BEACON Researchers at Work: How fast can hyenas learn?

This week’s BEACON Researchers at Work blog post is by MSU postdoc Agathe Laurence.

If monkeys could reach the point of being bored, they could turn into human beings,” said Goethe. More than a philosophical essay about boredom, that comparison between humans and monkeys is also a good illustration of what we now know about apes and monkeys’ cognitive abilities. In fact, as primates, we are a species that can claim one of the biggest relative brain size among mammals (Jerrison et al, 1973). However, you would be surprised by what some birds can do, or even honeybees (Komischke et al, 2002). Of course it all depends on what kind of cognitive tests you are considering. But, cognition can be divided in two categories: physical and social cognition. While the first helps us deal with inanimate objects, we are able to understand our conspecifics intentional actions, perception, and knowledge, thanks to the latest (Herrmann et al, 2008). Obviously, social cognition is most useful when living in a big enough society, which means frequent encounters with conspecifics. As a matter of fact, social complexity could be what drove individuals to have bigger and bigger brains throughout evolution (Byrne & Whitten, 1988). This indicates that living in a complex society involves the need to understand its social rules, as well as to predict and/or understand the intentions of your conspecifics, especially if they’re higher ranking.

Figure 1

Figure 1: Two of my study subjects resting in the Maasai Mara, Kenya

Even though I used to work with non-human primates, and then birds, it is the spotted hyenas studied by Kay Holekamp and her graduate students that have brought me from France to Michigan State University. Throughout 26 years of research about their anatomy, ecology, physiology and behavior, Holekamp and her collaborators have shown the complexity of their social groups (Holekamp et al, 2007). First of all, spotted hyenas (Crocuta crocuta) are the subject of many myths, so let’s reestablish some truths about them: they are not hermaphrodites, 80% of their food is what they hunt for themselves, and they are not ugly (although that might be a personal bias, but see Figure 1). Regarding social characteristics, they actually exhibit a lot of similarities to primate societies, particularly old-world monkeys (e.g. baboons or macaques). A group of hyenas, called clan, can contain up to 90 individuals, each of which is able to recognize every member. There is strict linear hierarchy within the group, where females are dominant, bigger, and more aggressive than males. Overall, they meet the criteria to fit the social intelligence hypothesis. Like monkeys, hyenas have a high level of social cognition: the acquisition process of one’s social rank happens through learning. Only then they are able to identify every conspecific’s social rank, predict the issue of an interaction between two other hyenas, and join other hyenas in an aggressive coalition against a third one.

On the other hand, we know very little regarding their physical cognition, and that is the focus of my work with wild spotted hyenas in the Maasai Mara, Kenya. The social intelligence hypothesis predicts a high level of physical cognition along with social cognition. The key is to choose a task that can be done by several species, so that a wide comparison between species is possible. Let’s forget about IQ tests right away, only a chimpanzee would give the pen back when he’s done. Behavioral flexibility, or the ability to adapt one’s behavior to solve a problem, is a good measure of general intelligence and can be adapted through various tasks.

The hard part was to choose a task that hyena could solve without the use of hands, as most of the experiments have been conducted on primates. Moreover, wild hyenas are very cautious toward man-made objects, hence I chose a task that could be done in several steps, to eliminate any bias of novelty on their ability to solve the task. So, to test their ability to display flexible behavior, I’m using a reversal learning test where the hyenas have to pull ropes to get access to meat as a reward (Figures 2 & 3). They first have to learn to discriminate between two colors (black versus yellow), one associated with the reward, the other one associated with the absence of that reward (Figure 4). Once they have learn that, the rewarded and the non-rewarded colors are reversed and they have to suppress one behavior in favor of another to get the meat.

Figure 2: That young subadult is pulling the rope so that the tray containing the meat slides to the edge of the device.

Figure 2: That young subadult is pulling the rope so that the tray containing the meat slides to the edge of the device.

Figure 3: After pulling the rope, the same hyena can get the meat and thus associate the color of the rope (here, the black one) with a positive reinforcement (the meat).

Figure 3: After pulling the rope, the same hyena can get the meat and thus associate the color of the rope (here, the black one) with a positive reinforcement (the meat).

Figure 4: After getting the reward on the "black side", this hyena is trying to get the meat on the "yellow side", which is blocked and the meat will remain inaccessible. After several trial, that hyena will learn to pull only the black rope.

Figure 4: After getting the reward on the “black side”, this hyena is trying to get the meat on the “yellow side”, which is blocked and the meat will remain inaccessible. After several trial, that hyena will learn to pull only the black rope.

Conducting an experiment on wild hyenas takes time, especially when I have to find them across their wide territory and to get them to interact with the device. After a very long habituation phase, I have now started the learning phase, trusting that my beloved subjects will learn it quick (because I believe that they are very smart). Then, the best part begins: reversal learning!

 

Picnic Trees, SerenaI went to Rennes 1 University in France, where I graduated with a Master’s degree in ethology studying hand preference in non-humans primates and then received a PhD studying the impact of chronic stress on behavioral development in birds. Afterwards I wished to study more specifically behaviors in a social mammal. I won a 2-year grant awarded by the Fyssen foundation (website) to study cognition in wild spotted hyenas at MSU.

References

  • Byrne RW, Whiten A (1988): Machiavellian Intelligence..Oxford, Clarendon Press.
  • Jerison HJ (1973): Evolution of the Brain and Intelligence. London, Academic Press.
  • Hermann et al (2007): Humans have evolved specialized skills for social cognition: the cultural intelligence hypothesis. Science (317), 1360-1365.
  • Holekamp et al (2007): Social intelligence in the spotted hyena (Crocuta crocuta). Phil. Trans. R. Soc. B (362), 523-538.
  • Komischke et al (2002): Successive olfactory reversal earning in honeybees. Learning & Memory (9), 122–129

For more information about Agathe’s work, you can contact her at ag dot laurence at gmail dot com.

 

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BEACON Researchers at Work: Seeing double? Genome duplication and the teleost fish retina

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

Figure 1. The cells and layers of the retina

Figure 1. The cells and layers of the retina

The sense of vision is mediated by a specialized projection of the central nervous system located in the back of the eye, the retina. The retina is a highly conserved structure consisting of cellular and synaptic layers that establish physiological pathways capable of converting light stimulus from our environment into a perceivable image (figure 1). This is possible because of the specialized circuits that detect different aspects of vision such as color, edges, and movement. Light first stimulates photoreceptors at the back of the retina, which propagate this information to innernerons that modulate this signal before the retinal ganglion cells send this information to the brain.

Our lab is interested in how this structure has evolved, and we operate with a focus on the visual system of teleost fish. The visual system observed in teleost fish is interesting because of its ability to regenerate damaged neurons, maintain constant neurogenesis throughout their lifespan, detect a wide range of light, and adapt to novel visual environments, and they contain an increased number of retinal cell types. We hypothesize that the specialization and unique traits that are observed in the teleost visual system was influenced by the Teleost Genome Duplication (TGD). This was a large scale genetic rearrangement event that took place roughly 350 million years ago, providing the the teleost lineage with a duplicated genome that resulted in an explosion of diversity.

Figure 2. Spotted Gar retina with antibody to short wavelength cone opsin. A clear gradient was observed in cellular density between dorsal and ventral regions of the retina. Density between the two regions was significant (p= 2.07x10-41).

Figure 2. Spotted Gar retina with antibody to short wavelength cone opsin. A clear gradient was observed in cellular density between dorsal and ventral regions of the retina. Density between the two regions was significant (p= 2.07×10-41).

As a first step to understanding the role of TGD on the evolution of the visual system, we have characterized a basal model, the spotted gar, which diverged in lineage prior to the TGD. This gave us the unique opportunity to study a retina that evolved under similar environmental pressures, but with only a single genome. Consistent with our hypothesis, we found the retina of the spotted gar to have conserved structure and cell types to that of the teleost, but some key differences were observed. The teleost retina is well known to have a highly ordered photoreceptor mosaic. The retina of the spotted gar was organized, but not to the level and consistency that was observed in teleost. The spotted gar retina also contains gradients of cellular density throughout the eye; a trait seen in mammals but not in teleost fish (figure 2). The spotted gar retina also contained thinner cellular layers, supporting the hypothesis that teleost retinas contain more retinal cell types overall. Through this characterization, we have been able to identify and label major cell popluations in the retina, as well as many different individual cell populations including cones and amacrine cells (figure 3). This will be beneficial to the our project going forward, as well as future research involving the spotted gar retina.

