Disease-specific loss of microbial cross-feeding interactions in the human gut.

Many gut microorganisms critical to human health rely on nutrients produced by each other for survival; however, these cross-feeding interactions are still challenging to quantify and remain poorly characterized. Here, we introduce a Metabolite Exchange Score (MES) to quantify those interactions. Using metabolic models of prokaryotic metagenome-assembled genomes from over 1600 individuals, MES allows us to identify and rank metabolic interactions that are significantly affected by a loss of cross-feeding partners in 10 out of 11 diseases. When applied to a Crohn's disease case-control study, our approach identifies a lack of species with the ability to consume hydrogen sulfide as the main distinguishing microbiome feature of disease. We propose that our conceptual framework will help prioritize in-depth analyses, experiments and clinical targets, and that targeting the restoration of microbial cross-feeding interactions is a promising mechanism-informed strategy to reconstruct a healthy gut ecosystem.

Extensive genome analysis identifies novel plasmid families in Clostridium perfringens.

Globally, the anaerobic bacterium Clostridium perfringens causes severe disease in a wide array of hosts; however, C. perfringens strains are also carried asymptomatically. Accessory genes are responsible for much of the observed phenotypic variation and virulence within this species, with toxins frequently encoded on conjugative plasmids and many isolates carrying up to 10 plasmids. Despite this unusual biology, current genomic analyses have largely excluded isolates from healthy hosts or environmental sources. Accessory genomes, including plasmids, also have often been excluded from broader scale phylogenetic investigations. Here we interrogate a comprehensive collection of 464 C. perfringens genomes and identify the first putative non-conjugative enterotoxin (CPE)-encoding plasmids and a putative novel conjugative locus (Bcp) with sequence similarity to a locus reported from Clostridium botulinum. We sequenced and archived 102 new C. perfringens genomes, including those from rarely sequenced toxinotype B, C, D and E isolates. Long-read sequencing of 11 C. perfringens strains representing all toxinotypes (A-G) identified 55 plasmids from nine distinct plasmid groups. Interrogation of the 464 genomes in this collection identified 1045 plasmid-like contigs from the nine plasmid families, with a wide distribution across the C. perfringens isolates. Plasmids and plasmid diversity play an essential role in C. perfringens pathogenicity and broader biology. We have expanded the C. perfringens genome collection to include temporal, spatial and phenotypically diverse isolates including those carried asymptomatically in the gastrointestinal microbiome. This analysis has resulted in the identification of novel C. perfringens plasmids whilst providing a comprehensive understanding of species diversity.

expam-high-resolution analysis of metagenomes using distance trees.

SUMMARY: Shotgun metagenomic sequencing provides the capacity to understand microbial community structure and function at unprecedented resolution; however, the current analytical methods are constrained by a focus on taxonomic classifications that may obfuscate functional relationships. Here, we present expam, a tree-based, taxonomy agnostic tool for the identification of biologically relevant clades from shotgun metagenomic sequencing. AVAILABILITY AND IMPLEMENTATION: expam is an open-source Python application released under the GNU General Public Licence v3.0. expam installation instructions, source code and tutorials can be found at https://github.com/seansolari/expam. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Review article: the future of microbiome-based therapeutics.

BACKGROUND: From consumption of fermented foods and probiotics to emerging applications of faecal microbiota transplantation, the health benefit of manipulating the human microbiota has been exploited for millennia. Despite this history, recent technological advances are unlocking the capacity for targeted microbial manipulation as a novel therapeutic. AIM: This review summarises the current developments in microbiome-based medicines and provides insight into the next steps required for therapeutic development. METHODS: Here we review current and emerging approaches and assess the capabilities and weaknesses of these technologies to provide safe and effective clinical interventions. Key literature was identified through Pubmed searches with the following key words, 'microbiome', 'microbiome biomarkers', 'probiotics', 'prebiotics', 'synbiotics', 'faecal microbiota transplant', 'live biotherapeutics', 'microbiome mimetics' and 'postbiotics'. RESULTS: Improved understanding of the human microbiome and recent technological advances provide an opportunity to develop a new generation of therapies. These therapies will range from dietary interventions, prebiotic supplementations, single probiotic bacterial strains, human donor-derived faecal microbiota transplants, rationally selected combinations of bacterial strains as live biotherapeutics, and the beneficial products or effects produced by bacterial strains, termed microbiome mimetics. CONCLUSIONS: Although methods to identify and refine these therapeutics are continually advancing, the rapid emergence of these new approaches necessitates accepted technological and ethical frameworks for measurement, testing, laboratory practices and clinical translation.

Key Technologies for Progressing Discovery of Microbiome-Based Medicines.

A growing number of experimental and computational approaches are illuminating the "microbial dark matter" and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research.