Teaching Competition and Predation from a Microbiological Perspective

Life has been busy, very busy.  And life has been good!  But the busy-ness has made it hard for me to keep up with this blog.  In the next few weeks, I hope to share some of the things that have kept me so occupied this past month.

For starters, I’d like to discuss some recent teaching where I tried to emphasize the interplay between theory and experiments in ecology.

I recently taught part of our graduate-level course called “Integrative Microbial Biology.”  Some years ago this course replaced several other graduate courses (microbial ecology, microbial physiology, microbial diversity, etc.) that each had a low enrollment.  The idea is that we now offer a single, annual, intensive, team-taught course that covers all these topics, albeit more superficially but with the hope that it encourages students and faculty alike to develop a more integrated perspective of microorganisms as organisms.  More specialized courses, with a focus on reading and discussion, are offered as occasional seminar-style courses.

I teach two parts of the course – one on aspects of microbial ecology, the other on microbial evolution.   Many of the students have not had an undergraduate course in general ecology or evolutionary biology, and so I try to bring them up to speed, albeit with examples that focus on microorganisms.

So, for the ecology portion I begin with population growth and competition.  I’m a fan of resource-based competition theory, as opposed to the more familiar logistic growth and Lotka-Volterra competition models.  The key strength of resource-based competition theory is that one can predict the outcome of competition based on parameters that can be measured separately for each species or strain, without requiring that one compete them in order to understand their competition.  Of course, there are many reasons the predictions might fail, but the resource-based model (and extensions to it) provide a mechanistic framework for understanding competition.

I then present predator-prey interactions, surveying the extraordinary diversity of microbe-on-microbe predation and parasitism, and then providing again a dynamical framework for understanding those interactions.  Here, Lotka-Volterra predator-prey models do provide a reasonable starting point because one can measure key parameters that have mechanistic interpretations (e.g., attack rates, conversion efficiencies) and use them to make new predictions about the dynamics of the system as a whole.

Besides presenting the general theory, I also present empirical studies from the primary literature.  In some cases, I summarize the papers in my lectures, while in other cases the students read the papers and we then discuss them.  Here are four of the papers with summaries; I hope to blog someday in greater depth on at least the Hansen & Hubbell and Rainey & Travisano papers, which I view as “must-read” papers in the field of ecology.

Hansen, S. R., and S. P. Hubbell.  1980.  Single-nutrient microbial competition: qualitative agreement between experimental and theoretically forecast outcomes.  Science 207:1491-1493.

This paper presented an early, concise, and compelling demonstration of the utility of resource-based competition theory.  By choosing three pairs of competitors that differed in various parameters, and then competing them in chemostats, the authors showed that the outcome depended on the two competitors’ relative “break-even” (equilibrium) concentrations of the growth-limiting resource.  For any student who wants more information on this approach – and every year at least some students ask for more – I recommend they read David Tilman’s outstanding book, Resource Competition and Community Structure (1982, Princeton University Press).

Rainey, P. B., and M. Travisano.  1998.  Adaptive radiation in a heterogeneous environment.  Nature 394:69-72.

This paper is a beauty.  The authors showed that the evolutionary emergence of diversity can sometimes depend on something as simple as whether a flask is shaken or not.  In the absence of shaking, an initially monotypic population of Pseudomonas fluorescens evolved into a community of three distinct ecotypes that differentially exploit the environmental gradients that arise without constant mixing; that diversity is stably maintained, as was shown by analyzing pairwise interactions.  By contrast, simply shaking the flask, with all else being equal, homogenizes the environment and the ecotypic diversity does not evolve; and if the diversity had already evolved, then it was eliminated as a single type came to dominate the well-mixed system.

Lenski, R. E., and B. R. Levin.  1985.  Constraints on the coevolution of bacteria and virulent phage: a model, some experiments, and predictions for natural communities.  American Naturalist 125:585-602.

