COMPUTATIONAL DEVELOPMENT TO INTEGRATE EPIGENOMICS, TRANSCRIPTOMICS AND METABOLOMICS PROFILES IN THE STUDY OF WHITE ADIPOSE TISSUE RESPONSE TO HIGH FAT DIET
|Author||Bao Khanh TRANG|
|Director of thesis||Prof. Béatrice Desvergne|
|Co-director of thesis||Nicolas Guex|
|Summary of thesis||
Nutrition inputs modulate cell metabolism and a number of critical links between metabolism and epigenetic control centers- including chromatin remodeling, histone modifications, DNA methylation, and microRNAs. In the last decade, the omics sciences have significantly thrived, and thus have produced myriad of metabolomics, proteomics, transcriptomics, and genomics data. As high-throughput and high content technologies agglomerate such information-rich databases, there are emerging demands for intensive analytical tools to comprehend every aspects of cell life regulation. However, the development of bioinformatic algorithms, tools and platforms to integrate multiple types of omics data is still very challenging, due to the vast variety of the data.
To improve our understanding of the molecular responses and perturbations occurring in obesity, we will integrate a series of metabolomics, proteomics, transcriptomics, and genomics datasets of mouse adipose tissue obtained from mouse subjected to a high fat diet, to mimic the high caloric intake. My project will focus on developing a methodology and bioinformatic tools to analyze, organize, and incorporate vast omics profiles through high throughput approaches. With this project, we aim to 1. explore ChipSeq data and identify genes with their functions that vary between time points and diets, 2. explore metabolomics data and identify affected metabolites, and 3. attempt to construct landscape pathway of interactions between epigenetic signals, identified genes and metabolites.
Our results will help to highlight novel mechanism underlying obesity-related inflammatory onset in adipose tissue and clarify the connection between the identified epigenetic-mediated and metabolite-driven regulation processes. We also aim to develop our method into a more standardized model that can be incorporated into variety of different pathology analyses with cell lines and organism. Furthermore, this project will provide necessary and profitable training opportunity at the starting of my academic career, which will facilitate my future development in subsequent researches.
|Administrative delay for the defence||11.07.2019|