Integration of disease-specific single nucleotide polymorphisms, expression quantitative trait loci and coexpression networks reveal novel candidate genes for type 2 diabetes

Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study

An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype–network–phenotype associations and the underlying network traits or network signatures with functional impact and importance.

Peroxiredoxins and Immune Infiltrations in Colon Adenocarcinoma: Their Negative Correlations and Clinical Significances, an In Silico Analysis

Background: Peroxiredoxins (PRDXs) were reported to be associated with inflammation response in previous studies. In colon adenocarcinoma (COAD), however, their correlations and clinical significance were unclear.

Methods: The RNA-seq data of 452 COAD patients with clinical information was downloaded from The Cancer Genome Atlas (TCGA) and transcripts per million (TPM) normalized. Comparisons of relative expressions of PRDXs between COAD tumor and normal controls were applied. PRDXs dy-regulations in COAD were validated via Oncomine, Human Protein Atlas (HPA) and Gene Expression Omnibus (GEO) repository. Through Tumor Immune Estimation Resource (TIMER), the immune estimation of TCGA-COAD patients was downloaded and the dy-regulated PRDXs were analyzed for their correlations with immune infiltrations in COAD. The TCGA-COAD patients were divided into younger group (age≤65 years) and older group (age>65 years) to investigate the prognostic roles of age, TNM stage, dy-regulated PRDXs and the immune infiltrations in different age groups through Kaplan-Meier survival and Cox regression analyses.

Results: Three of the PRDX members showed their expressional differences both at protein and mRNA level. PRDX2 was consistently up-regulated while PRDX6 down-regulated in COAD. PRDX1 was overexpressed (mRNA) while nuclear absent (protein) in the tumor tissues. PRDX1 overexpression and PRDX6 under-expression were also shown in the stem-like colonospheres from colon cancer cells. Via TIMER, PRDX1, PRDX2, and PRDX6 were found to be negatively correlated with the immune infiltrations in COAD. Both in the younger and older patients, TNM stage had prognostic effects on their overall survival (OS) and recurrence-free survival (RFS). CD4⁺ T cell had independent unfavorable effects on OS of the younger patients while age had similar effects on RFS of the older ones. CD8⁺ T cell was independently prognostic for RFS in the two groups.

Conclusions: Late diagnosis indicated poor prognosis in COAD and dy-regulated PRDXs w might be new markers for its early diagnosis. Age was prognostic and should be considered in the treatments of the older patients. Dy-regulated PRDXs were negatively correlated with immune infiltration levels. CD4⁺ T cell and CD8⁺ T cell infiltrations were prognostic in COAD and their potential as immune targets needed further investigation.

Increased Expression of Lipid Metabolism Genes in Early Stages of Wooden Breast Links Myopathy of Broilers to Metabolic Syndrome in Humans

Wooden breast is a muscle disorder affecting modern commercial broiler chickens that causes a palpably firm pectoralis major muscle and severe reduction in meat quality. Most studies have focused on advanced stages of wooden breast apparent at market age, resulting in limited insights into the etiology and early pathogenesis of the myopathy. Therefore, the objective of this study was to identify early molecular signals in the wooden breast transcriptional cascade by performing gene expression analysis on the pectoralis major muscle of two-week-old birds that may later exhibit the wooden breast phenotype by market age at 7 weeks. Biopsy samples of the left pectoralis major muscle were collected from 101 birds at 14 days of age. Birds were subsequently raised to 7 weeks of age to allow sample selection based on the wooden breast phenotype at market age. RNA-sequencing was performed on 5 unaffected and 8 affected female chicken samples, selected based on wooden breast scores (0 to 4) assigned at necropsy where affected birds had scores of 2 or 3 (mildly or moderately affected) while unaffected birds had scores of 0 (no apparent gross lesions). Differential expression analysis identified 60 genes found to be significant at an FDR-adjusted p-value of 0.05. Of these, 26 were previously demonstrated to exhibit altered expression or genetic polymorphisms related to glucose tolerance or diabetes mellitus in mammals. Additionally, 9 genes have functions directly related to lipid metabolism and 11 genes are associated with adiposity traits such as intramuscular fat and body mass index. This study suggests that wooden breast disease is first and foremost a metabolic disorder characterized primarily by ectopic lipid accumulation in the pectoralis major.

