Abstract
BackgroundPrecise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.Results and Principal FindingsWe propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.ConclusionsWe established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation.
Highlights
Cell division, ageing, and death are intricately regulated processes that depend on the balance between various growth promoting and inhibiting signals
We demonstrate the capability of this computational framework to infer regulatory modules associated with the cell cycle progression in Hela cells by combining information from time-course gene expression experiments [2], protein-protein interactions (PPI) [11,12,13,14,15,16,17,18,19,20,21,22], protein-DNA interactions (PDI) [23], and gene ontology (GO) [24]
We used three types of data to reconstruct the transcriptional regulatory networks (TRNs), namely PPIs derived from a collection of PPI databases, PDIs from the TRANSFAC database, and the time course gene expression profiles as published by [2]
Summary
Cell division, ageing, and death are intricately regulated processes that depend on the balance between various growth promoting and inhibiting signals. The intricacies of these processes are defined by complex genetic programs that allow certain genes to be expressed in a tightly regulated manner. Errors in regulation cause uncontrolled cell proliferation, a universal property of tumors This characteristic is driven by genes that exhibit abnormal activities in tumor cells, many of which have important roles in transducing growth-regulating signals to the nucleus and interfacing these signals to modify gene expression. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells
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