Phylogeny-corrected identification of microbial gene families relevant to human gut colonization

Microbiological Characteristics of the Gastrointestinal Tracts of Jersey and Holstein Cows

The gastrointestinal bacterial microbiota is essential for maintaining the health of dairy cows and ensuring their production potential, and it may also help explain the breed-related phenotypic differences. Therefore, investigating the differences in gastrointestinal bacterial microbiota between breeds is critical for deciphering the mechanisms behind these differences and exploring the potential for improving milk production by regulating the gastrointestinal bacterial microbiota. This study holistically examined the differences between rumen and hindgut bacterial microbiota in a large cohort of two breeds of dairy cows, comprising 184 Jersey cows and 165 Holstein cows. Significant distinctions were identified between the rumen and hindgut bacterial microbiota of dairy cows, with these differences being consistent across breeds. A total of 20 breed-differentiated microorganisms, comprising 14 rumen microorganisms and 6 hindgut microorganisms, were screened, which may be the primary drivers of the observed differences in lactation performance between Jersey and Holstein cows. The present study revealed the spatial heterogeneity of the gastrointestinal bacterial microbiota of Jersey and Holstein cows and identified microbial biomarkers of different breeds. These findings enhance our understanding of the differences in the gastrointestinal bacterial microbiota between Jersey and Holstein cows and may provide useful information for optimizing the composition of the intestinal bacterial microbiota of the two breeds of dairy cows.

Targeting microbiota in dietary obesity management: a systematic review on randomized control trials in adults

Obesity is an alarming public health problem. Tailored nutritional therapy is advisable since emerging evidence on complex cross-talks among multifactorial agents. In this picture, the gut microbiota is highly individualized and intricately dependent on dietary patterns, with implications for obesity management. Most of the papers on the topic are observational and often conflicting. This review aimed to systematically organize the body of evidence on microbiota deriving from dietary trials in adult obesity giving the most certain phylogenetic, and metabolomic signatures in relation to both the host metabolism and phenotype changes published until now. We retrieved 18 randomized control trials on 1385 subjects with obesity who underwent several dietary interventions, including standard diet and healthy dietary regimens. Some phyla and species were more related to diets rich in fibers and others to healthy diets. Weight loss, metabolism improvements, inflammatory markers decrease were specifically related to different microorganisms or functions. The Prevotella/Bacteroides ratio was one of the most reported predictors. People with the burden of obesity comorbidities had the most significant taxonomic changes in parallel with a general improvement. These data emphasize the possibility of using symbiotic approaches involving tailored diets, microbiota characteristics, and maybe drugs to treat obesity and metabolic disorders. We encourage Authors to search for specific phylogenetic associations beyond a too generally reported Firmicutes/Bacteroides ratio.

Culturing of a complex gut microbial community in mucin-hydrogel carriers reveals strain- and gene-associated spatial organization

Microbial community function depends on both taxonomic composition and spatial organization. While composition of the human gut microbiome has been deeply characterized, less is known about the organization of microbes between regions such as lumen and mucosa and the microbial genes regulating this organization. Using a defined 117 strain community for which we generate high-quality genome assemblies, we model mucosa/lumen organization with in vitro cultures incorporating mucin hydrogel carriers as surfaces for bacterial attachment. Metagenomic tracking of carrier cultures reveals increased diversity and strain-specific spatial organization, with distinct strains enriched on carriers versus liquid supernatant, mirroring mucosa/lumen enrichment in vivo. A comprehensive search for microbial genes associated with this spatial organization identifies candidates with known adhesion-related functions, as well as novel links. These findings demonstrate that carrier cultures of defined communities effectively recapitulate fundamental aspects of gut spatial organization, enabling identification of key microbial strains and genes.

