Biology subjects

Nielsen, H. B.

Publications and source records attributed to Nielsen, H. B..

3 recordsLinked to original sources

bioRxiv2026

Sex shapes the human genitourinary microbiome, including many species recognized as uropathogens: urine profiles from a population-level shotgun metagenomics study

Background: The urinary tract was long considered sterile. The discovery of the urinary microbiome has attracted interest in its role in urinary health and disease. Population-level urinary microbiome composition remains poorly defined and previous studies relied on selected populations and low-resolution sequencing. We characterized the microbial composition of voided urine from adults without urinary symptoms in a large Swedish population cohort to establish a population-based reference for microorganisms detected in voided urine using shotgun metagenomics. Methods: We performed shotgun metagenomic sequencing of voided urine samples from 2,062 participants of the population-based Swedish CArdioPulmonary bioImage Study (SCAPIS; 55% women, 50-65 years). Results: Among 3,668 identified species, 90% were classified at the species level. Microbial profiles differed substantially between sexes, with lactobacilli and bifidobacteria enriched in females, while Cutibacterium acnes, Enterococcus and Propionimicrobium lymphophilum were enriched in males. Recognized uropathogens, including Enterococcus faecalis and Escherichia coli, were frequently detected despite participants having no urinary symptoms. Males showed higher alpha diversity (Shannon and inverse Simpson indices), whereas richness did not differ by sex. Age showed only modest associations with alpha diversity, and beta diversity differed by sex but showed minimal age-related variation. Conclusions: This study provides a population-based reference for interpreting microorganisms detected in routine voided urine samples. Biological sex was the principal determinant of microbial composition, supporting routine consideration of sex in future studies of the urogenital microbiota using routine voided urine samples.

microbiology↗

bioRxiv2024

CHAMP delivers accurate taxonomic profiles of the prokaryotes, eukaryotes, and bacteriophages in the human microbiome

Accurate taxonomic profiling of the human microbiome composition is crucial for linking microbial species to health outcomes. Therefore, we created the Clinical Microbiomics Human Microbiome Profiler (CHAMP), a comprehensive tool designed for the profiling of prokaryotes, eukaryotes, and viruses across all body sites. Boasting a reference database derived from 30,382 human microbiome samples, CHAMP covers 6,567 prokaryotic and 244 eukaryotic species as well as 64,003 viruses. We used a diverse set of in silico metagenomes and DNA mock communities to benchmark CHAMP against the established profiling tools MetaPhlAn 4, Bracken 2, mOTUs 3, and Phanta. CHAMP demonstrated unparalleled species recall, F1 score, and significantly reduced false positives compared to all other tools benchmarked. Indeed, the false positive relative abundance (FPRA) for CHAMP was, on average, 50-fold lower than the second-best performing profiler. CHAMP also proved to be more robust than the other tools to low sequencing depths, highlighting its application for low biomass samples. Taken together, this establishes CHAMP as a best-in-class human microbiome profiler of prokaryotes, eukaryotes, and viruses in diverse and complex communities across low and high biomass samples. CHAMP profiling is offered as a service by Clinical Microbiomics A/S and available for a fee at https://cosmosidhub.com.

bioinformatics↗

bioRxiv2018

Binning microbial genomes using deep learning

Identification and reconstruction of microbial species from metagenomics wide genome sequencing data is an important and challenging task. Current existing approaches rely on gene or contig co-abundance information across multiple samples and k-mer composition information in the sequences. Here we use recent advances in deep learning to develop an algorithm that uses variational autoencoders to encode co-abundance and compositional information prior to clustering. We show that the deep network is able to integrate these two heterogeneous datasets without any prior knowledge and that our method outperforms existing state-of-the-art by reconstructing 1.8 - 8 times more highly precise and complete genome bins from three different benchmark datasets. Additionally, we apply our method to a gene catalogue of almost 10 million genes and 1,270 samples from the human gut microbiome. Here we are able to cluster 1.3 - 1.8 million extra genes and reconstruct 117 - 246 more highly precise and complete bins of which 70 bins were completely new compared to previous methods. Our method Variational Autoencoders for Metagenomic Binning (VAMB) is freely available at: https://github.com/jakobnissen/vamb

bioinformatics↗