Meta-analysis approach identifies candidate genes and associated molecular networks for type-2 diabetes mellitus

The Effect of SOCS2 Polymorphisms on Type 2 Diabetes Mellitus Susceptibility and Diabetic Complications in the Chinese Han Population

BACKGROUND

SOCS2 is downregulated in diabetes, which might be related to diabetes. We explored the effect of SOCS2 polymorphisms on the development of type 2 diabetes mellitus (T2DM) and diabetic complications.

METHODS

The subjects consisted of 500 patients with T2DM and 501 healthy controls. Five variants in SOCS2 were genotyped by Agena MassARRAY system. RT-qPCR profiling was performed to detect the expression of SOCS2 mRNA. Logistic regression analysis was utilized to calculate odds ratio (OR) and 95% confidence intervals (95% CIs).

RESULTS

Rs3825199 (OR = 1.44, p = 0.007), rs11107116 (OR = 1.39, p = 0.014) and rs10492321 (OR = 1.48, p = 0.004) had an increased T2DM risk of T2DM. Moreover, the contribution of SOCS2 polymorphisms to T2DM risk was associated with age, gender, smoking, drinking, and BMI. SOCS2 variants also had a reduced risk for T2DM patients with diabetic nephropathy, diabetic retinopathy and coronary heart disease. SOCS2 rs10492321 was the best single locus model. SOCS2 mRNA was downregulated in patients with T2DM compared to healthy controls (p = 0.029).

CONCLUSION

This study firstly reported that rs3825199, rs11107116 and rs10492321 in SOCS2 conferred to an increased risk for the occurrence of T2DM in the Chinese Han population. Moreover, SOCS2 mRNA was downregulated in patients with T2DM, suggesting that SOCS2 might have an important role in the occurrence of T2DM.

Integrated analysis of transcriptome profiling of lncRNAs and mRNAs in livers of type 2 diabetes mellitus

Long noncoding RNAs (lncRNAs) influence the progression of almost all human diseases, but the participation of lncRNAs in type 2 diabetes mellitus (T2DM) has not been fully elucidated. The present study aimed to systematically compare the transcriptome profiling of lncRNAs and mRNAs in livers between T2DM patients and controls, to identify key genes associated with T2DM pathogenesis, and to predict the underlying molecular mechanisms. As a result, a total of 1,512 differentially expressed (DE) lncRNAs and 1,923 DE mRNAs were identified through microarray analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that multiple metabolic processes were dysregulated such as small molecule, organic acid, lipid and branched chain amino acid metabolism. Protein-protein interaction network was constructed and 10 hub mRNAs were identified, including EHHADH, ATM, ACOX1, PIK3R1, EGFR, UQCRFS1, HMGCL, UQCRC2, NDUFS3 and F2. RT-qPCR was conducted to verify the validity of microarray results. Then, coding-noncoding co-expression network and competing endogenous RNA (ceRNA) network were analyzed to predict the lncRNA-mRNA and lncRNA-miRNA-mRNA regulatory patterns. Subsequently, 10 key intermediating miRNAs in ceRNA networks with a node degree > 80 were identified, including hsa-miR-5692a, hsa-miR-12136, hsa-miR-5680, hsa-miR-1305, hsa-miR-6833-5p, hsa-miR-7159-5p, hsa-miR-548as-3p, hsa-miR-6873-3p, hsa-miR-1290 and hsa-miR-4768-5p. In conclusion, the present study evaluated the transcriptome profiling of lncRNAs and mRNAs in livers from T2DM patients, with a value for understanding the molecular mechanism of disease pathogenesis and identifying effective biomarkers in clinical diagnosis.

Transcriptomic meta-analysis of disuse muscle atrophy vs. resistance exercise-induced hypertrophy in young and older humans

BACKGROUND

Skeletal muscle atrophy manifests across numerous diseases; however, the extent of similarities/differences in causal mechanisms between atrophying conditions in unclear. Ageing and disuse represent two of the most prevalent and costly atrophic conditions, with resistance exercise training (RET) being the most effective lifestyle countermeasure. We employed gene-level and network-level meta-analyses to contrast transcriptomic signatures of disuse and RET, plus young and older RET to establish a consensus on the molecular features of, and therapeutic targets against, muscle atrophy in conditions of high socio-economic relevance.

METHODS

Integrated gene-level and network-level meta-analysis was performed on publicly available microarray data sets generated from young (18-35 years) m. vastus lateralis muscle subjected to disuse (unilateral limb immobilization or bed rest) lasting ≥7 days or RET lasting ≥3 weeks, and resistance-trained older (≥60 years) muscle.

RESULTS

Disuse and RET displayed predominantly separate transcriptional responses, and transcripts altered across conditions were mostly unidirectional. However, disuse and RET induced directly inverted expression profiles for mitochondrial function and translation regulation genes, with COX4I1, ENDOG, GOT2, MRPL12, and NDUFV2, the central hub components of altered mitochondrial networks, and ZMYND11, a hub gene of altered translation regulation. A substantial number of genes (n = 140) up-regulated post-RET in younger muscle were not similarly up-regulated in older muscle, with young muscle displaying a more pronounced extracellular matrix (ECM) and immune/inflammatory gene expression response. Both young and older muscle exhibited similar RET-induced ubiquitination/RNA processing gene signatures with associated PWP1, PSMB1, and RAF1 hub genes.

CONCLUSIONS

Despite limited opposing gene profiles, transcriptional signatures of disuse are not simply the converse of RET. Thus, the mechanisms of unloading cannot be derived from studying muscle loading alone and provides a molecular basis for understanding why RET fails to target all transcriptional features of disuse. Loss of RET-induced ECM mechanotransduction and inflammatory profiles might also contribute to suboptimal ageing muscle adaptations to RET. Disuse and age-dependent molecular candidates further establish a framework for understanding and treating disuse/ageing atrophy.

Progressive Degenerative Myopathy and Myosteatosis in ASNSD1-Deficient Mice

Mice with an inactivating mutation in the gene encoding asparagine synthetase domain containing 1 (ASNSD1) develop a progressive degenerative myopathy that results in severe sarcopenia and myosteatosis. ASNSD1 is conserved across many species, and whole body gene expression surveys show maximal expression levels of ASNSD1 in skeletal muscle. However, potential functions of this protein have not been previously reported. Asnsd1^-/- mice demonstrated severe muscle weakness, and their normalized body fat percentage on both normal chow and high fat diets was greater than 2 SD above the mean for 3651 chow-fed and 2463 high-fat-diet-fed knockout (KO) lines tested. Histologic lesions were essentially limited to the muscle and were characterized by a progressive degenerative myopathy with extensive transdifferentiation and replacement of muscle by well-differentiated adipose tissue. There was minimal inflammation, fibrosis, and muscle regeneration associated with this myopathy. In addition, the absence of any signs of lipotoxicity in Asnsd1^-/- mice despite their extremely elevated body fat percentage and low muscle mass suggests a role for metabolic dysfunctions in the development of this phenotype. Asnsd1^-/- mice provide the first insight into the function of this protein, and this mouse model could prove useful in elucidating fundamental metabolic interactions between skeletal muscle and adipose tissue.

Diabetes in Pregnancy and MicroRNAs: Promises and Limitations in Their Clinical Application

Maternal diabetes is associated with an increased risk of complications for the mother and her offspring. The latter have an increased risk of foetal macrosomia, hypoglycaemia, respiratory distress syndrome, preterm delivery, malformations and mortality but also of life-long development of obesity and diabetes. Epigenetics have been proposed as an explanation for this long-term risk, and microRNAs (miRNAs) may play a role, both in short- and long-term outcomes. Gestation is associated with increasing maternal insulin resistance, as well as β-cell expansion, to account for the increased insulin needs and studies performed in pregnant rats support a role of miRNAs in this expansion. Furthermore, several miRNAs are involved in pancreatic embryonic development. On the other hand, maternal diabetes is associated with changes in miRNA both in maternal and in foetal tissues. This review aims to summarise the existing knowledge on miRNAs in gestational and pre-gestational diabetes, both as diagnostic biomarkers and as mechanistic players, in the development of gestational diabetes itself and also of short- and long-term complications for the mother and her offspring.

A Unified Pathophysiological Construct of Diabetes and its Complications

Advances in understanding diabetes mellitus (DM) through basic and clinical research have helped clarify and reunify a disease state fragmented into numerous etiologies and subtypes. It is now understood that a common pathophysiology drives the diabetic state throughout its natural history and across its varied clinical presentations, a pathophysiology involving metabolic insults, oxidative damage, and vicious cycles that aggravate and intensify organ dysfunction and damage. This new understanding of the disease requires that we revisit existing diagnostics and treatment approaches, which were built upon outmoded assumptions. 'The Common Pathophysiologic Origins of Diabetes Mellitus and its Complications Construct' is presented as a more accurate, foundational, and translatable construct of DM that helps make sense of the hitherto ambiguous findings of long-term outcome studies.

Effects of High-Fat Feeding on Skeletal Muscle Gene Expression in Diabetic Goto-Kakizaki Rats

In the present report, we examined the responses of diabetic Goto-Kakizaki (GK) rats and control Wistar-Kyoto (WKY) rats fed either a standard chow or high-fat diet (HFD) from weaning to 20 weeks of age. This comparison included gene expression profiling of skeletal muscle using Affymetrix gene array chips. The expression profiling is interpreted within the context of a wide array of physiological measurements. Genes whose expressions are different between the 2 strains regardless of diet, as well as genes that differ between strains only with HFD, were identified. In addition, genes that were regulated by diet in 1 or both strains were identified. The results suggest that both strains respond to HFD by an increased capacity to oxidize lipid fuels in the musculature but that this adaptation occurs more rapidly in WKY rats. The results also demonstrated an impaired cytokine signalling and heightened inflammatory status in the GK rats.

Gene signature associated with benign neurofibroma transformation to malignant peripheral nerve sheath tumors

Benign neurofibromas, the main phenotypic manifestations of the rare neurological disorder neurofibromatosis type 1, degenerate to malignant tumors associated to poor prognosis in about 10% of patients. Despite efforts in the field of (epi)genomics, the lack of prognostic biomarkers with which to predict disease evolution frustrates the adoption of appropriate early therapeutic measures. To identify potential biomarkers of malignant neurofibroma transformation, we integrated four human experimental studies and one for mouse, using a gene score-based meta-analysis method, from which we obtained a score-ranked signature of 579 genes. Genes with the highest absolute scores were classified as promising disease biomarkers. By grouping genes with similar neurofibromatosis-related profiles, we derived panels of potential biomarkers. The addition of promoter methylation data to gene profiles indicated a panel of genes probably silenced by hypermethylation. To identify possible therapeutic treatments, we used the gene signature to query drug expression databases. Trichostatin A and other histone deacetylase inhibitors, as well as cantharidin and tamoxifen, were retrieved as putative therapeutic means to reverse the aberrant regulation that drives to malignant cell proliferation and metastasis. This in silico prediction corroborated reported experimental results that suggested the inclusion of these compounds in clinical trials. This experimental validation supported the suitability of the meta-analysis method used to integrate several sources of public genomic information, and the reliability of the gene signature associated to the malignant evolution of neurofibromas to generate working hypotheses for prognostic and drug-responsive biomarkers or therapeutic measures, thus showing the potential of this in silico approach for biomarker discovery.

A Chromosome 13 locus is associated with male-specific mortality in mice

BACKGROUND AND AIM

Mortality is a highly complex trait influenced by a wide array of genetic factors.

METHODS

We examined a population of 1200 mice that were F2 generation offspring of a 4-way reciprocal cross between C57BL6/J and DBA2/J strains. Animals were sacrificed at age 200, 500, or 800 days and genotyped at 96 markers. The 800 days old cohort, which were the survivors of a much larger breeding group, were examined for enriched frequency of alleles that benefit survival and depletion of alleles that reduce survival.

