Identifying molecular intervention points as therapeutic targets for disease

SCHEME: CORE

CALL: 2013

DOMAIN: BM - Translational Biomedical Research

FIRST NAME: Carole

LAST NAME: Linster

INDUSTRY PARTNERSHIP / PPP: No

INDUSTRY / PPP PARTNER:

HOST INSTITUTION: University of Luxembourg

KEYWORDS: Transcriptomics, Metabolomics, Proteomics, Phenotype, Quantitative trait loci, Causal predictions, Disease intervention points

START: 2014-04-01

END: 2016-03-31

WEBSITE: https://www.uni.lu

Submitted Abstract

As universal therapies for disease are increasingly difficult to find, research efforts should be targeted towards the development of personalized treatments that are tailored to the genetic constitution and environmental exposures of patients. To achieve this goal, it is important to identify and intervene in the precise molecular pathways that cause disease in an individual, particularly because targeting causative genetic variants is usually not feasible. Given the wealth of genetic and molecular profiling data that are currently being generated, the key challenge resides in developing suitable analysis approaches that can distinguish genuine causal relationships that lead from genotype to disease, from mere correlations. Environmental factors play an important role in the etiology of many diseases, and therefore must also be accounted for. The purpose of this project is to develop strategies to identify points in molecular networks that can be targeted to modulate phenotype in different genetic and environmental contexts. Our project has three specific aims: (1) Generate transcriptome, proteome, and metabolome profiles in a collection of yeast segregants across several environments; (2) Establish a computational pipeline that allows to integrate genotype with the transcriptomic, proteomic, and metabolomic data produced; (3) Predict and validate transcriptional, translational, and metabolic intervention points to modulate growth rates. Our preliminary implementation of the proposed approach using transcriptome profiles has already yielded valid predictions and relevant insights. In particular, we observed that joint modeling of genotype, gene expression and growth phenotype in multiple environments substantially increases the chances to identify genes that are causally associated with growth. This encourages us to extend our studies to supplementary growth environments and to profile proteins and metabolites as potential molecular mediators of growth in addition to RNA transcripts. The principles of experimental design and data analysis uncovered during the proposed study will be instrumental in developing combined experimental and computational approaches tailored towards the identification of molecular intervention points in cellular models of human disease.

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