The second All-BEACON Congress is being held August 10-12 at Michigan State University. Look to this space for more information.
For current BEACON members who want to suggest meeting/tutorials ideas or present research results, please go to our internal wiki.
This week’s BEACON Researchers at Work blog post is by Keck Graduate Institute graduate student Nicolas Chaumont, who is currently a visiting scholar at MSU.
Everybody I’ve talked to who is aware of Karl Sims’ work on the evolution of his 3D blocky creatures was impressed by the seemingly elaborate behavior they were capable of, despite their simple morphologies. The presentation of his results in 1994 was seen as a technical tour de force. Even by today’s standards, his work remains impressive. I was poised in the summer of 2000 to start a Master’s project at Sherbrooke University in Canada, with the goal of reproducing some of his results. This is how the EVO project has begun. Many students who underwent similar efforts to reproduce Sims’ work typically graduate and move on to a different project. Unlike them, I was fortunate to have Prof. Chris Adami as my Ph.D. adviser who saw how this program could be expanded to study Speciation, and other aspects of Evolutionary Biology.
EVO stands for Environment, Virtual objects, and Optimization Algorithm. EVO belongs to the few evolutionary platforms that can co-evolve virtual organisms’ morphology and controller (neural network) in 3D physically realistic environments. It is cross-platform (used on Windows, Mac OSX and Linux) and plugin based, so that extensions are easy to write. It is probably the only simulation platform that is designed to support arbitrary environments, virtual organisms and any optimization algorithm and 3D physics engines that exist. In EVO, you can evolve controllers for robots, catapults, blocky creatures that are able to walk, jump, swim, and follow one or multiple targets in various physics engines. It is also optimized to support large simulations with several hundreds of organisms within the same world on a single computer. Most of those capabilities have actually been developed or expanded within the last year (2010-2011) at BEACON.
Even though those features seem numerous, almost all of them are necessary to achieve my main goal: Use EVO to study speciation. To begin with, I need a self-sustaining population of similar or identical organisms, which I hope, will differentiate into several species if given enough time. Those organisms ought to be able to forage autonomously. This happens to be a hard problem: How to evolve them to become autonomous foragers? If you simulate a random population of artificial creatures for weeks and you replenish the population with organisms built from scratch (to maintain a constant population size), nothing happens (believe me, I’ve tried it!). Locomotion itself is rare: in a random population, there is about one in a hundred that move at all, and among those, about another one in a hundred that go blindly to an arbitrary direction, without using any sensor. Fortunately, thanks to selection, evolving walkers in EVO is systematic and takes about 2 hours.
The difficulty is obtaining organisms that generalize over the position of food sources. If I vary the position of the food sources too little, they will take those positions for granted and won’t be able to cope with other positions (overfitting). If I vary the positions too much, they can’t know what information to use, and they drift at random. So the problem is to strike a balance between those two extremes. They also have to hunt for several food sources in sequence. All those requirements and the exploratory nature of this work necessitates a lot of computing power, and ICER and the HPCC at Michigan State University have become an essential part of this research.
After the first foragers appear, the project will move on to a new phase: The open-ended simulation of many organisms in the same virtual world. This kind of simulation poses its own technical challenges. First, EVO’s simulation speed for such environments will have to be drastically optimized. Simulating in the order of thousand of blocks (that potentially collide together), tens of thousands of sensors (that have to be fed with environmental variables) and close to a million neurons poses a challenge to any modern desktop computer (as of 2011). This will probably involve using parallel programming techniques such as threads, MPI or OpenMP. Fortunately, ICER provides courses on those topics as well as one-to-one help on problems related to parallel programming. If the HPCC is not enough, we also have access to the TeraGrid, with much more computing power. Once the simulation will run at acceptable speed, we will have to explore critical environmental factors, such as conditions for reproduction, for finding a mate, transfer of energy to the offspring, energy contained in food sources, how to place them, at what rate, how much energy to use for motion, metabolic maintenance, thinking, etc. As an example, the population will have to be fed appropriately: Too many food sources, and the organisms won’t need to forage and might likely lose this ability whereas too little food sources will starve the entire population and drive it to extinction.
Once all those problems are overcome and evolution is happening, we’ll face somewhat the same problem as those faced on the field: What clue to look for? We can’t monitor every single variable, that would result in far more data than we can process. Even a fraction, such as a tenth of a percent, is completely infeasible. On the other hand, unlike field experiment, the measurements are 100% accurate, and we can re-run experiments multiple times to see if an event reoccurs consistently or if it is an accident.
For more information about Nicolas’ work, you can contact him at nicolas dot chaumont at gmail dot com.
This week’s BEACON Researchers at Work post is by MSU graduate student Carlos Anderson.
That the structure and laws of our universe enabled the origin of life is an incredible coincidence. Without the gravity that aggregates matter into galaxies, stars, and planets, or without the light that radiates from the sun and heats the Earth, or without the chemical bonds between atoms that store and release energy, life, as we know it, would have probably never arisen. But would life, as we don’t know it, have?