RESULTS

Loci on Chr 13 in males and on Chr X in females were significantly distorted from Mendelian expectations, even after conservative correction for multiple testing. DBA2/J alleles between 35 and 80 Mb on Chr 13 were underrepresented in the age 800 male animals. D2 genotypes in this region were also associated with premature death during behavioral testing. Furthermore, confirmatory analysis showed BXD recombinant inbred strains carrying the D2 alleles in this region had shorter median survival. Exploration of available pathology data indicated that a syndrome involving dental malocclusions, pancreatic islet hypertrophy, and kidney lipidosis may have mediated the effects of DBA alleles on mortality specifically in male mice. The heterozygote advantage locus on the X Chr was not found to be associated with any pathology.

CONCLUSIONS

These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps via a metabolic disorder that emerges by 200 days of age in male animals.

A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms of Action and Chemical Structure

When evaluating compound similarity, addressing multiple sources of information to reach conclusions about common pharmaceutical and/or toxicological mechanisms of action is a crucial strategy. In this chapter, we describe a systems biology approach that incorporates analyses of hepatotoxicant data for 33 compounds from three different sources: a chemical structure similarity analysis based on the 3D Tanimoto coefficient, a chemical structure-based protein target prediction analysis, and a cross-study/cross-platform meta-analysis of in vitro and in vivo human and rat transcriptomics data derived from public resources (i.e., the diXa data warehouse). Hierarchical clustering of the outcome scores of the separate analyses did not result in a satisfactory grouping of compounds considering their known toxic mechanism as described in literature. However, a combined analysis of multiple data types may hypothetically compensate for missing or unreliable information in any of the single data types. We therefore performed an integrated clustering analysis of all three data sets using the R-based tool iClusterPlus. This indeed improved the grouping results. The compound clusters that were formed by means of iClusterPlus represent groups that show similar gene expression while simultaneously integrating a similarity in structure and protein targets, which corresponds much better with the known mechanism of action of these toxicants. Using an integrative systems biology approach may thus overcome the limitations of the separate analyses when grouping liver toxicants sharing a similar mechanism of toxicity.

A Computational Pipeline for Cross-Species Analysis of RNA-seq Data Using R and Bioconductor

RNA sequencing (RNA-seq) has revolutionized transcriptome analysis through profiling the expression of thousands of genes at the same time. Systematic analysis of orthologous transcripts across species is critical for understanding the evolution of gene expression and uncovering important information in animal models of human diseases. Several computational methods have been published for analyzing gene expression between species, but they often lack crucial details and therefore cannot serve as a practical guide. Here, we present the first step-by-step protocol for cross-species RNA-seq analysis with a concise workflow that is largely based on the free open-source R language and Bioconductor packages. This protocol covers the entire process from short-read mapping, gene expression quantification, differential expression analysis to pathway enrichment. Many useful utilities for data visualization are included. This complete and easy-to-follow protocol provides hands-on guidance for users who are new to cross-species gene expression analysis.

Identification of novel targets for the diagnosis and treatment of liver fibrosis

Liver fibrosis is characterized by the excessive deposition of extracellular matrix (ECM) in the hepatic parenchyma and represents an intrinsic response to chronic injury, maintaining organ integrity when extensive necrosis or apoptosis occurs. Hepatic stellate cells (HSCs) are the major cell type responsible for liver fibrosis. Following liver injury, HSCs become activated and transdifferentiate into myofibroblasts (MFBs) that lead to intrahepatic ECM accumulation. In the present study, we performed a meta‑analysis of datasets which included whole-genome transcriptional data on HSCs in the quiescent and activated state from two different rodent species and identified commonly regulated genes. Several of the genes identified, including ECM components, metalloproteinases and growth factors, were found to be well‑known markers for HSC activation. However, other significant genes also appeared to play important roles in hepatic fibrosis. The elucidation of the molecular events underlying HSC activation may be key to the identification of potential novel pharmacological targets for the prevention and treatment of liver fibrosis.

Prognostic interaction patterns in diabetes mellitus II: A random-matrix-theory relation

We analyze protein-protein interactions in diabetes mellitus II and its normal counterpart under the combined framework of random matrix theory and network biology. This disease is the fifth-leading cause of death in high-income countries and an epidemic in developing countries, affecting around 8% of the total adult population in the world. Treatment at the advanced stage is difficult and challenging, making early detection a high priority in the cure of the disease. Our investigation reveals specific structural patterns important for the occurrence of the disease. In addition to the structural parameters, the spectral properties reveal the top contributing nodes from localized eigenvectors, which turn out to be significant for the occurrence of the disease. Our analysis is time-efficient and cost-effective, bringing a new horizon in the field of medicine by highlighting major pathways involved in the disease. The analysis provides a direction for the development of novel drugs and therapies in curing the disease by targeting specific interaction patterns instead of a single protein.

Genome-wide analysis of hepatic lipid content in extreme obesity

AIMS

Individuals with type 2 diabetes have an increased risk of developing non-alcoholic fatty liver disease (NAFLD), and NAFLD patients are also at greater risk for developing type 2 diabetes. Although the relationship between type 2 diabetes and NAFLD is highly interconnected, the pathogenic mechanisms linking the two diseases are poorly understood. The goal of this study was to identify genetic determinants of hepatic lipid accumulation through association analysis using histological phenotypes in obese individuals.

METHODS

Using the Illumina HumanOmniExpress BeadChip assay, we genotyped 2,300 individuals on whom liver biopsy data were available.

RESULTS

We analyzed total bilirubin levels, which are linked to fatty liver in severe obesity, and observed the strongest evidence for association with rs4148325 in UGT1A (P < 5.0 × 10(-93)), replicating previous findings. We assessed hepatic fat level and found strong evidence for association with rs4823173, rs2896019, and rs2281135, all located in PNPLA3 and rs10401969 in SUGP1. Analysis of liver transcript levels of 20 genes residing at the SUGP1/NCAN locus identified a 1.6-fold change in the expression of the LPAR2 gene in fatty liver. We also observed suggestive evidence for association between low-grade fat accumulation and rs10859525 and rs1294908, located upstream from SOCS2 and RAMP3, respectively. SOCS2 was differentially expressed between fatty and normal liver.

