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ABSTRACT Bacterially secreted proteins play an important role in microbial physiology and ecology in many environments, including the mammalian gut. While gut microbes have been extensively studied over the past decades, little is known about the proteins that they secrete into the gastrointestinal tract. In this study, we developed and applied a computational pipeline to a comprehensive catalog of human-associated metagenome-assembled genomes in order to predict and analyze the bacterial metasecretome of the human gut, i.e., the collection of proteins secreted out of the cytoplasm by human gut bacteria. We identified the presence of large and diverse families of secreted carbohydrate-active enzymes and assessed their phylogenetic distributions across different taxonomic groups, which revealed an enrichment in Bacteroidetes and Verrucomicrobia . By mapping secreted proteins to available metagenomic data from endoscopic sampling of the human gastrointestinal tract, we specifically pinpointed regions in the upper and lower intestinal tract along the lumen and mucosa where specific glycosidases are secreted by resident microbes. The metasecretome analyzed in this study constitutes the most comprehensive list of secreted proteins produced by human gut bacteria reported to date and serves as a useful resource for the microbiome research community. IMPORTANCE Bacterially secreted proteins are necessary for the proper functioning of bacterial cells and communities. Secreted proteins provide bacterial cells with the ability to harvest resources from the exterior, import these resources into the cell, and signal to other bacteria. In the human gut microbiome, these actions impact host health and allow the maintenance of a healthy gut bacterial community. We utilized computational tools to identify the major components of human gut bacterially secreted proteins and determined their spatial distribution in the gastrointestinal tract. Our analysis of human gut bacterial secreted proteins will allow a better understanding of the impact of gut bacteria on human health and represents a step toward identifying new protein functions with interesting applications in biomedicine and industry. 

Abstract Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype–genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns betweenRuminococcaceae,Bacteroidaceae,CoriobacteriaceaeandBifidobacteriaceaefamilies that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies.