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• W2159577812 abstract "Marine cone snails have developed sophisticated chemical strategies to capture prey and defend themselves against predators. Among the vast array of bioactive molecules in their venom, peptide components called conotoxins or conopeptides dominate, with many binding with high affinity and selectivity to a broad range of cellular targets, including receptors and transporters of the nervous system. Whereas the conopeptide gene precursor organization has a conserved topology, the peptides in the venom duct are highly processed. Indeed, deep sequencing transcriptomics has uncovered on average fewer than 100 toxin gene precursors per species, whereas advanced proteomics has revealed >10-fold greater diversity at the peptide level. In the present study, second-generation sequencing technologies coupled to highly sensitive mass spectrometry methods were applied to rapidly uncover the conopeptide diversity in the venom of a worm-hunting species, Conus miles. A total of 662 putative conopeptide encoded sequences were retrieved from transcriptomic data, comprising 48 validated conotoxin sequences that clustered into 10 gene superfamilies, including 3 novel superfamilies and a novel cysteine framework (C-C-C-CCC-C-C) identified at both transcript and peptide levels. A surprisingly large number of conopeptide gene sequences were expressed at low levels, including a series of single amino acid variants, as well as sequences containing deletions and frame and stop codon shifts. Some of the toxin variants generate alternative cleavage sites, interrupted or elongated cysteine frameworks, and highly variable isoforms within families that could be identified at the peptide level. Together with the variable peptide processing identified previously, background genetic and phenotypic levels of biological messiness in venoms contribute to the hypervariability of venom peptides and their ability to evolve rapidly. Marine cone snails have developed sophisticated chemical strategies to capture prey and defend themselves against predators. Among the vast array of bioactive molecules in their venom, peptide components called conotoxins or conopeptides dominate, with many binding with high affinity and selectivity to a broad range of cellular targets, including receptors and transporters of the nervous system. Whereas the conopeptide gene precursor organization has a conserved topology, the peptides in the venom duct are highly processed. Indeed, deep sequencing transcriptomics has uncovered on average fewer than 100 toxin gene precursors per species, whereas advanced proteomics has revealed >10-fold greater diversity at the peptide level. In the present study, second-generation sequencing technologies coupled to highly sensitive mass spectrometry methods were applied to rapidly uncover the conopeptide diversity in the venom of a worm-hunting species, Conus miles. A total of 662 putative conopeptide encoded sequences were retrieved from transcriptomic data, comprising 48 validated conotoxin sequences that clustered into 10 gene superfamilies, including 3 novel superfamilies and a novel cysteine framework (C-C-C-CCC-C-C) identified at both transcript and peptide levels. A surprisingly large number of conopeptide gene sequences were expressed at low levels, including a series of single amino acid variants, as well as sequences containing deletions and frame and stop codon shifts. Some of the toxin variants generate alternative cleavage sites, interrupted or elongated cysteine frameworks, and highly variable isoforms within families that could be identified at the peptide level. Together with the variable peptide processing identified previously, background genetic and phenotypic levels of biological messiness in venoms contribute to the hypervariability of venom peptides and their ability to evolve rapidly. Cone snails are predatory marine gastropods that feed on a variety of prey, including fish and invertebrates. With ∼700 cone snail species described, Conus represents the largest genus of all invertebrate marine animals. The current classification and phylogeny of cone snails are still a matter of debate and are being refined using genomic DNA and radula morphology data (1Bouchet P. Kantor Y.I. Sysoev A. Puillandre N. A new operational classification of the conoidea.J. Molluscan Stud. 2011; 77: 273-308Crossref Scopus (130) Google Scholar, 2Biggs J.S. Watkins M. Corneli P.S. Olivera B.M. Defining a clade by morphological, molecular and toxinological criteria: distinctive forms related to Conus praecellens A. Adams, 1854.Nautilus. 