CONCLUSIONS

These results replicate findings for several hepatic phenotypes in the setting of extreme obesity and implicate new loci that may play a role in the pathophysiology of hepatic lipid accumulation.

Identification of key nodes of type 2 diabetes mellitus protein interactome and study of their interactions with phloridzin

Network biology-inspired approaches could be used effectively in probing regulatory processes by which small molecules intervene with disease mechanisms. The present study aims at identification of key targets of type 2 diabetes mellitus (T2DM) by network analysis of the underlying protein interactome, and probing for mechanisms by which phloridzin could be critical at altering the disease phenotype. Towards this goal, we constructed a protein-protein interaction network associated with T2DM, starting from candidate genes and systems-level interactions data available. The relevance of the network constructed was verified with the help of gene ontology, node deletion, and biological essentiality studies. Using a network analysis method, MAPK1, EP300, and SMAD2 were identified as the most central proteins of potential therapeutic value. Phloridzin, a known antidiabetic agent, potentially interacts with proteins central to T2DM mechanisms. The structural understanding of interaction of phloridzin with these proteins of relevance to T2DM could provide better insight into its regulatory mechanisms and help in developing better therapeutic agents. The molecular docking results suggest that phloridzin is potentially involved in making critical interactions with MAPK1. These results could further be validated by experimental studies and could be used to design therapeutic agents for T2DM intervention.

Thioredoxins, glutaredoxins, and peroxiredoxins--molecular mechanisms and health significance: from cofactors to antioxidants to redox signaling

Thioredoxins (Trxs), glutaredoxins (Grxs), and peroxiredoxins (Prxs) have been characterized as electron donors, guards of the intracellular redox state, and "antioxidants". Today, these redox catalysts are increasingly recognized for their specific role in redox signaling. The number of publications published on the functions of these proteins continues to increase exponentially. The field is experiencing an exciting transformation, from looking at a general redox homeostasis and the pathological oxidative stress model to realizing redox changes as a part of localized, rapid, specific, and reversible redox-regulated signaling events. This review summarizes the almost 50 years of research on these proteins, focusing primarily on data from vertebrates and mammals. The role of Trx fold proteins in redox signaling is discussed by looking at reaction mechanisms, reversible oxidative post-translational modifications of proteins, and characterized interaction partners. On the basis of this analysis, the specific regulatory functions are exemplified for the cellular processes of apoptosis, proliferation, and iron metabolism. The importance of Trxs, Grxs, and Prxs for human health is addressed in the second part of this review, that is, their potential impact and functions in different cell types, tissues, and various pathological conditions.

Genome-wide association study of age at menarche in African-American women

African-American (AA) women have earlier menarche on average than women of European ancestry (EA), and earlier menarche is a risk factor for obesity and type 2 diabetes among other chronic diseases. Identification of common genetic variants associated with age at menarche has a potential value in pointing to the genetic pathways underlying chronic disease risk, yet comprehensive genome-wide studies of age at menarche are lacking for AA women. In this study, we tested the genome-wide association of self-reported age at menarche with common single-nucleotide polymorphisms (SNPs) in a total of 18 089 AA women in 15 studies using an additive genetic linear regression model, adjusting for year of birth and population stratification, followed by inverse-variance weighted meta-analysis (Stage 1). Top meta-analysis results were then tested in an independent sample of 2850 women (Stage 2). First, while no SNP passed the pre-specified P < 5 × 10(-8) threshold for significance in Stage 1, suggestive associations were found for variants near FLRT2 and PIK3R1, and conditional analysis identified two independent SNPs (rs339978 and rs980000) in or near RORA, strengthening the support for this suggestive locus identified in EA women. Secondly, an investigation of SNPs in 42 previously identified menarche loci in EA women demonstrated that 25 (60%) of them contained variants significantly associated with menarche in AA women. The findings provide the first evidence of cross-ethnic generalization of menarche loci identified to date, and suggest a number of novel biological links to menarche timing in AA women.

Prioritization of retinal disease genes: an integrative approach

The discovery of novel disease-associated variations in genes is often a daunting task in highly heterogeneous disease classes. We seek a generalizable algorithm that integrates multiple publicly available genomic data sources in a machine-learning model for the prioritization of candidates identified in patients with retinal disease. To approach this problem, we generate a set of feature vectors from publicly available microarray, RNA-seq, and ChIP-seq datasets of biological relevance to retinal disease, to observe patterns in gene expression specificity among tissues of the body and the eye, in addition to photoreceptor-specific signals by the CRX transcription factor. Using these features, we describe a novel algorithm, positive and unlabeled learning for prioritization (PULP). This article compares several popular supervised learning techniques as the regression function for PULP. The results demonstrate a highly significant enrichment for previously characterized disease genes using a logistic regression method. Finally, a comparison of PULP with the popular gene prioritization tool ENDEAVOUR shows superior prioritization of retinal disease genes from previous studies. The java source code, compiled binary, assembled feature vectors, and instructions are available online at https://github.com/ahwagner/PULP.

A novel method for cross-species gene expression analysis

Background

Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.

Results

In this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.

Conclusions

The method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.

An integrated approach of comparative genomics and heritability analysis of pig and human on obesity trait: evidence for candidate genes on human chromosome 2

Background

Traditional candidate gene approach has been widely used for the study of complex diseases including obesity. However, this approach is largely limited by its dependence on existing knowledge of presumed biology of the phenotype under investigation. Our combined strategy of comparative genomics and chromosomal heritability estimate analysis of obesity traits, subscapular skinfold thickness and back-fat thickness in Korean cohorts and pig (Sus scrofa), may overcome the limitations of candidate gene analysis and allow us to better understand genetic predisposition to human obesity.

