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1.  Prostaglandin E2 Receptor, EP3, Is Induced in Diabetic Islets and Negatively Regulates Glucose- and Hormone-Stimulated Insulin Secretion 
Diabetes  2013;62(6):1904-1912.
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.
doi:10.2337/db12-0769
PMCID: PMC3661627  PMID: 23349487
2.  Zinc, insulin, and the liver: a ménage à trois 
The Journal of Clinical Investigation  2013;123(10):4136-4139.
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.
doi:10.1172/JCI72325
PMCID: PMC3784553  PMID: 24051373
3.  A quantitative map of the liver mitochondrial phosphoproteome reveals post-translational control of ketogenesis 
Cell metabolism  2012;16(5):672-683.
Summary
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.
doi:10.1016/j.cmet.2012.10.004
PMCID: PMC3506251  PMID: 23140645
4.  Protein Sorting Motifs in the Cytoplasmic Tail of SorCS1 Control Generation of Alzheimer’s Amyloid-β Peptide 
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.
doi:10.1523/JNEUROSCI.5270-12.2013
PMCID: PMC3696125  PMID: 23595767
5.  Modeling Causality for Pairs of Phenotypes in System Genetics 
Genetics  2013;193(3):1003-1013.
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.
doi:10.1534/genetics.112.147124
PMCID: PMC3583988  PMID: 23288936
causality; model selection; hypothesis tests; systems genetics; quantitative trait loci
6.  Integrative Analysis of a Cross-Loci Regulation Network Identifies App as a Gene Regulating Insulin Secretion from Pancreatic Islets 
PLoS Genetics  2012;8(12):e1003107.
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.
Author Summary
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.
doi:10.1371/journal.pgen.1003107
PMCID: PMC3516550  PMID: 23236292
7.  Quantile-Based Permutation Thresholds for Quantitative Trait Loci Hotspots 
Genetics  2012;191(4):1355-1365.
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.
doi:10.1534/genetics.112.139451
PMCID: PMC3416013  PMID: 22661325
hotspots; permutation tests; multiple traits; LOD scores; quantitative trait loci (QTL)
8.  The Number of X Chromosomes Causes Sex Differences in Adiposity in Mice 
PLoS Genetics  2012;8(5):e1002709.
Sexual dimorphism in body weight, fat distribution, and metabolic disease has been attributed largely to differential effects of male and female gonadal hormones. Here, we report that the number of X chromosomes within cells also contributes to these sex differences. We employed a unique mouse model, known as the “four core genotypes,” to distinguish between effects of gonadal sex (testes or ovaries) and sex chromosomes (XX or XY). With this model, we produced gonadal male and female mice carrying XX or XY sex chromosome complements. Mice were gonadectomized to remove the acute effects of gonadal hormones and to uncover effects of sex chromosome complement on obesity. Mice with XX sex chromosomes (relative to XY), regardless of their type of gonad, had up to 2-fold increased adiposity and greater food intake during daylight hours, when mice are normally inactive. Mice with two X chromosomes also had accelerated weight gain on a high fat diet and developed fatty liver and elevated lipid and insulin levels. Further genetic studies with mice carrying XO and XXY chromosome complements revealed that the differences between XX and XY mice are attributable to dosage of the X chromosome, rather than effects of the Y chromosome. A subset of genes that escape X chromosome inactivation exhibited higher expression levels in adipose tissue and liver of XX compared to XY mice, and may contribute to the sex differences in obesity. Overall, our study is the first to identify sex chromosome complement, a factor distinguishing all male and female cells, as a cause of sex differences in obesity and metabolism.
Author Summary
Differences exist between men and women in the development of obesity and related metabolic diseases such as type 2 diabetes and cardiovascular disease. Previous studies have focused on the sex-biasing role of hormones produced by male and female gonads, but these cannot account fully for the sex differences in metabolism. We discovered that removal of the gonads uncovers an important genetic determinant of sex differences in obesity—the presence of XX or XY sex chromosomes. We used a novel mouse model to tease apart the effects of male and female gonads from the effects of XX or XY chromosomes. Mice with XX sex chromosomes (relative to XY), regardless of their type of gonad, had increased body fat and ate more food during the sleep period. Mice with two X chromosomes also had accelerated weight gain, fatty liver, and hyperinsulinemia on a high fat diet. The higher expression levels of a subset of genes on the X chromosome that escape inactivation may influence adiposity and metabolic disease. The effect of X chromosome genes is present throughout life, but may become particularly significant with increases in longevity and extension of the period spent with reduced gonadal hormone levels.
doi:10.1371/journal.pgen.1002709
PMCID: PMC3349739  PMID: 22589744
9.  Diabetes-associated SorCS1 regulates Alzheimer’s amyloid-β metabolism: Evidence for involvement of SorL1 and the retromer complex 
The Journal of Neuroscience  2010;30(39):13110-13115.
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.
doi:10.1523/JNEUROSCI.3872-10.2010
PMCID: PMC3274732  PMID: 20881129
AD; T1DM; T2DM; protein trafficking; APP; SorCS1; Vps10 domain; retromer
10.  Positional Cloning of a Type 2 Diabetes Quantitative Trait Locus; Tomosyn-2, a Negative Regulator of Insulin Secretion 
PLoS Genetics  2011;7(10):e1002323.