Figure 3. Spotted Gar retina section stained with Recoverin (photoreceptors), PNA (cone pedicles), and DAPI (nuclear stain). Retina structure is conserved compared to zebrafish and mammals. Scale bar is 20 um.

Figure 3. Spotted Gar retina section stained with Recoverin (photoreceptors), PNA (cone pedicles), and DAPI (nuclear stain). Retina structure is conserved compared to zebrafish and mammals. Scale bar is 20 um.

Next we hope to assay the function of paralogs in the zebrafish retina. The zebrafish is a member of the teleost lineage, and is a common model for eye research. When gene duplication occurs, there are three possible outcomes. Neofunctionalization occurs when the duplicated gene develops a completely novel function(s). Pseudogenization occurs when the gene becomes obsolete and its expression is lost. Subfunctionalization occurs where the two genes each retain a “subset” of the original gene function and are possibly relegated to expression in a specific tissue or cell type.

We will initially target two cell adhesion molecule families, the sidekick (SDK) and Down syndrome cell adhesion molecule (DSCAM) families, both essential in the organization of the retina. These are ideal candidates because the zebrafish genome contains twice as many copies as the spotted gar (and mouse). We will use genomic informatics and molecular approaches to assay whether both copies of the duplicated genes have been retained in teleost retina.

We will begin by performing in situ hybridization assays on the paralogs of our target genes. This will be done across development for the zebrafish to determine whether expression patterns have diverged. We will utilize developmental assays performed with the use of two different gene knockdown techniques. Morpholino oligonucleotides are small miRNA sequences that target specific mRNA sequences and reduce translation of these transcripts. The CRISPR/Cas9 technique was only recently discovered, but provides a unique way to target specific sequences in the genome directly. This will allow us to assay if the respective function of each gene has diverged compared to its paralog and therefore test if neofunctionalization has occurred. We predict the genome duplication and associated increase in genetic material could have enhanced the potential of the teleost lineage to develop novel visual circuitry. Future plans include adapting a place preference test in order to look at visual acuity in the mutant fish we develop.

Joshua SukeenaMy path to a PhD program has not been traditional. I began at The College of Idaho in pursuit of a degree in history and education. However, spending time in a cognitive research lab drastically changed my outlook for my future. This experience inspired me, but I knew that my biggest interests were at the molecular level; specifically in the field of genetics. I joined the University of Idaho in the lab of Dr. Peter Fuerst in the fall of 2013. Having grown up in Idaho, I have become aware of the problems facing scientific education. It is a state that ranks near the bottom of the country in number of students that go on to get a technical degree, college degree, or post-graduation degree. I am determined to make sure these statistics do not persist. I know that a doctoral degree will not only lead to an opportunity to conduct research and contribute the scientific community as a whole, but also put me in a position to make a significant impact on the way my community looks at science and education as tools for improvement. Idaho has abundant environmental resources and opportunity for young scientists. I have a dream of establishing an educational program which allows children to travel and learn about different disciplines of science all over our state from geology to wildlife science and microbiology. I believe in a future where scientific inquiry and research understanding is a commonplace in our educational system; allowing it to persist and lead to a more stable future.

 For more information about Joshua’s work, you can contact him at jmsukeena at gmail dot com.

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BEACON Researchers at Work: Of Milk and Microbes

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

JanetMilk and microbes, what do these two things have to do with each other? For many years, milk was thought to be sterile and any bacteria present were considered to be pathogenic or due to “contamination” of milk. It is true that certain bacteria, like Salmonella, E. coli and Listeria, in unpasteurized milk can be cause for concern. However, recent evidence has clearly demonstrated that “healthy” milk contains diverse bacterial communities (Figures 1 and 2 below)1,2. And what’s even more intriguing is the thought that certain bacteria in milk are actually good for the newborn. These bacterial communities in milk likely serve important roles in maturation of the nursing newborn’s gastrointestinal (GI) tract and immune system.3 Hmmm… bacteria in a food being consumed and conferring a health benefit to the host. Sound familiar? Sounds like a probiotic to me. This concept may or may not surprise you. In fact, you can now most likely find infant formulas supplemented with probiotics on the shelves of your local grocery store. If you are curious and want to read more about this, check out the recent review by McGuire and McGuire (2014)4 that explores the idea that milk is actually Mother Nature’s prototypical probiotic food.

