Differential Correlation Network Research Articles

Predicting and understanding the response to short-term intensive insulin therapy in people with early type 2 diabetes

ObjectiveShort-term intensive insulin therapy (IIT) early in the course of type 2 diabetes acutely improves beta-cell function with long-lasting effects on glycemic control. However, conventional measures cannot determine which patients are better suited for IIT, and little is known about the molecular mechanisms determining response. Therefore, this study aimed to develop a model that could accurately predict the response to IIT and provide insight into molecular mechanisms driving such response in humans. MethodsTwenty-four patients with early type 2 diabetes were assessed at baseline and four weeks after IIT, consisting of basal detemir and premeal insulin aspart. Twelve individuals had a beneficial beta-cell response to IIT (responders) and 12 did not (nonresponders). Beta-cell function was assessed by multiple methods, including Insulin Secretion-Sensitivity Index-2. MicroRNAs (miRNAs) were profiled in plasma samples before and after IIT. The response to IIT was modeled using a machine learning algorithm and potential miRNA-mediated regulatory mechanisms assessed by differential expression, correlation, and functional network analyses (FNA). ResultsBaseline levels of circulating miR-145-5p, miR-29c-3p, and HbA1c accurately (91.7%) predicted the response to IIT (OR = 121 [95% CI: 6.7, 2188.3]). Mechanistically, a previously described regulatory loop between miR-145-5p and miR-483-3p/5p, which controls TP53-mediated apoptosis, appears to also occur in our study population of humans with early type 2 diabetes. In addition, significant (fold change > 2, P < 0.05) longitudinal changes due to IIT in the circulating levels of miR-138-5p, miR-192-5p, miR-195-5p, miR-320b, and let-7a-5p further characterized the responder group and significantly correlated (|r| > 0.4, P < 0.05) with the changes in measures of beta-cell function and insulin sensitivity. FNA identified a network of coordinately/cooperatively regulated miRNA-targeted genes that potentially drives the IIT response through negative regulation of apoptotic processes that underlie beta cell dysfunction and concomitant positive regulation of proliferation. ConclusionsResponses to IIT in people with early type 2 diabetes are associated with characteristic miRNA signatures. This study represents a first step to identify potential responders to IIT (a current limitation in the field) and provides important insight into the pathophysiologic determinants of the reversibility of beta-cell dysfunction.ClinicalTrial.gov identifier: NCT01270789.

Niacin action in the atherogenic mixed dyslipidemia of metabolic syndrome: Insights from metabolic biomarker profiling and network analysis

Niacin as an adjunct to statin treatment to reduce cardiovascular risk is questioned. To evaluate interrelationships between the effects of niacin on mixed dyslipidemia and a spectrum of metabolic and inflammatory biomarkers. Obese, nondiabetic, hypertriglyceridemic males (n=19) with low high-density lipoprotein-cholesterol levels received extended-release nicotinic acid for 8weeks. Multiple biomarkers were measured using enzyme-linked immunosorbent assay, enzymatic/absorptiometric, or multiplex biochip assays. Treatment effects were determined for each variable and a differential correlation network created on the basis of univariate correlations between baseline and response to niacin treatment for all pairs of variables. Extended-release niacin treatment favoured normalization of plasma lipid and apolipoprotein profile. Plasma markers of inflammation, hepatic function, cellular adhesion and proliferation, and macrophage phenotype were attenuated; however, insulin resistance increased. Differential network analysis revealed that changes in triglycerides and high-density lipoprotein-cholesterol were closely linked; equally, niacin mediated reductions in total cholesterol, apolipoprotein B, low-density lipoprotein-cholesterol and lipoprotein(a) clustered together, as did homeostatic model assessment of insulin resistance, insulin, and interleukin-6 levels. Two clusters of inflammatory markers were identified, involving (1) intercellular adhesion molecule 1 and high-sensitive C-reactive protein and (2) soluble tumor necrosis factor receptors; and novel clusters involving matrix metallopeptidase 9 and apolipoprotein E, and adiponectin and cystatin C, respectively, were equally revealed. At lower stringency, lipid and insulin resistance clusters were linked; a C-reactive protein-centered cluster linked reduction in apolipoprotein CIII to intercellular adhesion molecule 1, gamma-glutamyltransferase, soluble tumor necrosis factor receptors, and E-selectin. A niacin-mediated trend to normalize atherogenic mixed dyslipidemia was intimately linked to attenuation of biomarkers of inflammation, cell adhesion, hepatic dysfunction and cell proliferation, but to enhanced insulin resistance and plasma homocysteine elevation.

