The inferred cardiogenic gene regulatory network in the mammalian heart

Jason N. Bazil, Karl D. Stamm, Xing Li, Raghuram Thiagarajan, Timothy J Nelson, Aoy Tomita-Mitchell, Daniel A. Beard

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Cardiac development is a complex, multiscale process encompassing cell fate adoption, differentiation and morphogenesis. To elucidate pathways underlying this process, a recently developed algorithm to reverse engineer gene regulatory networks was applied to time-course microarray data obtained from the developing mouse heart. Approximately 200 genes of interest were input into the algorithm to generate putative network topologies that are capable of explaining the experimental data via model simulation. To cull specious network interactions, thousands of putative networks are merged and filtered to generate scale-free, hierarchical networks that are statistically significant and biologically relevant. The networks are validated with known gene interactions and used to predict regulatory pathways important for the developing mammalian heart. Area under the precision-recall curve and receiver operator characteristic curve are 9% and 58%, respectively. Of the top 10 ranked predicted interactions, 4 have already been validated. The algorithm is further tested using a network enriched with known interactions and another depleted of them. The inferred networks contained more interactions for the enriched network versus the depleted network. In all test cases, maximum performance of the algorithm was achieved when the purely data-driven method of network inference was combined with a data-independent, functional-based association method. Lastly, the network generated from the list of approximately 200 genes of interest was expanded using gene-profile uniqueness metrics to include approximately 900 additional known mouse genes and to form the most likely cardiogenic gene regulatory network. The resultant network supports known regulatory interactions and contains several novel cardiogenic regulatory interactions. The method outlined herein provides an informative approach to network inference and leads to clear testable hypotheses related to gene regulation.

Original languageEnglish (US)
Article numbere100842
JournalPLoS One
Volume9
Issue number6
DOIs
StatePublished - Jun 27 2014

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Gene Regulatory Networks
Genes
heart
genes
gene interaction
operator regions
mice
engineers
topology
morphogenesis
simulation models
Morphogenesis
methodology
Microarrays
gene regulatory networks
Gene expression
Topology
Association reactions
Engineers
testing

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Bazil, J. N., Stamm, K. D., Li, X., Thiagarajan, R., Nelson, T. J., Tomita-Mitchell, A., & Beard, D. A. (2014). The inferred cardiogenic gene regulatory network in the mammalian heart. PLoS One, 9(6), [e100842]. https://doi.org/10.1371/journal.pone.0100842

The inferred cardiogenic gene regulatory network in the mammalian heart. / Bazil, Jason N.; Stamm, Karl D.; Li, Xing; Thiagarajan, Raghuram; Nelson, Timothy J; Tomita-Mitchell, Aoy; Beard, Daniel A.

In: PLoS One, Vol. 9, No. 6, e100842, 27.06.2014.

Research output: Contribution to journalArticle

Bazil, JN, Stamm, KD, Li, X, Thiagarajan, R, Nelson, TJ, Tomita-Mitchell, A & Beard, DA 2014, 'The inferred cardiogenic gene regulatory network in the mammalian heart', PLoS One, vol. 9, no. 6, e100842. https://doi.org/10.1371/journal.pone.0100842
Bazil, Jason N. ; Stamm, Karl D. ; Li, Xing ; Thiagarajan, Raghuram ; Nelson, Timothy J ; Tomita-Mitchell, Aoy ; Beard, Daniel A. / The inferred cardiogenic gene regulatory network in the mammalian heart. In: PLoS One. 2014 ; Vol. 9, No. 6.
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