Living Laboratories: Using islands to track natural selection in wild lizards

This post is by MSU postdocs Melissa Kjelvik and Liz Schultheis, and BEACON education director Louise Mead

The National Association of Biology Teachers (NABT) annual Professional Development Conference provides biology educators from across the nation the opportunity to join other leaders in biology and life science education for four days of renowned speakers, hands-on workshops, informative sessions, and special events. In November 2017 the BEACON Center partnered with the American Society of Naturalists to sponsor a special symposium highlighting cutting edge evolutionary research and introducing attendees to a new study system, research questions, and related resources they could incorporate into their classrooms.

Bob presenting his lab’s research on reproduction-survival tradeoffs in anole lizards.

This year’s Evolution Symposium: Emerging Research in Evolutionary Biology at NABT featured a research talk by Dr. Robert Cox (Bob) on selection and fitness in anole lizards, followed by a Data Nugget activity highlighting data from his research. Bob, an evolutionary biologist from the University of Virginia, studies these charismatic lizards, native to Cuba and the Bahamas. He describes anoles as “ecological popcorn” because they are so abundant and are eaten by many organisms. In Florida, where the anoles are invasive, you can shake a bush and 10 will fall out! Bob shared an amazing talk describing his lab’s research on brown anoles and the challenges and opportunities of studying natural selection in the wild. Bob uses real-time studies of wild animal populations to understand the ecological basis of natural selection as it happens. He chose to work with anoles because they are ideal organisms for studies of natural selection; they are abundant, easy to catch, and have short lifespans.

When Charles Darwin talked about the “struggle for existence” he was making the observation that many individuals in the wild don’t survive long enough to reach adulthood. Many die before they have the chance to reproduce and pass on their genes to the next generation. Darwin also noted that in every species there is variation in physical traits such as size, color, and shape. Is it simply that those who survive to reproduce are lucky, or do these traits affect which individuals have a greater or lesser chance of surviving? While evolutionary biology is often viewed as a historical science, exploring processes that have played out over millions of years, Bob stressed that natural selection, the primary force of adaptive evolution, is happening all the time and can be measured in natural populations! Along with field studies, Bob and his lab use genetic methods to help them track the reproductive success of thousands of individuals across multiple generations. Experimental manipulations of predators and competitors also help in understanding the ecological basis of natural selection.

Aaron sharing engaging stories about his experience working in the field during grad school.

The talk was followed by a hands-on workshop, led by Aaron Reedy, Elizabeth Schultheis, and Melissa Kjelvik, where participants worked together as students on two Data Nugget activities. This open time gives teachers the opportunity to have discussions about connections to educational standards or pedagogical strategies that are helpful for them to translate the research and associated data back to their classroom settings. Data Nuggets (http://datanuggets.org) are free classroom activities, designed to improve the scientific and quantitative abilities of K-16 students by providing them with authentic data collected by practicing scientists. The research in Data Nuggets on the anole lizards focused on two traits – their size, and their dewlaps. The Data Nuggets take students through the process of exploring two different hypotheses. In Part I students calculate and graph % survival of lizards according to their size at the beginning of the season, to explore the hypothesis that size influences survival and overall fitness. In Part II, students graph % survival as a function of dewlap size. The dewlap is an extendable red and yellow flap of skin on their throat. To communicate with other brown anoles, they extend their dewlap and move their head and body. Males have particularly large dewlaps, which they often display in territorial defense against other males and during courtship with females. Females have much smaller dewlaps and use them less often. This trait comes with a trade off – while it attracts the desired attention of females, it also attracts predators. There could be sexual selection favoring this trait, while natural selection works against it.

Participants working through Data Nuggets highlighting Bob and Aaron’s research.

In these and other Data Nugget activities, students read background information on a study system and scientist, graph and interpret authentic data from their research, and use their graphs to construct explanations based on sound reasoning and evidence. By relying on authentic research and data, Data Nuggets’ innovative approach reveals to students how the process of science really works, while building their quantitative abilities and interest in science. One teacher from the workshop shared that, “the beauty of the activity is in the simplicity,” which is a great testament to Bob and Aaron’s ability to take complex evolution research and distill it down to a core message. The Data Nuggets include the story behind Aaron and Bob’s research to further engage students in the journey taken by the scientist as they formulated their research questions and ideas.

For more information, and to check out the talk slides and Data Nuggets, check out this page (http://datanuggets.org/2017/10/nabt-2017/). To learn more about Bob and the research in his lab, check out his webpage (https://www.coxlabuva.org), and to learn more about Aaron’s research and outreach, check out his website (http://aaronmreedy.com).

