Gene network and proteomic analyses of cardiac responses to pathological and physiological stress

Ignat Drozdov, Athanasios Didangelos, Xiaoke Yin, Anna Zampetaki, Mélanie Abonnenc, Colin Murdoch, Min Zhang, Christos A. Ouzounis, Manuel Mayr, Sophia Tsoka, Ajay M. Shah

Research output: Contribution to journalArticle

Abstract

Background—The molecular mechanisms underlying similarities and differences between physiological and pathological left ventricular hypertrophy (LVH) are of intense interest. Most previous work involved targeted analysis of individual signaling pathways or screening of transcriptomic profiles. We developed a network biology approach using genomic and proteomic data to study the molecular patterns that distinguish pathological and physiological LVH.
Methods and Results—A network-based analysis using graph theory methods was undertaken on 127 genome-wide expression arrays of in vivo murine LVH. This revealed phenotype-specific pathological and physiological gene coexpression networks. Despite >1650 common genes in the 2 networks, network structure is significantly different. This is largely because of rewiring of genes that are differentially coexpressed in the 2 networks; this novel concept of differential wiring was further validated experimentally. Functional analysis of the rewired network revealed several distinct cellular pathways and gene sets. Deeper exploration was undertaken by targeted proteomic analysis of mitochondrial, myofilament, and extracellular subproteomes in pathological LVH. A notable finding was that mRNA–protein correlation was greater at the cellular pathway level than for individual loci.
Conclusions—This first combined gene network and proteomic analysis of LVH reveals novel insights into the integrated pathomechanisms that distinguish pathological versus physiological phenotypes. In particular, we identify differential gene wiring as a major distinguishing feature of these phenotypes. This approach provides a platform for the investigation of potentially novel pathways in LVH and offers a freely accessible protocol (http://sites.google.com/site/cardionetworks) for similar analyses in other cardiovascular diseases.
Original languageEnglish
Pages (from-to)588-597
Number of pages10
JournalCirculation Cardiovascular Genetics
Volume6
Issue number6
Early online date8 Nov 2013
DOIs
Publication statusPublished - Dec 2013

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Physiological Stress
Gene Regulatory Networks
Left Ventricular Hypertrophy
Proteomics
Phenotype
Genes
Myofibrils
Cardiovascular Diseases
Genome

Keywords

  • computational biology
  • genetics
  • genomics
  • proteomics

Cite this

Drozdov, I., Didangelos, A., Yin, X., Zampetaki, A., Abonnenc, M., Murdoch, C., ... Shah, A. M. (2013). Gene network and proteomic analyses of cardiac responses to pathological and physiological stress. Circulation Cardiovascular Genetics, 6(6), 588-597. https://doi.org/10.1161/CIRCGENETICS.113.000063
Drozdov, Ignat ; Didangelos, Athanasios ; Yin, Xiaoke ; Zampetaki, Anna ; Abonnenc, Mélanie ; Murdoch, Colin ; Zhang, Min ; Ouzounis, Christos A. ; Mayr, Manuel ; Tsoka, Sophia ; Shah, Ajay M. / Gene network and proteomic analyses of cardiac responses to pathological and physiological stress. In: Circulation Cardiovascular Genetics. 2013 ; Vol. 6, No. 6. pp. 588-597.
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Drozdov, I, Didangelos, A, Yin, X, Zampetaki, A, Abonnenc, M, Murdoch, C, Zhang, M, Ouzounis, CA, Mayr, M, Tsoka, S & Shah, AM 2013, 'Gene network and proteomic analyses of cardiac responses to pathological and physiological stress', Circulation Cardiovascular Genetics, vol. 6, no. 6, pp. 588-597. https://doi.org/10.1161/CIRCGENETICS.113.000063

Gene network and proteomic analyses of cardiac responses to pathological and physiological stress. / Drozdov, Ignat ; Didangelos, Athanasios ; Yin, Xiaoke ; Zampetaki, Anna ; Abonnenc, Mélanie ; Murdoch, Colin; Zhang, Min; Ouzounis, Christos A.; Mayr, Manuel ; Tsoka, Sophia ; Shah, Ajay M.

In: Circulation Cardiovascular Genetics, Vol. 6, No. 6, 12.2013, p. 588-597.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Gene network and proteomic analyses of cardiac responses to pathological and physiological stress

AU - Drozdov, Ignat

AU - Didangelos, Athanasios

AU - Yin, Xiaoke

AU - Zampetaki, Anna

AU - Abonnenc, Mélanie

AU - Murdoch, Colin

AU - Zhang, Min

AU - Ouzounis, Christos A.

AU - Mayr, Manuel

AU - Tsoka, Sophia

AU - Shah, Ajay M.

PY - 2013/12

Y1 - 2013/12

N2 - Background—The molecular mechanisms underlying similarities and differences between physiological and pathological left ventricular hypertrophy (LVH) are of intense interest. Most previous work involved targeted analysis of individual signaling pathways or screening of transcriptomic profiles. We developed a network biology approach using genomic and proteomic data to study the molecular patterns that distinguish pathological and physiological LVH. Methods and Results—A network-based analysis using graph theory methods was undertaken on 127 genome-wide expression arrays of in vivo murine LVH. This revealed phenotype-specific pathological and physiological gene coexpression networks. Despite >1650 common genes in the 2 networks, network structure is significantly different. This is largely because of rewiring of genes that are differentially coexpressed in the 2 networks; this novel concept of differential wiring was further validated experimentally. Functional analysis of the rewired network revealed several distinct cellular pathways and gene sets. Deeper exploration was undertaken by targeted proteomic analysis of mitochondrial, myofilament, and extracellular subproteomes in pathological LVH. A notable finding was that mRNA–protein correlation was greater at the cellular pathway level than for individual loci. Conclusions—This first combined gene network and proteomic analysis of LVH reveals novel insights into the integrated pathomechanisms that distinguish pathological versus physiological phenotypes. In particular, we identify differential gene wiring as a major distinguishing feature of these phenotypes. This approach provides a platform for the investigation of potentially novel pathways in LVH and offers a freely accessible protocol (http://sites.google.com/site/cardionetworks) for similar analyses in other cardiovascular diseases.

AB - Background—The molecular mechanisms underlying similarities and differences between physiological and pathological left ventricular hypertrophy (LVH) are of intense interest. Most previous work involved targeted analysis of individual signaling pathways or screening of transcriptomic profiles. We developed a network biology approach using genomic and proteomic data to study the molecular patterns that distinguish pathological and physiological LVH. Methods and Results—A network-based analysis using graph theory methods was undertaken on 127 genome-wide expression arrays of in vivo murine LVH. This revealed phenotype-specific pathological and physiological gene coexpression networks. Despite >1650 common genes in the 2 networks, network structure is significantly different. This is largely because of rewiring of genes that are differentially coexpressed in the 2 networks; this novel concept of differential wiring was further validated experimentally. Functional analysis of the rewired network revealed several distinct cellular pathways and gene sets. Deeper exploration was undertaken by targeted proteomic analysis of mitochondrial, myofilament, and extracellular subproteomes in pathological LVH. A notable finding was that mRNA–protein correlation was greater at the cellular pathway level than for individual loci. Conclusions—This first combined gene network and proteomic analysis of LVH reveals novel insights into the integrated pathomechanisms that distinguish pathological versus physiological phenotypes. In particular, we identify differential gene wiring as a major distinguishing feature of these phenotypes. This approach provides a platform for the investigation of potentially novel pathways in LVH and offers a freely accessible protocol (http://sites.google.com/site/cardionetworks) for similar analyses in other cardiovascular diseases.

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KW - proteomics

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