Population Genetics in the Human Microbiome

Vaginal microbiomes show ethnic evolutionary dynamics and positive selection of Lactobacillus adhesins driven by a long-term niche-specific process

The vaginal microbiome's composition varies among ethnicities. However, the evolutionary landscape of the vaginal microbiome in the multi-ethnic context remains understudied. We perform a systematic evolutionary analysis of 351 vaginal microbiome samples from 35 multi-ethnic pregnant women, in addition to two validation cohorts, totaling 462 samples from 90 women. Microbiome alpha diversity and community state dynamics show strong ethnic signatures. Lactobacillaceae have a higher ratio of non-synonymous to synonymous polymorphism and lower nucleotide diversity than non-Lactobacillaceae in all ethnicities, with a large repertoire of positively selected genes, including the mucin-binding and cell wall anchor genes. These evolutionary dynamics are driven by the long-term evolutionary process unique to the human vaginal niche. Finally, we propose an evolutionary model reflecting the environmental niches of microbes. Our study reveals the extensive ethnic signatures in vaginal microbial ecology and evolution, highlighting the importance of studying the host-microbiome ecosystem from an evolutionary perspective.

The pseudoentropy of allele frequency trajectories, the persistence of variation, and the effective population size

To concisely describe how genetic variation, at individual loci or across whole genomes, changes over time, and to follow transitory allelic changes, we introduce a quantity related to entropy, that we term pseudoentropy. This quantity emerges in a diffusion analysis of the mean time a mutation segregates in a population. For a neutral locus with an arbitrary number of alleles, the mean time of segregation is generally proportional to the pseudoentropy of initial allele frequencies. After the initial time point, pseudoentropy generally decreases, but other behaviours are possible, depending on the genetic diversity and selective forces present. For a biallelic locus, pseudoentropy and entropy coincide, but they are distinct quantities with more than two alleles. Thus for populations with multiple biallelic loci, the language of entropy suffices. Then entropy, combined across loci, serves as a concise description of genetic variation. We used individual based simulations to explore how this entropy behaves under different evolutionary scenarios. In agreement with predictions, the entropy associated with unlinked neutral loci decreases over time. However, deviations from free recombination and neutrality have clear and informative effects on the entropy's behaviour over time. Analysis of publicly available data of a natural D. melanogaster population, that had been sampled over seven years, using a sliding-window approach, yielded considerable variation in entropy trajectories of different genomic regions. These mostly follow a pattern that suggests a substantial effective population size and a limited effect of positive selection on genome-wide diversity over short time scales.

Impact of evolution on lifestyle in microbiome

This chapter analyses the interaction between microbiota and humans from an evolutionary point of view. Long-term interactions between gut microbiota and host have been generated as a result of dietary choices through coevolutionary processes, where mutuality of advantage is essential. Likewise, the characteristics of the intestinal environment have made it possible to describe different intrahost evolutionary mechanisms affecting microbiota. For its part, the intestinal microbiota has been of great importance in the evolution of mammals, allowing the diversification of dietary niches, phenotypic plasticity and the selection of host phenotypes. Although the origin of the human intestinal microbial community is still not known with certainty, mother-offspring transmission plays a key role, and it seems that transmissibility between individuals in adulthood also has important implications. Finally, it should be noted that certain aspects inherent to modern lifestyle, including refined diets, antibiotic intake, exposure to air pollutants, microplastics, and stress, could negatively affect the diversity and composition of our gut microbiota. This chapter aims to combine current knowledge to provide a comprehensive view of the interaction between microbiota and humans throughout evolution.

Hierarchical eco-evo dynamics mediated by the gut microbiome

The concept of eco-evolutionary (eco-evo) dynamics, stating that ecological and evolutionary processes occur at similar time scales and influence each other, has contributed to our understanding of responses of populations, communities, and ecosystems to environmental change. Phenotypes, central to these eco-evo processes, can be strongly impacted by the gut microbiome. The gut microbiome shapes eco-evo dynamics in the host community through its effects on the host phenotype. Complex eco-evo feedback loops between the gut microbiome and the host communities might thus be common. Bottom-up dynamics occur when eco-evo interactions shaping the gut microbiome affect host phenotypes with consequences at population, community, and ecosystem levels. Top-down dynamics occur when eco-evo dynamics shaping the host community structure the gut microbiome.

Association between the skin microbiome and MHC class II diversity in an amphibian

Microbiomes play an important role in determining the ecology and behaviour of their hosts. However, questions remain pertaining to how host genetics shape microbiomes, and how microbiome composition influences host fitness. We explored the effects of geography, evolutionary history and host genetics on the skin microbiome diversity and structure in a widespread amphibian. More specifically, we examined the association between bacterial diversity and composition and the major histocompatibility complex class II exon 2 diversity in 12 moor frog (Rana arvalis) populations belonging to two geographical clusters that show signatures of past and ongoing differential selection. We found that while bacterial alpha diversity did not differ between the two clusters, MHC alleles/supertypes and genetic diversity varied considerably depending on geography and evolutionary history. Bacterial alpha diversity was positively correlated with expected MHC heterozygosity and negatively with MHC nucleotide diversity. Furthermore, bacterial community composition showed significant variation between the two geographical clusters and between specific MHC alleles/supertypes. Our findings emphasize the importance of historical demographic events on hologenomic variation and provide new insights into how immunogenetic host variability and microbial diversity may jointly influence host fitness with consequences for disease susceptibility and population persistence.

Cross-cohort single-nucleotide-variant profiling of gut microbiota suggests a novel gut-health assessment approach

IMPORTANCE

Most studies focused much on the change in abundance and often failed to explain the microbiome variation related to disease conditions, Herein, we argue that microbial genetic changes can precede the ecological changes associated with the host physiological changes and, thus, would offer a new information layer from metagenomic data for predictive modeling of diseases. Interestingly, we preliminarily found a few genetic biomarkers on SCFA production can cover most chronic diseases involved in the meta-analysis. In the future, it is of both scientific and clinical significance to further explore the dynamic interactions between adaptive evolution and ecology of gut microbiota associated with host health status.