Parallel expansion and divergence of an adhesin family in pathogenic yeasts

Opportunistic yeast pathogens arose multiple times in the Saccharomycetes class, including the recently emerged, multidrug-resistant (MDR) Candida auris. We show that homologs of a known yeast adhesin family in Candida albicans, the Hyr/Iff-like (Hil) family, are enriched in distinct clades of Candida species as a result of multiple, independent expansions. Following gene duplication, the tandem repeat-rich region in these proteins diverged extremely rapidly and generated large variations in length and β-aggregation potential, both of which are known to directly affect adhesion. The conserved N-terminal effector domain was predicted to adopt a β-helical fold followed by an α-crystallin domain, making it structurally similar to a group of unrelated bacterial adhesins. Evolutionary analyses of the effector domain in C. auris revealed relaxed selective constraint combined with signatures of positive selection, suggesting functional diversification after gene duplication. Lastly, we found the Hil family genes to be enriched at chromosomal ends, which likely contributed to their expansion via ectopic recombination and break-induced replication. Combined, these results suggest that the expansion and diversification of adhesin families generate variation in adhesion and virulence within and between species and are a key step toward the emergence of fungal pathogens.

Within-host evolution of the gut microbiome

Gut bacteria inhabit a complex environment that is shaped by interactions with their host and the other members of the community. While these ecological interactions have evolved over millions of years, mounting evidence suggests that gut commensals can evolve on much shorter timescales as well, by acquiring new mutations within individual hosts. In this review, we highlight recent progress in understanding the causes and consequences of short-term evolution in the mammalian gut, from experimental evolution in murine hosts to longitudinal tracking of human cohorts. We also discuss new opportunities for future progress by expanding the repertoire of focal species, hosts, and surrounding communities, and by combining deep-sequencing technologies with quantitative frameworks from population genetics.

Efficient computation of contributional diversity metrics from microbiome data with FuncDiv

MOTIVATION

Microbiome datasets with taxa linked to the functions (e.g. genes) they encode are becoming more common as metagenomics sequencing approaches improve. However, these data are challenging to analyze due to their complexity. Summary metrics, such as the alpha and beta diversity of taxa contributing to each function (i.e. contributional diversity), represent one approach to investigate these data, but currently there are no straightforward methods for doing so.

RESULTS

We addressed this gap by developing FuncDiv, which efficiently performs these computations. Contributional diversity metrics can provide novel insights that would be impossible to identify without jointly considering taxa and functions.

AVAILABILITY AND IMPLEMENTATION

FuncDiv is distributed under a GNU Affero General Public License v3.0 and is available at https://github.com/gavinmdouglas/FuncDiv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Host and gut bacteria share metabolic pathways for anti-cancer drug metabolism

Pharmaceuticals have extensive reciprocal interactions with the microbiome, but whether bacterial drug sensitivity and metabolism is driven by pathways conserved in host cells remains unclear. Here we show that anti-cancer fluoropyrimidine drugs inhibit the growth of gut bacterial strains from 6 phyla. In both Escherichia coli and mammalian cells, fluoropyrimidines disrupt pyrimidine metabolism. Proteobacteria and Firmicutes metabolized 5-fluorouracil to its inactive metabolite dihydrofluorouracil, mimicking the major host mechanism for drug clearance. The preTA operon was necessary and sufficient for 5-fluorouracil inactivation by E. coli, exhibited high catalytic efficiency for the reductive reaction, decreased the bioavailability and efficacy of oral fluoropyrimidine treatment in mice and was prevalent in the gut microbiomes of colorectal cancer patients. The conservation of both the targets and enzymes for metabolism of therapeutics across domains highlights the need to distinguish the relative contributions of human and microbial cells to drug efficacy and side-effect profiles.

Integrating phylogenetic and functional data in microbiome studies

MOTIVATION

Microbiome functional data are frequently analyzed to identify associations between microbial functions (e.g. genes) and sample groups of interest. However, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed POMS, a package that implements multiple phylogeny-aware frameworks to more robustly identify enriched functions.

RESULTS

The key contribution is an extended balance-tree workflow that incorporates functional and taxonomic information to identify functions that are consistently enriched in sample groups across independent taxonomic lineages. Our package also includes a workflow for running phylogenetic regression. Based on simulated data we demonstrate that these approaches more accurately identify gene families that confer a selective advantage compared with commonly used tools. We also show that POMS in particular can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome.

