Biology subjects

Michel, B.

Publications and source records attributed to Michel, B..

3 recordsLinked to original sources

bioRxiv2026

Conservation and evolution of the ribosome across the photosynthetic lineage

Regulated biogenesis of photosynthetic proteins by the chloroplast ribosomes is central to plant development and environmental adaptation. Here, we describe changes to the ribosome that occurred during evolution of chloroplasts from cyanobacteria in the photosynthetic lineage based on structural, proteomic and bioinformatic analyses. We identify structural features common to oxygenic photosynthetic organisms and distinct from other bacteria, as well as the gain and loss of ribosomal proteins, rRNA modifications and hibernation factors upon endosymbiosis. We uncover structural variation among land plant lineages at sites that remodel the peptide exit tunnel and features associated with translation initiation. The data challenge the prevailing view of ribosomal features that are specific to the chloroplast by demonstrating that many originated earlier, in cyanobacteria, or later, during land plant evolution.

biochemistry↗

bioRxiv2024

Additions to the list of arthropods of Reunion Island

Contribution to the inventory of arthropods of Reunion Island This work is a synthesis of new taxa for the Reunion Island identified between 1963 and 2022 and not published to date. The collections were carried out mainly by researchers and technicians from CIRAD or FDGDON. Sampling mainly targeted at crop pests and their natural enemies. The list includes 101 taxa new for Reunion. The Hymenoptera and Hemiptera are the most represented orders.

zoology↗

bioRxiv2020

Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches

Triple-negative breast cancer (TNBC) heterogeneity represents one of the main impediment to precision medicine for this disease. Recent concordant transcriptomics studies have shown that TNBC could be splitted into at least three subtypes with potential therapeutic implications. Although, a few studies have been done to predict TNBC subtype by means of transcriptomics data, subtyping was partially sensitive and limited by batch effect and dependence to a given dataset, which may penalize the switch to routine diagnostic testing. Therefore, we sought to build an absolute predictor (i.e. intra-patient diagnosis) based on machine learning algorithm with a limited number of probes. To this end, we started by introducing probe binary comparison for each patient (indicators). We based predictive analysis on this transformed data. Probe selection was first performed by combining both filter and wrapper methods for variable selection using cross validation. We thus tested three prediction models (random forest, gradient boosting [GB] and extreme gradient boosting) using this optimal subset of indicators as inputs. Nested cross-validation allowed us to consistently choose the best model. Results showed that the 50 selected indicators highlighted biological characteristics associated with each TNBC subtype. The GB based on this subset of indicators has better performances as compared to the other models.

bioinformatics↗