adhesiomeR: a tool for Escherichia coli adhesin classification and analysis.

Adhesins are crucial factors in the virulence of bacterial pathogens such as Escherichia coli. However, to date no resources have been dedicated to the detailed analysis of E. coli adhesins. Here, we provide adhesiomeR software that enables characterization of the complete adhesin repertoire, termed the adhesiome. AdhesiomeR incorporates the most comprehensive database of E. coli adhesins and facilitates an extensive analysis of adhesiome. We demonstrate that adhesiomeR achieves 98% accuracy when compared with experimental analyses. Based on analysis of 15,000 E. coli genomes, we define novel adhesiome profiles and clusters, providing a nomenclature for a unified comparison of E. coli adhesiomes.

The reproduction process of Gram-positive protocells.

Protocells are believed to have existed on early Earth prior to the emergence of prokaryotes. Due to their rudimentary nature, it is widely accepted that these protocells lacked intracellular mechanisms to regulate their reproduction, thereby relying heavily on environmental conditions. To understand protocell reproduction, we adopted a top-down approach of transforming a Gram-positive bacterium into a lipid-vesicle-like state. In this state, cells lacked intrinsic mechanisms to regulate their morphology or reproduction, resembling theoretical propositions on protocells. Subsequently, we grew these proxy-protocells under the environmental conditions of early Earth to understand their impact on protocell reproduction. Despite the lack of molecular biological coordination, cells in our study underwent reproduction in an organized manner. The method and the efficiency of their reproduction can be explained by an interplay between the physicochemical properties of cell constituents and environmental conditions. While the overall reproductive efficiency in these top-down modified cells was lower than their counterparts with a cell wall, the process always resulted in viable daughter cells. Given the simplicity and suitability of this reproduction method to early Earth environmental conditions, we propose that primitive protocells likely reproduced by a process like the one we described below.

Precision enzyme discovery through targeted mining of metagenomic data.

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.

Pervasive associations between dark septate endophytic fungi with tree root and soil microbiomes across Europe.

Trees interact with a multitude of microbes through their roots and root symbionts such as mycorrhizal fungi and root endophytes. Here, we explore the role of fungal root symbionts as predictors of the soil and root-associated microbiomes of widespread broad-leaved trees across a European latitudinal gradient. Our results suggest that, alongside factors such as climate, soil, and vegetation properties, root colonization by ectomycorrhizal, arbuscular mycorrhizal, and dark septate endophytic fungi also shapes tree-associated microbiomes. Notably, the structure of root and soil microbiomes across our sites is more strongly and consistently associated with dark septate endophyte colonization than with mycorrhizal colonization and many abiotic factors. Root colonization by dark septate endophytes also has a consistent negative association with the relative abundance and diversity of nutrient cycling genes. Our study not only indicates that root-symbiotic interactions are an important factor structuring soil communities and functions in forest ecosystems, but also that the hitherto less studied dark septate endophytes are likely to be central players in these interactions.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing.

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Potential therapeutic implications of histidine catabolism by the gut microbiota in NAFLD patients with morbid obesity.

The gut microbiota contributes to the pathophysiology of non-alcoholic fatty liver disease (NAFLD). Histidine is a key energy source for the microbiota, scavenging it from the host. Its role in NAFLD is poorly known. Plasma metabolomics, liver transcriptomics, and fecal metagenomics were performed in three human cohorts coupled with hepatocyte, rodent, and Drosophila models. Machine learning analyses identified plasma histidine as being strongly inversely associated with steatosis and linked to a hepatic transcriptomic signature involved in insulin signaling, inflammation, and trace amine-associated receptor 1. Circulating histidine was inversely associated with Proteobacteria and positively with bacteria lacking the histidine utilization (Hut) system. Histidine supplementation improved NAFLD in different animal models (diet-induced NAFLD in mouse and flies, ob/ob mouse, and ovariectomized rats) and reduced de novo lipogenesis. Fecal microbiota transplantation (FMT) from low-histidine donors and mono-colonization of germ-free flies with Enterobacter cloacae increased triglyceride accumulation and reduced histidine content. The interplay among microbiota, histidine catabolism, and NAFLD opens therapeutic opportunities.

A single dietary factor, daily consumption of a fermented beverage, can modulate the gut bacteria and fecal metabolites within the same ethnic community.

IMPORTANCE: Our study investigated how a traditional drink called Apong, made from fermented rice, affects the gut and health of the Mishing community in India. We compared two groups of people who drink Apong to a group of people who do not drink it. To accomplish this, we studied the gut bacteria, fecal metabolites, and blood samples of the participants. It was found that the people who drank Apong had higher blood pressure but lower blood sugar and protein levels than people who did not drink it. We also found that the gut microbiome composition of people who drank Apong was different from those who did not drink it. Moreover, people who drank Apong had lower levels of isovaleric acid in their feces. Overall, this study shows that a traditional drink like Apong can affect the gut bacteria of a community.

