Accurate and robust inference of microbial growth dynamics from metagenomic sequencing reveals personalized growth rates.

Patterns of sequencing coverage along a bacterial genome-summarized by a peak-to-trough ratio (PTR)-have been shown to accurately reflect microbial growth rates, revealing a new facet of microbial dynamics and host-microbe interactions. Here, we introduce Compute PTR (CoPTR): a tool for computing PTRs from complete reference genomes and assemblies. Using simulations and data from growth experiments in simple and complex communities, we show that CoPTR is more accurate than the current state of the art while also providing more PTR estimates overall. We further develop a theory formalizing a biological interpretation for PTRs. Using a reference database of 2935 species, we applied CoPTR to a case-control study of 1304 metagenomic samples from 106 individuals with inflammatory bowel disease. We show that growth rates are personalized, are only loosely correlated with relative abundances, and are associated with disease status. We conclude by showing how PTRs can be combined with relative abundances and metabolomics to investigate their effect on the microbiome.

Directional Gaussian Mixture Models of the Gut Microbiome Elucidate Microbial Spatial Structure.

The gut microbiome is spatially heterogeneous, with environmental niches contributing to the distribution and composition of microbial populations. A recently developed mapping technology, MaPS-seq, aims to characterize the spatial organization of the gut microbiome by providing data about local microbial populations. However, information about the global arrangement of these populations is lost by MaPS-seq. To address this, we propose a class of Gaussian mixture models (GMM) with spatial dependencies between mixture components in order to computationally recover the relative spatial arrangement of microbial communities. We demonstrate on synthetic data that our spatial models can identify global spatial dynamics, accurately cluster data, and improve parameter inference over a naive GMM. We applied our model to three MaPS-seq data sets taken from various regions of the mouse intestine. On cecal and distal colon data sets, we find our model accurately recapitulates known spatial behaviors of the gut microbiome, including compositional differences between mucus and lumen-associated populations. Our model also seems to capture the role of a pH gradient on microbial populations in the mouse ileum and proposes new behaviors as well. IMPORTANCE The spatial arrangement of the microbes in the gut microbiome is a defining characteristic of its behavior. Various experimental studies have attempted to provide glimpses into the mechanisms that contribute to microbial arrangements. However, many of these descriptions are qualitative. We developed a computational method that takes microbial spatial data and learns many of the experimentally validated spatial factors. We can then use our model to propose previously unknown spatial behaviors. Our results demonstrate that the gut microbiome, while exceptionally large, has predictable spatial patterns that can be used to help us understand its role in health and disease.

An Introduction to Whole-Metagenome Shotgun Sequencing Studies.

Microbial communities are found across diverse environments, including within and across the human body. As many microbes are unculturable in the lab, much of what is known about a microbiome-a collection of bacteria, fungi, archaea, and viruses inhabiting an environment--is from the sequencing of DNA from within the constituent community. Here, we provide an introduction to whole-metagenome shotgun sequencing studies, a ubiquitous approach for characterizing microbial communities, by reviewing three major research areas in metagenomics: assembly, community profiling, and functional profiling. Though not exhaustive, these areas encompass a large component of the metagenomics literature. We discuss each area in depth, the challenges posed by whole-metagenome shotgun sequencing, and approaches fundamental to the solutions of each. We conclude by discussing promising areas for future research. Though our emphasis is on the human microbiome, the methods discussed are broadly applicable across study systems.

Efficient and Accurate Inference of Mixed Microbial Population Trajectories from Longitudinal Count Data.

The recently completed second phase of the Human Microbiome Project has highlighted the relationship between dynamic changes in the microbiome and disease, motivating new microbiome study designs based on longitudinal sampling. Yet, analysis of such data is hindered by presence of technical noise, high dimensionality, and data sparsity. Here, we introduce LUMINATE (longitudinal microbiome inference and zero detection), a fast and accurate method for inferring relative abundances from noisy read count data. We demonstrate that LUMINATE is orders of magnitude faster than current approaches, with better or similar accuracy. We further show that LUMINATE can accurately distinguish biological zeros, when a taxon is absent from the community, from technical zeros, when a taxon is below the detection threshold. We conclude by demonstrating the utility of LUMINATE on a real dataset, showing that LUMINATE smooths trajectories observed from noisy data. LUMINATE is freely available from https://github.com/tyjo/luminate.

Compositional Lotka-Volterra describes microbial dynamics in the simplex.

Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical datasets only provide estimates of relative abundances. Here, we systematically investigate models of microbial dynamics in the simplex of relative abundances. We derive a new nonlinear dynamical system for microbial dynamics, termed "compositional" Lotka-Volterra (cLV), unifying approaches using generalized Lotka-Volterra (gLV) equations from community ecology and compositional data analysis. On three real datasets, we demonstrate that cLV recapitulates interactions between relative abundances implied by gLV. Moreover, we show that cLV is as accurate as gLV in forecasting microbial trajectories in terms of relative abundances. We further compare cLV to two other models of relative abundance dynamics motivated by common assumptions in the literature-a linear model in a log-ratio transformed space, and a linear model in the space of relative abundances-and provide evidence that cLV more accurately describes community trajectories over time. Finally, we investigate when information about direct effects can be recovered from relative data that naively provide information about only indirect effects. Our results suggest that strong effects may be recoverable from relative data, but more subtle effects are challenging to identify.

Genetic history from the Middle Neolithic to present on the Mediterranean island of Sardinia.

The island of Sardinia has been of particular interest to geneticists for decades. The current model for Sardinia's genetic history describes the island as harboring a founder population that was established largely from the Neolithic peoples of southern Europe and remained isolated from later Bronze Age expansions on the mainland. To evaluate this model, we generate genome-wide ancient DNA data for 70 individuals from 21 Sardinian archaeological sites spanning the Middle Neolithic through the Medieval period. The earliest individuals show a strong affinity to western Mediterranean Neolithic populations, followed by an extended period of genetic continuity on the island through the Nuragic period (second millennium BCE). Beginning with individuals from Phoenician/Punic sites (first millennium BCE), we observe spatially-varying signals of admixture with sources principally from the eastern and northern Mediterranean. Overall, our analysis sheds light on the genetic history of Sardinia, revealing how relationships to mainland populations shifted over time.

Inference of Population Structure from Time-Series Genotype Data.

Sequencing ancient DNA can offer direct probing of population history. Yet, such data are commonly analyzed with standard tools that assume DNA samples are all contemporary. We present DyStruct, a model and inference algorithm for inferring shared ancestry from temporally sampled genotype data. DyStruct explicitly incorporates temporal dynamics by modeling individuals as mixtures of unobserved populations whose allele frequencies drift over time. We develop an efficient inference algorithm for our model using stochastic variational inference. On simulated data, we show that DyStruct outperforms the current state of the art when individuals are sampled over time. Using a dataset of 296 modern and 80 ancient samples, we demonstrate DyStruct is able to capture a well-supported admixture event of steppe ancestry into modern Europe. We further apply DyStruct to a genome-wide dataset of 2,067 modern and 262 ancient samples used to study the origin of farming in the Near East. We show that DyStruct provides new insight into population history when compared with alternate approaches, within feasible run time.

FEAST: fast expectation-maximization for microbial source tracking.

A major challenge of analyzing the compositional structure of microbiome data is identifying its potential origins. Here, we introduce fast expectation-maximization microbial source tracking (FEAST), a ready-to-use scalable framework that can simultaneously estimate the contribution of thousands of potential source environments in a timely manner, thereby helping unravel the origins of complex microbial communities ( https://github.com/cozygene/FEAST ). The information gained from FEAST may provide insight into quantifying contamination, tracking the formation of developing microbial communities, as well as distinguishing and characterizing bacteria-related health conditions.

Comparative Population Genomics of African Montane Forest Mammals Support Population Persistence across a Climatic Gradient and Quaternary Climatic Cycles.

The Eastern Afromontane biodiversity hotspot (EABH) has the highest concentration of biodiversity in tropical Africa, yet few studies have investigated recent historical diversification processes in EABH lineages. Herein, we analyze restriction-site associated DNA-sequences (RAD-Seq) to study recent historical processes in co-distributed mouse (Hylomyscus) and shrew (Sylvisorex) species complexes, with an aim to better determine how historical paleoenvironmental processes might have contributed to the EABH's high diversity. We analyzed complete SNP matrices of > 50,000 RAD loci to delineate populations, reconstruct the history of isolation and admixture, and discover geographic patterns of genetic partitioning. These analyses demonstrate that persistently unsuitable habitat may have isolated multiple populations distributed across montane habitat islands in the Itombwe Massif and Albertine Rift to the west as well as Mt Elgon and Kenyan Highlands to the east. We detected low genetic diversity in Kenyan Highland populations of both genera, consistent with smaller historical population sizes in this region. We additionally tested predictions that Albertine Rift populations are older and more persistently isolated compared to the Kenyan Highlands. Phylogenetic analyses support greater historical isolation among Albertine Rift populations of both shrews and mice compared to the Kenyan Highlands and suggest that there are genetically isolated populations from both focal genera in the Itombwe Massif, Democratic Republic of Congo. The Albertine Rift ecoregion has the highest mammalian tropical forest species richness per unit area on earth. Our results clearly support accelerating efforts to conserve this diversity.