 

 

 

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Determining functionality in a genome

This post is written by MSU grad student Bethany Moore

Bethany Moore, at work programming

Imagine you are traveling in space, searching for a hospitable planet. Not only does the planet have to have elements present on earth, but it has to be the right distance from a star in order to avoid extreme temperatures, and has to have the correct proportions of water, oxygen, and carbon. There are millions light years of empty space between stars and planets, and you cannot see a planet given its close proximity to a bright star. How can you know where to search for such a planet? A similar conundrum is experienced in finding out the function of the DNA in a genome. First, you have to find where a gene (or planet) is in the midst of a galaxy of DNA that includes many non-functional regions (empty space) and thousands of genes (potentially hospitable planets). As you might imagine, matching up genes to their function is tricky to say the least.

Gene expression, or the measurement of RNA a gene produces or “expresses” is one way to determine function—genes that are expressed at high levels might be doing something important, while genes expressed at the same time or under similar conditions (called co-expression) might be involved in the same kinds of processes. A more direct approach is gene knockout, an experimental procedure where a gene is mutated in some way to make it non-functional, and the phenotype of the mutant is recorded. While this shows a more definitive relationship between the gene and the function, this process can take weeks or months for each gene in question.

The Shiu lab focuses on predicting gene function using computational approaches such as machine-learning. Given a set of example inputs and desired outputs, a computer program “learns” the general rule by which you can get from the input to the output. When the computer program is thus “trained” by given inputs, the type of machine-learning is called supervised-learning. How can this approach be applied to finding gene function? If we train our program with inputs from genes whose function we know, we receive output as to whether an unknown gene looks like our known gene. This approach can be highly efficient and accurate in predicting gene function and narrowing down a set of candidate genes that can be experimentally validated using more time-consuming techniques, such as making a gene knockout.

Predicting Lethal Gene Phenotypes

A previous graduate student in our lab to predicted essential genes in plants (Lloyd et al., 2015). Only a small proportion (15%) of genes in the well-annotated genome of the model plant A. thaliana have experimental evidence that connects a gene to a function in the plant. The goal of our project was then to predict what genes are essential, or in other words cause a lethal phenotype, in A. thaliana. Characteristics of known essential genes and non-functional genes (pseudogenes) were used to create a model capable of predicting the likelihood of an uncharacterized gene to be functional. Characteristics such as mechanisms of gene duplication, gene expression, evolution and conservation, and gene networks were compared between lethal phenotype genes and pseudogenes. Using a supervised machine-learning approach, we combined these characteristics to model what lethal phenotype genes and pseudogenes look like. Finally, we applied the model to genes with unknown function, predicting 1,970 undocumented genes to have a lethal phenotype. Not only did this model enable us to document the functionality of genes without a known phenotype, but can help future research in prioritizing candidate genes for further study.

Predicting Gene Regulation

Cis-elements (CREs) important in predicting the up-regulation of salt stress in the shoots of A. thaliana from Uygun, et al., 2017. This first and second columns are sequence logos, which represent the sequence of a CRE, and their corresponding reverse complement sequence. The third column contains the sequence logos and transcription factor family of the best matching transcription factor binding site.

Some regions of DNA in the genome that are not genes can play a role in how and when genes are expressed. This is known as gene regulation, and can be thought of as turning genes on or off. Many genes can be described as “cryptic”, in that they are only turned on under certain conditions, for example during viral infection, so both the gene function and how it is regulated can be difficult to detect unless a given stress is present. This sort of cryptic expression has allowed plants to adapt to many diverse environments around the globe, from deserts to alpine regions to marshes. What regions of a plant genome can actually respond to drought, or cold, or flooding? Does this have implications for our crop plants? What if we could grow crops that under a particular stress, like drought, turn on genes that increase that crop’s resistance to that stress?

To answer these questions, we looked at DNA sequences in specific regions that are frequently involved in regulation. These regions are adjacent to genes, and commonly known as the cis region. We asked if there was a cis-regulatory “code” that can turn on a gene under a given stress. Using gene expression data from plants under salt stress, we were able to determine important cis-elements that tend to regulate genes under this condition, and elements that responded in specific parts of the plant, including the root and the shoot (Uygun, et. al., 2017). We then used machine-learning to predict how well our putative cis-regulatory codes explained plant gene expression under salt stress, and found our putative cis-elements explained approximately 79% of expression. Currently the Shiu lab is working on finding cis-elements that regulate wounding, heat, and drought stress.

Conclusions

When I first joined the Shiu lab in 2014, I had no previous computational experience, only a desire to learn more about genes and genomes of plants. By learning how to program and how to deal with big datasets, I developed my skill set in bioinformatics and with it the future job market looks a little brighter. Additionally, I have gained insight into the biology and complexity of what is happening in a given genome, and tools of how to parse that complexity into meaningful data.

