The inferred cardiogenic gene regulatory network in the mammalian heart

A cis-regulatory-directed pipeline for the identification of genes involved in cardiac development and disease

BACKGROUND

Congenital heart diseases are the major cause of death in newborns, but the genetic etiology of this developmental disorder is not fully known. The conventional approach to identify the disease-causing genes focuses on screening genes that display heart-specific expression during development. However, this approach would have discounted genes that are expressed widely in other tissues but may play critical roles in heart development.

RESULTS

We report an efficient pipeline of genome-wide gene discovery based on the identification of a cardiac-specific cis-regulatory element signature that points to candidate genes involved in heart development and congenital heart disease. With this pipeline, we retrieve 76% of the known cardiac developmental genes and predict 35 novel genes that previously had no known connectivity to heart development. Functional validation of these novel cardiac genes by RNAi-mediated knockdown of the conserved orthologs in Drosophila cardiac tissue reveals that disrupting the activity of 71% of these genes leads to adult mortality. Among these genes, RpL14, RpS24, and Rpn8 are associated with heart phenotypes.

CONCLUSIONS

Our pipeline has enabled the discovery of novel genes with roles in heart development. This workflow, which relies on screening for non-coding cis-regulatory signatures, is amenable for identifying developmental and disease genes for an organ without constraining to genes that are expressed exclusively in the organ of interest.

Improving network inference algorithms using resampling methods

Background

Relatively small changes to gene expression data dramatically affect co-expression networks inferred from that data which, in turn, can significantly alter the subsequent biological interpretation. This error propagation is an underappreciated problem that, while hinted at in the literature, has not yet been thoroughly explored. Resampling methods (e.g. bootstrap aggregation, random subspace method) are hypothesized to alleviate variability in network inference methods by minimizing outlier effects and distilling persistent associations in the data. But the efficacy of the approach assumes the generalization from statistical theory holds true in biological network inference applications.

Results

We evaluated the effect of bootstrap aggregation on inferred networks using commonly applied network inference methods in terms of stability, or resilience to perturbations in the underlying expression data, a metric for accuracy, and functional enrichment of edge interactions.

Conclusion

Bootstrap aggregation results in improved stability and, depending on the size of the input dataset, a marginal improvement to accuracy assessed by each method’s ability to link genes in the same functional pathway.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2402-0) contains supplementary material, which is available to authorized users.

The feasibility of genome-scale biological network inference using Graphics Processing Units

Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called ‘big data’ applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications. For example, both inverse identification and forward simulations of genome-scale gene regulatory network models pose compute-intensive problems. This issue is addressed here by combining the processing power of Graphics Processing Units (GPUs) and a parallel reverse engineering algorithm for inference of regulatory networks. It is shown that, given an appropriate data set, information on genome-scale networks (systems of 1000 or more state variables) can be inferred using a reverse-engineering algorithm in a matter of days on a small-scale modern GPU cluster.

The Local Edge Machine: inference of dynamic models of gene regulation

We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle. Finally, we use an atlas of transcription data in a mammalian circadian system to illustrate how the method can be used for discovery in the context of large complex networks.

Computational modeling of cardiac fibroblasts and fibrosis

Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.

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

AIMS

MicroRNAs (miRs) regulate processes involved in both cardiac remodeling and obesity. We investigated if the expression of selected miRs in patients with heart failure (HF) is influenced by the presence of obesity.

METHODS

In this case-control study, we compared plasma levels of miR-21, -130b, -221, -423-5p, and the -221/-130b ratio in 57 age- and gender-matched subjects: 40 HF patients (20 obese HF and 20 lean HF) and 17 lean healthy controls. Body composition was estimated by bioelectrical impedance analysis. MiRs were measured by quantitative reverse transcription-PCR. Bioinformatics analysis was performed based on miRs findings to predict their putative targets and investigate their biological function.

RESULTS

HF was associated with increased miR-423-5p levels in both lean and obese patients (P<0.05 vs. controls) without differences between HF groups. MiR-130b levels were reduced in obese HF patients compared with HF lean (P=0.036) and controls (P=0.025). MiR-221 levels were non-significantly increased in obese HF patients. MiR-21 levels were not different among the groups. MiR-221/-130b ratio was increased in obese HF patients, and was positively associated with body fat percentage (r=0.43; P=0.002), body mass index (r=0.44; P=0.002), and waist circumference (r=0.40; P=0.020). Computational prediction of target genes followed by functional enrichment analysis indicated a relevant role of miR-130b and miR-221 in modulating the expression of genes associated to cardiovascular and endocrine diseases, and suggested their influence in important signaling mechanisms and in numerous processes related to the circulatory and endocrine systems.

CONCLUSIONS

In HF patients, the presence of obesity is associated with a differential expression of selected miRs and the miR-221/-130b ratio had significant correlations with adiposity parameters. Computational target prediction analysis identified several interrelated pathways targeted by miR-130b and miR-221 with a known relationship with endocrine and cardiovascular diseases, representing potential mechanisms to be further validated.

Big Data Analytics in Healthcare

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.