Virulent phage infect bacteria, and they have life-cycles like those of insect parasitoids; that is, a successful infection is lethal to the host, and many phage are produced from a single infection.  In this paper, we examined the ecological and evolutionary dynamics of the interactions between E. coli and four different virulent phages.  First, the Lotka-Volterra predator-prey model – modified to include resource-based growth for the prey (bacteria) and a time-lag associated with predator reproduction (phage replicating inside bacteria) – predicted reasonably well the short-term dynamics of the interaction between E. coli and one of the phages, called T4.  Second, the model was extended to include the evolution of bacteria that are resistant to phage attack.  Resistance mutations changed the equilibrium density of the bacteria by several orders of magnitude, as the bacterial population went from top-down predator limitation to bottom-up resource limitation.  Yet despite complete resistance, the phage population persisted because there was a “cost of resistance” – in the absence of phage, the sensitive bacteria out-competed the resistant mutants.  In essence, the system becomes one of predator-mediated coexistence of sensitive and resistant prey populations.  Third, the interactions between E. coli and three other phages were examined.  Each interaction had somewhat different dynamics depending on whether resistance was costly or not, whether resistance was partial or complete, and whether the phage population produced host-range mutants that could infect the mutant bacteria that had become resistant to the progenitor phage.  [This paper built on related work that Lin Chao had done a few years earlier with Bruce Levin, and which inspired me to contact Bruce about joining his lab.]

Bohannan, B. J. M., and R. E. Lenski.  2000.  Linking genetic change to community evolution: insights from studies of bacteria and bacteriophage.  Ecology Letters 3:362-377.

This paper reviews the research that Brendan Bohannan did for his dissertation in my lab.  His work examined the same four bacteria-phage interactions studied in the Lenski and Levin paper above, but the work was extended to include some elegant new manipulations and analyses.  In particular, by changing the levels of resource available to the bacteria, the classic “paradox of enrichment” predicted by Lotka-Volterra predator-prey models was confirmed, with respect to the effects of enrichment on both equilibrium densities and the temporal fluctuations in population densities.  These experiments also provided compelling evidence for predator-prey cycles and the effects of bacterial resistance on the dynamics of the interaction between the remaining sensitive bacteria and phage populations.

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Chao and Levin, 1981, PNAS

This is the third in my series of must-read papers.  It’s an elegant paper that sits right at the interface of ecology, evolution, and behavior.  And like the last paper that I wrote about, this one is superb for teaching and capturing the interest of students.

Chao, L., and Levin, B. R.  1981. Structured habitats and the evolution of anticompetitor toxins in bacteria.  Proc. Natl. Acad. Sci. USA 78, 6324-6328.

Short summary:  Some bacterial strains produce and release toxins that kill members of their own species – except, that is, close kin that possess a linked immunity function.  The production of the toxins is also lethal to the small fraction of cells that actually do so in any given generation.  Lin Chao and Bruce Levin sought to understand when and how this trait would be beneficial.  When killer and sensitive strains competed in liquid, the killer strain prevailed, but only if it started out above a threshold frequency.  That raised the question of how the killer strain could reach that frequency, because it was at a disadvantage when it was below that threshold.  When the same strains competed in a structured environment (a gel-like matrix), this conundrum was resolved—the killer strain could invade a population of sensitive cells even if the killers started at an arbitrarily low frequency.  The difference arises because, in the structured environment, the resources made available by the killers accrue disproportionately to the killers’ kin.  This paper was ahead of its time, but it set the conceptual stage for the now-blossoming field that uses microbes to study the evolution of social traits and interactions.

Some additional background and explanation:  Many bacteria can produce and release toxins that kill other members of the same species.  These toxins are called bacteriocins in general; those studied by Chao and Levin are also called colicins because they are produced by, and used against, E. coli.  The toxin production and immunity functions are tightly linked in a genetic module, and such modules are often located on extra-chromosomal elements called plasmids.  Interestingly, the production of the toxin is lethal to the individual cell that does so, because the cell must lyse to release the toxin.  However, only a small proportion (maybe 1%) of the potential killers that carry the toxin/immunity module actually produce toxin in a given generation, while the others constitutively express the immunity function.

How can a function evolve that is lethal to the individual organism that expresses it?  Chao and Levin began by competing two otherwise identical E. coli strains—one that carries the toxin/immunity module, the other sensitive to the toxin—in a well-mixed liquid medium.  Let’s call the strains K and S for killer and sensitive, respectively.  If there were enough K cells (above ~2% in their experimental conditions), then K rose in frequency and drove the S type extinct.  Although the K population experienced some deaths from the production of the toxin, the resulting toxin concentration was so high that the death rate of S exceeded its growth rate.