AVAILABILITY AND IMPLEMENTATION

These workflows are freely available in the POMS R package at https://github.com/gavinmdouglas/POMS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Horizontal gene transfer and adaptive evolution in bacteria

Horizontal gene transfer (HGT) is arguably the most conspicuous feature of bacterial evolution. Evidence for HGT is found in most bacterial genomes. Although HGT can considerably alter bacterial genomes, not all transfer events may be biologically significant and may instead represent the outcome of an incessant evolutionary process that only occasionally has a beneficial purpose. When adaptive transfers occur, HGT and positive selection may result in specific, detectable signatures in genomes, such as gene-specific sweeps or increased transfer rates for genes that are ecologically relevant. In this Review, we first discuss the various mechanisms whereby HGT occurs, how the genetic signatures shape patterns of genomic variation and the distinct bioinformatic algorithms developed to detect these patterns. We then discuss the evolutionary theory behind HGT and positive selection in bacteria, and discuss the approaches developed over the past decade to detect transferred DNA that may be involved in adaptation to new environments.

Tree-aggregated predictive modeling of microbiome data

Modern high-throughput sequencing technologies provide low-cost microbiome survey data across all habitats of life at unprecedented scale. At the most granular level, the primary data consist of sparse counts of amplicon sequence variants or operational taxonomic units that are associated with taxonomic and phylogenetic group information. In this contribution, we leverage the hierarchical structure of amplicon data and propose a data-driven and scalable tree-guided aggregation framework to associate microbial subcompositions with response variables of interest. The excess number of zero or low count measurements at the read level forces traditional microbiome data analysis workflows to remove rare sequencing variants or group them by a fixed taxonomic rank, such as genus or phylum, or by phylogenetic similarity. By contrast, our framework, which we call trac (tree-aggregation of compositional data), learns data-adaptive taxon aggregation levels for predictive modeling, greatly reducing the need for user-defined aggregation in preprocessing while simultaneously integrating seamlessly into the compositional data analysis framework. We illustrate the versatility of our framework in the context of large-scale regression problems in human gut, soil, and marine microbial ecosystems. We posit that the inferred aggregation levels provide highly interpretable taxon groupings that can help microbiome researchers gain insights into the structure and functioning of the underlying ecosystem of interest.

Mechanisms underlying gut microbiota-host interactions in insects

Insects are the most diverse group of animals and colonize almost all environments on our planet. This diversity is reflected in the structure and function of the microbial communities inhabiting the insect digestive system. As in mammals, the gut microbiota of insects can have important symbiotic functions, complementing host nutrition, facilitating dietary breakdown or providing protection against pathogens. There is an increasing number of insect models that are experimentally tractable, facilitating mechanistic studies of gut microbiota-host interactions. In this Review, we will summarize recent findings that have advanced our understanding of the molecular mechanisms underlying the symbiosis between insects and their gut microbiota. We will open the article with a general introduction to the insect gut microbiota and then turn towards the discussion of particular mechanisms and molecular processes governing the colonization of the insect gut environment as well as the diverse beneficial roles mediated by the gut microbiota. The Review highlights that, although the gut microbiota of insects is an active field of research with implications for fundamental and applied science, we are still in an early stage of understanding molecular mechanisms. However, the expanding capability to culture microbiomes and to manipulate microbe-host interactions in insects promises new molecular insights from diverse symbioses.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge.

phylogenize: correcting for phylogeny reveals genes associated with microbial distributions

Summary

Phylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization.

Availability and implementation

phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize.

Supplementary information

Supplementary data are available at Bioinformatics online.

Population Genetics in the Human Microbiome

While the human microbiome's structure and function have been extensively studied, its within-species genetic diversity is less well understood. However, genetic mutations in the microbiome can confer biomedically relevant traits, such as the ability to extract nutrients from food, metabolize drugs, evade antibiotics, and communicate with the host immune system. The population genetic processes by which these traits evolve are complex, in part due to interacting ecological and evolutionary forces in the microbiome. Advances in metagenomic sequencing, coupled with bioinformatics tools and population genetic models, facilitate quantification of microbiome genetic variation and inferences about how this diversity arises, evolves, and correlates with traits of both microbes and hosts. In this review, we explore the population genetic forces (mutation, recombination, drift, and selection) that shape microbiome genetic diversity within and between hosts, as well as efforts towards predictive models that leverage microbiome genetics.

New insights from uncultivated genomes of the global human gut microbiome

The genome sequences of many species of the human gut microbiome remain unknown, largely owing to challenges in cultivating microorganisms under laboratory conditions. Here we address this problem by reconstructing 60,664 draft prokaryotic genomes from 3,810 faecal metagenomes, from geographically and phenotypically diverse humans. These genomes provide reference points for 2,058 newly identified species-level operational taxonomic units (OTUs), which represents a 50% increase over the previously known phylogenetic diversity of sequenced gut bacteria. On average, the newly identified OTUs comprise 33% of richness and 28% of species abundance per individual, and are enriched in humans from rural populations. A meta-analysis of clinical gut-microbiome studies pinpointed numerous disease associations for the newly identified OTUs, which have the potential to improve predictive models. Finally, our analysis revealed that uncultured gut species have undergone genome reduction that has resulted in the loss of certain biosynthetic pathways, which may offer clues for improving cultivation strategies in the future.

Computational Structural Biology: Successes, Future Directions, and Challenges

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

Evolutionary dynamics of bacteria in the gut microbiome within and across hosts

Gut microbiota are shaped by a combination of ecological and evolutionary forces. While the ecological dynamics have been extensively studied, much less is known about how species of gut bacteria evolve over time. Here, we introduce a model-based framework for quantifying evolutionary dynamics within and across hosts using a panel of metagenomic samples. We use this approach to study evolution in approximately 40 prevalent species in the human gut. Although the patterns of between-host diversity are consistent with quasi-sexual evolution and purifying selection on long timescales, we identify new genealogical signatures that challenge standard population genetic models of these processes. Within hosts, we find that genetic differences that accumulate over 6-month timescales are only rarely attributable to replacement by distantly related strains. Instead, the resident strains more commonly acquire a smaller number of putative evolutionary changes, in which nucleotide variants or gene gains or losses rapidly sweep to high frequency. By comparing these mutations with the typical between-host differences, we find evidence that some sweeps may be seeded by recombination, in addition to new mutations. However, comparisons of adult twins suggest that replacement eventually overwhelms evolution over multi-decade timescales, hinting at fundamental limits to the extent of local adaptation. Together, our results suggest that gut bacteria can evolve on human-relevant timescales, and they highlight the connections between these short-term evolutionary dynamics and longer-term evolution across hosts.

The Glutaminase-Dependent Acid Resistance System: Qualitative and Quantitative Assays and Analysis of Its Distribution in Enteric Bacteria

Neutralophilic bacteria have developed several strategies to overcome the deleterious effects of acid stress. In particular, the amino acid-dependent systems are widespread, with their activities overlapping, covering a rather large pH range, from 6 to <2. Recent reports showed that an acid resistance (AR) system relying on the amino acid glutamine (AR2_Q), the most readily available amino acid in the free form, is operative in Escherichia coli, Lactobacillus reuteri, and some Brucella species. This system requires a glutaminase active at acidic pH and the antiporter GadC to import L-glutamine and export either glutamate (the glutamine deamination product) or GABA. The latter occurs when the deamination of glutamine to glutamate, via acid-glutaminase (YbaS/GlsA), is coupled to the decarboxylation of glutamate to GABA, via glutamate decarboxylase (GadB), a structural component of the glutamate-dependent AR (AR2) system, together with GadC. Taking into account that AR2_Q could be widespread in bacteria and that until now assays based on ammonium ion detection were typically employed, this work was undertaken with the aim to develop assays that allow a straightforward identification of the acid-glutaminase activity in permeabilized bacterial cells (qualitative assay) as well as a sensitive method (quantitative assay) to monitor in the pH range 2.5-4.0 the transport of the relevant amino acids in vivo. The qualitative assay is colorimetric, rapid and reliable and provides several additional information, such as co-occurrence of AR2 and AR2_Q in the same bacterial species and assessment of the growth conditions that support maximal expression of glutaminase at acidic pH. The quantitative assay is HPLC-based and allows to concomitantly measure the uptake of glutamine and the export of glutamate and/or GABA via GadC in vivo and depending on the external pH. Finally, an extensive bioinformatic genome analysis shows that the gene encoding the glutaminase involved in AR2_Q is often nearby or in operon arrangement with the genes coding for GadC and GadB. Overall, our results indicate that AR2_Q is likely to be of prominent importance in the AR of enteric bacteria and that it modulates the enzymatic as well as antiport activities depending on the imposed acidic stress.