase their flexibility, allowing metabolic reactions to speed up. Beyond a certain temperature, though, the increased flexibility causes the enzymes to malfunction and degrade. One this starts to occur, fitness decreases very rapidly till the organism can no longer grow. As the example below illustrates, the fitnesses of all ectothermic species increase slowly with increasing temperatures till a peak (the optimum temperature), after which they drop off rapidly.  This means that changes in temperature above the optimum of a species can be much more harmful than those below.

A number of important experiments examining this topic were done in Rich Lenski’s lab here at MSU using E. coli, but there are plenty of interesting questions remaining to be answered. Some are specific to the phytoplankton we’re interested in but nonetheless important when trying to understand these communities, such as, ‘How fast can these species adapt to changes in temperature?’ As is common, the more interesting questions transcend the specific taxon we’re working on. These include 1) how much of the genome undergoes changes as part of this adaptation?, 2) are there multiple genetic ways to adapt to high temperatures?, and 3) is it more ‘costly’ in terms of physiology to adapt to a fluctuating environment than a constant one?

So, in collaboration with the lab of Virginia Armbrust at University of Washington, we are performing an experiment on a marine diatom called Thalassiosira pseudonana to examine  adaptation to four different temperature conditions (high and low temperatures, and within each of those, constant and fluctuating environments). Thalassiosira is a large (well, for a microbe) eukaryote that doesn’t growth quite as fast as bacteria like E. coli does, but it nonetheless divides more than once per day, which should allow us to see evolution take place relatively quickly. The experiment is fairly simple. We’ll have a number of test tubes filled with identical populations of T. pseudonana suspended in water baths maintaining the temperature. Every two days, we’ll supply the populations with fresh nutrients to grow on. Mutations and new genetic combinations from sexual reproduction will produce cells that differ in physiology, some of which will tend to tolerate higher temperatures better. These will grow fastest will slowly take over the test tubes kept at high temperatures, which we will track by measuring how temperature affects their fitness over time. Tracking changes in the other groups of tubes as well and subsequent genetic analysis will help us answer the questions I posed.

As I mentioned earlier, learning about the costs of adaptation through lab experiments should help us predict how phytoplankton communities will be affected by climate change. But one of the nice things about addressing questions such as these is that the answers can sometimes have broader implications. For example, our attempts to understand adaptation to environmental temperatures in marine phytoplankton showed patterns that were strikingly similar to those seen in terrestrial insects. This probably reflects similar underlying processes and physiology. Similarly, we expect that answers we get will not just be specific to phytoplankton, but will reflect patterns that can help us understand temperature adaptation in larger ectotherms as well, both aquatic and terrestrial. That’s a large part of the pleasure of this work: the experiments are small-scale, but the processes they help illuminate are global.

For more information about Mridul’s work, you can contact him at thomasmr at msu dot edu.

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Origins of Life: Experiment art installation at Ars Electronica 2011

Posted on September 15, 2011 by Danielle Whittaker

The art installation “Origins of Life: Experiment #1.6” by Adam Brown, BEACON’s  artist-in-residence, and Robert Root-Bernstein was featured this month at Ars Electronica 2011 in Austria. This series of experiments is a re-enactment of the famous Urey-Miller experiment which simulated the conditions of early Earth in order to understand the chemical process leading to the creation of organic compounds.

This year’s theme for the art festival was “Origin,”  focusing on understanding the basic nature of the cosmos and the origins of the universe and life itself. “Origins of Life: Experiment #1.6” was part of the exhibit Symmetries:

A heterogeneous array of experimental assemblies, images and exhibits invites visitors to confront highly diverse manifestations of the human spirit of inquiry and the joy of discovery. […] To find out whether or not a scientific experiment can also be a work of art, artists/scientists Adam Brown (US) and Bob Root-Bernstein (US) conducted the Origins of Life: Experiment 1.6. It explores imaginary worlds of gas under glass.

Learn more about Ars Electronica, whose awards are known as the Oscars of the digital media arts world, here.

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BEACON Researchers at Work: Lessons in bacterial evolvability from eventual winners

Posted on September 12, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work blog post is by University of Texas at Austin faculty member Jeffrey Barrick.