Life history strategies of soil bacterial communities across global terrestrial biomes.

The life history strategies of soil microbes determine their metabolic potential and their response to environmental changes. Yet these strategies remain poorly understood. Here we use shotgun metagenomes from terrestrial biomes to characterize overarching covariations of the genomic traits that capture dominant life history strategies in bacterial communities. The emerging patterns show a triangle of life history strategies shaped by two trait dimensions, supporting previous theoretical and isolate-based studies. The first dimension ranges from streamlined genomes with simple metabolisms to larger genomes and expanded metabolic capacities. As metabolic capacities expand, bacterial communities increasingly differentiate along a second dimension that reflects a trade-off between increasing capacities for environmental responsiveness or for nutrient recycling. Random forest analyses show that soil pH, C:N ratio and precipitation patterns together drive the dominant life history strategy of soil bacterial communities and their biogeographic distribution. Our findings provide a trait-based framework to compare life history strategies of soil bacteria.

A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses.

Environmental DNA (eDNA) metabarcoding has gained growing attention as a strategy for monitoring biodiversity in ecology. However, taxa identifications produced through metabarcoding require sophisticated processing of high-throughput sequencing data from taxonomically informative DNA barcodes. Various sets of universal and taxon-specific primers have been developed, extending the usability of metabarcoding across archaea, bacteria and eukaryotes. Accordingly, a multitude of metabarcoding data analysis tools and pipelines have also been developed. Often, several developed workflows are designed to process the same amplicon sequencing data, making it somewhat puzzling to choose one among the plethora of existing pipelines. However, each pipeline has its own specific philosophy, strengths and limitations, which should be considered depending on the aims of any specific study, as well as the bioinformatics expertise of the user. In this review, we outline the input data requirements, supported operating systems and particular attributes of thirty-two amplicon processing pipelines with the goal of helping users to select a pipeline for their metabarcoding projects.

Exploring Endotypes in Chronic Rhinosinusitis (ExpRess): Protocol for a cohort study.

BACKGROUND: Chronic Rhinosinusitis (CRS) affects approximately 1 in 10 UK adults and impacts quality of life quality of life significantly. Response to treatment may be driven by individual CRS endotypes and therefore work to delineate biomarker clusters that may separate responders from non-responders is needed. The ongoing MACRO three-arm parallel-group trial randomises adult CRS patients to endoscopic sinus surgery, macrolide therapy or placebo. AIM: This study aims to correlate CRS endotypes with clinical parameters from the ongoing MACRO trial, including olfactory function and outcomes in terms of response to treatment using core biomarkers sets. METHODS: Adult CRS patients enrolled into the MACRO trial will be recruited from participating UK otorhinolaryngology departments. Nasal tissue samples and swabs will be obtained in theatre or clinic from patients randomised to all three trial arms. Nasal tissue will be analysed with multiplex electrochemiluminescence for 32 cytokines including IL-5, IL-13, IgE and periostin. Bacterial swabs will be analysed using illumina miSeq 16S amplicon sequencing. Mean expression for each biomarker will be reported for treatment responder and non-responder groups. Correlation of biomarkers with MACRO trial outcome data such as endoscopic evaluation scores and quality-of-life improvement scores will be reported. DISCUSSION: Defining clear endotypes in CRS will contribute to refining patient pathways for the efficient use of clinical resources. This work may lay the groundwork for future studies to predict which patients might respond to medical or surgical therapy.

Enterosignatures define common bacterial guilds in the human gut microbiome.

The human gut microbiome composition is generally in a stable dynamic equilibrium, but it can deteriorate into dysbiotic states detrimental to host health. To disentangle the inherent complexity and capture the ecological spectrum of microbiome variability, we used 5,230 gut metagenomes to characterize signatures of bacteria commonly co-occurring, termed enterosignatures (ESs). We find five generalizable ESs dominated by either Bacteroides, Firmicutes, Prevotella, Bifidobacterium, or Escherichia. This model confirms key ecological characteristics known from previous enterotype concepts, while enabling the detection of gradual shifts in community structures. Temporal analysis implies that the Bacteroides-associated ES is "core" in the resilience of westernized gut microbiomes, while combinations with other ESs often complement the functional spectrum. The model reliably detects atypical gut microbiomes correlated with adverse host health conditions and/or the presence of pathobionts. ESs provide an interpretable and generic model that enables an intuitive characterization of gut microbiome composition in health and disease.

