As summarized in , genetic perturbation of eight out of the nine (~90%) candidate genes which were predicted to be causal for obesity in mice using our LCMS procedure caused significant alterations in fat/muscle ratios as well as relevant changes in body weight, adiposity (total fat/body weight), individual fat pad masses or plasma lipids. Furthermore, we identified corresponding changes in the liver expression of genes involved in metabolic pathways previously identified to be differentially regulated between fat and lean mice 13
. Therefore, a large majority of the candidate causal genes for abdominal obesity predicted by LCMS were validated at both a phenotypic and a gene expression level. The liver gene expression signature genes from the validation mouse models highly overlapped with one another and with the metabolic trait-associated MEMN module genes. All of these causal candidate genes were found to impact a common liver transcriptional subnetwork that is enriched for GO metabolic pathways and the MEMN module. These multiple lines of evidence suggest that the perturbation of these predicted causal genes influences obesity via a common functional mechanism.
Significant phenotypic traits observed in the mouse models.
Interestingly, sex specificity in phenotypic effect was common, and we observed opposing effects on abdominal obesity between the sexes in C3ar1
ko and Tgfbr2
heterozygous ko mice. Sex hormones can affect Tgfbr2
, and C3a stimulates the release of ACTH, which is involved in the production of androgens 33
. Down-regulation of both genes in the ko mouse models may alter the impact of sex hormones and lead to sex-specific phenotypes.
Among the newly validated genes, Gas7
was originally identified as a gene that was expressed in serum-starved NIH3T3 cells, and its protein structure resembles Oct2 and synapsins, which are involved in neuronal development and neurotransmitter release, respectively 34,35
. It is selectively expressed in mature cerebral cortical, hippocampal, and cerebellar neurons 35
. Our studies now indicate relevance of this gene to fat metabolism and other previously unknown pathways such as the insulin signaling pathway.
is involved in cellular protection against oxidative damage through the reduction of peroxides 36
. The cytosolic isoform of Gpx3
, has been associated with obesity 37
, and recently the dysregulation of Gpx3
in the plasma and adipose of obese subjects has been implicated in the increase in inflammatory signals and oxidative stress and hence obesity-related metabolic disorders 38
. Our study provides primary evidence that Gpx3
is a causal gene for obesity and supports that Gpx3
overexpression modifies insulin resistance 38
. Although the magnitude of the effect of Gpx3
tg on phenotype was relatively weak (), the liver gene expression signature from the Gpx3
animals highly overlaps that of Gas7
, and Lpl
as well as significantly overlaps the MEMN genes (), suggesting that Gpx3
is causally affecting abdominal obesity along with the other genes validated in our study. The weak phenotypic validation might be a result of low copy number of the transgene 7
and susceptibility to compensatory mechanisms in gene networks 9
encodes a cytosolic NADP(+)-dependent enzyme involved in the regeneration of pyruvate from malate back to the mitochondria, forming a link between the glycolytic pathway and the citric acid cycle 39
. By assisting with the release of acetyl-CoA and NADPH from the mitochondria into the cytosol, they are made available for de novo
fatty acid biosynthesis and other metabolic processes. Me1
is considered lipogenic and altered levels of Me1 enzyme activity has been associated with obesity mouse and rat models 40,41
. Recently, Me1
was identified as a primary candidate gene underlying a porcine QTL associated with backfat thickness 42
encodes an enzyme responsible for the metabolism of endogenous and dietary glycerolipids 8
. Deficiency in Gyk
has been linked to altered fat and lipid metabolism 8,43
, and deficiency in Aqp7
which elevates Gyk
expression has been associated with obesity development 44
. In this study, we did not validate this gene at the phenotypic level. However, pathway analysis of the liver Gyk
signature indicated that 5 out of 13 of the metabolic pathways previously linked to fat content were affected, and the Gyk
heterozygous ko liver signature highly overlapped with the signatures derived from mouse models of other validated genes including Gas7
, and Me1
, supporting a causal role. The lack of phenotypic validation might be a result of insufficient perturbation represented by the heterozygous ko.
When directly comparing our causal genes with the findings from the recent human GWAS studies, we only found limited overlaps. We reason that the GWAS genes/loci represent the cis
variations in human population that confer disease risk, whereas by requiring multiple overlapping loci between the expression and fat mass traits in our LCMS method we implicitly required the causal genes to be affected in trans
by a given genetic locus and then cause variations in the obesity traits. Thus, it is not surprising to observe a limited overlap between the GWAS genes and the mouse causal genes we have identified. The causal genes that are affected by DNA variation in trans
may not have been identified in the GWAS because the signals were too subtle to detect with the current scale, yet are still of interest because they are supported as causal. The fact that we found a weak enrichment for SNPs with low association p values to BMI from the Broad Institute GWAS population 31
when the mouse MEMN genes within the core subnetwork were considered supports this hypothesis.
In summary, we have validated the majority of the top genes predicted to be causal for abdominal obesity through phenotypic characterization and gene expression profiling, thus supporting the LCMS as a powerful tool in predicting causal genes for diseases. Although the genes are seemingly disparate, each appears to affect metabolic pathways that are linked to the TCA cycle. Future directions include the application of these network approaches to additional relevant tissues such as adipose, with incorporation of potential cross-tissue interactions, as well as environmental variations. Also, the investigation of negative predictive value of the LCMS procedure would be of value, though this is a more complicated problem than it appears on the surface, given that a list of LCMS predicted causal genes from one tissue is by no means comprehensive as many tissues are involved in the regulation of body fat. Considering that a large number of genes influence body weight, focusing on pathways and networks rather than pinpointing individual genes may be more efficient in elucidating the pathogenesis of obesity and the development of novel treatments.