I thought about this question in the summer of 2003, after reading a curious novel by Michael Crichton called Prey. In it, scientists create self-reproducing artificial nanobots, which evolve out of control and prey on their creators (most of them die, of course). But the concept of an artificial life fascinated me, and I soon began reading on evolutionary computation, starting with a wonderful book by Melanie Mitchell. She describes a class of algorithms that use the principles of evolution by natural selection—inheritance, variation, and differential reproduction—to search for solutions to various kinds of problems, such as the design of efficient engines or the sequence reconstruction of the human genome. These ‘genetic algorithms’ demonstrate that evolution is not unique to biological life but to any system having certain basic properties.
With this new interest in evolution I began taking classes in biology, and although I had recently obtained a B.S. degree in Computer Science, I decided to pursue an M.S. degree in Biology. This first exposure to graduate school helped me transition from by background in computer science to biology, and it allowed me to develop a specific interest within evolution that I could study further as a dissertation.
This interest was speciation, the process by which new species arise. There are many definitions of ‘species,’ some based on morphology, others on ecology, and still others on genetic differences. But the one most widely accepted is the biological species concept, in which populations are considered to be different species if they are reproductively isolated; that is, if they cannot produce fertile or viable offspring, either because they are unable to mate or because their hybrid offspring are sterile or inviable. Reproductive isolation is thought to evolve most readily when populations become geographically isolated and each subpopulation adapts independently to its local environment. Two big questions in speciation are (1) can speciation proceed even if environments are similar to each other, and (2) can speciation proceed when migration between populations occurs? I wanted to study speciation in an artificial life system, where my findings could be generalized to life as we don’t know it.
Searching for Ph.D. programs in 2006, I found Michigan State University, where a team of scientists and students have been developing Avida. Avida is an artificial life software designed to study questions in evolution and ecology. In Avida, digital organisms consist of a sequence of instructions (or ‘genome’) that encodes their ability to replicate and perform computational functions. The precise sequence of instructions that allow organisms to perform functions evolve through natural selection and genetic drift, two evolutionary processes that occur in biological organisms. With Avida, one can observe millions of generations of evolution in a short period of time, perform many replications, easily manipulate genomes, and accurately record measurements like fitness and events like mutations.
One of my studies addresses whether speciation can occur when environments are similar between populations. One hypothesis is that speciation can happen by compensatory adaptation, in which a deleterious mutation (a mutation that has a negative effect on the organism) rises in frequency in a population and is subsequently compensated by secondary mutations. Imagine that two populations become divided and each undergoes the process of compensatory adaptation. If the populations were now to come into contact, their hybrids would inherit a combination of deleterious and compensatory mutations, which, because they evolved independently, may not be compatible with each other and possibly cause inviability or sterility. I found this hybrid incompatibility in Avida, and it wasn’t simply because hybrids inherited deleterious mutations, but also because compensatory mutations between populations were incompatible. These findings show that compensatory adaptation is one way in which speciation can occur when the external environment does not change.
The "Ecological" treatment evolved populations to two novel but different environments, the "Mut-order 1" and "Mut-order 2" treatments evolved populations to two novel but similar environments, and the "Drift" treatment evolved populations to the same environment as the ancestral population.
Another of my studies tests the effect of migration between diverging populations on the probability of speciation. I evolved populations that had to adapt to a new environment while migration between them occurred. Although the environments were new, I had treatments in which the two environments were different from each other and in which they were the same. I found that when the environments are different, migration does not prevent speciation from starting—even at 10% migration. However, when the environments are the same, even 1% migration prevents speciation. It appears that when the environments are the same, the population that adapts to it first and lets an individual migrate to the other population, effectively gives away its solution. This causes both populations to adapt similarly, preventing reproductive isolation between them.
Interestingly, these findings agree with theoretical expectations. Such unsurprising results are actually good because they show that Avida behaves like biology, and therefore demonstrates that evolution does not require the intricacies of biological life. Although Avida was not designed to study the origin of life, it does make one ponder whether artificial life could evolve de novo, and thus show that life can emerge not from specific universal structures or laws but from general ones.
For more information on Carlos’ work, you can contact him at carlosja at msu dot edu.
This week’s BEACON Researchers at Work post is by MSU postdoc Bjørn Østman.
We would like to know how soil bacteria evolve. They are important for humans and other living things, as they are involved in chemical processes that are both beneficial and harmful to us. They emit and absorb greenhouse gases: carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), and they are part of the nitrogen cycle, which is important for agriculture. Which you care about even if you don’t eat your veggies.
There is a reason why soil bacteria are not model organisms in biology. They are hard to study because they are difficult to grow in the laboratory. Also, there are many different species of soil bacteria, so even if we did mange to grow a couple in the lab, we would not likely learn much about the overall function of the bacterial communities in soil. So what to do?
You take a handful of soil and you sequence that – after removing earthworms. Simple. It’s called metagenomics.
That gives you a bunch of sequences that you at first don’t know what to do with, but after thinking about it for a while, you realize that you can find specific genes among these metagenomes. But you also realize that they are from many different species of bacteria – bacteria that you don’t know what are. Some of them can be inferred to be from known species, but there are likely millions of different species of bacteria in soil, so no doubt many of them are utterly foreign to us. We can, in other words, not know if one metagenomic sequence is from the same organism or even species as another.
