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Leonardi A.,Jozef Stefan Institute | Biass D.,Atheris Laboratories | Kordis D.,Jozef Stefan Institute | Stocklin R.,Atheris Laboratories | And 3 more authors.
Journal of Proteome Research | Year: 2012

For some decades, cone snail venoms have been providing peptides, generally termed conopeptides, that exhibit a large diversity of pharmacological properties. However, little attention has been devoted to the high molecular mass (HMM) proteins in venoms of mollusks. In order to shed more light on cone snail venom HMM components, the proteins of dissected and injected venom of a fish-hunting cone snail, Conus consors, were extensively assessed. HMM venom proteins were separated by two-dimensional polyacrylamide gel electrophoresis and analyzed by mass spectrometry (MS). The MS data were interpreted using UniProt database, EST libraries from C. consors venom duct and salivary gland, and their genomic information. Numerous protein families were discovered in the lumen of the venom duct and assigned a biological function, thus pointing to their potential role in venom production and maturation. Interestingly, the study also revealed original proteins defining new families of unknown function. Only two groups of HMM proteins passing the venom selection process, echotoxins and hyaluronidases, were clearly present in the injected venom. They are suggested to contribute to the envenomation process. This newly devised integrated HMM proteomic analysis is a big step toward identification of the protein arsenal used in a cone snail venom apparatus for venom production, maturation, and function. © 2012 American Chemical Society.


Koua D.,Atheris Laboratories | Koua D.,Swiss Institute of Bioinformatics | Brauer A.,Bioinformatics Workgroup | Laht S.,Bioinformatics Workgroup | And 5 more authors.
Nucleic Acids Research | Year: 2012

ConoDictor is a tool that enables fast and accurate classification of conopeptides into superfamilies based on their amino acid sequence. ConoDictor combines predictions from two complementary approaches-profile hidden Markov models and generalized profiles. Results appear in a browser as tables that can be downloaded in various formats. This application is particularly valuable in view of the exponentially increasing number of conopeptides that are being identified. ConoDictor was written in Perl using the common gateway interface module with a php submission page. Sequence matching is performed with hmmsearch from HMMER 3 and ps-scan.pl from the pftools 2.3 package. ConoDictor is freely accessible at http://conco.ebc.ee. © 2012 The Author(s).


Eugster P.J.,University of Geneva | Biass D.,Atheris Laboratories | Guillarme D.,University of Geneva | Favreau P.,Atheris Laboratories | And 2 more authors.
Journal of Chromatography A | Year: 2012

The high resolution profiling of complex mixtures is indispensable for obtaining online structural information on the highest possible number of the analytes present. This is particularly relevant for natural extracts, as for the venom of the predatory marine snail Conus consors, which contains numerous bioactive peptides with molecular masses ranging between 1000 and 5000. Da. The goal of the present work was to maximise peak capacity of peptides separations by LC-MS while maintaining a reasonable analysis time. The best gradient performance using the C. consors venom as a real sample was obtained with a mobile phase flow rate as high as possible to maximise performance in the gradient mode, and gradient time comprised between 75 and 350. min when using a 150 mm column length. The present study also confirmed that an elevated temperature (up to 90. °C) improves performance under ultra-high pressure liquid chromatography (UHPLC) conditions. However, the thermal stability of the analytes had to be critically evaluated. For the profiling of C. consors, analyte degradation was not clearly observable at 90. °C with analysis times of approximately 100. min. Finally, the MS source was found to cause significant additional band broadening in the UHPLC mode (σext2 was 10-24 times higher using TOF-MS vs. UV detection). Thus, if the MS contributes strongly to the peak capacity loss, classical 2.1 mm I.D. columns can be replaced by 3.0 mm I.D. to mitigate this problem. Based on these considerations, the optimal generic profiling conditions applied to the C. consors venom provided a peak capacity higher than 1100 for a gradient time of around 100. min, doubling the values reached by classical HPLC separation. UHPLC-QTOF-MS/MS experiments carried out in these conditions provided exploitable data that matched with peptides present in the C. consors venom. These optimal LC conditions are thus compatible with online peptide deconvolution and matching against transcriptomic data and, to some extent, de novo sequencing in such complex mixtures. © 2012 Elsevier B.V.


Coissac E.,CNRS Alpine Ecology Laboratory | Riaz T.,CNRS Alpine Ecology Laboratory | Puillandre N.,Atheris Laboratories | Puillandre N.,French Natural History Museum
Molecular Ecology | Year: 2012

Almost all empirical studies in ecology have to identify the species involved in the ecological process under examination. DNA metabarcoding, which couples the principles of DNA barcoding with next generation sequencing technology, provides an opportunity to easily produce large amounts of data on biodiversity. Microbiologists have long used metabarcoding approaches, but use of this technique in the assessment of biodiversity in plant and animal communities is under-explored. Despite its relationship with DNA barcoding, several unique features of DNA metabarcoding justify the development of specific data analysis methodologies. In this review, we describe the bioinformatics tools available for DNA metabarcoding of plants and animals, and we revisit others developed for DNA barcoding or microbial metabarcoding. We also discuss the principles and associated tools for evaluating and comparing DNA barcodes in the context of DNA metabarcoding, for designing new custom-made barcodes adapted to specific ecological question, for dealing with PCR and sequencing errors, and for inferring taxonomical data from sequences. © 2012 Blackwell Publishing Ltd.


