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Krakau, S.

Publications and source records attributed to Krakau, S..

3 recordsLinked to original sources

Resistance Training Reshapes the Gut Microbiome for Better Health

ObjectivesThe gut microbiome plays a critical role in metabolism, immunity, and aging. While endurance training has been shown to beneficially modulate the microbiome, the effects of resistance training remain less clear, with some studies reporting minimal changes. This project aims to investigate whether structured resistance training elicits significant changes in gut microbiome composition and diversity in sedentary, healthy adults. Methods150 participants completed an 8-week supervised resistance training program. Session-level training data, including weights and repetitions, were recorded alongside metrics like load and compliance. Fecal samples were collected throughout the study period at designated timepoints for 16S rRNA gene amplicon sequencing to assess microbiome composition and for metabolomics analyses to evaluate microbial metabolic activity. ResultsNo differences in microbial diversity were observed, and there were no significant changes in microbial community composition or fecal metabolomics across all participants post-training. However, within-individual microbial community changes significantly correlated with strength improvement, and significantly stronger shifts in beta diversity were observed in participants with high average strength gains compared to those with smaller gains. In these high responders, differential abundance analysis revealed time-dependent microbial changes, with more taxa enriched or depleted by week 8 of training. Notably, Faecalibacterium and Roseburia hominis--both associated with a healthier, anti-inflammatory microbiome--were significantly enriched. Many differentially abundant taxa belonged to the Lachnospiraceae family. ConclusionResistance training drives significant, time-dependent gut microbiome changes, particularly in those demonstrating greater improvements in strength. These shifts mirror endurance training effects and may reflect improved overall health. SummaryWhile endurance training has been consistently shown to enhance gut microbiome composition, the effects of resistance training remain less well defined, with findings to date being variable. Our results indicate that resistance training can induce meaningful, time-dependent shifts in the gut microbiome, particularly among sedentary individuals who experience substantial strength gains. Notably, we observed enrichment of key health-associated taxa, including Faecalibacterium and Roseburia hominis, both linked to anti-inflammatory effects and improved gut function. These findings suggest that resistance training may contribute to gut health in conjunction with physical fitness, supporting its broader application in health promotion strategies and future microbiome-focused research.

microbiology↗

nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install - all dependencies are provided within containers -, portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All code is hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

bioinformatics↗

PureCLIP: Capturing target-specific protein-RNA interaction footprints from single-nucleotide CLIP-seq data

iCLIP and eCLIP techniques facilitate the detection of protein-RNA interaction sites at high resolution, based on diagnostic events at crosslink sites. However, previous methods do not explicitly model the specifics of iCLIP and eCLIP truncation patterns and possible biases. We developed PureCLIP, a hidden Markov model based approach, which simultaneously performs peak calling and individual crosslink site detection. It explicitly incorporates RNA abundances and, for the first time, non-specific sequence biases. On both simulated and real data, PureCLIP is more accurate in calling crosslink sites than other state-of-the-art methods and has a higher agreement across replicates. Link: https://github.com/skrakau/PureCLIP.

bioinformatics↗