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Discovering regulatory motifs in the Plasmodium genome using comparative genomics.

ABSTRACT: Understanding gene regulation in Plasmodium, the causative agent of malaria, is an important step in deciphering its complex life cycle as well as leading to possible new targets for therapeutic applications. Very little is known about gene regulation in Plasmodium, and in particular, few regulatory elements have been identified. Such discovery has been significantly hampered by the high A-T content of some of the genomes of Plasmodium species, as well as the challenge in associating discovered regulatory elements to gene regulatory cascades due to Plasmodium's complex life cycle.We report a new method of using comparative genomics to systematically discover motifs in Plasmodium without requiring any functional data. Different from previous methods, our method does not depend on sequence alignments, and thus is particularly suitable for highly divergent genomes. We applied our method to discovering regulatory motifs between the human parasite, P.falciparum, and its rodent-infectious relative, P.yoelii. We also tested our procedure against comparisons between P.falciparum and the primate-infectious, P.knowlesi. Our computational effort leads to an initial catalog of 38 distinct motifs, corresponding to over 16 200 sites in the Plasmodium genome. The functionality of these motifs was further supported by their defined distribution within the genome as well as a correlation with gene expression patterns. This initial map provides a systematic view of gene regulation in Plasmodium, which can be refined as additional genomes become available.The new algorithm, named motif discovery using orthologous sequences (MDOS), is available at http://www.ics.uci.edu/ approximately xhx/project/mdos/.

SUBMITTER: Wu J

PROVIDER: S-EPMC4168299 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

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Discovering regulatory motifs in the Plasmodium genome using comparative genomics.

Wu Jie J Sieglaff Douglas H DH Gervin Joshua J Xie Xiaohui S XS

Bioinformatics (Oxford, England) 20080708 17

<h4>Motivation</h4>Understanding gene regulation in Plasmodium, the causative agent of malaria, is an important step in deciphering its complex life cycle as well as leading to possible new targets for therapeutic applications. Very little is known about gene regulation in Plasmodium, and in particular, few regulatory elements have been identified. Such discovery has been significantly hampered by the high A-T content of some of the genomes of Plasmodium species, as well as the challenge in asso ...[more]

PMID: 18611947

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Project description:In silico comparative genomics approaches have been efficiently used for functional prediction and reconstruction of metabolic and regulatory networks. Riboswitches are metabolite-sensing structures often found in bacterial mRNA leaders controlling gene expression on transcriptional or translational levels.An increasing number of riboswitches and other cis-regulatory RNAs have been recently classified into numerous RNA families in the Rfam database. High conservation of these RNA motifs provides a unique advantage for their genomic identification and comparative analysis.A comparative genomics approach implemented in the RegPredict tool was used for reconstruction and functional annotation of regulons controlled by RNAs from 43 Rfam families in diverse taxonomic groups of Bacteria. The inferred regulons include ~5200 cis-regulatory RNAs and more than 12000 target genes in 255 microbial genomes. All predicted RNA-regulated genes were classified into specific and overall functional categories. Analysis of taxonomic distribution of these categories allowed us to establish major functional preferences for each analyzed cis-regulatory RNA motif family. Overall, most RNA motif regulons showed predictable functional content in accordance with their experimentally established effector ligands. Our results suggest that some RNA motifs (including thiamin pyrophosphate and cobalamin riboswitches that control the cofactor metabolism) are widespread and likely originated from the last common ancestor of all bacteria. However, many more analyzed RNA motifs are restricted to a narrow taxonomic group of bacteria and likely represent more recent evolutionary innovations.The reconstructed regulatory networks for major known RNA motifs substantially expand the existing knowledge of transcriptional regulation in bacteria. The inferred regulons can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. The obtained genome-wide collection of reference RNA motif regulons is available in the RegPrecise database (http://regprecise.lbl.gov/).

