Transcriptomic Profiling Reveals Extraordinary Diversity of Venom Peptides in Unexplored Predatory Gastropods of the Genus Clavus

Predatory gastropods of the superfamily Conoidea number over 12,000 living species. The evolutionary success of this lineage can be explained by the ability of conoideans to produce complex venoms for hunting, defense, and competitive interactions. Whereas venoms of cone snails (family Conidae) have become increasingly well studied, the venoms of most other conoidean lineages remain largely uncharacterized. In the present study, we present the venom gland transcriptomes of two species of the genus Clavus that belong to the family Drilliidae. Venom gland transcriptomes of two specimens of Clavus canalicularis and two specimens of Clavus davidgilmouri were analyzed, leading to the identification of a total of 1,176 putative venom peptide toxins (drillipeptides). Based on the combined evidence of secretion signal sequence identity, entire precursor similarity search (BLAST), and the orthology inference, putative Clavus toxins were assigned to 158 different gene families. The majority of identified transcripts comprise signal, pro-, mature peptide, and post-regions, with a typically short (<50 amino acids) and cysteine-rich mature peptide region. Thus, drillipeptides are structurally similar to conotoxins. However, convincing homology with known groups of Conus toxins was only detected for very few toxin families. Among these are Clavus counterparts of Conus venom insulins (drillinsulins), porins (drilliporins), and highly diversified lectins (drillilectins). The short size of most drillipeptides and structural similarity to conotoxins were unexpected, given that most related conoidean gastropod families (Terebridae and Turridae) possess longer mature peptide regions. Our findings indicate that, similar to conotoxins, drillipeptides may represent a valuable resource for future pharmacological exploration.

Discovery of Novel Conotoxin Candidates Using Machine Learning

Cone snails (genus Conus) are venomous marine snails that inject prey with a lethal cocktail of conotoxins, small, secreted, and cysteine-rich peptides. Given the diversity and often high affinity for their molecular targets, consisting of ion channels, receptors or transporters, many conotoxins have become invaluable pharmacological probes, drug leads, and therapeutics. Transcriptome sequencing of Conus venom glands followed by de novo assembly and homology-based toxin identification and annotation is currently the state-of-the-art for discovery of new conotoxins. However, homology-based search techniques, by definition, can only detect novel toxins that are homologous to previously reported conotoxins. To overcome these obstacles for discovery, we have created ConusPipe, a machine learning tool that utilizes prominent chemical characters of conotoxins to predict whether a certain transcript in a Conus transcriptome, which has no otherwise detectable homologs in current reference databases, is a putative conotoxin. By using ConusPipe on RNASeq data of 10 species, we report 5148 new putative conotoxin transcripts that have no homologues in current reference databases. 896 of these were identified by at least three out of four models used. These data significantly expand current publicly available conotoxin datasets and our approach provides a new computational avenue for the discovery of novel toxin families.

Discovery of Novel Conotoxin Candidates Using Machine Learning

Cone snails (genus Conus) are venomous marine snails that inject prey with a lethal cocktail of conotoxins, small, secreted, cysteine-rich peptides. Given the diversity and often high affinity for their molecular targets, consisting of ion channels, receptors or transporters, many conotoxins have become invaluable pharmacological probes, drug leads and therapeutics. Transcriptome sequencing of Conus venom glands followed by de novo assembly and homology-based toxin identification and annotation is currently the state-of-the-art for discovery of new conotoxins. However, homology-based search techniques, by definition, can only detect novel toxins that are homologous to previously reported conotoxins. To overcome these obstacles for discovery we have created ConusPipe, a machine learning tool that utilizes prominent chemical characters of conotoxins to predict whether a certain transcript in a Conus transcriptome, which has no otherwise detectable homologs in current reference databases, is a putative conotoxin. By using ConusPipe on RNASeq data of 10 species, we report 5,230 new putative conotoxin transcripts that have no homologues in current reference databases. 893 of these were identified by at least 3 out of 4 models used. These data significantly expand current publicly available conotoxin datasets and our approach provides a new computational avenue for the discovery of novel toxin families.