Inference of the demographic histories and selective effects of human gut commensal microbiota over the course of human history

Despite the importance of gut commensal microbiota to human health, there is little knowledge about their evolutionary histories, including their population demographic histories and their distributions of fitness effects (DFE) of new mutations. Here, we infer the demographic histories and DFEs of 27 of the most highly prevalent and abundant commensal gut microbial species in North Americans over timescales exceeding human generations using a collection of lineages inferred from a panel of healthy hosts. We find overall reductions in genetic variation among commensal gut microbes sampled from a Western population relative to an African rural population. Additionally, some species in North American microbiomes display contractions in population size and others expansions, potentially occurring at several key historical moments in human history. DFEs across species vary from highly to mildly deleterious, with accessory genes experiencing more drift compared to core genes. Within genera, DFEs tend to be more congruent, reflective of underlying phylogenetic relationships. Taken together, these findings suggest that human commensal gut microbes have distinct evolutionary histories, possibly reflecting the unique roles of individual members of the microbiome.

Bacterial SNPs in the human gut microbiome associate with host BMI

Genome-wide association studies (GWASs) have provided numerous associations between human single-nucleotide polymorphisms (SNPs) and health traits. Likewise, metagenome-wide association studies (MWASs) between bacterial SNPs and human traits can suggest mechanistic links, but very few such studies have been done thus far. In this study, we devised an MWAS framework to detect SNPs and associate them with host phenotypes systematically. We recruited and obtained gut metagenomic samples from a cohort of 7,190 healthy individuals and discovered 1,358 statistically significant associations between a bacterial SNP and host body mass index (BMI), from which we distilled 40 independent associations. Most of these associations were unexplained by diet, medications or physical exercise, and 17 replicated in a geographically independent cohort. We uncovered BMI-associated SNPs in 27 bacterial species, and 12 of them showed no association by standard relative abundance analysis. We revealed a BMI association of an SNP in a potentially inflammatory pathway of Bilophila wadsworthia as well as of a group of SNPs in a region coding for energy metabolism functions in a Faecalibacterium prausnitzii genome. Our results demonstrate the importance of considering nucleotide-level diversity in microbiome studies and pave the way toward improved understanding of interpersonal microbiome differences and their potential health implications.

Adaptive evolution and co-evolution of chloroplast genomes in Pteridaceae species occupying different habitats: overlapping residues are always highly mutated

BACKGROUND

The evolution of protein residues depends on the mutation rates of their encoding nucleotides, but it may also be affected by co-evolution with other residues. Chloroplasts function as environmental sensors, transforming fluctuating environmental signals into different physiological responses. We reasoned that habitat diversity may affect their rate and mode of evolution, which might be evidenced in the chloroplast genome. The Pteridaceae family of ferns occupy an unusually broad range of ecological niches, which provides an ideal system for analysis.

RESULTS

We conducted adaptive evolution and intra-molecular co-evolution analyses of Pteridaceae chloroplast DNAs (cpDNAs). The results indicate that the residues undergoing adaptive evolution and co-evolution were mostly independent, with only a few residues being simultaneously involved in both processes, and these overlapping residues tend to exhibit high mutations. Additionally, our data showed that Pteridaceae chloroplast genes are under purifying selection. Regardless of whether we grouped species by lineage (which corresponded with ecological niches), we determined that positively selected residues mainly target photosynthetic genes.

CONCLUSIONS

Our work provides evidence for the adaptive evolution of Pteridaceae cpDNAs, especially photosynthetic genes, to different habitats and sheds light on the adaptive evolution and co-evolution of proteins.

Intrahost evolution of the gut microbiota

A massive number of microorganisms, belonging to different species, continuously divide inside the guts of animals and humans. The large size of these communities and their rapid division times imply that we should be able to watch microbial evolution in the gut in real time, in a similar manner to what has been done in vitro. Here, we review recent findings on how natural selection shapes intrahost evolution (also known as within-host evolution), with a focus on the intestines of mice and humans. The microbiota of a healthy host is not as static as initially thought from the information measured at only one genomic marker. Rather, the genomes of each gut-colonizing species can be highly dynamic, and such dynamism seems to be related to the microbiota species diversity. Genetic and bioinformatic tools, and analysis of time series data, allow quantification of the selection strength on emerging mutations and horizontal transfer events in gut ecosystems. The drivers and functional consequences of gut evolution can now begin to be grasped. The rules of this intrahost microbiota evolution, and how they depend on the biology of each species, need to be understood for more effective development of microbiota therapies to help maintain or restore host health.

Microdiversity of the vaginal microbiome is associated with preterm birth

Preterm birth (PTB) is the leading cause of neonatal morbidity and mortality. The vaginal microbiome has been associated with PTB, yet the mechanisms underlying this association are not fully understood. Understanding microbial genetic adaptations to selective pressures, especially those related to the host, may yield insights into these associations. Here, we analyze metagenomic data from 705 vaginal samples collected during pregnancy from 40 women who delivered preterm spontaneously and 135 term controls from the Multi-Omic Microbiome Study-Pregnancy Initiative. We find that the vaginal microbiome of pregnancies that ended preterm exhibited unique genetic profiles. It was more genetically diverse at the species level, a result which we validate in an additional cohort, and harbored a higher richness and diversity of antimicrobial resistance genes, likely promoted by transduction. Interestingly, we find that Gardnerella species drove this higher genetic diversity, particularly during the first half of the pregnancy. We further present evidence that Gardnerella spp. underwent more frequent recombination and stronger purifying selection in genes involved in lipid metabolism. Overall, our population genetics analyses reveal associations between the vaginal microbiome and PTB and suggest that evolutionary processes acting on vaginal microbes may play a role in adverse pregnancy outcomes such as PTB.

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.