Figure 1: Example of immune and bacterial cells found in “healthy” human milk

Figure 1: Example of immune and bacterial cells found in “healthy” human milk

Here is another interesting thought… milk has not only evolved to contain nutrients to “meet the diverse reproductive and environmental demands of different species” but to also contain bacteria that increase the chance of survival and development of the nursing young in diverse environments.

So are milk bacterial communities similar across different mammals? Are milk bacterial communities similar across different human populations? Does maternal diet influence milk bacterial communities? Do host genetics play a role in structuring the milk bacterial communities? What components in milk influence the structure of milk bacterial communities? How has the evolution of those components impacted the microbial diversity found in milk? What types of bacteria-bacteria interactions may be at play in structuring the bacterial communities? I could go on for a long time adding to this list. I’m fascinated with how all these factors are intertwined and how together they influence maternal and newborn health. 

Figure 2: Community composition of 15 abundant bacterial genera in milk samples, from Hunt et al 2011

Figure 2: Community composition of 15 abundant bacterial genera in milk samples, from Hunt et al 2011

Right now, we don’t have the answers to many of these questions. This is one of the reasons I am pursuing a PhD in Bioinformatics and Computational Biology at the University of Idaho. I work in the laboratory of Dr. Mark McGuire (Animal & Veterinary Sciences Dept, UI) and in close collaboration with Dr. Shelley McGuire (School of Biological Sciences, WSU). The McGuire labs have been engaged in the study and hands-on collection, extraction, and analysis of various components of interest from human and cow milk (e.g., lipid, protein, sugars, host RNA) for many years. We are now venturing into the computational arena of processing and interpreting massive amounts of sequencing data from bacterial DNA.

I work on both the “wet lab bench” side and the computational side of things in the laboratory. Although most of my experience prior to starting the PhD program was at the lab bench, my current focus is analyzing 16S rRNA sequence data from a variety of samples (human and cow milk, human fecal, dairy cow rumen and fecal, and newt skin swabs). I am trying to understand some of the complexities in the dynamics in the microbial communities and how other factors (e.g. diet and/or spatial/geographical location) may influence the structure of these communities.

For the human milk samples, we are currently working to address the question of how diet influences the milk microbiome in much the same way that others have looked at how diet influences the gastrointestinal (GI) microbiota. Initially it’s just about characterization of what bacteria populations are present over the course of lactation at the different taxonomic levels. The next step is to see if there is any structure to the variation of the bacterial communities. Can a large part of the variation in the milk bacterial communities be explained by maternal diet? By time postpartum? By some other nutrient or component in human milk? It’s important to understand the composition of the bacterial communities that are present as it has been suggested that the milk microbiota may be directly involved in colonization of the newborn GI tract and the GI microbial composition has potential short- and long-term effects on the health of that individual.

One of the reasons that I’m a part of BEACON is because I’d also like to try and investigate the evolutionary forces that potentially influence milk composition and the microbial communities in milk. And that’s where you, the reader, might be able to provide insight or suggestions. Now that you know some of what we do, what thoughts or ideas have come to mind as you have read this in how we might approach some of the questions above? Let me know what you think. I’d be interested in having a conversation with you. 

1Hunt K, Foster J, Forney L, Schütte U, Beck D, Abdo Z, et al. (2011). Characterization of the diversity and temporal stability of bacterial communities in human milk. PloS ONE 6:e21313.

2Quigley L, O’Sullivan O, Stanton C, Beresford TP, Ross RP, Fitzgerald GF, Cotter PD (2013) The complex microbiota of raw milk. FEMS Microbiol Rev 37:664-698.

3Fernández L, Langa S, Martín V, Maldonado A, Jiménez E, Martín R, Rodríguez JM (2013) The human milk microbiota: Origin and potential roles in health and disease. Pharm Res 69:1-10.

4McGuire MK, McGuire MA (2015) Human milk: Mother nature’s prototypical probiotic food? Adv Nutr (in press).

For more information about Janet’s work, you can contact her at janetw at uidaho dot edu.

