Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples

Microbiome-wide association studies link dynamic microbial consortia to disease

Rapid advances in DNA sequencing, metabolomics, proteomics and computational tools are dramatically increasing access to the microbiome and identification of its links with disease. In particular, time-series studies and multiple molecular perspectives are facilitating microbiome-wide association studies, which are analogous to genome-wide association studies. Early findings point to actionable outcomes of microbiome-wide association studies, although their clinical application has yet to be approved. An appreciation of the complexity of interactions among the microbiome and the host's diet, chemistry and health, as well as determining the frequency of observations that are needed to capture and integrate this dynamic interface, is paramount for developing precision diagnostics and therapies that are based on the microbiome.

plasmidSPAdes: assembling plasmids from whole genome sequencing data

MOTIVATION

Plasmids are stably maintained extra-chromosomal genetic elements that replicate independently from the host cell's chromosomes. Although plasmids harbor biomedically important genes, (such as genes involved in virulence and antibiotics resistance), there is a shortage of specialized software tools for extracting and assembling plasmid data from whole genome sequencing projects.

RESULTS

We present the plasmidSPAdes algorithm and software tool for assembling plasmids from whole genome sequencing data and benchmark its performance on a diverse set of bacterial genomes.

AVAILABILITY AND IMPLEMENTATION

plasmidSPAdes is publicly available at http://spades.bioinf.spbau.ru/plasmidSPAdes/ CONTACT: d.antipov@spbu.ruSupplementary information: Supplementary data are available at Bioinformatics online.

Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond

Metagenomic approaches are now commonly used in microbial ecology to study microbial communities in more detail, including many strains that cannot be cultivated in the laboratory. Bioinformatic analyses make it possible to mine huge metagenomic datasets and discover general patterns that govern microbial ecosystems. However, the findings of typical metagenomic and bioinformatic analyses still do not completely describe the ecology and evolution of microbes in their environments. Most analyses still depend on straightforward sequence similarity searches against reference databases. We herein review the current state of metagenomics and bioinformatics in microbial ecology and discuss future directions for the field. New techniques will allow us to go beyond routine analyses and broaden our knowledge of microbial ecosystems. We need to enrich reference databases, promote platforms that enable meta- or comprehensive analyses of diverse metagenomic datasets, devise methods that utilize long-read sequence information, and develop more powerful bioinformatic methods to analyze data from diverse perspectives.

Species-function relationships shape ecological properties of the human gut microbiome

Despite recent progress, the organization and ecological properties of the intestinal microbial ecosystem remain under-investigated. Here, using a manually curated metabolic module framework for (meta-)genomic data analysis, we studied species-function relationships in gut microbial genomes and microbiomes. Half of gut-associated species were found to be generalists regarding overall substrate preference, but we observed significant genus-level metabolic diversification linked to bacterial life strategies. Within each genus, metabolic consistency varied significantly, being low in Firmicutes genera and higher in Bacteroides. Differentiation of fermentable substrate degradation potential contributed to metagenomic functional repertoire variation between individuals, with different enterotypes showing distinct saccharolytic/proteolytic/lipolytic profiles. Finally, we found that module-derived functional redundancy was reduced in the low-richness Bacteroides enterotype, potentially indicating a decreased resilience to perturbation, in line with its frequent association to dysbiosis. These results provide insights into the complex structure of gut microbiome-encoded metabolic properties and emphasize the importance of functional and ecological assessment of gut microbiome variation in clinical studies.

Meta-omics in Inflammatory Bowel Disease Research: Applications, Challenges, and Guidelines

Meta-omics [metagenomics, metatranscriptomics, and metaproteomics] are rapidly expanding our knowledge of the gut microbiota in health and disease. These technologies are increasingly used in inflammatory bowel disease [IBD] research. Yet, meta-omics data analysis, interpretation, and among-study comparison remain challenging. In this review we discuss the role these techniques are playing in IBD research, highlighting their strengths and limitations. We give guidelines on proper sample collection and preparation methods, and on performing the analyses and interpreting the results, reporting available user-friendly tools and pipelines.

