Uncovering the drivers of host-associated microbiota with joint species distribution modelling

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

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

A mixed-model approach for estimating drivers of microbiota community composition and differential taxonomic abundance

Next-generation sequencing (NGS) and metabarcoding approaches are increasingly applied to wild animal populations, but there is a disconnect between the widely applied generalized linear mixed model (GLMM) approaches commonly used to study phenotypic variation and the statistical toolkit from community ecology typically applied to metabarcoding data. Here, we describe the suitability of a novel GLMM-based approach for analyzing the taxon-specific sequence read counts derived from standard metabarcoding data. This approach allows decomposition of the contribution of different drivers to variation in community composition (e.g., age, season, individual) via interaction terms in the model random-effects structure. We provide guidance to implementing this approach and show how these models can identify how responsible specific taxonomic groups are for the effects attributed to different drivers. We applied this approach to two cross-sectional data sets from the Soay sheep population of St. Kilda. GLMMs showed agreement with dissimilarity-based approaches highlighting the substantial contribution of age and minimal contribution of season to microbiota community compositions, and simultaneously estimated the contribution of other technical and biological factors. We further used model predictions to show that age effects were principally due to increases in taxa of the phylum Bacteroidetes and declines in taxa of the phylum Firmicutes. This approach offers a powerful means for understanding the influence of drivers of community structure derived from metabarcoding data. We discuss how our approach could be readily adapted to allow researchers to estimate contributions of additional factors such as host or microbe phylogeny to answer emerging questions surrounding the ecological and evolutionary roles of within-host communities. IMPORTANCE NGS and fecal metabarcoding methods have provided powerful opportunities to study the wild gut microbiome. A wealth of data is, therefore, amassing across wild systems, generating the need for analytical approaches that can appropriately investigate simultaneous factors at the host and environmental scale that determine the composition of these communities. Here, we describe a generalized linear mixed-effects model (GLMM) approach to analyze read count data from metabarcoding of the gut microbiota, allowing us to quantify the contributions of multiple host and environmental factors to within-host community structure. Our approach provides outputs that are familiar to a majority of field ecologists and can be run using any standard mixed-effects modeling packages. We illustrate this approach using two metabarcoding data sets from the Soay sheep population of St. Kilda investigating age and season effects as worked examples.

An Overview of Modern Applications of Negative Binomial Modelling in Ecology and Biodiversity

Negative binomial modelling is one of the most commonly used statistical tools for analysing count data in ecology and biodiversity research. This is not surprising given the prevalence of overdispersion (i.e., evidence that the variance is greater than the mean) in many biological and ecological studies. Indeed, overdispersion is often indicative of some form of biological aggregation process (e.g., when species or communities cluster in groups). If overdispersion is ignored, the precision of model parameters can be severely overestimated and can result in misleading statistical inference. In this article, we offer some insight as to why the negative binomial distribution is becoming, and arguably should become, the default starting distribution (as opposed to assuming Poisson counts) for analysing count data in ecology and biodiversity research. We begin with an overview of traditional uses of negative binomial modelling, before examining several modern applications and opportunities in modern ecology/biodiversity where negative binomial modelling is playing a critical role, from generalisations based on exploiting its Poisson-gamma mixture formulation in species distribution models and occurrence data analysis, to estimating animal abundance in negative binomial N-mixture models, and biodiversity measures via rank abundance distributions. Comparisons to other common models for handling overdispersion on real data are provided. We also address the important issue of software, and conclude with a discussion of future directions for analysing ecological and biological data with negative binomial models. In summary, we hope this overview will stimulate the use of negative binomial modelling as a starting point for the analysis of count data in ecology and biodiversity studies.

Robust bacterial co-occurence community structures are independent of r- and K-selection history

Selection for bacteria which are K-strategists instead of r-strategists has been shown to improve fish health and survival in aquaculture. We considered an experiment where microcosms were inoculated with natural seawater and the selection regime was switched from K-selection (by continuous feeding) to r-selection (by pulse feeding) and vice versa. We found the networks of significant co-occurrences to contain clusters of taxonomically related bacteria having positive associations. Comparing this with the time dynamics, we found that the clusters most likely were results of similar niche preferences of the involved bacteria. In particular, the distinction between r- or K-strategists was evident. Each selection regime seemed to give rise to a specific pattern, to which the community converges regardless of its prehistory. Furthermore, the results proved robust to parameter choices in the analysis, such as the filtering threshold, level of random noise, replacing absolute abundances with relative abundances, and the choice of similarity measure. Even though our data and approaches cannot directly predict ecological interactions, our approach provides insights on how the selection regime affects the composition of the microbial community, providing a basis for aquaculture experiments targeted at eliminating opportunistic fish pathogens.

Modelling spatial patterns in host-associated microbial communities

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.

Host specificity of microbiome assembly and its fitness effects in phytoplankton

Insights into symbiosis between eukaryotic hosts and their microbiomes have shifted paradigms on what determines host fitness, ecology, and behavior. Questions remain regarding the roles of host versus environment in shaping microbiomes, and how microbiome composition affects host fitness. Using a model system in ecology, phytoplankton, we tested whether microbiomes are host-specific, confer fitness benefits that are host-specific, and remain conserved in time in their composition and fitness effects. We used an experimental approach in which hosts were cleaned of bacteria and then exposed to bacterial communities from natural environments to permit recruitment of microbiomes. We found that phytoplankton microbiomes consisted of a subset of taxa recruited from these natural environments. Microbiome recruitment was host-specific, with host species explaining more variation in microbiome composition than environment. While microbiome composition shifted and then stabilized over time, host specificity remained for dozens of generations. Microbiomes increased host fitness, but these fitness effects were host-specific for only two of the five species. The shifts in microbiome composition over time amplified fitness benefits to the hosts. Overall, this work solidifies the importance of host factors in shaping microbiomes and elucidates the temporal dynamics of microbiome compositional and fitness effects.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Host development overwhelms environmental dispersal in governing the ecological succession of zebrafish gut microbiota

Clarifying mechanisms underlying the ecological succession of gut microbiota is a central theme of gut ecology. Under experimental manipulations of zebrafish hatching and rearing environments, we test our core hypothesis that the host development will overwhelm environmental dispersal in governing fish gut microbial community succession due to host genetics, immunology, and gut nutrient niches. We find that zebrafish developmental stage substantially explains the gut microbial community succession, whereas the environmental effects do not significantly affect the gut microbiota succession from larvae to adult fish. The gut microbiotas of zebrafish are clearly separated according to fish developmental stages, and the degree of homogeneous selection governing gut microbiota succession is increasing with host development. This study advances our mechanistic understanding of the gut microbiota assembly and succession by integrating the host and environmental effects, which also provides new insights into the gut ecology of other aquatic animals.

Assuming independence in spatial latent variable models: Consequences and implications of misspecification

Multivariate spatial data, where multiple responses are simultaneously recorded across spatially indexed observational units, are routinely collected in a wide variety of disciplines. For example, the Southern Ocean Continuous Plankton Recorder survey collects records of zooplankton communities in the Indian sector of the Southern Ocean, with the aim of identifying and quantifying spatial patterns in biodiversity in response to environmental change. One increasingly popular method for modeling such data is spatial generalized linear latent variable models (GLLVMs), where the correlation across sites is captured by a spatial covariance function in the latent variables. However, little is known about the impact of misspecifying the latent variable correlation structure on inference of various parameters in such models. To address this gap in the literature, we investigate how misspecifying and assuming independence for the latent variables' correlation structure impacts estimation and inference in spatial GLLVMs. Through both theory and numerical studies, we show that performance of maximum likelihood estimation and inference on regression coefficients under misspecification depends on a combination of the response type, the magnitude of true regression coefficient, and the corresponding loadings, and, most importantly, whether the corresponding covariate is (also) spatially correlated. On the other hand, estimation and inference of truly nonzero loadings and prediction of latent variables is consistently not robust to misspecification of the latent variable correlation structure.

Comparable Ecological Processes Govern the Temporal Succession of Gut Bacteria and Microeukaryotes as Shrimp Aged

Understanding the rules that govern the successions of gut microbiota is prerequisite for testing general ecological theories and sustaining a desirable microbiota. However, the ignorance of microeukaryotes raises the question of whether gut microeukaryotes are assembled according to the same rules as bacteria. We tracked the shrimp gut bacterial and microeukaryotic communities by a longitudinal dense sampling. The successions of both domains were significantly correlated with host age, with relatively stable microeukaryotic communities in adult shrimp. Gut microeukaryotes exhibited significantly higher turnover rate, but fewer transient species, lower proportion of temporal generalists, and narrower habitat niche breadth than bacteria. The γ-diversity partitioning analysis revealed that the successions of gut microbiotas were primarily ascribed to the high dissimilarity as shrimp aged ([Formula: see text]_IntraTimes), whereas the relative importance of [Formula: see text]_IntraTimes was significantly higher for microeukaryotes than that for bacteria. Compared with contrasting ecological processes in governing free-living bacteria and microeukaryotes, the ecological patterns were comparable between host-associated gut counterparts. However, the gut microeukaryotes were governed more strongly by deterministic selection relative to nestedness compared with the gut bacteria, which supports the "size-plasticity" hypothesis. Our results highlight the importance of independently interpreting free-living and host-associated meta-communities for a comprehensive understanding of the processes that govern microbial successions.