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Tips for Thriving in Your Research Career

This blog post is written by University Texas at Austin graduate student Rayna Harris, and was inspired by the “NIH and You: How to Survive and Thrive in Your Research Career” Symposium at the 2014 Society for Neuroscience Annual Meeting in Washington D.C. on Saturday, November 15, 2014.

NIH Panel Members included:

  • Stephen J. Korn, Director of the Office of Training, Career Development, and Workforce Diversity
  • Nancy L. Desmond, Office Director and Associate Director for Research Training and Career Development
  • Michelle Jones-London, Director of Diversity Training and Workforce Development
  • Alan L. Williard, Acting Deputy Director of NINDS

#1. When it comes to choosing mentors, be promiscuous!

Successful experimenting! L-R: Manisha Sinha, Hilary Katz, Dalia Salloum. Photo credit: Rayna Harris

Successful experimenting! L-R: Manisha Sinha, Hilary Katz, Dalia Salloum. Photo credit: Rayna Harris

Choosing the right mentor is one of the most critical decisions grad students and post-doctoral fellows must make (see # 2). However, don’t forget the importance of having multiple mentors during each stage of your research career.

Other mentors will not only nurture and advise you, but they can also fill the voids in your relationship with your primary mentor. For instance, if your principal investigator (PI) is not a statistician, seek the advice of one who is to verify that your results are statistically sound. Or, if your mentor is a single male and you are a soon to be mother, seek the guidance of a female PI with children to discuss work-family balance.

#2. But seriously, choose the right mentor for you

It is important to join a lab where you will be supported in your training and your career; receiving good mentorship support is pivotal for success in your career. When choosing a lab, do your homework first and find out where former trainees have gone. Did they continue down their chosen career path? Do they still have a good relationship with the PI? These are important questions you need to have the answers to.

A good mentor should have the experience and the connections to get you were you want to be!

Altmetric score for Barres 2013 Neuron article.

Altmetric score for Barres 2013 Neuron article.

Once you join a lab, develop a relationship with your mentor that is built on good communication. How, when, and how often you communicate will be different for each mentor-mentee relationship, so find a strategy that works for both of you. Don’t be afraid to talk to your mentor about your goals! Work together to create an individual development plan and revisit it periodically.

For more on this subject, the following articles are highly recommended:

  1. Barres, Ben A. (2013) How to pick a graduate advisor. Neuron 80: 275-9.
  2. Wood, Charles (2012) When lab leaders take too much control. Nature 491: 785-786
  3. Raman, Indira M (2014) How to Be a Graduate Advisee. Neuron 81: 9-11.

#3. Be a good advisee

It would not be fair to demand quality from your mentor without returning the favor. By being a good advisee, you can actually help your mentor be a good mentor. Be proactive, and ask for your mentor’s time or advice when you need it. This way, both of you can shine!

If you ever find yourself in the unfortunate situation of being in a toxic relationship, swallow your pride and ask for outside help. Talk to your graduate program director, your department chair, or one of your other mentors. These people can either help you work it out with your mentor or can help you find a new lab.

Be proactive and talk to your mentors. Downloaded from http://www.phdcomics.com/comics/archive.php?comicid=1025

Be proactive and talk to your mentors. Downloaded from http://www.phdcomics.com/comics/archive.php?comicid=1025

 

#4. Have plans and follow through with them.

I recall The Serial Mentor saying that the number one common mistake grad students make is proposing an overly ambitious thesis. Don’t be one of those folks! Propose a doable project. Then do it. Persist even when parts of it fail, and do not take rejection personally.

Stay focused and learn to balance the time and effort you spend on your projects with classes, grant writing (see #8), reading, publishing, exercising, relaxing, and the plethora of other responsibilities you may have.

If you are a post-doctoral fellow, your focus should be to develop a research program that you can take with you! Discuss this early on with your mentor, and don’t join if you suspect that you won’t be able to.

Of course, a healthy dose of ambition is fantastic. Ambition is probably one of the most common shared traits among people who are “the first” to do something. The trick is, though, to not be so overly ambitious that you have little to present in your next job talk or award acceptance speech.