Differential gene regulatory networks in development and disease.

Gene regulatory networks, in which differential expression of regulator genes induce differential expression of their target genes, underlie diverse biological processes such as embryonic development, organ formation and disease pathogenesis. An archetypical systems biology approach to mapping these networks involves the combined application of (1) high-throughput sequencing-based transcriptome profiling (RNA-seq) of biopsies under diverse network perturbations and (2) network inference based on gene-gene expression correlation analysis. The comparative analysis of such correlation networks across cell types or states, differential correlation network analysis, can identify specific molecular signatures and functional modules that underlie the state transition or have context-specific function. Here, we review the basic concepts of network biology and correlation network inference, and the prevailing methods for differential analysis of correlation networks. We discuss applications of gene expression network analysis in the context of embryonic development, cancer, and congenital diseases.

DGCA: A comprehensive R package for Differential Gene Correlation Analysis.

BackgroundDissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition.ResultsIn this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC).ConclusionsDGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0349-1) contains supplementary material, which is available to authorized users.

Identifying module biomarkers from gastric cancer by differential correlation network.

Gastric cancer (stomach cancer) is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer.

Abstract B2-43: Bioinformatics analysis of TCGA tumors to identify cancer associated epigenetic regulators

Abstract Many cancer cells show distorted epigenetic landscapes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors, allowing the discovery of somatic alterations in the epigenetic machinery and the identification of potential cancer drivers among members of epigenetic protein families. We integrated mutation, expression, and copy number data from thousands of tumors from major cancer types to train a classification model that predicts the likelihood of being an oncogene (OG), tumor suppressor (TSG) or neutral gene (NG). Using classical driver genes to train an OG/TSG predictor, we determined mutation parameters as the most predictive features. We applied this predictor to epigenetic regulator genes (ERGs), and used differential expression and correlation network analysis to identify dysregulated ERGs along with co-expressed cancer genes. Mutation-based classifiers uncovered the TSG-like profiles for several ERGs, but did not detect any OG-like profile. Differential gene expression and correlation network analyses revealed that EZH2 is the most significantly over-expressed ERG in cancer and is co-regulated with a cell cycle network. Furthermore we quantified global proteomic changes by mass spectrometry after EZH2 inhibition. Proteomic analysis showed that EZH2 inhibition induced down-regulation of cell cycle regulators in lymphoma cells. We have now expanded our analysis to other types of proteins including kinases and secreted proteins. Note: This abstract was not presented at the conference. Citation Format: Florian Gnad, Zemin Zhang. Bioinformatics analysis of TCGA tumors to identify cancer associated epigenetic regulators. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-43.

Evidence of perturbations of the cytokine network in preterm labor

Intraamniotic inflammation/infection is the only mechanism of disease with persuasive evidence of causality for spontaneous preterm labor/delivery. Previous studies about the behavior of cytokines in preterm labor have been largely based on the analysis of the behavior of each protein independently. Emerging evidence indicates that the study of biologic networks can provide insight into the pathobiology of disease and improve biomarker discovery. The goal of this study was to characterize the inflammatory-related protein network in the amniotic fluid of patients with preterm labor. A retrospective cohort study was conducted that included women with singleton pregnancies who had spontaneous preterm labor and intact membranes (n = 135). These patients were classified according to the results of amniotic fluid culture, broad-range polymerase chain reaction coupled with electrospray ionization mass spectrometry, and amniotic fluid concentration of interleukin (IL)-6 into the following groups: (1) those without intraamniotic inflammation (n = 85), (2) those with microbial-associated intraamniotic inflammation (n = 15), and (3) those with intraamniotic inflammation without detectable bacteria (n = 35). Amniotic fluid concentrations of 33 inflammatory-related proteins were determined with the use of a multiplex bead array assay. Patients with preterm labor and intact membranes who had microbial-associated intraamniotic inflammation had a higher amniotic fluid inflammatory-related protein concentration correlation than those without intraamniotic inflammation (113 perturbed correlations). IL-1β, IL-6, macrophage inflammatory protein (MIP)-1α, and IL-1α were the most connected nodes (highest degree) in this differential correlation network (degrees of 20, 16, 12, and 12, respectively). Patients with sterile intraamniotic inflammation had correlation patterns of inflammatory-related proteins, both increased and decreased, when compared to those without intraamniotic inflammation (50 perturbed correlations). IL-1α, MIP-1α, and IL-1β were the most connected nodes in this differential correlation network (degrees of 12, 10, and 7, respectively). There were more coordinated inflammatory-related protein concentrations in the amniotic fluid of women with microbial-associated intraamniotic inflammation than in those with sterile intraamniotic inflammation (60 perturbed correlations), with IL-4 and IL-33 having the largest number of perturbed correlations (degrees of 15 and 13, respectively). We report for the first time an analysis of the inflammatory-related protein network in spontaneous preterm labor. Patients with preterm labor and microbial-associated intraamniotic inflammation had more coordinated amniotic fluid inflammatory-related proteins than either those with sterile intraamniotic inflammation or those without intraamniotic inflammation. The correlations were also stronger in patients with sterile intraamniotic inflammation than in those without intraamniotic inflammation. The findings herein could be of value in the development of biomarkers of preterm labor.