Kellogg Biological Station has been running a Long-Term Ecological Research program for over twenty years where different agricultural treatments have been managed for research purposes. From KBS we have obtained metagenomic sequences from agricultural soils (AG) used for growing wheat, soy, and corn that are fertilized with ammonium nitrate, as well as from unfertilized deciduous forest soils (DF), where trees have not been cut down in recorded history.
Our question is if and how the soil bacteria evolve differently in response to transforming DF to AG. What happens to the bacterial communities when we cut down the forest, grow one crop a year, add fertilizer, and reduce the amount of oxygen by increasing the level of moisture?
In the soils, nitrate (fertilizer) is chemically reduced in the denitrification pathway:
Notice that one byproduct is nitrous oxide, the third-most potent greenhouse gas. Each of these reactions are catalyzed by a different gene. We look at nitrite reductase (nirK), which reduces nitrite (NO2–) to nitric oxide (NO). The first indicator that this is an important gene is that an estimated 1/9 of bacteria in DF have this gene, while 1/3 of bacteria in AG have it. This increase in nirK abundance suggests that bacteria with nirK have higher fitness in AG than in DF. That nirK should affect fitness makes sense because under anaerobic conditions (no oxygen), some bacteria can use nitrate to make energy. They basically breathe nitrate. It’s not as efficient as using oxygen, but clearly much better than doing nothing at all in times of limited O2.
In order to estimate the selection pressure that the change of environment exerts on the bacteria, we turn to the formalism of dN/dS. This is the ratio between the rates of non-synonymous and synonymous substitutions. dN is the rate at which nucleotide substitutions change an amino acid in the protein-coding sequence of a gene (e.g., CTG → CCG, which code for leucine and proline). Similarly, dS is the rate of nucleotide substitutions that changes a codon, but not the amino acid (e.g., CTG → CTA, which both code for leucine).
Assuming that synonymous nucleotide substitutions don’t change the efficacy of the protein (a fair assumption, but not always a certainty), we can infer that if dN=dS, then it doesn’t matter what the sequence of amino acids is, and that there is no selection acting on the gene (or amino acid site). If the environment dictates that the protein must consist of a certain sequence of amino acids, then selection will favor those that are of that particular sequence, and purge those that deviate from it. This is purifying selection, and results in dN being smaller than dS. On the other hand, if the protein is not optimal, some amino acid changes will be favorable to the organism, and we might observe that dN is larger than dS. Thus, measuring dN/dS is informative about the selection pressure exerted on the gene in question.
Given a phylogenetic tree based on the nirK sequences, we can compute dN/dS for each amino acid site in the gene. Using some 30 unique sequences from both AG and DF, we can compare dN/dS between the two.
Two phylogenetic trees from AG (red nodes) and DF (green nodes). The sequences at every internal node is estimated based on a substitution model and on the known sequences at the tips. dN/dS is then computed between all pairs of sequences separated by a branch. The two trees have 30 sequences that are chosen at random among the full set of sequences from KBS.
The result is that for both the bacteria from the agricultural soils and the forest soils the majority of amino acid sites have a dN/dS much smaller than one. Most sites are thus under purifying selection. However, DF does contain a fair number of sites with a dN/dS close to one, while there are nearly none of those in AG. In other words, not only are bacteria in AG more likely to have a copy of nirK, those copies are also more stringently optimized in AG than they are in DF. It thus seems to be of higher importance for the bacteria to possess a well-functioning nirK gene when their environment is farmland.
And this is bad news for us, because it means that traditional farming practices increases the amount of N2O in the atmosphere, thereby exacerbating global climate change. The good news is that the selection pressure in AG does not cause the bacteria that thrived in DF to disappear completely, so it is possible that the bacterial communities in farmland left to its own devices will eventually change their composition back into what they were in the past.
For more information about Bjørn’s work, please contact him at ostman at msu dot edu. Bjørn blogs at Pleiotropy.
This week’s BEACON Researchers at Work post is by University of Washington graduate student Carrie Glenney.
Proteins are the workhorses of life and they play an essential role in just about every biological function, from DNA replication to the immune system. Just as the structure of proteins can drastically change how proteins work, the way in which proteins interact with each other determines if and how essential biological tasks are performed. Protein interactions are determined in large part by their binding affinity and specificity for one another. The binding specificity of a protein describes its “choosiness”: does the protein interact with one specific protein or many different proteins? Binding affinity then determines the strength of the bond between interacting proteins: when the affinity is high, the proteins will “hug” even tighter and for a longer period of time.
A major evolutionary problem involves understanding how high affinity and high specificity protein interactions can diversify when they are critical for cell survival. Small changes in one protein may impact this binding intimacy to the detriment of the organism, so we expect changes to be rare.
Antimicrobial production in Escherichia coli exemplifies such a high-stakes game of protein diversification. These E. coli strains produce colicin proteins, narrow-spectrum antimicrobials that can kill other bacterial cells by destroying DNA, RNA, or by poking holes in the cell wall. When a cell produces these toxic colicin proteins, they must simultaneously produce a neutralizing immunity protein to avoid self-poisoning. Once released from the cell, the colicin can kill cells that do not produce their own immunity to the colicin. Neutralization of the colicin protein is essential to cell survival; thus, each colicin protein has its own unique immunity protein that binds it with high affinity and specificity. In fact, colicin-immunity protein pairs have among the highest binding affinities of all proteins. Despite these high stakes, colicin-immunity protein binding pairs have undergone extensive diversification: but how?
One of the first hypotheses to explain the diversification of colicins invokes positive selection. First, changes in the immunity protein confer an advantageous broadened immunity function: the protein can still bind its own colicin, and it can also bind another functionally distinct colicin. Since colicin-producing E. coli naturally reside in ecologies where polymorphic populations are present, the ability to bind other types of colicin proteins could confer an advantage. Subsequently, complimentary changes occur in the colicin, leading to a novel colicin-immunity protein pair and the inability of the ancestral immunity protein to bind the evolved colicin protein.
I am currently using a method called directed evolution to explore this hypothesis. Directed evolution is a method used to engineer proteins with desirable properties. First, a library of protein variants is created using genetic mutagenesis. Next, this library is screened for the trait of interest. Mutants with the desired phenotype can then be isolated and sequenced to see which mutations have occurred. Using this method, I am able to create a library of immunity genes with mutations and screen them for broadened immunity function. I am particularly interested in exploring changes in binding affinities of broadened immunity mutants and measuring how this has impacted the fitness of the organism. I then plan to use broadened immunity mutants to select for the evolution of novel colicin proteins.
Understanding how proteins evolve is a fundamental problem in evolutionary biology and it transcends disciplines. I am excited about the opportunity BEACON provides to facilitate new ideas and form interdisciplinary partnerships with other researchers who are exploring the same types of questions.
I came to love science in a round-about manner. In high school and college, I had very little interest in science and I struggled to pass my math classes. I was working in social work when I discovered my fascination with the natural world through hiking and camping in the Pacific Northwest.
I share BEACON’s passion for increasing diversity in the sciences and making all science, particularly evolutionary biology, accessible to the public. Most people have an inherent curiosity about life and how it works, even if they don’t immediately think of it as scientific curiosity. Based on my own experience, I believe that harnessing this innate inquisitiveness is the gateway to an understanding of and excitement for science. In collaboration with BEACON, I hope to contribute to expanding science outreach and hands-on science opportunities for the public and K-12 students that might not otherwise have these experiences.
Many thanks to the Kerr lab at the University of Washington for their support of me and this research.
For more information about Carrie’s work, please contact her at cglenney at u dot washington dot edu.
This week’s BEACON Researchers at Work post is by MSU postdoc Kevin Theis.
Kevin Theis with an anesthetized adult male hyena named Detroit.
My research, conducted in collaboration with Michigan State University and BEACON researchers Kay Holekamp, Tom Schmidt and Tracy Teal, lies at the intersection of two broad ideas that have BEACON’s mission of studying “evolution in action” at their core.
The first idea is that many, perhaps even all, animals can be appropriately viewed as supraorganisms. A supraorganism is an organism that is actually a composite of multiple contributing organisms. Consider yourself, for example. There are roughly 100 trillion cells and 3 million genes associated with your body. Amazingly, ninety percent of those cells and ninety-nine percent of those genes are bacterial. You are simultaneously a veritable ecosystem, affording a stable home to multiple generations of trillions of bacteria, and a supraorganism because those bacteria are having as great an effect on your biology as you are having on theirs! Obviously, some of these bacteria are detrimental to your health, but most are truly beneficial. For example, your symbiotic bacteria were instrumental in the proper development of some of your tissues, they primed your immune system against many pathogenic bacteria, and today they continue to competitively exclude additional pathogens and to provide you with critical vitamins and nutrients. Furthermore, recent research suggests that your microbes can affect your behavior in beneficial ways as well, a phenomenon that likely pertains to most members of the animal kingdom.
The second idea is that communication is the glue that holds animal societies together. Many animals are social because, at a functional level, they reap fitness benefits from living in prolonged and close proximity to conspecifics. Specifically, they benefit from decreased predation risk, increased foraging efficiency, greater access to potential mates, and improved ability to secure and defend limited resources. However, there are also costs inherent with living in societies, most notably increased competition over “shared” resources. The adhesive quality of communication stems from its value in facilitating the benefits and modulating the costs associated with living in animal societies. Again, you yourself provide an excellent example, although this time I’ll allow you to walk through the argument on your own.
So, animals can often be appropriately viewed as supraorganisms and communication is the glue that holds societies together, but where do these two seemingly disparate ideas intersect? In a hypothesis suggesting that the bacteria associated with the bodies of many animals underlie the chemical communication systems of those animals, and thus are critical linchpins in the operation and maintenance of those animals’ societies. At a mechanistic level, the idea specifically suggests that the odorants many animals use to communicate with one another are by-products of their symbionts’ metabolisms, and that variation in the information content of animal chemical signals is a direct consequence of underlying variation in the diversity of their bacterial communities.
A spotted hyena pasting a grass stalk in the Masai Mara National Reserve, Kenya.