Inhibin βE (INHBE) is a possible insulin resistance-associated hepatokine identified by comprehensive gene expression analysis in human liver biopsy samples

The liver plays a major role in whole-body energy homeostasis by releasing secretory factors, termed hepatokines. To identify novel target genes associated with insulin resistance, we performed a comprehensive analysis of gene expression profiles using a DNA chip method in liver biopsy samples from humans with varying degrees of insulin resistance. Inhibin βE (INHBE) was identified as a novel putative hepatokine with hepatic gene expression that positively correlated with insulin resistance and body mass index in humans. Quantitative real time-PCR analysis also showed an increase in INHBE gene expression in independent liver samples from insulin-resistant human subjects. Additionally, Inhbe gene expression increased in the livers of db/db mice, a rodent model of type 2 diabetes. To preliminarily screen the role of Inhbe in vivo in whole-body energy metabolic status, hepatic mRNA was knocked down with siRNA for Inhbe (siINHBE) in db/db mice. Treatment with siINHBE suppressed body weight gain during the two-week experimental period, which was attributable to diminished fat rather than lean mass. Additionally, treatment with siINHBE decreased the respiratory quotient and increased plasma total ketone bodies compared with treatment with non-targeting siRNA, both of which suggest enhanced whole-body fat utilization. Our study suggests that INHBE functions as a possible hepatokine to alter the whole-body metabolic status under obese insulin-resistant conditions.

Gene Expression Meta-Analysis of Seven Candidate Gene Sets for Diabetes Traits Following a GWAS Pathway Study

Seven gene sets were significantly enriched for SNP associations with diabetes, and considered as potential diabetes pathways in a previous meta-analysis of diabetes GWAS. This study aims to examine if these gene sets also have expression associations with diabetes. The analysis was conducted using pooled data from 23 diabetes gene expression studies. Gene associations were examined using linear modeling with an empirical Bayes approach, and pathway associations were investigated by testing enrichment for significant genes. Meta-analyses were performed to investigate gene and pathway associations in all studies and tissue types. The analysis showed that six gene sets and three member genes of ACADSB, RASSF2, and KLF12 had significant associations with diabetes traits. The findings suggest that these gene sets are related to diabetes regulation, and their functions tend to be tissue non-specific.

Consensus Genome-Wide Expression Quantitative Trait Loci and Their Relationship with Human Complex Trait Disease

Most of the risk loci identified from genome-wide association (GWA) studies do not provide direct information on the biological basis of a disease or on the underlying mechanisms. Recent expression quantitative trait locus (eQTL) association studies have provided information on genetic factors associated with gene expression variation. These eQTLs might contribute to phenotype diversity and disease susceptibility, but interpretation is handicapped by low reproducibility of the expression results. To address this issue, we have generated a set of consensus eQTLs by integrating publicly available data for specific human populations and cell types. Overall, we find over 4000 genes that are involved in high-confidence eQTL relationships. To elucidate the role that eQTLs play in human common diseases, we matched the high-confidence eQTLs to a set of 335 disease risk loci identified from the Wellcome Trust Case Control Consortium GWA study and follow-up studies for 7 human complex trait diseases-bipolar disorder (BD), coronary artery disease (CAD), Crohn's disease (CD), hypertension (HT), rheumatoid arthritis (RA), type 1 diabetes (T1D), and type 2 diabetes (T2D). The results show that the data are consistent with ∼50% of these disease loci arising from an underlying expression change mechanism.