But if the initial frequency of the K type was below that ~2% threshold, then the outcome was reversed—the S population rose in frequency, and the K population declined, although the exclusion played out more slowly than when K started out above the threshold.  What’s happening here?  The K cells still had the extra cell deaths caused by the release of toxin, but the concentration of toxin was not sufficient to wipe out the S population.  Some S cells were killed, and their resources—those released upon their death plus those they could no longer consume—became available to other cells.  Because the competition environment was well mixed, any cell—whether K or S—had equal access to the freed-up resources.  If the death rate of the K type (the proportion that produces toxin and then lyses) were greater than the kill rate of the S type, then K would decline in frequency because the resulting benefit—the extra resource that became available—was equally available to all survivors, regardless of whether they had the K or S genotype.

From an ecological standpoint, it’s a nice example of a dynamically unstable equilibrium between two competitors.  However, it raises a problem from an evolutionary perspective.  If possession of the toxin/immunity module is beneficial when it is common in a population, but disadvantageous when it is rare, then how can it go from being rare to common?

Chao and Levin recognized that a physically structured environment might be important, because it would change the distribution of the freed-up resources to the two cell types.  So they repeated the competitions between K and S strains, again varying the initial frequency of the K type, except now in a semi-solid medium called “soft agar.”   (The procedures get more complicated here; to propagate the competing cell types, each day they had to free the cells from the soft-agar matrix and transfer them into a new matrix.)  When the two types competed in this structured environment, the unstable equilibrium disappeared, and the K strain could invade and take over from an arbitrarily low initial frequency.  That is, the K genotype could now go from being rare to common.

Why this difference between the liquid and semi-solid environments?  In the structured environment, the bacteria grew as colonies, not as individuals floating about at random.  As a consequence, the extra resources made available by the killers flowed disproportionately to their own kin.  Here a picture is worth a thousand words; I show a figure from Chao and Levin below that makes this point graphically.  In a sea of crowded S colonies, there’s one K colony.  The K colony is larger than most of the S colonies.  Each colony began from a single cell; the fact that the K colony is larger than most means that it got more than its share of resources.  Even more strikingly, the K colony is surrounded by a large zone that is entirely devoid of colonies—the toxins released by the small proportion of K cells that lysed have diffused into this zone and prevented growth of S cells.  The resources diffused randomly, but the K colony sat alone in the middle of this zone of inhibition that it generated, and so indeed it got more than its share of resources.

The figure above is from Chao and Levin, 1981, Proc. Natl. Acad. Sci. USA; it is shown here under the doctrine of fair use.  The image is centered on a single colony of toxin-producing bacteria surrounded by an inhibition zone and, further out, by colonies of sensitive bacteria.  The scale bar is 0.5 mm.

A Later, Related Paper:  There’s another nice paper by Ben Kerr, Peg Riley, Marc Feldman and Brendan Bohannan (2002, Nature) that builds on the work by Chao and Levin.  Kerr et al. added a third “player”—a third strain—into these experiments, one that was resistant to the toxin but did not produce it.  In a physically structured environment, the toxin-producing killer strain could invade and displace the sensitive strain, just as Levin and Chao saw.  However, the resistant strain could invade and displace the toxin-producer, because the physiological cost of resistance was less than the combined costs of toxin-production and immunity.  And the sensitive strain could invade and displace the resistant strain, because the sensitive strain did not pay the cost of resistance.  In other words, the pairwise interactions were non-transitive, just like the game of rock-paper-scissors.  But although each pairwise interaction had a winner and a loser, the three types could coexist indefinitely in a spatially structured environment provided different spatial regions were out of phase—in effect, the three populations chased one another around in space and time.

Why I like this paper so much:  First, the paper by Chao and Levin beautifully illustrates how population biologists frame, dissect and analyze a complex problem—one that involves frequency-dependent effects, tradeoffs, spatial structure, and genetic relatedness along with both scramble and interference competition.  Out of all these complications, there comes that “Aha!” moment when it all makes sense—just like the feeling one gets from the Luria and Delbrück experiment.

