Early Exposure to a High Fat/High Sugar Diet Increases the Mammary Stem Cell Compartment and Mammary Tumor Risk in Female Mice.

Obesity and alterations in metabolic programming from early diet exposures can affect the propensity to disease in later life. Through dietary manipulation, developing mouse pups were exposed to a hyperinsulinemic, hyperglycemic milieu during three developmental phases: gestation, lactation, and postweaning. Analyses showed that a postweaning high fat/high sugar (HF/HS) diet had the main negative effect on adult body weight, glucose tolerance, and insulin resistance. However, dimethylbenz[a]anthracene (DMBA)-induced carcinogenesis revealed that animals born to a mother fed a HF/HS gestation diet, nursed by a mother on a mildly diet-restricted, low fat/low sugar diet (DR) and weaned onto a HF/HS diet (HF/DR/HF) had the highest mammary tumor incidence, while HF/HF/DR had the lowest tumor incidence. Cox proportional hazards analysis showed that a HF/HS postweaning diet doubled mammary cancer risk, and a HF/HS diet during gestation and postweaning increased risk 5.5 times. Exposure to a HF/HS diet during gestation, when combined with a postweaning DR diet, had a protective effect, reducing mammary tumor risk by 86% (HR = 0.142). Serum adipocytokine analysis revealed significant diet-dependent differences in leptin/adiponectin ratio and IGF-1. Flow cytometry analysis of cells isolated from mammary glands from a high tumor incidence group, DR/HF/HF, showed a significant increase in the size of the mammary stem cell compartment compared with a low tumor group, HF/HF/DR. These results indicate that dietary reprogramming induces an expansion of the mammary stem cell compartment during mammary development, increasing likely carcinogen targets and mammary cancer risk. Cancer Prev Res; 10(10); 553-62. ©2017 AACRSee related editorial by Freedland, p. 551-2.

Host-microbe interactions in the neonatal intestine: role of human milk oligosaccharides.

The infant intestinal microbiota is shaped by genetics and environment, including the route of delivery and early dietary intake. Data from germ-free rodents and piglets support a critical role for the microbiota in regulating gastrointestinal and immune development. Human milk oligosaccharides (HMO) both directly and indirectly influence intestinal development by regulating cell proliferation, acting as prebiotics for beneficial bacteria and modulating immune development. We have shown that the gut microbiota, the microbial metatranscriptome, and metabolome differ between porcine milk-fed and formula-fed (FF) piglets. Our goal is to define how early nutrition, specifically HMO, shapes host-microbe interactions in breast-fed (BF) and FF human infants. We an established noninvasive method that uses stool samples containing intact sloughed epithelial cells to quantify intestinal gene expression profiles in human infants. We hypothesized that a systems biology approach, combining i) HMO composition of the mother's milk with the infant's gut gene expression and fecal bacterial composition, ii) gene expression, and iii short-chain fatty acid profiles would identify important mechanistic pathways affecting intestinal development of BF and FF infants in the first few months of life. HMO composition was analyzed by HLPC Chip/time-of-flight MS and 3 HMO clusters were identified using principle component analysis. Initial findings indicated that both host epithelial cell mRNA expression and the microbial phylogenetic profiles provided strong feature sets that distinctly classified the BF and FF infants. Ongoing analyses are designed to integrate the host transcriptome, bacterial phylogenetic profiles, and functional metagenomic data using multivariate statistical analyses.

Integrated microRNA and mRNA expression profiling in a rat colon carcinogenesis model: effect of a chemo-protective diet.

We have recently demonstrated that nutritional bioactives (fish oil and pectin) modulate microRNA molecular switches in the colon. Since integrated analysis of microRNA and mRNA expression at an early stage of colon cancer development is lacking, in this study, four computational approaches were utilized to test the hypothesis that microRNAs and their posttranscriptionally regulated mRNA targets, i.e., both total mRNAs and actively translated mRNA transcripts, are differentially modulated by carcinogen and diet treatment. Sprague-Dawley rats were fed diets containing corn oil ± fish oil with pectin ± cellulose and injected with azoxymethane or saline (control). Colonic mucosa was assayed at an early time of cancer progression, and global gene set enrichment analysis was used to obtain those microRNAs significantly enriched by the change in expression of their putative target genes. In addition, cumulative distribution function plots and functional network analyses were used to evaluate the impact of diet and carcinogen combination on mRNA levels induced via microRNA alterations. Finally, linear discriminant analysis was used to identify the best single-, two-, and three-microRNA combinations for classifying dietary effects and colon tumor development. We demonstrate that polysomal profiling is tightly related to microRNA changes when compared with total mRNA profiling. In addition, diet and carcinogen exposure modulated a number of microRNAs (miR-16, miR-19b, miR-21, miR26b, miR27b, miR-93, and miR-203) linked to canonical oncogenic signaling pathways. Complementary gene expression analyses showed that oncogenic PTK2B, PDE4B, and TCF4 were suppressed by the chemoprotective diet at both the mRNA and protein levels.

Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models.

Birthweight and gestational age are closely related and represent important indicators of a healthy pregnancy. Customary modeling for birthweight is conditional on gestational age. However, joint modeling directly addresses the relationship between gestational age and birthweight, and provides increased flexibility and interpretation as well as a strategy to avoid using gestational age as an intermediate variable. Previous proposals have utilized finite mixtures of bivariate regression models to incorporate well-established risk factors into analysis (e.g. sex and birth order of the baby, maternal age, race, and tobacco use) while examining the non-Gaussian shape of the joint birthweight and gestational age distribution. We build on this approach by demonstrating the inferential (prognostic) benefits of joint modeling (e.g. investigation of 'age inappropriate' outcomes like small for gestational age) and hence re-emphasize the importance of capturing the non-Gaussian distributional shapes. We additionally extend current models through a latent specification which admits interval-censored gestational age. We work within a Bayesian framework which enables inference beyond customary parameter estimation and prediction as well as exact uncertainty assessment. The model is applied to a portion of the 2003-2006 North Carolina Detailed Birth Record data (n=336129) available through the Children's Environmental Health Initiative and is fitted using the Bayesian methodology and Markov chain Monte Carlo approaches.