Gut microbial diversity across a contact zone for California voles: Implications for lineage divergence of hosts and mitonuclear mismatch in the assembly of the mammalian gut microbiome

Gut microbial diversity is thought to reflect the co-evolution of microbes and their hosts as well as current host-specific attributes such as genetic background and environmental setting. To explore interactions among these parameters, we characterized variation in gut microbiome composition of California voles (Microtus californicus) across a contact zone between two recently diverged lineages of this species. Because this contact zone contains individuals with mismatched mitochondrial-nuclear genomes (cybrids), it provides an important opportunity to explore how different components of the genotype contribute to gut microbial diversity. Analyses of bacterial 16S rRNA sequences and joint species distribution modelling revealed that host genotypes and genetic differentiation among host populations together explained more than 50% of microbial community variation across our sampling transect. The ranked importance (most to least) of factors contributing to gut microbial diversity in our study populations were: genome-wide population differentiation, local environmental conditions, and host genotypes. However, differences in microbial communities among vole populations (β-diversity) did not follow patterns of lineage divergence (i.e., phylosymbiosis). Instead, among-population variation was best explained by the spatial distribution of hosts, as expected if the environment is a primary source of gut microbial diversity (i.e., dispersal limitation hypothesis). Across the contact zone, several bacterial taxa differed in relative abundance between the two parental lineages as well as among individuals with mismatched mitochondrial and nuclear genomes. Thus, genetic divergence among host lineages and mitonuclear genomic mismatches may also contribute to microbial diversity by altering interactions between host genomes and gut microbiota (i.e., hologenome speciation hypothesis).

Biochemical and structural insights into the cytochrome P450 reductase from Candida tropicalis

Cytochrome P450 reductases (CPRs) are diflavin oxidoreductases that supply electrons to type II cytochrome P450 monooxygenases (CYPs). In addition, it can also reduce other proteins and molecules, including cytochrome c, ferricyanide, and different drugs. Although various CPRs have been functionally and structurally characterized, the overall mechanism and its interaction with different redox acceptors remain elusive. One of the main problems regarding electron transfer between CPRs and CYPs is the so-called "uncoupling", whereby NAD(P)H derived electrons are lost due to the reduced intermediates' (FAD and FMN of CPR) interaction with molecular oxygen. Additionally, the decay of the iron-oxygen complex of the CYP can also contribute to loss of reducing equivalents during an unproductive reaction cycle. This phenomenon generates reactive oxygen species (ROS), leading to an inefficient reaction. Here, we present the study of the CPR from Candida tropicalis (CtCPR) lacking the hydrophobic N-terminal part (Δ2-22). The enzyme supports the reduction of cytochrome c and ferricyanide, with an estimated 30% uncoupling during the reactions with cytochrome c. The ROS produced was not influenced by different physicochemical conditions (ionic strength, pH, temperature). The X-ray structures of the enzyme were solved with and without its cofactor, NADPH. Both CtCPR structures exhibited the closed conformation. Comparison with the different solved structures revealed an intricate ionic network responsible for the regulation of the open/closed movement of CtCPR.

Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research

To date, most insights into the processes shaping vertebrate gut microbiomes have emerged from studies with cross-sectional designs. While this approach has been valuable, emerging time series analyses on vertebrate gut microbiomes show that gut microbial composition can change rapidly from 1 day to the next, with consequences for host physical functioning, health, and fitness. Hence, the next frontier of microbiome research will require longitudinal perspectives. Here we argue that primatologists, with their traditional focus on tracking the lives of individual animals and familiarity with longitudinal fecal sampling, are well positioned to conduct research at the forefront of gut microbiome dynamics. We begin by reviewing some of the most important ecological processes governing microbiome change over time, and briefly summarizing statistical challenges and approaches to microbiome time series analysis. We then introduce five questions of general interest to microbiome science where we think field-based primate studies are especially well positioned to fill major gaps: (a) Do early life events shape gut microbiome composition in adulthood? (b) Do shifting social landscapes cause gut microbial change? (c) Are gut microbiome phenotypes heritable across variable environments? (d) Does the gut microbiome show signs of host aging? And (e) do gut microbiome composition and dynamics predict host health and fitness? For all of these questions, we highlight areas where primatologists are uniquely positioned to make substantial contributions. We review preliminary evidence, discuss possible study designs, and suggest future directions.