Violette A.,Atheris Laboratories | Biass D.,Atheris Laboratories | Dutertre S.,Atheris Laboratories | Dutertre S.,University of Queensland | And 5 more authors.
Journal of Proteomics | Year: 2012

Predatory marine snails of the genus Conus use venom containing a complex mixture of bioactive peptides to subdue their prey. Here we report on a comprehensive analysis of the protein content of injectable venom from Conus consors, an indo-pacific fish-hunting cone snail. By matching MS/MS data against an extensive set of venom gland transcriptomic mRNA sequences, we identified 105 components out of ~ 400 molecular masses detected in the venom. Among them, we described new conotoxins belonging to the A, M- and O1-superfamilies as well as a novel superfamily of disulphide free conopeptides. A high proportion of the deduced sequences (36%) corresponded to propeptide regions of the A- and M-superfamilies, raising the question of their putative role in injectable venom. Enzymatic digestion of higher molecular mass components allowed the identification of new conkunitzins (~ 7 kDa) and two proteins in the 25 and 50. kDa molecular mass ranges respectively characterised as actinoporin-like and hyaluronidase-like protein. These results provide the most exhaustive and accurate proteomic overview of an injectable cone snail venom to date, and delineate the major protein families present in the delivered venom. This study demonstrates the feasibility of this analytical approach and paves the way for transcriptomics-assisted strategies in drug discovery. © 2012 Elsevier B.V.


Koua D.,Atheris Laboratories | Koua D.,Swiss Institute of Bioinformatics | Laht S.,Estonian Biocentre | Kaplinski L.,Estonian Biocentre | And 4 more authors.
Biochimica et Biophysica Acta - Proteins and Proteomics | Year: 2013

Classified into 16 superfamilies, conopeptides are the main component of cone snail venoms that attract growing interest in pharmacology and drug discovery. The conventional approach to assigning a conopeptide to a superfamily is based on a consensus signal peptide of the precursor sequence. While this information is available at the genomic or transcriptomic levels, it is not present in amino acid sequences of mature bioactives generated by proteomic studies. As the number of conopeptide sequences is increasing exponentially with the improvement in sequencing techniques, there is a growing need for automating superfamily elucidation. To face this challenge we have defined distinct models of the signal sequence, propeptide region and mature peptides for each of the superfamilies containing more than 5 members (14 out of 16). These models rely on two robust techniques namely, Position-Specific Scoring Matrices (PSSM, also named generalized profiles) and hidden Markov models (HMM). A total of 50 PSSMs and 47 HMM profiles were generated. We confirm that propeptide and mature regions can be used to efficiently classify conopeptides lacking a signal sequence. Furthermore, the combination of all three-region models demonstrated improvement in the classification rates and results emphasise how PSSM and HMM approaches complement each other for superfamily determination. The 97 models were validated and offer a straightforward method applicable to large sequence datasets. © 2013 Elsevier B.V.


Laht S.,Estonian Biocentre | Koua D.,Atheris Laboratories | Koua D.,Swiss Institute of Bioinformatics | Kaplinski L.,Estonian Biocentre | And 3 more authors.
Biochimica et Biophysica Acta - Proteins and Proteomics | Year: 2012

Conopeptides are small toxins produced by predatory marine snails of the genus Conus. They are studied with increasing intensity due to their potential in neurosciences and pharmacology. The number of existing conopeptides is estimated to be 1 million, but only about 1000 have been described to date. Thanks to new high-throughput sequencing technologies the number of known conopeptides is likely to increase exponentially in the near future. There is therefore a need for a fast and accurate computational method for identification and classification of the novel conopeptides in large data sets. 62 profile Hidden Markov Models (pHMMs) were built for prediction and classification of all described conopeptide superfamilies and families, based on the different parts of the corresponding protein sequences. These models showed very high specificity in detection of new peptides. 56 out of 62 models do not give a single false positive in a test with the entire UniProtKB/Swiss-Prot protein sequence database. Our study demonstrates the usefulness of mature peptide models for automatic classification with accuracy of 96% for the mature peptide models and 100% for the pro- and signal peptide models. Our conopeptide profile HMMs can be used for finding and annotation of new conopeptides from large datasets generated by transcriptome or genome sequencing. To our knowledge this is the first time this kind of computational method has been applied to predict all known conopeptide superfamilies and some conopeptide families. © 2012 Elsevier B.V. All rights reserved.


