Abstract A31: Correlations between diet, gut microbiota and epithelial methylation pattern with CRC risk

Purpose: To determine which dietary factors contribute to microbiota variation and identify correlations with gut epithelial methylation pattern associated with the presence of high-risk polyps. Dietary exposures have long been suggested as important risk factors for cancers, especially those of the gastrointestinal tract. Diet is known to affect the commensal gut microbiota, which contributes important activities to the human host that are required for maintaining normal health. However, distortions in microbiota composition and associated microbial activities might contribute to disease processes including colorectal carcinogenesis. High-throughput sequencing based microbiota studies have revealed a large degree of intra- and inter-individual variation, the sources of which are currently not well understood. As part of this project we explored various ‘Big Data’ approaches that can facilitate an efficient mining of the large dataset generated by combining dietary intake data with output from multiple high-throughput technologies. We hypothesized that specific nutrients can affect microbiota composition, and that microbiota correlates with an epithelial methylation pattern associated with CRC risk. Methods: The study included 126 human healthy volunteers undergoing a screening colonoscopy. Dietary habits were assessed using 4-day food records, FFQ (Block 98) and Meat Module Questionnaire. Microbiota composition was examined in a stool samples collected before colonoscopy and in multiple biopsy samples retrieved during the screening procedure. The colonoscopy results were used for ascertainment of polyp status. Microbiota composition was analyzed in DNA extracted from stool and biopsy samples using a 16S rRNA based approach. In addition, in a subset of 39 stool samples a shotgun metagenomic analysis was undertaken. Sequence reads were binned into operational taxonomic units (OTUs) using ESPRIT and further analyzed using our in house analytical pipeline as well as publicly available platforms such as QIIME. We then performed a discriminant analysis to identify a microbiota pattern associated with polyp prevalence. Methylation analysis was performed in DNA extracted from biopsies obtained from six high-risk cases and six matched controls using the Infinium HumanMethylation450 beadchip. We used regression analysis approaches as well as the popular apriori rule-mining algorithm to identify associations in the complex heterogeneous dataset. Results: We observed microbiota associations with dietary intake as well as differences in the presence of OTUs in subjects with and without polyps (p Conclusions: Our observations suggest complex interactions between diet and microbiota that correlate with epithelial methylation pattern and CRC risk. An efficient mining of this and other large datasets, generated by combining multiple layers of complex data, will require adaptation of ‘Big Data’ analytical approaches that can handle the unique characteristics of high dimensional but sparse data. Citation Format: Tyler Culpepper, Maria Ukhanova, Lusine Yaghjyan, Samantha Sites, Yijun Sun, Prosperi Mattia, Volker Mai. Correlations between diet, gut microbiota and epithelial methylation pattern with CRC risk [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr A31.