Colorectal (CRC) cancer is the third most common cancer in the United States and its incidence is rising in younger populations. Diet is an important risk factor for CRC and dietary constituents are modified by gut microbial action. Microbial fermentation of dietary fiber and plant lignans in high-fiber foods produces bioactive endproducts, such as butyrate and enterolignans. The enterolignans, enterolactone (ENL) and enterodiol (END) have been associated with lower CRC in some epidemiologic studies. Lignans and dietary fiber have been shown to reduce colon tumors in animal models and butyrate and END and ENL influence cellular pathways important to cancer risk in vitro. We propose a 3-period randomized, cross-over intervention in 70 healthy men and women (ages 20-45) with the goal to test the effect of a flaxseed lignan supplement and defatted flaxseed meal (containing lignans + dietary fiber) as compared to placebo on: 1) host gene expression in epithelial and stromal cells from colon biopsies and exfoliated colonocytes extracted from feces;2) gut microbial community composition, and 3) the interaction of the gut microbiome, enterolignan exposure, and colonic gene expression in high- and low-ENL excreters. Colon biopsies, stool, and urine will be collected at the end of each of the three 60-day periods to evaluate effects of the lignan treatments. We will use an innovative application of RNA-seq to measure gene expression in human colon cells from biopsy and stool. The gut microbiome will be characterized using pyrosequencing and QPCR of the 16S rRNA gene. Lignans will be measured in urine by GC-MS. Further, using samples from a subset of intervention participants who are low-and high-ENL excreters, we will measure the functional metagenomics of the gut microbiome in vitro. This proposed project will be the first to integrate and characterize, through a unique interdisciplinary collaboration, the relationships between lignan exposure, gut microbial ecology, and gene expression in cell- signaling pathways. Results of this placebo-controlled intervention will bridge the current knowledge from pre-clinical animal models and epidemiologic studies and will help to inform approaches for CRC prevention.
Intake of foods high in dietary fiber is associated with lower risk of colorectal cancer. Gut bacteria convert constituents of plant foods, sugh as lignans and dietary flber, to biologically active compounds that in animal models prevent the development of colon cancer. In a human dietary intervention, we will study how these biologically active compounds affect colon cell-signaling pathways important to colorectal cancer risk.
|Kim, Eunji; Ivanov, Ivan; Hua, Jianping et al. (2017) The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data. Cancer Inform 16:1176935117710530|
|Knight, Jason M; Kim, Eunji; Ivanov, Ivan et al. (2016) Comprehensive site-specific whole genome profiling of stromal and epithelial colonic gene signatures in human sigmoid colon and rectal tissue. Physiol Genomics 48:651-9|
|Zoh, Roger S; Mallick, Bani; Ivanov, Ivan et al. (2016) PCAN: Probabilistic correlation analysis of two non-normal data sets. Biometrics 72:1358-1368|
|Hullar, Meredith A J; Lancaster, Samuel M; Li, Fei et al. (2015) Enterolignan-producing phenotypes are associated with increased gut microbial diversity and altered composition in premenopausal women in the United States. Cancer Epidemiol Biomarkers Prev 24:546-54|
|Knight, Jason; Ivanov, Ivan; Triff, Karen et al. (2015) Detecting Multivariate Gene Interactions in RNA-Seq Data Using Optimal Bayesian Classification. IEEE/ACM Trans Comput Biol Bioinform :|
|Chapkin, Robert S; DeClercq, Vanessa; Kim, Eunjoo et al. (2014) Mechanisms by Which Pleiotropic Amphiphilic n-3 PUFA Reduce Colon Cancer Risk. Curr Colorectal Cancer Rep 10:442-452|
|Hullar, Meredith A J; Burnett-Hartman, Andrea N; Lampe, Johanna W (2014) Gut microbes, diet, and cancer. Cancer Treat Res 159:377-99|
|Donovan, Sharon M; Wang, Mei; Monaco, Marcia H et al. (2014) Noninvasive molecular fingerprinting of host-microbiome interactions in neonates. FEBS Lett 588:4112-9|
|Hullar, Meredith A J; Fu, Benjamin C (2014) Diet, the gut microbiome, and epigenetics. Cancer J 20:170-5|
|Knight, Jason M; Ivanov, Ivan; Dougherty, Edward R (2014) MCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification. BMC Bioinformatics 15:401|
Showing the most recent 10 out of 14 publications