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Global identification of conserved post-transcriptional regulatory programs in trypanosomatids.

ABSTRACT: While regulatory programs are extensively studied at the level of transcription, elements that are involved in regulation of post-transcriptional processes are largely unknown, and methods for systematic identification of these elements are in early stages. Here, using a novel computational framework, we have integrated sequence information with several functional genomics data sets to characterize conserved regulatory programs of trypanosomatids, a group of eukaryotes that almost entirely rely on post-transcriptional processes for regulation of mRNA abundance. This analysis revealed a complex network of linear and structural RNA elements that potentially govern mRNA abundance across different life stages and environmental conditions. Furthermore, we show that the conserved regulatory network that we have identified is responsive to chemical perturbation of several biological functions in trypanosomatids. We have further characterized one of the most abundant regulatory RNA elements that we discovered, an AU-rich element (ARE) that can be found in 3' untranslated region of many trypanosomatid genes. Using bioinformatics approaches as well as in vitro and in vivo experiments, we have identified three ELAV-like homologs, including the developmentally critical protein TbRBP6, which regulate abundance of a large number of trypanosomatid ARE-containing transcripts. Together, these studies lay out a roadmap for characterization of mechanisms that modulate development and metabolic pathways in trypanosomatids.

SUBMITTER: Najafabadi HS

PROVIDER: S-EPMC3794602 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Global identification of conserved post-transcriptional regulatory programs in trypanosomatids.

Najafabadi Hamed S HS Lu Zhiquan Z MacPherson Chad C Mehta Vaibhav V Adoue Véronique V Pastinen Tomi T Salavati Reza R

Nucleic acids research 20130722 18

While regulatory programs are extensively studied at the level of transcription, elements that are involved in regulation of post-transcriptional processes are largely unknown, and methods for systematic identification of these elements are in early stages. Here, using a novel computational framework, we have integrated sequence information with several functional genomics data sets to characterize conserved regulatory programs of trypanosomatids, a group of eukaryotes that almost entirely rely ...[more]

PMID: 23877242

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Project description:BACKGROUND: Dysregulation of transcriptional programs leads to cell malfunctioning and can have an impact in cancer development. Our study aims to characterize global differences between transcriptional regulatory programs of normal and tumor cells of the colon. METHODS: Affymetrix Human Genome U219 expression arrays were used to assess gene expression in 100 samples of colon tumor and their paired adjacent normal mucosa. Transcriptional networks were reconstructed using ARACNe algorithm using 1,000 bootstrap replicates consolidated into a consensus network. Networks were compared regarding topology parameters and identified well-connected clusters. Functional enrichment was performed with SIGORA method. ENCODE ChIP-Seq data curated in the hmChIP database was used for in silico validation of the most prominent transcription factors. RESULTS: The normal network contained 1,177 transcription factors, 5,466 target genes and 61,226 transcriptional interactions. A large loss of transcriptional interactions in the tumor network was observed (11,585; 81% reduction), which also contained fewer transcription factors (621; 47% reduction) and target genes (2,190; 60% reduction) than the normal network. Gene silencing was not a main determinant of this loss of regulatory activity, since the average gene expression was essentially conserved. Also, 91 transcription factors increased their connectivity in the tumor network. These genes revealed a tumor-specific emergent transcriptional regulatory program with significant functional enrichment related to colorectal cancer pathway. In addition, the analysis of clusters again identified subnetworks in the tumors enriched for cancer related pathways (immune response, Wnt signaling, DNA replication, cell adherence, apoptosis, DNA repair, among others). Also multiple metabolism pathways show differential clustering between the tumor and normal network. CONCLUSIONS: These findings will allow a better understanding of the transcriptional regulatory programs altered in colon cancer and could be an invaluable methodology to identify potential hubs with a relevant role in the field of cancer diagnosis, prognosis and therapy.

Project description:Transcriptional responses often consist of regulatory modules - sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA binding, and promoter sequences. In cases where physical protein-DNA data are lacking, such methods are essential for the analysis of the underlying regulatory program.Here, we present a novel approach for the analysis of modular regulatory programs. Our method - Biochemical Regulatory Network Inference (BRNI) - is based on an algorithm that learns from expression data a biochemically-motivated regulatory program. It describes the expression profiles of gene modules consisting of hundreds of genes using a small number of regulators and affinity parameters. We developed an ensemble learning algorithm that ensures the robustness of the learned model. We then use the topology of the learned regulatory program to guide the discovery of a library of cis-regulatory motifs, and determined the motif compositions associated with each module.We test our method on the cell cycle regulatory program of the fission yeast. We discovered 16 coherent modules, covering diverse processes from cell division to metabolism and associated them with 18 learned regulatory elements, including both known cell-cycle regulatory elements (MCB, Ace2, PCB, ACCCT box) and novel ones, some of which are associated with G2 modules. We integrate the regulatory relations from the expression- and motif-based models into a single network, highlighting specific topologies that result in distinct dynamics of gene expression in the fission yeast cell cycle.Our approach provides a biologically-driven, principled way for deconstructing a set of genes into meaningful transcriptional modules and identifying their associated cis-regulatory programs. Our analysis sheds light on the architecture and function of the regulatory network controlling the fission yeast cell cycle, and a similar approach can be applied to the regulatory underpinnings of other modular transcriptional responses.

Dataset Information

Global identification of conserved post-transcriptional regulatory programs in trypanosomatids.

Publications

Global identification of conserved post-transcriptional regulatory programs in trypanosomatids.

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