Voltage-gated calcium channels: Novel targets for cancer therapy

Voltage-gated calcium channels (VGCCs) comprise five subtypes: The L-type; R-type; N-type; P/Q-type; and T-type, which are encoded by α₁ subunit genes. Calcium ion channels also have confirmed roles in cellular functions, including mitogenesis, proliferation, differentiation, apoptosis and metastasis. An association between VGCCs, a reduction in proliferation and an increase in apoptosis in prostate cancer cells has also been reported. Therefore, in the present study, the online clinical database Oncomine was used to identify the alterations in the mRNA expression level of VGCCs in 19 cancer subtypes. Overall, VGCC family genes exhibited under-expression in numerous types of cancer, including brain, breast, kidney and lung cancers. Notably, the majority of VGCC family members (CACNA1C, CACNA1D, CACNA1A, CACNA1B, CACNA1E, CACNA1H and CACNA1I) exhibited low expression in brain tumors, with mRNA expression levels in the top 1–9% of downregulated gene rankings. A total of 5 VGCC family members (CACNA1A, CACNA1B, CACNA1E, CACNA1G and CACNA1I) were under-expressed in breast cancer, with a gene ranking in the top 1–10% of the low-expressed genes compared with normal tissue. In kidney and lung cancers, CACNA1S, CACNA1C, CACNA1D, CACNA1A and CACNA1H exhibited low expression, with gene rankings in the top 1–8% of downregulated genes. In conclusion, the present findings may contribute to the development of new cancer treatment approaches by identifying target genes involved in specific types of cancer.

Functional regression method for whole genome eQTL epistasis analysis with sequencing data

Background

Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges.

Methods

We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions.

Results

By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction identified using FRGM, RPKM and DESeq were 16,2361, 260 and 51, respectively, from the 350 European samples.

Conclusions

The proposed FRGM for epistasis analysis of RNA-seq can capture isoform and position-level information and will have a broad application. Both simulations and real data analysis highlight the potential for the FRGM to be a good choice of the epistatic analysis with sequencing data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3777-4) contains supplementary material, which is available to authorized users.

The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus

BACKGROUND

The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation.

RESULTS

We validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine tumor necrosis factor alpha (TNFα) promote pathogenesis, presumably through excessive inflammation.

CONCLUSIONS

The current study provides validation of network modeling approaches for identifying important players in virus infection pathogenesis, and a step forward in understanding the host response to an important infectious disease. The results presented here suggest the role of Kepi in the host response to SARS-CoV, as well as inflammatory activity driving pathogenesis through TNFα signaling in SARS-CoV infections. Though we have reported the utility of this approach in bacterial and cell culture studies previously, this is the first comprehensive study to confirm that network topology can be used to predict phenotypes in mice with experimental validation.

The sequenced rat brain transcriptome--its use in identifying networks predisposing alcohol consumption

A quantitative genetic approach, which involves correlation of transcriptional networks with the phenotype in a recombinant inbred (RI) population and in selectively bred lines of rats, and determination of coinciding quantitative trait loci for gene expression and the trait of interest, has been applied in the present study. In this analysis, a novel approach was used that combined DNA-Seq data, data from brain exon array analysis of HXB/BXH RI rat strains and six pairs of rat lines selectively bred for high and low alcohol preference, and RNA-Seq data (including rat brain transcriptome reconstruction) to quantify transcript expression levels, generate co-expression modules and identify biological functions that contribute to the predisposition of consuming varying amounts of alcohol. A gene co-expression module was identified in the RI rat strains that contained both annotated and unannotated transcripts expressed in the brain, and was associated with alcohol consumption in the RI panel. This module was found to be enriched with differentially expressed genes from the selected lines of rats. The candidate genes within the module and differentially expressed genes between high and low drinking selected lines were associated with glia (microglia and astrocytes) and could be categorized as being related to immune function, energy metabolism and calcium homeostasis, as well as glial-neuronal communication. The results of the present study show that there are multiple combinations of genetic factors that can produce the same phenotypic outcome. Although no single gene accounts for predisposition to a particular level of alcohol consumption in every animal model, coordinated differential expression of subsets of genes in the identified pathways produce similar phenotypic outcomes.

DATABASE

The datasets supporting the results of the present study are available at http://phenogen.ucdenver.edu.

Pharmacogenomics of Drug Response in Type 2 Diabetes: Toward the Definition of Tailored Therapies?

