Biology subjects

Kieser, S.

Publications and source records attributed to Kieser, S..

4 recordsLinked to original sources

bioRxiv2026

Shared species, strains and resistome between humans and pigs: A metagenomic analysis

Pig farming is one of the most intensive human-animal interfaces, and farm workers carry gut microbiotas that differ from those of non-farmers. Whether this overlap reflects genuine strain transmission or shared environmental exposure is unclear. Using shotgun metagenomics, we profiled the gut microbiomes of pigs at three growth stages (suckling, weaning, fattening), their farmers, and non-farmer controls, at species and strain resolution, and characterized the resistome in parallel. Farmers shared more species with pigs than controls, with Prevotellaceae the most consistently enriched family. Strain-level analysis of 137 shared species showed that most (71%) maintain host-specific sub-species clades. Strain sharing ran an order of magnitude below species sharing and was confined to early growth stages; its direction could not be determined. Pigs and farmers shared a farm-associated resistome signature, including the beta-lactamase cfxA4 and the methyltransferases ermF and cfrE. ermF and tet(X) were carried together on the same clonal Tn4351-family transposon, present in 86% of farmers and 80% of pigs but only 19% of controls. We found no evidence for transmission of mobile genetic elements between pigs and farmers; the shared resistome is best explained by carriage within shared gut taxa. Species sharing, strain transmission, and resistance gene carriage thus reflect different scales of microbial exchange at the livestock-human interface. Our results argue against ongoing strain or mobile genetic element transmission from handled animals: the shared species and resistome are best explained by shared environmental exposure and by carriage of resistance genes within shared gut taxa.

microbiology↗

bioRxiv2025

Human gut microbiota subspecies carry implicit information for in-depth microbiome research

Microbial strains from same species can have distinct functional characteristics owing to their different gene content. As the highest resolution, strains are mainly host-specific, thus obscuring unbiased associations, and hindering deductive research. Here, we comprehensively define the human gut microbiota at consistently-annotated subspecies resolution in an unbiased, cohort-independent manner, and demonstrate that we can generalize across distinct populations worldwide while maintaining specificity and improving interstudy reproducibility. We developed panhashome, a sketching-based method for rapid subspecies quantification and identification of genes that drive the intraspecies variations, and showed that subspecies carry implicit information undetectable at species level. By meta-analysis of colorectal cancer (CRC) datasets, we identified disease-associated subspecies whose sibling subspecies or species are not. Subspecies-based machine-learning CRC diagnostic algorithm outperformed species-level methods by leveraging the unique subspecies-level information. This subspecies catalogue allows identification of genes that drive the functional differences between subspecies as fundamental step in mechanistically understanding microbiome-phenotype interactions.

bioinformatics↗

bioRxiv2021

Comprehensive mouse gut metagenomecatalog reveals major difference to thehuman counterpart

Mouse is the most used model for studying the impact of microbiota on its host, but the repertoire of species from the mouse gut microbiome remains largely unknown. Here, we construct a Comprehensive Mouse Gut Metagenome (CMGM) catalog by assembling all currently available mouse gut metagenomes and combining them with published reference and metagenome-assembled genomes. The 50011 genomes cluster into 1699 species, of which 78.1% are uncultured, and we discovered 226 new genera, 7 new families, and 1 new order. Rarefaction analysis indicates comprehensive sampling of the species from the mouse gut. CMGM enables an unprecedented coverage of the mouse gut microbiome exceeding 90%. Comparing CMGM to the human gut microbiota shows an overlap 64% at the genus, but only 16% at the species level, demonstrating that human and mouse gut microbiota are largely distinct.

microbiology↗

bioRxiv2019

ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data

BackgroundMetagenomics and metatranscriptomics studies provide valuable insight into the composition and function of microbial populations from diverse environments, however the data processing pipelines that rely on mapping reads to gene catalogs or genome databases for cultured strains yield results that underrepresent the genes and functional potential of uncultured microbes. Recent improvements in sequence assembly methods have eased the reliance on genome databases, thereby allowing the recovery of genomes from uncultured microbes. However, configuring these tools, linking them with advanced binning and annotation tools, and maintaining provenance of the processing continues to be challenging for researchers.\n\nResultsHere we present ATLAS, a software package for customizable data processing from raw sequence reads to functional and taxonomic annotations using state-of-the-art tools to assemble, annotate, quantify, and bin metagenome and metatranscriptome data. Genome-centric resolution and abundance estimates are provided for each sample in a dataset. ATLAS is written in Python and the workflow implemented in Snakemake; it operates in a Linux environment, and is compatible with Python 3.5+ and Anaconda 3+ versions. The source code for ATLAS is freely available, distributed under a BSD-3 license.\n\nConclusionATLAS provides a user-friendly, modular and customizable Snakemake workflow for metagenome and metatranscriptome data processing; it is easily installable with conda and maintained as open-source on GitHub at https://github.com/metagenome-atlas/atlas.

bioinformatics↗