Biology subjects

Duval, L.

Publications and source records attributed to Duval, L..

3 recordsLinked to original sources

bioRxiv2021

Marine gregarine genomes illuminate current understanding of apicomplexan glideosome

Our current view of the evolutionary history, coding and adaptive capacities of Apicomplexa, protozoan parasites of a wide range of metazoan, is currently strongly biased toward species infecting humans, as data on early diverging apicomplexan lineages infecting invertebrates is extremely limited. Here, we characterized the genome of the marine eugregarine Porospora gigantea, intestinal parasite of Lobsters, remarkable for the macroscopic size of its vegetative feeding forms (trophozoites) and its gliding speed, the fastest so far recorded for Apicomplexa. Two highly syntenic genomes named A and B were assembled. Similar in size (~9 Mb) and coding capacity (~5300 genes), A and B genomes are 10.8% divergent at the nucleotide level, corresponding to 16-38 My in divergent time. Orthogroup analysis across 25 (proto)Apicomplexa species, including Gregarina niphandrodes, showed that A and B are highly divergent from all other known apicomplexan species, revealing an unexpected breadth of diversity. Phylogenetically these two species branch sister to Cephaloidophoroidea, and thus expand the known crustacean gregarine superfamily. The genomes were mined for genes encoding proteins necessary for gliding, a key feature of apicomplexans parasites, currently studied through the molecular model called glideosome. Sequence analysis shows that actin-related proteins and regulatory factors are strongly conserved within apicomplexans. In contrast, the predicted protein sequences of core glideosome proteins and adhesion proteins are highly variable among apicomplexan lineages, especially in gregarines. These results confirm the importance of studying gregarines to widen our biological and evolutionary view of apicomplexan species diversity, and to deepen our understanding of the molecular bases of key functions enabling parasitism, such as the glideosome.

genomics↗

bioRxiv2020

Glucose-lactose mixture feeds in industry-like conditions: a gene regulatory network analysis on the hyperproducing Trichoderma reesei strain Rut-C30

BackgroundThe degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei. Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network. ResultsExperimental results confirmed the impact of sugar mixture on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network (GRN) built using the BRANE Cut software reveals three sub-networks related to i a positive correlation between lactose concentration and cellulase production, ii a particular dependence of the lactose onto the {beta}-glucosidase regulation and iii a negative regulation of the development process and growth. ConclusionsThis work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1, clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the {beta}-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains.

bioinformatics↗

bioRxiv2017

BRANE Clust: Cluster-Assisted Gene Regulatory Network Inference Refinement

Discovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells. Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy issues. BRANE Clust (Biologically-Related A priori Network Enhancement with Clustering) refines gene regulatory network (GRN) inference thanks to cluster information. It works as a post-processing tool for inference methods (i.e. CLR, GENIE3). In BRANE Clust, the clustering is based on the inversion of a system of linear equations involving a graph-Laplacian matrix promoting a modular structure. Our approach is validated on DREAM4 and DREAM5 datasets with objective measures, showing significant comparative improvements. We provide additional insights on the discovery of novel regulatory or co-expressed links in the inferred Escherichia coli network evaluated using the STRING database. The comparative pertinence of clustering is discussed computationally (SIMoNe, WGCNA, X-means) and biologically (RegulonDB). BRANE Clust software is available at: http://www-syscom.univ-mlv.fr/[~]pirayre/Codes-GRN-BRANE-clust.html

bioinformatics↗