Peptidomic and transcriptomic profiling of four distinct spider venoms.

Vera Oldrati,Reto Stöcklin,Lucia Kuhn-Nentwig,Wolfgang Nentwig,Dominique Koua,Miriam Arrell,Pierre-Marie Allard,Jean-Luc Wolfender,Nicolas Hulo,Frédérique Lisacek,José María Gutiérrez

doi:10.1371/journal.pone.0172966

Abstract

Venom based research is exploited to find novel candidates for the development of innovative pharmacological tools, drug candidates and new ingredients for cosmetic and agrochemical industries. Moreover, venomics, as a well-established approach in systems biology, helps to elucidate the genetic mechanisms of the production of such a great molecular biodiversity. Today the advances made in the proteomics, transcriptomics and bioinformatics fields, favor venomics, allowing the in depth study of complex matrices and the elucidation even of minor compounds present in minute biological samples. The present study illustrates a rapid and efficient method developed for the elucidation of venom composition based on NextGen mRNA sequencing of venom glands and LC-MS/MS venom proteome profiling. The analysis of the comprehensive data obtained was focused on cysteine rich peptide toxins from four spider species originating from phylogenetically distant families for comparison purposes. The studied species were Heteropoda davidbowie (Sparassidae), Poecilotheria formosa (Theraphosidae), Viridasius fasciatus (Viridasiidae) and Latrodectus mactans (Theridiidae). This led to a high resolution profiling of 284 characterized cysteine rich peptides, 111 of which belong to the Inhibitor Cysteine Knot (ICK) structural motif. The analysis of H. davidbowie venom revealed a high richness in term of venom diversity: 95 peptide sequences were identified; out of these, 32 peptides presented the ICK structural motif and could be classified in six distinct families. The profiling of P. formosa venom highlighted the presence of 126 peptide sequences, with 52 ICK toxins belonging to three structural distinct families. V. fasciatus venom was shown to contain 49 peptide sequences, out of which 22 presented the ICK structural motif and were attributed to five families. The venom of L. mactans, until now studied for its large neurotoxins (Latrotoxins), revealed the presence of 14 cysteine rich peptides, out of which five were ICK toxins belonging to the CSTX superfamily. This in depth profiling of distinct ICK peptide families identified across the four spider species highlighted the high conservation of these neurotoxins among spider families.

Highlights

Venoms consist of small organic molecules, peptides and proteins, optimized for defensive and predatory purpose by Nature through 300 million years of evolution
The present study aims to compare the toxin composition of one mygalomorph and three araneomorph spiders (S1 Fig): Heteropoda davidbowie (Sparassidae), Poecilotheria formosa (Theraphosidae), Viridasius fasciatus (Viridasiidae) and Latrodectus mactans (Theridiidae)
Transcriptome profiling of the four spider species was achieved by whole venom gland mRNA sequencing using a NextGen instrument

Summary

Introduction

Venoms consist of small organic molecules, peptides and proteins, optimized for defensive and predatory purpose by Nature through 300 million years of evolution. Toxins from spider venom reported in the UniProt/SwissProt database, are to date limited to 1’446, clearly representing only the tip of the iceberg of this unexplored biodiversity (http://www.uniprot.org/uniprot/) [1,2,3] This lack of identified toxins reflects two major drawbacks in spider venom-based studies: the small size of the majority of spider species, the limited amount of venom produced and the difficult sample collection on the one hand; and the lack of optimized methodologies for in depth studies of such complex matrices, on the other hand [4]. Venomics exploits improvements in LC-MS/MS technology, often in conjunction with NextGen mRNA and DNA sequencing, in order to provide a global high resolution profiling of venom and venom gland of the species of interest This allows considerable amounts of data to be generated, without any discrimination for minor peptides. This approach, combined to dedicated bioinformatics and databases, supports the identification and characterization of an increasing number of toxins

Methods

Results

Discussion

Conclusion