Microdiversity of the Vaginal Microbiome is Associated with Preterm Birth

Preterm birth (PTB) is the leading cause of neonatal morbidity and mortality. The vaginal microbiome has been associated with PTB, yet the mechanisms underlying this association are not fully understood. Understanding microbial genetic adaptations to selective pressures, especially those related to the host, may yield new insights into these associations. To this end, we analyzed metagenomic data from 705 vaginal samples collected longitudinally during pregnancy from 40 women who delivered preterm spontaneously and 135 term controls from the Multi-Omic Microbiome Study-Pregnancy Initiative (MOMS-PI). We find that the vaginal microbiome of pregnancies that ended preterm exhibits unique genetic profiles. It is more genetically diverse at the species level, a result which we validate in an additional cohort, and harbors a higher richness and diversity of antimicrobial resistance genes, likely promoted by transduction. Interestingly, we find that Gardnerella species, a group of central vaginal pathobionts, are driving this higher genetic diversity, particularly during the first half of the pregnancy. We further present evidence that Gardnerella spp. undergoes more frequent recombination and stronger purifying selection in genes involved in lipid metabolism. Overall, our results reveal novel associations between the vaginal microbiome and PTB using population genetics analyses, and suggest that evolutionary processes acting on the vaginal microbiome may play a vital role in adverse pregnancy outcomes such as preterm birth.

Evolutionary implications of host genetic control for engineering beneficial microbiomes

Engineering new functions in the microbiome requires understanding how host genetic control and microbe-microbe interactions shape the microbiome. One key genetic mechanism underlying host control is the immune system. The immune system can promote stability in the composition of the microbiome by reshaping the ecological dynamics of its members, but the degree of stability will depend on the interplay between ecological context, immune system development, and higher-order microbe-microbe interactions. The eco-evolutionary interplay affecting composition and stability should inform the strategies used to engineer new functions in the microbiome. We conclude with recent methodological developments that provide an important path forward for both engineering new functionality in the microbiome and broadly understanding how ecological interactions shape evolutionary processes in complex biological systems.

Genome-wide association studies in plant pathosystems: success or failure?

Harnessing natural genetic variation is an established alternative to artificial genetic variation for investigating the molecular dialog between partners in plant pathosystems. Herein, we review the successes of genome-wide association studies (GWAS) in both plants and pathogens. While GWAS in plants confirmed that the genetic architecture of disease resistance is polygenic, dynamic during the infection kinetics, and dependent on the environment, GWAS shortened the time of identification of quantitative trait loci (QTLs) and revealed both complex epistatic networks and a genetic architecture dependent upon the geographical scale. A similar picture emerges from the few GWAS in pathogens. In addition, the ever-increasing number of functionally validated QTLs has revealed new molecular plant defense mechanisms and pathogenicity determinants. Finally, we propose recommendations to better decode the disease triangle.

Identifying species-specific k-mers for fast and accurate metagenotyping with Maast and GT-Pro

Genotyping single-nucleotide polymorphisms (SNPs) in microbiomes enables strain-level quantification. In this protocol, we describe a computational pipeline that performs fast and accurate SNP genotyping using metagenomic data. We first demonstrate how to use Maast to catalog SNPs from microbial genomes. Then we use GT-Pro to extract unique SNP-covering k-mers, optimize a data structure for storing these k-mers, and finally perform metagenotyping. For proof of concept, the protocol leverages public whole-genome sequences to metagenotype a synthetic community. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022a)1 and Shi et al. (2022b).2.

Structure-informed microbial population genetics elucidate selective pressures that shape protein evolution

Comprehensive sampling of natural genetic diversity with metagenomics enables highly resolved insights into the interplay between ecology and evolution. However, resolving adaptive, neutral, or purifying processes of evolution from intrapopulation genomic variation remains a challenge, partly due to the sole reliance on gene sequences to interpret variants. Here, we describe an approach to analyze genetic variation in the context of predicted protein structures and apply it to a marine microbial population within the SAR11 subclade 1a.3.V, which dominates low-latitude surface oceans. Our analyses reveal a tight association between genetic variation and protein structure. In a central gene in nitrogen metabolism, we observe decreased occurrence of nonsynonymous variants from ligand-binding sites as a function of nitrate concentrations, revealing genetic targets of distinct evolutionary pressures maintained by nutrient availability. Our work yields insights into the governing principles of evolution and enables structure-aware investigations of microbial population genetics.

Pitfalls of genotyping microbial communities with rapidly growing genome collections

Detecting genetic variants in metagenomic data is a priority for understanding the evolution, ecology, and functional characteristics of microbial communities. Many tools that perform this metagenotyping rely on aligning reads of unknown origin to a database of sequences from many species before calling variants. In this synthesis, we investigate how databases of increasingly diverse and closely related species have pushed the limits of current alignment algorithms, thereby degrading the performance of metagenotyping tools. We identify multi-mapping reads as a prevalent source of errors and illustrate a trade-off between retaining correct alignments versus limiting incorrect alignments, many of which map reads to the wrong species. Then we evaluate several actionable mitigation strategies and review emerging methods showing promise to further improve metagenotyping in response to the rapid growth in genome collections. Our results have implications beyond metagenotyping to the many tools in microbial genomics that depend upon accurate read mapping.

Animal Age Affects the Gut Microbiota and Immune System in Captive Koalas (Phascolarctos cinereus)

Gut microbiota is one of the major elements in the control of host health. However, the composition of gut microbiota in koalas has rarely been investigated. Here, we performed 16S rRNA gene sequencing to determine the individual and environmental determinants of gut microbiota diversity and function in 35 fecal samples collected from captive koalas. Meanwhile, blood immune-related cytokine levels were examined by quantitative reverse transcription-PCR to initially explore the relationship between the gut microbiota and the immune system in koalas. The relative abundance of many bacteria, such as Lonepinella koalarum, varies at different ages in koalas and decreases with age. Conversely, Ruminococcus flavefaciens increases with age. Moreover, bacterial pathways involved in lipid metabolism, the biosynthesis of other secondary metabolites, and infectious disease show a significant correlation with age. Age affects the relationship between the microbiota and the host immune system. Among them, the gut microbiota of subadult and aged koalas was closely correlated with CD8β and CD4, whereas adult koalas were correlated with CLEC4E. We also found that sex, reproductive status, and living environment have little impact on the koala gut microbiota and immune system. These results shed suggest age is a key factor affecting gut microbiota and immunity in captive koalas and thus provide new insight into its role in host development and the host immune system. IMPORTANCE Although we have a preliminary understanding of the gut microbiota of koalas, we lack insight into which factors potentially impact captive koalas. This study creates the largest koala gut microbiota data set in China to date and describes several factors that may affect gut microbiota and the immune system in captive koalas, highlighting that age may be a key factor affecting captive koalas. Moreover, this study is the first to characterize the correlation between gut microbiota and cytokines in koalas. Better treatment strategies for infectious disorders may be possible if we can better understand the interactions between the immune system and the microbiota.

