A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.

Transcription factor (TF) regulation is often post-translational. TF modifications such as reversible phosphorylation and missense mutations, which can act independent of TF expression level, are overlooked by differential expression analysis. Using bovine Piedmontese myostatin mutants as proof-of-c...

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Autores principales:	Nicholas J Hudson, Antonio Reverter, Brian P Dalrymple
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e1cc8001612048df96b3827d02b672ef
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spelling	oai:doaj.org-article:e1cc8001612048df96b3827d02b672ef2021-11-25T05:42:24ZA differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.1553-734X1553-735810.1371/journal.pcbi.1000382https://doaj.org/article/e1cc8001612048df96b3827d02b672ef2009-05-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19412532/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Transcription factor (TF) regulation is often post-translational. TF modifications such as reversible phosphorylation and missense mutations, which can act independent of TF expression level, are overlooked by differential expression analysis. Using bovine Piedmontese myostatin mutants as proof-of-concept, we propose a new algorithm that correctly identifies the gene containing the causal mutation from microarray data alone. The myostatin mutation releases the brakes on Piedmontese muscle growth by translating a dysfunctional protein. Compared to a less muscular non-mutant breed we find that myostatin is not differentially expressed at any of ten developmental time points. Despite this challenge, the algorithm identifies the myostatin 'smoking gun' through a coordinated, simultaneous, weighted integration of three sources of microarray information: transcript abundance, differential expression, and differential wiring. By asking the novel question "which regulator is cumulatively most differentially wired to the abundant most differentially expressed genes?" it yields the correct answer, "myostatin". Our new approach identifies causal regulatory changes by globally contrasting co-expression network dynamics. The entirely data-driven 'weighting' procedure emphasises regulatory movement relative to the phenotypically relevant part of the network. In contrast to other published methods that compare co-expression networks, significance testing is not used to eliminate connections.Nicholas J HudsonAntonio ReverterBrian P DalrymplePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 5, p e1000382 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Nicholas J Hudson Antonio Reverter Brian P Dalrymple A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
description	Transcription factor (TF) regulation is often post-translational. TF modifications such as reversible phosphorylation and missense mutations, which can act independent of TF expression level, are overlooked by differential expression analysis. Using bovine Piedmontese myostatin mutants as proof-of-concept, we propose a new algorithm that correctly identifies the gene containing the causal mutation from microarray data alone. The myostatin mutation releases the brakes on Piedmontese muscle growth by translating a dysfunctional protein. Compared to a less muscular non-mutant breed we find that myostatin is not differentially expressed at any of ten developmental time points. Despite this challenge, the algorithm identifies the myostatin 'smoking gun' through a coordinated, simultaneous, weighted integration of three sources of microarray information: transcript abundance, differential expression, and differential wiring. By asking the novel question "which regulator is cumulatively most differentially wired to the abundant most differentially expressed genes?" it yields the correct answer, "myostatin". Our new approach identifies causal regulatory changes by globally contrasting co-expression network dynamics. The entirely data-driven 'weighting' procedure emphasises regulatory movement relative to the phenotypically relevant part of the network. In contrast to other published methods that compare co-expression networks, significance testing is not used to eliminate connections.
format	article
author	Nicholas J Hudson Antonio Reverter Brian P Dalrymple
author_facet	Nicholas J Hudson Antonio Reverter Brian P Dalrymple
author_sort	Nicholas J Hudson
title	A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
title_short	A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
title_full	A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
title_fullStr	A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
title_full_unstemmed	A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
title_sort	differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/e1cc8001612048df96b3827d02b672ef
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A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation.

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