Blog
About

123
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Complex Modulation of the Aedes aegypti Transcriptome in Response to Dengue Virus Infection

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Dengue fever is the most important arboviral disease world-wide, with Aedes aegypti being the major vector. Interactions between the mosquito host and dengue viruses (DENV) are complex and vector competence varies among geographically-distinct Ae. aegypti populations. Additionally, dengue is caused by four antigenically-distinct viral serotypes (DENV1–4), each with multiple genotypes. Each virus genotype interacts differently with vertebrate and invertebrate hosts. Analyses of alterations in mosquito transcriptional profiles during DENV infection are expected to provide the basis for identifying networks of genes involved in responses to viruses and contribute to the molecular-genetic understanding of vector competence. In addition, this knowledge is anticipated to support the development of novel disease-control strategies. RNA-seq technology was used to assess genome-wide changes in transcript abundance at 1, 4 and 14 days following DENV2 infection in carcasses, midguts and salivary glands of the Ae. aegypti Chetumal strain. DENV2 affected the expression of 397 Ae. aegypti genes, most of which were down-regulated by viral infection. Differential accumulation of transcripts was mainly tissue- and time-specific. Comparisons of our data with other published reports reveal conservation of functional classes, but limited concordance of specific mosquito genes responsive to DENV2 infection. These results indicate the necessity of additional studies of mosquito-DENV interactions, specifically those focused on recently-derived mosquito strains with multiple dengue virus serotypes and genotypes.

          Related collections

          Most cited references 59

          • Record: found
          • Abstract: found
          • Article: not found

          TopHat: discovering splice junctions with RNA-Seq

          Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

            High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1-3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes

              The TRANSFAC® database on transcription factors, their binding sites, nucleotide distribution matrices and regulated genes as well as the complementing database TRANSCompel® on composite elements have been further enhanced on various levels. A new web interface with different search options and integrated versions of Match™ and Patch™ provides increased functionality for TRANSFAC®. The list of databases which are linked to the common GENE table of TRANSFAC® and TRANSCompel® has been extended by: Ensembl, UniGene, EntrezGene, HumanPSD™ and TRANSPRO™. Standard gene names from HGNC, MGI and RGD, are included for human, mouse and rat genes, respectively. With the help of InterProScan, Pfam, SMART and PROSITE domains are assigned automatically to the protein sequences of the transcription factors. TRANSCompel® contains now, in addition to the COMPEL table, a separate table for detailed information on the experimental EVIDENCE on which the composite elements are based. Finally, for TRANSFAC®, in respect of data growth, in particular the gain of Drosophila transcription factor binding sites (by courtesy of the Drosophila DNase I footprint database) and of Arabidopsis factors (by courtesy of DATF, Database of Arabidopsis Transcription Factors) has to be stressed. The here described public releases, TRANSFAC® 7.0 and TRANSCompel® 7.0, are accessible under .
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                27 November 2012
                : 7
                : 11
                Affiliations
                [1 ]Program in Public Health, University of California Irvine, Irvine, California, United States of America
                [2 ]Department of Molecular Biology and Biochemistry, University of California Irvine, Irvine, California, United States of America
                [3 ]Institute for Genomics and Bioinformatics, University of California Irvine, Irvine, California, United States of America
                [4 ]Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, United States of America
                [5 ]Department of Microbiology and Molecular Genetics, University of California Irvine, Irvine, California, United States of America
                Centro de Pesquisas René Rachou, Brazil
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MB WAD CLC KEO OM AAJ. Performed the experiments: MB WAD CLC. Analyzed the data: MB WAD CLC KEO OM AAJ. Contributed reagents/materials/analysis tools: MB WAD CLC. Wrote the paper: MB WAD CLC KEO OM AAJ.

                Article
                PONE-D-12-19714
                10.1371/journal.pone.0050512
                3507784
                23209765

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 14
                Funding
                This work was supported by grant U54AI065359 from the National Institute of Allergy and Infectious Diseases. WAD is supported in part by T15LM07443 from the National Library of Medicine, United States National Institutes of Health (NIH). C.L. Campbell and K.E. Olson are supported in part by a grant to the Regents of the University of California from the Foundation for the NIH through the Grand Challenges in Global Health initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Computational Biology
                Genomics
                Genome Analysis Tools
                Transcriptomes
                Genetics
                Gene Expression
                Genomics
                Genome Analysis Tools
                Transcriptomes
                Genome Expression Analysis
                Microbiology
                Vector Biology
                Mosquitoes
                Emerging Infectious Diseases
                Host-Pathogen Interaction
                Virology
                Medicine
                Infectious Diseases
                Neglected Tropical Diseases
                Dengue Fever

                Uncategorized

                Comments

                Comment on this article