How longitudinal data can contribute to our understanding of host genetic effects on the gut microbiome

A key component of microbiome research is understanding the role of host genetic influence on gut microbial composition. However, it can be difficult to link host genetics with gut microbial composition because host genetic similarity and environmental similarity are often correlated. Longitudinal microbiome data can supplement our understanding of the relative role of genetic processes in the microbiome. These data can reveal environmentally contingent host genetic effects, both in terms of controlling for environmental differences and in comparing how genetic effects differ by environment. Here, we explore four research areas where longitudinal data could lend new insights into host genetic effects on the microbiome: microbial heritability, microbial plasticity, microbial stability, and host and microbiome population genetics. We conclude with a discussion of methodological considerations for future studies.

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.

Genotyping Microbial Communities with MIDAS2: From Metagenomic Reads to Allele Tables

The Metagenomic Intra-Species Diversity Analysis System 2 (MIDAS2) is a scalable pipeline that identifies single nucleotide variants and gene copy number variants in metagenomes using comprehensive reference databases built from public microbial genome collections (metagenotyping). MIDAS2 is the first metagenotyping tool with functionality to control metagenomic read mapping filters and to customize the reference database to the microbial community, features that improve the precision and recall of detected variants. In this article we present four basic protocols for the most common use cases of MIDAS2, along with supporting protocols for installation and use. In addition, we provide in-depth guidance on adjusting command line parameters, editing the reference database, optimizing hardware utilization, and understanding the metagenotyping results. All the steps of metagenotyping, from raw sequencing reads to population genetic analysis, are demonstrated with example data in two downloadable sequencing libraries of single-end metagenomic reads representing a mixture of multiple bacterial species. This set of protocols empowers users to accurately genotype hundreds of species in thousands of samples, providing rich genetic data for studying the evolution and strain-level ecology of microbial communities. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Species prescreening Basic Protocol 2: Download MIDAS reference database Basic Protocol 3: Population single nucleotide variant calling Basic Protocol 4: Pan-genome copy number variant calling Support Protocol 1: Installing MIDAS2 Support Protocol 2: Command line inputs Support Protocol 3: Metagenotyping with a custom collection of genomes Support Protocol 4: Metagenotyping with advanced parameters.

Loyal gut microbes

Bacterial strains in the gut microbiota diversified as humans spread across the globe.

Gut Bacteria and Neurotransmitters

Gut bacteria play an important role in the digestion of food, immune activation, and regulation of entero-endocrine signaling pathways, but also communicate with the central nervous system (CNS) through the production of specific metabolic compounds, e.g., bile acids, short-chain fatty acids (SCFAs), glutamate (Glu), γ-aminobutyric acid (GABA), dopamine (DA), norepinephrine (NE), serotonin (5-HT) and histamine. Afferent vagus nerve (VN) fibers that transport signals from the gastro-intestinal tract (GIT) and gut microbiota to the brain are also linked to receptors in the esophagus, liver, and pancreas. In response to these stimuli, the brain sends signals back to entero-epithelial cells via efferent VN fibers. Fibers of the VN are not in direct contact with the gut wall or intestinal microbiota. Instead, signals reach the gut microbiota via 100 to 500 million neurons from the enteric nervous system (ENS) in the submucosa and myenteric plexus of the gut wall. The modulation, development, and renewal of ENS neurons are controlled by gut microbiota, especially those with the ability to produce and metabolize hormones. Signals generated by the hypothalamus reach the pituitary and adrenal glands and communicate with entero-epithelial cells via the hypothalamic pituitary adrenal axis (HPA). SCFAs produced by gut bacteria adhere to free fatty acid receptors (FFARs) on the surface of intestinal epithelial cells (IECs) and interact with neurons or enter the circulatory system. Gut bacteria alter the synthesis and degradation of neurotransmitters. This review focuses on the effect that gut bacteria have on the production of neurotransmitters and vice versa.

A Review of the Role of Oral Microbiome in the Development, Detection, and Management of Head and Neck Squamous Cell Cancers

The role of the microbiome in the development and propagation of head and neck squamous cell cancer (HNSCC) is largely unknown and the surrounding knowledge lags behind what has been discovered related to the microbiome and other malignancies. In this review, the authors performed a structured analysis of the available literature from several databases. The authors discuss the merits and detriments of several studies discussing the microbiome of the structures of the aerodigestive system throughout the development of HNSCC, the role of the microbiome in the development of malignancies (generally and in HNSCC) and clinical applications of the microbiome in HNSCC. Further studies will be needed to adequately describe the relationship between HNSCC and the microbiome, and to push this relationship into a space where it is clinically relevant outside of a research environment.

Microbial Populations Are Shaped by Dispersal and Recombination in a Low Biomass Subseafloor Habitat

The subseafloor is a vast habitat that supports microorganisms that have a global scale impact on geochemical cycles. Many of the endemic microbial communities inhabiting the subseafloor consist of small populations under growth-limited conditions. For small populations, stochastic evolutionary events can have large impacts on intraspecific population dynamics and allele frequencies. These conditions are fundamentally different from those experienced by most microorganisms in surface environments, and it is unknown how small population sizes and growth-limiting conditions influence evolution and population structure in the subsurface. Using a 2-year, high-resolution environmental time series, we examine the dynamics of microbial populations from cold, oxic crustal fluids collected from the subseafloor site North Pond, located near the mid-Atlantic ridge. Our results reveal rapid shifts in overall abundance, allele frequency, and strain abundance across the time points observed, with evidence for homologous recombination between coexisting lineages. We show that the subseafloor aquifer is a dynamic habitat that hosts microbial metapopulations that disperse frequently through the crustal fluids, enabling gene flow and recombination between microbial populations. The dynamism and stochasticity of microbial population dynamics in North Pond suggest that these forces are important drivers in the evolution of microbial populations in the vast subseafloor habitat.

