Proteomic insight into the venom composition of the largest European rear-fanged snake, Malpolon monspessulanus monspessulanus.

Snake envenomations constitute a worldwide neglected tropical disease, with the vast majority of lethal bites inflicted by front-fanged snakes from the viperid and elapid groups. Rear-fanged snakes (colubrids) were often considered harmless and as a result, are much less studied, but several documented deaths have suggested potent venom in this group as well. The largest European snake (Malpolon monspessulanus monspessulanus), known as the "Montpellier snake", is such a rear-fanged snake that belongs to the Lamprophiidae family. Its venom remains largely unknown but cases of envenomation with neurological symptoms have been reported. Here, we provide the first insights into the composition of its venom using mass spectrometry methods. First, liquid chromatography coupled mass spectrometry analysis of the manually collected venom samples reveals a complex profile, with the majority of masses encompassing the range 500-3000 Da, 4000-8000 Da, and 10 000-30 000 Da. Next, shotgun proteomics allowed the identification of a total of 42 different known families of proteins, including snake venom metalloproteinases, peptidase M1, and cysteine-rich secretory proteins, as the most prominent. Interestingly, three-finger toxins were not detected, suggesting that neurotoxicity may occur via other, yet to be determined, toxin types. Overall, our results provide the basis for a better understanding of the effects of a peculiar snake venom on human symptomatology, but also on the main prey consumed by this species.

The Rhizobial Microbiome from the Tropical Savannah Zones in Northern Côte d'Ivoire.

Over the past decade, many projects have been initiated worldwide to decipher the composition and function of the soil microbiome, including the African Soil Microbiome (AfSM) project that aims at providing new insights into the presence and distribution of key groups of soil bacteria from across the African continent. In this national study, carried out under the auspices of the AfSM project, we assessed the taxonomy, diversity and distribution of rhizobial genera in soils from the tropical savannah zones in Northern Côte d'Ivoire. Genomic DNA extracted from seven sampled soils was analyzed by sequencing the V4-V5 variable region of the 16S rDNA using Illumina's MiSeq platform. Subsequent bioinformatic and phylogenetic analyses showed that these soils harbored 12 out of 18 genera of Proteobacteria harboring rhizobia species validly published to date and revealed for the first time that the Bradyrhizobium genus dominates in tropical savannah soils, together with Microvirga and Paraburkholderia. In silico comparisons of different 16S rRNA gene variable regions suggested that the V5-V7 region could be suitable for differentiating rhizobia at the genus level, possibly replacing the use of the V4-V5 region. These data could serve as indicators for future rhizobial microbiome explorations and for land-use decision-making.

Combined Proteotranscriptomic-Based Strategy to Discover Novel Antimicrobial Peptides from Cone Snails.

Despite their impressive diversity and already broad therapeutic applications, cone snail venoms have received less attention as a natural source in the investigation of antimicrobial peptides than other venomous animals such as scorpions, spiders, or snakes. Cone snails are among the largest genera (Conus sp.) of marine invertebrates, with more than seven hundred species described to date. These predatory mollusks use their sophisticated venom apparatus to capture prey or defend themselves. In-depth studies of these venoms have unraveled many biologically active peptides with pharmacological properties of interest in the field of pain management, the treatment of epilepsy, neurodegenerative diseases, and cardiac ischemia. Considering sequencing efficiency and affordability, cone snail venom gland transcriptome analyses could allow the discovery of new, promising antimicrobial peptides. We first present here the need for novel compounds like antimicrobial peptides as a viable alternative to conventional antibiotics. Secondly, we review the current knowledge on cone snails as a source of antimicrobial peptides. Then, we present the current state of the art in analytical methods applied to crude or milked venom followed by how antibacterial activity assay can be implemented for fostering cone snail antimicrobial peptides studies. We also propose a new innovative profile Hidden Markov model-based approach to annotate full venom gland transcriptomes and speed up the discovery of potentially active peptides from cone snails.

Improved prediction of conopeptide superfamilies with ConoDictor 2.0.

Motivation: Cone snails are among the richest sources of natural peptides with promising pharmacological and therapeutic applications. With the reduced costs of RNAseq, scientists now heavily rely on venom gland transcriptomes for the mining of novel bioactive conopeptides, but the bioinformatic analyses often hamper the discovery process. Results: Here, we present ConoDictor 2.0 as a standalone and user-friendly command-line program. We have updated the program originally published as a web server 10 years ago using novel and updated tools and algorithms and improved our classification models with new and higher quality sequences. ConoDictor 2.0 is now more accurate, faster, multiplatform and able to deal with a whole cone snail venom gland transcriptome (raw reads or contigs) in a very short time. The new version of Conodictor also improves the identification and subsequent classification for entirely novel or relatively distant conopeptides. We conducted various tests on known conopeptides from public databases and on the published venom duct transcriptome of Conus geographus, and compared previous results with the output of ConoDictor 2.0, ConoSorter and BLAST. Overall, ConoDictor 2.0 is 4 to 8 times faster for the analysis of a whole transcriptome on a single core computer and performed better at predicting gene superfamily. Availability and implementation: ConoDictor 2.0 is available as a python 3 git folder at https://github.com/koualab/conodictor. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Proteotranscriptomic Insights into the Venom Composition of the Wolf Spider Lycosa tarantula.

Spider venoms represent an original source of novel compounds with therapeutic and agrochemical potential. Whereas most of the research efforts have focused on large mygalomorph spiders, araneomorph spiders are equally promising but require more sensitive and sophisticated approaches given their limited size and reduced venom yield. Belonging to the latter group, the genus Lycosa ("wolf spiders") contains many species widely distributed throughout the world. These spiders are ambush predators that do not build webs but instead rely strongly on their venom for prey capture. Lycosa tarantula is one of the largest species of wolf spider, but its venom composition is unknown. Using a combination of RNA sequencing of the venom glands and venom proteomics, we provide the first overview of the peptides and proteins produced by this iconic Mediterranean spider. Beside the typical small disulfide rich neurotoxins, several families of proteins were also identified, including cysteine-rich secretory proteins (CRISP) and Hyaluronidases. Proteomic analysis of the electrically stimulated venom validated 30 of these transcriptomic sequences, including nine putative neurotoxins and eight venom proteins. Interestingly, LC-MS venom profiles of manual versus electric stimulation, as well as female versus male, showed some marked differences in mass distribution. Finally, we also present some preliminary data on the biological activity of L. tarantula crude venom.

The Dual Prey-Inactivation Strategy of Spiders-In-Depth Venomic Analysis of Cupiennius salei.

Most knowledge of spider venom concerns neurotoxins acting on ion channels, whereas proteins and their significance for the envenomation process are neglected. The here presented comprehensive analysis of the venom gland transcriptome and proteome of Cupiennius salei focusses on proteins and cysteine-containing peptides and offers new insight into the structure and function of spider venom, here described as the dual prey-inactivation strategy. After venom injection, many enzymes and proteins, dominated by α-amylase, angiotensin-converting enzyme, and cysteine-rich secretory proteins, interact with main metabolic pathways, leading to a major disturbance of the cellular homeostasis. Hyaluronidase and cytolytic peptides destroy tissue and membranes, thus supporting the spread of other venom compounds. We detected 81 transcripts of neurotoxins from 13 peptide families, whereof two families comprise 93.7% of all cysteine-containing peptides. This raises the question of the importance of the other low-expressed peptide families. The identification of a venom gland-specific defensin-like peptide and an aga-toxin-like peptide in the hemocytes offers an important clue on the recruitment and neofunctionalization of body proteins and peptides as the origin of toxins.

Identification of a precursor processing protease from the spider Cupiennius salei essential for venom neurotoxin maturation.

Spider venom neurotoxins and cytolytic peptides are expressed as elongated precursor peptides, which are post-translationally processed by proteases to yield the active mature peptides. The recognition motifs for these processing proteases, first published more than 10 years ago, include the processing quadruplet motif (PQM) and the inverted processing quadruplet motif (iPQM). However, the identification of the relevant proteases was still pending. Here we describe the purification of a neurotoxin precursor processing protease from the venom of the spider Cupiennius salei The chymotrypsin-like serine protease is a 28-kDa heterodimer with optimum activity at venom's pH of 6.0. We designed multiple synthetic peptides mimicking the predicted cleavage sites of neurotoxin precursors. Using these peptides as substrates, we confirm the biochemical activity of the protease in propeptide removal from neurotoxin precursors by cleavage C-terminal of the PQM. Furthermore, the PQM protease also cleaves the iPQM relevant for heterodimerization of a subgroup of neurotoxins. An involvement in the maturing of cytolytic peptides is very likely, due to high similarity of present protease recognition motifs. Finally, bioinformatics analysis, identifying sequences of homolog proteins from 18 spiders of 9 families, demonstrate the wide distribution and importance of the isolated enzyme for spiders. In summary, we establish the first example of a PQM protease, essential for maturing of spider venom neurotoxins. In the future, the here described protease may be established as a powerful tool for production strategies of recombinant toxic peptides, adapted to the maturing of spider venom toxins.

Spider Neurotoxins, Short Linear Cationic Peptides and Venom Protein Classification Improved by an Automated Competition between Exhaustive Profile HMM Classifiers.

Spider venoms are rich cocktails of bioactive peptides, proteins, and enzymes that are being intensively investigated over the years. In order to provide a better comprehension of that richness, we propose a three-level family classification system for spider venom components. This classification is supported by an exhaustive set of 219 new profile hidden Markov models (HMMs) able to attribute a given peptide to its precise peptide type, family, and group. The proposed classification has the advantages of being totally independent from variable spider taxonomic names and can easily evolve. In addition to the new classifiers, we introduce and demonstrate the efficiency of hmmcompete, a new standalone tool that monitors HMM-based family classification and, after post-processing the result, reports the best classifier when multiple models produce significant scores towards given peptide queries. The combined used of hmmcompete and the new spider venom component-specific classifiers demonstrated 96% sensitivity to properly classify all known spider toxins from the UniProtKB database. These tools are timely regarding the important classification needs caused by the increasing number of peptides and proteins generated by transcriptomic projects.

Peptidomic and transcriptomic profiling of four distinct spider venoms.

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.

Position-specific scoring matrix and hidden Markov model complement each other for the prediction of conopeptide superfamilies.

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.

[Water access and storage: survey in a peri-urban area of Abidjan in 2010]. / Conditions d'accès et de stockage de l'eau : enquête dans les ménages en zone périurbaine à Abidjan en 2010.

A health survey on access to water and a chemical and bacteriological analysis were conducted between May and October 2010 on 200 tanks of drinking water in 669 households in a peri-urban area of Abidjan. The results show that 70% of the population used piped water and that 64% of the population used approximately 20 litres of water per person per day. The study found that households that used alternative sources of water spent more than those that used piped water (p < 0,001). The study also found that 75.6% of the surveyed households stored water. The survey showed that 81% of the samples contained coliforms and 42.5% contained Escherichia coli. The presence of bacteria can be explained by the large quantities of water stored in open containers (i.e. containers without lids). Basic water supply combined with health education and safe water storage containers are needed.

ConoDictor: a tool for prediction of conopeptide superfamilies.

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.

Large-scale discovery of conopeptides and conoproteins in the injectable venom of a fish-hunting cone snail using a combined proteomic and transcriptomic approach.

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.

Identification and classification of conopeptides using profile Hidden Markov Models.

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.

Venomic study on cone snails (Conus spp.) from South Africa.

From six Conus species (Conus coronatus, Conus lividus, Conus mozambicus f. lautus, Conus pictus, Conus sazanka, Conus tinianus) collected off the eastern coast of South Africa the venoms were analyzed using MALDI-TOF mass spectrometry. Between 56 and 151 molecular masses most in a range of 1000 to 2500 Da, were identified. Among the six venoms, between 0 and 27% (C. coronatus versus C. sazanka) of the peptide masses were found to be similar. In a study on venoms from 6 Conus species collected in the Philippines, the percentage of identical masses was between none and 9% only. The venoms from the South African Conus species antagonized the rat neuronal nicotinic acetylcholine receptors (nAChRs) α3ß2, α4ß2, and α7, except for C. coronatus venom that blocked the α4ß2 and α7 nAChRs only. HPLC-fractionation of C. tinianus venom led to the isolation of a peptide that is active on all three receptor subtypes. It consists of 16 amino acid residues cross-linked by two disulfide bridges as revealed by de novo sequencing using tandem mass spectrometry: GGCCSHPACQNNPDYC. Posttranslational modifications include C-terminal amidation and tyrosine sulfation. The new peptide is a member of the α-conotoxin family that are competitive antagonists of nAChRs. Phylogenetic analysis of the 16S RNA from numerous Conus species has clarified the evolutionary position of endemic South African Conus species and provided the first evidence for their close genetic relationships.

PeroxiBase: a powerful tool to collect and analyse peroxidase sequences from Viridiplantae.

Peroxidases are enzymes that are implicated in several biological processes and are detected in all living organisms. The increasing number of sequencing projects and the poor quality of annotation justified the creation of an efficient tool that was suitable for collecting and annotating the huge quantity of data. Started in 2004 to collect only class III peroxidases, PeroxiBase has undergone important updates since then and, currently, the majority of peroxidase sequences from all kingdoms of life is stored in the database. In addition, the web site (http://peroxibase.isb-sib.ch) provides a series of bioinformatics tools and facilities suitable for analysing these stored sequences. In particular, the high number of isoforms in each organism makes phylogenetic studies extremely useful to elucidate the complex evolution of these enzymes, not only within the plant kingdom but also between the different kingdoms. This paper provides a general overview of PeroxiBase, focusing on its tools and the stored data. The main goal is to give researchers some guidelines to extract classified and annotated sequences from the data base in a quick and easy way in order to perform alignments and phylogenetic analysis. The description of the database is accompanied by the updates we have recently carried out in order to improve its completeness and make it more user-friendly.

PeroxiBase: a database with new tools for peroxidase family classification.

Peroxidases (EC 1.11.1.x), which are encoded by small or large multigenic families, are involved in several important physiological and developmental processes. They use various peroxides as electron acceptors to catalyse a number of oxidative reactions and are present in almost all living organisms. We have created a peroxidase database (http://peroxibase.isb-sib.ch) that contains all identified peroxidase-encoding sequences (about 6000 sequences in 940 organisms). They are distributed between 11 superfamilies and about 60 subfamilies. All the sequences have been individually annotated and checked. PeroxiBase can be consulted using six major interlink sections 'Classes', 'Organisms', 'Cellular localisations', 'Inducers', 'Repressors' and 'Tissue types'. General documentation on peroxidases and PeroxiBase is accessible in the 'Documents' section containing 'Introduction', 'Class description', 'Publications' and 'Links'. In addition to the database, we have developed a tool to classify peroxidases based on the PROSITE profile methodology. To improve their specificity and to prevent overlaps between closely related subfamilies the profiles were built using a new strategy based on the silencing of residues. This new profile construction method and its discriminatory capacity have been tested and validated using the different peroxidase families and subfamilies present in the database. The peroxidase classification tool called PeroxiScan is accessible at the following address: http://peroxibase.isb-sib.ch/peroxiscan.php.