Gut microbiome and lipid metabolism: from associations to mechanisms

PURPOSE OF REVIEW

The gut microbiome has now been convincingly linked to human metabolic health but the underlying causality and mechanisms remain poorly understood. This review focuses on the recent progress in establishing the associations between gut microbiome species and lipid metabolism in humans and discusses how to move from association toward causality and mechanistic understanding, which is essential knowledge to bring the observed associations into clinical use.

RECENT FINDINGS

Recent population-based association studies have shown that the gut microbiota composition can explain a substantial proportion of the interindividual variation in blood triglycerides and HDL-cholesterol level and predict metabolic response to diet and drug. Faecal transplantation has suggested that this is a causal effect of microbiome on host metabolism, although the underlying mechanism remains largely unexplored.

SUMMARY

The gut microbiome is transitioning from being a 'missing' organ to a potential target for therapeutic applications. Due to the complex interplay between the gut microbiome, the host genome, and diet, a systematic approach is required to pave the way for therapeutic development.

A Model-Based Approach For Species Abundance Quantification Based On Shotgun Metagenomic Data

The human microbiome, which includes the collective microbes residing in or on the human body, has a profound influence on the human health. DNA sequencing technology has made the large-scale human microbiome studies possible by using shotgun metagenomic sequencing. One important aspect of data analysis of such metagenomic data is to quantify the bacterial abundances based on the metagenomic sequencing data. Existing methods almost always quantify such abundances one sample at a time, which ignore certain systematic differences in read coverage along the genomes due to GC contents, copy number variation and the bacterial origin of replication. In order to account for such differences in read counts, we propose a multi-sample Poisson model to quantify microbial abundances based on read counts that are assigned to species-specific taxonomic markers. Our model takes into account the marker-specific effects when normalizing the sequencing count data in order to obtain more accurate quantification of the species abundances. Compared to currently available methods on simulated data and real data sets, our method has demonstrated an improved accuracy in bacterial abundance quantification, which leads to more biologically interesting results from downstream data analysis.

High definition for systems biology of microbial communities: metagenomics gets genome-centric and strain-resolved

The systems biology of microbial communities, organismal communities inhabiting all ecological niches on earth, has in recent years been strongly facilitated by the rapid development of experimental, sequencing and data analysis methods. Novel experimental approaches and binning methods in metagenomics render the semi-automatic reconstructions of near-complete genomes of uncultivable bacteria possible, while advances in high-resolution amplicon analysis allow for efficient and less biased taxonomic community characterization. This will also facilitate predictive modeling approaches, hitherto limited by the low resolution of metagenomic data. In this review, we pinpoint the most promising current developments in metagenomics. They facilitate microbial systems biology towards a systemic understanding of mechanisms in microbial communities with scopes of application in many areas of our daily life.

Antibiotics as a selective driver for conjugation dynamics

It is generally assumed that antibiotics can promote horizontal gene transfer. However, because of a variety of confounding factors that complicate the interpretation of previous studies, the mechanisms by which antibiotics modulate horizontal gene transfer remain poorly understood. In particular, it is unclear whether antibiotics directly regulate the efficiency of horizontal gene transfer, serve as a selection force to modulate population dynamics after such gene transfer has occurred, or both. Here, we address this question by quantifying conjugation dynamics in the presence and absence of antibiotic-mediated selection. Surprisingly, we find that sublethal concentrations of antibiotics from the most widely used classes do not significantly increase the conjugation efficiency. Instead, our modelling and experimental results demonstrate that conjugation dynamics are dictated by antibiotic-mediated selection, which can both promote and suppress conjugation dynamics. Our findings suggest that the contribution of antibiotics to the promotion of horizontal gene transfer may have been overestimated. These findings have implications for designing effective antibiotic treatment protocols and for assessing the risks of antibiotic use.

Network-based metabolic analysis and microbial community modeling

Network inference is being applied to studies of microbial ecology to visualize and characterize microbial communities. Network representations can allow examination of the underlying organizational structure of a microbial community, and identification of key players or environmental conditions that influence community assembly and stability. Microbial co-association networks provide information on the dynamics of community structure as a function of time or other external variables. Community metabolic networks can provide a mechanistic link between species through identification of metabolite exchanges and species specific resource requirements. When used together, co-association networks and metabolic networks can provide a more in-depth view of the hidden rules that govern the stability and dynamics of microbial communities.