Results

We found common genes including FTO, the fat mass and obesity associated gene, identified from significant SNPs by association studies of each trait. These common genes were related to blood pressure and arterial stiffness (P = 1.65E-05) and type 2 diabetes (P = 0.00578). Through the estimation of variance of genetic component (heritability) for each chromosome by SNPs, we observed a significant positive correlation (r = 0.479) between genetic contributions of human and pig to obesity traits. Furthermore, we noted that human chromosome 2 (syntenic to pig chromosomes 3 and 15) was most important in explaining the phenotypic variance for obesity.

Conclusions

Obesity genetics still awaits further discovery. Navigating syntenic regions suggests obesity candidate genes on chromosome 2 that are previously known to be associated with obesity-related diseases: MRPL33, PARD3B, ERBB4, STK39, and ZNF385B.

Genome-wide meta-analysis of genetic susceptible genes for Type 2 Diabetes

Background

Many genetic studies, including single gene studies and Genome-wide association studies (GWAS), aim to identify risk alleles for genetic diseases such as Type II Diabetes (T2D). However, in T2D studies, there is a significant amount of the hereditary risk that cannot be simply explained by individual risk genes. There is a need for developing systems biology approaches to integrate comprehensive genetic information and provide new insight on T2D biology.

Methods

We performed comprehensive integrative analysis of Single Nucleotide Polymorphisms (SNP's) individually curated from T2D GWAS results and mapped them to T2D candidate risk genes. Using protein-protein interaction data, we constructed a T2D-specific molecular interaction network consisting of T2D genetic risk genes and their interacting gene partners. We then studied the relationship between these T2D genes and curated gene sets.

Results

We determined that T2D candidate risk genes are concentrated in certain parts of the genome, specifically in chromosome 20. Using the T2D genetic network, we identified highly-interconnected network "hub" genes. By incorporating T2D GWAS results, T2D pathways, and T2D genes' functional category information, we further ranked T2D risk genes, T2D-related pathways, and T2D-related functional categories. We found that highly-interconnected T2D disease network “hub” genes most highly associated to T2D genetic risks to be PI3KR1, ESR1, and ENPP1. The well-characterized TCF7L2, contractor to our expectation, was not among the highest-ranked T2D gene list. Many interacted pathways play a role in T2D genetic risks, which includes insulin signalling pathway, type II diabetes pathway, maturity onset diabetes of the young, adipocytokine signalling pathway, and pathways in cancer. We also observed significant crosstalk among T2D gene subnetworks which include insulin secretion, regulation of insulin secretion, response to peptide hormone stimulus, response to insulin stimulus, peptide secretion, glucose homeostasis, and hormone transport. Overview maps involving T2D genes, gene sets, pathways, and their interactions are all reported.

Conclusions

Large-scale systems biology meta-analyses of GWAS results can improve interpretations of genetic variations and genetic risk factors. T2D genetic risks can be attributable to the summative genetic effects of many genes involved in a broad range of signalling pathways and functional networks. The framework developed for T2D studies may serve as a guide for studying other complex diseases.

Biosignatures for Parkinson's disease and atypical parkinsonian disorders patients

Diagnosis of Parkinson' disease (PD) carries a high misdiagnosis rate due to failure to recognize atypical parkinsonian disorders (APD). Usually by the time of diagnosis greater than 60% of the neurons in the substantia nigra are dead. Therefore, early detection would be beneficial so that therapeutic intervention may be initiated early in the disease process. We used splice variant-specific microarrays to identify mRNAs whose expression is altered in peripheral blood of early-stage PD patients compared to healthy and neurodegenerative disease controls. Quantitative polymerase chain reaction assays were used to validate splice variant transcripts in independent sample sets. Here we report a PD signature used to classify blinded samples with 90% sensitivity and 94% specificity and an APD signature that resulted in a diagnosis with 95% sensitivity and 94% specificity. This study provides the first discriminant functions with coherent diagnostic signatures for PD and APD. Analysis of the PD biomarkers identified a regulatory network with nodes centered on the transcription factors HNF4A and TNF, which have been implicated in insulin regulation.

Insights into the pathogenesis of axial spondyloarthropathy from network and pathway analysis

Background

Complex chronic diseases are usually not caused by changes in a single causal gene but by an unbalanced regulating network resulting from the dysfunctions of multiple genes or their products. Therefore, network based systems approach can be helpful for the identification of candidate genes related to complex diseases and their relationships. Axial spondyloarthropathy (SpA) is a group of chronic inflammatory joint diseases that mainly affect the spine and the sacroiliac joints. The pathogenesis of SpA remains largely unknown.

Results

In this paper, we conducted a network study of the pathogenesis of SpA. We integrated data related to SpA, from the OMIM database, proteomics and microarray experiments of SpA, to prioritize SpA candidate disease genes in the context of human protein interactome. Based on the top ranked SpA related genes, we constructed a SpA specific PPI network, identified potential pathways associated with SpA, and finally sketched an overview of biological processes involved in the development of SpA.

Conclusions

The protein-protein interaction (PPI) network and pathways reflect the link between the two pathological processes of SpA, i.e., immune mediated inflammation, as well as imbalanced bone modelling caused new boneformation and bone loss. We found that some known disease causative genes, such as TNFand ILs, play pivotal roles in this interaction.

PPINGUIN: Peptide Profiling Guided Identification of Proteins improves quantitation of iTRAQ ratios

Background

Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins.

Results

We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates.

Conclusions

Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.

Association of pyruvate dehydrogenase kinase 4 gene polymorphisms with type 2 diabetes and metabolic syndrome

AIMS

Pyruvate dehydrogenase kinase 4 (PDK4) plays a crucial role in glucose utilization and lipid metabolism by regulating the pyruvate dehydrogenase complex (PDC) and is an emerging therapeutic target for type 2 diabetes. To date, no study has specifically examined the relationship between PDK4 gene polymorphisms and type 2 diabetes or metabolic syndrome.

METHODS

The association of common single nucleotide polymorphisms (SNPs) was examined in PDK4 [-208A/G (rs10085637), IVS3+192C/T (rs3779478), IVS6+31A/G (rs2301630), IVS7+514A/G (rs12668651), IVS10+75C/T (rs10247649)] with type 2 diabetes and metabolic syndrome in 651 Korean subjects with type 2 diabetes and 350 nondiabetic Korean subjects. The association of these SNPs with clinical parameters related to metabolic syndromes including obesity, hyperglycemia, hypertension, and dyslipidemia was also examined.

