Comprehensive site-specific whole genome profiling of stromal and epithelial colonic gene signatures in human sigmoid colon and rectal tissue.

The strength of associations between various exposures (e.g., diet, tobacco, chemopreventive agents) and colorectal cancer risk may partially depend on the complex interaction between epithelium and stroma across anatomic subsites.

Data describing the effects of dietary bioactive agents on colonic stem cell microRNA and mRNA expression.

With the identification of Lgr5 as a definitive marker for intestinal stem cells, we used the highly novel, recently described, Lgr5-EGFP-IRES-cre ER (T2) knock in mouse model. Mice were injected with azoxymethane (AOM, a colon carcinogen) or saline (control) and fed a chemo-protective diet containing n-3 fatty acids and fermentable fiber (n-3 PUFA+pectin) or a control diet (n-6 PUFA + cellulose). Single cells were isolated from colonic mucosa crypts and three discrete populations of cells were collected via fluorescence activated cell sorting (FACS): Lgr5(high) (stem cells), Lgr5(low) (daughter cells) and Lgr5(negative) (differentiated cells).

Comparative effects of diet and carcinogen on microRNA expression in the stem cell niche of the mouse colonic crypt.

There is mounting evidence that noncoding microRNAs (miRNA) are modulated by select chemoprotective dietary agents. For example, recently we demonstrated that the unique combination of dietary fish oil (containing n-3 fatty acids) plus pectin (fermented to butyrate in the colon) (FPA) up-regulates a subset of putative tumor suppressor miRNAs in intestinal mucosa, and down-regulates their predicted target genes following carcinogen exposure as compared to control (corn oil plus cellulose (CCA)) diet. To further elucidate the biological effects of diet and carcinogen modulated miR's in the colon, we verified that miR-26b and miR-203 directly target PDE4B and TCF4, respectively.

Detecting Multivariate Gene Interactions in RNA-Seq Data Using Optimal Bayesian Classification.

Differential gene expression testing is an analysis commonly applied to RNA-Seq data. These statistical tests identify genes that are significantly different across phenotypes. We extend this testing paradigm to multivariate gene interactions from a classification perspective with the goal to detect novel gene interactions for the phenotypes of interest.

MCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification.

Background: Sequencing datasets consist of a finite number of reads which map to specific regions of a reference genome. Most effort in modeling these datasets focuses on the detection of univariate differentially expressed genes. However, for classification, we must consider multiple genes and their interactions.

Non-invasive analysis of intestinal development in preterm and term infants using RNA-Sequencing.

The state and development of the intestinal epithelium is vital for infant health, and increased understanding in this area has been limited by an inability to directly assess epithelial cell biology in the healthy newborn intestine. To that end, we have developed a novel, noninvasive, molecular approach that utilizes next generation RNA sequencing on stool samples containing intact epithelial cells for the purpose of quantifying intestinal gene expression. We then applied this technique to compare host gene expression in healthy term and extremely preterm infants.

Generating stochastic gene regulatory networks consistent with pathway information and steady-state behavior.

We present a procedure to generate a stochastic genetic regulatory network model consistent with pathway information. Using the stochastic dynamics of Markov chains, we produce a model constrained by the prior knowledge despite the sometimes incomplete, time independent, and often conflicting nature of these pathways. We apply the Markov theory to study the model's long run behavior and introduce a biologically important transformation to aid in comparison with real biological outcome prediction in the steady-state domain.

A simple approach to overcoming mutual coupling effects in some transmit array coils for magnetic resonance imaging.

Single Echo Acquisition (SEA) is a method of completely parallel MR imaging that uses coil elements for spatial localization during receive, replacing the need for phase encoding repetitions. In this receive-only form, SEA imaging requires the use of a phase compensation gradient, the value of which is dependent on coil geometry, imaging distance from the elements, and element orientation. Operation of the arrays in transmit-receive mode, while adding significant complexity, is one potential method of eliminating the restrictions imposed by the phase compensation gradient.