#5. Learn to cope with failure and develop grit

In addition to technical training, accumulate transferable skills throughout your career. These skills will help you succeed no matter what you choose to pursue and include (but are not limited to) critical thinking, communication, leadership, reasoning, grit, and perseverance.

Empowerment, resiliency, and grit are essential characteristics in a good researcher. Learn to cope with failure and you will have much more success in life. Take control of your academic environment rather than stumbling along after failure. Your mentors are there to help you up when you fall, but you must empower yourself.

#6. “You’ve got to know when to hold ‘em, know when to fold ‘em”

Don’t let failure stress you out! Image from: http://goo.gl/XOrfHq

Don’t let failure stress you out! Image from: http://goo.gl/XOrfHq

This quote is actually from a song about gambling by Kenny Rogers, but I think the advice really applies publishing goals and whether or not you really want to stay on the tenure track.

Set your aims high. If you aim to publish in top tier journals, then will you have a good chance of publishing in journals ranging from good to the very best. However, don’t spend 6 years trying to get one project into the best journal and then never publish. Ask yourself if publishing small bits early in a solid journal is a better career move or if you really want to hold out for that chance to revolutionize the field with one great piece.

Remember, industry is not easier; it’s just different.

Many of my peers struggle with deciding whether or not to stay in academia. The most common advice I’ve heard is to stick with research as long as you passionately love it and to not quit until you have to. Every minute you spend in academia is useful, so don’t think that you’re wasting your time. If you are considering leaving academia, peruse opportunities as they present themselves and seize the right one when it comes along.

#7. Network whenever possible and don’t burn bridges.

Networking at conferences is a must #SfN14

Networking at conferences is a must #SfN14

When you go to meetings, don’t just socialize with people from home. Schedule lunch or coffee with your letter writers to keep them updated or with potential employers to get to know them better. Meet new people at posters or socials or during interactive sessions.

Along those lines, try to keep positive relationships with all your colleagues and don’t burn bridges. Our communities are small, so try to be nice to even to your bad colleagues. You never know you will need something from them or someone they know.

#8. Talk to your program officer before and after applying for grants

I’ve saved the final tip for the topic of funding. This could probably be a 1000 word blog all by itself, but I’ll keep it short. Visit the National Institute for Allergy and Infectious Disease (NIAID) for more online resources.

Remember, your program officer (PO) is there to help you get funding! I’m sure you have heard that you should call or email them before submitting a grant, but what’s the best approach? The POs say that the best way is to send an email with your Specific Aims page and your Biosketch attached.

Also, contact your PO to discuss interpreting the summary statement of a grant that is not funded. This is especially useful if you have a hard time understanding the essence of the comments or if the reviews are conflicting.

Applying for grants as a grad student or post doc is a great idea because it gives you experience with the whole process and will help you thrive in your research career. However, you don’t need a grant at this stage to get a faculty position. If you have heard this, know that it is a myth! According toDr. Stephen J. Korn only 15% of new assistant professors had a K99 award.

Final thoughts

There is a pretty good chance you have heard most of this advice before. My mentors (yes I have multiple) and other great scientists have said this over and over again. But, sometimes it’s good to hear things more than once

I hope you found pieces of advice contained herein useful and worth sharing with others. Best wishes in your journey as a research scientist!

E.O. Wilson’s advice for thriving in sciencing

E.O. Wilson’s advice for thriving in sciencing

 

Disclaimer

I have a great mentor and a good relationship with him. But, I strive for perfection and am always looking for advice on how to do things better.

One of my live tweets from #SfN14

One of my live tweets from #SfN14

 

Acknowledgments

Many thanks to @karinaalbab and @maruca221 for comments and suggestions for this blog, the organizers of #SfN14 for providing a great forum for discussion, and to @PLOSNeuro and @emilyjanedennis for inspiring me to blog and tweet at #SfN14.

This story was originally published here on Medium and here on PLOS as part of the PLOS Neuroscience Community.

For more information, you can contact Rayna at rayna.harris at utexas dot edu.

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BEACON Researchers at Work: Can’t we all get along? Overcoming evolutionary conflict

This week’s BEACON Researchers at Work blog post is by University of Washington postdoc Sylvie Estrela.

