Effective models and the search for quantitative principles in microbial evolution

New avenues for potentially seeking microbial responses to climate change beneath Antarctic ice shelves

The signs of climate change are undeniable, and the impact of these changes on ecosystem function heavily depends on the response of microbes that underpin the food web. Antarctic ice shelf is a massive mass of floating ice that extends from the continent into the ocean, exerting a profound influence on global carbon cycles. Beneath Antarctic ice shelves, marine ice stores valuable genetic information, where marine microbial communities before the industrial revolution are archived. Here, in this proof-of-concept, by employing a combination of single-cell technologiesand metagenomics, we have been able to sequence frozen microbial DNA (≈300 years old) stored in the marine ice core B15 collected from the Filchnner-Ronne Ice Shelf. Metagenomic data indicated that Proteobacteria and Thaumarchaeota (e.g., Nitrosopumilus spp.), followed by Actinobacteria (e.g., Actinomarinales), were abundant. Remarkably, our data allow us to "travel to the past" and calibrate genomic and genetic evolutionary changes for ecologically relevant microbes and functions, such as Nitrosopumilus spp., preserved in the marine ice (≈300 years old) with those collected recently in seawater under an ice shelf (year 2017). The evolutionary divergence for the ammonia monooxygenase gene amoA involved in chemolithoautotrophy was about 0.88 amino acid and 2.8 nucleotide substitution rate per 100 sites in a century, while the accumulated rate of genomic SNPs was 2,467 per 1 Mb of genome and 100 years. Whether these evolutionary changes remained constant over the last 300 years or accelerated during post-industrial periods remains an open question that will be further elucidated.

IMPORTANCE

Several efforts have been undertaken to predict the response of microbes under climate change, mainly based on short-term microcosm experiments under forced conditions. A common concern is that manipulative experiments cannot properly simulate the response of microbes to climate change, which is a long-term evolutionary process. In this proof-of-concept study with a limited sample size, we demonstrate a novel approach yet to be fully explored in science for accessing genetic information from putative past marine microbes preserved under Antarctic ice shelves before the industrial revolution. This potentially allows us estimating evolutionary changes as exemplified in our study. We advocate for gathering a more comprehensive Antarctic marine ice core data sets across various periods and sites. Such a data set would enable the establishment of a robust baseline, facilitating a better assessment of the potential effects of climate change on key genetic signatures of microbes.

Eco-evolutionary dynamics: The repeatability of diversification in an experimental microbial community

Understanding the evolution and subsequent maintenance of ecological diversity is a daunting task. Using a historical microbial evolution experiment, a new paper demonstrates the extent to which diversity can re-emerge in reduced communities and the traits through which rediversification occurs.

Investigating macroecological patterns in coarse-grained microbial communities using the stochastic logistic model of growth

The structure and diversity of microbial communities are intrinsically hierarchical due to the shared evolutionary history of their constituents. This history is typically captured through taxonomic assignment and phylogenetic reconstruction, sources of information that are frequently used to group microbes into higher levels of organization in experimental and natural communities. Connecting community diversity to the joint ecological dynamics of the abundances of these groups is a central problem of community ecology. However, how microbial diversity depends on the scale of observation at which groups are defined has never been systematically examined. Here, we used a macroecological approach to quantitatively characterize the structure and diversity of microbial communities among disparate environments across taxonomic and phylogenetic scales. We found that measures of biodiversity at a given scale can be consistently predicted using a minimal model of ecology, the Stochastic Logistic Model of growth (SLM). This result suggests that the SLM is a more appropriate null-model for microbial biodiversity than alternatives such as the Unified Neutral Theory of Biodiversity. Extending these within-scale results, we examined the relationship between measures of biodiversity calculated at different scales (e.g. genus vs. family), an empirical pattern previously evaluated in the context of the Diversity Begets Diversity (DBD) hypothesis (Madi et al., 2020). We found that the relationship between richness estimates at different scales can be quantitatively predicted assuming independence among community members, demonstrating that the DBD can be sufficiently explained using the SLM as a null model of ecology. Contrastingly, only by including correlations between the abundances of community members (e.g. as the consequence of interactions) can we predict the relationship between estimates of diversity at different scales. The results of this study characterize novel microbial patterns across scales of organization and establish a sharp demarcation between recently proposed macroecological patterns that are not and are affected by ecological interactions.

