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Microbiome studies have recently transitioned from experimental designs with a few hundred samples to designs spanning tens of thousands of samples. Modern studies such as the Earth Microbiome Project (EMP) afford the statistics crucial for untangling the many factors that influence microbial community composition. Analyzing those data used to require access to a compute cluster, making it both expensive and inconvenient. We show that recent improvements in both hardware and software now allow to compute key bioinformatics tasks on EMP-sized data in minutes using a gaming-class laptop, enabling much faster and broader microbiome science insights.
We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, amore »
Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivityThe human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS). Studies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner. Here, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex. Common human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified. The prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls. The presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age. In vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells. HS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection. Understanding the impact of shifts in microbial community composition andmore »