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      The Odorant Binding Protein Gene Family from the Genome of Silkworm, Bombyx mori

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          Abstract

          Background

          Chemosensory systems play key roles in the survival and reproductive success of insects. Insect chemoreception is mediated by two large and diverse gene superfamilies, chemoreceptors and odorant binding proteins (OBPs). OBPs are believed to transport hydrophobic odorants from the environment to the olfactory receptors.

          Results

          We identified a family of OBP-like genes in the silkworm genome and characterized their expression using oligonucleotide microarrays. A total of forty-four OBP genes were annotated, a number comparable to the 57 OBPs known from Anopheles gambiae and 51 from Drosophila melanogaster. As seen in other fully sequenced insect genomes, most silkworm OBP genes are present in large clusters. We defined six subfamilies of OBPs, each of which shows lineage-specific expansion and diversification. EST data and OBP expression profiles from multiple larvae tissues of day three fifth instars demonstrated that many OBPs are expressed in chemosensory-specific tissues although some OBPs are expressed ubiquitously and others exclusively in non-chemosensory tissues. Some atypical OBPs are expressed throughout development. These results reveal that, although many OBPs are chemosensory-specific, others may have more general physiological roles.

          Conclusion

          Silkworms possess a number of OBPs genes similar to other insects. Their expression profiles suggest that many OBPs may be involved in olfaction and gustation as well as general carriers of hydrophobic molecules. The expansion of OBP gene subfamilies and sequence divergence indicate that the silkworm OBP family acquired functional diversity concurrently with functional constraints. Further investigation of the OBPs of the silkworm could give insights in the roles of OBPs in chemoreception.

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          Most cited references60

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          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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            Ab initio gene finding in Drosophila genomic DNA.

            Ab initio gene identification in the genomic sequence of Drosophila melanogaster was obtained using (human gene predictor) and Fgenesh programs that have organism-specific parameters for human, Drosophila, plants, yeast, and nematode. We did not use information about cDNA/EST in most predictions to model a real situation for finding new genes because information about complete cDNA is often absent or based on very small partial fragments. We investigated the accuracy of gene prediction on different levels and designed several schemes to predict an unambiguous set of genes (annotation CGG1), a set of reliable exons (annotation CGG2), and the most complete set of exons (annotation CGG3). For 49 genes, protein products of which have clear homologs in protein databases, predictions were recomputed by Fgenesh+ program. The first annotation serves as the optimal computational description of new sequence to be presented in a database. Reliable exons from the second annotation serve as good candidates for selecting the PCR primers for experimental work for gene structure verification. Our results shows that we can identify approximately 90% of coding nucleotides with 20% false positives. At the exon level we accurately predicted 65% of exons and 89% including overlapping exons with 49% false positives. Optimizing accuracy of prediction, we designed a gene identification scheme using Fgenesh, which provided sensitivity (Sn) = 98% and specificity (Sp) = 86% at the base level, Sn = 81% (97% including overlapping exons) and Sp = 58% at the exon level and Sn = 72% and Sp = 39% at the gene level (estimating sensitivity on std1 set and specificity on std3 set). In general, these results showed that computational gene prediction can be a reliable tool for annotating new genomic sequences, giving accurate information on 90% of coding sequences with 14% false positives. However, exact gene prediction (especially at the gene level) needs additional improvement using gene prediction algorithms. The program was also tested for predicting genes of human Chromosome 22 (the last variant of Fgenesh can analyze the whole chromosome sequence). This analysis has demonstrated that the 88% of manually annotated exons in Chromosome 22 were among the ab initio predicted exons. The suite of gene identification programs is available through the WWW server of Computational Genomics Group at http://genomic.sanger.ac.uk/gf. html.
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              G protein-coupled receptors in Anopheles gambiae.

              We used bioinformatic approaches to identify a total of 276 G protein-coupled receptors (GPCRs) from the Anopheles gambiae genome. These include GPCRs that are likely to play roles in pathways affecting almost every aspect of the mosquito's life cycle. Seventy-nine candidate odorant receptors were characterized for tissue expression and, along with 76 putative gustatory receptors, for their molecular evolution relative to Drosophila melanogaster. Examples of lineage-specific gene expansions were observed as well as a single instance of unusually high sequence conservation.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2009
                23 July 2009
                : 10
                : 332
                Affiliations
                [1 ]The Key Sericultural Laboratory of Agricultural Ministry, Southwest University, Chongqing, PR China
                [2 ]Key Laboratory for Tobacco Quality Control, Ministry of Agriculture, Tobacco Research Institute, Chinese Academy of Agricultural Science, Qingdao, PR China
                [3 ]Institute of Agronomy and Life Sciences, Chongqing University, Chongqing, PR China
                Article
                1471-2164-10-332
                10.1186/1471-2164-10-332
                2722677
                19624863
                83775cdc-88f2-4be2-8f8c-c70399f9fa7d
                Copyright © 2009 Gong 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 January 2009
                : 23 July 2009
                Categories
                Research Article

                Genetics
                Genetics

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