As a lab, we have found computational methods, particularly machine-learning, can be combined with gene expression data to make powerful predictions of gene function. If we can predict the function of a gene, or a genomic region, this can provide a starting point for experimental validation, reducing the amount of guesswork and time involved in the validation process. As many genes and functional regions of the genome remain unknown or uncharacterized, gene prediction or predictions of functional regions are important to discover and is a way to navigate the seas of genomic data.

 

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Bringing Together a World through Science

This post is written by UT undergraduate researchers Zachary Martinez and Andrew Ly

UT Austin undergraduates (L->R) Rachel Johnson, Zachary Martinez, Andrew Ly, Andrea Martinez, and Milki Negeri. Behind them is their poster, entitled “Yo GABA GABA”. The student researchers also presented their work orally.

The University of Texas at Austin is known for many things: from being a powerhouse in Division 1 sports, to leading the world in innovation and cutting-edge research. However, there is one historic fact that many Longhorns do not know, and that is the success of the UT Austin iGEM team. For the past six years at the International Genetically Engineered Machine (iGEM) conference, UT Austin has earned a gold medal each year, an honor bestowed only to teams fulfilling the highest and strictest research requirements. This annual synthetic biology conference takes place in Boston, where over 300 teams from universities around the world present their research. This year, the UT Austin team consisted of a wide range of students, from underclassmen that have just started doing research, to more seasoned upperclassmen that have participated in iGEM previously. The 2017 project focused on engineering an effective GABA-producing probiotic.

The indigenous gut flora of humans possesses the ability to synthesize neurotransmitters, such as GABA, that are hypothesized to influence behavioral, cognitive, and emotional processes of the body via the gut-brain axis. The microbiome-gut-brain axis is a bi-directional communication system in which the microbiome of the gut affects the central nervous system, and vice-versa. Using this information along with our background in microbiology, molecular biology, and synthetic biology, we set out to engineer this microbiome as a way to potentially treat mental illnesses.

Gamma-Aminobutyric acid, or GABA, is the chief inhibitory neurotransmitter in the body and is responsible for reducing neuronal signaling in the central nervous system. Medications, such as alprazolam and diazepam, that increase GABA signaling are typically used for treating anxiety disorders. However, such drugs can lead to a physical dependence, and if given to children, a “pill-popping” habit. Due to these reasons, we began researching potential probiotics that we could study and engineer in order to produce GABA. We ended up picking Lactobacillus plantarum, which is not only indigenous to the human gut, but also expresses GABA in small amounts by converting glutamate to GABA via a glutamate decarboxylase enzyme encoded by the gadB gene. Our goal was to engineer this microbe to produce high levels of GABA and implement it into fermentable foods (such as kombucha, kimchi, or yogurt), which could then be ingested as an alternative form of medicine for patients suffering from anxiety.

Bacterial plate of transformed L. plantarum.

In order to engineer our probiotic to produce high levels of GABA in the human gut, we first wanted to assemble a plasmid in which the gadB gene was overexpressed. To accomplish this, we employed a cloning technique called Golden Gate Assembly, which utilizes type IIS restriction enzymes that cut adjacent to the recognition sites. This allows for the scarless and simultaneous interchanging of different DNA parts, such as origins of replication, antibiotic resistance cassettes, coding sequences, and promoters, all while maintaining directionality in a single reaction. As such, we chose this assembly method due to its ability to rapidly create functional plasmid prototypes that would allow us to interchange parts quickly as we begin experimenting with L. plantarum. After successfully assembling our intended gadB overexpression plasmid using Golden Gate Assembly, we would then introduce it into our probiotic.

While trying to overexpress GABA, we observed various mutational inactivations of our gadB gene. Given that glutamate is an important substrate in biosynthesis and that GABA production requires the conversion of glutamate into GABA, we hypothesized that the functionally active form of gadB was ultimately toxic to cells. As a result, cells containing a mutated gadB gene were more evolutionarily fit and thus selected for. This explains why we were only able to obtain cells with the mutated gadB gene. We then constructed plasmids with either lower copy numbers and/or inducible promoters that would downregulate or control the expression of the gadB gene. However, we still found mutations within the gadB gene. Some possible solutions to address this issue are to utilize an inducible promoter with tighter regulation in our plasmid assembly, perform DNA transformations with a strain with a lower mutation rate, or even simply growing the bacteria in media supplemented with high levels of glutamate. Our future directions include developing a quorum sensing system in our engineered probiotic for controlled GABA production and potentially introducing our probiotic into the microbial ecosystem of the fermented beverage, kombucha, which was the main focus of our iGEM project last year. This year’s project, much like our 2015 iGEM project regarding evolutionary stability, has highlighted the importance of creating evolutionarily stable genetic circuits with low metabolic burdens: a problem synthetic biology has long had.