Population-level impacts of antibiotic usage on the human gut microbiome.

The widespread usage of antimicrobials has driven the evolution of resistance in pathogenic microbes, both increased prevalence of antimicrobial resistance genes (ARGs) and their spread across species by horizontal gene transfer (HGT). However, the impact on the wider community of commensal microbes associated with the human body, the microbiome, is less well understood. Small-scale studies have determined the transient impacts of antibiotic consumption but we conduct an extensive survey of ARGs in 8972 metagenomes to determine the population-level impacts. Focusing on 3096 gut microbiomes from healthy individuals not taking antibiotics we demonstrate highly significant correlations between both the total ARG abundance and diversity and per capita antibiotic usage rates across ten countries spanning three continents. Samples from China were notable outliers. We use a collection of 154,723 human-associated metagenome assembled genomes (MAGs) to link these ARGs to taxa and detect HGT. This reveals that the correlations in ARG abundance are driven by multi-species mobile ARGs shared between pathogens and commensals, within a highly connected central component of the network of MAGs and ARGs. We also observe that individual human gut ARG profiles cluster into two types or resistotypes. The less frequent resistotype has higher overall ARG abundance, is associated with certain classes of resistance, and is linked to species-specific genes in the Proteobacteria on the periphery of the ARG network.

Gut microbiota dysbiosis in Parkinson disease: A systematic review and pooled analysis.

BACKGROUND AND PURPOSE: The role of the gut microbiome in the pathogenesis of Parkinson disease (PD) is under intense investigation, and the results presented are still very heterogeneous. These discrepancies arise not only from the highly heterogeneous pathology of PD, but also from widely varying methodologies at all stages of the workflow, from sampling to final statistical analysis. The aim of the present work is to harmonize the workflow across studies to reduce the methodological heterogeneity and to perform a pooled analysis to account for other sources of heterogeneity. METHODS: We performed a systematic review to identify studies comparing the gut microbiota of PD patients to healthy controls. A workflow was designed to harmonize processing across all studies from bioinformatics processing to final statistical analysis using a Bayesian random-effects meta-analysis based on individual patient-level data. RESULTS: The results show that harmonizing workflows minimizes differences between statistical methods and reveals only a small set of taxa being associated with the pathogenesis of PD. Increased shares of the genera Akkermansia and Bifidobacterium and decreased shares of the genera Roseburia and Faecalibacterium were most characteristic for PD-associated microbiota. CONCLUSIONS: Our study summarizes evidence that reduced levels of butyrate-producing taxa in combination with possible degradation of the mucus layer by Akkermansia may promote intestinal inflammation and reduced permeability of the gut mucosal layer. This may allow potentially pathogenic metabolites to transit and enter the enteric nervous system.

Microbiota Dysbiosis and Gut Barrier Dysfunction Associated with Non-Alcoholic Fatty Liver Disease Are Modulated by a Specific Metabolic Cofactors' Combination.

The gut is a selective barrier that not only allows the translocation of nutrients from food, but also microbe-derived metabolites to the systemic circulation that flows through the liver. Microbiota dysbiosis occurs when energy imbalances appear due to an unhealthy diet and a sedentary lifestyle. Dysbiosis has a critical impact on increasing intestinal permeability and epithelial barrier deterioration, contributing to bacterial and antigen translocation to the liver, triggering non-alcoholic fatty liver disease (NAFLD) progression. In this study, the potential therapeutic/beneficial effects of a combination of metabolic cofactors (a multi-ingredient; MI) (betaine, N-acetylcysteine, L-carnitine, and nicotinamide riboside) against NAFLD were evaluated. In addition, we investigated the effects of this metabolic cofactors' combination as a modulator of other players of the gut-liver axis during the disease, including gut barrier dysfunction and microbiota dysbiosis. Diet-induced NAFLD mice were distributed into two groups, treated with the vehicle (NAFLD group) or with a combination of metabolic cofactors (NAFLD-MI group), and small intestines were harvested from all animals for histological, molecular, and omics analysis. The MI treatment ameliorated gut morphological changes, decreased gut barrier permeability, and reduced gene expression of some proinflammatory cytokines. Moreover, epithelial cell proliferation and the number of goblet cells were increased after MI supplementation. In addition, supplementation with the MI combination promoted changes in the intestinal microbiota composition and diversity, as well as modulating short-chain fatty acids (SCFAs) concentrations in feces. Taken together, this specific combination of metabolic cofactors can reverse gut barrier disruption and microbiota dysbiosis contributing to the amelioration of NAFLD progression by modulating key players of the gut-liver axis.

LotuS2: an ultrafast and highly accurate tool for amplicon sequencing analysis.