Project description:BackgroundThe regulation of gene expression is complex and occurs at many levels, including transcriptional and post-transcriptional, in metazoans. Transcriptional regulation is mainly determined by sequence elements within the promoter regions of genes while sequence elements within the 3' untranslated regions of mRNAs play important roles in post-transcriptional regulation such as mRNA stability and translation efficiency. Identifying cis-regulatory elements, or motifs, in multicellular eukaryotes is more difficult compared to unicellular eukaryotes due to the larger intergenic sequence space and the increased complexity in regulation. Experimental techniques for discovering functional elements are often time consuming and not easily applied on a genome level. Consequently, computational methods are advantageous for genome-wide cis-regulatory motif detection. To decrease the search space in metazoans, many algorithms use cross-species alignment, although studies have demonstrated that a large portion of the binding sites for the same trans-acting factor do not reside in alignable regions. Therefore, a computational algorithm should account for both conserved and nonconserved cis-regulatory elements in metazoans.ResultsWe present CompMoby (Comparative MobyDick), software developed to identify cis-regulatory binding sites at both the transcriptional and post-transcriptional levels in metazoans without prior knowledge of the trans-acting factors. The CompMoby algorithm was previously shown to identify cis-regulatory binding sites in upstream regions of genes co-regulated in embryonic stem cells. In this paper, we extend the software to identify putative cis-regulatory motifs in 3' UTR sequences and verify our results using experimentally validated data sets in mouse and human. We also detail the implementation of CompMoby into a user-friendly tool that includes a web interface to a streamlined analysis. Our software allows detection of motifs in the following three categories: one, those that are alignable and conserved; two, those that are conserved but not alignable; three, those that are species specific. One of the output files from CompMoby gives the user the option to decide what category of cis-regulatory element to experimentally pursue based on their biological problem. Using experimentally validated biological datasets, we demonstrate that CompMoby is successful in detecting cis-regulatory target sites of known and novel trans-acting factors at the transcriptional and post-transcriptional levels.ConclusionCompMoby is a powerful software tool for systematic de novo discovery of evolutionarily conserved and nonconserved cis-regulatory sequences involved in transcriptional or post-transcriptional regulation in metazoans. This software is freely available to users at http://genome.ucsf.edu/compmoby/.

Project description:BACKGROUND: For many metalloproteins, sequence motifs characteristic of metal-binding sites have not been found or are so short that they would not be expected to be metal-specific. Striking examples of such metalloproteins are those containing Mg2+, one of the most versatile metal cofactors in cellular biochemistry. Even when Mg2+-proteins share insufficient sequence homology to identify Mg2+-specific sequence motifs, they may still share similarity in the Mg2+-binding site structure. However, no structural motifs characteristic of Mg2+-binding sites have been reported. Thus, our aims are (i) to develop a general method for discovering structural patterns/motifs characteristic of ligand-binding sites, given the 3D protein structures, and (ii) to apply it to Mg2+-proteins sharing <30% sequence identity. Our motif discovery method employs structural alphabet encoding to convert 3D structures to the corresponding 1D structural letter sequences, where the Mg2+-structural motifs are identified as recurring structural patterns. RESULTS: The structural alphabet-based motif discovery method has revealed the structural preference of Mg2+-binding sites for certain local/secondary structures: compared to all residues in the Mg2+-proteins, both first and second-shell Mg2+-ligands prefer loops to helices. Even when the Mg2+-proteins share no significant sequence homology, some of them share a similar Mg2+-binding site structure: 4 Mg2+-structural motifs, comprising 21% of the binding sites, were found. In particular, one of the Mg2+-structural motifs found maps to a specific functional group, namely, hydrolases. Furthermore, 2 of the motifs were not found in non metalloproteins or in Ca2+-binding proteins. The structural motifs discovered thus capture some essential biochemical and/or evolutionary properties, and hence may be useful for discovering proteins where Mg2+ plays an important biological role. CONCLUSION: The structural motif discovery method presented herein is general and can be applied to any set of proteins with known 3D structures. This new method is timely considering the increasing number of structures for proteins with unknown function that are being solved from structural genomics incentives. For such proteins, which share no significant sequence homology to proteins of known function, the presence of a structural motif that maps to a specific protein function in the structure would suggest likely active/binding sites and a particular biological function.

Project description:BackgroundComparative genomics methods enable the reconstruction of bacterial regulatory networks using available experimental data. In spite of their potential for accelerating research into the composition and evolution of bacterial regulons, few comparative genomics suites have been developed for the automated analysis of these regulatory systems. Available solutions typically rely on precomputed databases for operon and ortholog predictions, limiting the scope of analyses to processed complete genomes, and several key issues such as the transfer of experimental information or the integration of regulatory information in a probabilistic setting remain largely unaddressed.ResultsHere we introduce CGB, a flexible platform for comparative genomics of prokaryotic regulons. CGB has few external dependencies and enables fully customized analyses of newly available genome data. The platform automates the merging of experimental information and uses a gene-centered, Bayesian framework to generate and integrate easily interpretable results. We demonstrate its flexibility and power by analyzing the evolution of type III secretion system regulation in pathogenic Proteobacteria and by characterizing the SOS regulon of a new bacterial phylum, the Balneolaeota.ConclusionsOur results demonstrate the applicability of the CGB pipeline in multiple settings. CGB's ability to automatically integrate experimental information from multiple sources and use complete and draft genomic data, coupled with its non-reliance on precomputed databases and its easily interpretable display of gene-centered posterior probabilities of regulation provide users with an unprecedented level of flexibility in launching comparative genomics analyses of prokaryotic transcriptional regulatory networks. The analyses of type III secretion and SOS response regulatory networks illustrate instances of convergent and divergent evolution of these regulatory systems, showcasing the power of formal ancestral state reconstruction at inferring the evolutionary history of regulatory networks.

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Discovering regulatory motifs in the Plasmodium genome using comparative genomics.

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Discovering regulatory motifs in the Plasmodium genome using comparative genomics.

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