Type 2 diabetes is one of the major causes of mortality with rapidly increasing prevalence. Pharmacological treatment is the first recommended approach after failure in lifestyle changes. However, a significant number of patients shows-or develops along time and disease progression-drug resistance. In addition, not all type 2 diabetic patients have the same responsiveness to drug treatment. Despite the presence of nongenetic factors (hepatic, renal, and intestinal), most of such variability is due to genetic causes. Pharmacogenomics studies have described association between single nucleotide variations and drug resistance, even though there are still conflicting results. To date, the most reliable approach to investigate allelic variants is Next-Generation Sequencing that allows the simultaneous analysis, on a genome-wide scale, of nucleotide variants and gene expression. Here, we review the relationship between drug responsiveness and polymorphisms in genes involved in drug metabolism (CYP2C9) and insulin signaling (ABCC8, KCNJ11, and PPARG). We also highlight the advancements in sequencing technologies that to date enable researchers to perform comprehensive pharmacogenomics studies. The identification of allelic variants associated with drug resistance will constitute a solid basis to establish tailored therapeutic approaches in the treatment of type 2 diabetes.

Integrative network analysis reveals different pathophysiological mechanisms of insulin resistance among Caucasians and African Americans

Background

African Americans (AA) have more pronounced insulin resistance and higher insulin secretion than European Americans (Caucasians or CA) when matched for age, gender, and body mass index (BMI). We hypothesize that physiological differences (including insulin sensitivity [S_I]) between CAs and AAs can be explained by co-regulated gene networks in tissues involved in glucose homeostasis.

Methods

We performed integrative gene network analyses of transcriptomic data in subcutaneous adipose tissue of 99 CA and 37 AA subjects metabolically characterized as non-diabetic, with a range of S_I and BMI values.

Results

Transcripts negatively correlated with S_I in only the CA or AA subjects were enriched for inflammatory response genes and integrin-signaling genes, respectively. A sub-network (module) with TYROBP as a hub enriched for genes involved in inflammatory response (corrected p = 1.7E-26) was negatively correlated with S_I (r = −0.426, p = 4.95E-04) in CA subjects. S_I was positively correlated with transcript modules enriched for mitochondrial metabolism in both groups. Several S_I-associated co-expressed modules were enriched for genes differentially expressed between groups. Two modules involved in immune response to viral infections and function of adherens junction, are significantly correlated with S_I only in CAs. Five modules involved in drug/intracellular transport and oxidoreductase activity, among other activities, are correlated with S_I only in AAs. Furthermore, we identified driver genes of these race-specific S_I-associated modules.

Conclusions

S_I-associated transcriptional networks that were deranged predominantly in one ethnic group may explain the distinctive physiological features of glucose homeostasis among AA subjects.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0078-0) contains supplementary material, which is available to authorized users.

Genetic variation at glucose and insulin trait loci and response to glucose-insulin-potassium (GIK) therapy: the IMMEDIATE trial

The mechanistic effects of intravenous glucose, insulin and potassium (GIK) in cardiac ischemia are not well understood. We conducted a genetic sub-study of the Immediate Myocardial Metabolic Enhancement During Initial Assessment and Treatment in Emergency care (IMMEDIATE) Trial to explore effects of common and rare glucose and insulin-related genetic loci on initial to 6-h and 6- to 12-h change in plasma glucose and potassium. We identified 27 NOTCH2/ADAM30 and 8 C2CD4B variants conferring a 40-57% increase in glucose during the first 6 h of infusion (P<5.96 × 10(-6)). Significant associations were also found for ABCB11 and SLC30A8 single-nucleotide polymorphisms (SNPs) and glucose responses, and an SEC61A2 SNP with a potassium response to GIK. These studies identify genetic factors that may impact the metabolic response to GIK, which could influence treatment benefits in the setting of acute coronary syndromes (ACS).

Integrated genome-wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis

BACKGROUND

Allergic rhinitis is a common disease whose genetic basis is incompletely explained. We report an integrated genomic analysis of allergic rhinitis.