I am currently studying this idea in the spotted hyena. Spotted hyenas are large carnivores found throughout sub-Saharan Africa. They live in complex societies, called clans, that typically contain 40 to 80 hyenas. Hyena societies are extremely competitive, but unlike most mammalian societies, they are female-dominated and dominance status within clans is not determined by body size or fighting ability. Instead, offspring inherit their mothers’ social ranks. Adult immigrant males, all of whom are subordinate to all natal individuals, must queue for social status. To mediate the complex social interactions both within and among clans, hyenas use a rich repertoire of visual, vocal and chemical signaling behaviors. A common and conspicuous chemical signaling behavior of hyenas is pasting, wherein a hyena straddles a grass stalk and drags its extruded anal scent pouch across the stalk, leaving behind a thin layer of smelly secretion, called paste. At a functional level, pasting helps maintain clan territories, and also appears to effectively advertise the dominance status of males and the reproductive condition of females.
A scanning electron micrograph of bacteria associated with spotted hyena paste.
To demonstrate that variation in the bacterial communities within hyena scent pouches underlies chemical signaling among hyenas, I am confirming, using next-generation sequencing technologies and gas chromatography – mass spectrometry, that bacterial and odorant profiles of paste co-vary, and that pure cultures of bacteria isolated from the paste of wild hyenas yield the expected odorants. MSU research assistants Kwi Kim and John Dover, and undergraduate students Jacquelyn Dycus and Emily Schmitt-Matzen have been instrumental in this process.
Although, to date, my research has largely explored the ecological aspects of spotted hyenas’ relationships with their bacterial symbionts, coevolution among hyenas and their scent pouch bacteria has always been implicit in the model. Recently, along with collaborators Holekamp, Schmidt and Teal, I have begun to explore the evolutionary aspects of these relationships as well. Specifically, we are using recent advances in bioinformatics to test whether spotted and striped hyenas have coevolved with the bacteria inhabiting their scent pouches. Striped hyenas are largely solitary animals found in northern Africa and the Middle East, although the ranges of the two species do overlap in parts of Kenya, East Africa. Given that the lineages of these two hyena species diverged more than 4 million years ago, the bacterial communities inhabiting their scent pouches should differ. Also, as there is variation in the selection pressures shaping symbiotic communities among body sites, the bacterial communities in the anal scent pouch of a given hyena species should differ from those associated with its other body sites (e.g. nose, mouth, rectum). We have now begun evaluating both these predictions.
Although this evolutionary aspect of my research is in its infancy, it is a direct testament to the strength of BEACON’s mission to bring ecological, evolutionary and computational biologists together to study “evolution in action.”
For more information about Kevin’s work, please contact him at theiskev at msu dot edu.
This week’s BEACON Researchers at Work post is by University of Washington postdoc Sean Sleight.
Synthetic biology is a relatively new field that uses engineering principles to design and build novel biological functions and systems. In 2000, Michael Elowitz and colleagues constructed the first synthetic gene network called the repressilator. This genetic circuit consisted of three repressor genes connected in a feedback loop, such that each gene represses the next gene in the loop, like a genetic version of the paper-rock-scissors game. The output of the circuit was Green Fluorescent Protein (GFP) to read out the oscillatory behavior of the network using fluorescence microscopy.
Fast forward to 2011. Now we have several synthetic biology labs worldwide and hundreds of undergraduate teams that compete every year in the International Genetically Engineered Machine competition (iGEM) to engineer novel organisms. At the beginning of the summer, the students are given a kit of plasmids that encode standardized biological parts such as promoters, ribosome binding sites, coding sequences, and transcriptional terminators. These parts, called BioBricks, have been used to engineer bacteria to function as a black and white photographic film, generate colored pigments, smell like bananas, and develop a designer vaccine for Helicobacter pylori (the bacterium that causes ulcers). Successes in synthetic biology groups include the overproduction of an anti-malaria compound, multicellular pattern formation, Craig Venter‘s synthetic cell, and the development of various biofuels.
Besides its numerous applications, synthetic biology is also a powerful system for studying evolution. After finishing my graduate work doing experimental evolution in Richard Lenski‘s lab, I was intrigued by the possibility of being able to assemble large numbers of BioBricks together on plasmids and watching how these modular DNA sequences change over time. I decided that my project should tackle one of the biggest problems in synthetic biology, evolutionary stability of genetic circuits, while at the same time involve the study of evolution. So my research deals with understanding the evolutionary stability dynamics and loss-of-function mutations in genetic circuits, then using this information to engineer mutationally robust circuits.
Genetic circuits are destined to fail unless they impart some beneficial function to the cell or there is a selective environment designed for maintaining circuit function over evolutionary time. Due to the metabolic load of having to express foreign genes, as cells divide, one with a mutant plasmid may grow slighly faster than cells having all functional plasmids. As plasmids segregate to daughter cells, a new cell may have multiple copies of the mutant plasmid and grow even faster. Eventually a cell emerges with no copies of the original plasmid and this cell can outcompete the functional cells in the population.
For my project (referenced below), I measured the stability of several BioBrick-assembled genetic circuits propagated in Escherichia coli over multiple generations and found the mutations that caused their loss-of-function. In this post, I will focus on discussing the results of one circuit called T9002. T9002 works by expressing an activator protein called LuxR. When the inducer molecule AHL is added to the media, it binds to LuxR and activates GFP expression downstream from the luxR promoter. This circuit loses function in less than 20 generations and the mutation that causes its loss-of-function is a deletion between two repeated transcriptional terminators. To measure the effect between transcriptional terminator sequence similarity and evolutionary stability, six versions of T9002 were re-engineered with a different transcriptional terminator at the end of the circuit. The figure below shows the BioBrick ID numbers/names with promoters (arrows), ribosome binding sites (ovals), coding sequences (arrows), and double transcriptional terminators (octagons) for the original circuit (top) and re-engineered circuit (bottom).
