Location: Children's Nutrition Research Center
2022 Annual Report
Objectives
Objective 1: Use transgenic mouse models, microdissection, nuclear sorting, next-generation sequencing and innovative computational approaches to alter DNA methylation in specific subpopulations of hypothalamic neurons and evaluate lifelong effects on energy metabolism, food intake, and physical activity; isolate specific neuronal (and potentially non-neuronal) hypothalamic cell types to evaluate cell type-specific alterations in DNA methylation in established models of nutritional programming.
Objective 2: Advance understanding of the causes of interindividual epigenetic variation and consequences for human energy balance by conducting target-capture bisulfite sequencing in multiple tissues from an existing cohort of molecularly-phenotyped individuals to determine associations between genetic variation, epigenetic variation, and gene expression at human metastable epialleles; identify human metastable epialleles that predict risk of obesity by exploiting existing longitudinal cohorts of metabolically-phenotyped individuals; assess how DNA methylation at obesity-associated metastable epialleles is affected by maternal periconceptional nutrition.
Objective 3: Determine the functional impact of folic acid supplementation and establish the functional role of age-related p16 epimutation in genetically and epigenetically engineered mouse models of colon cancer and in intestinal carcinogenesis.
New Project (JY):
Objective 1. Examine the tumorigenic effects of HFCS on a humanized colon tumor mouse model
Objective 2. Investigate the effects of HFCS on the gut microbiota of a humanized colon tumor mouse model.
Objective 3. Determine the role of HFCS-induced gut microbiota in CRC development
New Project (HY):
Objective 1. Create multi-omic nutritional data share portal to resolve the unmet demand for an efficient access to the large volumes of heterogeneous multi-omic data across various research labs and centers.
Objective 2. Integrate heterogeneous multi-omic datasets such as genetic (SNPs), transcriptomic, epigenetic, proteomic, metabolomic and microbiome to infer molecular network structures illustrating eating disorder dynamics.
Objective 3. Decode genetic and epigenetic patterns of disordered eating using machine learning methods.
Approach
Developmental programming occurs when nutrition and other environmental exposures affect prenatal or early postnatal development, causing structural or functional changes that persist to influence health throughout life. Researchers are working to understand epigenetic mechanisms of developmental programming. Epigenetic mechanisms regulate cell-type specific gene expression, are established during development, and persist for life. Importantly, nutrition during prenatal and early postnatal development can induce epigenetic changes that persist to adulthood. We focus on DNA methylation because this is the most stable epigenetic mechanism. The inherent cell-type specificity of epigenetic regulation motivates development of techniques to isolate and study specific cell types of relevance to obesity and digestive diseases. These projects integrate both detailed studies of animal models and characterization of epigenetic mechanisms in humans. We will use mouse models of developmental epigenetics in the hypothalamus to understand cell type-specific epigenetic mechanisms mediating developmental programming of body weight regulation. Mouse models will also be used to investigate how folic acid intake affects epigenetic mechanisms regulating intestinal epithelial stem cell (IESC) development and characterize the involvement of these mechanisms in metabolic programming related to obesity, inflammation, and gastrointestinal cancer. In human studies, we will identify human genomic loci at which interindividual variation in DNA methylation is both sensitive to maternal nutrition in early pregnancy and associated with risk of later weight gain. An improved understanding of how nutrition affects developmental epigenetics should eventually lead to the creation of early-life nutritional interventions to improve human health. Additional researchers will use humanized Apc-mutant CA mice in which a human microbiome has been established through fecal microbiota transplantation. Using this innovative mouse model, we can strictly control genetics, environment, and diet, thereby eliminating confounding variables. We will establish humanized CA mice that recapitulate the human gut microbiome and determine the tumorigenic effect of high fructose corn syrup on this mouse model. And scientists will elucidate the molecular interplay of epigenome and transcriptome in aberrant eating behaviors using robust genome-wide computational analyses. They will conduct a multi-omic integrative study to systematically decipher the regulatory aspects of DNA methylation and histone modifications on alternative splicing and alternative polyadenylation in disordered eating. Novel machine learning approaches will be designed to address specific analytical challenges.
