Dataset Information

Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication.

ABSTRACT: Inter-tissue molecular interactions are critical to the function and behavior of biological systems in multicellular organisms, but systematic studies of interactions between tissues are lacking. Also, existing studies of inter-tissue interactions are based on direct gene expression correlations, which can't distinguish correlations due to common genetic architectures versus biochemical or molecular signal exchange between tissues.We developed a novel strategy to study inter-tissue interaction by removing effects of genetic regulation of gene expression (genetic decorrelation). We applied our method to the comprehensive atlas of gene expression across nine human tissues in the Genotype-Tissue Expression (GTEx) project to generate novel genetically decorrelated inter-tissue networks. From this we derived modules of genes important in inter-tissue interactions that are likely driven by biological signal exchange instead of their common genetic basis. Importantly we highlighted communication between tissues and elucidated gene activities in one tissue inducing gene expression changes in others.We reveal global unidirectional inter-tissue coordination of specific biological pathways such as protein synthesis. Using our data, we highlighted a clinically relevant example whereby heart expression of DPP4 was coordinated with a gene expression signature characteristic for whole blood proliferation, potentially impacting peripheral stem cell mobilization. We also showed that expression of the poorly characterized FOCAD in heart correlated with protein biosynthetic processes in the lung.In summary, this is the first resource of human multi-tissue networks enabling the investigation of molecular inter-tissue interactions. With the networks in hand, we may systematically design combination therapies that simultaneously target multiple tissues or pinpoint potential side effects of a drug in other tissues.

SUBMITTER: Long Q

PROVIDER: S-EPMC4746932 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication.

Long Quan Q Argmann Carmen C Houten Sander M SM Huang Tao T Peng Siwu S Zhao Yong Y Tu Zhidong Z Zhu Jun J

Genome medicine 20160209 1

<h4>Background</h4>Inter-tissue molecular interactions are critical to the function and behavior of biological systems in multicellular organisms, but systematic studies of interactions between tissues are lacking. Also, existing studies of inter-tissue interactions are based on direct gene expression correlations, which can't distinguish correlations due to common genetic architectures versus biochemical or molecular signal exchange between tissues.<h4>Methods</h4>We developed a novel strategy ...[more]

PMID: 26856537

Similar Datasets

Project description:BACKGROUND: Analysis of gene expression data using genome-wide microarrays is a technique often used in genomic studies to find coexpression patterns and locate groups of co-transcribed genes. However, most studies done at global "omic" scale are not focused on human samples and when they correspond to human very often include heterogeneous datasets, mixing normal with disease-altered samples. Moreover, the technical noise present in genome-wide expression microarrays is another well reported problem that many times is not addressed with robust statistical methods, and the estimation of errors in the data is not provided. METHODOLOGY/PRINCIPAL FINDINGS: Human genome-wide expression data from a controlled set of normal-healthy tissues is used to build a confident human gene coexpression network avoiding both pathological and technical noise. To achieve this we describe a new method that combines several statistical and computational strategies: robust normalization and expression signal calculation; correlation coefficients obtained by parametric and non-parametric methods; random cross-validations; and estimation of the statistical accuracy and coverage of the data. All these methods provide a series of coexpression datasets where the level of error is measured and can be tuned. To define the errors, the rates of true positives are calculated by assignment to biological pathways. The results provide a confident human gene coexpression network that includes 3327 gene-nodes and 15841 coexpression-links and a comparative analysis shows good improvement over previously published datasets. Further functional analysis of a subset core network, validated by two independent methods, shows coherent biological modules that share common transcription factors. The network reveals a map of coexpression clusters organized in well defined functional constellations. Two major regions in this network correspond to genes involved in nuclear and mitochondrial metabolism and investigations on their functional assignment indicate that more than 60% are house-keeping and essential genes. The network displays new non-described gene associations and it allows the placement in a functional context of some unknown non-assigned genes based on their interactions with known gene families. CONCLUSIONS/SIGNIFICANCE: The identification of stable and reliable human gene to gene coexpression networks is essential to unravel the interactions and functional correlations between human genes at an omic scale. This work contributes to this aim, and we are making available for the scientific community the validated human gene coexpression networks obtained, to allow further analyses on the network or on some specific gene associations. The data are available free online at http://bioinfow.dep.usal.es/coexpression/.

Project description:Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS-associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots (D(ABD-GLU)?=?0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response-related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS-associated versus un-associated modules (ABD: 0.48 versus 0.18, P?=?0.08; GLU: 0.54 versus 0.20, P?=?7.8×10(-4)). Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS-related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P?=?6.0×10(-4)); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P?=?8.7×10(-4)) and BMI-adjusted waist-to-hip ratio (P?=?2.4×10(-4)). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.

Dataset Information

Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication.

Publications

Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets