Dataset Information

De Novo sequencing and transcriptome analysis for Tetramorium bicarinatum: a comprehensive venom gland transcriptome analysis from an ant species.

ABSTRACT: BACKGROUND: Arthropod venoms are invaluable sources of bioactive substances with biotechnological application. The limited availability of some venoms, such as those from ants, has restricted the knowledge about the composition and the potential that these biomolecules could represent. In order to provide a global insight on the transcripts expressed in the venom gland of the Brazilian ant species Tetramorium bicarinatum and to unveil the potential of its products, high-throughput approach using Illumina technology has been applied to analyze the genes expressed in active venom glands of this ant species. RESULTS: A total of 212,371,758 pairs of quality-filtered, 100-base-pair Illumina reads were obtained. The de novo assemblies yielded 36,042 contigs for which 27,873 have at least one predicted ORF among which 59.77% produce significant hits in the available databases. The investigation of the reads mapping toxin class revealed a high diversification with the major part consistent with the classical hymenopteran venom protein signature represented by venom allergen (33.3%), followed by a diverse toxin-expression profile including several distinct isoforms of phospholipase A1 and A2, venom serine protease, hyaluronidase, protease inhibitor and secapin. Moreover, our results revealed for the first time the presence of toxin-like peptides that have been previously identified from unrelated venomous animals such as waprin-like (snakes) and agatoxins (spiders and conus).The non-toxin transcripts were mainly represented by contigs involved in protein folding and translation, consistent with the protein-secretory function of the venom gland tissue. Finally, about 40% of the generated contigs have no hits in the databases with 25% of the predicted peptides bearing signal peptide emphasizing the potential of the investigation of these sequences as source of new molecules. Among these contigs, six putative novel peptides that show homologies with previously identified antimicrobial peptides were identified. CONCLUSIONS: To the best of our knowledge, this work reports the first large-scale analysis of genes transcribed by the venomous gland of the ant species T. bicarinatum and helps with the identification of Hymenoptera toxin arsenal. In addition, results from this study demonstrate that de novo transcriptome assembly allows useful venom gene expression analysis in a species lacking a genome sequence database.

SUBMITTER: Bouzid W

PROVIDER: S-EPMC4256838 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

De Novo sequencing and transcriptome analysis for Tetramorium bicarinatum: a comprehensive venom gland transcriptome analysis from an ant species.

Bouzid Wafa W Verdenaud Marion M Klopp Christophe C Ducancel Frédéric F Noirot Céline C Vétillard Angélique A

BMC genomics 20141118

<h4>Background</h4>Arthropod venoms are invaluable sources of bioactive substances with biotechnological application. The limited availability of some venoms, such as those from ants, has restricted the knowledge about the composition and the potential that these biomolecules could represent. In order to provide a global insight on the transcripts expressed in the venom gland of the Brazilian ant species Tetramorium bicarinatum and to unveil the potential of its products, high-throughput approac ...[more]

PMID: 25407482

Similar Datasets

Project description:Venom-gland transcriptomics is a key tool in the study of the evolution, ecology, function, and pharmacology of animal venoms. In particular, gene-expression variation and coding sequences gained through transcriptomics provide key information for explaining functional venom variation over both ecological and evolutionary timescales. The accuracy and usefulness of inferences made through transcriptomics, however, is limited by the accuracy of the transcriptome assembly, which is a bioinformatic problem with several possible solutions. Several methods have been employed to assemble venom-gland transcriptomes, with the Trinity assembler being the most commonly applied among them. Although previous evidence of variation in performance among assembly software exists, particularly regarding recovery of difficult-to-assemble multigene families such as snake venom metalloproteinases, much work to date still employs a single assembly method. We evaluated the performance of several commonly used de novo assembly methods for the recovery of both nontoxin transcripts and complete, high-quality venom-gene transcripts across eleven snake and four scorpion transcriptomes. We varied k-mer sizes used by some assemblers to evaluate the impact of k-mer length on transcript recovery. We showed that the recovery of nontoxin transcripts and toxin transcripts is best accomplished through different assembly software, with SDT at smaller k-mer lengths and Trinity being best for nontoxin recovery and a combination of SeqMan NGen and a seed-and-extend approach implemented in Extender as the best means of recovering a complete set of toxin transcripts. In particular, Extender was the only means tested capable of assembling multiple isoforms of the diverse snake venom metalloproteinase family, while traditional approaches such as Trinity recovered at most one metalloproteinase transcript. Our work demonstrated that traditional metrics of assembly performance are not predictive of performance in the recovery of complete and high quality toxin genes. Instead, effective venom-gland transcriptomic studies should combine and quality-filter the results of several assemblers with varying algorithmic strategies.

Dataset Information

De Novo sequencing and transcriptome analysis for Tetramorium bicarinatum: a comprehensive venom gland transcriptome analysis from an ant species.

Publications

De Novo sequencing and transcriptome analysis for Tetramorium bicarinatum: a comprehensive venom gland transcriptome analysis from an ant species.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets