An intact microbiota is required for the gastrointestinal toxicity of the immunosuppressant mycophenolate mofetil.

BACKGROUND: Mycophenolate mofetil (MMF) is commonly prescribed after transplantation and has major advantages over other immunosuppressive drugs, but frequent gastrointestinal (GI) side-effects limit its use. The mechanism(s) underlying MMF-related GI toxicity have yet to be elucidated. METHODS: To investigate MMF-related GI toxicity, experimental mice were fed chow containing MMF (0.563%) and multiple indices of toxicity, including weight loss and colonic inflammation, were measured. Changes in intestinal microbial composition were detected using 16S rRNA Illumina sequencing, and downstream PICRUSt analysis was used to predict metagenomic pathways involved. Germ-free (GF) mice and mice treated with orally administered broad-spectrum antibiotics (ABX) were utilized to interrogate the importance of the microbiota in MMF-induced GI toxicity. RESULTS: Mice treated with MMF exhibited significant weight loss, related to loss of body fat and muscle, and marked colonic inflammation. MMF exposure was associated with changes in gut microbial composition, as demonstrated by a loss of overall diversity, expansion of Proteobacteria (specifically Escherichia/Shigella), and enrichment of genes involved in lipopolysaccharide (LPS) biosynthesis, which paralleled increased levels of LPS in the feces and serum. MMF-related GI toxicity was dependent on the intestinal microbiota, as MMF did not induce weight loss or colonic inflammation in GF mice. Furthermore, ABX prevented and reversed MMF-induced weight loss and colonic inflammation. CONCLUSIONS: An intact intestinal microbiota is required to initiate and sustain the GI toxicity of MMF. MMF treatment causes dynamic changes in the composition of the intestinal microbiota that may be a targetable driver of the GI side-effects of MMF.

CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database.

The Comprehensive Antibiotic Resistance Database (CARD; http://arpcard.mcmaster.ca) is a manually curated resource containing high quality reference data on the molecular basis of antimicrobial resistance (AMR), with an emphasis on the genes, proteins and mutations involved in AMR. CARD is ontologically structured, model centric, and spans the breadth of AMR drug classes and resistance mechanisms, including intrinsic, mutation-driven and acquired resistance. It is built upon the Antibiotic Resistance Ontology (ARO), a custom built, interconnected and hierarchical controlled vocabulary allowing advanced data sharing and organization. Its design allows the development of novel genome analysis tools, such as the Resistance Gene Identifier (RGI) for resistome prediction from raw genome sequence. Recent improvements include extensive curation of additional reference sequences and mutations, development of a unique Model Ontology and accompanying AMR detection models to power sequence analysis, new visualization tools, and expansion of the RGI for detection of emergent AMR threats. CARD curation is updated monthly based on an interplay of manual literature curation, computational text mining, and genome analysis.

Clinical utilization of genomics data produced by the international Pseudomonas aeruginosa consortium.

The International Pseudomonas aeruginosa Consortium is sequencing over 1000 genomes and building an analysis pipeline for the study of Pseudomonas genome evolution, antibiotic resistance and virulence genes. Metadata, including genomic and phenotypic data for each isolate of the collection, are available through the International Pseudomonas Consortium Database (http://ipcd.ibis.ulaval.ca/). Here, we present our strategy and the results that emerged from the analysis of the first 389 genomes. With as yet unmatched resolution, our results confirm that P. aeruginosa strains can be divided into three major groups that are further divided into subgroups, some not previously reported in the literature. We also provide the first snapshot of P. aeruginosa strain diversity with respect to antibiotic resistance. Our approach will allow us to draw potential links between environmental strains and those implicated in human and animal infections, understand how patients become infected and how the infection evolves over time as well as identify prognostic markers for better evidence-based decisions on patient care.

IslandViewer 3: more flexible, interactive genomic island discovery, visualization and analysis.

IslandViewer (http://pathogenomics.sfu.ca/islandviewer) is a widely used web-based resource for the prediction and analysis of genomic islands (GIs) in bacterial and archaeal genomes. GIs are clusters of genes of probable horizontal origin, and are of high interest since they disproportionately encode genes involved in medically and environmentally important adaptations, including antimicrobial resistance and virulence. We now report a major new release of IslandViewer, since the last release in 2013. IslandViewer 3 incorporates a completely new genome visualization tool, IslandPlot, enabling for the first time interactive genome analysis and gene search capabilities using synchronized circular, horizontal and vertical genome views. In addition, more curated virulence factors and antimicrobial resistance genes have been incorporated, and homologs of these genes identified in closely related genomes using strict filters. Pathogen-associated genes have been re-calculated for all pre-computed complete genomes. For user-uploaded genomes to be analysed, IslandViewer 3 can also now handle incomplete genomes, with an improved queuing system on compute nodes to handle user demand. Overall, IslandViewer 3 represents a significant new version of this GI analysis software, with features that may make it more broadly useful for general microbial genome analysis and visualization.