Identification of shared and disease-specific host gene-microbiome associations across human diseases using multi-omic integration.

While gut microbiome and host gene regulation independently contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here we developed a machine learning-based framework to jointly analyse paired host transcriptomic (n = 208) and gut microbiome (n = 208) profiles from colonic mucosal samples of patients with colorectal cancer, inflammatory bowel disease and irritable bowel syndrome. We identified associations between gut microbes and host genes that depict shared as well as disease-specific patterns. We found that a common set of host genes and pathways implicated in gastrointestinal inflammation, gut barrier protection and energy metabolism are associated with disease-specific gut microbes. Additionally, we also found that mucosal gut microbes that have been implicated in all three diseases, such as Streptococcus, are associated with different host pathways in each disease, suggesting that similar microbes can affect host pathophysiology in a disease-specific manner through regulation of different host genes. Our framework can be applied to other diseases for the identification of host gene-microbiome associations that may influence disease outcomes.

Longitudinal Multi-omics Reveals Subset-Specific Mechanisms Underlying Irritable Bowel Syndrome.

The gut microbiome has been implicated in multiple human chronic gastrointestinal (GI) disorders. Determining its mechanistic role in disease has been difficult due to apparent disconnects between animal and human studies and lack of an integrated multi-omics view of disease-specific physiological changes. We integrated longitudinal multi-omics data from the gut microbiome, metabolome, host epigenome, and transcriptome in the context of irritable bowel syndrome (IBS) host physiology. We identified IBS subtype-specific and symptom-related variation in microbial composition and function. A subset of identified changes in microbial metabolites correspond to host physiological mechanisms that are relevant to IBS. By integrating multiple data layers, we identified purine metabolism as a novel host-microbial metabolic pathway in IBS with translational potential. Our study highlights the importance of longitudinal sampling and integrating complementary multi-omics data to identify functional mechanisms that can serve as therapeutic targets in a comprehensive treatment strategy for chronic GI diseases. VIDEO ABSTRACT.

Interactions between the gut microbiome and host gene regulation in cystic fibrosis.

BACKGROUND: Cystic fibrosis is the most common autosomal recessive genetic disease in Caucasians. It is caused by mutations in the CFTR gene, leading to poor hydration of mucus and impairment of the respiratory, digestive, and reproductive organ functions. Advancements in medical care have led to markedly increased longevity of patients with cystic fibrosis, but new complications have emerged, such as early onset of colorectal cancer. Although the pathogenesis of colorectal cancer in cystic fibrosis remains unclear, altered host-microbe interactions might play a critical role. To investigate this, we characterized changes in the microbiome and host gene expression in the colonic mucosa of cystic fibrosis patients relative to healthy controls, and identified host gene-microbiome interactions in the colon of cystic fibrosis patients. METHODS: We performed RNA-seq on colonic mucosa samples from cystic fibrosis patients and healthy controls to determine differentially expressed host genes. We also performed 16S rRNA sequencing to characterize the colonic mucosal microbiome and identify gut microbes that are differentially abundant between patients and healthy controls. Lastly, we modeled associations between relative abundances of specific bacterial taxa in the gut mucosa and host gene expression. RESULTS: We find that 1543 genes, including CFTR, show differential expression in the colon of cystic fibrosis patients compared to healthy controls. These genes are enriched with functions related to gastrointestinal and colorectal cancer, such as metastasis of colorectal cancer, tumor suppression, p53, and mTOR signaling pathways. In addition, patients with cystic fibrosis show decreased gut microbial diversity, decreased abundance of butyrate producing bacteria, such as Ruminococcaceae and Butyricimonas, and increased abundance of other taxa, such as Actinobacteria and Clostridium. An integrative analysis identified colorectal cancer-related genes, including LCN2 and DUOX2, for which gene expression is correlated with the abundance of colorectal cancer-associated bacteria, such as Ruminococcaceae and Veillonella. CONCLUSIONS: In addition to characterizing host gene expression and mucosal microbiome in cystic fibrosis patients, our study explored the potential role of host-microbe interactions in the etiology of colorectal cancer in cystic fibrosis. Our results provide biomarkers that may potentially serve as targets for stratifying risk of colorectal cancer in patients with cystic fibrosis.

Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers.

BACKGROUND: Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. METHODS: We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks. RESULTS: We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC. CONCLUSIONS: Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

Transposon mutagenesis screen in mice identifies TM9SF2 as a novel colorectal cancer oncogene.

New therapeutic targets for advanced colorectal cancer (CRC) are critically needed. Our laboratory recently performed an insertional mutagenesis screen in mice to identify novel CRC driver genes and, thus, potential drug targets. Here, we define Transmembrane 9 Superfamily 2 (TM9SF2) as a novel CRC oncogene. TM9SF2 is an understudied protein, belonging to a well conserved protein family characterized by their nine putative transmembrane domains. Based on our transposon screen we found that TM9SF2 is a candidate progression driver in digestive tract tumors. Analysis of The Cancer Genome Atlas (TCGA) data revealed that approximately 35% of CRC patients have elevated levels of TM9SF2 mRNA, data we validated using an independent set of CRC samples. RNAi silencing of TM9SF2 reduced CRC cell growth in an anchorage-independent manner, a hallmark of cancer. Furthermore, CRISPR/Cas9 knockout of TM9SF2 substantially diminished CRC tumor fitness in vitro and in vivo. Transcriptome analysis of TM9SF2 knockout cells revealed a potential role for TM9SF2 in cell cycle progression, oxidative phosphorylation, and ceramide signaling. Lastly, we report that increased TM9SF2 expression correlates with disease stage and low TM9SF2 expression correlate with a more favorable relapse-free survival. Taken together, this study provides evidence that TM9SF2 is a novel CRC oncogene.

Colorectal cancer mutational profiles correlate with defined microbial communities in the tumor microenvironment.

Variation in the gut microbiome has been linked to colorectal cancer (CRC), as well as to host genetic variation. However, we do not know whether, in addition to baseline host genetics, somatic mutational profiles in CRC tumors interact with the surrounding tumor microbiome, and if so, whether these changes can be used to understand microbe-host interactions with potential functional biological relevance. Here, we characterized the association between CRC microbial communities and tumor mutations using microbiome profiling and whole-exome sequencing in 44 pairs of tumors and matched normal tissues. We found statistically significant associations between loss-of-function mutations in tumor genes and shifts in the abundances of specific sets of bacterial taxa, suggestive of potential functional interaction. This correlation allows us to statistically predict interactions between loss-of-function tumor mutations in cancer-related genes and pathways, including MAPK and Wnt signaling, solely based on the composition of the microbiome. In conclusion, our study shows that CRC microbiomes are correlated with tumor mutational profiles, pointing towards possible mechanisms of molecular interaction.

A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.

Textual eligibility criteria in clinical trial protocols contain important information about potential clinically relevant pharmacogenomic events. Manual curation for harvesting this evidence is intractable as it is error prone and time consuming. In this paper, we develop and evaluate a Semantic Web-based system that captures and manages mutation evidences and related contextual information from cancer clinical trials. The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager. We evaluated the performance of the annotator in terms of precision and recall. We demonstrated the usefulness of the system by conducting case studies in retrieving relevant clinical trials using a collection of mutations identified from TCGA Leukemia patients and Atlas of Genetics and Cytogenetics in Oncology and Haematology. In conclusion, our system using Semantic Web technologies provides an effective framework for extraction, annotation, standardization and management of genetic mutations in cancer clinical trials.