Session M12: Variability in Biological and Living Systems

Using a tiny symbiosis to answer big questions about how host-microbe interactions evolve

Understanding how the microbiome evolves, and how the microbiome shapes host evolution, requires measuring host and microbe phenotypes and fitness across multiple host genotypes (G), microbial genotypes or community compositions (also often represented by G), and environments (E). When measuring outcomes across these multiple sources of variation, experiments quickly become very large, especially when we want to estimate G x G, G x E, or G x G x E interaction effects, which are often of substantial interest. For example, there must be G x G effects for both host and microbial fitness for hosts and microbes to co-evolve, while local adaptation of hosts, microbes, or both implies G x E or G x G x E effects. My lab has developed a new experimental system to manipulate many host genotypes and microbiomes across environmental gradients at high throughput using tiny, fast-growing aquatic plants (duckweeds in the genus Lemna) and their microbiota. I will describe the results of several recent experiments in this system that quantify the main and interactive effects of host genetic variation, microbial community composition variation, and environmental variation on host and microbe fitness, thereby shedding light on how host-microbe interactions evolve.

    

Explaining the trajectories sick animals take through disease space

Explaining the trajectories sick animals take through disease space. Ideally when you are infected with a pathogen, you get sick and then recover back to your original health.  We like to plot the trajectory a sick individual takes through multidimensional disease spaces to better understand how we might nudge this trajectory into a more healthy position.  As we’ve begun looking at sick hosts that vary in their responses we find that mice don’t occupy all of the mathematically available space available to them.  We think we can explain this by invoking Pareto optimization.  There are some hosts that are great at fighting one type of pathogen but not another and the reverse is true.  The mice occupy the space between these archetypes.  It looks like the mouse phenotypes are contained within a relatively low complexity n-polytope, suggesting there just aren’t that many ways of getting sick.  Now our job is to understand the nature of these different types of illness. 

    

Emergent song: the polyphonies of living timekeepers

Living timekeepers, stochastic yet precise, tunable yet robust, idiosyncratic yet organized, have captured the imagination of many a curious onlooker in many a context for the longest time. This talk represents a quest for unifying ideas, frameworks, and principles governing temporal ordering in a variety of natural phenomena, with special emphasis on the emergence of patterns and tessellations in the space-time interplay between living timekeepers and their space analog: the living ruler.   

Modeling transmission chain heterogeneity from experimental data

Heterogeneity is inherent in biological processes. Many distributions of biological data are characterized by asymmetry/skew and long, sometimes bumpy tails, and it is difficult to generate models that fit the relevant features of these distributions. Here we show, using experimental data from small host models, how distributions of both transmission and sensitivity to colonization affect the shape of transmission chains, with distributions of secondary infections as the primary measure of interest. These results provide novel insights into the effects of individual heterogeneity on the outcomes of the stochastic process of transmission.

    

Resource competition predicts assembly of in vitro gut bacterial communities

Members of microbial communities interact via a plethora of mechanisms, including resource competition, cross-feeding, and pH modulation. However, the relative contributions of these mechanisms to community dynamics remain uncharacterized. Here, we develop a framework to distinguish the effects of resource competition from other interaction mechanisms by integrating data from growth measurements in spent media, synthetic community assembly, and metabolomics with consumer-resource models. When applied to human gut commensals, our framework revealed that resource competition alone could explain most pairwise interactions. The resource-competition landscape inferred from metabolomic profiles of individual species successfully predicted assembly compositions, demonstrating that resource competition is a dominant driver of in vitro community assembly. Moreover, identification and incorporation of interactions other than resource competition, including pH-mediated growth inhibition and cross-feeding, improved model predictions. Our work provides a foundation to characterize and quantify interspecies interactions in vitro that should advance mechanistically principled engineering of microbial communities.