Personalized Nutrition by Prediction of Glycemic Responses

Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.

Analysis of Lung Microbiota in Bronchoalveolar Lavage, Protected Brush and Sputum Samples from Subjects with Mild-To-Moderate Cystic Fibrosis Lung Disease

Individuals with cystic fibrosis (CF) often acquire chronic lung infections that lead to irreversible damage. We sought to examine regional variation in the microbial communities in the lungs of individuals with mild-to-moderate CF lung disease, to examine the relationship between the local microbiota and local damage, and to determine the relationships between microbiota in samples taken directly from the lung and the microbiota in spontaneously expectorated sputum. In this initial study, nine stable, adult CF patients with an FEV₁>50% underwent regional sampling of different lobes of the right lung by bronchoalveolar lavage (BAL) and protected brush (PB) sampling of mucus plugs. Sputum samples were obtained from six of the nine subjects immediately prior to the procedure. Microbial community analysis was performed on DNA extracted from these samples and the extent of damage in each lobe was quantified from a recent CT scan. The extent of damage observed in regions of the right lung did not correlate with specific microbial genera, levels of community diversity or composition, or bacterial genome copies per ml of BAL fluid. In all subjects, BAL fluid from different regions of the lung contained similar microbial communities. In eight out of nine subjects, PB samples from different regions of the lung were also similar in microbial community composition, and were similar to microbial communities in BAL fluid from the same lobe. Microbial communities in PB samples were more diverse than those in BAL samples, suggesting enrichment of some taxa in mucus plugs. To our knowledge, this study is the first to examine the microbiota in different regions of the CF lung in clinically stable individuals with mild-to-moderate CF-related lung disease.

Linking Microbiota to Human Diseases: A Systems Biology Perspective

The human gut microbiota encompasses a densely populated ecosystem that provides essential functions for host development, immune maturation, and metabolism. Alterations to the gut microbiota have been observed in numerous diseases, including human metabolic diseases such as obesity, type 2 diabetes (T2D), and irritable bowel syndrome, and some animal experiments have suggested causality. However, few studies have validated causality in humans and the underlying mechanisms remain largely to be elucidated. We discuss how systems biology approaches combined with new experimental technologies may disentangle some of the mechanistic details in the complex interactions of diet, microbiota, and host metabolism and may provide testable hypotheses for advancing our current understanding of human-microbiota interaction.

A distributed cell division counter reveals growth dynamics in the gut microbiota

Microbial population growth is typically measured when cells can be directly observed, or when death is rare. However, neither of these conditions hold for the mammalian gut microbiota, and, therefore, standard approaches cannot accurately measure the growth dynamics of this community. Here we introduce a new method (distributed cell division counting, DCDC) that uses the accurate segregation at cell division of genetically encoded fluorescent particles to measure microbial growth rates. Using DCDC, we can measure the growth rate of Escherichia coli for >10 consecutive generations. We demonstrate experimentally and theoretically that DCDC is robust to error across a wide range of temperatures and conditions, including in the mammalian gut. Furthermore, our experimental observations inform a mathematical model of the population dynamics of the gut microbiota. DCDC can enable the study of microbial growth during infection, gut dysbiosis, antibiotic therapy or other situations relevant to human health.

Metagenomics: A New Way to Illustrate the Crosstalk between Infectious Diseases and Host Microbiome

Microbes have co-evolved with human beings for millions of years. They play a very important role in maintaining the health of the host. With the advancement in next generation sequencing technology, the microbiome profiling in the host can be obtained under different circumstances. This review focuses on the current knowledge of the alteration of complex microbial communities upon the infection of different pathogens, such as human immunodeficiency virus, hepatitis B virus, influenza virus, and Mycobacterium tuberculosis, at different body sites. It is believed that the increased understanding of the correlation between infectious disease and the alteration of the microbiome can contribute to better management of disease progression in the future. However, future studies may need to be more integrative so as to establish the exact causality of diseases by analyzing the correlation between microorganisms within the human host and the pathogenesis of infectious diseases.