Can mutational robustness be increased by removing the sequence similarity between the first and second terminators? How predictable are mutations in genetic circuits?
The figure below shows the evolutionary stability dynamics of the original T9002 circuit vs. three of the re-engineered circuits. The circuit expression level (shown in fluorescence/OD ) is plotted vs. generations. It turns out that changing the terminator of the circuit also changes its expression level since terminator strength can change RNA degradation. The table in the corner shows the relationship of each circuit to expression level and sequence similarity between terminators. The T9002 circuit, with high sequence similarity between terminators and high expression, loses function in less than 20 generations. The T9002-E circuit has an increased evolutionary half-life of over 2-fold on average and this is likely due to having no terminator sequence similarity since its expression level is similar to T9002. The T9002-F circuit is the most robust, with a 17-fold increase in evolutionary half-life, due to having both a low expression level and low sequence similarity. The T9002-D circuit, with medium expression level and sequence similarity, has a half-life in between the T9002 and T9002-F circuits. After noticing the pattern between expression level and evolutionary half-life, I tested this relationship with several circuits and found that on average half-life decreases exponentially with expression level. 
To understand the predictability of mutations in these circuits, the loss-of-function mutations in one clone from nine populations were discovered and shown in the table below. When there are repeated terminators, as in the T9002 circuit, the same exact deletion is found between terminators in all nine populations. However, when sequence similarity is decreased, as in the T9002-D circuit, there are sometimes deletions between repeated sequences, but also less predictable mutations. The T9002-E and F circuits, with no sequence similarity between terminators, have mutations of various types that involve inactivating either the luxR gene or luxR promoter.
Overall, the T9002 circuit can be re-engin
eered to be more mutationally robust by decreasing its expression level and sequence similarity between terminators. Presumably deletions between repeated terminators occur at a high rate and therefore this circuit can be re-engineered to have a lower mutation rate by removing a certain class of mutations from occurring. Although decreasing mutation rate effectively increases mutational robustness (T9002 vs. T9002-E), decreasing expression level has a stronger relative effect on evolutionary half-life (T9002 and T9002-E vs. T9002-F).
My current BEACON project extends this work by understanding mutational robustness in metabolic pathways. For this project, instead of rationally re-engineering pathways, I will be using a directed evolution approach to identify designs that are the most robust.
References: Sleight S.C., B.A. Bartley, J.A. Lieviant, and H.M. Sauro. Designing and engineering evolutionary robust genetic circuits. 2010. Journal of Biological Engineering, 4:12.
For more information about Sean’s work, contact him at sleight at u dot washington dot edu.
Adam Brown’s work “Origins of Life: Experiment #1.4” is now on display at the Synth-ethic: Art and Synthetic Biology exhibition at the BIO:FICTION science, art and film festival in Vienna.
From the website:
Origins of Life: Experiment #1.4
Could life still be originating on earth today? This is a question asked by many. The standard answer is “no.” Origins-of-life chemists assert that the oxygen in the current atmosphere would prevent the reactions necessary to produce prebiotic molecules such as amino acids and nucleic acids. Origins of Life: Experiment #1.4, developed by artist Adam Brown in collaboration with physiologist Robert Root-Bernstein and atmospheric chemist Maxine Davis at Michigan State University, is a performative art installation that stages a miniature model of the earth today as a live experiment. Will the common assumption prove true or false in this public laboratory setting?
Origins of Life: Experiment #1.4 is a further development of Adam Brown’s earlier installations of his Origins of Life series in which he re-enacted the famous experiments carried out by Stanley Miller and Harold Urey at the University of Chicago in the 1950s. Their setting simulated the chemical and energetic conditions of the early earth, 4 billion years ago, resulting in the production of a number of organic molecules necessary for the origin of life. Likewise, Adam Brown succeeded in obtaining similar molecules in his aesthetic and sensual gallery installations, reminding visitors that all organisms and all biology are the product of a natural synthesis.
While the early atmosphere simulated by Miller and Urey consisted of methane, ammonia, hydrogen and heated de-ionized water, subjected to electric sparks, Origins of Life: Experiment #1.4 now also integrates currently existing gases such as nitrogen, oxygen and carbon dioxide to drive the reactions. The display also reflects the presence, today, of large oceans and thus contains seawater rich in sodium, chlorine, calcium, sulfur and potassium. It further includes minerals such as calcium carbonate and calcium phosphate, representing lime, calcite, marble and apatite. The outcomes are unpredictable – will unexpected molecules indeed be synthesized?
For more information about this science, art & film festival, please see the BIO:FICTION website and the article in New Scientist, in which Adam Brown and collaborator Robert Root-Bernstein are featured.
This week’s BEACON Researchers at Work blog post is by University of Texas at Austin graduate student Emily Jane McTavish.