SylvieEstrela_photoConflict is widespread in nature and that is no exception in the microbial world. Examples of competitive interactions between microbes include competition for shared limiting nutrients, competition for space, and the production of compounds such as toxins and antibiotics that inhibit or kill competitors. In the face of such conflict, how can we explain the occurrence of mutually beneficial associations between unrelated organisms, known as mutualisms?

Microbes are intrinsically leaky, that is, they produce a broad range of metabolites into their environment as a result of their metabolism. When these waste products of metabolism are used as nutrients for growth, this is called cross-feeding. Thus, a cross-feeder reaps some benefit from the association with a producer. If the waste product is toxic to the producer, then waste removal by the cross-feeder is beneficial to the producer. This can be seen as trading a service (detoxification) for a resource (food). At a first glance, it seems that both partners would benefit from the association, setting out the ground for mutualism to occur. To gain a better insight into the dynamics of this interaction, I started by developing a simple mathematical model. The model revealed that this simple cross-feeding interaction can generate a variety of possible ecological outcomes, spanning mutualism, exploitation, and competition. Furthermore, it highlighted the importance of the metabolic constraints of individual species and the features of their shared environment, such as toxicity level and decay rate of the waste product, in determining the conditions for mutualism [1].

This was the beginning of my academic journey into exploring how mutualism may arise at the first place and be maintained, and which ended up being the main focus of my PhD research supervised by Dr. Sam Brown at the University of Edinburgh. At this point, the model described two species growing in a well-mixed (planktonic-like) environment. But in natural environments, most microbes live in surface-attached, spatially-structured communities such as biofilms. An interesting feature of growth in a structured environment is the stronger potential for demographic feedbacks between interacting partners. This is mostly due to the fact that an individual cell has a stronger effect (either positive or negative) on its neighbouring cells than on the cells that are further apart, which in turn feeds back on its own growth. So how do metabolic interactions and demographic feedbacks combine to shape the spatial organisation and functioning of polymicrobial communities?

Figure 1. Simulation of a two species community where species are engaged in a food for detoxification metabolic interaction. While strong metabolic interdependence drives species mixing, weak metabolic interdependence drives species segregation.

Figure 1. Simulation of a two species community where species are engaged in a food for detoxification metabolic interaction. While strong metabolic interdependence drives species mixing, weak metabolic interdependence drives species segregation.

To address this question, I used a spatially-explicit model that simulates the growth of the two-species community on a surface. I found that strong metabolic interdependence generates mutualism and species mixing, and community behaviour is less sensitive to variation in initial conditions (initial species frequency and spatial distribution). In contrast, weak metabolic interdependence generates competition and species segregation, and community behaviour is highly contingent on initial conditions (fig. 1, [2]). Hence, these findings suggest that demographic feedbacks between species are central to the community development, shaping whether and how potential metabolic interactions come to be strengthened or attenuated between expanding species [3].

Now as a postdoc in Prof. Ben Kerr’s lab (UW), I’m interested in exploring further some of these questions by specifically focusing on the evolution of mutualisms and interdependencies when traits are costly to perform rather than just a waste product of metabolism. Because of the lack of relatedness between partners, evolutionary conflicts of interest will be strong. But despite conflict, interspecific mutualism can prevail when the conditions are such that partners’ interests are aligned and potential conflicts are kept in check. A critical question is how this can be achieved. In collaboration with Prof. Ben Kerr and Prof. Eric Klavins (UW), I’m using the ‘gro’ simulation platform to address this question (fig. 2).

Figure 2. Snapshot of a ‘gro’ simulation showing the emergent spatial pattern of two species exchanging costly essential functions.

Figure 2. Snapshot of a ‘gro’ simulation showing the emergent spatial pattern of two species exchanging costly essential functions.

 

Key references

[1] Estrela, S. et al. (2012) From Metabolism to Ecology: Cross-Feeding Interactions Shape the Balance between Polymicrobial Conflict and Mutualism. Am. Nat. 180, 566–576

[2] Estrela, S. and Brown, S.P. (2013) Metabolic and demographic feedbacks shape the emergent spatial structure and function of microbial communities. PLoS Comput. Biol. 9, e1003398

[3] Estrela S, Whiteley M, and Brown SP (in press) The demographic determinants of human microbiome health. Trends in Microbiology

For more information about Sylvie’s work, you can contact her at sestrela at uw dot edu.

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