Second, there’s been a boom in the study of the evolution of social behaviors using microbes over the last 15 years or so.  The current phase began with papers by Paul Tuner and Lin Chao on interactions among viruses infecting the same cell leading to a Prisoner’s Dilemma (Nature, 1999); by Greg Velicer, Lee Kroos, and myself on cheating during multicellular fruiting-body development in the bacterium Myxococcus xanthus (Nature, 2000); and by Joan Strassmann, Yong Zhu, and David Queller on cooperation and cheating in aggregations of the social amoeba Dictyostelium discoideum (Nature, 2000).  Today, there are many groups around the world who study quorum sensing, fruiting-body formation, biofilms, toxin degradation, and other microbial behaviors from an evolutionary perspective.  The 1981 paper by Chao and Levin showed that microbial systems could serve as model systems for studying social evolution while being fascinating in their own right.  (It’s also fitting to note that John Bonner, who pioneered the study of D. discoideum, served as the editor for Chao and Levin’s paper.)

Third, Bruce Levin was my postdoctoral mentor, and Lin Chao did his graduate work with Bruce.  Lin had moved on to a postdoc position before I joined the lab, but this paper was one of my formative exposures to the conceptual elegance and experimental power of using microbes to study population dynamics.  Lin and Bruce had also written two papers on the dynamics of interactions between bacteria and phage (Levin et al., 1977, Am. Nat.; Chao et al., 1977, Ecology), and those papers were the ones that first led me to write Bruce about the possibility of joining his group as a postdoc.

Finally, this paper provides a sobering reminder that we humans are not as special as we often imagine, even in warfare.  Mindless bacteria were killing each other billions of years before we came on the scene.  Perhaps we can use our minds to suppress the worst of our primal urges.

[ADDED 13 Sept. 2013] Lin Chao emailed me that “The inspiration of that work was a lecture that Bruce gave in his Pop Biology class at UMass where he discussed the limitations of Lotka Volterra equations for interference competition.  That sat in my mind for a couple of years until it became a real project.” So this must-read paper also provides a nice example of the productive interplay between teaching and research.

Lieberman et al., 2011, Nature Genetics

My second “must-read” paper is a recent one.  Unlike the last paper I discussed, I suspect that most of you have not read this one and probably don’t even know about it.  I hope this post will convince you to go out and read it.

And if you do, you might also use this paper in your teaching.  It should be a terrific paper to explain or discuss in all sorts of courses from an undergrad evolution course filled with pre-meds to a graduate-level seminar on … well, almost anything, from genomics and molecular evolution to Darwinian medicine and evolution in action.

Lieberman TD, Michel JB, Aingaran M, Potter-Bynoe G, Roux D, Davis MR, Skurnik D, Leiby N, LiPuma JJ, Goldberg JB, McAdam AJ, Priebe GP, Kishony R.  2011.  Parallel bacterial evolution within multiple patients identifies candidate pathogenicity genes.  Nature Genetics 43, 1275-1280.

There was also an accompanying News and Views piece by yours truly.

Lenski RE.  2011.  Chance and necessity in the evolution of a bacterial pathogen.  Nature Genetics 43, 1174-1176.

Short summary:  This paper provides a striking demonstration of the power of combining genomic, epidemiological, and evolutionary data and analyses.  Tami Lieberman and Jean-Baptiste Michel, two graduate students in Roy Kishony’s group, and colleagues sequenced 112 clonal isolates of an opportunistic pathogen, Burkholderia dolosa, that were sampled from 14 patients over the course of 16 years.  Using phylogenomics, they first traced the history of transmission events and used the resulting phylogeny to distinguish between mutations that were shared by descent and those that arose within a particular patient.  They then identified 17 genes that exhibited significant signatures of parallel evolution, and they inferred that mutations in those genes contributed to the pathogen’s adaptation to the host environment.

Some additional context and findings:  The patients in this study were individuals with cystic fibrosis (CF), an inherited disease that makes them vulnerable to chronic and life-threatening infections of the lungs.  A number of different bacterial strains can cause these opportunistic infections, and they are sometimes transmitted between CF patients in the same clinic or other settings.  In the 1990s, there was an outbreak of B. dolosa infections among 39 CF patients in Boston.  Foresighted clinicians and researchers saved isolates from these patients, and some of the isolates were then sequenced and analyzed in this study.