2010; 124: 1-19PubMed Google Scholar, 3Duda Jr., T.F. Bolin M.B. Meyer C.P. Kohn A.J. Hidden diversity in a hyperdiverse gastropod genus: discovery of previously unidentified members of a Conus species complex.Mol. Phylogenet. Evol. 2008; 49: 867-876Crossref PubMed Scopus (41) Google Scholar). Cone snails are classified in the taxonomic class Neogastropoda, which comprises three superfamilies, Muricoidea, Cancellarioidea, and Conoidea (4Modica M.V. Holford M. The Neogastropoda: evolutionary innovations of predatory marine snails with remarkable pharmacological potential.in: Pontarotti P. Evolutionary Biology—Concepts, Molecular and Morphological Evolution. Springer-Verlag, Berlin Heidelberg2010: 249-270Google Scholar). The Conidae family belongs to the Conoidea branch, and the only genus of this family is Conus (1Bouchet P. Kantor Y.I. Sysoev A. Puillandre N. A new operational classification of the conoidea.J. Molluscan Stud. 2011; 77: 273-308Crossref Scopus (130) Google Scholar). Cone snails have evolved potent venoms that they use for defense and capturing prey (5Olivera B.M. E. E. Just Lecture, 1996. Conus venom peptides, receptor and ion channel targets, and drug design: 50 million years of neuropharmacology.Mol. Biol. Cell. 1997; 8: 2101-2109Crossref PubMed Scopus (332) Google Scholar). These venoms are highly complex mixtures of dominant cysteine-rich conotoxins as well as cysteine-poor conopeptides, enzymes, and proteins (6Violette A. Biass D. Dutertre S. Koua D. Piquemal D. Pierrat F. Stocklin R. Favreau P. Large-scale discovery of conopeptides and conoproteins in the injectable venom of a fish-hunting cone snail using a combined proteomic and transcriptomic approach,.J. Proteomics. 2012; 75: 5215-5225Crossref PubMed Scopus (68) Google Scholar, 7Czerwiec E. Begley G.S. Bronstein M. Stenflo J. Taylor K. Furie B.C. Furie B. Expression and characterization of recombinant vitamin K-dependent gamma-glutamyl carboxylase from an invertebrate, Conus textile.Eur. J. Biochem. 2002; 269: 6162-6172Crossref PubMed Scopus (45) Google Scholar, 8Marsh H. The caseinase activity of some vermivorous cone shell venoms.Toxicon. 1971; 9: 63-67Crossref PubMed Scopus (12) Google Scholar, 9Moller C. Vanderweit N. Bubis J. Mari F. Comparative analysis of proteases in the injected and dissected venom of cone snail species.Toxicon. 2013; 65: 59-67Crossref PubMed Scopus (12) Google Scholar, 10Violette A. Leonardi A. Piquemal D. Terrat Y. Biass D. Dutertre S. Noguier F. Ducancel F. Stocklin R. Krizaj I. Favreau P. Recruitment of glycosyl hydrolase proteins in a cone snail venomous arsenal: further insights into biomolecular features of Conus venoms.Marine Drugs. 2012; 10: 258-280Crossref PubMed Scopus (27) Google Scholar). Conopeptides are produced as mRNA precursors displaying a mostly conserved topological organization comprising an N-terminus signal sequence followed by an intervening propeptide region, the mature toxin region, and, for some, an additional C-terminal propeptide region (11Woodward S.R. Cruz L.J. Olivera B.M. Hillyard D.R. Constant and hypervariable regions in conotoxin propeptides.EMBO J. 1990; 9: 1015-1020Crossref PubMed Scopus (172) Google Scholar). Based on signal sequence similarities, conopeptides have been classified into 18 gene superfamilies (12Kaas Q. Westermann J.C. Halai R. Wang C.K. Craik D.J. ConoServer, a database for conopeptide sequences and structures.Bioinformatics. 