Microbiome breeding: conceptual and practical issues

Microbiome breeding is a new artificial selection technique that seeks to change the genetic composition of microbiomes in order to benefit plant or animal hosts. Recent experimental and theoretical analyses have shown that microbiome breeding is possible whenever microbiome-encoded genetic factors affect host traits (e.g., health) and microbiomes are transmissible between hosts with sufficient fidelity, such as during natural microbiome transmission between individuals of social animals, or during experimental microbiome transplanting between plants. To address misunderstandings that stymie microbiome-breeding programs, we (i) clarify and visualize the corresponding elements of microbiome selection and standard selection; (ii) elucidate the eco-evolutionary processes underlying microbiome selection within a quantitative genetic framework to summarize practical guidelines that optimize microbiome breeding; and (iii) characterize the kinds of host species most amenable to microbiome breeding.

Scalable Microbial Strain Inference in Metagenomic Data Using StrainFacts

While genome databases are nearing a complete catalog of species commonly inhabiting the human gut, their representation of intraspecific diversity is lacking for all but the most abundant and frequently studied taxa. Statistical deconvolution of allele frequencies from shotgun metagenomic data into strain genotypes and relative abundances is a promising approach, but existing methods are limited by computational scalability. Here we introduce StrainFacts, a method for strain deconvolution that enables inference across tens of thousands of metagenomes. We harness a “fuzzy” genotype approximation that makes the underlying graphical model fully differentiable, unlike existing methods. This allows parameter estimates to be optimized with gradient-based methods, speeding up model fitting by two orders of magnitude. A GPU implementation provides additional scalability. Extensive simulations show that StrainFacts can perform strain inference on thousands of metagenomes and has comparable accuracy to more computationally intensive tools. We further validate our strain inferences using single-cell genomic sequencing from a human stool sample. Applying StrainFacts to a collection of more than 10,000 publicly available human stool metagenomes, we quantify patterns of strain diversity, biogeography, and linkage-disequilibrium that agree with and expand on what is known based on existing reference genomes. StrainFacts paves the way for large-scale biogeography and population genetic studies of microbiomes using metagenomic data.

Rapid evolution and strain turnover in the infant gut microbiome

While the ecological dynamics of the infant gut microbiome have been intensely studied, relatively little is known about evolutionary dynamics in the infant gut microbiome. Here we analyze longitudinal fecal metagenomic data from >700 infants and their mothers over the first year of life and find that the evolutionary dynamics in infant gut microbiomes are distinct from that of adults. We find evidence for more than 10-fold increase in the rate of evolution and strain turnover in the infant gut compared to healthy adults, with the mother-infant transition at delivery being a particularly dynamic period in which gene loss dominates. Within a few months after birth, these dynamics stabilize, and gene gains become increasingly frequent as the microbiome matures. We furthermore find that evolutionary changes in infants show signatures of being seeded by a mixture of de novo mutations and transmissions of pre-evolved lineages from the broader family. Several of these evolutionary changes occur in parallel across infants, highlighting candidate genes that may play important roles in the development of the infant gut microbiome. Our results point to a picture of a volatile infant gut microbiome characterized by rapid evolutionary and ecological change in the early days of life.

Dissecting the dominant hot spring microbial populations based on community-wide sampling at single-cell genomic resolution

With advances in DNA sequencing and miniaturized molecular biology workflows, rapid and affordable sequencing of single-cell genomes has become a reality. Compared to 16S rRNA gene surveys and shotgun metagenomics, large-scale application of single-cell genomics to whole microbial communities provides an integrated snapshot of community composition and function, directly links mobile elements to their hosts, and enables analysis of population heterogeneity of the dominant community members. To that end, we sequenced nearly 500 single-cell genomes from a low diversity hot spring sediment sample from Dewar Creek, British Columbia, and compared this approach to 16S rRNA gene amplicon and shotgun metagenomics applied to the same sample. We found that the broad taxonomic profiles were similar across the three sequencing approaches, though several lineages were missing from the 16S rRNA gene amplicon dataset, likely the result of primer mismatches. At the functional level, we detected a large array of mobile genetic elements present in the single-cell genomes but absent from the corresponding same species metagenome-assembled genomes. Moreover, we performed a single-cell population genomic analysis of the three most abundant community members, revealing differences in population structure based on mutation and recombination profiles. While the average pairwise nucleotide identities were similar across the dominant species-level lineages, we observed differences in the extent of recombination between these dominant populations. Most intriguingly, the creek's Hydrogenobacter sp. population appeared to be so recombinogenic that it more closely resembled a sexual species than a clonally evolving microbe. Together, this work demonstrates that a randomized single-cell approach can be useful for the exploration of previously uncultivated microbes from community composition to population structure.

Introduction of Plasmid to the Murine Gut via Consumption of an Escherichia coli Carrier and Examining the Impact of Bacterial Dosing and Antibiotics on Persistence

Purpose

We examine the impacts of dosing strategies of plasmids on bacterial communities in the murine gut by measuring the quantity of plasmids in mouse feces.

Methods

We fed mice carrier bacteria, E. coli, that contain plasmids with both a reporter gene and an antibiotic resistant gene. We varied the quantity of the plasmid-carrying bacteria and the length of time the mice consumed the bacteria. We also pretreated the gut with broad-spectrum antibiotics and used continuous antibiotic treatment to investigate selection pressure. We collected bacteria from fecal pellets to quantify the number of plasmid-carrying bacteria via plate assay.

Results

Dosing regimens with plasmid-carrying bacteria resulted in a significantly increased duration of persistence of the plasmid within the gut when supplemented continuously with kanamycin during as well as after completion of bacterial dosing. The carrier bacteria concentration influenced the short-term abundance of carrier bacteria.