Overall, the iGEM conference was an invaluable experience where we were able to meet and network with numerous people from around the world, ranging from China to Ghana. We spoke to researchers who were looking into creating more robust genetic systems in a wide array of bacteria, something we have had an interest in for several years. Additionally, students from Vilnius, Lithuania discussed how they were able to use multiple plasmids within a single bacterium while controlling the copy number and maintaining this entire set of plasmids (five in total!) over multiple generations. As we prepare for next year’s iGEM competition, we hope to take what we have learned from this year’s experience and apply it to our 2018 research project.

 

 

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Lessons from your parents: “Fool me once, shame on you. Fool me twice, shame on me” – Randall Terry.

This post is by UW faculty Cynthia Chang and Thelma Madzima, research tech Colin Feng, and undergraduate researcher Jackelyn Garcia

“I told you so” – All parents?

Can the lessons from your parent’s experiences be passed on to you for your benefit, and if so, how?

In many organisms, the memory of our experiences often influences our behavior and how we respond to similar situations in the future. In humans, these ‘learned’ lessons are often passed on verbally from one generation to the next. (Sometimes, no matter what our parents tell us, we have to experience things for ourselves). However, in some organisms like plants, life lessons and behavior are not ‘verbally communicated’, but rely on other methods so that clues of past experiences are passed on to offspring.

In living organisms, memory information can be passed on from one generation to the next at the molecular level; through signals added on top of DNA, referred to as ‘epigenetic modifications’ (Greek for epi = on top or above. Therefore, on top of genetic information). Epigenetic modifications (such as DNA methylation) can be inherited, are reversible and can influence phenotype or how an organism develops. Epigenetic modifications can also be induced by the environment, therefore, by studying the inheritance of epigenetic signals, we can understand how the environment experienced in one generation (parents) can impact developmental responses in the offspring. Thus, epigenetic modification may prove to be an important signal to understanding how species remember and respond to a rapidly changing climate (Donelson et al. 2017).

Climate change: “It’s getting hot in her(r)e” – Nelly

Current climate change models predict greater periods of drought as well as a more variable environment, both of which will drive the evolution of how plants respond to changing environmental conditions (Jump and Penuelas 2005). Our project focuses on determining if plants that experience high environmental stress (drought) pass on molecular signals (epigenetic modifications) to their offspring which allows the offspring to learn from their parents, and better adapt to a variable environment.

To do this, we are using the model plant Arabidopsis thaliana, a fast-growing, primarily selfing plant. As part of our experimental design, we will collect physiological and epigenetic data from Arabidopsis plants exposed to different stresses over multiple generations. In the first 3 generations, we will expose half of our plants to high-drought conditions, and the other half to normal conditions (low-stress; non-drought). Offspring seeds will be collected from each plant and planted in the same treatment their parent experienced. In the fourth generation, we will determine if these life experiences are inherited. We will compare historically stressed plants to non-stressed plants, when grown in either a low, high, or variable water stress environment. We hypothesize that historically stressed plants will grow better than non-stressed plants when grown in a high or variable water stress environment. However, it is also possible that plants have to ‘learn for themselves’ each time.

What’s in it for me?”

This research will provide insight into how a plant population’s past experiences can help or hinder its ability to adapt to a rapidly changing environment. Understanding how plants will respond to climate change is a major motivation for our whole research team.

“The molecular mechanisms of epigenetic inheritance are particularly relevant to all plants, especially in agriculturally important plants. But first, it’s important and more feasible to study these mechanisms in a model plant like Arabidopsis thaliana”. – Thelma Madzima

“I am excited to see how the epigenetic modifications will affect Arabidopsis in the future generations of this project. More specifically, I want to see what genetic differences do stressed plants have compared to unstressed (if any) and how that impacts their ability to respond to stresses.” – Colin Feng

With an interdisciplinary team, we are able to tackle this research question with our different areas of expertise.

Jackelyn Garcia, 2nd year undergraduate researcher at the University of Washington-Bothell, watering the first generation of experimental plants.

“Understanding the evolutionary implications of epigenetics is an exciting way to bridge the gap between molecular biology and ecology.” – Cynthia Chang

Finally, this Beacon research is providing first-hand research experience to young undergraduate researchers. Jackelyn Garcia, a 2nd year UW-Bothell aspiring Biology major has dedicated her time to understanding the phenotypic (trait) patterns of the plants. She hopes to use this research to connect her coursework to real research, and learn more about evolution, ecology, and genetics.

Scientific experience and inheritance

This research is being conducted in collaboration with the undergraduates phenotyping Arabidopsis knockouts (unPAK) network (http://arabidopsisunpak.org/). unPAK is a network of undergraduate research institutions working towards to goal of understanding the relationship between genotype and phenotype, using Arabidopsis. In addition to answering our own research questions, the plants grown in our experiment will provide data for this growing database of unPAK genotype-phenotype data. Both Assistant Professors are particularly excited to incorporate this research in their undergraduate Investigative Biology courses with the hope of adding to our growing understand of how plants can adapt to climate change and the molecular signals that are transmitted, and inspiring new researchers to tackle this complex problem.

Literature Cited

Donelson, J. M., S. Salinas, P. L. Munday, and L. N. S. Shama. 2017. Transgenerational plasticity and climate change experiments: Where do we go from here? Global Change Biology.

Jump, A. S., and J. Penuelas. 2005. Running to stand still: adaptation and the response of plants to rapid climate change. Ecology Letters 8:1010-1020.

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BEACON’s China Collaborations Expanding

BEACON Director Erik Goodman just returned from a 2-week whirlwind trip to four cities in China. He was seeking new collaborations and following up on existing ones, including two of long standing. His first stop was Shanghai’s Tongji University, visiting the laboratory of Prof. Lihong Xu, a longtime collaborator on the evolutionary design of greenhouse controllers for the new generation of greenhouses being built across China. MSU Ph.D. student José Llera-Ortiz is preparing to defend his Ph.D. thesis on controller evolution, and Goodman presented their latest results, and also heard from about a dozen Chinese researchers on a variety of topics concerning modern agricultural automation. Prof. Xu’s lab will provide some of the data to be used to validate Llera-Ortiz’s evolved controllers and greenhouse model.

Goodman, Prof. Lihong Xu (to his left) and some of Xu’s students (including Dr. Leilei Cao, a previous BEACON visitor) at Tongji University’s Jiading Campus

The second stop was Shantou University, hosted by Prof. Zhun Fan. He directs the International Joint Research Center “Evolutionary Intelligence and Engineering Applications” which was established between BEACON and the Guangdong Provincial Key Laboratory of Digital Signal and Image Processing. Goodman presented seminar for students and affiliated faculty members, and discussed their individual research projects with many of them. He also opened discussions on possible collaboration with the dean of the College of Science.

Stop three was the IDEAL Conference, this year hosted at Southern University of Science and Technology (SUSTech), in Shenzhen, very close to Hong Kong. This relatively new and gigantic city (population 20 million) is China’s high-tech capital, and SUSTech is hiring faculty from among the best in the world! Two of BEACON’s collaborators were among the three winners of Best Paper Awards. Goodman gave a keynote lecture at the founding of SUSTech’s new Key Laboratory for Computational Intelligence, directed by Xin Yao, one of the most distinguished researchers and leaders in the field.

Final stop was at the Chinese Academy of Science’s Beijing labs, where Goodman gave an invited talk introducing BEACON’s research on land use planning and on multi-level optimization of logistics. Researchers from the Academy and from Beijing University of Technology (BJUT) are interested in establishing future collaboration on these topics, and Goodman met with the BJUT president and with the director of the Beijing Advanced Innovation Center for Future Internet Technology.

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Fall 2017 elementary school science nights

We are excited to share some pictures from three local elementary school science nights that we had the pleasure of attending this semester. We ran booths at Marble, Whitehills, and Glencairn elementary schools here in Lansing introducing kids to evolution using a couple of highly interactive activities. Thanks everyone for volunteering and thank you Matthew Moreno for taking photos!

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Two BEACONites receive awards from Web of Science for their publication records

We are very excited to congratulate two amazing researchers, Amir Gandomi and Kalyanmoy Deb for their recent awards from Clarivate Analytics, formerly the Intellectual Property and Science business of Thomson Reuters, and owner of Web of Science.

Amir Gandomi was named a 2017 Clarivate Analytics Highly Cited Researcher based on his publication record, publishing Highly Cited Papers defined as those that rank in the top 1% by citations for field and publication year in the Web of Science. This list of researchers represent some of the world’s most influential minds as determined by a citation analysis of Web of Science data.

Kalyanmoy Deb was awarded the Lifetime Achievement Award for his highly cited research contributions in a wonderful event organized by Clarivate Analytics attended by a large audience in New Delhi including other awardees, scientific advisers to Government of India, and many dignitaries from Indian universities and industries.

One of his papers published in 2002 passed 10,000 citations by Web of Science and is ranked 174 out of 45,602,967 journal and conference articles recorded by Web of Science from 1900 to 2017. Kalyanmoy is the first recipient of this award. Besides this award, the Citation Awards 2017 event also gave away Research Excellence Awards to individual researchers and institutions in India for their impacting contributions.

The award citation stated “Professor Deb is recognized for research on multi-objective optimization using evolutionary algorithms, which are capable of solving complex problems across a range of fields involving trade-offs between conflicting preferences. A 2002 paper, with more than 10,000 citations, ranks among the 200 most-cited papers recorded in the Web of Science, 1900-2017, and is by far the most influential paper ever produced by an Indian scientist, as reflected by citations.”

