Biology subjects

Mathe, C.

Publications and source records attributed to Mathe, C..

3 recordsLinked to original sources

bioRxiv2025

The uptake of metallic nanoparticles in breast cancer cell lines is modulated by the HA-CD44 axis.

Radiation enhancement is a promising anti-cancer approach based on a local radiation dose increase due to the presence of metallic nanoparticles (NPs) within cancer cells. Depending on their composition, size and cellular properties, NPs can follow multiple cellular pathways and entry routes. We observed that gold, platinum and TiO2 NPs are internalized at higher levels in mesenchymal cells compared to epithelial cells in breast cancer models. A global survey of gene expression between epithelial and mesenchymal cells exposed to 4 different NP types revealed an involvement of membrane structure, and further experiments confirmed that the hyaluronic acid (HA) and its receptor CD44 are mediators of metallic NP uptake into cells. We extended our results to a larger panel of breast cancer cell lines and again showed a preferential uptake of all NPs tested in mesenchymal cells and relying on the HA/CD44 axis. These data provide considerations for the design of NP-based therapies targeting mesenchymal cancer cells, which are often resistant to treatment and correlate with poor prognosis and tumor recurrence.

cancer biology↗

bioRxiv2025

Semi-automatic quantification of 3D Histone H3 phosphorylation signals during cell division in Arabidopsis root meristems

O_LIPosttranslational modification of histones during the cell cycle is a major process controlling many aspects of cell division. Among the variety of histone modifications, mitotic phosphorylation of histone H3 at serine 10 (H3S10ph) plays a crucial role, particularly in proper chromosome segregation. Here we aimed at precisely quantifying this phosphorylation dynamics during mitosis in plant cells, in order to reveal molecular pathways involved in this process. C_LIO_LIWe describe an analysis pipeline based on 3D image analysis that allows to semi-automatically quantify H3S10 phosphorylation in mitotic Arabidopsis root cells. We also developed a new method for the compensation of signal attenuation in Z, based on measurement of objects of interest themselves. C_LIO_LIWe show that this new attenuation correction method allows significant gains in accuracy and statistical power. Using this pipeline, we were able to reveal small H3S10ph differences between cells treated with hesperadin, an inhibitor of an H3S10ph kinase, or between Arabidopsis mutants affected in PP2A phosphatase activity. C_LIO_LIThis tool opens new avenues to explore such regulation pathway in plants, using the wealth of genetic material available in Arabidopsis. It can also be applied to study other histone post-translational modifications, and more generally to any discrete 3D signals. C_LI

cell biology↗

bioRxiv2023

P-GRe : an efficient pipeline to maximised pseudogene prediction in plants/eucaryotes

Formerly considered as part of "junk DNA", pseudogenes are nowadays known for their role in the post-transcriptional regulation of functional genes. In addition, their identification allows a better understanding of gene evolution in the frame of multigenic families. Despite this, there is, to our knowledge, no fully automatic user-friendly software allowing the annotation of pseudogenes on a whole genome. Here, we present Pseudo-Gene Retriever (P-GRe), a fully automated pseudogene prediction software requiring only a genome sequence and its corresponding GFF annotation file. P-GRe detects the sequences of the pseudogenes on a whole genome and returns to the user all their genomic sequences and their pseudo-coding sequences. The ability of P-GRe to finely reconstruct the structure of pseudogenes also allow to obtain a set of proteins virtually encoded by the predicted pseudogenes. We show here that in 70% of the cases, virtual proteins constructed by P-GRe from Arabidopsis thaliana proteome and genome aligned better to their parent protein than their annotated counterpart.

bioinformatics↗