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.
Author Summary
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.
doi:10.1371/journal.pgen.1002323
PMCID: PMC3188574  PMID: 21998599
11.  CAUSAL GRAPHICAL MODELS IN SYSTEMS GENETICS: A UNIFIED FRAMEWORK FOR JOINT INFERENCE OF CAUSAL NETWORK AND GENETIC ARCHITECTURE FOR CORRELATED PHENOTYPES1 
The annals of applied statistics  2010;4(1):320-339.
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.
PMCID: PMC3017382  PMID: 21218138
Causal graphical models; QTL mapping; joint inference of phenotype network and genetic architecture; systems genetics; homogeneous conditional Gaussian regression models; Markov chain Monte Carlo
12.  Genetic validation of whole-transcriptome sequencing for mapping expression affected by cis-regulatory variation 
BMC Genomics  2010;11:473.
Background
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.
Results
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.
Conclusion
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.
doi:10.1186/1471-2164-11-473
PMCID: PMC3091669  PMID: 20707912
13.  Obesity and genetics regulate microRNAs in islets, liver and adipose of diabetic mice 
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.
doi:10.1007/s00335-009-9217-2
PMCID: PMC2879069  PMID: 19727952
14.  Liver and Adipose Expression Associated SNPs Are Enriched for Association to Type 2 Diabetes 
PLoS Genetics  2010;6(5):e1000932.
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.
Author Summary
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.
doi:10.1371/journal.pgen.1000932
PMCID: PMC2865508  PMID: 20463879
15.  Metabolic syndrome: from epidemiology to systems biology 
Nature reviews. Genetics  2008;9(11):819-830.
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.
doi:10.1038/nrg2468
PMCID: PMC2829312  PMID: 18852695
16.  Hypoxia-Inducible Factor 1α Induces Fibrosis and Insulin Resistance in White Adipose Tissue ▿ §  
Molecular and Cellular Biology  2009;29(16):4467-4483.
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.
doi:10.1128/MCB.00192-09
PMCID: PMC2725728  PMID: 19546236
18.  Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling 
PLoS Genetics  2008;4(3):e1000034.
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.
Author Summary
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.
doi:10.1371/journal.pgen.1000034
PMCID: PMC2265422  PMID: 18369453
19.  Defending science education against intelligent design: a call to action 
Journal of Clinical Investigation  2006;116(5):1134-1138.
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.
doi:10.1172/JCI28449
PMCID: PMC1451210  PMID: 16670753
20.  The Republican war on science 
doi:10.1172/JCI28068
PMCID: PMC1386128
21.  Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping 
PLoS Genetics  2006;2(1):e6.
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.
Synopsis
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.
doi:10.1371/journal.pgen.0020006
PMCID: PMC1331977  PMID: 16424919
22.  Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping 
PLoS Genetics  2006;2(1):e6.
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.
Synopsis
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.
doi:10.1371/journal.pgen.0020006
PMCID: PMC1331977  PMID: 16424919
23.  Insig: a significant integrator of nutrient and hormonal signals 
Journal of Clinical Investigation  2004;113(8):1112-1114.
Lipogenesis is regulated by sterols and by insulin through the regulated expression and activation of the sterol regulatory element–binding proteins (SREBPs). A new study shows one way in which sterol and insulin regulation can be decoupled. In transgenic mice overexpressing a protein that regulates SREBP activation, lipogenesis is more sensitive to cholesterol and less sensitive to insulin.
doi:10.1172/JCI200421450
PMCID: PMC385410  PMID: 15085189
24.  The New Industrialized Approach to Biology 
Cell Biology Education  2003;2:150-151.
doi:10.1187/cbe.03-02-0008
PMCID: PMC192444
25.  The role of the LDL receptor in apolipoprotein B secretion 
Journal of Clinical Investigation  2000;105(4):521-532.
Familial hypercholesterolemia is caused by mutations in the LDL receptor gene (Ldlr). Elevated plasma LDL levels result from slower LDL catabolism and a paradoxical lipoprotein overproduction. We explored the relationship between the presence of the LDL receptor and lipoprotein secretion in hepatocytes from both wild-type and LDL receptor–deficient mice. Ldlr–/– hepatocytes secreted apoB100 at a 3.5-fold higher rate than did wild-type hepatocytes. ApoB mRNA abundance, initial apoB synthetic rate, and abundance of the microsomal triglyceride transfer protein 97-kDa subunit did not differ between wild-type and Ldlr–/– cells. Pulse-chase analysis and multicompartmental modeling revealed that in wild-type hepatocytes, approximately 55% of newly synthesized apoB100 was degraded. However, in Ldlr–/– cells, less than 20% of apoB was degraded. In wild-type hepatocytes, approximately equal amounts of LDL receptor–dependent apoB100 degradation occured via reuptake and presecretory mechanisms. Adenovirus-mediated overexpression of the LDL receptor in Ldlr–/– cells resulted in degradation of approximately 90% of newly synthesized apoB100. These studies show that the LDL receptor alters the proportion of apoB that escapes co- or post-translational presecretory degradation and mediates the reuptake of newly secreted apoB-containing lipoprotein particles.
PMCID: PMC289165  PMID: 10683382

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