Bioinformatics analysis of thousands of TCGA tumors to determine the involvement of epigenetic regulators in human cancer

BackgroundMany cancer cells show distorted epigenetic landscapes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors, allowing the discovery of somatic alterations in the epigenetic machinery and the identification of potential cancer drivers among members of epigenetic protein families.MethodsWe integrated mutation, expression, and copy number data from 5943 tumors from 13 cancer types to train a classification model that predicts the likelihood of being an oncogene (OG), tumor suppressor (TSG) or neutral gene (NG). We applied this predictor to epigenetic regulator genes (ERGs), and used differential expression and correlation network analysis to identify dysregulated ERGs along with co-expressed cancer genes. Furthermore, we quantified global proteomic changes by mass spectrometry after EZH2 inhibition.ResultsMutation-based classifiers uncovered the OG-like profile of DNMT3A and TSG-like profiles for several ERGs. Differential gene expression and correlation network analyses revealed that EZH2 is the most significantly over-expressed ERG in cancer and is co-regulated with a cell cycle network. Proteomic analysis showed that EZH2 inhibition induced down-regulation of cell cycle regulators in lymphoma cells.ConclusionsUsing classical driver genes to train an OG/TSG predictor, we determined the most predictive features at the gene level. Our predictor uncovered one OG and several TSGs among ERGs. Expression analyses elucidated multiple dysregulated ERGs including EZH2 as member of a co-expressed cell cycle network.

Integrative genomics identifies APOE ε4 effectors in Alzheimer's disease

Late-onset Alzheimer's disease (LOAD) risk is strongly influenced by genetic factors such as the presence of the apolipoprotein E ε4 allele (referred to here as APOE4), as well as non-genetic determinants including ageing. To pursue mechanisms by which these affect human brain physiology and modify LOAD risk, we initially analysed whole-transcriptome cerebral cortex gene expression data in unaffected APOE4 carriers and LOAD patients. APOE4 carrier status was associated with a consistent transcriptomic shift that broadly resembled the LOAD profile. Differential co-expression correlation network analysis of the APOE4 and LOAD transcriptomic changes identified a set of candidate core regulatory mediators. Several of these--including APBA2, FYN, RNF219 and SV2A--encode known or novel modulators of LOAD associated amyloid beta A4 precursor protein (APP) endocytosis and metabolism. Furthermore, a genetic variant within RNF219 was found to affect amyloid deposition in human brain and LOAD age-of-onset. These data implicate an APOE4 associated molecular pathway that promotes LOAD.

A study of Caenorhabditis elegans DAF-2 mutants by metabolomics and differential correlation networks

daf-2 is one of the most studied mutants in C. elegans: it contains a deletion in the gene orthologue of the insulin/insulin-like growth factor (IGF) receptor. Using high resolution (1)H NMR spectroscopy, metabolomics has helped to dissect the metabolic consequences of altered daf-2 signalling. Here, we present a detailed metabolomic analysis of daf-2, using NMR spectroscopy, gas chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) to integrate information from different pathways. We have then used Pearson and partial correlation analysis to build networks to explore the central role of daf-2 in regulating fatty acid and amino acid metabolism. The results show the tight regulation between these two parts of the metabolome.