Increasing evidence has shown that proper control of mitochondrial dynamics (fusion and fission) is required for high capacity ATP production in heart. The transcriptional coactivators, peroxisome proliferator-activated receptor gamma coactivator 1 (PGC-1) α and β have been shown to regulate mitochondrial biogenesis in heart at the time of birth. The function of the PGC-1 coactivators in heart after birth is incompletely understood.
To assess the role of the PGC-1 coactivators during postnatal cardiac development and in the adult heart in mice.
Methods and Results
Conditional gene targeting was used in mice to explore the role of the PGC-1 coactivators during postnatal cardiac development and in adult heart. Marked mitochondrial structural derangements were observed in hearts of PGC-1α/β-deficient mice during postnatal growth, including fragmentation and elongation, associated with the development of a lethal cardiomyopathy. The expression of genes involved in mitochondrial fusion [mitofusin 1 (Mfn1), optic atrophy 1 (Opa1)] and fission [dynamin-related protein 1 (Drp1), fission protein 1 (Fis1)] was altered in hearts of PGC-1α/β-deficient mice. PGC-lα was shown to directly regulate Mfn1 gene transcription by coactivating the estrogen-related receptor α (ERRα) upon a conserved DNA element. Surprisingly, PGC-1α/β deficiency in the adult heart did not result in evidence of abnormal mitochondrial dynamics or heart failure. However, transcriptional profiling demonstrated that the PGC-1 coactivators are required for high level expression of nuclear- and mitochondrial-encoded genes involved in mitochondrial dynamics and energy transduction in adult heart.
These results reveal distinct developmental stage-specific programs involved in cardiac mitochondrial dynamics.
PGC-1 coactivators; mitochondrial fusion; cardiac energy metabolism; cardiomyopathy; mitofusin
We describe a chemical tag for duplex proteome quantification using neutron encoding (NeuCode). The method utilizes the straightforward, efficient, and inexpensive carbamylation reaction. We demonstrate the utility of NeuCode carbamylation by accurately measuring quantitative ratios from tagged yeast lysates mixed in known ratios and by applying this method to quantify differential protein expression in mice fed a either control or high-fat diet.
We previously positionally cloned Sorcs1 as a diabetes quantitative trait locus. Sorcs1 belongs to the Vacuolar protein sorting-10 (Vps10) gene family. In yeast, Vps10 transports enzymes from the trans-Golgi network (TGN) to the vacuole. Whole-body Sorcs1 KO mice, when made obese with the leptinob mutation (ob/ob), developed diabetes. β Cells from these mice had a severe deficiency of secretory granules (SGs) and insulin. Interestingly, a single secretagogue challenge failed to consistently elicit an insulin secretory dysfunction. However, multiple challenges of the Sorcs1 KO ob/ob islets consistently revealed an insulin secretion defect. The luminal domain of SORCS1 (Lum-Sorcs1), when expressed in a β cell line, acted as a dominant-negative, leading to SG and insulin deficiency. Using syncollin-dsRed5TIMER adenovirus, we found that the loss of Sorcs1 function greatly impairs the rapid replenishment of SGs following secretagogue challenge. Chronic exposure of islets from lean Sorcs1 KO mice to high glucose and palmitate depleted insulin content and evoked an insulin secretion defect. Thus, in metabolically stressed mice, Sorcs1 is important for SG replenishment, and under chronic challenge by insulin secretagogues, loss of Sorcs1 leads to diabetes. Overexpression of full-length SORCS1 led to a 2-fold increase in SG content, suggesting that SORCS1 is sufficient to promote SG biogenesis.
Massively parallel RNA sequencing (RNA-seq) has yielded a wealth of new insights into transcriptional regulation. A first step in the analysis of RNA-seq data is the alignment of short sequence reads to a common reference genome or transcriptome. Genetic variants that distinguish individual genomes from the reference sequence can cause reads to be misaligned, resulting in biased estimates of transcript abundance. Fine-tuning of read alignment algorithms does not correct this problem. We have developed Seqnature software to construct individualized diploid genomes and transcriptomes for multiparent populations and have implemented a complete analysis pipeline that incorporates other existing software tools. We demonstrate in simulated and real data sets that alignment to individualized transcriptomes increases read mapping accuracy, improves estimation of transcript abundance, and enables the direct estimation of allele-specific expression. Moreover, when applied to expression QTL mapping we find that our individualized alignment strategy corrects false-positive linkage signals and unmasks hidden associations. We recommend the use of individualized diploid genomes over reference sequence alignment for all applications of high-throughput sequencing technology in genetically diverse populations.
RNA-seq; expression QTL; Diversity Outbred mice; Diversity Outbred (DO); QTL mapping; haplotype reconstruction; high-density genotyping; mixed models; Multiparent Advanced Generation Inter-Cross (MAGIC); multiparental populations; MPP
The leading cause of death in diabetic patients is cardiovascular disease. Apolipoprotein B (ApoB)-containing lipoprotein particles, which are secreted and cleared by the liver, are essential for the development of atherosclerosis. Insulin plays a key role in the regulation of ApoB. Insulin decreases ApoB secretion by promoting ApoB degradation in the hepatocyte. In parallel, insulin promotes clearance of circulating ApoB particles in the liver via the low density lipoprotein receptor (LDLR), LDLR-related protein 1 (LRP1), and heparan sulfate proteoglycans (HSPGs). Consequently, the insulin resistant state of Type 2 diabetes (T2D) is associated with increased secretion and decreased clearance of ApoB. Here, we review the mechanisms by which insulin controls the secretion and uptake of ApoB in normal and diabetic livers.
apolipoprotein B; insulin; diabetes; very low density lipoprotein (VLDL); cardiovascular disease; selective insulin resistance
BTBR mice develop severe diabetes in response to genetically induced obesity due to a failure of the β-cells to compensate for peripheral insulin resistance. In analyzing BTBR islet gene expression patterns, we observed that Pgter3, the gene for the prostaglandin E receptor 3 (EP3), was upregulated with diabetes. The EP3 receptor is stimulated by prostaglandin E2 (PGE2) and couples to G-proteins of the Gi subfamily to decrease intracellular cAMP, blunting glucose-stimulated insulin secretion (GSIS). Also upregulated were several genes involved in the synthesis of PGE2. We hypothesized that increased signaling through EP3 might be coincident with the development of diabetes and contribute to β-cell dysfunction. We confirmed that the PGE2-to-EP3 signaling pathway was active in islets from confirmed diabetic BTBR mice and human cadaveric donors, with increased EP3 expression, PGE2 production, and function of EP3 agonists and antagonists to modulate cAMP production and GSIS. We also analyzed the impact of EP3 receptor activation on signaling through the glucagon-like peptide (GLP)-1 receptor. We demonstrated that EP3 agonists antagonize GLP-1 signaling, decreasing the maximal effect that GLP-1 can elicit on cAMP production and GSIS. Taken together, our results identify EP3 as a new therapeutic target for β-cell dysfunction in T2D.
Insulin and Zn2+ enjoy a multivalent relationship. Zn2+ binds insulin in pancreatic β cells to form crystalline aggregates in dense core vesicles (DCVs), which are released in response to physiological signals such as increased blood glucose. This transition metal is an essential cofactor in insulin-degrading enzyme and several key Zn2+ finger transcription factors that are required for β cell development and insulin gene expression. Studies are increasingly revealing that fluctuations in Zn2+ concentration can mediate signaling events, including dynamic roles that extend beyond that of a static structural or catalytic cofactor. In this issue of the JCI, Tamaki et al. propose an additional function for Zn2+ in relation to insulin: regulation of insulin clearance from the bloodstream.
Mitochondria are dynamic organelles that play a central role in a diverse array of metabolic processes. Elucidating mitochondrial adaptations to changing metabolic demands and the pathogenic alterations that underlie metabolic disorders represent principal challenges in cell biology. Here, we performed multiplexed quantitative mass spectrometry-based proteomics to chart the remodeling of the mouse liver mitochondrial proteome and phosphoproteome during both acute and chronic physiological transformations in more than 50 mice. Our analyses reveal that reversible phosphorylation is widespread in mitochondria, and is a key mechanism for regulating ketogenesis during the onset of obesity and type 2 diabetes. Specifically, we have demonstrated that phosphorylation of a conserved serine on Hmgcs2 (S456) significantly enhances its catalytic activity in response to increased ketogenic demand. Collectively, our work describes the plasticity of this organelle at high resolution and provides a framework for investigating the roles of proteome restructuring and reversible phosphorylation in mitochondrial adaptation.
