Systematic interrogation of the Conus marmoreus venom duct transcriptome with ConoSorter reveals 158 novel conotoxins and 13 new gene superfamilies

Lavergne, Vincent, Dutertre, Sebastien, Jin, Ai-hua, Lewis, Richard J., Taft, Ryan J. and Alewood, Paul F. (2013) Systematic interrogation of the Conus marmoreus venom duct transcriptome with ConoSorter reveals 158 novel conotoxins and 13 new gene superfamilies. Bmc Genomics, 14 1: 708.1-708.11. doi:10.1186/1471-2164-14-708


Author Lavergne, Vincent
Dutertre, Sebastien
Jin, Ai-hua
Lewis, Richard J.
Taft, Ryan J.
Alewood, Paul F.
Title Systematic interrogation of the Conus marmoreus venom duct transcriptome with ConoSorter reveals 158 novel conotoxins and 13 new gene superfamilies
Journal name Bmc Genomics   Check publisher's open access policy
ISSN 1471-2164
Publication date 2013-10
Year available 2013
Sub-type Article (original research)
DOI 10.1186/1471-2164-14-708
Open Access Status DOI
Volume 14
Issue 1
Start page 708.1
End page 708.11
Total pages 12
Place of publication London, United Kingdom
Publisher BioMed Central Ltd.
Collection year 2014
Language eng
Formatted abstract
Background: Conopeptides, often generically referred to as conotoxins, are small neurotoxins found in the venom of predatory marine cone snails. These molecules are highly stable and are able to efficiently and selectively interact with a wide variety of heterologous receptors and channels, making them valuable pharmacological probes and potential drug leads. Recent advances in next-generation RNA sequencing and high-throughput proteomics have led to the generation of large data sets that require purpose-built and dedicated bioinformatics tools for efficient data mining.

Results: Here we describe ConoSorter, an algorithm that categorizes cDNA or protein sequences into conopeptide superfamilies and classes based on their signal, pro- and mature region sequence composition. ConoSorter also catalogues key sequence characteristics (including relative sequence frequency, length, number of cysteines, N-terminal hydrophobicity, sequence similarity score) and automatically searches the ConoServer database for known precursor sequences, facilitating identification of known and novel conopeptides. When applied to ConoServer and UniProtKB/Swiss-Prot databases, ConoSorter is able to recognize 100% of known conotoxin superfamilies and classes with a minimum species specificity of 99%. As a proof of concept, we performed a reanalysis of Conus marmoreus venom duct transcriptome and (i) correctly classified all sequences previously annotated, (ii) identified 158 novel precursor conopeptide transcripts, 106 of which were confirmed by protein mass spectrometry, and (iii) identified another 13 novel conotoxin gene superfamilies.

Conclusions:
Taken together, these findings indicate that ConoSorter is not only capable of robust classification of known conopeptides from large RNA data sets, but can also facilitate de novo identification of conopeptides which may have pharmaceutical importance.
Keyword ConoSorter
Cone snail
Conopeptides
Transcriptome
Proteome
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
Institute for Molecular Bioscience - Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 17 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 18 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Thu, 21 Nov 2013, 15:26:19 EST by Susan Allen on behalf of Institute for Molecular Bioscience