Progress Report
Objective 1 focuses on mouse models of epigenetic development in the hypothalamus of the brain, to understand developmental programming of obesity. We used a model of developmental programming of energy balance, coupled with an innovative approach to test if early postnatal overnutrition induces epigenetic changes within specific subclasses of neurons in the hypothalamus. We conducted small litter experiments in transgenic mice in which a hypothalamic neuron (Agrp), is fluorescently tagged. This allows us to isolate Agrp neurons from mice who were overnourished postnatally and compare them with mice fed normally. We worked with the Baylor Human Genome Sequencing Center to optimize the preparation of the DNA samples for whole-genome DNA methylation analysis ('library preparation'). Likely due to the very small quantities of DNA recovered, we have been unable to reliably perform library preparation. We pivoted our focus to perform an unbiased analysis of postnatal epigenetic maturation in both neurons and glia in the arcuate nucleus of the hypothalamus (from which Agrp neurons originate). We identified marked sex differences in epigenetic maturation in this part of the brain and showed that this postnatal maturation in the mouse occurs in genomic regions associated with obesity in humans.
Objective 2 focuses on identifying human metastable epialleles and assessing their association with obesity. Metastable epialleles are regions of the genome that show epigenetic variation among individuals but not between different tissues of the same individual. Rather than focus on identifying canonical metastable epialleles (at which individual variation in DNA methylation is largely independent of genetic variation), we have shown that systemic interindividual epigenetic variants in humans can have a stochastic (probabilistic) component, a genetic component, and be influenced by periconceptional nutrition. Rather than limiting our focus to metastable epialleles, we introduced a new term: Correlated Regions of Systemic Interindividual Variation in DNA methylation (CoRSIVs). Previously we generated and analyzed a large data set on DNA methylation at 4,086 CoRSIVs in multiple tissues from 188 donors from the National Institutes of Health Genotype-Tissue Expression Consortium. We submitted our findings to various journals but are facing resistance due to the innovative nature of our findings. Nearly all scientists studying population epigenetics in humans are using standard methylation arrays manufactured by one company. Unfortunately, these arrays do not target regions of interindividual epigenetic variation. Our latest findings demonstrate that focusing on interindividual methylation variants is essential in population epigenetics.
The goal of Objective 3 is to determine if dietary folate supplementation affects a causal epigenetic mechanism leading to increased colon cancer risk. We generated a mouse colon cancer model in which a tumor suppressor gene p16 is epigenetically silenced by DNA methylation (hypermethylation) during aging. With this model, we demonstrated that dietary methyl-donor supplementation exacerbates not only the age-related p16 methylation, but also consequent epigenetic silencing. We also showed that the supplemented mice had a significantly shortened survival as compared to the controls. Furthermore, single-cell transcriptome profiling of tumor tissues provided comprehensive analysis of tumor evolution and identified specific tumor-associated immune cells, including immunosuppressive regulatory T cells, that contribute to tumor progression. Thus, we have established a novel animal model and combinatory strategies to understand whether and how nutritional status during critical stages of development modules the tendency for hypermethylation and consequent cancer.
A new project was started with the addition of a new scientist. Technology has enabled researchers to profile biomolecules at various levels: genes, proteins, metabolites and epigenetic factors. Integrative analysis of these data can provide deeper insights into the underlying biological system. With numerous analytical standards and tools data integration is hindered across studies. Efficient access and uniform data processing standards are the fundamental challenges inherent to sharing and exploring multi-omic data. We aim to develop a user-friendly multi-omic nutritional data share portal. We acquired two high-performance servers with significant RAM and storage and loaded the Ubuntu computing platform. We queried multiple publicly available Next Generation Sequencing (NGS) data repositories for nutrition or diet factored datasets. Multi-omic datasets including Ribonucleic acid sequencing (RNA-Seq), Chromatin Immunoprecipitation Sequencing (ChIP-Seq), Reduced Representation Bisulfite Sequencing (RRBS) and Whole-Genome Bisulfite Sequencing (WGBS) were downloaded. Multiple complementary datasets can be identified from Gene Expression Omnibus by mapping organism, tissue, and genotype. Datasets from other center scientists were also collected. Required NGS analysis, bioinformatic and computational programming tools were set up/tested. We developed a uniform analytical pipeline focusing on quality control, read trimming, adapter contamination, mapping, quantification, clustering, batch effects, differential expression, binding, and methylation profiles. We tested the alignment tools STAR (Spliced Transcripts Alignment to a Reference) and HISAT2 (hierarchical indexing for spliced alignment of transcripts) in terms of downstream splicing analysis. For splicing analysis, we evaluated algorithms like CrypSplice, rMATS, MISO, MAJIQ and SUPPA. We developed a new version of CrypSplice with enhanced splice junction annotation and statistical testing. For alternative polyadenylation (APA) analysis we tested DaPars, APALyzer and PolyA-miner. We developed PolyA-miner-Bulk to retrofit bulk RNA-Seq datasets. For binding profiles (ChIP-Seq) data we tested serval peak calling algorithms like MACS2, SICER, WACS and HOMER. For methylation datasets (RRBS and WGBS) we implemented a Bismark and methylKit pipeline. We developed uniform gene expression, splicing, alternative polyadenylation, binding, and methylation profile pipelines using R, python and shell script. The pipelines were deployed in the local computational platform and are ready to use. This multi-omic platform will resolve the unmet demand for an efficient access to large volumes of heterogeneous data across various research labs and presents the data in an online environment.
A new project was started this year with the addition of another new scientist. We achieved our first milestone to establish a humanized mouse model of colon tumor that contain human gut bacteria instead of mouse gut bacteria. We depleted the number of existing bacteria in the mouse gut by treating mice with broad-spectrum antibiotics. Then, we transferred a slurry of feces derived from healthy adults or colorectal cancer patients into these antibiotics-treated mice. We confirmed that these mice shared ~80% of bacteria compared to human donors' bacteria (thus, humanized mice) throughout the period of 1; 3; and 5 weeks from the initial human bacterial transfer to mice. Colon tumor mouse models develop tumors in the colon quickly after we induce tumors and expire within 5 weeks. In stark contrast, conventional mice share only ~15% of human bacteria after undergoing the same procedure. Next year we plan to evaluate the tumorigenic effect of sugary drinks in these humanized mice to see if sugary drinks can facilitate tumor initiation and growth.
For Objective 6, while establishing a humanized mouse model of colon tumor, we investigated which bacteria would increase/decrease following a sugar treatment in humanized normal wild-type (WT) mice. We observed that sugary treatment in water (mimicking sugary drinks) increased the number of different bacterial species in the mouse gut, which is usually correlated with unhealthy conditions. We found that many bacteria were significantly altered following sugar treatment in these humanized WT mice. For example, good bacteria (probiotics) such as Odoribacter and Lactobacillus were decreased while bad bacteria such as Akkermansia were increased following sugar treatment. We will repeat the same experiment using a humanized mouse model of colon tumor and identify specific bacteria enriched or depleted by sugar treatment. In addition to the alteration of the gut bacteria, we will examine the alterations of gene expression and small molecules (metabolites) in the colon cells and colon tumors in the humanized mice. Ultimately, we will integrate these studies to identify the pathways and molecules involved in sugar-induced bacterial changes and colon cancer development.
For our Objective 7, we established one of the critical resources, which is human intestinal organoids and tumor organoids. We successfully cultured, maintained, and expanded these 3-dimensional organoids, which will be used for understanding the causal roles of sugar-induced specific bacteria on colon cancer development.
Accomplishments