Conclusion

We evaluated the persistence of plasmid-carrying bacteria in the murine gut over time using varying dosage strategies. In future work, we will study how bacterial diversity in the gut impacts the degree of plasmid transfer and the prevalence of plasmid-carrying bacteria over time.

Lay Summary

Observing how plasmids persist within the gut can help us understand how newly introduced genes, including antibiotic resistance, are transmitted within the gut microbiome. In our experiments, mice were given bacteria containing a genetically engineered plasmid and were examined for the persistence of the plasmid in the gut. We found long-term persistence of the plasmid in the gut when administering antibiotics during and following dosing of the mice with bacteria carrying the plasmid. The use of higher concentrations of carrier bacteria influenced the short-term abundance of the plasmid-carrying bacteria in the gut.

Description of Future Works

Building on evidence from these initial studies that persistence of plasmids within the gut can be regulated by the dosage strategy, we will explore future studies and models of gene uptake in the context of spatial and taxonomic control and further determine if dosing strategies alter the compositional diversity of the gut microbiome.

Graphical abstract

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the “one-size-fits-all” approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.

Fast and accurate metagenotyping of the human gut microbiome with GT-Pro

Single nucleotide polymorphisms (SNPs) in metagenomics are used to quantify population structure, track strains and identify genetic determinants of microbial phenotypes. However, existing alignment-based approaches for metagenomic SNP detection require high-performance computing and enough read coverage to distinguish SNPs from sequencing errors. To address these issues, we developed the GenoTyper for Prokaryotes (GT-Pro), a suite of methods to catalog SNPs from genomes and use unique k-mers to rapidly genotype these SNPs from metagenomes. Compared to methods that use read alignment, GT-Pro is more accurate and two orders of magnitude faster. Using high-quality genomes, we constructed a catalog of 104 million SNPs in 909 human gut species and used unique k-mers targeting this catalog to characterize the global population structure of gut microbes from 7,459 samples. GT-Pro enables fast and memory-efficient metagenotyping of millions of SNPs on a personal computer.

Evaluating supervised and unsupervised background noise correction in human gut microbiome data

The ability to predict human phenotypes and identify biomarkers of disease from metagenomic data is crucial for the development of therapeutics for microbiome-associated diseases. However, metagenomic data is commonly affected by technical variables unrelated to the phenotype of interest, such as sequencing protocol, which can make it difficult to predict phenotype and find biomarkers of disease. Supervised methods to correct for background noise, originally designed for gene expression and RNA-seq data, are commonly applied to microbiome data but may be limited because they cannot account for unmeasured sources of variation. Unsupervised approaches address this issue, but current methods are limited because they are ill-equipped to deal with the unique aspects of microbiome data, which is compositional, highly skewed, and sparse. We perform a comparative analysis of the ability of different denoising transformations in combination with supervised correction methods as well as an unsupervised principal component correction approach that is presently used in other domains but has not been applied to microbiome data to date. We find that the unsupervised principal component correction approach has comparable ability in reducing false discovery of biomarkers as the supervised approaches, with the added benefit of not needing to know the sources of variation apriori. However, in prediction tasks, it appears to only improve prediction when technical variables contribute to the majority of variance in the data. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses.

The human gut microbiome is known to play a major role in health and is associated with many diseases including colorectal cancer, obesity, and diabetes. The prediction of host phenotypes and identification of biomarkers of disease is essential for harnessing the therapeutic potential of the microbiome. However, many metagenomic datasets are affected by technical variables that introduce unwanted variation that can confound the ability to predict phenotypes and identify biomarkers. Currently, supervised methods originally designed for gene expression and RNA-seq data are commonly applied to microbiome data for correction of background noise, but they are limited in that they cannot correct for unmeasured sources of variation. Unsupervised approaches address this issue, but current methods are limited because they are ill-equipped to deal with the unique aspects of microbiome data, which is compositional, highly skewed, and sparse. We perform a comparative analysis of the ability of different denoising transformations in combination with supervised correction methods as well as an unsupervised principal component correction approach and find that all correction approaches reduce false positives for biomarker discovery. In the task of predicting phenotypes, different approaches have varying success where the unsupervised correction can improve prediction when technical variables contribute to the majority of variance in the data. As new and larger metagenomic datasets become increasingly available, background noise correction will become essential for generating reproducible microbiome analyses.

The influence of host genetics on liver microbiome composition in patients with NAFLD

Background

Human body microbiotas are influenced by several factors, including the interaction of the host with the environment and dietary preferences. The role of host genetics in modulating the liver microbiota in the context of NAFLD remains unknown. To address this gap, we examined the interplay between the liver metataxonomic profile and host genetics.

Methods

We obtained 16S rRNA gene sequences from liver biopsies and genotypes by Taqman-assays in 116 individuals. We compared taxon abundance at the genus level across host genotypes using dominant models of inheritance. We focused the analysis on variants influencing the risk/ protection against NAFLD-histological severity (PNPLA3-rs738409, TM6SF2-rs58542926, MBOAT7-rs641738, and HSD17B13-rs72613567) and a variant influencing macronutrient intake (FGF21-rs838133). We also explored the variants' combined effect via a polygenic risk score (PRS).

Findings

We identified at least 18 bacterial taxa associated with variants in the selected loci. Members of the Gammaproteobacteria class were significantly enriched in carriers of the rs738409 and rs58542926 risk-alleles, including Enterobacter (fold change [FC]=6.2) and Pseudoalteromonas (FC=2) genera, respectively. Lawsonella (1.6-FC), Prevotella_9 (FC=1.5), and Staphylococcus (FC=1.3) genera were enriched in rs838133-minor allele carriers, which is linked to sugar consumption and carbohydrate intake. Tyzzerella abundance (FC=2.64) exhibited the strongest association (p = 0.0019) with high PRS values (>4 risk alleles). The percentage of genus-level taxa variation explained by the PRS was ∼7.4%, independently of liver steatosis score and obesity.

Interpretation

We provided evidence that genetic variation may influence the liver microbial DNA composition. These observations may represent potentially actionable mechanisms of disease.