2008; 24: 445-446Crossref PubMed Scopus (175) Google Scholar, 13Kaas Q. Yu R. Jin A.H. Dutertre S. Craik D.J. ConoServer: updated content, knowledge, and discovery tools in the conopeptide database.Nucleic Acids Res. 2012; 40: D325-D330Crossref PubMed Scopus (256) Google Scholar), which reveal evolutionary relationships between different conopeptides. Indeed, the higher evolution rate of mature peptide regions prevents the establishment of reliable phylogeny using these regions only, and only the conservation of signal sequences offers the possibility of relating conopeptide precursors (14Puillandre N. Koua D. Favreau P. Olivera B.M. Stocklin R. Molecular phylogeny, classification and evolution of conopeptides.J. Mol. Evol. 2012; 74: 297-309Crossref PubMed Scopus (71) Google Scholar). During their journey in the endoplasmic reticulum and the export machinery, conopeptides are excised from the precursors with proteases (15Milne T.J. Abbenante G. Tyndall J.D. Halliday J. Lewis R.J. Isolation and characterization of a cone snail protease with homology to CRISP proteins of the pathogenesis-related protein superfamily.J. Biol. Chem. 2003; 278: 31105-31110Abstract Full Text Full Text PDF PubMed Scopus (194) Google Scholar) and at the same time are heavily post-translationally modified. Currently, 14 different post-translational modifications are identified in conopeptides (13Kaas Q. Yu R. Jin A.H. Dutertre S. Craik D.J. ConoServer: updated content, knowledge, and discovery tools in the conopeptide database.Nucleic Acids Res. 2012; 40: D325-D330Crossref PubMed Scopus (256) Google Scholar). The most common post-translational modification is the formation of disulfide bonds, and the conopeptides with more than one disulfide bond are commonly referred to as conotoxins (16Olivera B.M. Rivier J. Scott J.K. Hillyard D.R. Cruz L.J. Conotoxins.J. Biol. Chem. 1991; 266: 22067-22070Abstract Full Text PDF PubMed Google Scholar). Conotoxins are currently divided into 24 cysteine frameworks, designated using Roman numerals, according to the arrangement of cysteines in the mature peptide region (17Kaas Q. Westermann J.C. Craik D.J. Conopeptide characterization and classifications: an analysis using ConoServer.Toxicon. 2010; 55: 1491-1509Crossref PubMed Scopus (169) Google Scholar, 18Luo S. Christensen S. Zhangsun D. Wu Y. Hu Y. Zhu X. Chhabra S. Norton R.S. McIntosh J.M. A novel inhibitor of alpha9alpha10 nicotinic acetylcholine receptors from Conus vexillum delineates a new conotoxin superfamily.PloS One. 2013; 8: e54648Crossref PubMed Scopus (44) Google Scholar). The disulfide bond connectivities are usually important for the folding and activity of conotoxins, although they are not part of the definition of the cysteine frameworks. Second-generation transcriptomics has to date uncovered on average fewer than 100 toxin cDNA precursors per Conus species (19Dutertre S. Jin A.H. Kaas Q. Jones A. Alewood P.F. Lewis R.J. Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom.Mol. Cell. Proteomics. 2013; 12: 312-329Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar, 20Hu H. Bandyopadhyay P.K. Olivera B.M. Yandell M. Characterization of the Conus bullatus genome and its venom-duct transcriptome.BMC Genomics. 2011; 12: 60Crossref PubMed Scopus (97) Google Scholar, 21Hu H. Bandyopadhyay P.K. Olivera B.M. Yandell M. Elucidation of the molecular envenomation strategy of the cone snail Conus geographus through transcriptome sequencing of its venom duct.BMC Genomics. 2012; 13: 284Crossref PubMed Scopus (75) Google Scholar, 22Lluisma A.O. Milash B.A. Moore B. Olivera B.M. Bandyopadhyay P.K. Novel venom peptides from the cone snail Conus pulicarius discovered through next-generation sequencing of its venom duct transcriptome.Mar. Genomics. 2012; 5: 43-51Crossref PubMed Scopus (58) Google Scholar, 23Terrat Y. Biass D. Dutertre S. Favreau P. Remm M. Stocklin R. Piquemal D. Ducancel F. High-resolution picture of a venom gland transcriptome: case study with the marine snail Conus consors.Toxicon. 2012; 59: 34-46Crossref PubMed Scopus (73) Google Scholar). A more impressive molecular diversity has been described at the peptide level in cone snail venoms, with >1000 detected masses observed in a single specimen (19Dutertre S. Jin A.H. Kaas Q. Jones A. Alewood P.F. Lewis R.J. Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom.Mol. Cell. Proteomics. 2013; 12: 312-329Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar, 24Davis J. Jones A. Lewis R.J. Remarkable inter- and intra-species complexity of conotoxins revealed by LC/MS.Peptides. 2009; 30: 1222-1227Crossref PubMed Scopus (150) Google Scholar), and even closely related species can display a completely different set of conopeptides in their venom (24Davis J. Jones A. Lewis R.J. Remarkable inter- and intra-species complexity of conotoxins revealed by LC/MS.Peptides. 