Uncovering the liver's role in immunity through RNA co-expression networks

. 2016 Oct ; 27 (9-10) : 469-84. [epub] 20160711

Jazyk angličtina Země Spojené státy americké Médium print-electronic

Typ dokumentu časopisecké články, práce podpořená grantem, Research Support, N.I.H., Extramural

Perzistentní odkaz   https://www.medvik.cz/link/pmid27401171

Grantová podpora
R24 AA013162 NIAAA NIH HHS - United States
R24 AA012885 NIAAA NIH HHS - United States
P30 DK048522 NIDDK NIH HHS - United States
P50 AA011999 NIAAA NIH HHS - United States
P01 HL035018 NHLBI NIH HHS - United States

Odkazy

PubMed 27401171
PubMed Central PMC5002042
DOI 10.1007/s00335-016-9656-5
PII: 10.1007/s00335-016-9656-5
Knihovny.cz E-zdroje

Gene co-expression analysis has proven to be a powerful tool for ascertaining the organization of gene products into networks that are important for organ function. An organ, such as the liver, engages in a multitude of functions important for the survival of humans, rats, and other animals; these liver functions include energy metabolism, metabolism of xenobiotics, immune system function, and hormonal homeostasis. With the availability of organ-specific transcriptomes, we can now examine the role of RNA transcripts (both protein-coding and non-coding) in these functions. A systems genetic approach for identifying and characterizing liver gene networks within a recombinant inbred panel of rats was used to identify genetically regulated transcriptional networks (modules). For these modules, biological consensus was found between functional enrichment analysis and publicly available phenotypic quantitative trait loci (QTL). In particular, the biological function of two liver modules could be linked to immune response. The eigengene QTLs for these co-expression modules were located at genomic regions coincident with highly significant phenotypic QTLs; these phenotypes were related to rheumatoid arthritis, food preference, and basal corticosterone levels in rats. Our analysis illustrates that genetically and biologically driven RNA-based networks, such as the ones identified as part of this research, provide insight into the genetic influences on organ functions. These networks can pinpoint phenotypes that manifest through the interaction of many organs/tissues and can identify unannotated or under-annotated RNA transcripts that play a role in these phenotypes.

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