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Kunath, B. J.

Publications and source records attributed to Kunath, B. J..

4 recordsLinked to original sources

The Food Additive Xanthan Gum Drives Adaptation of the Human Gut Microbiota

The diets of industrialized countries reflect the increasing use of processed foods, often with the introduction of novel food additives. Xanthan gum is a complex polysaccharide with unique rheological properties that have established its use as a widespread stabilizer and thickening agent1. However, little is known about its direct interaction with the gut microbiota, which plays a central role in digestion of other, chemically-distinct dietary fiber polysaccharides. Here, we show that the ability to digest xanthan gum is surprisingly common in industrialized human gut microbiomes and appears to be contingent on the activity of a single bacterium that is a member of an uncultured bacterial genus in the family Ruminococcaceae. We used a combination of enrichment culture, multi-omics, and recombinant enzyme studies to identify and characterize a complete pathway in this uncultured bacterium for the degradation of xanthan gum. Our data reveal that this keystone degrader cleaves the xanthan gum backbone with a novel glycoside hydrolase family 5 (GH5) enzyme before processing the released oligosaccharides using additional enzymes. Surprisingly, some individuals harbor a Bacteroides species that is capable of consuming oligosaccharide products generated by the keystone Ruminococcaceae or a purified form of the GH5 enzyme. This Bacteroides symbiont is equipped with its own distinct enzymatic pathway to cross-feed on xanthan gum breakdown products, which still harbor the native linkage complexity in xanthan gum, but it cannot directly degrade the high molecular weight polymer. Thus, the introduction of a common food additive into the human diet in the past 50 years has promoted the establishment of a food chain involving at least two members of different phyla of gut bacteria.

microbiology

Synergistic effect of short- and long-read sequencing on functional meta-omics

Real-world evaluations of metagenomic reconstructions are challenged by distinguishing reconstruction artefacts from genes and proteins present in situ. Here, we evaluate short-read-only, long-read-only, and hybrid assembly approaches on four different metagenomic samples of varying complexity and demonstrate how they affect gene and protein inference which is particularly relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic, and metaproteomic data to evaluate the metagenomic data-based protein predictions. Our findings pave the way for critical assessments of metagenomic reconstructions and we propose a reference-independent solution based on the synergistic effects of multi-omic data integration for the in situ study of microbiomes using long-read sequencing data.

microbiology

Critical Assessment of Metaproteome Investigation (CAMPI): a Multi-Lab Comparison of Established Workflows

Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carried out the first community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluated the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observed that variability at the peptide level was predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappeared at the protein group level. While differences were observed for predicted community composition, similar functional profiles were obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.

microbiology

Integration of absolute multi-omics reveals translational and metabolic interplay in mixed-kingdom microbiomes.

Microbiology is founded on well-known model organisms. For example, the majority of our fundamental knowledge regarding the quantitative levels of DNA, RNA, and protein backdates to keystone pure culture-based studies. Nowadays, meta-omic approaches allow us to directly access the molecules that constitute microbes and microbial communities, however due to a lack of absolute measurements, many original culture-derived "microbiology statutes" have not been updated or adapted to more complex microbiome settings. Within a cellulose-degrading and methanogenic consortium, we temporally measured genome-centric absolute RNA and protein levels per gene, and obtained a protein-to-RNA ratio of 102-104 for bacterial populations, whereas Archaeal RNA/protein dynamics (103-105: Methanothermobacter thermoautotrophicus) were more comparable to Eukaryotic representatives humans and yeast. The linearity between transcriptome and proteome had a population-specific change over time, highlighting a minimal subset of four functional carriers (cellulose degrader, fermenter, syntrophic acetate-oxidizer and methanogen) that coordinated their respective metabolisms, cumulating in the overarching community phenotype of converting polysaccharides to methane. Our findings show that upgrading multi-omic toolkits with traditional absolute measurements unlocks the scaling of core biological questions to dynamic and complex microbiomes, creating a deeper insight into inter-organismal relationships that drive the greater community function.

microbiology