Host, microbiome, and complex space: applying population and landscape genetic approaches to gut microbiome research in wild populations

In recent years, emerging sequencing technologies and computational tools have driven a tidal wave of research on host-associated microbiomes, particularly the gut microbiome. These studies demonstrate numerous connections between the gut microbiome and vital host functions, primarily in humans, model organisms, and domestic animals. As the adaptive importance of the gut microbiome becomes clearer, interest in studying the gut microbiomes of wild populations has increased, in part due to the potential for discovering conservation applications. The study of wildlife gut microbiomes holds many new challenges and opportunities due to the complex genetic, spatial, and environmental structure of wild host populations, and the potential for these factors to interact with the microbiome. The emerging picture of adaptive coevolution in host-microbiome relationships highlights the importance of understanding microbiome variation in the context of host population genetics and landscape heterogeneity across a wide range of host populations. We propose a conceptual framework for understanding wildlife gut microbiomes in relation to landscape variables and host population genetics, including the potential of approaches derived from landscape genetics. We use this framework to review current research, synthesize important trends, highlight implications for conservation, and recommend future directions for research. Specifically, we focus on how spatial structure and environmental variation interact with host population genetics and microbiome variation in natural populations, and what we can learn from how these patterns of covariation differ depending on host ecological and evolutionary traits.

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.

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

Modeling host-associating microbes under selection

The concept of fitness is often reduced to a single component, such as the replication rate in a given habitat. For species with multi-step life cycles, this can be an unjustified oversimplification, as every step of the life cycle can contribute to the overall reproductive success in a specific way. In particular, this applies to microbes that spend part of their life cycles associated to a host. In this case, there is a selection pressure not only on the replication rates, but also on the phenotypic traits associated to migrating from the external environment to the host and vice-versa (i.e., the migration rates). Here, we investigate a simple model of a microbial lineage living, replicating, migrating and competing in and between two compartments: a host and an environment. We perform a sensitivity analysis on the overall growth rate to determine the selection gradient experienced by the microbial lineage. We focus on the direction of selection at each point of the phenotypic space, defining an optimal way for the microbial lineage to increase its fitness. We show that microbes can adapt to the two-compartment life cycle through either changes in replication or migration rates, depending on the initial values of the traits, the initial distribution across the two compartments, the intensity of competition, and the time scales involved in the life cycle versus the time scale of adaptation (which determines the adequate probing time to measure fitness). Overall, our model provides a conceptual framework to study the selection on microbes experiencing a host-associated life cycle.

Microcoleus (Cyanobacteria) form watershed-wide populations without strong gradients in population structure

The relative importance of separation by distance and by environment to population genetic diversity can be conveniently tested in river networks, where these two drivers are often independently distributed over space. To evaluate the importance of dispersal and environmental conditions in shaping microbial population structures, we performed genome‐resolved metagenomic analyses of benthic Microcoleus‐dominated cyanobacterial mats collected in the Eel and Russian River networks (California, USA). The 64 Microcoleus genomes were clustered into three species that shared >96.5% average nucleotide identity (ANI). Most mats were dominated by one strain, but minor alleles within mats were often shared, even over large spatial distances (>300 km). Within the most common Microcoleus species, the ANI between the dominant strains within mats decreased with increasing spatial separation. However, over shorter spatial distances (tens of kilometres), mats from different subwatersheds had lower ANI than mats from the same subwatershed, suggesting that at shorter spatial distances environmental differences between subwatersheds in factors like canopy cover, conductivity, and mean annual temperature decreases ANI. Since mats in smaller creeks had similar levels of nucleotide diversity (π) as mats in larger downstream subwatersheds, within‐mat genetic diversity does not appear to depend on the downstream accumulation of upstream‐derived strains. The four‐gamete test and sequence length bias suggest recombination occurs between almost all strains within each species, even between populations separated by large distances or living in different habitats. Overall, our results show that, despite some isolation by distance and environmental conditions, sufficient gene‐flow occurs among cyanobacterial strains to prevent either driver from producing distinctive population structures across the watershed.

A Scale-Free, Fully Connected Global Transition Network Underlies Known Microbiome Diversity

Microbiomes are inherently linked by their structural similarity, yet the global features of such similarity are not clear. Here, we propose as a solution a search-based microbiome transition network. By traversing a composition-similarity-based network of 177,022 microbiomes, we show that although the compositions are distinct by habitat, each microbiome is on-average only seven neighbors from any other microbiome on Earth, indicating the inherent homology of microbiomes at the global scale. This network is scale-free, suggesting a high degree of stability and robustness in microbiome transition. By tracking the minimum spanning tree in this network, a global roadmap of microbiome dispersal was derived that tracks the potential paths of formulating and propagating microbiome diversity. Such search-based global microbiome networks, reconstructed within hours on just one computing node, provide a readily expanded reference for tracing the origin and evolution of existing or new microbiomes. IMPORTANCE It remains unclear whether and how compositional changes at the "community to community" level among microbiomes are linked to the origin and evolution of global microbiome diversity. Here we propose a microbiome transition model and a network-based analysis framework to describe and simulate the variation and dispersal of the global microbial beta-diversity across multiple habitats. The traversal of a transition network with 177,022 samples shows the inherent homology of microbiome at the global scale. Then a global roadmap of microbiome dispersal derived from the network tracks the potential paths of formulating and propagating microbiome diversity. Such search-based microbiome network provides a readily expanded reference for tracing the origin and evolution of existing or new microbiomes at the global scale.

Ultra-resolution Metagenomics: When Enough Is Not Enough

Technological advances in community sequencing have steadily increased the taxonomic resolution at which microbes can be delineated. In high-resolution metagenomics, bacterial strains can now be resolved, enhancing medical microbiology and the description of microbial evolution in vivo. In the Hildebrand lab, we are researching novel approaches to further increase the phylogenetic resolution of metagenomics. I propose that ultra-resolution metagenomics will be the next qualitative level of community sequencing, classified by the accurate resolution of ultra-rare genetic events, such as subclonal mutations present in all populations of evolving cells. This will be used to quantify evolutionary processes at ecologically relevant scales, monitor the progress of infections within a patient, and accurately track pathogens in food and infection chains. However, to develop this next metagenomic generation, we first need to understand the currently imposed limits of sequencing technologies, metagenomic strain delineation, and genome reconstructions.