BACKGROUND: Amplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (less OTU scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences. RESULTS: In LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines, yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxon composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified taxa and a higher fraction of reads assigned to true taxa (48% and 57% at species; 83% and 98% at genus level, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reported 16S sequences. CONCLUSION: LotuS2 is a lightweight and user-friendly pipeline that is fast, precise, and streamlined, using extensive pre- and post-ASV/OTU clustering steps to further increase data quality. High data usage rates and reliability enable high-throughput microbiome analysis in minutes. AVAILABILITY: LotuS2 is available from GitHub, conda, or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/ . Video Abstract.

Glacier melt-down changes habitat characteristics and unique microbial community composition and physiology in alpine lake sediments.

Glacial melt-down alters hydrological and physico-chemical conditions in downstream aquatic habitats. In this study, we tested if sediment-associated microbial communities respond to the decrease of glaciers and associated meltwater flows in high-alpine lakes. We analyzed 16 lakes in forefield catchments of three glaciers in the Eastern Swiss Alps on physico-chemical and biological parameters. We compared lakes fed by glacier meltwater with hydrologically disconnected lakes, as well as "mixed" lakes that received water from both other lake types. Glacier-fed lakes had a higher turbidity (94 NTU) and conductivity (47 µS/cm), but were up to 5.2°C colder than disconnected lakes (1.5 NTU, 26 µS/cm). Nutrient concentration was low in all lakes (TN < 0.05 mg/l, TP < 0.02 mg/l). Bacterial diversity in the sediments decreased significantly with altitude. Bacterial community composition correlated with turbidity, temperature, conductivity, nitrate, and lake age and was distinctly different between glacier-fed compared to disconnected and mixed water lakes, but not between catchments. Chemoheterotrophic processes were more abundant in glacier-fed compared to disconnected and mixed water lakes where photoautotrophic processes dominated. Our study suggests that the loss of glaciers will change sediment bacterial community composition and physiology that are unique for glacier-fed lakes in mountain and polar regions.

Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease.

Patients with inflammatory bowel disease (IBD) wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome's composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools. We collected fifteen 16S rRNA microbiome datasets (7,707 samples) from North America to benchmark combinations of gut microbiome features, data normalization and transformation methods, batch effect correction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with two binary classification metrics following leave-one-dataset-out cross (LODO) validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features processed with a compositional transformation method and batch effect correction with the naive zero-centering method attain the best classification performance. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. These findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.

Landscape of mobile genetic elements and their antibiotic resistance cargo in prokaryotic genomes.

Prokaryotic Mobile Genetic Elements (MGEs) such as transposons, integrons, phages and plasmids, play important roles in prokaryotic evolution and in the dispersal of cargo functions like antibiotic resistance. However, each of these MGE types is usually annotated and analysed individually, hampering a global understanding of phylogenetic and environmental patterns of MGE dispersal. We thus developed a computational framework that captures diverse MGE types, their cargos and MGE-mediated horizontal transfer events, using recombinases as ubiquitous MGE marker genes and pangenome information for MGE boundary estimation. Applied to ∼84k genomes with habitat annotation, we mapped 2.8 million MGE-specific recombinases to six operational MGE types, which together contain on average 13% of all the genes in a genome. Transposable elements (TEs) dominated across all taxa (∼1.7 million occurrences), outnumbering phages and phage-like elements (<0.4 million). We recorded numerous MGE-mediated horizontal transfer events across diverse phyla and habitats involving all MGE types, disentangled and quantified the extent of hitchhiking of TEs (17%) and integrons (63%) with other MGE categories, and established TEs as dominant carriers of antibiotic resistance genes. We integrated all these findings into a resource (proMGE.embl.de), which should facilitate future studies on the large mobile part of genomes and its horizontal dispersal.

Structure and function of the soil microbiome underlying N2O emissions from global wetlands.

Wetland soils are the greatest source of nitrous oxide (N2O), a critical greenhouse gas and ozone depleter released by microbes. Yet, microbial players and processes underlying the N2O emissions from wetland soils are poorly understood. Using in situ N2O measurements and by determining the structure and potential functional of microbial communities in 645 wetland soil samples globally, we examined the potential role of archaea, bacteria, and fungi in nitrogen (N) cycling and N2O emissions. We show that N2O emissions are higher in drained and warm wetland soils, and are correlated with functional diversity of microbes. We further provide evidence that despite their much lower abundance compared to bacteria, nitrifying archaeal abundance is a key factor explaining N2O emissions from wetland soils globally. Our data suggest that ongoing global warming and intensifying environmental change may boost archaeal nitrifiers, collectively transforming wetland soils to a greater source of N2O.