December Reading List: Evolution of Microbiomes

Introduction to Evolutionary processes

The microbiome is the aggregation of all the microorganisms in a system; this can be the human gut microbiome or a biofilm on a rock in a stream. Regardless of the system, the microbes inhabiting that environment are constantly competing for resources (e.g., physical space, nutrients, etc.). Thus, community assembly can be highly deterministic if the environmental conditions and interspecific interactions determine which species persist in a local community. Typically, we associate  microbe's traits to the environment, as these traits underlie an organism's response to both biotic and abiotic factors. These traits are inherently based on evolutionary history. So a major question is how can we observe evolution in these systems? 

For one, environmental disturbances can lead to community turnover - this is typically associated with ecological processes. However, is it time to start reevaluating these processes from an evolutionary standpoint? After all, a change in allele frequency is the definition of evolution. Typically, evolution is studied at the population level, but this is increasingly hard to do in microbiomes. What defines a natural population? Moreover, with rapid population sizes and fast generation times, how can we disseminate ecological and evolutionary processes in microbiomes? Maybe they are one in the same...This month's reading list will highlight some recent (and not so recent) papers addressing evolutionary processes and how they relate to microbiomes. I will not go into population-level processes since I have covered them in other posts here and here. Instead, I want to focus on the challenges in microbiome studies on the aggregate.

Human Microbiome

Example of ecological shifts in species abundances vs. genetic changes in a system.

The first paper I want to go over provides a nice introduction into this topic broadly [1]. It centers around the human microbiome but all of the concepts can readily be applied to other microbiome systems. The first point I want to highlight in this paper is:

A population genetic view of the microbiome provides the opportunity to discover the genetic contribution to traits within microbial populations and to infer the processes creating and maintaining trait-associated genotypes.

The goal is to understand how microbial traits, and their underlying genetic modifications influence both ecological and evolutionary processes. I think this paper does a wonderful job outlining evolutionary processes and ways to go about measuring them in microbial systems. For instance, interpreting recombination metrics and the challenges in identifying such events within communities. Further, utilizing and understanding various genome estimates (e.g., dN/dS, FST, recombination : selection ratios) are extremely powerful tools to understand population genetics in microbial systems. Inevitably, it comes down to connecting genotype to phenotype, a major goal for my research as well. Definitely worth a read!

Niche expansion and adaptation

If mutations can lead to increased fitness, what prevents the "optimal" phenotype from dominating a given system. Certainly, a generalist approach can utilize diverse resources and outcompete specialists with limited resources and narrower niche breadth. The next paper address the idea of evolvability [2] with respect to the generalist v. specialist strategy. In particular, the cost of a generalist resides in their reduced capacity to evolve to changing environments. In other words, selection is weakened or relaxed in the generalist strategy since it can readily access a wider range of niches, albeit at local peaks of lower fitness. The authors describe this in the context of a fitness landscape where specialists can rapidly respond to environmental changes whereas the generalist must maneuver through a rugged fitness landscape resulting in a "lag load" to peak fitness.

So then what qualifies as peak fitness? The idea that natural selection favors traits at any given time is often a misnomer. Natural selection operates at the level of "good enough" with many genes being suboptimal. This next preprint addresses this idea that modules will steadily be improved; however, different modules are improved with fitness effects while others will stall [3]. Especially in regards to microbial clonal interference, modules will essentially compete against one another preventing optimization of other modules, or at least delaying. This observation may explain how, given similar environmental conditions, populations may converge on similar fitnesses, albeit at different trajectories. A recent paper discusses these ideas on how cryptic genetic variation can assist in maneuvering these fitness landscapes (side Figure) by providing access to diverse, otherwise inaccessible fitness trajectories [4]. Together these results suggest that natural selection is a process, slowing improving certain modules to navigate a fitness landscape. However, epistatic interactions render complex fitness landscapes with beneficial mutations in one module affecting fitness trajectories, and possibly leading to suboptimal peaks.

Different fitness trajectories along a fitness landscape. Divergence from an ancestral population can lead to the accumulation of cryptic variation (red open circles). A change in the environment enables this variation to lead to variable fitness peaks.

Observing evolution and molecular adaptation

All of this has been amazing work and definitely worth pursuing more in microbial systems. But a major question is how to observe these evolutionary dynamics in high resolution. This last paper utilizes a barcoding system to track adaptation in yeast [5]. Using this method, the authors can re-barcode each population to tract specific lineages arising from mutations. Initially the ancestral population accumulates a distribution of beneficial mutations. Lower fitness clones die out and are replaced by newer, other beneficial mutants that maintain diversity in the population. This distribution of continuously generated variation provides a broad distribution across a fitness mean.

Papers:

1. Garud NR, Pollard KS. (2019). Population genetics in the human microbiome. Trends in Genetics

​2. Bono LM, Draghi JA, Turner PE. (2019). Evolvability costs of niche expansion. Trends in Genetics 

3. Venkataram S, Monasky R, Sikaroodi SH, Kryazhimskiy S, Kacar B. (2019). Evolutionary stalling in the optimization of the translation machinery. bioRxiv

4. Zheng J, Payne JL, Wagner A. (2019). Cryptic genetic variation accelerates evolution by opening access to diverse adaptive peaks. Science 365: 347-353.

5. Nguyen Ba AN, Cvijovic I, Rojas Echenique JI, Lawrence KR, Rego-Costa A, Liu X, Levy SF, Desai MM. (2019). High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast. Nature 575: 494–499.