Expanded microbiome niches of RAG-deficient patients.

The complex interplay between microbiota and immunity is important to human health. To explore how altered adaptive immunity influences the microbiome, we characterize skin, nares, and gut microbiota of patients with recombination-activating gene (RAG) deficiency-a rare genetically defined inborn error of immunity (IEI) that results in a broad spectrum of clinical phenotypes. Integrating de novo assembly of metagenomes from RAG-deficient patients with reference genome catalogs provides an expansive multi-kingdom view of microbial diversity. RAG-deficient patient microbiomes exhibit inter-individual variation, including expansion of opportunistic pathogens (e.g., Corynebacterium bovis, Haemophilus influenzae), and a relative loss of body site specificity. We identify 35 and 27 bacterial species derived from skin/nares and gut microbiomes, respectively, which are distinct to RAG-deficient patients compared to healthy individuals. Underscoring IEI patients as potential reservoirs for viral persistence and evolution, we further characterize the colonization of eukaryotic RNA viruses (e.g., Coronavirus 229E, Norovirus GII) in this patient population.

Predicting cancer immunotherapy response from gut microbiomes using machine learning models.

Cancer immunotherapy has significantly improved patient survival. Yet, half of patients do not respond to immunotherapy. Gut microbiomes have been linked to clinical responsiveness of melanoma patients on immunotherapies; however, different taxa have been associated with response status with implicated taxa inconsistent between studies. We used a tumor-agnostic approach to find common gut microbiome features of response among immunotherapy patients with different advanced stage cancers. A combined meta-analysis of 16S rRNA gene sequencing data from our mixed tumor cohort and three published immunotherapy gut microbiome datasets from different melanoma patient cohorts found certain gut bacterial taxa correlated with immunotherapy response status regardless of tumor type. Using multivariate selbal analysis, we identified two separate groups of bacterial genera associated with responders versus non-responders. Statistical models of gut microbiome community features showed robust prediction accuracy of immunotherapy response in amplicon sequencing datasets and in cross-sequencing platform validation with shotgun metagenomic datasets. Results suggest baseline gut microbiome features may be predictive of clinical outcomes in oncology patients on immunotherapies, and some of these features may be generalizable across different tumor types, patient cohorts, and sequencing platforms. Findings demonstrate how machine learning models can reveal microbiome-immunotherapy interactions that may ultimately improve cancer patient outcomes.

Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions.

Human skin functions as a physical barrier to foreign pathogen invasion and houses numerous commensals. Shifts in the human skin microbiome have been associated with conditions ranging from acne to atopic dermatitis. Previous metagenomic investigations into the role of the skin microbiome in health or disease have found that much of the sequenced data do not match reference genomes, making it difficult to interpret metagenomic datasets. We combined bacterial cultivation and metagenomic sequencing to assemble the Skin Microbial Genome Collection (SMGC), which comprises 622 prokaryotic species derived from 7,535 metagenome-assembled genomes and 251 isolate genomes. The metagenomic datasets that we generated were combined with publicly available skin metagenomic datasets to identify members and functions of the human skin microbiome. The SMGC collection includes 174 newly identified bacterial species and 12 newly identified bacterial genera, including the abundant genus 'Candidatus Pellibacterium', which has been newly associated with the skin. The SMGC increases the characterized set of known skin bacteria by 26%. We validated the SMGC metagenome-assembled genomes by comparing them with sequenced isolates obtained from the same samples. We also recovered 12 eukaryotic species and assembled thousands of viral sequences, including newly identified clades of jumbo phages. The SMGC enables classification of a median of 85% of skin metagenomic sequences and provides a comprehensive view of skin microbiome diversity, derived primarily from samples obtained in North America.