The genes that Lieberman et al. identified as having mutations under positive selection in the CF host environment include several likely candidates, specifically genes related to therapeutic interventions (antibiotic resistance) and host immune responses (cell-surface antigens).  However, the genes with mutations under positive selection also included others not previously known to play a role in these infections, including some involved in oxygen-dependent regulation and others of unknown function.

The evidence for positive selection based on parallel evolution was further supported in two additional ways.  For two phenotypes (antibiotic resistance and antigenicity) that can be readily scored in the lab, genome-wide association tests provided compelling evidence of a causal connection between specific mutations and phenotypic differences among isolates.  More generally, the dN/dS ratio – reflecting the relative rates of change at non-synonymous and synonymous sites in protein-coding sequences – was substantially elevated (above unity) in the 17 genes identified on the basis of parallel changes, but that ratio was not elevated in the remainder of the genome.

Why I like this paper so much:  First, this paper shows just how important evolutionary thinking is becoming to fields like genomics and medicine.  Remember how dreary those early genome papers became after the novelty of seeing foldout figures with giant circles (or lines, for those of you working on eukaryotes), funny colors, and tiny labels had worn off?  Sure, there was phylogenetic information to be gleaned, and maybe some hints about something interesting that happened in one lineage or another.  But if history is “just one damned thing after another”, then genomics was looking like “just one damned nucleotide after another.”  The paper by Lieberman et al., by contrast, shows the beauty and power of evolutionary thinking when applied to an interesting collection of genomes.  This study shows how a rigorous evolutionary analysis can generate new insights and new leads with respect to mechanisms of pathogenesis and potential targets of therapy.

Second, this paper provides a wonderful illustration of just how far science and technology have come.  It was four decades from Watson and Crick’s elucidation of the structure of DNA in 1953 to the publication of the first bacterial genome sequence in 1995, undoubtedly at great expense.  Now, even a basic-science, curiosity-driven lab like mine can afford to sequence dozens of bacterial genomes to study the dynamics of evolution and the complex genetic basis of a novel phenotype.  And when it comes to health-related and other applied research, advances are no longer limited by the costs of obtaining complete genome sequences of many samples, but rather by the ingenuity of scientists in analyzing, interpreting, and understanding the data.  The huge advances in computing power in recent decades were also essential for this study; for example, the authors generated the null distribution for genetic parallelism by randomizing, 1000 times over, the placement of the 561 independent mutations seen in their data across the ~6.4 million sites in the B. dolosa genome.

Third, I am fascinated by the tension between “chance and necessity” – randomness and repeatability – in evolution.  Understanding the cause of that tension was the point of Luria and Delbruck’s paper; exploring the effects of that tension lies at the heart of our long-term evolution experiment; and exploiting that tension provides the power behind the inferences in the study by Lieberman et al.

Fourth, the senior (last) author on the paper, Roy Kishony, visited my lab for a few days to discuss evolution when he was a graduate student making the transition from physics to biology.  Roy has gone on to do beautiful work, both basic and applied, in the area of microbial evolution.

Last, but not least, I use Lieberman et al. as a discussion paper in a course that I co-teach on “Integrative Microbial Biology.”  Most of the students are in their first semester of graduate school, and they are interested in different areas of microbiology – from infectious disease and immunology through genetics and molecular biology to ecology and evolution.  This paper interests all of them and, more importantly, it helps them to see how these different fields of inquiry can and do fit together.

The figure above comes from Lieberman et al., 2011, Nature Genetics; it is shown here under the doctrine of fair use. The figure illustrates the transmission history inferred from the authors’ phylogenomic analysis of B. dolosa isolates from 14 infected patients.

Luria and Delbrück, 1943, Genetics

Here’s the first of my blog posts on “must-read” papers.  I hope others will find these papers interesting and useful.

Luria, S. E., and Delbrück, M.  1943.  Mutations of bacteria from virus sensitivity to virus resistance.  Genetics 28, 491–511.

[I’ve cobbled this post together by borrowing from a couple of previous writings where I explained why the Luria and Delbrück’s experiment is my all-time favorite.  One of these earlier pieces was a Q & A in Current Biology (2003); the other was an essay that appeared in Microbe (2011) and then in the book Microbes and Evolution: The World That Darwin Never Saw (2012).  I’ve also tweaked the text and added some bits to make things flow.]

Short summary:  An elegant experiment — sometimes called the “fluctuation test” — by Salvador Luria and Max Delbrück showed that new mutations that made bacteria resistant to phage (viruses that infect bacteria) arose before the bacteria were exposed to the phage.  This paper removed the specter of Lamarckian inheritance from microbiology, and it set the stage for the tremendous advances in microbial genetics and molecular biology that took place over the next several decades.  Theirs was a beautifully simple, yet subtle, experiment on a fundamental concept.

Personal influence:  I first read about Luria and Delbrück’s paper as an undergraduate at Oberlin College, in a course taught by Richard Levin using Gunther Stent’s 1971 textbook, Molecular Genetics: An Introductory Narrative.  Stent took a historical approach to microbial and molecular genetics by emphasizing the ideas, questions, and experiments that led to the growth and success of those fields.  I remember the challenge of reading about Luria and Delbrück’s experiment, trying to wrap my head around how it worked and what it meant, and then the wonderful “Aha!” moment when I got it.

But I did not go directly on to work with microbes.  Instead, I did my Ph.D. in zoology, with my dissertation research on ground beetles in the mountains of western North Carolina.  Despite the pleasures of working outdoors, the research was slow, heavy rains often drowned the beetles in my pitfall traps, and it was difficult to imagine feasible experiments that would really test the scientific ideas that most excited me.  So as I pondered future directions, I recalled the Luria and Delbrück experiment that I had encountered as an undergraduate.  I remembered not only its elegance, but also the profound insight it gave into the tension between randomness and direction in evolution — a tension that continues to fascinate me and lies at the heart of the long-term evolution experiment in my lab.

It wasn’t until I was a postdoc, learning how to work with bacteria, that I actually read the Luria and Delbrück paper.  It’s not an easy paper to read.  If the experiment is unfamiliar to you, then you might want to read about it before reading the original paper.  Pages 556-558 in Sniegowski and Lenski (1995, Ann. Rev. Ecol. Syst.) briefly explain the experiment.

Historical perspective:  The science of genetics took hold with the rediscovery of Gregor Mendel’s experiments on pea plants in the early 1900s.  However, microbiologists remained baffled by the question of heredity in bacteria for several more decades.  They saw that bacteria could “adapt” to various challenges, but they couldn’t tell whether spontaneous mutants had appeared and been selected or, alternatively, whether the challenge had induced the cells to change themselves.  In 1934, a microbiologist, I. M. Lewis, wrote that “The subject of bacterial variation and heredity has reached an almost hopeless state of confusion . . . There are many advocates of the Lamarckian mode of bacterial inheritance, while others hold to the view that it is essentially Darwinian.”  And in 1942, Julian Huxley wrote Evolution: The Modern Synthesis and explicitly excluded bacteria from the then-modern synthesis on the grounds that “They have no genes in the sense of accurately quantized portions of hereditary substance …”

This confusion cleared the very next year with the publication of what is, to me, the single greatest experiment in the history of biology.  Working together, Luria, a biologist, and Delbrück, a physicist-turned-biologist, employed subtle reasoning and an elegant design to demonstrate that some mutations in E. coli occurred before the selective challenge was imposed, and therefore the mutations could not have been caused by the challenge.  In other words, mutations are random changes that occur whether or not they prove useful, while selection provides the direction in evolution by disproportionately retaining those mutations that are advantageous to their carriers and discarding others that are harmful.

Luria and Delbrück’s paper launched a tidal wave of research that led to the discovery of DNA as the hereditary material and to cracking the genetic code.  But it had little immediate impact on evolutionary research.  The new molecular biologists pursued their reductionist methods, while evolutionary biologists, grounded in natural history, didn’t want to study things they couldn’t even see.  These naturalists preferred beautiful butterflies and even homely fruit flies to E. coli that, after all, come from a rather uninviting habitat.