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 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.

Figure 1. Heat map of correlations between liver metabolites.

Each square represents the Spearman's correlation coefficient between the metabolite of the column with that of the row (|r|>0.254, p<0.05; |r|>0.330, p<0.01). Metabolite order is determined as in hierarchical clustering using the distance function 1-correlation. Self-self correlations are identified in black. Acyl-carnitines are annotated according to clinical acyl-carnitine profile shorthand and amino acids by three letter code; other metabolite abbreviations are found in Table S1. Individual correlation coefficients can be found in Table S2.

Figure 2. Linkage hot spots for metabolic quantitative trait loci (mQTL).

Each row represents a marker; each column represents a metabolite. Metabolites are ordered as in hierarchical clustering using the distance function 1-correlation (as in Figure 1). The LOD color scale is indicated, showing blue (red) when the B6 (BTBR) allele at that marker results in an elevated level of metabolite.

Figure 3. Heat map of correlations between liver metabolites and select liver transcripts.

Each square represents the Spearman's correlation coefficient between the metabolite of the column with the transcript of the row (|r|>0.254, p<0.05; |r|>0.330, p<0.01). Metabolites are organized into their biochemical class; transcripts are selected based on gene ontology terms relating to biological processes in which they play a role. Correlation coefficients between individual amino acids with select transcripts are found in Table S3.

Figure 4. Glx network.

This network consists of a select number of transcripts (grey circles) among the 250 mRNA that are most correlated to glx (black rectangle) (p<0.002). The microsatellite marker (Mb) for peak eQTL or mQTL altering levels of transcripts and metabolites, respectively, are given. For any two phenotypes connected by an edge, the direction LOD score and p-value are indicated (insert). The best solution was determined by an approximate Bayes factor (BF) and indicated in solid lines, the second best solution in dotted lines.

Figure 5. Glutamine changes hepatic gene expression.

Hepatocytes from 10-week old lean B6 (A) and BTBR (B) were treated overnight +/− 10 mM glutamine (n = 5 per strain). Transcripts were measured by RT-PCR and expression was normalized to Actb control. Significance calculated based on the difference of delta CT value of each transcript between the untreated and glutamine treated hepatocytes for each individual animal (*p<0.05, **p<0.005).