The data presented here show that several transcriptomic signatures previously identified as relevant to lung cancer pathogenesis are associated with enrichment of the lower airway microbiota with oral commensals.

Integrating multi-omics datasets is critical for microbiome research, but multiple statistical challenges can confound traditional correlation techniques. We solve this problem by using neural networks to estimate the conditional probability that each molecule is present given the presence of each specific microbe. We show with known environmental (desert biological soil crust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially-produced metabolites and inflammatory bowel disease.

In lung cancer, enrichment of the lower airway microbiota with oral commensals commonly occurs and ex vivo models support that some of these bacteria can trigger host transcriptomic signatures associated with carcinogenesis. Here, we show that this lower airway dysbiotic signature was more prevalent in group IIIB-IV TNM stage lung cancer and is associated with poor prognosis, as shown by decreased survival among subjects with early stage disease (I-IIIA) and worse tumor progression as measured by RECIST scores among subjects with IIIB-IV stage disease. In addition, this lower airway microbiota signature was associated with upregulation of IL-17, PI3K, MAPK and ERK pathways in airway transcriptome, and we identified Veillonella parvula as the most abundant taxon driving this association. In a KP lung cancer model, lower airway dysbiosis with V. parvula led to decreased survival, increased tumor burden, IL-17 inflammatory phenotype and activation of checkpoint inhibitor markers. Statement of Significance (50 word limit)Multiple lines of investigations have shown that the gut microbiota affects host immune response to immunotherapy in cancer. Here we support that the local airway microbiota modulates the host immune tone in lung cancer affecting tumor progression and prognosis.Research.

The early-life intestinal microbiota plays a key role in shaping host immune system development. We found that a single early-life antibiotic course (1PAT) accelerated type 1 diabetes (T1D) development in male NOD mice. The single course had deep and persistent effects on the intestinal microbiome, leading to altered cecal, hepatic, and serum metabolites. The exposure elicited sex-specific effects on chromatin states in the ileum and liver and perturbed ileal gene expression, altering normal maturational patterns. The global signature changes included specific genes controlling both innate and adaptive immunity. Microbiome analysis revealed four taxa each that potentially protect against or accelerate T1D onset, that were linked in a network model to specific differences in ileal gene expression. This simplified animal model reveals multiple potential pathways to understand pathogenesis by which early-life gut microbiome perturbations alter a global suite of intestinal responses, contributing to the accelerated and enhanced T1D development.

Objective To characterize the ecological effects of biologic therapies on the gut bacterial and fungal microbiome in psoriatic arthritis (PsA)/spondyloarthritis (SpA) patients. Methods Fecal samples from PsA/SpA patients pre‐ and posttreatment with tumor necrosis factor inhibitors (TNFi; n = 15) or an anti–interleukin‐17A monoclonal antibody inhibitor (IL‐17i; n = 14) underwent sequencing (16S ribosomal RNA, internal transcribed spacer and shotgun metagenomics) and computational microbiome analysis. Fecal levels of fatty acid metabolites and cytokines/proteins implicated in PsA/SpA pathogenesis or intestinal inflammation were correlated with sequence data. Additionally, ileal biopsies obtained from SpA patients who developed clinically overt Crohn's disease (CD) after treatment with IL‐17i (n = 5) were analyzed for expression of IL‐23/Th17–related cytokines, IL‐25/IL‐17E–producing cells, and type 2 innate lymphoid cells (ILC2s). Results There were significant shifts in abundance of specific taxa after treatment with IL‐17i compared to TNFi, particularly Clostridiales (P = 0.016) and Candida albicans (P = 0.041). These subclinical alterations correlated with changes in bacterial community co‐occurrence, metabolic pathways, IL‐23/Th17–related cytokines, and various fatty acids. Ileal biopsies showed that clinically overt CD was associated with expansion of IL‐25/IL‐17E–producing tuft cells and ILC2s (P < 0.05), compared to pre–IL‐17i treatment levels. Conclusion In a subgroup of SpA patients, the initiation of IL‐17A blockade correlated with features of subclinical gut inflammation and intestinal dysbiosis of certain bacterial and fungal taxa, most notably C albicans. Further, IL‐17i–related CD was associated with overexpression of IL‐25/IL‐17E–producing tuft cells and ILC2s. These results may help to explain the potential link between inhibition of a specific IL‐17 pathway and the (sub)clinical gut inflammation observed in SpA.

Estimation of statistical associations in microbial genomic survey count data is fundamental to microbiome research. Experimental limitations, including count compositionality, low sample sizes and technical variability, obstruct standard application of association measures and require data normalization prior to statistical estimation. Here, we investigate the interplay between data normalization, microbial association estimation and available sample size by leveraging the large-scale American Gut Project (AGP) survey data. We analyze the statistical properties of two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis workflows and log-ratio transformations. We show that shrinkage estimation, a standard statistical regularization technique, can universally improve the quality of taxon–taxon association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as downstream data analysis examples, we show that variance-stabilizing and log-ratio approaches enable the most taxonomically and structurally coherent estimates. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.

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Bacteriophages are abundant members of all microbiomes studied to date, influencing microbial communities through interactions with their bacterial hosts. Despite their functional importance and ubiquity, phages have been underexplored in urban environments compared to their bacterial counterparts. We profiled the viral communities in New York City (NYC) wastewater using metagenomic data collected in November 2014 from 14 wastewater treatment plants. We show that phages accounted for the largest viral component of the sewage samples and that specific virus communities were associated with local environmental conditions within boroughs. The vast majority of the virus sequences had no homology matches in public databases, forming an average of 1,700 unique virus clusters (putative genera). These new clusters contribute to elucidating the overwhelming proportion of data that frequently goes unidentified in viral metagenomic studies. We assigned potential hosts to these phages, which appear to infect a wide range of bacterial genera, often outside their presumed host. We determined that infection networks form a modular-nested pattern, indicating that phages include a range of host specificities, from generalists to specialists, with most interactions organized into distinct groups. We identified genes in viral contigs involved in carbon and sulfur cycling, suggesting functional importance of viruses in circulating pathways and gene functions in the wastewater environment. In addition, we identified virophage genes as well as a nearly complete novel virophage genome. These findings provide an understanding of phage abundance and diversity in NYC wastewater, previously uncharacterized, and further examine geographic patterns of phage-host association in urban environments. IMPORTANCE Wastewater is a rich source of microbial life and contains bacteria, viruses, and other microbes found in human waste as well as environmental runoff sources. As part of an effort to characterize the New York City wastewater metagenome, we profiled the viral community of sewage samples across all five boroughs of NYC and found that local sampling sites have unique sets of viruses. We focused on bacteriophages, or viruses of bacteria, to understand how they may influence the microbial ecology of this system. We identified several new clusters of phages and successfully associated them with bacterial hosts, providing insight into virus-host interactions in urban wastewater. This study provides a first look into the viral communities present across the wastewater system in NYC and points to their functional importance in this environment.