Interactions between strains govern the eco-evolutionary dynamics of microbial communities

Critically evaluating the relative importance of phage in shaping microbial community composition

The ubiquity of bacteriophages (phages) and the major evolutionary and ecological impacts they can have on their microbial hosts has resulted in phages often cited as key drivers shaping microbial community composition (the relative abundances of species). However, the evidence for the importance of phages is mixed. Here, we critically review the theory and data exploring the role of phages in communities, identifying the conditions when phages are likely to be important drivers of community composition. At ecological scales, we conclude that phages are often followers rather than drivers of microbial population and community dynamics. While phages can affect strain diversity within species, there is yet to be strong evidence suggesting that fluctuations in species' strains affects community composition.

Predicting the first steps of evolution in randomly assembled communities

Microbial communities can self-assemble into highly diverse states with predictable statistical properties. However, these initial states can be disrupted by rapid evolution of the resident strains. When a new mutation arises, it competes for resources with its parent strain and with the other species in the community. This interplay between ecology and evolution is difficult to capture with existing community assembly theory. Here, we introduce a mathematical framework for predicting the first steps of evolution in large randomly assembled communities that compete for substitutable resources. We show how the fitness effects of new mutations and the probability that they coexist with their parent depends on the size of the community, the saturation of its niches, and the metabolic overlap between its members. We find that successful mutations are often able to coexist with their parent strains, even in saturated communities with low niche availability. At the same time, these invading mutants often cause extinctions of metabolically distant species. Our results suggest that even small amounts of evolution can produce distinct genetic signatures in natural microbial communities.

Punctuated virus-driven succession generates dynamical alternations in CRISPR-mediated microbe-virus coevolution

The coevolutionary dynamics of lytic viruses and microbes with CRISPR-Cas immunity exhibit alternations between sustained host control of viral proliferation and major viral epidemics in previous computational models. These alternating dynamics have yet to be observed in other host-pathogen systems. Here, we address the breakdown of control and transition to large outbreaks with a stochastic eco-evolutionary model. We establish the role of host density-dependent competition in punctuated virus-driven succession and associated diversity trends that concentrate escape pathways during control phases. Using infection and escape networks, we derive the viral emergence probability whose fluctuations of increasing size and frequency characterize the approach to large outbreaks. We explore alternation probabilities as a function of non-dimensional parameters related to the probability of viral escape and host competition. Our results demonstrate how emergent feedbacks between host competition and viral diversification render the host immune structure fragile, potentiating a dynamical transition to large epidemics.

Metabolic complexity drives divergence in microbial communities

Microbial communities are shaped by environmental metabolites, but the principles that govern whether different communities will converge or diverge in any given condition remain unknown, posing fundamental questions about the feasibility of microbiome engineering. Here we studied the longitudinal assembly dynamics of a set of natural microbial communities grown in laboratory conditions of increasing metabolic complexity. We found that different microbial communities tend to become similar to each other when grown in metabolically simple conditions, but they diverge in composition as the metabolic complexity of the environment increases, a phenomenon we refer to as the divergence-complexity effect. A comparative analysis of these communities revealed that this divergence is driven by community diversity and by the assortment of specialist taxa capable of degrading complex metabolites. An ecological model of community dynamics indicates that the hierarchical structure of metabolism itself, where complex molecules are enzymatically degraded into progressively simpler ones that then participate in cross-feeding between community members, is necessary and sufficient to recapitulate our experimental observations. In addition to helping understand the role of the environment in community assembly, the divergence-complexity effect can provide insight into which environments support multiple community states, enabling the search for desired ecosystem functions towards microbiome engineering applications.

Floria: fast and accurate strain haplotyping in metagenomes

SUMMARY

Shotgun metagenomics allows for direct analysis of microbial community genetics, but scalable computational methods for the recovery of bacterial strain genomes from microbiomes remains a key challenge. We introduce Floria, a novel method designed for rapid and accurate recovery of strain haplotypes from short and long-read metagenome sequencing data, based on minimum error correction (MEC) read clustering and a strain-preserving network flow model. Floria can function as a standalone haplotyping method, outputting alleles and reads that co-occur on the same strain, as well as an end-to-end read-to-assembly pipeline (Floria-PL) for strain-level assembly. Benchmarking evaluations on synthetic metagenomes show that Floria is  > 3× faster and recovers 21% more strain content than base-level assembly methods (Strainberry) while being over an order of magnitude faster when only phasing is required. Applying Floria to a set of 109 deeply sequenced nanopore metagenomes took <20 min on average per sample and identified several species that have consistent strain heterogeneity. Applying Floria's short-read haplotyping to a longitudinal gut metagenomics dataset revealed a dynamic multi-strain Anaerostipes hadrus community with frequent strain loss and emergence events over 636 days. With Floria, accurate haplotyping of metagenomic datasets takes mere minutes on standard workstations, paving the way for extensive strain-level metagenomic analyses.

AVAILABILITY AND IMPLEMENTATION

Floria is available at https://github.com/bluenote-1577/floria, and the Floria-PL pipeline is available at https://github.com/jsgounot/Floria_analysis_workflow along with code for reproducing the benchmarks.

Predicting the First Steps of Evolution in Randomly Assembled Communities

Microbial communities can self-assemble into highly diverse states with predictable statistical properties. However, these initial states can be disrupted by rapid evolution of the resident strains. When a new mutation arises, it competes for resources with its parent strain and with the other species in the community. This interplay between ecology and evolution is difficult to capture with existing community assembly theory. Here, we introduce a mathematical framework for predicting the first steps of evolution in large randomly assembled communities that compete for substitutable resources. We show how the fitness effects of new mutations and the probability that they coexist with their parent depends on the size of the community, the saturation of its niches, and the metabolic overlap between its members. We find that successful mutations are often able to coexist with their parent strains, even in saturated communities with low niche availability. At the same time, these invading mutants often cause extinctions of metabolically distant species. Our results suggest that even small amounts of evolution can produce distinct genetic signatures in natural microbial communities.

Probiotic Insights from the Genomic Exploration of Lacticaseibacillus paracasei Strains Isolated from Fermented Palm Sap

This study focused on L. paracasei strains isolated from fermented palm sap in southern Thailand that exhibit potential probiotic characteristics, including antibiotic susceptibility, resistance to gastrointestinal stresses, and antimicrobial activity against various pathogens. However, a thorough investigation of the whole genome sequences of L. paracasei isolates is required to ensure their safety and probiotic properties for human applications. This study aimed to sequence the genome of L. paracasei isolated from fermented palm sap, to assess its safety profile, and to conduct a comprehensive comparative genomic analysis with other Lacticaseibacillus species. The genome sizes of the seven L. paracasei strains ranged from 3,070,747 bp to 3,131,129 bp, with a GC content between 46.11% and 46.17% supporting their classification as nomadic lactobacilli. In addition, the minimal presence of cloud genes and a significant number of core genes suggest a high degree of relatedness among the strains. Meanwhile, phylogenetic analysis of core genes revealed that the strains possessed distinct genes and were grouped into two distinct clades. Genomic analysis revealed key genes associated with probiotic functions, such as those involved in gastrointestinal, oxidative stress resistance, vitamin synthesis, and biofilm disruption. This study is consistent with previous studies that used whole-genome sequencing and bioinformatics to assess the safety and potential benefits of probiotics in various food fermentation processes. Our findings provide valuable insights into the potential use of seven L. paracasei strains isolated from fermented palm sap as probiotic and postbiotic candidates in functional foods and pharmaceuticals.

Deciphering Microbial Adaptation in the Rhizosphere: Insights into Niche Preference, Functional Profiles, and Cross-Kingdom Co-occurrences

Rhizosphere microbial communities are to be as critical factors for plant growth and vitality, and their adaptive differentiation strategies have received increasing amounts of attention but are poorly understood. In this study, we obtained bacterial and fungal amplicon sequences from the rhizosphere and bulk soils of various ecosystems to investigate the potential mechanisms of microbial adaptation to the rhizosphere environment. Our focus encompasses three aspects: niche preference, functional profiles, and cross-kingdom co-occurrence patterns. Our findings revealed a correlation between niche similarity and nucleotide distance, suggesting that niche adaptation explains nucleotide variation among some closely related amplicon sequence variants (ASVs). Furthermore, biological macromolecule metabolism and communication among abundant bacteria increase in the rhizosphere conditions, suggesting that bacterial function is trait-mediated in terms of fitness in new habitats. Additionally, our analysis of cross-kingdom networks revealed that fungi act as intermediaries that facilitate connections between bacteria, indicating that microbes can modify their cooperative relationships to adapt. Overall, the evidence for rhizosphere microbial community adaptation, via differences in gene and functional and co-occurrence patterns, elucidates the adaptive benefits of genetic and functional flexibility of the rhizosphere microbiota through niche shifts.

Emergent competition shapes top-down versus bottom-up control in multi-trophic ecosystems

Ecosystems are commonly organized into trophic levels-organisms that occupy the same level in a food chain (e.g., plants, herbivores, carnivores). A fundamental question in theoretical ecology is how the interplay between trophic structure, diversity, and competition shapes the properties of ecosystems. To address this problem, we analyze a generalized Consumer Resource Model with three trophic levels using the zero-temperature cavity method and numerical simulations. We derive the corresponding mean-field cavity equations and show that intra-trophic diversity gives rise to an effective "emergent competition" term between species within a trophic level due to feedbacks mediated by other trophic levels. This emergent competition gives rise to a crossover from a regime of top-down control (populations are limited by predators) to a regime of bottom-up control (populations are limited by primary producers) and is captured by a simple order parameter related to the ratio of surviving species in different trophic levels. We show that our theoretical results agree with empirical observations, suggesting that the theoretical approach outlined here can be used to understand complex ecosystems with multiple trophic levels.

Pairing metagenomics and metaproteomics to characterize ecological niches and metabolic essentiality of gut microbiomes

The genome of a microorganism encodes its potential functions that can be implemented through expressed proteins. It remains elusive how a protein's selective expression depends on its metabolic essentiality to microbial growth or its ability to claim resources as ecological niches. To reveal a protein's metabolic or ecological role, we developed a computational pipeline, which pairs metagenomics and metaproteomics data to quantify each protein's gene-level and protein-level functional redundancy simultaneously. We first illustrated the idea behind the pipeline using simulated data of a consumer-resource model. We then validated it using real data from human and mouse gut microbiome samples. In particular, we analyzed ABC-type transporters and ribosomal proteins, confirming that the metabolic and ecological roles predicted by our pipeline agree well with prior knowledge. Finally, we performed in vitro cultures of a human gut microbiome sample and investigated how oversupplying various sugars involved in ecological niches influences the community structure and protein abundance. The presented results demonstrate the performance of our pipeline in identifying proteins' metabolic and ecological roles, as well as its potential to help us design nutrient interventions to modulate the human microbiome.

Molecular ecology of microbiomes in the wild: Common pitfalls, methodological advances and future directions

The study of microbiomes across organisms and environments has become a prominent focus in molecular ecology. This perspective article explores common challenges, methodological advancements, and future directions in the field. Key research areas include understanding the drivers of microbiome community assembly, linking microbiome composition to host genetics, exploring microbial functions, transience and spatial partitioning, and disentangling non-bacterial components of the microbiome. Methodological advancements, such as quantifying absolute abundances, sequencing complete genomes, and utilizing novel statistical approaches, are also useful tools for understanding complex microbial diversity patterns. Our aims are to encourage robust practices in microbiome studies and inspire researchers to explore the next frontier of this rapidly changing field.

Microbial interactions shape cheese flavour formation

Cheese fermentation and flavour formation are the result of complex biochemical reactions driven by the activity of multiple microorganisms. Here, we studied the roles of microbial interactions in flavour formation in a year-long Cheddar cheese making process, using a commercial starter culture containing Streptococcus thermophilus and Lactococcus strains. By using an experimental strategy whereby certain strains were left out from the starter culture, we show that S. thermophilus has a crucial role in boosting Lactococcus growth and shaping flavour compound profile. Controlled milk fermentations with systematic exclusion of single Lactococcus strains, combined with genomics, genome-scale metabolic modelling, and metatranscriptomics, indicated that S. thermophilus proteolytic activity relieves nitrogen limitation for Lactococcus and boosts de novo nucleotide biosynthesis. While S. thermophilus had large contribution to the flavour profile, Lactococcus cremoris also played a role by limiting diacetyl and acetoin formation, which otherwise results in an off-flavour when in excess. This off-flavour control could be attributed to the metabolic re-routing of citrate by L. cremoris from diacetyl and acetoin towards α-ketoglutarate. Further, closely related Lactococcus lactis strains exhibited different interaction patterns with S. thermophilus, highlighting the significance of strain specificity in cheese making. Our results highlight the crucial roles of competitive and cooperative microbial interactions in shaping cheese flavour profile.

Phosphate-related genomic islands as drivers of environmental adaptation in the streamlined marine alphaproteobacterial HIMB59

IMPORTANCE

These results shed light on the evolutionary strategies of microbes with streamlined genomes to adapt and survive in the oligotrophic conditions that dominate the surface waters of the global ocean. At the individual level, these microbes have been subjected to evolutionary constraints that have led to a more efficient use of nutrients, removing non-essential genes named as "streamlining theory." However, at the population level, they conserve a highly diverse gene pool in flexible genomic islands resulting in polyclonal populations on the same genomic background as an evolutionary response to environmental pressures. Localization of these islands at equivalent positions in the genome facilitates horizontal transfer between clonal lineages. This high level of environmental genomic heterogeneity could explain their cosmopolitan distribution. In the case of the order HIMB59 within the class Alphaproteobacteria, two factors exert evolutionary pressure and determine this intraspecific diversity: phages and the concentration of P in the environment.

Turnover of strain-level diversity modulates functional traits in the honeybee gut microbiome between nurses and foragers

BACKGROUND

Strain-level diversity is widespread among bacterial species and can expand the functional potential of natural microbial communities. However, to what extent communities undergo consistent shifts in strain composition in response to environmental/host changes is less well understood.

RESULTS

Here, we used shotgun metagenomics to compare the gut microbiota of two behavioral states of the Western honeybee (Apis mellifera), namely nurse and forager bees. While their gut microbiota is composed of the same bacterial species, we detect consistent changes in strain-level composition between nurses and foragers. Single nucleotide variant profiles of predominant bacterial species cluster by behavioral state. Moreover, we identify strain-specific gene content related to nutrient utilization, vitamin biosynthesis, and cell-cell interactions specifically associated with the two behavioral states.

CONCLUSIONS

Our findings show that strain-level diversity in host-associated communities can undergo consistent changes in response to host behavioral changes modulating the functional potential of the community.

Eco-evolutionary feedbacks in the human gut microbiome

Gut microbiota can evolve within their hosts on human-relevant timescales, but little is known about how these changes influence (or are influenced by) the composition of their local community. Here, by combining ecological and evolutionary analyses of a large cohort of human gut metagenomes, we show that the short-term evolution of the microbiota is linked with shifts in its ecological structure. These correlations are not simply explained by expansions of the evolving species, and often involve additional fluctuations in distantly related taxa. We show that similar feedbacks naturally emerge in simple resource competition models, even in the absence of cross-feeding or predation. These results suggest that the structure and function of host microbiota may be shaped by their local evolutionary history, which could have important implications for personalized medicine and microbiome engineering.

Starvation responses impact interaction dynamics of human gut bacteria Bacteroides thetaiotaomicron and Roseburia intestinalis

Bacterial growth often alters the environment, which in turn can impact interspecies interactions among bacteria. Here, we used an in vitro batch system containing mucin beads to emulate the dynamic host environment and to study its impact on the interactions between two abundant and prevalent human gut bacteria, the primary fermenter Bacteroides thetaiotaomicron and the butyrate producer Roseburia intestinalis. By combining machine learning and flow cytometry, we found that the number of viable B. thetaiotaomicron cells decreases with glucose consumption due to acid production, while R. intestinalis survives post-glucose depletion by entering a slow growth mode. Both species attach to mucin beads, but only viable cell counts of B. thetaiotaomicron increase significantly. The number of viable co-culture cells varies significantly over time compared to those of monocultures. A combination of targeted metabolomics and RNA-seq showed that the slow growth mode of R. intestinalis represents a diauxic shift towards acetate and lactate consumption, whereas B. thetaiotaomicron survives glucose depletion and low pH by foraging on mucin sugars. In addition, most of the mucin monosaccharides we tested inhibited the growth of R. intestinalis but not B. thetaiotaomicron. We encoded these causal relationships in a kinetic model, which reproduced the observed dynamics. In summary, we explored how R. intestinalis and B. thetaiotaomicron respond to nutrient scarcity and how this affects their dynamics. We highlight the importance of understanding bacterial metabolic strategies to effectively modulate microbial dynamics in changing conditions.

Geometry of ecological coexistence and niche differentiation

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze consumer resource models using geometric arguments. This has led to broadly applicable principles such as Tilman's R * and species coexistence cones. Here, we extend these arguments by constructing a novel geometric framework for understanding species coexistence based on convex polytopes in the space of consumer preferences. We show how the geometry of consumer preferences can be used to predict species which may coexist and enumerate ecologically-stable steady states and transitions between them. Collectively, these results provide a framework for understanding the role of species traits within niche theory.

