Dietary fat and fiber interact to uniquely modify global histone post-translational epigenetic programming in a rat colon cancer progression model.

Dietary fermentable fiber generates short-chain fatty acids (SCFA), for example, butyrate, in the colonic lumen which serves as a chemoprotective histone deacetylase inhibitor and/or as an acetylation substrate for histone acetylases. In addition, n-3 polyunsaturated fatty acids (n-3 PUFA) in fish oil can affect the chromatin landscape by acting as ligands for tumor suppressive nuclear receptors. In an effort to gain insight into the global dimension of post-translational modification of histones (including H3K4me3 and H3K9ac) and clarify the chemoprotective impact of dietary bioactive compounds on transcriptional control in a preclinical model of colon cancer, we generated high-resolution genome-wide RNA (RNA-Seq) and "chromatin-state" (H3K4me3-seq and H3K9ac-seq) maps for intestinal (epithelial colonocytes) crypts in rats treated with a colon carcinogen and fed diets containing bioactive (i) fish oil, (ii) fermentable fiber (a rich source of SCFA), (iii) a combination of fish oil plus pectin, or (iv) control, devoid of fish oil or pectin. In general, poor correlation was observed between differentially transcribed (DE) and enriched genes (DERs) at multiple epigenetic levels. The combinatorial diet (fish oil + pectin) uniquely affected transcriptional profiles in the intestinal epithelium, for example, upregulating lipid catabolism and beta-oxidation associated genes. These genes were linked to activated ligand-dependent nuclear receptors associated with n-3 PUFA and were also correlated with the mitochondrial L-carnitine shuttle and the inhibition of lipogenesis. These findings demonstrate that the chemoprotective fish oil + pectin combination diet uniquely induces global histone state modifications linked to the expression of chemoprotective genes.

Assessment of histone tail modifications and transcriptional profiling during colon cancer progression reveals a global decrease in H3K4me3 activity.

During colon cancer, epigenetic alterations contribute to the dysregulation of major cellular functions and signaling pathways. Modifications in chromatin signatures such as H3K4me3 and H3K9ac, which are associated with transcriptionally active genes, can lead to genomic instability and perturb the expression of gene sets associated with oncogenic processes. In order to further elucidate early pre-tumorigenic epigenetic molecular events driving CRC, we integrated diverse, genome-wide, epigenetic inputs (by high throughput sequencing of RNA, H3K4me3, and H3K9ac) and compared differentially expressed transcripts (DE) and enriched regions (DER) in an in-vivo rat colon cancer progression model. Carcinogen (AOM) effects were detected genome-wide at the RNA (116 DE genes), K9ac (49 DERs including 24 genes) and K4me3 (7678 DERs including 3792 genes) level. RNA-seq differential expression and pathway analysis indicated that interferon-associated innate immune responses were impacted by AOM exposure. Despite extensive associations between K4me3 DERs and colon tumorigenesis (1210 genes were linked to colorectal carcinoma) including FOXO3, GNAI2, H2AFX, MSH2, NR3C1, PDCD4 and VEGFA, these changes were not reflected at the RNA gene expression level during early cancer progression. Collectively, our results indicate that carcinogen-induced changes in gene K4me3 DERs are harbingers of future transcriptional events, which drive malignant transformation of the colon.

Functional Generalized Additive Models.

We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number of principal components as in Müller and Yao (2008), our model incorporates the functional predictor directly and thus our model can be viewed as the natural functional extension of generalized additive models. We estimate F(·,·) using tensor-product B-splines with roughness penalties. A pointwise quantile transformation of the functional predictor is also considered to ensure each tensor-product B-spline has observed data on its support. The methods are evaluated using simulated data and their predictive performance is compared with other competing scalar-on-function regression alternatives. We illustrate the usefulness of our approach through an application to brain tractography, where X(t) is a signal from diffusion tensor imaging at position, t, along a tract in the brain. In one example, the response is disease-status (case or control) and in a second example, it is the score on a cognitive test. R code for performing the simulations and fitting the FGAM can be found in supplemental materials available online.