A distinct Fusobacterium nucleatum clade dominates the colorectal cancer niche.

Fusobacterium nucleatum (Fn), a bacterium present in the human oral cavity and rarely found in the lower gastrointestinal tract of healthy individuals1, is enriched in human colorectal cancer (CRC) tumours2-5. High intratumoural Fn loads are associated with recurrence, metastases and poorer patient prognosis5-8. Here, to delineate Fn genetic factors facilitating tumour colonization, we generated closed genomes for 135 Fn strains; 80 oral strains from individuals without cancer and 55 unique cancer strains cultured from tumours from 51 patients with CRC. Pangenomic analyses identified 483 CRC-enriched genetic factors. Tumour-isolated strains predominantly belong to Fn subspecies animalis (Fna). However, genomic analyses reveal that Fna, considered a single subspecies, is instead composed of two distinct clades (Fna C1 and Fna C2). Of these, only Fna C2 dominates the CRC tumour niche. Inter-Fna analyses identified 195 Fna C2-associated genetic factors consistent with increased metabolic potential and colonization of the gastrointestinal tract. In support of this, Fna C2-treated mice had an increased number of intestinal adenomas and altered metabolites. Microbiome analysis of human tumour tissue from 116 patients with CRC demonstrated Fna C2 enrichment. Comparison of 62 paired specimens showed that only Fna C2 is tumour enriched compared to normal adjacent tissue. This was further supported by metagenomic analysis of stool samples from 627 patients with CRC and 619 healthy individuals. Collectively, our results identify the Fna clade bifurcation, show that specifically Fna C2 drives the reported Fn enrichment in human CRC and reveal the genetic underpinnings of pathoadaptation of Fna C2 to the CRC niche.

Genomic and functional characterization of a mucosal symbiont involved in early-stage colorectal cancer.

Colorectal cancer is a major health concern worldwide. Growing evidence for the role of the gut microbiota in the initiation of CRC has sparked interest in approaches that target these microorganisms. However, little is known about the composition and role of the microbiota associated with precancerous polyps. Here, we found distinct microbial signatures between patients with and without polyps and between polyp subtypes using sequencing and culturing techniques. We found a correlation between Bacteroides fragilis recovered and the level of inflammatory cytokines in the mucosa adjacent to the polyp. Additional analysis revealed that B. fragilis from patients with polyps are bft-negative, activate NF-κB through Toll-like receptor 4, induce a pro-inflammatory response, and are enriched in genes associated with LPS biosynthesis. This study provides fundamental insight into the microbial microenvironment of the pre-neoplastic polyp by highlighting strain-specific genomic and proteomic differences, as well as more broad compositional differences in the microbiome.

geneshot: gene-level metagenomics identifies genome islands associated with immunotherapy response.

Researchers must be able to generate experimentally testable hypotheses from sequencing-based observational microbiome experiments to discover the mechanisms underlying the influence of gut microbes on human health. We describe geneshot, a novel bioinformatics tool for identifying testable hypotheses based on gene-level metagenomic analysis of WGS microbiome data. By applying geneshot to two independent previously published cohorts, we identify microbial genomic islands consistently associated with response to immune checkpoint inhibitor (ICI)-based cancer treatment in culturable type strains. The identified genomic islands are within operons involved in type II secretion, TonB-dependent transport, and bacteriophage growth.

Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease.

BACKGROUND: Whole-genome "shotgun" (WGS) metagenomic sequencing is an increasingly widely used tool for analyzing the metagenomic content of microbiome samples. While WGS data contains gene-level information, it can be challenging to analyze the millions of microbial genes which are typically found in microbiome experiments. To mitigate the ultrahigh dimensionality challenge of gene-level metagenomics, it has been proposed to cluster genes by co-abundance to form Co-Abundant Gene groups (CAGs). However, exhaustive co-abundance clustering of millions of microbial genes across thousands of biological samples has previously been intractable purely due to the computational challenge of performing trillions of pairwise comparisons. RESULTS: Here we present a novel computational approach to the analysis of WGS datasets in which microbial gene groups are the fundamental unit of analysis. We use the Approximate Nearest Neighbor heuristic for near-exhaustive average linkage clustering to group millions of genes by co-abundance. This results in thousands of high-quality CAGs representing complete and partial microbial genomes. We applied this method to publicly available WGS microbiome surveys and found that the resulting microbial CAGs associated with inflammatory bowel disease (IBD) and colorectal cancer (CRC) were highly reproducible and could be validated independently using multiple independent cohorts. CONCLUSIONS: This powerful approach to gene-level metagenomics provides a powerful path forward for identifying the biological links between the microbiome and human health. By proposing a new computational approach for handling high dimensional metagenomics data, we identified specific microbial gene groups that are associated with disease that can be used to identify strains of interest for further preclinical and mechanistic experimentation.

Follicle number, not assessments of the ovarian stroma, represents the best ultrasonographic marker of polycystic ovary syndrome.

OBJECTIVE: To compare the diagnostic potential of ultrasonographic markers of ovarian morphology, used alone or in combination, to predict polycystic ovary syndrome (PCOS). DESIGN: A diagnostic test study using cross-sectional data collected from 2006-2011. SETTING: Academic hospital and clinical research unit. PATIENT(S): Eighty-two women with PCOS and 60 healthy female volunteers. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Follicle number per ovary (FNPO), ovarian volume (OV), follicle number per single cross-section (FNPS), follicle distribution pattern, stromal area, ovarian area, stromal-to-ovarian area ratio (S:A), and stromal index (SI). RESULT(S): Follicle number per ovary best predicted PCOS (R(2) = 67%) with 85% sensitivity and 98% specificity, followed by OV (R(2) = 44%), and FNPS (R(2) = 36%). Neither S:A nor SI had predictive power for PCOS. In combination, FNPO+S:A and FNPO+SI most significantly predicted PCOS (R(2) = 74% vs. 73%, respectively). The diagnostic potentials of OV and FNPS were substantially improved when used in combination (OV+FNPO, R(2) = 55%). CONCLUSION(S): As a single metric, FNPO best predicted PCOS. Although the addition of S:A or SI improved the predictive power of FNPO, gains were marginal, suggesting limited use in clinical practice. When image quality precludes a reliable estimation of FNPO, measurements of OV+FNPS provide the next closest level of diagnostic potential.