Leveraging phylogenetic signal to unravel microbiome function and assembly rules

Clarifying the general rules behind microbial community assembly will foster the development of microbiome-based technological solutions. Here, we study microbial community assembly through a computational analysis of phylogenetic core groups (PCGs): discrete portions of the bacterial phylogeny with high prevalence in the ecosystem under study. We first show that the existence of PCGs was a predominant feature of the varied set of microbial ecosystems studied. Then, we re-analyzed an in vitro experimental dataset using a PCG-based approach, drawing only from its community composition data and from publicly available genomic databases. Using mainly genome scale metabolic models and population dynamics modeling, we obtained ecological insights on metabolic niche structure and population dynamics comparable to those gained after canonical experimentation. Thus, leveraging phylogenetic signal to help unravel microbiome function and assembly rules offers a potential avenue to gain further insight on Earth's microbial ecosystems.

Geometry of ecological coexistence and niche differentiation

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze consumer resource models using geometric arguments. This has led to broadly applicable principles such as Tilman's R^{*} and species coexistence cones. Here, we extend these arguments by constructing a geometric framework for understanding species coexistence based on convex polytopes in the space of consumer preferences. We show how the geometry of consumer preferences can be used to predict species which may coexist and enumerate ecologically stable steady states and transitions between them. Collectively, these results provide a framework for understanding the role of species traits within niche theory.

Environmental fluctuations explain the universal decay of species-abundance correlations with phylogenetic distance

Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches-which combine phylogenetic relationships between species with community ecology-have the potential to disentangle such forces but are often hard to connect with quantitative predictions from theoretical models. On the other hand, macroecology, which focuses on statistical patterns of abundance and diversity, provides natural connections with theoretical models but often neglects interspecific correlations and interactions. Here, we propose a unified framework combining both such approaches to analyze microbial communities. In particular, by using both cross-sectional and longitudinal metagenomic data for species abundances, we reveal the existence of an empirical macroecological law establishing that correlations in species-abundance fluctuations across communities decay from positive to null values as a function of phylogenetic dissimilarity in a consistent manner across ecologically distinct microbiomes. We formulate three variants of a mechanistic model-each relying on alternative ecological forces-that lead to radically different predictions. From these analyses, we conclude that the empirically observed macroecological pattern can be quantitatively explained as a result of shared population-independent fluctuating resources, i.e., environmental filtering and not as a consequence of, e.g., species competition. Finally, we show that the macroecological law is also valid for temporal data of a single community and that the properties of delayed temporal correlations can be reproduced as well by the model with environmental filtering.

Investigating the eco-evolutionary response of microbiomes to environmental change

Microorganisms are the primary engines of biogeochemical processes and foundational to the provisioning of ecosystem services to human society. Free-living microbial communities (microbiomes) and their functioning are now known to be highly sensitive to environmental change. Given microorganisms' capacity for rapid evolution, evolutionary processes could play a role in this response. Currently, however, few models of biogeochemical processes explicitly consider how microbial evolution will affect biogeochemical responses to environmental change. Here, we propose a conceptual framework for explicitly integrating evolution into microbiome-functioning relationships. We consider how microbiomes respond simultaneously to environmental change via four interrelated processes that affect overall microbiome functioning (physiological acclimation, demography, dispersal and evolution). Recent evidence in both the laboratory and the field suggests that ecological and evolutionary dynamics occur simultaneously within microbiomes; however, the implications for biogeochemistry under environmental change will depend on the timescales over which these processes contribute to a microbiome's response. Over the long term, evolution may play an increasingly important role for microbially driven biogeochemical responses to environmental change, particularly to conditions without recent historical precedent.

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.

Algae drive convergent bacterial community assembly at low dilution frequency

Microbial community assembly is a complex dynamical process that determines community structure and function. The interdependence of inter-species interactions and nutrient availability presents a challenge for understanding community assembly. We sought to understand how external nutrient supply rate modulated interactions to affect the assembly process. A statistical decomposition of taxonomic structures of bacterial communities assembled with and without algae and at varying dilution frequencies allowed the separation of the effects of biotic (presence of algae) and abiotic (dilution frequency) factors on community assembly. For infrequent dilutions, the algae strongly impact community assembly, driving initially diverse bacterial consortia to converge to a common structure. Analyzing sequencing data revealed that this convergence is largely mediated by a decline in the relative abundance of specific taxa in the presence of algae. This study shows that complex phototroph-heterotroph communities can be powerful model systems for understanding assembly processes relevant to the global ecosystem functioning.

Fucosylated Human Milk Oligosaccharides Drive Structure-Specific Syntrophy between Bifidobacterium infantis and Eubacterium hallii within a Modeled Infant Gut Microbiome

SCOPE

Fucosylated human milk oligosaccharides (fHMOs) are metabolized by Bifidobacterium infantis and promote syntrophic interactions between microbiota that colonize the infant gut. The role of fHMO structure on syntrophic interactions and net microbiome function is not yet fully understood.

METHODS AND RESULTS

Metabolite production and microbial populations are tracked during mono- and co-culture fermentations of 2'fucosyllactose (2'FL) and difucosyllactose (DFL) by two B. infantis strains and Eubacterium hallii. This is also conducted in an in vitro modeled microbiome supplemented by B. infantis and/or E. hallii. Metabolites are quantified by high performance liquid chromatography. Total B. infantis and E. hallii populations are quantified through qRT-PCR and community composition through 16S amplicon sequencing. Differential metabolism of 2'FL and DFL by B. infantis strains gives rise to strain- and fHMO structure-specific syntrophy with E. hallii. Within the modeled microbial community, fHMO structure does not strongly alter metabolite production in aggregate, potentially due to functional redundancy within the modeled community. In contrast, community composition is dependent on fHMO structure.

CONCLUSION

Whereas short chain fatty acid production is not significantly altered by the specific fHMO structure introduced to the modeled community, specific fHMO structure influences the composition of the gut microbiome.

Evolutionary repeatability of emergent properties of ecological communities

Most species belong to ecological communities where their interactions give rise to emergent community-level properties, such as diversity and productivity. Understanding and predicting how these properties change over time has been a major goal in ecology, with important practical implications for sustainability and human health. Less attention has been paid to the fact that community-level properties can also change because member species evolve. Yet, our ability to predict long-term eco-evolutionary dynamics hinges on how repeatably community-level properties change as a result of species evolution. Here, we review studies of evolution of both natural and experimental communities and make the case that community-level properties at least sometimes evolve repeatably. We discuss challenges faced in investigations of evolutionary repeatability. In particular, only a handful of studies enable us to quantify repeatability. We argue that quantifying repeatability at the community level is critical for approaching what we see as three major open questions in the field: (i) Is the observed degree of repeatability surprising? (ii) How is evolutionary repeatability at the community level related to repeatability at the level of traits of member species? (iii) What factors affect repeatability? We outline some theoretical and empirical approaches to addressing these questions. Advances in these directions will not only enrich our basic understanding of evolution and ecology but will also help us predict eco-evolutionary dynamics. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Ecological Stability Emerges at the Level of Strains in the Human Gut Microbiome

The human gut microbiome harbors substantial ecological diversity at the species level as well as at the strain level within species. In healthy hosts, species abundance fluctuations in the microbiome are thought to be stable, and these fluctuations can be described by macroecological laws. However, it is less clear how strain abundances change over time. An open question is whether individual strains behave like species themselves, exhibiting stability and following the macroecological relationships known to hold at the species level, or whether strains have different dynamics, perhaps due to the relatively close phylogenetic relatedness of cocolonizing lineages. Here, we analyze the daily dynamics of intraspecific genetic variation in the gut microbiomes of four healthy, densely longitudinally sampled hosts. First, we find that the overall genetic diversity of a large majority of species is stationary over time despite short-term fluctuations. Next, we show that fluctuations in abundances in approximately 80% of strains analyzed can be predicted with a stochastic logistic model (SLM), an ecological model of a population experiencing environmental fluctuations around a fixed carrying capacity, which has previously been shown to capture statistical properties of species abundance fluctuations. The success of this model indicates that strain abundances typically fluctuate around a fixed carrying capacity, suggesting that most strains are dynamically stable. Finally, we find that the strain abundances follow several empirical macroecological laws known to hold at the species level. Together, our results suggest that macroecological properties of the human gut microbiome, including its stability, emerge at the level of strains. IMPORTANCE To date, there has been an intense focus on the ecological dynamics of the human gut microbiome at the species level. However, there is considerable genetic diversity within species at the strain level, and these intraspecific differences can have important phenotypic effects on the host, impacting the ability to digest certain foods and metabolize drugs. Thus, to fully understand how the gut microbiome operates in times of health and sickness, its ecological dynamics may need to be quantified at the level of strains. Here, we show that a large majority of strains maintain stable abundances for periods of months to years, exhibiting fluctuations in abundance that can be well described by macroecological laws known to hold at the species level, while a smaller percentage of strains undergo rapid, directional changes in abundance. Overall, our work indicates that strains are an important unit of ecological organization in the human gut microbiome.

