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|Title:||Inferring the Transcriptional Landscape of Bovine Skeletal Muscle by Integrating Co-Expression Networks||Contributor(s):||Hudson, Nicholas J (author); Reverter, Antonio (author); Wang, YongHong (author); Greenwood, Paul (author); Dalrymple, Brian P (author)||Publication Date:||2009||DOI:||10.1371/journal.pone.0007249||Handle Link:||https://hdl.handle.net/1959.11/9021||Abstract:||Background: Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches. Methodology/Principal Findings: Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1), glycolysis (HLF), mitochondrial transcription (TFB2M), adipogenesis (PIAS1), neuronal development (TLX3), immune function (IRF1) and vasculogenesis (SOX17), within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C) were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal) to 30 months post natal (adulthood) for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a 'metabolic axis' formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes. Conclusions/Significance: The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo transcriptional regulation of skeletal muscle development.||Publication Type:||Journal Article||Source of Publication:||PLoS One, v.4 (10)||Publisher:||Public Library of Science (PLoS)||Place of Publication:||San Francisco, United States of America||ISSN:||1932-6203||Field of Research (FOR):||060603 Animal Physiology - Systems||Socio-Economic Outcome Codes:||970106 Expanding Knowledge in the Biological Sciences||Peer Reviewed:||Yes||HERDC Category Description:||C1 Refereed Article in a Scholarly Journal||Statistics to Oct 2018:||Visitors: 224
|Appears in Collections:||Journal Article|
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