METHODS

We performed genome wide association studies (GWAS) of allergic rhinitis in 5633 ethnically diverse North American subjects. Next, we profiled gene expression in disease-relevant tissue (peripheral blood CD4+ lymphocytes) collected from subjects who had been genotyped. We then integrated the GWAS and gene expression data using expression single nucleotide (eSNP), coexpression network, and pathway approaches to identify the biologic relevance of our GWAS.

RESULTS

GWAS revealed ethnicity-specific findings, with 4 genome-wide significant loci among Latinos and 1 genome-wide significant locus in the GWAS meta-analysis across ethnic groups. To identify biologic context for these results, we constructed a coexpression network to define modules of genes with similar patterns of CD4+ gene expression (coexpression modules) that could serve as constructs of broader gene expression. 6 of the 22 GWAS loci with P-value ≤ 1x10-6 tagged one particular coexpression module (4.0-fold enrichment, P-value 0.0029), and this module also had the greatest enrichment (3.4-fold enrichment, P-value 2.6 × 10-24) for allergic rhinitis-associated eSNPs (genetic variants associated with both gene expression and allergic rhinitis). The integrated GWAS, coexpression network, and eSNP results therefore supported this coexpression module as an allergic rhinitis module. Pathway analysis revealed that the module was enriched for mitochondrial pathways (8.6-fold enrichment, P-value 4.5 × 10-72).

CONCLUSIONS

Our results highlight mitochondrial pathways as a target for further investigation of allergic rhinitis mechanism and treatment. Our integrated approach can be applied to provide biologic context for GWAS of other diseases.

Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease

The majority of the heritability of coronary artery disease (CAD) remains unexplained, despite recent successes of genome-wide association studies (GWAS) in identifying novel susceptibility loci. Integrating functional genomic data from a variety of sources with a large-scale meta-analysis of CAD GWAS may facilitate the identification of novel biological processes and genes involved in CAD, as well as clarify the causal relationships of established processes. Towards this end, we integrated 14 GWAS from the CARDIoGRAM Consortium and two additional GWAS from the Ottawa Heart Institute (25,491 cases and 66,819 controls) with 1) genetics of gene expression studies of CAD-relevant tissues in humans, 2) metabolic and signaling pathways from public databases, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments. We not only detected CAD-associated gene networks of lipid metabolism, coagulation, immunity, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network based on the topology of the gene regulatory networks. In particular, we found a gene network involved in antigen processing to be strongly associated with CAD. The key driver genes of this network included glyoxalase I (GLO1) and peptidylprolyl isomerase I (PPIL1), which we verified as regulatory by siRNA experiments in human aortic endothelial cells. Our results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk. The key driver genes for these networks highlight potential novel targets for further mechanistic studies and therapeutic interventions.

Sudden death due to heart attack ranks among the top causes of death in the world, and family studies have shown that genetics has a substantial effect on heart disease risk. Recent studies suggest that multiple genetic factors each with modest effects are necessary for the development of CAD, but the genes and molecular processes involved remain poorly understood. We conducted an integrative genomics study where we used the information of gene-gene interactions to capture groups of genes that are most likely to increase heart disease risk. We not only confirmed the importance of several known CAD risk processes such as the metabolism and transport of cholesterol, immune response, and blood coagulation, but also revealed many novel processes such as neuroprotection, cell cycle, and proteolysis that were not previously implicated in CAD. In particular, we highlight several genes such as GLO1 with key regulatory roles within these processes not detected by the first wave of genetic analyses. These results highlight the value of integrating population genetic data with diverse resources that functionally annotate the human genome. Such integration facilitates the identification of novel molecular processes involved in the pathogenesis of CAD as well as potential novel targets for the development of efficacious therapeutic interventions.

