Dominique Kadio Koua

Cone snails are carnivorous marine predators that prey on mollusks, worms, or fish. They purposefully inject a highly diversified and peptide-rich venom, which can vary according to the predatory or defensive intended use. Previous studies have shown some correlations between the predation- and defense-evoked venoms and specific sections of the venom gland. In this study, we focus on the characterization of the venom of Cylinder canonicus, a molluscivorous species collected from Mayotte Island. Integrated proteomics and transcriptomics studies allowed for the identification of 108 conotoxin sequences from 24 gene superfamilies, with the most represented sequences belonging to the O1, O2, M, and conkunitzin superfamilies. A comparison of the predatory injected venom and the distal, central, and proximal sections of the venom duct suggests mostly distal origin. Identified conotoxins will contribute to a better understanding of venom–ecology relationships in cone snails and provide a novel resource for potential drug discovery.

Archaea and bacteria communities play pivotal roles in tropical forest ecosystems’ functioning, especially nutrient cycling, plant phenology, and health. The objective of this study was to explore the diversity of archaeal and bacterial communities in forest soil ecosystem of Côte d’Ivoire and to identify abiotic factors that influence their composition. Using high-throughput amplicon sequencing targeting the V4V5 hypervariable region of the 16S ribosomal RNA gene, we analyzed 22 soil samples taken from the 2 main forest areas of Côte d’Ivoire, namely the semi-deciduous moist forest and the evergreen moist forest, both of which are located in the humid and sub-humid areas of the country. The analysis revealed that the biodiversity at the phyla level was congruent with previous studies. Richness and Shannon diversity indices revealed the dominance of bacteria over archaea in all studied soils. Moreover, the predominant bacterial community consisted of Proteobacteria (29.8%), Acidobacteria (15.5%), and Actinobacteria (14.2%), while the archaeal community was dominated by Thaumarchaeota (1.93%). However, at the genus level, patterns emerged. The most abundant and ubiquitous members at the genus level included Bradyrhizobium, Rhodoplanes, Bacillus (bacteria), and Nitrosophaera (archaea). While bacterial core microbiome members were found in almost all soils, Nitrososphaera genus were selective to sub-humid bioclimate and cropland land use. These patterns were correlated to the soils’ physicochemical characteristics, bioclimate, and land use. This study sheds light on the intricate relationships between abiotic factors and microbial communities in Côte d’Ivoire’s forest soils and helps to identify keys species for future soil management.

The 16S ribosomal RNA gene is one of the most studied genes in biology. This 16S ribosomal RNA importance is due to its wide application in phylogenetics and taxonomic elucidation of bacteria and archaea. Indeed, 16S ribosomal RNA is present in almost all bacteria and archaea and has, among many other useful characteristics, a low mutation rate. The 16S ribosomal RNA is composed of nine hypervariable regions which are commonly targeted by high throughput sequencing technologies in identification or community studies like metabarcoding studies. Unfortunately, the hypervariable regions do not have the same taxonomic resolution among all bacteria taxa. This requires a preliminary in silico analysis to determine the best hypervariable regions to target in a particular study. Nevertheless, to the best of our knowledge, no automated primer-based open-source tool exists to extract hypervariable regions from complete or near-complete 16S rRNA sequencing data. Here we present HyperEx which efficiently extracts the hypervariable region of interest based on embedded primers or user-given primers. HyperEx implements the Myers algorithm for the exact pairwise sequence alignment. HyperEx is freely available under the MIT license as an operating system independent Rust command-line tool at https://github.com/Ebedthan/hyperex and https://crates.io .

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.

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.

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. 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. ConoDictor 2.0 is available as a python 3 git folder at https://github.com/koualab/conodictor. Supplementary data are available at Bioinformatics Advances online.

In Côte d’Ivoire, banana ( Musa sp. ) ranks third among exportation products and represents 3% of the Gross Domestic Product with a national production up to 500000 tons in 2019. Banana is subject to numerous disease agents among which viruses cause significant losses. To figure out the impact of viruses in Ivorian industrial banana fields, surveys were conducted in the 7 main banana production departments. A total of 260 leaf fragments presenting viral symptoms were collected and analyzed. From the 65 leaf fragments used for biological indexing, 14 showed symptoms related to Cucumber mosaic virus (CMV). CMV presence was confirmed by double-antibody sandwich enzyme-linked immunosorbent assay (DAS-ELISA) using CMV polyclonal antibodies. CMV strains we isolated, appeared to be highly infectious and to produce various symptoms like mosaic, chlorosis, and necrotic spots on Cucumis sativus , Cucurbita pepo , and Nicotiana tabacum . Satellite RNAs (SatRNAs) associated with CMV isolates were also detected using reverse transcription polymerase chain reaction (RT-PCR) with a degenerate primer pair. CMV’s coat protein as well as satRNAs was sequenced. Novel Ivorian coat proteins and satRNAs were compared to publicly available sequences. We noticed a single amino acid substitution (Serine to Leucine) at position 73 of the novel coat protein that allowed us to divide Ivorian CMV strains into two groups. Molecular and phylogenetic analysis suggested that Ivorian strains might be classified into CMV Subgroup IA. We also discovered that satellite RNA associated with Ivorian CMVs form a separate clade.