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      Functional transcriptomic analysis of the role of MAB-5/Hox in Q neuroblast migration in Caenorhabditis elegans

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          Abstract

          Background

          Directed cell migration is a fundamental process in normal development and in tumor metastasis. In C. elegans the MAB-5/Hox transcription factor is a determinant of posterior migration of the Q neuroblast descendants. In this work, mab-5 transcriptional targets that control Q descendant migration are identified by comparing RNA-seq profiles in wild type and mab-5 mutant backgrounds.

          Results

          Transcriptome profiling is a widely-used and potent tool to identify genes involved in developmental and pathological processes, and is most informative when RNA can be isolated from individual cell or tissue types. Cell-specific RNA samples can be difficult to obtain from invertebrate model organisms such as Drosophila and C. elegans. Here we test the utility of combining a whole organism RNA-seq approach with mab-5 loss and gain-of-function mutants and functional validation using RNAi to identify genes regulated by MAB-5 to control Q descendant migration. We identified 22 genes whose expression was controlled by mab-5 and that controlled Q descendant migration. Genes regulated by mab-5 were enriched for secreted and transmembrane molecules involved in basement membrane interaction and modification, and some affected Q descendant migration.

          Conclusions

          Our results indicate that a whole-organism RNA-seq approach, when combined with mutant analysis and functional validation, can be a powerful method to identify genes involved in a specific developmental process, in this case Q descendant posterior migration. These genes could act either autonomously in the Q cells, or non-autonomously in other cells that express MAB-5. The identities of the genes regulated by MAB-5 indicate that MAB-5 acts by modifying interactions with the basement membrane, resulting in posterior versus anterior migration.

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          Most cited references35

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          Improving RNA-Seq expression estimates by correcting for fragment bias

          The biochemistry of RNA-Seq library preparation results in cDNA fragments that are not uniformly distributed within the transcripts they represent. This non-uniformity must be accounted for when estimating expression levels, and we show how to perform the needed corrections using a likelihood based approach. We find improvements in expression estimates as measured by correlation with independently performed qRT-PCR and show that correction of bias leads to improved replicability of results across libraries and sequencing technologies.
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            Identification of novel transcripts in annotated genomes using RNA-Seq.

            We describe a new 'reference annotation based transcript assembly' problem for RNA-Seq data that involves assembling novel transcripts in the context of an existing annotation. This problem arises in the analysis of expression in model organisms, where it is desirable to leverage existing annotations for discovering novel transcripts. We present an algorithm for reference annotation-based transcript assembly and show how it can be used to rapidly investigate novel transcripts revealed by RNA-Seq in comparison with a reference annotation. The methods described in this article are implemented in the Cufflinks suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks. The software is released under the BOOST license. cole@broadinstitute.org; lpachter@math.berkeley.edu Supplementary data are available at Bioinformatics online.
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              OrthoList: A Compendium of C. elegans Genes with Human Orthologs

              Background C. elegans is an important model for genetic studies relevant to human biology and disease. We sought to assess the orthology between C. elegans and human genes to understand better the relationship between their genomes and to generate a compelling list of candidates to streamline RNAi-based screens in this model. Results We performed a meta-analysis of results from four orthology prediction programs and generated a compendium, “OrthoList”, containing 7,663 C. elegans protein-coding genes. Various assessments indicate that OrthoList has extensive coverage with low false-positive and false-negative rates. Part of this evaluation examined the conservation of components of the receptor tyrosine kinase, Notch, Wnt, TGF-ß and insulin signaling pathways, and led us to update compendia of conserved C. elegans kinases, nuclear hormone receptors, F-box proteins, and transcription factors. Comparison with two published genome-wide RNAi screens indicated that virtually all of the conserved hits would have been obtained had just the OrthoList set (∼38% of the genome) been targeted. We compiled Ortholist by InterPro domains and Gene Ontology annotation, making it easy to identify C. elegans orthologs of human disease genes for potential functional analysis. Conclusions We anticipate that OrthoList will be of considerable utility to C. elegans researchers for streamlining RNAi screens, by focusing on genes with apparent human orthologs, thus reducing screening effort by ∼60%. Moreover, we find that OrthoList provides a useful basis for annotating orthology and reveals more C. elegans orthologs of human genes in various functional groups, such as transcription factors, than previously described.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2013
                4 May 2013
                : 14
                : 304
                Affiliations
                [1 ]Department of Molecular Biosciences, Programs in Genetics and Molecular, Cellular, and Developmental Biology, The University of Kansas, 1200 Sunnyside Avenue, Lawrence KS 66045, USA
                Article
                1471-2164-14-304
                10.1186/1471-2164-14-304
                3651406
                23642123
                8ab05080-87cd-4b9e-afa8-8dd2e29659fd
                Copyright ©2013 Tamayo et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 9 February 2013
                : 1 May 2013
                Categories
                Research Article

                Genetics
                Genetics

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