Tracking and Reconstructing Interactions to Understand the Missing Parts of Heritability (TRIUMPH).

• Applicants: Professor Dr. Julien Gagneur; Professor Lars Steinmetz, Ph.D.
• Subject Area: General Genetics and Functional Genome Biology
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 535971044

Complex traits, such as metabolism, are controlled by many genes. While the genes involved in metabolic pathways (e.g. tricarboxylic acid cycle, glycolysis, oxidative phosphorylation) are known, how the single nucleotide polymorphisms (SNPs) distinguishing individuals combine with environmental factors to influence metabolic phenotypes is unknown. We propose to map this ‘missing heritability’ to gene-gene (GxG) and gene-environment (GxE) interactions using a yeast model. As these core metabolic pathways are conserved, we can benefit from the ability to reliably generate SNPs and, more importantly, combinations of SNPs at a scale large enough to interrogate epistatic interactions as well as the ability to screen growth across multiple environments in yeast. We will use MAGESTIC technology to reciprocally introduce SNPs in five different yeast backgrounds, focusing on coding and non-coding SNPs within 1000bp of genes associated with mitochondrial metabolism (~6000 SNPs). We will then test the growth of these edited strains in various media that challenge different aspects of metabolism to determine which natural variants cause growth differences and whether they are influenced by environment or genetic background. Causal variants that differ across genetic background could be indicative of GxG interactions. With this in mind, we will select variants of interest for transcriptome and proteome profiling in two backgrounds across four environments. This will reveal the pathways that are perturbed by the SNPs. We will then perform epistatic analysis between these variants by implementing MARVEL, a technology based on MAGESTIC that allows tracking of sequential edits. We will perform growth, RNA, and protein analyses to determine the additive, synergistic, or antagonistic effects of the combinatorial mutations. This information will allow the development of models predicting epistatic interactions and their effects on growth and gene expression. Based on these models, we will select 20 SNPs to observe how their interactions fine-tune metabolic phenotypes. We will first perform four rounds of MARVEL, resulting in ~5000 potential combinations of these SNPs, and measure growth of the edited strains. This data will be used to further refine the models predicting the outcome of higher-order interactions. Using these refined models, we will construct edited strains (with up to 12 edits) predicted to show optimum growth in a selected environment. We will validate the mechanisms predicted to optimise growth by profiling the transcriptomes and proteomes of selected strains. This will be the largest available dataset exploring the molecular pathways underlying GxE and epistasis. Understanding the basis for how SNPs, which are far more prevalent in the population than are gene deletions, interact to coordinate phenotypes may prove important for interpreting how genetic risk factors contribute to disease.

• DFG Programme: Research Grants
• International Connection: Luxembourg
• Partner Organisation: Fonds National de la Recherche
• Cooperation Partner: Professor Evan Williams, Ph.D.