Determinants of microbiome composition: Insights from free-ranging hybrid zebras (Equus quagga × grevyi).

The composition of mammalian gut microbiomes is highly conserved within species, yet the mechanisms by which microbiome composition is transmitted and maintained within lineages of wild animals remain unclear. Mutually compatible hypotheses exist, including that microbiome fidelity results from inherited dietary habits, shared environmental exposure, morphophysiological filtering and/or maternal effects. Interspecific hybrids are a promising system in which to interrogate the determinants of microbiome composition because hybrids can decouple traits and processes that are otherwise co-inherited in their parent species. We used a population of free-living hybrid zebras (Equus quagga × grevyi) in Kenya to evaluate the roles of these four mechanisms in regulating microbiome composition. We analysed faecal DNA for both the trnL-P6 and the 16S rRNA V4 region to characterize the diets and microbiomes of the hybrid zebra and of their parent species, plains zebra (E. quagga) and Grevy's zebra (E. grevyi). We found that both diet and microbiome composition clustered by species, and that hybrid diets and microbiomes were largely nested within those of the maternal species, plains zebra. Hybrid microbiomes were less variable than those of either parent species where they co-occurred. Diet and microbiome composition were strongly correlated, although the strength of this correlation varied between species. These patterns are most consistent with the maternal-effects hypothesis, somewhat consistent with the diet hypothesis, and largely inconsistent with the environmental-sourcing and morphophysiological-filtering hypotheses. Maternal transmittance likely operates in conjunction with inherited feeding habits to conserve microbiome composition within species.

Chromatin activity identifies differential gene regulation across human ancestries.

BACKGROUND: Current evidence suggests that cis-regulatory elements controlling gene expression may be the predominant target of natural selection in humans and other species. Detecting selection acting on these elements is critical to understanding evolution but remains challenging because we do not know which mutations will affect gene regulation. RESULTS: To address this, we devise an approach to search for lineage-specific selection on three critical steps in transcriptional regulation: chromatin activity, transcription factor binding, and chromosomal looping. Applying this approach to lymphoblastoid cells from 831 individuals of either European or African descent, we find strong signals of differential chromatin activity linked to gene expression differences between ancestries in numerous contexts, but no evidence of functional differences in chromosomal looping. Moreover, we show that enhancers rather than promoters display the strongest signs of selection associated with sites of differential transcription factor binding. CONCLUSIONS: Overall, our study indicates that some cis-regulatory adaptation may be more easily detected at the level of chromatin than DNA sequence. This work provides a vast resource of genomic interaction data from diverse human populations and establishes a novel selection test that will benefit future study of regulatory evolution in humans and other species.

Reassessing the modularity of gene co-expression networks using the Stochastic Block Model.

Finding communities in gene co-expression networks is a common first step toward extracting biological insight from these complex datasets. Most community detection algorithms expect genes to be organized into assortative modules, that is, groups of genes that are more associated with each other than with genes in other groups. While it is reasonable to expect that these modules exist, using methods that assume they exist a priori is risky, as it guarantees that alternative organizations of gene interactions will be ignored. Here, we ask: can we find meaningful communities without imposing a modular organization on gene co-expression networks, and how modular are these communities? For this, we use a recently developed community detection method, the weighted degree corrected stochastic block model (SBM), that does not assume that assortative modules exist. Instead, the SBM attempts to efficiently use all information contained in the co-expression network to separate the genes into hierarchically organized blocks of genes. Using RNA-seq gene expression data measured in two tissues derived from an outbred population of Drosophila melanogaster , we show that (a) the SBM is able to find ten times as many groups as competing methods, that (b) several of those gene groups are not modular, and that (c) the functional enrichment for non-modular groups is as strong as for modular communities. These results show that the transcriptome is structured in more complex ways than traditionally thought and that we should revisit the long-standing assumption that modularity is the main driver of the structuring of gene co-expression networks.

Relative abundance data can misrepresent heritability of the microbiome.