Emily Jane McTavish with Prairie Dancer at the Double Helix Ranch
In the ranch lands of Texas it can feel like cattle have always been a part of the western landscape. However, neither domesticated cattle (Bos taurus) nor their wild ancestor, the auroch (Bos primigenius), are native to the Americas – there were no cattle prior to 1493. The first cattle were brought by Christopher Columbus on his second voyage across the Atlantic to the Caribbean island of Hispaniola, what is today Haiti and the Dominican Republic. The cattle he brought were likely from the Canary Islands, off the western coast of Africa, then a Portuguese colony, which were in turn probably descended from animals of Portuguese origin. The imported cattle reproduced rapidly, and by 1512 importation of cattle by ship was no longer necessary. Caribbean cattle were introduced into Mexico in 1521, and had reached north into what is now Texas and south into Colombia and Venezuela within a few decades (Barragy 2003).
The settlers relied on these cattle for meat, but largely allowed them free range in the unfenced wilderness. The artificial selection was imposed mostly by the choice of which individuals to castrate for steers, and which to leave as bulls. Otherwise, natural selection drove the evolution of this group for the next 400 years, roughly 100 generations. These cattle are the ancestors of the present day new world breeds including Corriente cattle from Mexico, Texas Longhorns, and Romosinuano cattle from Colombia. This long period of selection has left these groups better adapted to these landscapes than breeds of more recent European import. Texas Longhorns are known to be immune to Tick Fever, a disease caused by bacteria in the genus Babesia, which can be fatal. They have also been described to have far greater drought resistance than other European breeds.
Neat history, sure, but how does the history of domestic cattle in the United States tie into BEACON’s mission of studying “evolution in action”?
I am a graduate student in my fourth year in David Hillis’s lab at the University of Texas at Austin, a BEACON partner institution. I am interested in the role of gene flow and hybridization in how populations diverge and adapt to their environments. My interests lie not in how divergence occurs when populations are completely isolated, but rather how differentiation between populations can occur in the face of continuous gene flow or interbreeding. In addition, within the patterns of divergence between populations is a signature of past processes, which can allow us to reconstruct the geographic history of a species. Gene flow between two groups that have been evolutionarily separated for a long period of time injects new adaptive variation into both groups, which can then be acted on by natural selection. In my research, I am using a genome wide single nucleotide polymorphism (SNP) data set to assess the population structure of cattle breeds world-wide, and specifically determine the role of hybridization two subspecies of cattle. Domesticated cattle actually consists of two subspecies (or species, depending on whom you ask), which are derived from independent domestications of the same progenitor species, the auroch; Bos taurus taurus, likely domesticated in the middle east or Europe, and Bos taurus indicus, domesticated on the Indian sub-continent. Although these domestication events likely occurred only 7,000-10,000 years ago, due to pre-existing spatial genetic structure in the auroch population these two subspecies share a most recent common ancestor 200,000 or more years ago. The clearest phenotypic difference between these groups is that indicine cattle have a noticeable hump at the withers. Brahman cattle is the most common B. t. indicus breed in the United States. Generally, B. t. indicus are more feed-stress and water-stress tolerant than taurine breeds, and more tropically adapted.
This will allow me to both assess the possible influence of indicus-derived genes on these cattle’s adaptation to the Americas, but also to track the history of hybridization, and determine whether it has occurred before or after their introduction.
I have found that New World cattle are of B. t. taurus origin, as expected from their European ancestry, but show significant introgression (approximately 10-20%) of B. t. indicus alleles into their genome. Preliminary results suggest that southern European breeds of cattle similarly show elevated levels of B. t. indicus introgression, suggesting that the hybridization may predate the introduction of cattle to the Americas. This hybridization may have given these conquistador cattle the some of genetic variation they have used to adapt to their novel environment. I am currently working on mapping what portions of the genome of breeds that show introgression are derived from each subspecies. This information will also allow me to determine whether introgression occurred in independently in several breeds, or ancestrally. One historically plausible explanation is that indicine cattle from North Africa were transported across the Mediterranean, and crossed with cattle from the Iberian Peninsula. Alternatively, hybridization may have happened recently, after the introduction of Brahman cattle to the United States.
The figure demonstrates how gene flow may have occurred, based on historic records and my preliminary data. I am continuing work on this project, and look forward to clarifying the history of this group.
I am very excited about applying genomic data to question of dispersal and gene flow, and am at this moment on a plane to Okinawa to participate in a three week genomics workshop with a focus on linkage and recombination, at the Okinawa Institute for Science and Technology (OIST), which will provide me with many more analytical tools to bring to bear on this question as well as in future projects. In particular this will be useful for a project I am beginning in the fall in collaboration with fellow BEACON members Jack Sullivan and James Foster at the University of Idaho. We will be examining genomic patterns of differentiation under divergence with gene flow. My role will be using spatial explicit simulations (DIM SUM) to parameterize expectations for patterns of genetic divergence between populations, across selected and neutral regions of the genome, under a range of dispersal distributions.
Being involved in BEACON has been a great opportunity for me to meet and collaborate with fascinating scientists, research exciting questions, and tie my work into a broader context.
References:
Barragy, JT. 2003. Gathering Texas Gold. Caye del Grullo Press.
Brown, J. K. Savidge, and E. J. McTavish, 2011. DIM SUM: Demography and Individual Migration Simulated Using a Markov chain. Molecular Ecology Resources, 11: 358–363. doi: 10.1111/j.1755-0998.2010.02925.x
For more information, contact Emily Jane at emily dot mctavish at mail dot utexas dot edu.