RESULTS

No significant association was found between the studied SNPs and type 2 diabetes, metabolic syndrome, or clinical parameters. The PDK4 gene haplotype ACAGC showed a modest association with type 2 diabetes. However, the significance of this association was lost after considering for multiple comparisons.

CONCLUSIONS

PDK4 polymorphisms may not be associated with type 2 diabetes or metabolic syndrome. Further studies utilizing a larger study population are required to confirm these results.

Uncovering suitable reference proteins for expression studies in human adipose tissue with relevance to obesity

Background

Protein expression studies based on the two major intra-abdominal human fat depots, the subcutaneous and the omental fat, can shed light into the mechanisms involved in obesity and its co-morbidities. Here we address, for the first time, the identification and validation of reference proteins for data standardization, which are essential for accurate comparison of protein levels in expression studies based on fat from obese and non-obese individuals.

Methodology and Findings

To uncover adipose tissue proteins equally expressed either in omental and subcutaneous fat depots (study 1) or in omental fat from non-obese and obese individuals (study 2), we have reanalyzed our previously published data based on two-dimensional fluorescence difference gel electrophoresis. Twenty-four proteins (12 in study 1 and 12 in study 2) with similar expression levels in all conditions tested were selected and identified by mass spectrometry. Immunoblotting analysis was used to confirm in adipose tissue the expression pattern of the potential reference proteins and three proteins were validated: PARK7, ENOA and FAA. Western Blot analysis was also used to test customary loading control proteins. ENOA, PARK7 and the customary loading control protein Beta-actin showed steady expression profiles in fat from non-obese and obese individuals, whilst FAA maintained steady expression levels across paired omental and subcutaneous fat samples.

Conclusions

ENOA, PARK7 and Beta-actin are proper reference standards in obesity studies based on omental fat, whilst FAA is the best loading control for the comparative analysis of omental and subcutaneous adipose tissues either in obese and non-obese subjects. Neither customary loading control proteins GAPDH and TBB5 nor CALX are adequate standards in differential expression studies on adipose tissue. The use of the proposed reference proteins will facilitate the adequate analysis of proteins differentially expressed in the context of obesity, an aim difficult to achieve before this study.

The SOCS2 ubiquitin ligase complex regulates growth hormone receptor levels

Growth Hormone is essential for the regulation of growth and the homeostatic control of intermediary metabolism. GH actions are mediated by the Growth Hormone Receptor; a member of the cytokine receptor super family that signals chiefly through the JAK2/STAT5 pathway. Target tissue responsiveness to GH is under regulatory control to avoid excessive and off-target effects upon GHR activation. The suppressor of cytokine signalling 2 (SOCS) is a key regulator of GHR sensitivity. This is clearly shown in mice where the SOCS2 gene has been inactivated, which show 30–40% increase in body length, a phenotype that is dependent on endogenous GH secretion. SOCS2 is a GH-stimulated, STAT5b-regulated gene that acts in a negative feedback loop to downregulate GHR signalling. Since the biochemical basis for these actions is poorly understood, we studied the molecular function of SOCS2. We demonstrated that SOCS2 is part of a multimeric complex with intrinsic ubiquitin ligase activity. Mutational analysis shows that the interaction with Elongin B/C controls SOCS2 protein turnover and affects its molecular activity. Increased GHR levels were observed in livers from SOCS2^−/− mice and in the absence of SOCS2 in in vitro experiments. We showed that SOCS2 regulates cellular GHR levels through direct ubiquitination and in a proteasomally dependent manner. We also confirmed the importance of the SOCS-box for the proper function of SOCS2. Finally, we identified two phosphotyrosine residues in the GHR to be responsible for the interaction with SOCS2, but only Y487 to account for the effects of SOCS2. The demonstration that SOCS2 is an ubiquitin ligase for the GHR unveils the molecular basis for its physiological actions.

Positional integratomic approach in identification of genomic candidate regions for Parkinson's disease

MOTIVATION

Recent abundance of data from studies employing high-throughput technologies to reveal alterations in human disease on genomic, transcriptomic, proteomic and other levels, offer the possibility to integrate this information into a comprehensive picture of molecular events occurring in human disease. Diversity of data originating from these studies presents a methodological obstacle in the integration process, also due to difficulties in choosing the optimal unified denominator that would allow inclusion of variables from various types of studies. We present a novel approach for integration of such multi-origin data based on positions of genetic alterations occurring in human diseases. Parkinson's disease (PD) was chosen as a model for evaluation of our methodology.

METHODS

Datasets from various types of studies in PD (linkage, genome-wide association, transcriptomic and proteomic studies) were obtained from online repositories or were extracted from available research papers. Subsequently, human genome assembly was subdivided into 10 kb regions, and significant signals from aforementioned studies were arranged into their corresponding regions according to their genomic position. For each region, rank product values were calculated and significance values were estimated by permuting the original dataset.

RESULTS

Altogether, 179 regions (representing 33 contiguous genomic regions) had significant accumulation of signals when P-value cut-off was set at 0.0001. Identified regions with significant accumulation of signals contained 29 plausible candidate genes for PD. In conclusion, we present a novel approach for identification of candidate regions and genes for various human disorders, based on the positional integration of data across various types of omic studies.

Meta-analysis of heterogeneous Down Syndrome data reveals consistent genome-wide dosage effects related to neurological processes

Background

Down syndrome (DS; trisomy 21) is the most common genetic cause of mental retardation in the human population and key molecular networks dysregulated in DS are still unknown. Many different experimental techniques have been applied to analyse the effects of dosage imbalance at the molecular and phenotypical level, however, currently no integrative approach exists that attempts to extract the common information.