Geometry of ecological coexistence and niche differentiation

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze Consumer Resource Models (CRMs) using geometric arguments. This has led to broadly applicable principles such as Tilman's R* and species coexistence cones. Here, we extend these arguments by constructing a novel geometric framework for understanding species coexistence based on convex polytopes in the space of consumer preferences. We show how the geometry of consumer preferences can be used to predict species coexistence and enumerate ecologically-stable steady states and transitions between them. Collectively, these results constitute a qualitatively new way of understanding the role of species traits in shaping ecosystems within niche theory.

Community diversity is associated with intra-species genetic diversity and gene loss in the human gut microbiome

How the ecological process of community assembly interacts with intra-species diversity and evolutionary change is a longstanding question. Two contrasting hypotheses have been proposed: Diversity Begets Diversity (DBD), in which taxa tend to become more diverse in already diverse communities, and Ecological Controls (EC), in which higher community diversity impedes diversification. Previously, using 16S rRNA gene amplicon data across a range of microbiomes, we showed a generally positive relationship between taxa diversity and community diversity at higher taxonomic levels, consistent with the predictions of DBD (Madi et al., 2020). However, this positive 'diversity slope' plateaus at high levels of community diversity. Here we show that this general pattern holds at much finer genetic resolution, by analyzing intra-species strain and nucleotide variation in static and temporally sampled metagenomes from the human gut microbiome. Consistent with DBD, both intra-species polymorphism and strain number were positively correlated with community Shannon diversity. Shannon diversity is also predictive of increases in polymorphism over time scales up to ~4-6 months, after which the diversity slope flattens and becomes negative - consistent with DBD eventually giving way to EC. Finally, we show that higher community diversity predicts gene loss at a future time point. This observation is broadly consistent with the Black Queen Hypothesis, which posits that genes with functions provided by the community are less likely to be retained in a focal species' genome. Together, our results show that a mixture of DBD, EC, and Black Queen may operate simultaneously in the human gut microbiome, adding to a growing body of evidence that these eco-evolutionary processes are key drivers of biodiversity and ecosystem function.

Genome-Centric Dynamics Shape the Diversity of Oral Bacterial Populations

Two major viewpoints have been put forward for how microbial populations change, differing in whether adaptation is driven principally by gene-centric or genome-centric processes. Longitudinal sampling at microbially relevant timescales, i.e., days to weeks, is critical for distinguishing these mechanisms. Because of its significance for both microbial ecology and human health and its accessibility and high level of curation, we used the oral microbiota to study bacterial intrapopulation genome dynamics. Metagenomes were generated by shotgun sequencing of total community DNA from the healthy tongues of 17 volunteers at four to seven time points obtained over intervals of days to weeks. We obtained 390 high-quality metagenome-assembled genomes (MAGs) defining population genomes from 55 genera. The vast majority of genes in each MAG were tightly linked over the 2-week sampling window, indicating that the majority of the population's genomes were temporally stable at the MAG level. MAG-defined populations were composed of up to 5 strains, as determined by single-nucleotide-variant frequencies. Although most were stable over time, individual strains carrying over 100 distinct genes that rose from low abundance to dominance in a population over a period of days were detected. These results indicate a genome-wide as opposed to a gene-level process of population change. We infer that genome-wide selection of ecotypes is the dominant mode of adaptation in the oral populations over short timescales. IMPORTANCE The oral microbiome represents a microbial community of critical relevance to human health. Recent studies have documented the diversity and dynamics of different bacteria to reveal a rich, stable ecosystem characterized by strain-level dynamics. However, bacterial populations and their genomes are neither monolithic nor static; their genomes are constantly evolving to lose, gain, or alter their functional potential. To better understand how microbial genomes change in complex communities, we used culture-independent approaches to reconstruct the genomes (MAGs) for bacterial populations that approximated different species, in 17 healthy donors' mouths over a 2-week window. Our results underscored the importance of strain-level dynamics, which agrees with and expands on the conclusions of previous research. Altogether, these observations reveal patterns of genomic dynamics among strains of oral bacteria occurring over a matter of days.

Fine-scale diversity of microbial communities due to satellite niches in boom and bust environments

Recent observations have revealed that closely related strains of the same microbial species can stably coexist in natural and laboratory settings subject to boom and bust dynamics and serial dilutions, respectively. However, the possible mechanisms enabling the coexistence of only a handful of strains, but not more, have thus far remained unknown. Here, using a consumer-resource model of microbial ecosystems, we propose that by differentiating along Monod parameters characterizing microbial growth rates in high and low nutrient conditions, strains can coexist in patterns similar to those observed. In our model, boom and bust environments create satellite niches due to resource concentrations varying in time. These satellite niches can be occupied by closely related strains, thereby enabling their coexistence. We demonstrate that this result is valid even in complex environments consisting of multiple resources and species. In these complex communities, each species partitions resources differently and creates separate sets of satellite niches for their own strains. While there is no theoretical limit to the number of coexisting strains, in our simulations, we always find between 1 and 3 strains coexisting, consistent with known experiments and observations.

The ability of Phaeobacter inhibens to produce tropodithietic acid influences the community dynamics of a microalgal microbiome

Microbial secondary metabolites facilitate microbial interactions and are crucial for understanding the complexity of microbial community dynamics. The purpose of the present study was to determine how a secondary metabolite producing marine bacteria or its metabolite deficient mutant affected the microbiome of the marine microalgae Tetraselmis suecica during a 70 day long co-evolution experiment. Using 16S rRNA gene amplicon sequencing, we found that neither the tropodithietic acid (TDA)-producing Phaeobacter inhibens wildtype nor the TDA-deficient mutant had major impacts on the community composition. However, a subset of strains, displayed temporally different relative abundance trajectories depending on the presence of P. inhibens. In particular, a Winogradskyella strain displayed temporal higher relative abundance when the TDA-producing wildtype was present. Numbers of the TDA-producing wildtype were reduced significantly more than those of the mutant over time indicating that TDA production was not an advantage. In communities without the P. inhibens wildtype strain, an indigenous population of Phaeobacter increased over time, indicating that indigenous Phaeobacter populations cannot co-exist with the TDA-producing wildtype. Despite that TDA was not detected chemically, we detected transcripts of the tdaC gene indicating that TDA could be produced in the microbial community associated with the algae. Our work highlights the importance of deciphering longitudinal strain dynamics when addressing the ecological effect of secondary metabolites in a relevant natural community.

Vaginal microbiota networks as a mechanistic predictor of aerobic vaginitis

Aerobic vaginitis (AV) is a complex vaginal dysbiosis that is thought to be caused by the micro-ecological change of the vaginal microbiota. While most studies have focused on how changes in the abundance of individual microbes are associated with the emergence of AV, we still do not have a complete mechanistic atlas of the microbe-AV link. Network modeling is central to understanding the structure and function of any microbial community assembly. By encapsulating the abundance of microbes as nodes and ecological interactions among microbes as edges, microbial networks can reveal how each microbe functions and how one microbe cooperate or compete with other microbes to mediate the dynamics of microbial communities. However, existing approaches can only estimate either the strength of microbe-microbe link or the direction of this link, failing to capture full topological characteristics of a network, especially from high-dimensional microbial data. We combine allometry scaling law and evolutionary game theory to derive a functional graph theory that can characterize bidirectional, signed, and weighted interaction networks from any data domain. We apply our theory to characterize the causal interdependence between microbial interactions and AV. From functional networks arising from different functional modules, we find that, as the only favorable genus from Firmicutes among all identified genera, the role of Lactobacillus in maintaining vaginal microbial symbiosis is enabled by upregulation from other microbes, rather than through any intrinsic capacity. Among Lactobacillus species, the proportion of L. crispatus to L. iners is positively associated with more healthy acid vaginal ecosystems. In a less healthy alkaline ecosystem, L. crispatus establishes a contradictory relationship with other microbes, leading to population decrease relative to L. iners. We identify topological changes of vaginal microbiota networks when the menstrual cycle of women changes from the follicular to luteal phases. Our network tool provides a mechanistic approach to disentangle the internal workings of the microbiota assembly and predict its causal relationships with human diseases including AV.

Prey identity affects fitness of a generalist consumer in a brown food web

The use of ever-advancing sequencing technologies has revealed incredible biodiversity at the microbial scale, and yet we know little about the ecological interactions in these communities. For example, in the phytotelmic community found in the purple pitcher plant, Sarracenia purpurea, ecologists typically consider the bacteria as a functionally homogenous group. In this food web, bacteria decompose detritus and are consumed by protozoa that are considered generalist consumers. Here, we tested whether a generalist consumer benefits from all bacteria equally. We isolated and identified 22 strains of bacteria, belonging to six genera, from S. purpurea plants. We grew the protozoa, Tetrahymena sp. with single isolates and strain mixtures of bacteria and measured Tetrahymena fitness. We found that different bacterial strains had different effects on protozoan fitness, both in isolation and in mixture. Our results demonstrate that not accounting for the composition of prey communities may affect the predicted outcome of predator-prey interactions.