See photos below of Kalyanmoy receiving his award.

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The phage from the local lagoon

This post is by MSU postdoc Sarah Doore

Earlier this year, I wrote a blog post about doing some hunting with a graduate class here at Michigan State University. I’m not talking about hunting in the traditional sense though, since what we sought were bacteriophages—or viruses that infect bacteria—in the local environment. We were specifically interested in bacteriophages that infect enteric bacteria, like Escherichia coli, Salmonella enterica, and Shigella flexneri.

We asked the students to come up with a location and methods they wanted to use to check for bacteriophages (“phages” for short), then did some hands-on isolation using non-pathogenic strains of bacteria in our lab. We got to look at some new phages and the students’ creativity helped us find at samples we never would’ve imagined testing before. The project was fun for the students and fun for us!

By spring, we’d already planned to repeat the activity in the same grad course and were trying to bring phage isolation to undergraduate biology labs at MSU. So, when a Nebraska high school teacher heard about the project and seemed interested in doing something similar for his science class, we brought him to our lab over the summer and showed him the ropes.

Fig 1. Jason fishing for some phage in Nebraska.

After taking him through the isolation procedure, we made a plan for how he could have his students isolate their own phages back in his classroom. We also sent Jason, one of the lab’s graduate students, to Nebraska to do a trial run and see what the classroom still needed. You can see Jason collecting a Nebraska water sample for this in Figure 1. Come fall, we sent a box full of supplies to the high school and anxiously waited for the phage hunting module to begin. We were SO curious to see where students would decide to look—and, even better, what types of phages they’d find.

On collection day, each of the 50 students got their own sterile tube and went to a place they thought would most likely have phage, or at least somewhere they thought might have interesting results. They labeled the tube with their sampling location, then brought it back to the classroom to test it for phage.

A few common themes emerged among the sample locations the students picked: pond and river water were popular choices, as were the school’s baseball and football fields. There were also some more creative ideas, like the two students who sampled their dogs’ water bowls and the one who scooped a deer’s footprint in the woods. One of my personal favorites was “pond by frog,” which is just specific enough to make you wonder: what frog? What was the frog up to? Could this frog be an unknown reservoir for enteric phages? (Spoiler: it wasn’t, and I’m oddly disappointed by this result.)

Back in the classroom, the students tested their samples to see if they had found any phage. Out of the 50 samples, 16 of these had phages in them. Most phages—11 out of 16—came from ponds. The rest of the phages came from the baseball field (3), football field (1), and grass outside the classroom (1). The students sent their samples to our lab at MSU so we could also take a closer look at these new phages.

We’re still in the process of characterizing all of them, but so far it looks like some have interesting morphologies (see Figure 2 for an example of what I’d call an interesting plaque morphology). One also has a unique host range that we haven’t seen yet. This phage infects Salmonella enterica, Escherichia coli, and Citrobacter freundii: three types of enteric bacteria that belong to the same family but are otherwise significantly different. This particular phage came from pond water near a cattail. So…maybe cattails make better homes for phages than frogs do?

Fig 2. On an agar plate like this, phages kill their host and leave a clear spot where bacteria would have grown (“the circle of death”), known as a plaque. This one has fuzzy edges and a little belly button in the middle.

Although we’ve only been doing this kind of isolation for about a year, we’ve already discovered a total of 36 new phages, some of which are pretty rare and/or have really unique qualities (like the broad host range mentioned above). We plan to keep challenging students to sample their environment as long as we have the resources to keep up with them.

Although bacteriophages have been studied for over a century, and we’ve known for awhile that they’re abundant, we don’t know as much about the diversity or identity of the phages that are out there in the environment. Hopefully now we’re starting to answer that question too, and in the process we’re beginning to appreciate our own local composition of microbes.

If you’re interested in doing some of your own citizen science, we’ve got some resources for you! Maybe you’re a teacher who wants to try this in your classroom, or you know of someone who might be interested.

The protocols can all be found on the Parent lab’s website here (under “Video Protocols”). There’s a written version for the entire process of phage isolation, with videos for certain steps that are easier to understand and do yourself after seeing someone else do them. You can also follow us and/or ask us questions on Twitter @phage4lyfe.

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In search of evolutionary hotspots

This post is by MSU grad student Emily Dolson

Imagine that an alien species arrives on earth. It happens to be able to live and reproduce in any part of the world, and, over successive generations, it begins to adapt to its new environment. Among other things, it adapts to eat new kinds of food. Where in the world would you expect the first alien capable of eating papayas to be born?

Most people would probably say South or Central America, since papayas live there. When asked why they said that, there are two potential explanations they might give:

  1. The ability to eat papayas is more beneficial in regions where papayas actually grow.
  2. Regions where papayas grow are also populated by similar plants, like guavas.