Results

We have performed a statistical meta-analysis from 45 heterogeneous publicly available DS data sets in order to identify consistent dosage effects from these studies. We identified 324 genes with significant genome-wide dosage effects, including well investigated genes like SOD1, APP, RUNX1 and DYRK1A as well as a large proportion of novel genes (N = 62). Furthermore, we characterized these genes using gene ontology, molecular interactions and promoter sequence analysis. In order to judge relevance of the 324 genes for more general cerebral pathologies we used independent publicly available microarry data from brain studies not related with DS and identified a subset of 79 genes with potential impact for neurocognitive processes. All results have been made available through a web server under http://ds-geneminer.molgen.mpg.de/.

Conclusions

Our study represents a comprehensive integrative analysis of heterogeneous data including genome-wide transcript levels in the domain of trisomy 21. The detected dosage effects build a resource for further studies of DS pathology and the development of new therapies.

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Background

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics.

Results

We present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods.

Conclusions

The combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.

Influence of energy balance on the somatotrophic axis and matrix metalloproteinase expression in the endometrium of the postpartum dairy cow

Postpartum dairy cows enter a period of negative energy balance (NEB) associated with low circulating IGF1, during which the uterus must undergo extensive repair following calving. This study investigated the effects of NEB on expression of IGF family members and related genes in the involuting uterus. Cows were allocated to two treatments using differential feeding and milking regimes to produce mild NEB or severe NEB (SNEB). Uterine endometrial samples collected 2 weeks post partum were analysed by quantitative PCR. The expression of IGF-binding protein 4 (IGFBP4) mRNA increased in the endometrium of SNEB cows, with trends towards increased IGFBP1 and reduced IGFBP6 expression. There were no significant differences between treatments in mRNA expression of IGF1, IGF2 or of any hormone receptor studied, but significant correlations across all cows in the expression levels of groups of receptors suggested common regulatory mechanisms: type 1 IGF receptor (IGF1R), IGF2R and insulin receptor (INSR); GHR with ESR1; and ESR2 with NR3C1. The expression of IGF1R and INSR also positively correlated with the circulating urea concentration. Matrix metalloproteinases (MMPs) are important in tissue remodelling and can affect IGF signalling via interaction with IGFBPs. The expression levels of MMP1, MMP3, MMP9 and MMP13 mRNAs all showed major upregulation in the endometrium of cows in SNEB and all except MMP9 were highly correlated with expression of IGFBP4. Alpha(2)-HS-glycoprotein (AHSG) and PDK4, two genes implicated in insulin resistance, were also highly expressed in SNEB. These results suggest that cows in SNEB experience alterations to the IGF and insulin signalling pathways in the postpartum endometrium. This may affect the rate of tissue repair with a possible negative impact on subsequent fertility.

Evolution and applications of plant pathway resources and databases

Plants are important sources of food and plant products are essential for modern human life. Plants are increasingly gaining importance as drug and fuel resources, bioremediation tools and as tools for recombinant technology. Considering these applications, database infrastructure for plant model systems deserves much more attention. Study of plant biological pathways, the interconnection between these pathways and plant systems biology on the whole has in general lagged behind human systems biology. In this article we review plant pathway databases and the resources that are currently available. We lay out trends and challenges in the ongoing efforts to integrate plant pathway databases and the applications of database integration. We also discuss how progress in non-plant communities can serve as an example for the improvement of the plant pathway database landscape and thereby allow quantitative modeling of plant biosystems. We propose Good Database Practice as a possible model for collaboration and to ease future integration efforts.

Gene regulatory network reveals oxidative stress as the underlying molecular mechanism of type 2 diabetes and hypertension

Background

The prevalence of diabetes is increasing worldwide. It has been long known that increased rates of inflammatory diseases, such as obesity (OBS), hypertension (HT) and cardiovascular diseases (CVD) are highly associated with type 2 diabetes (T2D). T2D and/or OBS can develop independently, due to genetic, behavioral or lifestyle-related variables but both lead to oxidative stress generation. The underlying mechanisms by which theses complications arise and manifest together remain poorly understood. Protein-protein interactions regulate nearly every living process. Availability of high-throughput genomic data has enabled unprecedented views of gene and protein co-expression, co-regulations and interactions in cellular systems.

Methods

The present work, applied a systems biology approach to develop gene interaction network models, comprised of high throughput genomic and PPI data for T2D. The genes differentially regulated through T2D were 'mined' and their 'wirings' were studied to get a more complete understanding of the overall gene network topology and their role in disease progression.

Results

By analyzing the genes related to T2D, HT and OBS, a highly regulated gene-disease integrated network model has been developed that provides useful functional linkages among groups of genes and thus addressing how different inflammatory diseases are connected and propagated at genetic level. Based on the investigations around the 'hubs' that provided more meaningful insights about the cross-talk within gene-disease networks in terms of disease phenotype association with oxidative stress and inflammation, a hypothetical co-regulation disease mechanism model been proposed. The results from this study revealed that the oxidative stress mediated regulation cascade is the common mechanistic link among the pathogenesis of T2D, HT and other inflammatory diseases such as OBS.

Conclusion

The findings provide a novel comprehensive approach for understanding the pathogenesis of various co-associated chronic inflammatory diseases by combining the power of pathway analysis with gene regulatory network evaluation.

Microarray meta-analysis database (M(2)DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database

Background

Over the past decade, gene expression microarray studies have greatly expanded our knowledge of genetic mechanisms of human diseases. Meta-analysis of substantial amounts of accumulated data, by integrating valuable information from multiple studies, is becoming more important in microarray research. However, collecting data of special interest from public microarray repositories often present major practical problems. Moreover, including low-quality data may significantly reduce meta-analysis efficiency.

Results

M²DB is a human curated microarray database designed for easy querying, based on clinical information and for interactive retrieval of either raw or uniformly pre-processed data, along with a set of quality-control metrics. The database contains more than 10,000 previously published Affymetrix GeneChip arrays, performed using human clinical specimens. M²DB allows online querying according to a flexible combination of five clinical annotations describing disease state and sampling location. These annotations were manually curated by controlled vocabularies, based on information obtained from GEO, ArrayExpress, and published papers. For array-based assessment control, the online query provides sets of QC metrics, generated using three available QC algorithms. Arrays with poor data quality can easily be excluded from the query interface. The query provides values from two algorithms for gene-based filtering, and raw data and three kinds of pre-processed data for downloading.