For a long time, I thought that I’d become a synthetic organic chemist. Synthesizing intricate molecules would be a natural next step from a childhood (ok: permanent) obsession with building blocks: first Duplo, then Lego, and finally Atoms. This train of thought was derailed when, as a college freshman, I learned about methods that biochemists had devised to recapitulate the process of Darwinian evolution (select, replicate, repeat) on RNA and protein molecules in a test tube.

I was hooked. Why build one specific molecule or protein sequence when you could kick things up a level? You didn’t necessarily need to design a protein that would fold into a precise three-dimensional structure and speed up a specific chemical reaction (which is still an extremely difficult scientific challenge). Instead, you could design a clever test, crank the Darwinian cycle, and be surprised by what crawled out. Evolution would teach you the relevant biochemistry.

I did eventually find some “winning” proteins that solved a binding challenge in an unexpected way, but my research experiences also brought me face to face with the limitations of evolution. Sometimes you just didn’t get any solutions if you pushed too hard. There might not be any molecules in your test tube that could pass the test. Nature has eons for evolution to generate and test so many sequences that it can eventually get around many of these walls, but we can’t always wait for those rare innovations in our lifetimes. So, like many in the BEACON community, I started to think about how can we force, coax, and jumpstart evolutionary processes so that they will more quickly discover better solutions to more challenging problems.

Electron micrograph of E. coli courtesy Brian Wade

My focus shifted up a level of biological organization to bacteria in a postdoc with Richard Lenski at Michigan State University. We began sequencing genomes from one of twelve flasks of the lab’s 20-year evolution experiment with Escherichia coli (the subject of recent posts by Mike Wiser and Caroline Turner). One striking observation from our work was that the rate of sequence evolution was really quite slow. The genomes of these bacteria had only experienced one new mutation every 500 generations (roughly 75 days). Furthermore, even though these bacteria have lived in the laboratory under conditions where a majority of their genes are dispensable, only about 1% of their chromosome has been deleted after 20 years.

So, bacteria have evolved to be conservative in an evolutionary sense. This makes sense: mutations in genes are more likely to be disruptive than beneficial. It’s easier to “break” a protein and unbalance regulatory networks, metabolic pathways, and cell physiology with a random change than to improve this well-tuned engine. Mutations that remove the coding regions for entire genes are even worse. They can cause a bacterium and all of its descendants to completely “forget” how to utilize some nutrient. This loss might not have any immediate ramifications, but those genes might prove crucial for survival later if the environment changed.

It turns out that 6 of the other 11 flasks are now dominated by bacteria that are risk takers. The winning bacteria in these flasks are “hypermutators.” They accumulate mutations at 10–100 times the rate of their ancestors because they have evolved defects in DNA repair and proofreading. Why? Having an elevated mutation rate is not immediately beneficial—in fact, it can be deleterious for the reasons discussed above. However, hypermutators also have more chances to throw the mutational “dice” and generate descendants with highly beneficial but very rare mutations or to “yahtzee” together multiple beneficial mutations more rapidly. So, hypermutators can adapt more quickly than their competitors and win when opportunities outweigh risks. This result can be described as “second-order selection” for greater evolutionary potential.

We recently studied how differences in evolutionary potential of another kind were important in the history of another flask. This E. coli population had diverged from its ancestor into two contending groups characterized by different beneficial mutations by 500 generations. Individuals in one group had evolved a significant fitness advantage over the other, but it turned out that descendants of the less-fit group later prevailed and drove the ones that had been ahead extinct by 2,000 generations. That could have meant that the ones that were behind were just lucky and happened to “draw” an extremely beneficial mutation that caused them to leapfrog their more-fit competitors. However, by replaying evolution many times from these different starting points, Robert Woods was able to show in his thesis research that neither of these explanations was the case. The eventual winners were actually able to out-adapt the group that had been ahead earlier, on average.

To understand why this was the case, we sequenced the genomes of bacteria from these experiments. None had become hypermutators. Instead, we discovered that each group of bacteria had a different, beneficial mutation in a protein that regulates how tightly the DNA of the bacterial chromosome is wound (i.e., supercoiling). Supercoiling affects how accessible the genome is to polymerases that must unwind the chromosome to transcribe RNA. Therefore, this protein is a global regulator, and mutations in it can alter the expression levels of hundreds of cellular proteins. Interestingly, the bacteria that were initially less fit, but later won, often had subsequent, highly beneficial, mutations in a second global regulatory protein that turns many genes on and off in response to starvation, but mutations in this protein were never found in descendants of bacteria from the group that lost.

Tim Cooper’s lab at the University of Houston mixed and matched these key regulatory mutations together in different combinations to definitively show that the group that had been winning early in the experiment had, in a way, painted itself into a corner. Despite having the most beneficial set of mutations early, further mutations — including key ones in the starvation response protein — were no longer as beneficial in these bacteria. Furthermore, although each group had mutations in the supercoiling regulator early on, only the mutation from the eventual losers restricted further evolution. The slow-but-steady lineage that won in the end had maintained its capacity to benefit more from mutations in the starvation response gene (and probably elsewhere). This situation can be described as second-order selection to maintain a more evolvable “genetic architecture”.

Evolving a more creative bacterium? (Painting by Joan Miró)

In January, I moved to the Department of Chemistry and Biochemistry at the University of Texas at Austin. My new lab is using systems biology tools to better understand how the interactions between global regulatory proteins, such as these, can affect evolutionary potential, and we’re also looking for other eventual winner and eventual loser stories in hopes of discovering some commonalities. The eventual goal is to use synthetic biology and computational approaches to test ideas for how we can isolate, engineer, and evolve bacteria that are more capable of rapidly evolving in useful and creative ways.

As you can see, even though bacterial evolution appears to be fairly conservative most of the time in nature, it’s clear that both risk-takers and prudently mutating lineages can evolve as a second-order effect of natural selection. This gives us a chance to learn something from an evolution experiment about what it means on a biochemical level to unleash evolutionary potential.

You can learn more about the Barrick lab’s research on their website.

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Evolution keeps sex determination flexible

Posted on September 9, 2011 by Danielle Whittaker

There are many old wives’ tales about what determines a baby’s sex, yet it is the tight controls at the gene level which determine an organism’s sex in most species. Researchers at Michigan State University have found that even when genetic and genomic mechanisms are disrupted, organisms quickly evolve ways to compensate.

In research published this week in the journal Evolution, scientists from the BEACON Center for the Study of Evolution in Action headquartered at Michigan State University along with colleagues from other universities used an experimental evolution approach to study adaptations in sexual determination of nematodes, more commonly known as worms.

“Our findings show the nematodes evolved quickly to diminish any negative effects caused by mutations in the sex-determining mechanisms,” said Christopher Chandler, a post-doctoral researcher who led the study.

Chandler and a team of researchers studied 50 generations of nematodes, after introducing mutations in the genes that normally help worms develop into males or females. These mutations’ effects also depend on the environmental temperatures, so the team tested whether worms adapted to the mutations at just one temperature, or across a range of temperatures.

Researchers studied adaptations in sexual determination of 50 generations of female/hermaphrodite nematodes (above) and male nematodes (bottom), and their findings show the worms evolved rapidly to compensate for the effects of harmful mutations.

“Unless we grew them in pretty warm environments, it didn’t seem to matter much – the worms evolved to do better across a range of temperatures,” said Chandler.

“At the genetic level, the worms by-passed the problem rather than fixing it directly,” said Ian Dworkin, assistant professor of zoology. “There was little or no change in the genes involved, and instead they made the changes elsewhere. As they evolved, they swiftly compensated to create a balance with respect to their sex.”

The findings have big implications for how sex determination evolves. Sex determination is important for reproduction in all organisms and it is tightly controlled at the gene level.

“Our findings show the mechanisms themselves are flexible and adaptable from an evolutionary viewpoint,” said Chandler. “If something goes wrong with the control mechanisms, a work-around can quickly be found to restore the balance.”

The National Science Foundation funded the study. Dworkin and Chandler are members of the BEACON Center for the Study of Evolution in Action, an NSF-funded Science and Technology Center headquartered at Michigan State. The research team also included Genna Chadderdon and Fredric Janzen from Iowa State University, and Patrick Phillips from the University of Oregon.

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BEACON Researchers at Work: Portrait of a Damsel

Posted on September 5, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by MSU postdoc Idelle Cooper.

If damselflies were painters, they would surely be watercolorists, and probably impressionists, too. As soon as the morning sun strikes the vegetation along the riverbank, the damselflies are fluttering and basking, displaying their colorful bodies and dusky wings to potential mates, and ovipositing in the reflective surface of the water. For anyone who pauses along a stream in the summertime, these colors and behaviors stand out as lovely and interesting traits, and while they are the intriguing results of an evolutionary history of selection, they are also currently playing a role in future adaptation and speciation.

Damselflies are in the same order as dragonflies, but smaller and hold their wings behind them at rest. Males and females are often different colors, and if there are multiple species sharing the same stream, they are often distinct in their body and wing pigmentation. As an evolutionary biologist, I am interested in what selection pressures may lead to variation within and between species. Sexual dimorphism (which are differences between the sexes) can arise from selection differences between males and females in what traits enable them to mate (sexual selection) and differences that enable them to survive (ecological selection).

Some questions that I’m pursuing include: what traits are under sexual and ecological selection within a species? How do multiple species arise and then remain separate, without interbreeding? What determines the geographic distribution of species and how might they respond to climate and habitat changes? To explore these questions as a BEACON postdoc, I am working with folks at MSU (Tom Getty, Muraleedharan Nair, and Chris Klausmeier) and UT-Austin (Molly Cummings and Eben Gering). We are investigating these topics at many levels within nature, from the cellular and biochemical level within individuals to dynamics within populations and to interactions between species.

Color is often important for survival and mating success, but is dependent on the ecology of the organism. Think of the camouflage of mice in sand dunes and rabbits in snow, or the warning coloration of unpalatable butterflies that is effective only at certain frequencies in the population. There are many cases of color being used as a signal to predators or mates, but the pigment that causes the coloration may also be important under abiotic conditions and function in thermoregulation and UV protection. The effect of high UV levels may be particularly dire, as the anthropogenic depletion of stratospheric ozone and the resulting rise in ultraviolet radiation exposure are well documented, but we don’t yet understand the evolutionary consequences. Changes in UV levels may affect direct selection on organisms by affecting their survival, as well as indirect selection through changes in species ranges, species recognition, and mate choice. We are addressing both of these processes by studying the Megalagrion damselflies of Hawaii and the Calopteryx damselflies of the upper Midwest and southern Canada.

In Megalagrion, males of most species in this endemic genus are red in color and live in open, exposed habitats. Females are often green and live in the shade of the forest except when they are mating around water (see the photo of a male and female in copulation). What we know so far is that red pigmentation is prevalent under high solar radiation and functions as an antioxidant. Free radicals produced under high UV can cause tissue damage and mutation, making antioxidant production an important adaptation in plants and animals under stressful abiotic conditions. In humans, oxidative stress is thought to contribute to diseases such as Alzheimers, Parkinson’s, diabetes, and cardiovascular diseases. Foods rich in antioxidants, including tomatoes, red peppers, red wine, pomegranates, and others (note the red coloration) had help sequester the damaging effects of free radicals. While I certainly don’t advocate eating damselflies, it is interesting to consider how these tiny creatures have evolved a powerful antidote that allows them to live and mate in exposed habitat.

Males need the red pigmentation because they are constantly in the sun, defending territories around water. Females don’t need the extra protection because they are in the shade, except in some cases: at high elevation, above tree line. Amazingly, females at high elevation express the red coloration of the males, and at first glance look just like the opposite sex. This color switch doesn’t confuse the males, however, which readily find the females. What appears to be mimicry of males in this case is simply the result of females making use of a sort of sunscreen in the same way the males do. While we understand this pattern most thoroughly in one species, Megalagrion calliphya, we are finding that the same pattern in other species. Red coloration in high UV environments is prevalent throughout this endemic radiation, and the switch from green to red pigmentation in females evolved multiple, independent times. Protection from UV radiation is important in these damselflies, is related to body color, and affects their species ranges. But what will happen as UV levels rise?

While we are interested in how species may persist through trait changes or distribution shifts, these responses will also affect species interactions, particularly because color is often used as a sexual signal for mating or species recognition. A recent range overlap of the Calopteryx species C. maculata and C. aequabilis provides an opportunity to measure selection on body and wing coloration. Where the species don’t overlap, C. maculata and C. aequabilis look similar, especially in the dark coloration of female wings. However, in sympatry (where the species overlap), female C. maculata have dark wings and C. aequabilis have light wings. This example of character displacement is thought to be important for reinforcing speciation because it prevents mistaken identity during mating.

This system was well studied by Waage in the 1970s, and our current surveys indicate that since that time, at least C. aequabilis has moved northward. The southern boundary in Michigan is now several hundred miles north of where it was 35 years ago. Such northern movement of organisms is predicted as a response to global climate change, and though we haven’t verified the cause of this shift, changes it the area of species overlap may influence rapid evolution of those characters important in sexual selection. In our recent mating trials, male C. maculata mistake the dark-winged C. aequabilis females as mates much more than the light-winged C. aequabilis from areas of overlap. Our research will continue to explore the species distributions and mating preferences of these species. As for their artistic preferences, these damselflies would probably admire Monet’s lily pads, though perhaps instead they would favor Renoir’s nudes.

For more information, see www.msu.edu/~cooperi or email Idelle at cooperi at msu dot edu.

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BEACON Researchers at Work: Survival of the weakest – when doing poorly does best

Posted on August 29, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by University of Washington graduate student Joshua Nahum.

Brittany Harding and Joshua Nahum in front of the hundreds of RPS populations we evolved.

“Survival of the fittest” is a phrase coined by Herbert Spencer upon his reading of Darwin’s On the Origin of Species to describe the process of natural selection. In common parlance, the fittest member of a population is the strongest, fastest, biggest and, in general, the best. However, the word “fitness,” as used by biologists, is not identical to its lay usage. A small, unarmored fish may be more fit than more armored ones in the absence of predators. Pairs of wild turkey brothers who cooperate in wooing mates are more fit than aggressive loners. Fitness is a description of how many descendants an organism will leave, be it through strength, resistance to disease, ability to acquire resources or some other mechanism. Here, I will describe some work I’ve participated in where these two definitions of fitness disagree.

First, I should introduce myself. My name is Joshua Nahum, and I am a graduate student in Dr. Ben Kerr’s lab at the University of Washington, Seattle. The work I will describe below was done by Brittany Harding, Ben Kerr and myself. The work we do in the Kerr Lab is centered around performing evolution experiments in real time, where we can watch evolution as it happens. To do this, we use rapidly evolving systems (as experiments which last thousands of years are generally not awarded grants). These include microbial systems, such as E. coli and Pseudomonas fluorescens (which have a generation time of less than an hour) and digital systems, like Avida (that have a generation time of microseconds). We pick an organism to be the founding member, or ancestor, of a population. When put in a suitable environment, the ancestor replicates into a large population that with time evolves. At the conclusion of the experiment, we take isolates from the ending population (called descendants) and perform measurements to see how the population changed. We often perform competitions between the ancestor and descendants in different environments to see what traits are needed to succeed (to be fit in an environment). And now, on to this experiment.

E. coli and most other bacteria produce a number of compounds that are meant to inhibit the growth of competitors. One class of such toxins produced by E. coli are called colicins (we scientists are very inventive when we come up with names). For those who’ve been reading this blog regularly, this topic was covered in another BEACON Researchers at Work post, “Colicin and Immunity Binding: A Love Story,” by Carrie Glenney, a fellow graduate student in the Kerr Lab. Being a colicin producer is costly, as each cell needs to make immunity proteins to not be killed by its relatives. And, a proportion of the Producer population needs to die every generation to release the colicin into the environment. But these costs are borne because the colicin can kill other sensitive E. coli. If you mix a flask of Producer with Sensitive, no Sensitive cell will survive. However, there is a small chance that a Sensitive cell will evolve to be Resistant. You have probably heard of antibiotic-resistant bacteria; this is similar. The mechanism of resistance varies, but often involves the costly loss of a nutrient-uptake transporter (the colicin binds to the transporter to infiltrate the Sensitive cell). This loss of a transporter mildly cripples the cell, making it less efficient at taking up nutrients, and slows its growth rate. Unfortunately for pharmaceutical companies, Resistant populations can often evolve to reduce or eliminate this cost though compensatory mutations. So now we have three types of bacteria: Sensitive (fastest growers), Resistant (slower growing, but not killed by colicin), and Producers (slowest growers, but able to kill Sensitive).