The first of these explanations is based on either a misunderstanding of the question or a misunderstanding of evolution (but don’t worry, it’s a common one!). We didn’t ask where aliens that eat papayas are most likely to survive long term; we asked where the very first alien that could eat a papaya would be born. Recall how natural selection works: mutations occur randomly, some happen to be useful, and organisms that have mutations that happen to be useful usually go on to be successful. The first alien to be born with the ability to eat papayas has presumably had some sort of mutation that (in the context of the rest of its genome) gave it this ability. That ability hasn’t yet had a chance to prove to be useful or not. This alien may never even encounter a papaya in its life, in which case having the ability to eat them will have no effect. The presence or absence of papayas can have no influence on the birth of the first alien that can even interact with them.

The second explanation, however, is plausible. South and Central America are home to other tropical fruits, such as guavas. Tropical fruits have similar physical and chemical properties. If an alien can eat a guava, it probably only needs a few subtle mutational tweaks to be able to eat a papaya (assuming that papaya-eating even requires further adaptation). Once a subset of the aliens in the tropics gain the ability to eat some sort of fruit, their progeny will likely go on to be very successful in the regions where these fruit grow. These aliens will be well-positioned, in both physical and mutational space, to begin eating other fruits, such as papayas.

While aliens are unlikely to invade earth and eat our tropical fruits, the phenomenon of a population encountering a set of entirely new challenges as it moves into a new geographic region (or as the geographic region in which it currently lives changes) is common. And it’s common for these new challenges to be related to each other. It’s no accident that plants with similar fruit live in the same region; plants in the same region experience the same selective pressures and may also have a shared evolutionary history. These factors generalize across a wide variety of scenarios. Thus, if some of the challenges of thriving in a new area are easier to solve than others, the easy ones can serve as “evolutionary building blocks” for the harder ones. The presence of such building blocks, i.e. simple adaptations that provide a good starting point for more complex adaptations, has been shown in previous work to be important to the evolution of complex traits (Lenski et al., 2003).

If spatial layout does indeed impact the ease of adaptation, it would be useful to understand, both for evolutionary biology and evolutionary computation. As species shift their ranges in response to climate change, they will traverse regions where different traits are advantageous. Predicting how the positioning of these regions impacts evolution will help us predict whether the species will be able to survive. Evolutionary computation often takes advantage of evolutionary building blocks by rewarding solutions to different problems over time, but this is an imprecise art. If we could understand how to reward them differently across space to promote evolution of an overall solution, we could more easily generalize evolutionary computation to more problems.

Of course, it’s also entirely possible that these spatial effects are too small to care about. Recently, I’ve been trying to figure out whether or not that’s the case (Dolson and Ofria, 2017). Since this would be an incredibly labor intensive question to address in the lab or field, I’m using the Avida Digital Evolution platform to perform preliminary experiments on the computer. Once we know more, I’d be very interested in collaborating with wet lab biologists to see if our digital results are consistent with results from DNA-based systems.

Fig 1. Evolutionary hotspots across all eight environments. Background colors indicate which set of resources are present in each location. Polygons show the location of hotspots for the evolution of the ability to use each resource (legend indicates which resource corresponds to which line-type).

In Avida, I created eight different environments with different patterns of resources across space. I then let 100 different populations of digital organisms evolve independently in each environment. Within each of these runs, I found the location of the first organism with the ability to use each resource in each run. From these data, for each resource and each environment, I determined which regions appeared more often then we would expect to see by chance. These regions are “evolutionary hotspots.” Sure enough, each environment (except the control, which had all resources everywhere) had at least some hotspots (see Figure 1). In some environments, most of the hotspots overlap. In others, they are largely in different regions of the environment.

Fig 2. The sequences of environments experienced by the ancestors of the first organism to be able to use the XOR resource in one of the environments. Each color represents a different environment, and the length indicates long the lineage stayed in that environment for. Notice that many bars appear to end in a somewhat similar sequence of environments.

I’m now working on trying to predict why hotspots are where they are. Surprisingly, a number of seemingly obvious explanations (e.g. the number of resources present, the presence or absence of specific resources, and local diversity) do not appear to explain the pattern. Currently, it appears that the sequence of environments that a lineage experiences over evolutionary time may be a key variable (see Figure 2). I’m looking forward to understanding more soon!

References:

Lenski, R. E., Ofria, C., Pennock, R. T., & Adami, C. (2003). The evolutionary origin of complex features. Nature, 423(6936), 139-144.

Dolson, E. and Ofria, C. (2017).  Spatial resource heterogeneity creates local hotspots of evolutionary potential. In Proceedings of the 14th European Conference on Artificial Life. Vol. 14. pp. 122 – 129. MIT Press.

