The Inferred Cardiogenic Gene Regulatory Network in the Mammalian Heart

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.

Circulating microRNAs in obese and lean heart failure patients: A case–control study with computational target prediction analysis

“…TargetScan works by searching mRNAs sites with high complementarity to the seed region (nucleotides 2-7) of the miRs, assessing sequence features derived from empirically defined rules and site conservation across species. This bioinformatics analysis helps in the interpretation of the biological and molecular role of miRs [22]. TargetScan was specifically used because it is suggested to be one of the most reliable miR target prediction algorithms available, presenting a distinguished tradeoff between sensitivity and specificity [23,24,25].…”

“…In general, the use of miR targets prediction softwares that consider site conservation across species, such as TargetScan, is associated with high precision and sensitivity [60]. Comparison between in vivo results and in silico predictions provided by several A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT 22 computational methods has revealed that TargetScan is one of the best available algorithms and that the scores associated with predicted interactions correlated with protein downregulation, justifying our choice for this prediction tool [24]. We showed that a high proportion of putative targets found by TargetScan have also support from other computational resources, and that TargetScan presented the most expressive overlap with experimentally validated data among the three in silico methods used (Fig.…”

Big Data Analytics for Healthcare

“…Recon 2 (an improvement over Recon 1) is a model to represent human metabolism and incorporates [139,140] Pathway analysis Breast cancer [141] GoMiner [142] Pathway analysis Pancreatic cancer [143] ClueGo [144] Pathway analysis Colorectal tumors [145] GSEA [146] Pathway analysis Diabetes [147] Pathway-Express [148] P a t h w a y a n a l y s i s L e u k e m i a [ 149] Recon 2 [150] Reconstruction of metabolic networks Drug target prediction studies [151] Boolean methods [135,152,153] Reconstruction of gene regulatory networks Cardiac differentiation [154] ODE models [155][156][157][158] Reconstruction of gene regulatory networks Cardiac development [158] 7,440 reactions involving 5,063 metabolites. Recon 2 has been expanded to account for known drugs for drug target prediction studies [151] and to study off-target effects of drugs [173].…”

“…This Boolean model successfully captured the network dynamics for two different immunology microarray datasets. The dynamics of gene regulatory network can be captured using ordinary differential equations (ODEs) [155][156][157][158]. This approach has been applied to determine regulatory network for yeast [155].…”

“…Determining connections in the regulatory network for a problem of the size of the human genome, consisting of 30,000 to 35,000 genes [16,17], will require exploring close to a billion possible connections. The dynamical ODE model has been applied to reconstruct the cardiogenic gene regulatory network of the mammalian heart [158]. A summary of methods and toolkits with their applications is presented in Table 2.…”