BACKGROUND: Host genetics can shape microbiome composition, but to what extent it does, remains unclear. Like any other complex trait, this important question can be addressed by estimating the heritability (h2) of the microbiome-the proportion of variance in the abundance in each taxon that is attributable to host genetic variation. However, unlike most complex traits, microbiome heritability is typically based on relative abundance data, where taxon-specific abundances are expressed as the proportion of the total microbial abundance in a sample. RESULTS: We derived an analytical approximation for the heritability that one obtains when using such relative, and not absolute, abundances, based on an underlying quantitative genetic model for absolute abundances. Based on this, we uncovered three problems that can arise when using relative abundances to estimate microbiome heritability: (1) the interdependency between taxa can lead to imprecise heritability estimates. This problem is most apparent for dominant taxa. (2) Large sample size leads to high false discovery rates. With enough statistical power, the result is a strong overestimation of the number of heritable taxa in a community. (3) Microbial co-abundances lead to biased heritability estimates. CONCLUSIONS: We discuss several potential solutions for advancing the field, focusing on technical and statistical developments, and conclude that caution must be taken when interpreting heritability estimates and comparing values across studies. Video Abstract.

Capturing continuous, long timescale behavioral changes in Drosophila melanogaster postural data.

Animal behavior spans many timescales, from short, seconds-scale actions to circadian rhythms over many hours to life-long changes during aging. Most quantitative behavior studies have focused on short-timescale behaviors such as locomotion and grooming. Analysis of these data suggests there exists a hierarchy of timescales; however, the limited duration of these experiments prevents the investigation of the full temporal structure. To access longer timescales of behavior, we continuously recorded individual Drosophila melanogaster at 100 frames per second for up to 7 days at a time in featureless arenas on sucrose-agarose media. We use the deep learning framework SLEAP to produce a full-body postural data set for 47 individuals resulting in nearly 2 billion pose instances. We identify stereotyped behaviors such as grooming, proboscis extension, and locomotion and use the resulting ethograms to explore how the flies' behavior varies across time of day and days in the experiment. We find distinct circadian patterns in all of our stereotyped behavior and also see changes in behavior over the course of the experiment as the flies weaken and die.

Applying an evolutionary mismatch framework to understand disease susceptibility.

Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of "lifestyle" diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be "mismatched" and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions, with different health effects in "ancestral" versus "modern" environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the "matched" to "mismatched" spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.

Characterizing the landscape of gene expression variance in humans.

Gene expression variance has been linked to organismal function and fitness but remains a commonly neglected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease.

From GWAS to signal validation: An approach for estimating genetic effects while preserving genomic context.

Validating associations between genotypic and phenotypic variation remains a challenge, despite advancements in association studies. Common approaches for signal validation rely on gene-level perturbations, such as loss-of-function mutations or RNAi, which test the effect of genetic modifications usually not observed in nature. CRISPR-based methods can validate associations at the SNP level, but have significant drawbacks, including resulting off-target effects and being both time-consuming and expensive. Both approaches usually modify the genome of a single genetic background, limiting the generalizability of experiments. To address these challenges, we present a simple, low-cost experimental scheme for validating genetic associations at the SNP level in outbred populations. The approach involves genotyping live outbred individuals at a focal SNP, crossing homozygous individuals with the same genotype at that locus, and contrasting phenotypes across resulting synthetic outbred populations. We tested this method in Drosophila melanogaster, measuring the longevity effects of a polymorphism at a naturally-segregating cis-eQTL for the midway gene. Our results demonstrate the utility of this method in SNP-level validation of naturally occurring genetic variation regulating complex traits. This method provides a bridge between the statistical discovery of genotype-phenotype associations and their validation in the natural context of heterogeneous genomic contexts.

Evolutionary mismatch and the role of GxE interactions in human disease.