A macroecological perspective on genetic diversity in the human gut microbiome

While the human gut microbiome has been intensely studied, we have yet to obtain a sufficient understanding of the genetic diversity that it harbors. Research efforts have demonstrated that a considerable fraction of within-host genetic variation in the human gut is driven by the ecological dynamics of co-occurring strains belonging to the same species, suggesting that an ecological lens may provide insight into empirical patterns of genetic diversity. Indeed, an ecological model of self-limiting growth and environmental noise known as the Stochastic Logistic Model (SLM) was recently shown to successfully predict the temporal dynamics of strains within a single human host. However, its ability to predict patterns of genetic diversity across human hosts has yet to be tested. In this manuscript I determine whether the predictions of the SLM explain patterns of genetic diversity across unrelated human hosts for 22 common microbial species. Specifically, the stationary distribution of the SLM explains the distribution of allele frequencies across hosts and predicts the fraction of hosts harboring a given allele (i.e., prevalence) for a considerable fraction of sites. The accuracy of the SLM was correlated with independent estimates of strain structure, suggesting that patterns of genetic diversity in the gut microbiome follow statistically similar forms across human hosts due to the existence of strain-level ecology.

Tracking contemporary microbial evolution in a changing ocean

Ocean microbes are fundamental for the functioning of the Earth system. Yet, our understanding of how they are reacting to global change in terms of evolution is limited. Microbes typically grow in large populations and reproduce quickly, which may allow them to rapidly adapt to environmental stressors compared to larger organisms. However, genetic evidence of contemporary evolution in wild microbes is scarce. We must begin coordinated efforts to establish new microbial time-series and explore novel tools, experiments, and data to fill this knowledge gap. The development of coordinated microbial 'genomic' observatories will provide the unprecedented opportunity to track contemporary microbial evolution in the ocean and explore the role of evolution in enabling wild microbes to respond to global change.

Multiple phase transitions shape biodiversity of a migrating population

In a wide variety of natural systems, closely related microbial strains coexist stably, resulting in high levels of fine-scale biodiversity. However, the mechanisms that stabilize this coexistence are not fully understood. Spatial heterogeneity is one common stabilizing mechanism, but the rate at which organisms disperse throughout the heterogeneous environment may strongly impact the stabilizing effect that heterogeneity can provide. An intriguing example is the gut microbiome, where active mechanisms affect the movement of microbes and potentially maintain diversity. We investigate how biodiversity is affected by migration rate using a simple evolutionary model with heterogeneous selection pressure. We find that the biodiversity-migration rate relationship is shaped by multiple phase transitions, including a reentrant phase transition to coexistence. At each transition, an ecotype goes extinct and dynamics exhibit critical slowing down (CSD). CSD is encoded in the statistics of fluctuations due to demographic noise-this may provide an experimental means for detecting and altering impending extinction.

Predicting Parallelism and Quantifying Divergence in Microbial Evolution Experiments

The degree to which independent populations subjected to identical environmental conditions evolve in similar ways is a fundamental question in evolution. To address this question, microbial populations are often experimentally passaged in a given environment and sequenced to examine the tendency for similar mutations to repeatedly arise. However, there remains the need to develop an appropriate statistical framework to identify genes that acquired more mutations in one environment than in another (i.e., divergent evolution), genes that serve as genetic candidates of adaptation. Here, we develop a mathematical model to evaluate evolutionary outcomes among replicate populations in the same environment (i.e., parallel evolution), which can then be used to identify genes that contribute to divergent evolution. Applying this approach to data sets from evolve-and-resequence experiments, we found that the distribution of mutation counts among genes can be predicted as an ensemble of independent Poisson random variables with zero free parameters. Building on this result, we propose that the degree of divergent evolution at a given gene between populations from two different environments can be modeled as the difference between two Poisson random variables, known as the Skellam distribution. We then propose and apply a statistical test to identify specific genes that contribute to divergent evolution. By focusing on predicting patterns among replicate populations in a given environment, we are able to identify an appropriate test for divergence between environments that is grounded in first principles. IMPORTANCE There is currently no universally accepted framework for identifying genes that contribute to molecular divergence between microbial populations in different environments. To address this absence, we developed a null model to describe the distribution of mutation counts among genes. We find that divergent evolution within a given gene can be modeled as the absolute difference in the total number of mutations observed between two environments. This quantity is effectively captured by a probability distribution known as the Skellam distribution, providing an appropriate statistical test for researchers seeking to identify the set of genes that contribute to divergent evolution in microbial evolution experiments.

Comparative Population Genetics in the Human Gut Microbiome

Genetic variation in the human gut microbiome is responsible for conferring a number of crucial phenotypes like the ability to digest food and metabolize drugs. Yet, our understanding of how this variation arises and is maintained remains relatively poor. Thus, the microbiome remains a largely untapped resource, as the large number of coexisting species in the microbiome presents a unique opportunity to compare and contrast evolutionary processes across species to identify universal trends and deviations. Here we outline features of the human gut microbiome that, while not unique in isolation, as an assemblage make it a system with unparalleled potential for comparative population genomics studies. We consciously take a broad view of comparative population genetics, emphasizing how sampling a large number of species allows researchers to identify universal evolutionary dynamics in addition to new genes, which can then be leveraged to identify exceptional species that deviate from general patterns. To highlight the potential power of comparative population genetics in the microbiome, we reanalyze patterns of purifying selection across ∼40 prevalent species in the human gut microbiome to identify intriguing trends which highlight functional categories in the microbiome that may be under more or less constraint.

Characterizing spreading dynamics of subsampled systems with nonstationary external input

Many systems with propagation dynamics, such as spike propagation in neural networks and spreading of infectious diseases, can be approximated by autoregressive models. The estimation of model parameters can be complicated by the experimental limitation that one observes only a fraction of the system (subsampling) and potentially time-dependent parameters, leading to incorrect estimates. We show analytically how to overcome the subsampling bias when estimating the propagation rate for systems with certain nonstationary external input. This approach is readily applicable to trial-based experimental setups and seasonal fluctuations as demonstrated on spike recordings from monkey prefrontal cortex and spreading of norovirus and measles.

Molecular Evolutionary Dynamics of Energy Limited Microorganisms

Microorganisms have the unique ability to survive extended periods of time in environments with extremely low levels of exploitable energy. To determine the extent that energy limitation affects microbial evolution, we examined the molecular evolutionary dynamics of a phylogenetically diverse set of taxa over the course of 1,000 days. We found that periodic exposure to energy limitation affected the rate of molecular evolution, the accumulation of genetic diversity, and the rate of extinction. We then determined the degree that energy limitation affected the spectrum of mutations as well as the direction of evolution at the gene level. Our results suggest that the initial depletion of energy altered the direction and rate of molecular evolution within each taxon, though after the initial depletion the rate and direction did not substantially change. However, this consistent pattern became diminished when comparisons were performed across phylogenetically distant taxa, suggesting that while the dynamics of molecular evolution under energy limitation are highly generalizable across the microbial tree of life, the targets of adaptation are specific to a given taxon.

A percolation model of natural selection

A new approach has been proposed and developed: the selection of optimal variants in the evolutionary mutation flow is considered as an analogue of a percolation filter. Interaction of mutations in a series of generations and random processes of drift determine the collective behavior of nodes (individuals - carriers and converters of mutations) and bonds (mutations) in the space of percolation lattice. It is shown that the choice of the development trajectory at the population level depends on the spectrum of supporting and prohibiting mutations under the influence of conjugate deterministic and random factors. From the point of view of the fluctuation-bifurcation process, new concepts of the lower and upper thresholds of the percolation selection grid are defined in the hierarchical structure of speciation. The upper threshold determines the state of self-organized criticality, which, when overcome, leads to irreversible self-organization processes in the population caused by the accumulation of mutations.

Cooperation in Microbial Populations: Theory and Experimental Model Systems

Cooperative behavior, the costly provision of benefits to others, is common across all domains of life. This review article discusses cooperative behavior in the microbial world, mediated by the exchange of extracellular products called public goods. We focus on model species for which the production of a public good and the related growth disadvantage for the producing cells are well described. To unveil the biological and ecological factors promoting the emergence and stability of cooperative traits we take an interdisciplinary perspective and review insights gained from both mathematical models and well-controlled experimental model systems. Ecologically, we include crucial aspects of the microbial life cycle into our analysis and particularly consider population structures where ensembles of local communities (subpopulations) continuously emerge, grow, and disappear again. Biologically, we explicitly consider the synthesis and regulation of public good production. The discussion of the theoretical approaches includes general evolutionary concepts, population dynamics, and evolutionary game theory. As a specific but generic biological example, we consider populations of Pseudomonas putida and its regulation and use of pyoverdines, iron scavenging molecules, as public goods. The review closes with an overview on cooperation in spatially extended systems and also provides a critical assessment of the insights gained from the experimental and theoretical studies discussed. Current challenges and important new research opportunities are discussed, including the biochemical regulation of public goods, more realistic ecological scenarios resembling native environments, cell-to-cell signaling, and multispecies communities.