ArticleGenome biology2009
Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease.
Article in Genome biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 2 of them syntheses that pooled it.
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95 citing papers in PubMed, 2 syntheses or guidelines pooled it, 165 citations in OpenAlex.
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- Disease signatures are robust across tissues and experiments.Molecular systems biology · 2009Pooled it
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- Switch-like gene expression modulates disease risk.Nature communications · 2025Article
- A computational framework for detecting inter-tissue gene-expression coordination changes with aging.Scientific reports · 2025Article
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- Physiological Cooperation between Aquaporin 5 and TRPV4.International journal of molecular sciences · 2022Review
- Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets.BMC bioinformatics · 2022Article
- The Risk Correlation between N7-Methylguanosine Modification-Related lncRNAs and Survival Prognosis of Oral Squamous Cell Carcinoma Based on Comprehensive Bioinformatics Analysis.Applied bionics and biomechanics · 2022Article
- A mechanistic framework for cardiometabolic and coronary artery diseases.Nature cardiovascular research · 2022Article
- Decoding disease: from genomes to networks to phenotypes.Nature reviews. Genetics · 2021Review
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- Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity.BMC medical genomics · 2021Article
- An Atherogenic Diet DisturbsBiomedicines · 2021Article
35 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObesity is a particularly complex disease that at least partially involves genetic and environmental perturbations to gene-networks connecting the hypothalamus and several metabolic tissues, resulting in an energy imbalance at the systems level.
resultsTo provide an inter-tissue view of obesity with respect to molecular states that are associated with physiological states, we developed a framework for constructing tissue-to-tissue coexpression networks between genes in the hypothalamus, liver or adipose tissue. These networks have a scale-free architecture and are strikingly independent of gene-gene coexpression networks that are constructed from more standard analyses of single tissues. This is the first systematic effort to study inter-tissue relationships and highlights genes in the hypothalamus that act as information relays in the control of peripheral tissues in obese mice. The subnetworks identified as specific to tissue-to-tissue interactions are enriched in genes that have obesity-relevant biological functions such as circadian rhythm, energy balance, stress response, or immune response.
conclusionsTissue-to-tissue networks enable the identification of disease-specific genes that respond to changes induced by different tissues and they also provide unique details regarding candidate genes for obesity that are identified in genome-wide association studies. Identifying such genes from single tissue analyses would be difficult or impossible.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.