Expression quantitative trait analyses to identify causal genetic variants for type 2 diabetes susceptibility

Type 2 diabetes (T2D) is a common metabolic disorder which is caused by multiple genetic perturbations affecting different biological pathways. Identifying genetic factors modulating the susceptibility of this complex heterogeneous metabolic phenotype in different ethnic and racial groups remains challenging. Despite recent success, the functional role of the T2D susceptibility variants implicated by genome-wide association studies (GWAS) remains largely unknown. Genetic dissection of transcript abundance or expression quantitative trait (eQTL) analysis unravels the genomic architecture of regulatory variants. Availability of eQTL information from tissues relevant for glucose homeostasis in humans opens a new avenue to prioritize GWAS-implicated variants that may be involved in triggering a causal chain of events leading to T2D. In this article, we review the progress made in the field of eQTL research and knowledge gained from those studies in understanding transcription regulatory mechanisms in human subjects. We highlight several novel approaches that can integrate eQTL analysis with multiple layers of biological information to identify ethnic-specific causal variants and gene-environment interactions relevant to T2D pathogenesis. Finally, we discuss how the eQTL analysis mediated search for “missing heritability” may lead us to novel biological and molecular mechanisms involved in susceptibility to T2D.

Network analysis: a new approach to study endocrine disorders

Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.

Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data

Posttraumatic stress disorder (PTSD) and other deployment-related outcomes originate from a complex interplay between constellations of changes in DNA, environmental traumatic exposures, and other biological risk factors. These factors affect not only individual genes or bio-molecules but also the entire biological networks that in turn increase or decrease the risk of illness or affect illness severity. This review focuses on recent developments in the field of systems biology which use multidimensional data to discover biological networks affected by combat exposure and post-deployment disease states. By integrating large-scale, high-dimensional molecular, physiological, clinical, and behavioral data, the molecular networks that directly respond to perturbations that can lead to PTSD can be identified and causally associated with PTSD, providing a path to identify key drivers. Reprogrammed neural progenitor cells from fibroblasts from PTSD patients could be established as an in vitro assay for high throughput screening of approved drugs to determine which drugs reverse the abnormal expression of the pathogenic biomarkers or neuronal properties.

eQTL Epistasis - Challenges and Computational Approaches

The determination of expression quantitative trait loci (eQTL) epistasis – a form of functional interaction between genetic loci that affect gene expression – is an important step toward the thorough understanding of gene regulation. Since gene expression has emerged as an “intermediate” molecular phenotype eQTL epistasis might help to explain the relationship between genotype and higher level organismal phenotypes such as diseases. A characteristic feature of eQTL analysis is the big number of tests required to identify associations between gene expression and genetic loci variability. This problem is aggravated, when epistatic effects between eQTLs are analyzed. In this review, we discuss recent algorithmic approaches for the detection of eQTL epistasis and highlight lessons that can be learned from current methods.

Bridging the Gap between Genotype and Phenotype via Network Approaches

In the last few years we have witnessed tremendous progress in detecting associations between genetic variations and complex traits. While genome-wide association studies have been able to discover genomic regions that may influence many common human diseases, these discoveries created an urgent need for methods that extend the knowledge of genotype-phenotype relationships to the level of the molecular mechanisms behind them. To address this emerging need, computational approaches increasingly utilize a pathway-centric perspective. These new methods often utilize known or predicted interactions between genes and/or gene products. In this review, we survey recently developed network based methods that attempt to bridge the genotype-phenotype gap. We note that although these methods help narrow the gap between genotype and phenotype relationships, these approaches alone cannot provide the precise details of underlying mechanisms and current research is still far from closing the gap.

Alternative Approaches in Gene Discovery and Characterization in Alzheimer's Disease