Polymorphisms in genes related to oxidative stress and inflammation: Emerging links with the pathogenesis and severity of Cerebral Cavernous Malformation disease

Cerebral Cavernous Malformation (CCM) is a cerebrovascular disease of genetic origin affecting 0.5% of the population and characterized by abnormally enlarged and leaky capillaries that predispose to seizures, neurological deficits, and intracerebral hemorrhage (ICH). CCM occurs sporadically or is inherited as dominant condition with incomplete penetrance and highly variable expressivity. Three disease genes have been identified: KRIT1 (CCM1), CCM2 and CCM3. Previous results demonstrated that loss-of-function mutations of CCM genes cause pleiotropic effects, including defective autophagy, altered reactive oxygen species (ROS) homeostasis, and enhanced sensitivity to oxidative stress and inflammatory events, suggesting a novel unifying pathogenetic mechanism, and raising the possibility that CCM disease onset and severity are influenced by the presence of susceptibility and modifier genes. Consistently, genome-wide association studies (GWAS) in large and homogeneous cohorts of patients sharing the familial form of CCM disease and identical mutations in CCM genes have led to the discovery of distinct genetic modifiers of major disease severity phenotypes, such as development of numerous and large CCM lesions, and susceptibility to ICH. This review deals with the identification of genetic modifiers with a significant impact on inter-individual variability in CCM disease onset and severity, including highly polymorphic genes involved in oxidative stress, inflammatory and immune responses, such as cytochrome P450 monooxygenases (CYP), matrix metalloproteinases (MMP), and Toll-like receptors (TLR), pointing to their emerging prognostic value, and opening up new perspectives for risk stratification and personalized medicine strategies.

Global biogeography of chemosynthetic symbionts reveals both localized and globally distributed symbiont groups

In the ocean, most hosts acquire their symbionts from the environment. Due to the immense spatial scales involved, our understanding of the biogeography of hosts and symbionts in marine systems is patchy, although this knowledge is essential for understanding fundamental aspects of symbiosis such as host-symbiont specificity and evolution. Lucinidae is the most species-rich and widely distributed family of marine bivalves hosting autotrophic bacterial endosymbionts. Previous molecular surveys identified location-specific symbiont types that "promiscuously" form associations with multiple divergent cooccurring host species. This flexibility of host-microbe pairings is thought to underpin their global success, as it allows hosts to form associations with locally adapted symbionts. We used metagenomics to investigate the biodiversity, functional variability, and genetic exchange among the endosymbionts of 12 lucinid host species from across the globe. We report a cosmopolitan symbiont species, Candidatus Thiodiazotropha taylori, associated with multiple lucinid host species. Ca. T. taylori has achieved more success at dispersal and establishing symbioses with lucinids than any other symbiont described thus far. This discovery challenges our understanding of symbiont dispersal and location-specific colonization and suggests both symbiont and host flexibility underpin the ecological and evolutionary success of the lucinid symbiosis.

Mobile Gene Sequence Evolution within Individual Human Gut Microbiomes Is Better Explained by Gene-Specific Than Host-Specific Selective Pressures

Pangenomes—the cumulative set of genes encoded by a population or species—arise from the interplay of horizontal gene transfer, drift, and selection. The balance of these forces in shaping pangenomes has been debated, and studies to date focused on ancient evolutionary time scales have suggested that pangenomes generally confer niche adaptation to their bacterial hosts. To shed light on pangenome evolution on shorter evolutionary time scales, we inferred the selective pressures acting on mobile genes within individual human microbiomes from 176 Fiji islanders. We mapped metagenomic sequence reads to a set of known mobile genes to identify single nucleotide variants (SNVs) and calculated population genetic metrics to infer deviations from a neutral evolutionary model. We found that mobile gene sequence evolution varied more by gene family than by human social attributes, such as household or village. Patterns of mobile gene sequence evolution could be qualitatively recapitulated with a simple evolutionary simulation without the need to invoke the adaptive value of mobile genes to either bacterial or human hosts. These results stand in contrast with the apparent adaptive value of pangenomes over longer evolutionary time scales. In general, the most highly mobile genes (i.e., those present in more distinct bacterial host genomes) tend to have higher metagenomic read coverage and an excess of low-frequency SNVs, consistent with their rapid spread across multiple bacterial species in the gut. However, a subset of mobile genes—including those involved in defense mechanisms and secondary metabolism—showed a contrasting signature of intermediate-frequency SNVs, indicating species-specific selective pressures or negative frequency-dependent selection on these genes. Together, our evolutionary models and population genetic data show that gene-specific selective pressures predominate over human or bacterial host-specific pressures during the relatively short time scales of a human lifetime.

Host specificity of the gut microbiome

Developing general principles of host-microorganism interactions necessitates a robust understanding of the eco-evolutionary processes that structure microbiota. Phylosymbiosis, or patterns of microbiome composition that can be predicted by host phylogeny, is a unique framework for interrogating these processes. Identifying the contexts in which phylosymbiosis does and does not occur facilitates an evaluation of the relative importance of different ecological processes in shaping the microbial community. In this Review, we summarize the prevalence of phylosymbiosis across the animal kingdom on the basis of the current literature and explore the microbial community assembly processes and related host traits that contribute to phylosymbiosis. We find that phylosymbiosis is less prevalent in taxonomically richer microbiomes and hypothesize that this pattern is a result of increased stochasticity in the assembly of complex microbial communities. We also note that despite hosting rich microbiomes, mammals commonly exhibit phylosymbiosis. We hypothesize that this pattern is a result of a unique combination of mammalian traits, including viviparous birth, lactation and the co-evolution of haemochorial placentas and the eutherian immune system, which compound to ensure deterministic microbial community assembly. Examining both the individual and the combined importance of these traits in driving phylosymbiosis provides a new framework for research in this area moving forward.