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Uncovering the function of host-associated microbial communities

This post is by MSU grad student Connie Rojas

Microbes colonize every surface of their hosts. Once established, they do not live in isolated patches, but instead form highly regulated, structurally and functionally organized communities, termed ‘microbiota’. Due to the interplay of the host’s immune system with its microbiota, many members are commensals or mutualists, performing functions critical for host health and physiology. In the human mouth, resident microbiota secrete antimicrobials and enzymes that contribute to oral health. In the mammalian gastrointestinal tract, microbiota synthesize vitamins, and supply the host with energy released from the fermentation of indigestible carbohydrates. In the human vagina, members of the microbiota produce lactic acid, which maintains a low pH environment thought to protect against infection. However, despite the explosion in microbiome research, we know very little about the additional functions microbes are performing within their hosts. We also do not know whether or how they have affected the behavior and evolution of their hosts.

The host generally maintains a stable microbiota. Stability ensures that beneficial symbionts and their associated functions persist over time. Host regulatory mechanisms like physical barriers, mucosal antibodies, and immune systems work to promote the growth of certain microbes, exclude others, and keep the microbiota in check. Furthermore, because some microbes are functionally redundant and can substitute for one another, community function can be retained despite shifts in composition. In fact, alterations to the function of these naturally occurring communities have been implicated in diseases like inflammatory bowel syndrome, type 2 diabetes, bacterial vaginosis, and colorectal cancer. Nevertheless, it is unknown the extent to which fluctuations in host and microbial environments, and resultant variation in the microbiota, can be afforded before these changes become detrimental. Years of research show that microbiota are often host species- and niche-specific, and across hosts, the microbiota varies with a myriad of factors like diet, age, antibiotic use, habitat, season, and environmental stressors. However, it is very likely that numerous other factors are driving variation in microbiota composition and function among hosts, and determining how this variation affects host phenotype is a key line of inquiry.

I study the gut microbiome of spotted hyenas! Photo Credit: Lily Johnson-Ulrich

My research seeks to understand the stability, composition, and function of the microbiota at various body-sites and elucidate the socio-ecological traits of hosts influencing its structure. While most microbiome research is conducted in humans and mice, typically within the context of host health and disease; I study these questions in wild spotted-hyenas (Crocuta crocuta). Due to their complex social behavior, hyenas are an excellent model system to explore how microbiota both influence host behavior and respond to host ecology.

Spotted-hyenas are large, social carnivores inhabiting much of Sub-Saharan Africa. They live in large groups, called ‘clans’, which are structured by linear dominance hierarchies, where an individual’s position determines its priority of access to resources. Their societies are also characterized by female dominance, male-biased dispersal, and a high degree of fission-fusion dynamics, such that individuals move freely among subgroups several times per day. Hyenas are reared in communal dens for the first 9mo of life, are weaned at 12-18mo old, and reach reproductive maturity at 24mo, although most females do not bear young until they are at least 36mo. In my current research, I am investigating how host factors like social dominance rank, group membership, and patterns of association affect hyena microbiota structure and function. Do individuals of varying social ranks differ in the stability and functional potential of their microbial communities? Are certain microbial genetic pathways lacking in one group vs. another? How similar are the communities, in terms of composition and function, of hyenas that associate very closely? Once again, can this variation have implications for host phenotype?

About to collect body-swabs and other biological samples from a hyena, after darting

We use next-generation sequencing technologies, mainly 16Sr RNA sequencing to profile the taxonomic composition of the microbiota, and metagenomic sequencing to characterize its function. From shotgun metagenomic data, it is also possible to infer microbial community dynamics. Populations of microbes, like members of any ecological community, cooperate and compete with each other, break-down and synthesize metabolites, and adapt rapidly to ever-changing environmental conditions. A recently developed mathematical framework by Sung and colleagues (2017) reconstructs community metabolic networks from metagenomic and available metabolic data. In these networks, the nodes represent major taxa, and the edges, which are color-coded, represent interactions (gray: cooperative; red: competitive; see Figure below). Metabolites that are imported and degraded by a species are shown in purple, and those that are synthesized and exported are in blue. I hope to use this framework to identify, for example, the molecules that are important for hyena gut community function, and evaluate whether the microbial community is dominated by competitive or cooperative interactions. Changes in community function and dynamics in response to extreme fluctuations in prey abundance (e.g. arrival of migratory wildebeest) will also be assessed this way.

The human gut microbiota metabolic community-level network constructed by Sung and colleagues (2017). Also, Connie’s dream figure.

Despite the interesting questions being asked, and the multitude of research on diverse host-microbiome systems being conducted, we still have a long way to go as a field. The things we do not know are too many to list. But I hope that revival of innovative culture-based techniques, advances in single-cell genomics, and development of more encompassing bioinformatics tools can help address the many existing gaps in our knowledge.

Reference

Sung et al. (2017). Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis. Nature Communications 8: 1-12.

 

 

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