Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling

TLDR

QTL mapping identifies disease‑related loci, yet pinpointing the underlying genes and pathways remains elusive; this F2 intercross of C57BL/6 and BTBR leptin‑ob/ob mice, previously used to locate diabetes‑related QTL, provides a suitable genetic background. The study aims to deepen genetic architecture insight by integrating transcriptional and metabolic profiling of phenotypes. The authors generated liver mRNA expression data from >40,000 probe sets and quantified 67 metabolites via mass spectrometry in an F2 intercross, then integrated these datasets to build causal networks linking gene expression and metabolic processes. They found that liver metabolites are heritable, mapped to distinct genetic loci, and that integrating transcriptomic and metabolomic data yields causal networks—illustrated by a glutamate‑metabolism network responsive to glutamine/glutamate levels—highlighting the approach’s potential to uncover regulatory networks underlying obesity and diabetes.

Abstract

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin(ob/ob) and the diabetes-susceptible BTBR leptin(ob/ob) mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.