Puillandre N.,Atheris Laboratories | Puillandre N.,University of Utah | Koua D.,Atheris Laboratories | Koua D.,Swiss Institute of Bioinformatics | And 3 more authors.
Journal of Molecular Evolution | Year: 2012

Conopeptides are toxins expressed in the venom duct of cone snails (Conoidea, Conus). These are mostly well-structured peptides and mini-proteins with high potency and selectivity for a broad range of cellular targets. In view of these properties, they are widely used as pharmacological tools and many are candidates for innovative drugs. The conopeptides are primarily classified into superfamilies according to their peptide signal sequence, a classification that is thought to reflect the evolution of the multigenic system. However, this hypothesis has never been thoroughly tested. Here we present a phylogenetic analysis of 1,364 conopeptide signal sequences extracted from GenBank. The results validate the current conopeptide superfamily classification, but also reveal several important new features. The so-called "cysteine-poor" conopeptides are revealed to be closely related to "cysteine- rich" conopeptides; with some of them sharing very similar signal sequences, suggesting that a distinction based on cysteine content and configuration is not phylogenetically relevant and does not reflect the evolutionary history of conopeptides. A given cysteine pattern or pharmacological activity can be found across different superfamilies. Furthermore, a few conopeptides from GenBank do not cluster in any of the known superfamilies, and could represent yet-undefined superfamilies. A clear phylogenetically based classification should help to disentangle the diversity of conopeptides, and could also serve as a rationale to understand the evolution of the toxins in the numerous other species of conoideans and venomous animals at large. © Springer Science+Business Media, LLC 2012.


Hocking H.G.,University Utrecht | Gerwig G.J.,University Utrecht | Dutertre S.,Atheris Laboratories | Violette A.,Atheris Laboratories | And 4 more authors.
Chemistry - A European Journal | Year: 2013

The glycopeptide CcTx, isolated from the venom of the piscivorous cone snail Conus consors, belongs to the κA-family of conopeptides. These toxins elicit excitotoxic responses in the prey by acting on voltage-gated sodium channels. The structure of CcTx, a first in the κA-family, has been determined by high-resolution NMR spectroscopy together with the analysis of its O-glycan at Ser7. A new type of glycopeptide O-glycan core structure, here registered as core type9, containing two terminal L-galactose units {α-L-Galp-(1→4)-α-D-GlcpNAc-(1→6)-[α-L-Galp- (1→2)-β-D-Galp-(1→3)-]α-D-GalpNAc-(1→O)}, is highlighted. A sequence comparison to other putative members of the κA-family suggests that O-linked glycosylation might be more common than previously thought. This observation alone underlines the requirement for more careful and in-depth investigations into this type of post-translational modification in conotoxins. Name your poison: A new O-glycan was found in a conopeptide isolated from the venom of the piscivorous cone snail Conus consors. The glycopeptide, CcTx, belongs to the κA-family of conotoxins and contains a new type of glycopeptide O-glycan core structure, here registered as core type9, containing two terminal L-galactose units (see figure). Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.


Kuhn-Nentwig L.,University of Bern | Stocklin R.,Atheris Laboratories | Nentwig W.,University of Bern
Advances in Insect Physiology | Year: 2011

This review on all spider venom components known by the end of 2010 bases on 1618 records for venom compounds from 174 spider species (= 0.41% of all known species) belonging to 32 families (= 29% of all existing spider families). Spiders investigated for venom research are either big (many mygalomorph species, Nephilidae, Ctenidae and Sparassidae) or medically important for humans (e.g. Loxosceles or Latrodectus species). Venom research widely ignored so far the two most species-rich families (Salticidae and Linyphiidae) and strongly neglected several other very abundant families (Araneidae, Lycosidae, Theridiidae, Thomisidae and Gnaphosidae).We grouped the known 1618 records for venom compounds into six categories: low molecular mass compounds (16 % of all compounds), acylpolyamines (11 %), linear peptides (6 %), cysteine-knotted mini-proteins (60 %), neurotoxic proteins (1 %) and enzymes (6 %). Low molecular mass compounds are known from many spider families and contain organic acids, nucleosides, nucleotides, amino acids, amines, polyamines, and some further substances, many of them acting as neurotransmitters. Acylpolyamines contain amino acids (Araneidae and Nephilidae) or not (several other families) and show a very high diversity within one species. Linear peptides, also called cytolytic, membranolytic or antimicrobial, exert a highly specific structure and are so far only known from Ctenidae, Lycosidae, Oxyopidae and Zodariidae. Cysteine-knotted mini-proteins represent the majority of venom compounds because research so far focused on them. They probably occur in most but not all spider families. Neurotoxic proteins so far are only known from theridiid spiders. Enzymes had been neglected for some time but meanwhile it becomes obvious that they play an important role in spider venoms. Sixteen enzymes either cleave polymers in the extracellular matrix or target phospholipids and related compounds in membranes. The overall structure of these compounds is given and the function, as far as it is known, is described. Since several of these component groups are presented in one average spider venom, we discuss the known interactions and synergisms and give reasons for such a functional redundancy. We also discuss main evolutionary pathways for spider venom compounds such as high variability among components of one group, synergistic interactions between cysteine-knotted mini-proteins and other components (low molecular mass compounds and linear peptides), change of function from ion-channel acting mini-proteins to cytolytic effects and replacement of mini-proteins by linear peptides, acylpolyamines, large proteins or enzymes. We also add first phylogenetic considerations. © 2011 Elsevier Ltd.

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