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Open AccessResearch article

The use of Open Reading frame ESTs (ORESTES) for analysis of the honey bee transcriptome

Francis MF Nunes1,2 email, Valeria Valente3 email, Josane F Sousa3 email, Marco AV Cunha2 email, Daniel G Pinheiro1,2 email, Rafaela M Maia3 email, Daniela D Araujo3 email, Maria CR Costa2 email, Waleska K Martins4 email, Alex F Carvalho4 email, Nadia Monesi5 email, Adriana M Nascimento6 email, Pablo MV Peixoto7 email, Maria FR Silva7 email, Ricardo GP Ramos3 email, Luis FL Reis4 email, Emmanuel Dias-Neto8 email, Sandro J Souza4 email, Andrew JG Simpson9 email, Marco A Zago2,10 email, Ademilson EE Soares1 email, Marcia MG Bitondi6 email, Enilza M Espreafico3 email, Foued S Espindola7 email, Maria L Paco-Larson3 email, Zila LP Simoes6 email, Klaus Hartfelder3,6 email and Wilson A Silva Jr1,2 email

1  Departamento de Genética, Laboratório de Genética Molecular e Bioinformática, e Laboratório de Genética de Abelhas, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Av. Bandeirantes 3900, 14040-900 Ribeirão Preto, SP, Brazil

2  Centro de Terapia Celular e Centro Regional de Hemoterapia, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Rua Tenente Catão Roxo, 2501, 14051-140 Ribeirão Preto, SP, Brazil

3  Departamento de Biologia Celular e Molecular e Bioagentes Patogênicos, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Av. Bandeirantes 3900, 14040-900 Ribeirão Preto, SP, Brazil

4  Ludwig Institute for Cancer Research, São Paulo-Brazil

5  Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Av. do Café sn, 14040-903 Ribeirão Preto, SP, Brazil

6  Departamento de Biologia, Faculdade de Filosofia, Ciências e Letras, Universidade de São Paulo, Av. Bandeirantes 3900, 14040-900 Ribeirão Preto, SP, Brazil

7  Instituto de Genética e Bioquímica, Universidade Federal de Uberlândia, Av. Pará 1720 -Uberlândia 38400-982 MG, Brazil

8  Laboratório de Neurociências (LIM-27), Instituto de Psiquiatria, Faculdade de Medicina – Universidade de São Paulo, Rua Dr. Ovidio de Campos, s/n – Consolação 05403-010, São Paulo, SP – Brazil

9  Ludwig Institute for Cancer Research, 605 Third Avenue, New York, NY 10158

10  Departamento de Clínica Médica, Laboratório de Hematologia, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Av. Bandeirantes 3900, 14040-900 Ribeirão Preto, SP, Brazil

author email corresponding author email

BMC Genomics 2004, 5:84doi:10.1186/1471-2164-5-84

Published: 3 November 2004

Abstract

Background

The ongoing efforts to sequence the honey bee genome require additional initiatives to define its transcriptome. Towards this end, we employed the Open Reading frame ESTs (ORESTES) strategy to generate profiles for the life cycle of Apis mellifera workers.

Results

Of the 5,021 ORESTES, 35.2% matched with previously deposited Apis ESTs. The analysis of the remaining sequences defined a set of putative orthologs whose majority had their best-match hits with Anopheles and Drosophila genes. CAP3 assembly of the Apis ORESTES with the already existing 15,500 Apis ESTs generated 3,408 contigs. BLASTX comparison of these contigs with protein sets of organisms representing distinct phylogenetic clades revealed a total of 1,629 contigs that Apis mellifera shares with different taxa. Most (41%) represent genes that are in common to all taxa, another 21% are shared between metazoans (Bilateria), and 16% are shared only within the Insecta clade. A set of 23 putative genes presented a best match with human genes, many of which encode factors related to cell signaling/signal transduction. 1,779 contigs (52%) did not match any known sequence. Applying a correction factor deduced from a parallel analysis performed with Drosophila melanogaster ORESTES, we estimate that approximately half of these no-match ESTs contigs (22%) should represent Apis-specific genes.

Conclusions

The versatile and cost-efficient ORESTES approach produced minilibraries for honey bee life cycle stages. Such information on central gene regions contributes to genome annotation and also lends itself to cross-transcriptome comparisons to reveal evolutionary trends in insect genomes.

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