2009; 30: 1222-1227Crossref PubMed Scopus (150) Google Scholar). Phylogenetic studies of certain gene superfamilies of conopeptides revealed extensive gene turnover, rapid evolution, and diversification within relatively recent evolutionary time (25Chang D. Duda Jr., T.F. Extensive and continuous duplication facilitates rapid evolution and diversification of gene families.Mol. Biol. Evol. 2012; 29: 2019-2029Crossref PubMed Scopus (106) Google Scholar), with conopeptide genes being among the most rapidly evolving protein-coding genes in Metazoans, a phenomenon thought to be facilitated by extensive gene duplications (25Chang D. Duda Jr., T.F. Extensive and continuous duplication facilitates rapid evolution and diversification of gene families.Mol. Biol. Evol. 2012; 29: 2019-2029Crossref PubMed Scopus (106) Google Scholar). An understanding of how Conus venoms have evolved to generate this vast number of peptides from a limited set of genes is expected to shed light on the rapid molecular evolution of Conus venom peptides (19Dutertre S. Jin A.H. Kaas Q. Jones A. Alewood P.F. Lewis R.J. Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom.Mol. Cell. Proteomics. 2013; 12: 312-329Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar). In the present study, a 454 pyrosequencing approach was applied to uncover the transcriptome of a worm-hunting species of cone snail, Conus miles. To date, only a cDNA cloning strategy using conserved signal peptides has been applied to the discovery of conotoxins from this species, with three superfamilies (O1, D, and I2) and 10 conopeptide sequences currently identified (26Loughnan M.L. Nicke A. Lawrence N. Lewis R.J. Novel alpha D-conopeptides and their precursors identified by cDNA cloning define the D-conotoxin superfamily.Biochemistry. 2009; 48: 3717-3729Crossref PubMed Scopus (33) Google Scholar, 27Luo S. Zhangsun D. Feng J. Wu Y. Zhu X. Hu Y. Diversity of the O-superfamily conotoxins from Conus miles.J. Pept. Sci. 2007; 13: 44-53Crossref PubMed Scopus (12) Google Scholar, 28Mondal S. Babu R.M. Bhavna R. Ramakumar S. I-conotoxin superfamily revisited.J. Pept. Sci. 2006; 12: 679-685Crossref PubMed Scopus (11) Google Scholar). To fully characterize the conopeptide isoforms, cysteine frameworks, and gene superfamilies within C. miles venom, we integrated transcriptomic and proteomic data using bioinformatics. This approach revealed unsuspected messiness at the mRNA level (29Tawfik D.S. Messy biology and the origins of evolutionary innovations.Nat. Chem. Biol. 2010; 6: 692-696Crossref PubMed Scopus (158) Google Scholar), where we identified a series of single amino acid variants (type I variants), pre-mature stop codons (type II variants), and frame shifts (type III variants). These variations produced conopeptides with alternative cleavage sites (types I and III), interrupted or elongated cysteine frameworks (types I, II, and III), and highly variable isoforms including deletions and elongations (types II and III). Interestingly, most of these unusual toxin variants were expressed at very low levels, and given the high rates of evolution of conotoxin genes within families and the presence of these single read (mRNA) peptides in the venom, we hypothesize that this “background” genetic noise or “transcriptomic messiness” contributes to venom peptide hypervariability and, more broadly, to the rapid evolution of bioactive peptides. One single adult specimen of C. miles collected from the Great Barrier Reef (Queensland, Australia) and measuring 6 cm was dissected on ice. The venom duct was removed and directly placed in a 1.5-ml tube with 1 ml of TRIZOL reagent (Invitrogen). The extraction of total RNA was carried out following the manufacturer's instructions. mRNA was purified from the tRNA using a Qiagen mRNA extraction kit (Qiagen, Valencia, CA). The Australian Genomic Research Facility conducted the next-generation sequencing using a Roche GS FLX Titanium sequencer. The assembly was carried out using Newbler 2.3. Raw cDNA reads (expressed sequence tags) and isotigs were up-loaded in a Web-based searchable database set up by the Australian Genomic Research Facility. Sorting of raw cDNA