Uncovering the genetic risk and protective factors for complex diseases is of fundamental importance for advancing therapeutic and biomarker discoveries. This endeavor is particularly challenging for neuropsychiatric diseases where diagnoses predominantly rely on the clinical presentation, which may be heterogeneous, possibly due to the heterogeneity of the underlying genetic susceptibility factors and environmental exposures. Although genome-wide association studies of various neuropsychiatric diseases have recently identified susceptibility loci, there likely remain additional genetic risk factors that underlie the liability to these conditions. Furthermore, identification and characterization of the causal risk variant(s) in each of these novel susceptibility loci constitute a formidable task, particularly in the absence of any prior knowledge about their function or mechanism of action. Biologically relevant, quantitative phenotypes, i.e., endophenotypes, provide a powerful alternative to the more traditional, binary disease phenotypes in the discovery and characterization of susceptibility genes for neuropsychiatric conditions. In this review, we focus on Alzheimer's disease (AD) as a model neuropsychiatric disease and provide a synopsis of the recent literature on the use of endophenotypes in AD genetics. We highlight gene expression, neuropathology and cognitive endophenotypes in AD, with examples demonstrating the utility of these alternative approaches in the discovery of novel susceptibility genes and pathways. In addition, we discuss how these avenues generate testable hypothesis about the pathophysiology of genetic factors that have far-reaching implications for therapies.

Systems Biology Approaches and Applications in Obesity, Diabetes, and Cardiovascular Diseases

The metabolically connected triad of obesity, diabetes, and cardiovascular diseases is a major public health threat, and is expected to worsen due to the global shift toward energy-rich and sedentary living. Despite decades of intense research, a large part of the molecular pathogenesis behind complex metabolic diseases remains unknown. Recent advances in genetics, epigenomics, transcriptomics, proteomics and metabolomics enable us to obtain large-scale snapshots of the etiological processes in multiple disease-related cells, tissues and organs. These datasets provide us with an opportunity to go beyond conventional reductionist approaches and to pinpoint the specific perturbations in critical biological processes. In this review, we summarize systems biology methodologies such as functional genomics, causality inference, data-driven biological network construction, and higher-level integrative analyses that can produce novel mechanistic insights, identify disease biomarkers, and uncover potential therapeutic targets from a combination of omics datasets. Importantly, we also demonstrate the power of these approaches by application examples in obesity, diabetes, and cardiovascular diseases.

Identifying candidate genes for Type 2 Diabetes Mellitus and obesity through gene expression profiling in multiple tissues or cells

Type 2 Diabetes Mellitus (T2DM) and obesity have become increasingly prevalent in recent years. Recent studies have focused on identifying causal variations or candidate genes for obesity and T2DM via analysis of expression quantitative trait loci (eQTL) within a single tissue. T2DM and obesity are affected by comprehensive sets of genes in multiple tissues. In the current study, gene expression levels in multiple human tissues from GEO datasets were analyzed, and 21 candidate genes displaying high percentages of differential expression were filtered out. Specifically, DENND1B, LYN, MRPL30, POC1B, PRKCB, RP4-655J12.3, HIBADH, and TMBIM4 were identified from the T2DM-control study, and BCAT1, BMP2K, CSRNP2, MYNN, NCKAP5L, SAP30BP, SLC35B4, SP1, BAP1, GRB14, HSP90AB1, ITGA5, and TOMM5 were identified from the obesity-control study. The majority of these genes are known to be involved in T2DM and obesity. Therefore, analysis of gene expression in various tissues using GEO datasets may be an effective and feasible method to determine novel or causal genes associated with T2DM and obesity.

Epigenetic mechanisms and the development of asthma

Asthma is heritable, influenced by the environment, and modified by in utero exposures and aging; all of these features are also common to epigenetic regulation. Furthermore, the transcription factors that are involved in the development of mature T cells that are critical to the T(H)2 immune phenotype in asthmatic patients are regulated by epigenetic mechanisms. Epigenetic marks (DNA methylation, modifications of histone tails, and noncoding RNAs) work in concert with other components of the cellular regulatory machinery to control the spatial and temporal levels of expressed genes. Technology to measure epigenetic marks on a genomic scale and comprehensive approaches to data analysis have recently emerged and continue to improve. Alterations in epigenetic marks have been associated with exposures relevant to asthma, particularly air pollution and tobacco smoke, as well as asthma phenotypes, in a few population-based studies. On the other hand, animal studies have begun to decipher the role of epigenetic regulation of gene expression associated with the development of allergic airway disease. Epigenetic mechanisms represent a promising line of inquiry that might, in part, explain the inheritance and immunobiology of asthma.