Biology subjects

Blondin, L.

Publications and source records attributed to Blondin, L..

3 recordsLinked to original sources

bioRxiv2026

Monoclonal anti-dsRNA antibody-based metagenomics (MADAM) reveal Pyricularia oryzae mycovirome

This study introduces MADAM (Monoclonal Anti-dsRNA Antibody-Based Metagenomics), a novel approach that integrates multiple technical modules previously used independently in other protocols. MADAM combines monoclonal antibody-mediated double-stranded RNA (dsRNA) enrichment, sequence-independent RT-PCR, and Oxford Nanopore Technologies (ONT) sequencing. Applied to Pyricularia oryzae, the causal agent of rice blast disease, MADAM enabled the comprehensive characterization of mycovirus genomes from four fungal isolates collected in Yunnan, China. The approach achieved high viral read recovery rates (46.9-72.7%) and identified 18 P. oryzae-associated RNA viruses spanning seven families: Botourmiaviridae, Deltaormycoviridae, Mymonaviridae, Partitiviridae, Polymycoviridae, Splipalmiviridae, and Ambiguiviridae. Seventeen nearly complete to complete viral genomes (1,226-6,085 nucleotides) were recovered, with sequence coverage ranging from 88% to 100%. Co-infections were detected in three of the four isolates, with notable discoveries including the first deltaormycovirus reported in P. oryzae, a putative novel member of Botourmiaviridae, and an additional genomic segment of a polymycovirus. MADAM successfully detected positive-sense, negative-sense ssRNA, and dsRNA viruses, demonstrating its broad applicability. By uncovering novel viruses and resolving complex co-infections, this method proves invaluable for fungal virology, with potential applications in diagnostics, surveillance, and biological control. Ultimately, MADAM advances our understanding of fungal viral diversity and paves the way for further exploration of mycovirus ecology and evolution.

microbiology↗

bioRxiv2019

A young age of subspecific divergence in the desert locust Schistocerca gregaria

AO_SCPLOWBSTRACTC_SCPLOWDating population divergence within species from molecular data and relating such dating to climatic and biogeographic changes is not trivial. Yet it can help formulating evolutionary hypotheses regarding local adaptation and future responses to changing environments. Key issues include statistical selection of a demographic and historical scenario among a set of possible scenarios, and estimation of the parameter(s) of interest under the chosen scenario. Such inferences greatly benefit from new statistical approaches including approximate Bayesian computation - Random Forest (ABC-RF), the latter providing reliable inference at a low computational cost, with the possibility to take into account prior knowledge on both biogeographical history and genetic markers. Here, we used ABC-RF, including independent information on evolutionary rate and pattern at microsatellite markers, to decipher the evolutionary history of the African arid-adapted pest locust, Schistocerca gregaria. We found that the evolutionary processes that have shaped the present geographical distribution of the species in two disjoint northern and southern regions of Africa were recent, dating back 2.6 Ky (90% CI: 0.9 - 6.6 Ky). ABC-RF inferences also supported a southern colonization of Africa from a low number of founders of northern origin. The inferred divergence history is better explained by the peculiar biology of S. gregaria, which involves a density-dependent swarming phase with some exceptional spectacular migrations, rather than a continuous colonization resulting from the continental expansion of open vegetation habitats during more ancient Quaternary glacial climatic episodes.

evolutionary biology↗

bioRxiv2017

Challenges in estimating heritability of phase polyphenism: Insights from measured and simulated data in the desert locust

Quantitative genetics experiments aim at understanding and predicting the evolution of phenotypic traits. Running such experiments often bring the same questions: Should I bother with maternal effects? Could I estimate those effects? What is the best crossing scheme to obtain reliable estimates? Can I use molecular markers to spare time in the complex task of keeping track of the experimental pedigree?\n\nWe explored those practical issues in the desert locust, Schistocerca gregaria using morphologic and coloration traits, known to be influenced by maternal effects. We ran quantitative genetic analyses with an experimental dataset and used simulations to explore i) the efficiency of animal models to accurately estimate both heritability and maternal effects, ii) the influence of crossing schemes on the precision of estimates and iii) the performance of a marker-based method compared to the pedigree-based method.\n\nThe simulations indicated that maternal effects deeply affect heritability estimates and very large datasets are required to properly distinguish and estimate maternal effects and heritabilities. In particular, ignoring maternal effects in the animal model resulted in overestimation of heritabilities and a high rate of false positives whereas models specifying maternal variance suffer from lack of power. Maternal effects can be estimated more precisely than heritabilities but with low power. To obtain better estimates, bigger datasets are required and, in the presence of maternal effects, increasing the number of families over the number of offspring per families is recommended. Our simulations also showed that, in the desert locust, using relatedness based on available microsatellite markers may allow reasonably reliable estimates while rearing locusts in group.\n\nIn the light of the simulation results, our experimental dataset suggested that maternal effects affected various phase traits. However the statistical limitations, revealed by the simulation approach, didnt allow precise variance estimates. We stressed out that doing simulations is a useful step to design an experiment in quantitative genetics and interpret the outputs of the statistical models.

evolutionary biology↗