reads was performed with ConoSorter, a stand-alone program developed in-house to classify conopeptides into gene superfamilies and classes. Briefly, after translating nucleic acid sequences in the six reading frames, the algorithm isolates the corresponding coding regions and classifies them into superfamilies and classes by employing an approach based on the complementarity of regular expressions and profile hidden Markov models. Finally, the program searches the ConoServer database for sequences already characterized and generates additional statistical information about the matching hits (frequency of identical sequences in the raw data set, percent hydrophobicity of the signal region, sequence length, and number of cysteine residues present). Manual identification of conopeptide sequences was carried out from the retrieved data. Gene superfamilies, signal peptides, and cleavage sites were predicted using the ConoPrec tool implemented in ConoServer. The cut-off value for assigning a signal peptide to a gene superfamily was set at >75% sequence identity, as extrapolated from a recent analysis of all precursors deposited in ConoServer (17Kaas Q. Westermann J.C. Craik D.J. Conopeptide characterization and classifications: an analysis using ConoServer.Toxicon. 2010; 55: 1491-1509Crossref PubMed Scopus (169) Google Scholar, 19Dutertre S. Jin A.H. Kaas Q. Jones A. Alewood P.F. Lewis R.J. Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom.Mol. Cell. Proteomics. 2013; 12: 312-329Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar). The pooled venom obtained from three adult (≥6 cm) specimens of C. miles collected from the Great Barrier Reef (Queensland, Australia) was used for proteomic studies. Dissection was performed on ice, the venom ducts were squeezed, and the contents were collected in 1 ml of 0.1% formic acid and stored at −20° C until further use. Reduction and alkylation of the cysteine bonds was carried out as previously described (19Dutertre S. Jin A.H. Kaas Q. Jones A. Alewood P.F. Lewis R.J. Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom.Mol. Cell. Proteomics. 2013; 12: 312-329Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar). Sigma proteomics sequencing-grade trypsin and endoproteinase Glu-C were used to digest the reduced and alkylated venom samples, and the enzymes were activated in 40 mm NH4HCO3 buffer. A ratio of 1:100 (w/w) of enzyme to venom peptides was used. The digestion was carried out in a microwave apparatus for 4 min on the lowest power setting. Liquid chromatography–electrospray mass spectrometry (LC-ESI-MS) 1The abbreviations used are:LC-ESI-MSliquid chromatography–electrospray mass spectrometry. was performed on an AB Sciex 5600 TF as previously described (19Dutertre S. Jin A.H. Kaas Q. Jones A. Alewood P.F. Lewis R.J. Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom.Mol. Cell. Proteomics. 2013; 12: 312-329Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar). Briefly, the dissected venom extracted as described above (∼8 μl supernatant) was directly subjected to LC-ESI-MS in order to obtain a complete mass list of underivatized peptides. Information Dependent Acquisition was performed on the reduced, reduced/alkylated, and enzymatically digested venom samples (i.e. four sets of samples for MS/MS). We used ProteinPilot 4.0 software for peaklist generation and sequence identification by searching the LC-ESI-MS/MS spectra against the raw cDNA database (1,534,974 entries) generated by the Roche 454 GS FLX Titanium sequencer. For comparison, the spectra were also searched against a publicly accessible database extracted from UniProtKB using “venom protein” as the keyword (3906 entries). With the alkylated samples, the fixed modification was set as maleimide for cysteine alkylation. Nine different types of variable modifications that have been identified on conopeptides were considered: amidation, deamidation, hydroxylation of proline and valine, oxidation of methionine, carboxylation of glutamic acid, cyclization of N-terminal glutamine (pyroglutamate), bromination of tryptophan, and sulfation of tyrosine. The mass tolerance was set as 0.05 Da for precursor ions and 0.1 Da for the fragment ions. Tandem mass spectra were only acquired to the 2 to 5 charged ions, and the switch criteria were set to exclude former target ions for 8 s and to exclude isotopes within 4 Da. The threshold score for accepting individual peptide spectra was 99. The detected peptide pieces were manually inspected and validated. liquid chromatography–electrospray mass spectrometry. A single run on the Roche GS FLX Titanium sequencer (one-quarter of a plate equivalent for C. miles; see “Experimental Procedures”) generated 255,829 cDNA reads averaging 325 bp (minimum of 19 bp) after trimming and removal of low-quality sequences. The raw cDNA reads were assembled using Newbler 2.3 software. Both the raw cDNA reads and assembled isotigs (including contigs) were sorted by our in-house program ConoSorter. After translation and motif searching using parameters generated from the ConoServer database, 17,215 and 50 peptide precursors were retrieved from the raw data and the isotigs, respectively. These peptide precursors were manually examined according to homology analysis generated by the ConoSorter program. Interestingly, only a small fraction of the total number of conopeptide precursors found in the raw data were retrieved from the assembled isotigs, indicating that genetic diversity is underestimated if only isotigs are analyzed. Overall, 662 precursors were characterized as putative conopeptide sequences based on the conserved precursor structures. Because the raw cDNA reads library was not normalized, the level of mRNA transcription could be inferred from the number of cDNA reads that coded for each conopeptide (19Dutertre S. Jin A.H. Kaas Q. Jones A. Alewood P.F. Lewis R.J. Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom.Mol. Cell. Proteomics. 2013; 12: 312-329Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar). Dramatic differences at the mRNA level were observed between the different conopeptide precursors. Isoform MiEr95, belonging to the O1 gene superfamily, largely dominated the transcriptome with 4128 cDNA reads, whereas 495 putative conopeptide precursors were identified with only 1 cDNA read. These rare transcripts constituted ∼75% of the total putative conopeptide precursors retrieved (Fig. 1). In addition to these sequences, 35 high-level precursors (>10 cDNA reads) and 132 low-level precursors (2 to 10 cDNA reads) were also identified. Using a 75% signal peptide homology cut-off (17Kaas Q. Westermann J.C. Craik D.J. Conopeptide characterization and classifications: an analysis using ConoServer.Toxicon. 2010; 55: 1491-1509Crossref PubMed Scopus (169) Google Scholar, 19Dutertre S. Jin A.H. Kaas Q. Jones A. Alewood P.F. Lewis R.J. Deep venomics reveals the mechanism for expanded peptide diversity in cone snail venom.Mol. Cell. Proteomics. 2013; 12: 312-329Abstract Full Text Full Text PDF PubMed Scopus (165) Google Scholar), we clustered the 662 putative conopeptide precursors into eight known (i.e. O1, O2, D, M, T, I2, L, and P) and eight putative new (1 to 8) gene superfamilies. The signal peptides and cysteine frameworks are listed in Table I. The identification of known gene superfamilies was confirmed using the ConoPrec tool in ConoServer. Conopeptide isoforms from all three previously discovered superfamilies (O1, D, and I2) from C. miles (26Loughnan M.L. Nicke A. Lawrence N. Lewis R.J. Novel alpha D-conopeptides and their precursors identified by cDNA cloning define the D-conotoxin superfamily.Biochemistry. 2009; 48: 3717-3729Crossref PubMed Scopus (33) Google Scholar, 27Luo S. Zhangsun D. Feng J. Wu Y. Zhu X. Hu Y. Diversity of the O-superfamily conotoxins from Conus miles.J. Pept. Sci. 2007; 13: 44-53Crossref PubMed Scopus (12) Google Scholar, 28Mondal S. Babu R.M. Bhavna R. Ramakumar S. I-conotoxin superfamily revisited.J. Pept. Sci. 2006; 12: 679-685Crossref PubMed Scopus (11) Google Scholar) were observed. However, only 3 (MiEr95, MiEr93, and Ml20.1) of the 10 known sequences described in the literature were identified in this transcriptome, probably because of the well-known phenomena of intraspecific variation in cone snails (24Davis J. Jones A. Lewis R.J. Remarkable inter- and intra-species complexity of conotoxins revealed by LC/MS.Peptides. 2009; 30: 1222-1227Crossref PubMed Scopus (150) Google Scholar, 30Dutertre S. Biass D. Stocklin R. Favreau P. Dramatic intraspecimen variations within the injected venom of Conus consors: an unsuspected contribution to venom diversity,.Toxicon. 