Those familiar with the most distinguished of dispute-resolution games will recognize the the elements of a Rock-Paper-Scissors (RPS) being played amongst the three types of bacteria. Sensitive outgrows Resistant, Resistant outgrows Producer, but Producer kills Sensitive. Every type beats its victim, but loses to the other, its enemy. This bizarre RPS-like ecology has been observed in other natural systems, most notably by Barry Sinervo and colleagues to occur in male mating behavior of side-blotched lizards. No single type dominates the population, because as one comes to prominence (say Resistant), its enemy (Sensitive) is quick to rise as well. If you distribute the three types on a surface (like an agar petri dish, or in our case, the grid of hundreds of small containers of liquid growth media), they form many patches of each type, whose boundaries move according to the competitions described above (Sensitive invades Resistant, etc…). The patches appear to chase each other, if they were to be viewed with time-lapse photography.

But the most interesting things happen when you consider the evolution of the system. The Resistant types is most able to evolve (change its growth rate), because it can acquire compensatory mutations which reduce the cost of resistance, allowing it to grow faster. At a glance, faster growth would be highly advantageous, as it would help repel invasion from Sensitive types, and allow the Resistant population to better invade both Producer and slower growing Resistant populations. And indeed, the evolution of faster growth is what we find in most of our experiments. However, when we limit migration (which is how populations move across the world) to small distances (only allowed the bacteria to move slowly), the Resistant cells did not evolve faster growth rates.

What we observe has been called “Survival of the Weakest,” a situation where selection favors the “weaker” competitor. Here’s why: In our experiment there are many patches of each of the three types chasing each other. Let’s say one of the Resistant cells gains a compensatory mutation and can now grow faster (I’ll call it Superbug). Superbug will take over its patch of Resistant cells, until the patch of consists solely of Superbugs. The Superbug patch will quickly invade every adjacent patch of its victim, Producer. After all accessible Producer patches are consumed, the Superbug will be in a bad place, as it will be completely surrounded by its enemy, Sensitive. Superbug will then be invaded by Sensitive and go extinct. Meanwhile, the normal Resistant cells will continue to thrive by only invading Producer at a sustainable rate. At the end of the experiment, most Resistant cells will be the slower growers. We found restraint (slower growth) didn’t evolve when migration occurred over long distances (as patches couldn’t form). With long migrations distances, the three types mixed together more freely, and faster growth is favored because there is no need to conserve resources (in this case the resource is Resistant’s victim, Producer).

This result, the evolution of a worse (slower growing) competitor goes against conventional wisdom that the better competitor will also be the most fit. If I could impart a takeaway message, it would be that interesting ecologies (like RPS) can generate interesting evolutionary outcomes that aren’t necessarily intuitive.

For more information regarding this work, please see our recently published paper in PNAS.

For more about Josh’s work, you can contact him at nahumj at uw dot edu.

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BEACON Researchers at Work: Measuring fitness in the Long Term Evolution Experiment

Posted on August 22, 2011 by Danielle Whittaker

This week’s BEACON Researchers at Work post is by Michigan State University graduate student Mike Wiser.

If there’s one thing you can really depend on about life, it’s that it’s constantly changing.

Many of us learned in our biology classes that evolution is basically a change in a population or a species across generations. This change is something that takes some degree of time to happen, but can produce powerful results — dinosaurs changing into birds, for example. But it’s easy to fall into the trap of thinking that evolution is something that happened. That’s true, but it’s incomplete. Not only did evolution happen, but it’s happening right now. You just generally need a number of generations before you can see things change. This is why I work with bacteria; more generations in a shorter time gives us more to look at. But, first, a non-bacterial example:

If, like me, you’re not really careful about keeping your yard perfect, you’ll end up with some weeds mixed in with the grass. Dandelions, for example, are all over the place. But not all dandelions are the same. Sometimes, they flower and produce seeds when they’re much taller than the grass. Some of them, though, do so when they’re the same height as freshly mowed grass. Seeds coming off the taller dandelions might manage to spread much further than seeds coming off the shorter ones, which makes being tall good for having offspring launch out into the neighbors’ lawn. Short dandelions, though, might be more likely to survive long enough to produce seeds than their taller cousins, who have to face the mower. The frequency with which you mow the lawn will tend to select for one type or another. Assuming that dandelion height can be inherited across generations, you have the ability to control the way in which the population is likely to evolve. Even though you’re not consciously trying to make the dandelions taller or shorter, your actions will influence their growth, survival, and reproduction, and thus help shape future generations.

I work in the lab of Dr. Richard Lenski, with a project called the Long Term Evolution Experiment (LTEE). At its core, this project involves 12 different flasks of bacteria. Every day, we take 1% of the liquid volume of a flask and transfer it to a fresh, sterile flask, clean our tools, and repeat for each of the 12 flasks. Therefore, every day, each population has a chance to grow by 100 fold before it runs out of nutrients, which allows us to know just how many generations a flask can have each day: about 6 and 2/3. Every 75 days, which is every 500 generations, we take the part of the culture we didn’t transfer and freeze it, so we have frozen samples of each lineage that we can revive and study at any time we like.

The LTEE started back in February of 1988. That means we’re now more than 50,000 generations into the project, and all of those frozen samples are available to work with. There’s a lot that can be done with this project, so my work is just a subset of it. Broadly speaking, the questions I’m interested in all concern the evolutionary process itself: how repeatable it is and how predictable it is. Because of the unique nature of the LTEE in terms of how many generations it covers, and how the old samples can be revived for later analysis, it provides an unmatched system in which to look at the long term evolutionary dynamics of a single species adapting to a very simple environment.

In terms of evolutionary dynamics, what matters the most is fitness. Now, the grad students in my lab are pretty physically active: half of the lab bike or walk to work, one person has run marathons, some of us swim or work with weights or play sports pretty regularly, and we’re generally in our 20s and 30s. But from an evolutionary perspective, none of us are fit: we don’t have children, and haven’t found other ways to pass on our genes like helping a sibling care for our nieces or nephews. Thankfully, the way our LTEE system is designed, we can directly measure the evolutionary fitness of our bacteria.

Measuring fitness in our system is a fairly simple, albeit a little time consuming, process. I take two different strains out of the freezer, and revive them separately. After I give them a couple of transfers to shake off the effect of the freezer and physiologically adjust to the normal growth media, I then take a small sample of each strain and put them together into the same flask. I immediately take out a sample of this culture, dilute it, and spread it onto a Petri dish. That dish is filled with a chemical mixture that allows cells to grow into colonies, and that causes one of the strains to become red, and the other strain to become light pink. This difference is totally irrelevant in the environment in which they’ve been evolving — it depends on chemicals that aren’t present there, so it’s neutral there — but it merely allows us to distinguish the two types from each other in a head-to-head competition. The rest of the culture goes into the incubator for 24 hours, at the end of which I again sample the culture, dilute it, and spread it on a Petri dish, and allow that to grow into colonies. By counting the number of colonies of each of the two types at the start and at the end of the competition, I can calculate how many generations each of the two strains underwent over the course of the competition, which is a very direct measure of their evolutionary fitness.

For my work, I’m systematically going through each of the populations and measuring fitness at dozens of points along the time course from the start to generation 50,000. Each of the populations is being compared to the ancestors, so I can see how much the population has changed from the start of the experiment. I’m also comparing pairs of populations through time, to see how much they’re diverging from each other. These data will allow me to address a set of interrelated questions about how these populations change over time. Are things still changing substantially after 50,000 generations, or has adaptation to this system essentially stopped? What mathematical function best predicts change in fitness over time? Can I accurately predict fitness at all? Is there enough consistency between populations to make useful general predictions, or are the predictions from one population so different from another that each case is completely different? Is the variance in fitness between populations more consistent with populations finding the same overall solution to their challenges (though at different rates), or with them finding very different ways to deal with their environment?

And that’s one of the great things about science: while I have some ideas of what I think might happen, for some of my questions I have no strong intuition at all. Research is all about finding out things that not only did you not know, but no one else did either. Many people like to ask why a question is even worth answering, so I’ve learned to find justifications for my work. For example, our ecological models tend to assume that species out in nature are already perfectly adapted to their environment unless there’s been a recent disturbance, but if I show that a population is still adapting to a simple environment after 50,000 generations — a longer time frame than almost any population will be subject to a consistent environment and no evolving prey or predators or diseases — that calls into question the assumption of everything already being perfectly adapted to its natural environment. But at the core, I’m really an ivory tower academic, and to me, the mere fact that no one knows the answer is reason enough to find out. Evolutionary biology, therefore, is a great place for someone like me. We’ve got enough of a theoretical understanding that there are good mathematical predictions for many things, but there are still a lot of experiments waiting to be done to either support or refute the existing theories.

For more information about Mike’s work, you can contact him at mwiser at msu dot edu.

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