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

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 leptinob/ob and the diabetes-susceptible BTBR leptinob/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.

Whole-Genome Mapping of Agronomic and Metabolic Traits to Identify Novel Quantitative Trait Loci in Bread Wheat Grown in a Water-Limited Environment

“…For complete lists of genetic correlation coefficients and their P values, see Supplemental Data Sets S5 and S6. Consistent with the observations of Ferrara et al (2008), we found that genetic correlations between traits belonging to different suites of characters (metabolic and agronomic) were generally not as strong as those among metabolites (Supplemental Data Set S1) or among agronomic traits (Supplemental Data Set S3). Not surprisingly, a substantial number of the metabolic traits showed strong positive or negative links (genetic correlation coefficient [r G ] .…”

“…Metabolomic phenotypes have been used for QTL mapping in mapping populations of several plant species, including Arabidopsis (Arabidopsis thaliana; Keurentjes et al, 2006;Lisec et al, 2008;Rowe et al, 2008), tomato (Solanum lycopersicum; Schauer et al, 2006), potato (Solanum tuberosum; Carreno-Quintero et al, 2012), and maize (Zea mays; Lisec et al, 2011). In some experiments, transcriptomic and/or proteomic data have been used in combination with metabolomic data and QTL analysis (Wentzell et al, 2007;Ferrara et al, 2008). Ultimately, the combination of transcriptomics, proteomics, metabolomics, and QTL analysis has the potential to reveal interactions and relationships among genes, transcripts, proteins, metabolites, and traits.…”

Correlation analysis between genome-wide expression profiles and cytoarchitectural abnormalities in the prefrontal cortex of psychiatric disorders

“…29,30 We employed the approach to identify genes and biological processes that are associated with cytoarchitectural abnormalities found in the PFC of major psychiatric disorders. Our functional annotation analysis revealed that multiple biological processes including cell metabolism, neurodevelopment, cell motility and programmed cell death were commonly associated with the prefrontal cytoarchitectural deficits of perineuronal oligodendrocytes (Table 4) and calbindin-positive GABAergic neurons (Table 5).…”

Leptin therapy in insulin-deficient type I diabetes

“…Metabolic Profiling Studies. Acylcarnitines, fatty acyl CoAs, and amino acids were analyzed in liver homogenates by tandem MS/MS, and organic acids were analyzed by gas GC/MS as described previously (33)(34)(35)(36)(37)(38). All MS analyses employed stable-isotope dilution with internal standards from Isotec, Cambridge Isotope Laboratories, and CDN Isotopes.…”