Endosomal sorting of the Alzheimer amyloid precursor protein (APP) plays a key role in the biogenesis of the amyloid-β peptide (Aβ). Genetic lesions underlying Alzheimer disease (AD) can act by interfering with this physiological process. Specifically, proteins involved in trafficking between endosomal compartments and the trans Golgi network (TGN) (including the retromer complex (Vps35, Vps26) and its putative receptors (sortilin, SorL1, SorCS1) have been implicated in the molecular pathology of late onset-AD. Previously, we demonstrated a role for SorCS1 in APP metabolism and Aβ production and, while we implicated a role for the retromer in this regulation, the underlying mechanism remained poorly understood. Here, we provide evidence for a motif within the SorCS1c cytoplasmic tail that, when manipulated, results in perturbed sorting of APP and/or its fragments to endosomal compartments, decreased retrograde TGN trafficking, and increased Aβ production in H4 neuroglioma cells. These perturbations apparently do not involve turnover of the cell-surface APP pool, but rather they involve intracellular APP and/or its fragments, downstream of APP endocytosis.
Current efforts in systems genetics have focused on the development of statistical approaches that aim to disentangle causal relationships among molecular phenotypes in segregating populations. Reverse engineering of transcriptional networks plays a key role in the understanding of gene regulation. However, transcriptional regulation is only one possible mechanism, as methylation, phosphorylation, direct protein–protein interaction, transcription factor binding, etc., can also contribute to gene regulation. These additional modes of regulation can be interpreted as unobserved variables in the transcriptional gene network and can potentially affect its reconstruction accuracy. We develop tests of causal direction for a pair of phenotypes that may be embedded in a more complicated but unobserved network by extending Vuong’s selection tests for misspecified models. Our tests provide a significance level, which is unavailable for the widely used AIC and BIC criteria. We evaluate the performance of our tests against the AIC, BIC, and a recently published causality inference test in simulation studies. We compare the precision of causal calls using biologically validated causal relationships extracted from a database of 247 knockout experiments in yeast. Our model selection tests are more precise, showing greatly reduced false-positive rates compared to the alternative approaches. In practice, this is a useful feature since follow-up studies tend to be time consuming and expensive and, hence, it is important for the experimentalist to have causal predictions with low false-positive rates.
causality; model selection; hypothesis tests; systems genetics; quantitative trait loci
Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptinob/ob mutation (Lepob). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein–protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.
Alzheimer's disease and type 2 diabetes are two common aging-related diseases. Numerous studies have shown that the two diseases are associated. However, the mechanisms of such connection are not clear. Both diseases are complex diseases that are induced by multiple genetic factors and the environment. To understand the molecular network regulated by complex genetic factors causing type 2 diabetes, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant and diabetic mouse strains. We measured genotypes, clinical traits, and expression profiling in five tissues for each mouse. We then performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait, and developed a novel method for inferring key regulators for regulating plasma insulin. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.
Quantitative trait loci (QTL) hotspots (genomic locations affecting many traits) are a common feature in genetical genomics studies and are biologically interesting since they may harbor critical regulators. Therefore, statistical procedures to assess the significance of hotspots are of key importance. One approach, randomly allocating observed QTL across the genomic locations separately by trait, implicitly assumes all traits are uncorrelated. Recently, an empirical test for QTL hotspots was proposed on the basis of the number of traits that exceed a predetermined LOD value, such as the standard permutation LOD threshold. The permutation null distribution of the maximum number of traits across all genomic locations preserves the correlation structure among the phenotypes, avoiding the detection of spurious hotspots due to nongenetic correlation induced by uncontrolled environmental factors and unmeasured variables. However, by considering only the number of traits above a threshold, without accounting for the magnitude of the LOD scores, relevant information is lost. In particular, biologically interesting hotspots composed of a moderate to small number of traits with strong LOD scores may be neglected as nonsignificant. In this article we propose a quantile-based permutation approach that simultaneously accounts for the number and the LOD scores of traits within the hotspots. By considering a sliding scale of mapping thresholds, our method can assess the statistical significance of both small and large hotspots. Although the proposed approach can be applied to any type of heritable high-volume “omic” data set, we restrict our attention to expression (e)QTL analysis. We assess and compare the performances of these three methods in simulations and we illustrate how our approach can effectively assess the significance of moderate and small hotspots with strong LOD scores in a yeast expression data set.