Conclusion

M²DB utilizes a user-friendly interface for QC parameters, sample clinical annotations, and data formats to help users obtain clinical metadata. This database provides a lower entry threshold and an integrated process of meta-analysis. We hope that this research will promote further evolution of microarray meta-analysis.

Prioritizing genes for follow-up from genome wide association studies using information on gene expression in tissues relevant for type 2 diabetes mellitus

Background

Genome-wide association studies (GWAS) have emerged as a powerful approach for identifying susceptibility loci associated with polygenetic diseases such as type 2 diabetes mellitus (T2DM). However, it is still a daunting task to prioritize single nucleotide polymorphisms (SNPs) from GWAS for further replication in different population. Several recent studies have shown that genetic variation often affects gene-expression at proximal (cis) as well as distal (trans) genomic locations by different mechanisms such as altering rate of transcription or splicing or transcript stability.

Methods

To prioritize SNPs from GWAS, we combined results from two GWAS related to T2DM, the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC), with genome-wide expression data from pancreas, adipose tissue, liver and skeletal muscle of individuals with or without T2DM or animal models thereof to identify T2DM susceptibility loci.

Results

We identified 1,170 SNPs associated with T2DM with P < 0.05 in both GWAS and 243 genes that were located in the vicinity of these SNPs. Out of these 243 genes, we identified 115 differentially expressed in publicly available gene expression profiling data. Notably five of them, IGF2BP2, KCNJ11, NOTCH2, TCF7L2 and TSPAN8, have subsequently been shown to be associated with T2DM in different populations. To provide further validation of our approach, we reversed the approach and started with 26 known SNPs associated with T2DM and related traits. We could show that 12 (57%) (HHEX, HNF1B, IGF2BP2, IRS1, KCNJ11, KCNQ1, NOTCH2, PPARG, TCF7L2, THADA, TSPAN8 and WFS1) out of 21 genes located in vicinity of these SNPs were showing aberrant expression in T2DM from the gene expression profiling studies.

Conclusions

Utilizing of gene expression profiling data from different tissues of individuals with or without T2DM or animal models thereof is a powerful tool for prioritizing SNPs from WGAS for further replication studies.

Molecular networks for the study of TCM pharmacology

To target complex, multi-factorial diseases more effectively, there has been an emerging trend of multi-target drug development based on network biology, as well as an increasing interest in traditional Chinese medicine (TCM) that applies a more holistic treatment to diseases. Thousands of years' clinic practices in TCM have accumulated a considerable number of formulae that exhibit reliable in vivo efficacy and safety. However, the molecular mechanisms responsible for their therapeutic effectiveness are still unclear. The development of network-based systems biology has provided considerable support for the understanding of the holistic, complementary and synergic essence of TCM in the context of molecular networks. This review introduces available sources and methods that could be utilized for the network-based study of TCM pharmacology, proposes a workflow for network-based TCM pharmacology study, and presents two case studies on applying these sources and methods to understand the mode of action of TCM recipes.

Expression-based network biology identifies alteration in key regulatory pathways of type 2 diabetes and associated risk/complications

Type 2 diabetes mellitus (T2D) is a multifactorial and genetically heterogeneous disease which leads to impaired glucose homeostasis and insulin resistance. The advanced form of disease causes acute cardiovascular, renal, neurological and microvascular complications. Thus there is a constant need to discover new and efficient treatment against the disease by seeking to uncover various novel alternate signalling mechanisms that can lead to diabetes and its associated complications. The present study allows detection of molecular targets by unravelling their role in altered biological pathways during diabetes and its associated risk factors and complications. We have used an integrated functional networks concept by merging co-expression network and interaction network to detect the transcriptionally altered pathways and regulations involved in the disease. Our analysis reports four novel significant networks which could lead to the development of diabetes and other associated dysfunctions. (a) The first network illustrates the up regulation of TGFBRII facilitating oxidative stress and causing the expression of early transcription genes via MAPK pathway leading to cardiovascular and kidney related complications. (b) The second network demonstrates novel interactions between GAPDH and inflammatory and proliferation candidate genes i.e., SUMO4 and EGFR indicating a new link between obesity and diabetes. (c) The third network portrays unique interactions PTPN1 with EGFR and CAV1 which could lead to an impaired vascular function in diabetic nephropathy condition. (d) Lastly, from our fourth network we have inferred that the interaction of β-catenin with CDH5 and TGFBR1 through Smad molecules could contribute to endothelial dysfunction. A probability of emergence of kidney complication might be suggested in T2D condition. An experimental investigation on this aspect may further provide more decisive observation in drug target identification and better understanding of the pathophysiology of T2D and its complications.

Genetic and dietary effects on dendrites in the rat hypothalamic ventromedial nucleus

Both genetic and environmental factors contribute to individual differences in body weight regulation. The present study examined a possible role for the dendritic arbor of hypothalamic ventromedial nucleus (VMH) neurons in a model of diet-induced obesity (DIO) in male rats. Rats were screened and selectively bred for being either susceptible, i.e., exhibiting DIO, or diet resistant (DR) when exposed to a 31% fat diet. A 2x2 experimental design was used, based on these two strains of rats and exposure to rat chow versus the 31% fat diet for seven weeks. Golgi-impregnated neurons were measured for soma size and dendrite parameters, including number, length, and direction. As previously observed, each VMH neuron had a single long primary dendrite. Genetic background and diet did not affect soma size or the number of dendrites of VMH neurons. However, genetic background exerted a main effect on the length of the long primary dendrites. In particular, the long primary dendrites were approximately 12.5% shorter on the VMH neurons in the DIO rats compared with DR rats regardless of diet. This effect was isolated to the long primary dendrites extending in the dorsolateral direction, with these long primary dendrites 19% shorter for the DIO group compared with the DR group. This finding implicates the connectivity of the long primary dendrites on VMH neurons in the control of energy balance. The functional significance of these shortened dendrites and their afferents warrants further study.