This week’s BEACON Researchers at Work blog post is by MSU graduate student Caroline Turner.
Every day, human activities release a wide variety of chemical compounds into the environment, from fertilizers and pesticides to pharmaceuticals and industrial waste. Many of these compounds are produced only by humans and not otherwise found in natural environments. While some chemicals are benign, others are harmful to human and ecosystem health. For example, over 200 industrial chemicals present in the environment have been identified as neurotoxins that may cause learning disorders, cerebral palsy, and delayed development. Naturally, there is strong interest in removing these harmful compounds from the environment.
Image from: epa.state.il.us
Readers of the BEACON Center blog, knowing the importance of “Evolution in Action,” will not be surprised to learn that microbes can evolve the capacity to consume novel compounds produced by humans. These compounds can often act as an energy source, providing a selective benefit to any organisms able to consume them. For example, in groundwater contaminated with chlorobenzene, a solvent that is used in the production of herbicides and does not occur naturally, bacteria have evolved that consume the contaminant (van der Meer et al. 1996).
Microbes evolving to remove our waste from the environment are an example of evolution working to our benefit. This capacity can potentially be further enhanced by active attempts at bioremediation. However, because evolution in natural systems occurs in complex assemblages, we know very little about the evolutionary and ecological processes by which these novel pathways for consuming new compounds emerge and achieve high abundance. How do these novel degradative pathways affect the ecology and evolution of coexisting organisms? How do coexisting organisms affect the evolution of organisms which consume novel compounds? My research addresses these questions using a model system in which a novel metabolic pathway has evolved in the laboratory, where it can be further studied in great detail from evolutionary and ecological standpoints.
In 1988, my adviser, Dr. Richard Lenski, began the “Long-Term Evolution Experiment” (LTEE) with 12 populations of initially identical* Escherichia coli. Every day since then, the populations have been transferred to fresh growth media, for a total of over 50,000 generations of evolution. The growth medium has a limited amount of carbon (food) in the form of glucose for the bacteria, so each day bacterial growth stops when the glucose runs out. In addition to glucose, the growth medium also contains substantial amounts of citrate, another carbon molecule. However, E. coli are not capable of consuming citrate in the presence of oxygen, and that inability has long been recognized as a defining feature of this species.
Courtesy Brian Baer
Thus for the first 30,000 generations of the LTEE, all the E. coli lived solely on glucose. As you may have guessed, however, after 30,000 generations, the capacity to consume citrate arose in one of the 12 populations. This population no longer stopped growing when it ran out of glucose. Instead, it continued to grow using the now available citrate (Blount et al 2008). As a result this population reached a density several times that of any of the other populations. Interestingly, the group of bacteria that consume citrate (Cit+) coexisted with a second group that still could not use citrate (Cit–). I am using the evolution of the Cit+ group and the coexistence of the Cit– group as a model system to study the ecology and evolution associated with consumption of novel compounds.
So far I have been investigating how it is that Cit– bacteria are able to survive when the Cit+ population has access to both citrate and glucose while the Cit– cells can only consume glucose. One possibility is that the evolution of citrate consumption involves a decrease in the ability of Cit+ cells to consume glucose. Cit– cells could then survive by outcompeting the Cit+ population for glucose. Another possibility is that during their growth the Cit+ bacteria release one or more additional carbon sources into the medium and the Cit– bacteria survive by consuming this addition resource. This type of interaction between microbes is known as cross-feeding.
How can I test which of these possibilities is occurring? Imagine taking some Cit– bacteria from before the evolution of the Cit+ lineage (“early Cit–”) and growing them in a flask together with Cit– bacteria from after the evolution of the Cit+ lineage (“late Cit–”). The bacteria from the LTEE are frozen at regular intervals and can be revived, so this is an experiment I can actually do. If the Cit– bacteria persisted solely by being better competitors for glucose, then I would expect that the “late Cit–” cells would be equally as good as or better then the “early Cit–” cells at growing in glucose. On the other hand, if the “late Cit–” cells have evolved to cross-feed, then they will have an advantage against the “early Cit–” cells in the presence of the Cit+ population but not in their absence.
Thinking back to the evolution of microorganisms to consume novel compounds produced by humans, it may be important to consider the possibility for cross-feeding interactions to evolve. The consumption of novel compounds could affect not only the specific bacteria doing the consumption, but also the ecology and evolution of coexisting bacteria.
*Or rather nearly identical. They differed only in a neutral marker, a genetic change which does not affect competitive ability in the experiment, but which allows the populations with different marker states to be distinguished from each other.
References:
Blount, Z. D., C. Z. Borland, and R. E. Lenski. 2008. Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proceedings of the National Academy of Sciences of the United States of America 105:7899-7906.
van der Meer et al. 1996. Evolution of a pathway for chlorobenzene metabolism leads to natural attenuation in contaminated groundwater. Applied and Environmental Microbiology 64(11): 4185-93
This research was developed under STAR Fellowship Assistance Agreement no. FP917112 awarded by the U.S. Environmental Protection Agency (EPA). It has not been formally reviewed by EPA and the views expressed are solely those of Caroline Turner. For more information, please contact Caroline at cturner at msu dot edu.