Globally, we are witnessing the rise of complex, non-communicable diseases (NCDs) related to changes in our daily environments. Obesity, asthma, cardiovascular disease, and type 2 diabetes are part of a long list of "lifestyle" diseases that were rare throughout human history but are now common. A key idea from anthropology and evolutionary biology-the evolutionary mismatch hypothesis-seeks to explain this phenomenon. It posits that humans evolved in environments that radically differ from the ones experienced by most people today, and thus traits that were advantageous in past environments may now be "mismatched" and disease-causing. This hypothesis is, at its core, a genetic one: it predicts that loci with a history of selection will exhibit "genotype by environment" (GxE) interactions and have differential health effects in ancestral versus modern environments. Here, we discuss how this concept could be leveraged to uncover the genetic architecture of NCDs in a principled way. Specifically, we advocate for partnering with small-scale, subsistence-level groups that are currently transitioning from environments that are arguably more "matched" with their recent evolutionary history to those that are more "mismatched". These populations provide diverse genetic backgrounds as well as the needed levels and types of environmental variation necessary for mapping GxE interactions in an explicit mismatch framework. Such work would make important contributions to our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and sociocultural contexts.

Natural selection of immune and metabolic genes associated with health in two lowland Bolivian populations.

A growing body of work has addressed human adaptations to diverse environments using genomic data, but few studies have connected putatively selected alleles to phenotypes, much less among underrepresented populations such as Amerindians. Studies of natural selection and genotype-phenotype relationships in underrepresented populations hold potential to uncover previously undescribed loci underlying evolutionarily and biomedically relevant traits. Here, we worked with the Tsimane and the Moseten, two Amerindian populations inhabiting the Bolivian lowlands. We focused most intensively on the Tsimane, because long-term anthropological work with this group has shown that they have a high burden of both macro and microparasites, as well as minimal cardiometabolic disease or dementia. We therefore generated genome-wide genotype data for Tsimane individuals to study natural selection, and paired this with blood mRNA-seq as well as cardiometabolic and immune biomarker data generated from a larger sample that included both populations. In the Tsimane, we identified 21 regions that are candidates for selective sweeps, as well as 5 immune traits that show evidence for polygenic selection (e.g., C-reactive protein levels and the response to coronaviruses). Genes overlapping candidate regions were strongly enriched for known involvement in immune-related traits, such as abundance of lymphocytes and eosinophils. Importantly, we were also able to draw on extensive phenotype information for the Tsimane and Moseten and link five regions (containing PSD4, MUC21 and MUC22, TOX2, ANXA6, and ABCA1) with biomarkers of immune and metabolic function. Together, our work highlights the utility of pairing evolutionary analyses with anthropological and biomedical data to gain insight into the genetic basis of health-related traits.

Dietary stress remodels the genetic architecture of lifespan variation in outbred Drosophila.

Evolutionary theory suggests that lifespan-reducing alleles should be purged from the gene pool, and yet decades of genome-wide association and model organism studies have shown that they persist. One potential explanation is that alleles that regulate lifespan do so only in certain environmental contexts. We exposed outbred Drosophila to control and high-sugar diets and genotyped more than 10,000 adult flies to track allele frequency changes over the course of a single adult lifespan. We identified thousands of lifespan-associated alleles associated with early versus late-life trade-offs, late-onset effects and genotype-by-environment interactions. Remarkably, a third of lifespan-associated genetic variation had environmentally dependent effects on lifespan. We find that lifespan-reducing alleles are often recently derived, have stronger effects on a high-sugar diet and show signatures of selection in wild Drosophila populations, consistent with the evolutionary mismatch hypothesis. Our results provide insight into the highly polygenic and context-dependent genetic architecture of lifespan variation and the evolutionary processes that shape this key trait.

Diverse environmental perturbations reveal the evolution and context-dependency of genetic effects on gene expression levels.

There is increasing appreciation that, in addition to being shaped by an individual's genotype and environment, most complex traits are also determined by poorly understood interactions between these two factors. So-called "genotype × environment" (G×E) interactions remain difficult to map at the organismal level but can be uncovered using molecular phenotypes. To do so at large scale, we used TM3'seq to profile transcriptomes across 12 cellular environments in 544 immortalized B cell lines from the 1000 Genomes Project. We mapped the genetic basis of gene expression levels across environments and revealed a context-dependent genetic architecture: The average heritability of gene expression levels increased in treatment relative to control conditions, and on average, each treatment revealed new expression quantitative trait loci (eQTLs) at 11% of genes. Across our experiments, 22% of all identified eQTLs were context-dependent, and this group was enriched for trait- and disease-associated loci. Further, evolutionary analyses suggested that positive selection has shaped G×E loci involved in responding to immune challenges and hormones but not to man-made chemicals. We hypothesize that this reflects a reduced opportunity for selection to act on responses to molecules recently introduced into human environments. Together, our work highlights the importance of considering an exposure's evolutionary history when studying and interpreting G×E interactions, and provides new insight into the evolutionary mechanisms that maintain G×E loci in human populations.