Good neighbors aplenty: fungal endophytes rarely exhibit competitive exclusion patterns across a span of woody habitats

Environmental forces and biotic interactions, both positive and negative, structure ecological communities, but their relative roles remain obscure despite strong theory. For instance, ecologically similar species, based on the principle of limiting similarity, are expected to be most competitive and show negative interactions. Specious communities that assemble along broad environmental gradients afford the most power to test theory, but the communities often are difficult to quantify. Microbes, specifically fungal endophytes of wood, are especially suited for testing community assembly theory because they are relatively easy to sample across a comprehensive range of environmental space with clear axes of variation. Moreover, endophytes mediate key forest carbon cycle processes, and although saprophytic fungi from dead wood typically compete, endophytic fungi in living wood may enhance success through cooperative symbioses. To classify interactions within endophyte communities, we analyzed fungal DNA barcode variation across 22 woody plant species growing in woodlands near Richmond, New South Wales, Australia. We estimated the response of endophytes to the measured wood environment (i.e., 11 anatomical and chemical wood traits) and each other using latent-variable models and identified recurrent communities across wood environments using model-based classification. We used this information to evaluate whether (1) co-occurrence patterns are consistent with strong competitive exclusion, and (2) a priori classifications by trophic mode and phylum distinguish taxa that are more likely to have positive vs. negative associations under the principle of limiting similarity. Fungal endophytes were diverse (mean = 140 taxa/sample), with differences in community composition structured by wood traits. Variation in wood water content and carbon concentration were associated with especially large community shifts. Surprisingly, after accounting for wood traits, fungal species were still more than three times more likely to have positive than negative co-occurrence patterns. That is, patterns consistent with strong competitive exclusion were rare, and positive interactions among fungal endophytes were more common than expected. Confirming the frequency of positive vs. negative interactions among fungal taxa requires experimental tests, and our findings establish clear paths for further study. Evidence to date intriguingly suggests that, across a wide range of wood traits, cooperation may outweigh combat for these fungi.

Spatiotemporal successions of shrimp gut microbial colonization: high consistency despite distinct species pool

Aquatic animals encounter suites of novel planktonic microbes during their development. Although hosts have been shown to exert strong selection on their gut microbiota from surrounding environment, to what extent and the generality that the gut microbiota and the underlying ecological processes are affected by biotic and abiotic variations are largely unclear. Here, these concerns were explored by coupling spatiotemporal data on gut and rearing water bacterial communities with environmental variables over shrimp life stages at spatially distant locations. Shrimp gut microbiotas significantly changed mirroring their development, as evidenced by gut bacterial signatures of shrimp life stage contributing 95.5% stratification accuracy. Shrimp sourced little (2.6%-15.8%) of their gut microbiota from their rearing water. This microbial resistance was reflected by weak compositional differences between shrimp farming spatially distinct locations where species pools were distinct. Consistently, the assembly of shrimp gut microbiota was not adequately explained by the rearing water variables and bacterial community, but rather by host-age-associated biotic features. The successions of shrimp gut microbiota were droved by replacement (βsim), rather than by nestedness (βnes), while those of bacterioplankton communities were equally governed by replacement and nestedness. Our study highlights how shrimp gut bacterial community assembly is coupled to their development, rearing species pool, and that the successional pattern of host-associated communities is differed from that of free-living bacteria.

Endemic infection can shape exposure to novel pathogens: Pathogen co-occurrence networks in the Serengeti lions

Pathogens are embedded in a complex network of microparasites that can collectively or individually alter disease dynamics and outcomes. Endemic pathogens that infect an individual in the first years of life, for example, can either facilitate or compete with subsequent pathogens thereby exacerbating or ameliorating morbidity and mortality. Pathogen associations are ubiquitous but poorly understood, particularly in wild populations. We report here on 10 years of serological and molecular data in African lions, leveraging comprehensive demographic and behavioural data to test if endemic pathogens shape subsequent infection by epidemic pathogens. We combine network and community ecology approaches to assess broad network structure and characterise associations between pathogens across spatial and temporal scales. We found significant non‐random structure in the lion‐pathogen co‐occurrence network and identified both positive and negative associations between endemic and epidemic pathogens. Our results provide novel insights on the complex associations underlying pathogen co‐occurrence networks.