hotspots; permutation tests; multiple traits; LOD scores; quantitative trait loci (QTL)
SorCS1 and SorL1/SorLA/LR11 belong to the sortilin family of vacuolar protein sorting-10 (Vps10) domain-containing proteins. Both are genetically associated with Alzheimer’s disease (AD), and SORL1 expression is decreased in the brains of patients suffering from AD. SORCS1 is also genetically associated with types 1 and 2 diabetes mellitus (T1DM, T2DM). We have undertaken a study of the possible role(s) for SorCS1 in metabolism of the Alzheimer’s amyloid-β peptide (Aβ) and the Aβ precursor protein (APP), to test the hypothesis that Sorcs1-deficiency might be a common genetic risk factor underlying the predisposition to AD that is associated with T2DM. Overexpression of SorCS1Cβ-myc in cultured cells caused a reduction (p=0.002) in Aβ generation. Conversely, endogenous murine Aβ40 and Aβ42 levels were increased (Aβ40, p=0.044; Aβ42, p=0.007) in the brains of female Sorcs1 hypomorphic mice, possibly paralleling the sexual dimorphism that is characteristic of the genetic associations of SORCS1 with AD and DM. Since SorL1 directly interacts with Vps35 to modulate APP metabolism, we investigated the possibility that SorCS1Cβ-myc interacts with APP, SorL1, and/or Vps35. We readily recovered SorCS1:APP, SorCS1:SorL1, and SorCS1:Vps35 complexes from nontransgenic mouse brain. Notably, total Vps35 protein levels were decreased by 49% (p=0.009) and total SorL1 protein levels were decreased by 29% (p=0.003) in the brains of female Sorcs1-hypomorphic mice. From these data, we propose that dysfunction of SorCS1 may contribute to both the APP/Aβ disturbance underlying AD and the insulin/glucose disturbance underlying DM.
AD; T1DM; T2DM; protein trafficking; APP; SorCS1; Vps10 domain; retromer
We previously mapped a type 2 diabetes (T2D) locus on chromosome 16 (Chr 16) in an F2 intercross from the BTBR T (+) tf (BTBR) Lepob/ob and C57BL/6 (B6) Lepob/ob mouse strains. Introgression of BTBR Chr 16 into B6 mice resulted in a consomic mouse with reduced fasting plasma insulin and elevated glucose levels. We derived a panel of sub-congenic mice and narrowed the diabetes susceptibility locus to a 1.6 Mb region. Introgression of this 1.6 Mb fragment of the BTBR Chr 16 into lean B6 mice (B6.16BT36–38) replicated the phenotypes of the consomic mice. Pancreatic islets from the B6.16BT36–38 mice were defective in the second phase of the insulin secretion, suggesting that the 1.6 Mb region encodes a regulator of insulin secretion. Within this region, syntaxin-binding protein 5-like (Stxbp5l) or tomosyn-2 was the only gene with an expression difference and a non-synonymous coding single nucleotide polymorphism (SNP) between the B6 and BTBR alleles. Overexpression of the b-tomosyn-2 isoform in the pancreatic β-cell line, INS1 (832/13), resulted in an inhibition of insulin secretion in response to 3 mM 8-bromo cAMP at 7 mM glucose. In vitro binding experiments showed that tomosyn-2 binds recombinant syntaxin-1A and syntaxin-4, key proteins that are involved in insulin secretion via formation of the SNARE complex. The B6 form of tomosyn-2 is more susceptible to proteasomal degradation than the BTBR form, establishing a functional role for the coding SNP in tomosyn-2. We conclude that tomosyn-2 is the major gene responsible for the T2D Chr 16 quantitative trait locus (QTL) we mapped in our mouse cross. Our findings suggest that tomosyn-2 is a key negative regulator of insulin secretion.
Humans carry many genetic variants that confer small effects on metabolic traits relevant to type 2 diabetes. These effects are amplified by environmental stressors like obesity. We used morbid obesity as a sensitizer to identify genes that contribute to the diabetes susceptibility of the BTBR mouse strain. Using mapping and breeding strategies, we were able to narrow a genetic region to one containing just 13 genes. One of these genes, tomosyn-2, emerged as a prime candidate. Our functional studies showed that tomosyn-2 is an inhibitor of insulin secretion, and it binds to the proteins involved in the fusion of insulin containing granules with the plasma membrane. We found a coding mutation and demonstrated that this mutation affects the stability of the protein product. Our work with Tomosyn-2 provides new insights into the regulation of insulin secretion and emphasizes that negative regulation is critical for avoiding insulin-induced hypoglycemia.
Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenotype network structure. In this paper we develop a QTL-driven phenotype network method (QTLnet) to jointly infer a causal phenotype network and associated genetic architecture for sets of correlated phenotypes. Randomization of alleles during meiosis and the unidirectional influence of genotype on phenotype allow the inference of QTLs causal to phenotypes. Causal relationships among phenotypes can be inferred using these QTL nodes, enabling us to distinguish among phenotype networks that would otherwise be distribution equivalent. We jointly model phenotypes and QTLs using homogeneous conditional Gaussian regression models, and we derive a graphical criterion for distribution equivalence. We validate the QTLnet approach in a simulation study. Finally, we illustrate with simulated data and a real example how QTLnet can be used to infer both direct and indirect effects of QTLs and phenotypes that co-map to a genomic region.
Causal graphical models; QTL mapping; joint inference of phenotype network and genetic architecture; systems genetics; homogeneous conditional Gaussian regression models; Markov chain Monte Carlo
Identifying associations between genotypes and gene expression levels using microarrays has enabled systematic interrogation of regulatory variation underlying complex phenotypes. This approach has vast potential for functional characterization of disease states, but its prohibitive cost, given hundreds to thousands of individual samples from populations have to be genotyped and expression profiled, has limited its widespread application.
Here we demonstrate that genomic regions with allele-specific expression (ASE) detected by sequencing cDNA are highly enriched for cis-acting expression quantitative trait loci (cis-eQTL) identified by profiling of 500 animals in parallel, with up to 90% agreement on the allele that is preferentially expressed. We also observed widespread noncoding and antisense ASE and identified several allele-specific alternative splicing variants.
Monitoring ASE by sequencing cDNA from as little as one sample is a practical alternative to expression genetics for mapping cis-acting variation that regulates RNA transcription and processing.
Type 2 diabetes results from severe insulin resistance coupled with a failure of β-cells to compensate by secreting sufficient insulin. Multiple genetic loci are involved in the development of diabetes, although the effect of each gene on diabetes susceptibility is thought to be small. MicroRNAs (miRNA) are non-coding 19–22 nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand the relationship between miRNA regulation and obesity-induced diabetes, we quantitatively profiled ~220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs profiled were expressed in all 3 tissues, with many miRNAs in each tissue showing significant changes in response to genetic obesity. Further, several miRNAs in each tissue were differentially responsive to obesity in B6 versus BTBR mice, suggesting that they may be involved in the pathogenesis of diabetes. In liver, there were ~40 miRNAs that were down-regulated in response to obesity in B6, but not BTBR mice, indicating that genetic differences between the mouse strains play a critical role in miRNA regulation. In order to elucidate the genetic architecture of hepatic miRNA expression, we measured the expression of miRNAs in genetically obese F2 mice. Approximately 10% of the miRNAs measured showed significant linkage (miR-eQTLs), identifying loci that control miRNA abundance. Understanding the influence that obesity and genetics exert on the regulation of miRNA expression will reveal the role miRNAs play in the context of obesity-induced type 2 diabetes.
Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS.
Genome-wide association studies (GWAS) seek to identify loci in which changes in DNA are correlated with disease. However, GWAS do not necessarily lead directly to genes associated with disease, and they do not typically inform the broader context in which disease genes operate, thereby providing limited insights into the mechanisms driving disease. One critical task to providing further insights into GWAS is developing an understanding of the genetics of gene expression (GGE). We present the first empiric study demonstrating that SNPs in human cohorts that associate with gene expression in liver and adipose tissues are enriched for associating with Type 2 Diabetes (T2D) in humans. By filtering “eSNPs” based on causal gene networks defined in an experimental cross population segregating T2D traits, we demonstrate a dramatically increased enrichment of T2D SNPs that enhance our ability to assess T2D risk. We demonstrate the utility of this approach by identifying malic enzyme 1 (ME1) as a novel T2D susceptibility gene in humans and then functionally validating the causal connection between ME1 and T2D in a mouse knockout model for Me1. This approach provides a path to identifying disease susceptibility networks rather than single SNPs or genes traditionally identified through GWAS.
Metabolic syndrome (MetSyn) is a group of metabolic conditions that occur together and promote the development of cardiovascular disease (CVD) and diabetes. Recent genome-wide association studies have identified several novel susceptibility genes for MetSyn traits, and studies in rodent models have provided important molecular insights. However, as yet, only a small fraction of the genetic component is known. Systems-based approaches that integrate genomic, molecular and physiological data are complementing traditional genetic and biochemical approaches to more fully address the complexity of MetSyn.
Adipose tissue can undergo rapid expansion during times of excess caloric intake. Like a rapidly expanding tumor mass, obese adipose tissue becomes hypoxic due to the inability of the vasculature to keep pace with tissue growth. Consequently, during the early stages of obesity, hypoxic conditions cause an increase in the level of hypoxia-inducible factor 1α (HIF1α) expression. Using a transgenic model of overexpression of a constitutively active form of HIF1α, we determined that HIF1α fails to induce the expected proangiogenic response. In contrast, we observed that HIF1α initiates adipose tissue fibrosis, with an associated increase in local inflammation. “Trichrome- and picrosirius red-positive streaks,” enriched in fibrillar collagens, are a hallmark of adipose tissue suffering from the early stages of hypoxia-induced fibrosis. Lysyl oxidase (LOX) is a transcriptional target of HIF1α and acts by cross-linking collagen I and III to form the fibrillar collagen fibers. Inhibition of LOX activity by β-aminoproprionitrile treatment results in a significant improvement in several metabolic parameters and further reduces local adipose tissue inflammation. Collectively, our observations are consistent with a model in which adipose tissue hypoxia serves as an early upstream initiator for adipose tissue dysfunction by inducing a local state of fibrosis.
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.
Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identifying individual genes and their potential roles in molecular pathways leading to disease remains a challenge. In this study, we include transcriptional and metabolic profiling in genomic analyses to address this limitation. We investigated an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains that segregates for genotype and diabetes-related physiological traits; blood glucose, plasma insulin and body weight. Our study shows that liver metabolites (comprised of amino acids, organic acids, and acyl-carnitines) 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, testable networks for control of specific metabolic processes in liver. We apply an in vitro study to confirm the validity of this integrative method, and thus provide a novel approach to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.
We review here the current political landscape and our own efforts to address the attempts to undermine science education in Wisconsin. To mount an effective response, expertise in evolutionary biology and in the history of the public controversy is useful but not essential. However, entering the fray requires a minimal tool kit of information. Here, we summarize some of the scientific and legal history of this issue and list a series of actions that scientists can take to help facilitate good science education and an improved atmosphere for the scientific enterprise nationally. Finally, we provide some model legislation that has been introduced in Wisconsin to strengthen the teaching of science.
Coordinated regulation of gene expression levels across a series of experimental conditions provides valuable information about the functions of correlated transcripts. The consideration of gene expression correlation over a time or tissue dimension has proved valuable in predicting gene function. Here, we consider correlations over a genetic dimension. In addition to identifying coregulated genes, the genetic dimension also supplies us with information about the genomic locations of putative regulatory loci. We calculated correlations among approximately 45,000 expression traits derived from 60 individuals in an F2 sample segregating for obesity and diabetes. By combining the correlation results with linkage mapping information, we were able to identify regulatory networks, make functional predictions for uncharacterized genes, and characterize novel members of known pathways. We found evidence of coordinate regulation of 174 G protein–coupled receptor protein signaling pathway expression traits. Of the 174 traits, 50 had their major LOD peak within 10 cM of a locus on Chromosome 2, and 81 others had a secondary peak in this region. We also characterized a Riken cDNA clone that showed strong correlation with stearoyl-CoA desaturase 1 expression. Experimental validation confirmed that this clone is involved in the regulation of lipid metabolism. We conclude that trait correlation combined with linkage mapping can reveal regulatory networks that would otherwise be missed if we studied only mRNA traits with statistically significant linkages in this small cross. The combined analysis is more sensitive compared with linkage mapping alone.
In order to annotate gene function and identify potential members of regulatory networks, the authors explore correlation of expression profiles across a genetic dimension, namely genotypes segregating in a panel of 60 F2 mice derived from a cross used to explore diabetes in obese mice. They first identified 6,016 seed transcripts for which they observe that the gene expression is linked to a particular region of the genome. Then they searched for transcripts whose expression is highly correlated with the seed transcripts and tested for enrichment of common biological functions among the lists of correlated transcripts. They found and explored the properties of 1,341 sets of transcripts that share a particular “gene ontology” term. Thirty-eight seeds in the G protein–coupled receptor protein signaling pathway were correlated with 174 transcripts, all of which are also annotated as G protein–coupled receptor protein signaling pathway and 131 of which share a regulatory locus on Chromosome 2. The authors note many of these findings would have been missed by simple expression quantitative trait loci analysis without the correlation step. The approach was used to identify a common set of genes involved in lipid metabolism.