Drosophila melanogaster microbiome is shaped by strict filtering and neutrality along a latitudinal cline.

Microbiomes affect many aspects of host biology, but the eco-evolutionary forces that shape their diversity in natural populations remain poorly understood. Geographical gradients, such as latitudinal clines, generate predictable patterns in biodiversity at macroecological scales, but whether these macroscale processes apply to host-microbiome interactions is an open question. To address this question, we sampled the microbiomes of 13 natural populations of Drosophila melanogaster along a latitudinal cline in the eastern United States. The microbiomes were surprisingly consistent across the cline, as latitude did not predict either alpha or beta diversity. Only a narrow taxonomic range of bacteria were present in all microbiomes, indicating that strict taxonomic filtering by the host and neutral ecological dynamics are the primary factors shaping the fly microbiome. Our findings reveal the complexity of eco-evolutionary interactions shaping microbial variation in D. melanogaster and highlight the need for additional sampling of the microbiomes in natural populations along environmental gradients.

Precise Quantification of Behavioral Individuality From 80 Million Decisions Across 183,000 Flies.

Individual animals behave differently from each other. This variability is a component of personality and arises even when genetics and environment are held constant. Discovering the biological mechanisms underlying behavioral variability depends on efficiently measuring individual behavioral bias, a requirement that is facilitated by automated, high-throughput experiments. We compiled a large data set of individual locomotor behavior measures, acquired from over 183,000 fruit flies walking in Y-shaped mazes. With this data set we first conducted a "computational ethology natural history" study to quantify the distribution of individual behavioral biases with unprecedented precision and examine correlations between behavioral measures with high power. We discovered a slight, but highly significant, left-bias in spontaneous locomotor decision-making. We then used the data to evaluate standing hypotheses about biological mechanisms affecting behavioral variability, specifically: the neuromodulator serotonin and its precursor transporter, heterogametic sex, and temperature. We found a variety of significant effects associated with each of these mechanisms that were behavior-dependent. This indicates that the relationship between biological mechanisms and behavioral variability may be highly context dependent. Going forward, automation of behavioral experiments will likely be essential in teasing out the complex causality of individuality.

Study Protocol: Interactive Dynamics of Coral Reef Fisheries and the Nutrition Transition in Kiribati.

The Kiribati 2019 Integrated Household Income and Expenditure Survey (Integrated HIES) embeds novel ecological and human health research into an ongoing social and economic survey infrastructure implemented by the Pacific Community in partnership with national governments. This study seeks to describe the health status of a large, nationally representative sample of a geographically and socially diverse I-Kiribati population through multiple clinical measurements and detailed socio-economic surveys, while also conducting supporting food systems research on ecological, social, and institutional drivers of change. The specific hypotheses within this research relate to access to seafood and the potential nutritional and health benefits of these foods. We conducted this research in 21 of the 23 inhabited islands of Kiribati, excluding the two inhabited islands-Kanton Islands in the Phoenix Islands group with a population of 41 persons (2020 census) and Banaba Island in the Gilbert Islands group with a population of 333 persons (2020 census)-and focusing exclusively on the remaining islands in the Gilbert and Line Islands groups. Within this sample, we focused our intensive human health and ecological research in 10 of the 21 selected islands to examine the relationship between ecological conditions, resource governance, food system dynamics, and dietary patterns. Ultimately, this research has created a baseline for future Integrated HIES assessments to simultaneously monitor change in ecological, social, economic, and human health conditions and how they co-vary over time.

Natural selection for imprecise vertical transmission in host-microbiota systems.

How and when the microbiome modulates host adaptation remains an evolutionary puzzle, despite evidence that the extended genetic repertoire of the microbiome can shape host phenotypes and fitness. One complicating factor is that the microbiome is often transmitted imperfectly across host generations, leading to questions about the degree to which the microbiome contributes to host adaptation. Here, using an evolutionary model, we demonstrate that decreasing vertical transmission fidelity can increase microbiome variation, and thus phenotypic variation, across hosts. When the most beneficial microbial genotypes change unpredictably from one generation to the next (for example, in variable environments), hosts can maximize fitness by increasing the microbiome variation among offspring, as this improves the chance of there being an offspring with the right microbial combination for the next generation. Imperfect vertical transmission can therefore be adaptive in varying environments. We characterize how selection on vertical transmission is shaped by environmental conditions, microbiome changes during host development and the contribution of other factors to trait variation. We illustrate how environmentally dependent microbial effects can favour intermediate transmission and set our results in the context of examples from natural systems. We also suggest research avenues to empirically test our predictions. Our model provides a basis to understand the evolutionary pathways that potentially led to the wide diversity of microbe transmission patterns found in nature.

The structure of behavioral variation within a genotype.

Individual animals vary in their behaviors. This is true even when they share the same genotype and were reared in the same environment. Clusters of covarying behaviors constitute behavioral syndromes, and an individual's position along such axes of covariation is a representation of their personality. Despite these conceptual frameworks, the structure of behavioral covariation within a genotype is essentially uncharacterized and its mechanistic origins unknown. Passing hundreds of inbred Drosophila individuals through an experimental pipeline that captured hundreds of behavioral measures, we found sparse but significant correlations among small sets of behaviors. Thus, the space of behavioral variation has many independent dimensions. Manipulating the physiology of the brain, and specific neural populations, altered specific correlations. We also observed that variation in gene expression can predict an individual's position on some behavioral axes. This work represents the first steps in understanding the biological mechanisms determining the structure of behavioral variation within a genotype.

Ancestral polymorphisms shape the adaptive radiation of Metrosideros across the Hawaiian Islands.

Some of the most spectacular adaptive radiations begin with founder populations on remote islands. How genetically limited founder populations give rise to the striking phenotypic and ecological diversity characteristic of adaptive radiations is a paradox of evolutionary biology. We conducted an evolutionary genomics analysis of genus Metrosideros, a landscape-dominant, incipient adaptive radiation of woody plants that spans a striking range of phenotypes and environments across the Hawaiian Islands. Using nanopore-sequencing, we created a chromosome-level genome assembly for Metrosideros polymorpha var. incana and analyzed whole-genome sequences of 131 individuals from 11 taxa sampled across the islands. Demographic modeling and population genomics analyses suggested that Hawaiian Metrosideros originated from a single colonization event and subsequently spread across the archipelago following the formation of new islands. The evolutionary history of Hawaiian Metrosideros shows evidence of extensive reticulation associated with significant sharing of ancestral variation between taxa and secondarily with admixture. Taking advantage of the highly contiguous genome assembly, we investigated the genomic architecture underlying the adaptive radiation and discovered that divergent selection drove the formation of differentiation outliers in paired taxa representing early stages of speciation/divergence. Analysis of the evolutionary origins of the outlier single nucleotide polymorphisms (SNPs) showed enrichment for ancestral variations under divergent selection. Our findings suggest that Hawaiian Metrosideros possesses an unexpectedly rich pool of ancestral genetic variation, and the reassortment of these variations has fueled the island adaptive radiation.

The microbiome extends host evolutionary potential.

The microbiome shapes many host traits, yet the biology of microbiomes challenges traditional evolutionary models. Here, we illustrate how integrating the microbiome into quantitative genetics can help untangle complexities of host-microbiome evolution. We describe two general ways in which the microbiome may affect host evolutionary potential: by shifting the mean host phenotype and by changing the variance in host phenotype in the population. We synthesize the literature across diverse taxa and discuss how these scenarios could shape the host response to selection. We conclude by outlining key avenues of research to improve our understanding of the complex interplay between hosts and microbiomes.