The Inferred Cardiogenic Gene Regulatory Network in the Mammalian Heart

PLoS One

Aoy Tomita-Mitchell¹  Karl D. Stamm¹  Daniel A. Beard²  Jason N. Bazil²  Raghuram Thiagarajan²  Timothy J. Nelson³  Xing Li⁴ 

[1] Biotechnology and Bioengineering Center, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America;Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, United States of America;Departments of Medicine, Molecular Pharmacology and Experimental Therapeutics, and Mayo Clinic Center for Regenerative Medicine, Rochester, Minnesota, United States of America;Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America

关键词: Genetic networks;    Algorithms;    Gene expression;    Gene ontologies;    Scale-free networks;    Gene regulatory networks;    Gene regulation;    Heart;   

DOI  :  10.1371/journal.pone.0100842

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

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.

【 授权许可】

CC BY