2010; 55: 1453-1462Crossref PubMed Scopus (64) Google Scholar, 31Jakubowski J.A. Kelley W.P. Sweedler J.V. Gilly W.F. Schulz J.R. Intraspecific variation of venom injected by fish-hunting Conus snails.J. Exp. Biol. 2005; 208: 2873-2883Crossref PubMed Scopus (65) Google Scholar); previous discoveries were made using venom pooled from 9 to 15 specimens. In addition to these known sequences, new isoforms from five other gene superfamilies (M, O2, L, T, and P) were also found. Finally, eight putative new gene superfamilies (coded SF-mi1–8) were identified. SF-mi1 and -2 are closely related to superfamily M (64% and 57%), whereas SF-mi3 is closely related to superfamily O2 (69%). SF-mi4, -5, and -7 contained only two cysteine residues in their mature conopeptides; they all showed less than 50% homology to the signal peptides of other known superfamilies. In contrast, SF-mi6 and -8 had eight cysteine residues in their mature peptides, and their signal peptides were ∼ 60% homologous to M and I2, respectively.Table IRepresentative signal sequence(s) and cysteine framework for C. miles superfamiliesGene superfamilySignal sequence(s)Cysteine patternFrameworkO1MKLLCVLIVAMLPLMACHLIIAC-C-CC-C-CVI/VIIMKLTCALIITLLFLSITAGO2MEKLTVLILVATVLLTIQVLGC-C-CC-C-C-C-CXVDMPKLEMMLLVLLILPLSSFSAAC-CC-C-CC-C-C-C-CXXMMSKLGVVLFIFLVLFTMATLQLDACC-C-C-CCIIILMKLSVMFIVFLMLTMPMTDGC-CC-CXXIVTMCCLPVFIILLLLIPSASCC-CCVI2MMCRLTSLCCLLVIVLLNSAVDGC-C-CC-CC-C-CXIPMHLSLAGSAVLVLLLLFALGNFAGVQPC-C-C-C-C-CIXSF-mi1MSKTGLVLVVLYLLSSPVNLC-C-C-CC-C-C-CXIIISF-mi2MRFFFLLLTVALFLTSITGC-C-C-CCC-C-CNovelSF-mi3MGILTVLLPLVAVLVLTC-C-CC-C-CVI/VIISF-mi4MTPRMNLLLMTFVVMTVPLLLAC-C–SF-mi5MGLLPLQTSVLLLAPVVHQC-C–SF-mi6MSTLGKVLLLLLLLLPLGNPC-C-CC-C-C-C-CXVSF-mi7MSTLNPLTRIYWRASLVPAAAVIPAPIAYTC-C–SF-mi8MMYRLTLFCCLLLVIVPLNMAC-C-C-CC-C-C-CXIII Open table in a new tab The total numbers of isoforms and cDNA reads for each superfamily are plotted in Fig. 1. Overall, the greatest number of isoforms was discovered for superfamily O1, accounting for approximately four times more sequences than the isoforms from the remaining superfamilies combined, irrespective of read number (Fig. 1B). Only four other known superfamilies (O2, M, L, and I2) contain isoforms with high-level cDNA reads (>10 reads). D and T superfamily isoforms display only low-level cDNA reads (2 to 10 reads and 1 read, respectively). Four identified isoforms belonged to the P superfamily, all with only one cDNA read (Fig. 1C). Some isoforms in the three putative new superfamilies were identified with high cDNA reads (SF-mi2, -4, and -7). The remaining five putative new superfamilies had low- or very low-level cDNA read numbers (Fig. 1D). To interrogate our transcriptomic sequences at the peptide level, we employed a proteomic strategy involving LC-ESI-MS and LC-ESI-MS/MS to uncover the complexity of C. miles dissected venom. To improve MS/MS fragmentation, the whole venom was reduced and alkylated prior to digestion (32Hale J.E. Butler J.P. Gelfanova V. You J.-S. Knierman M.D. A simplified procedure for the reduction and alkylation of cysteine residues in proteins prior to proteolytic digestion and mass spectral analysis.Anal. Biochem. 2004; 333: 174-181Crossref PubMed Scopus (94) Google Scholar) by either endo-GluC or trypsin. The total ion chromatogram (Fig. 2) illustrates the mass profile of the native venom sample. The pooled dissected venom of C. miles from three specimens was complex; nevertheless, 9 out of the 10 previously published conopeptide sequences from C. miles could be identified as major components (see Fig. 2). MS/MS coverage was obtained across all gene precursors regardless of their level of transcription; that is, some mRNA precursors with high-level cDNA reads could be identified at the peptide level, and surprisingly some mRNA transcri" @default.
• W2159577812 created "2016-06-24" @default.
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• W2159577812 date "2013-12-01" @default.
• W2159577812 modified "2023-10-11" @default.
• W2159577812 title "Transcriptomic Messiness in the Venom Duct of Conus miles Contributes to Conotoxin Diversity" @default.
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