Single-cell discovery of m6A RNA modifications in the hippocampus.

N 6-Methyladenosine (m6A) is a prevalent and highly regulated RNA modification essential for RNA metabolism and normal brain function. It is particularly important in the hippocampus, where m6A is implicated in neurogenesis and learning. Although extensively studied, its presence in specific cell types remains poorly understood. We investigated m6A in the hippocampus at a single-cell resolution, revealing a comprehensive landscape of m6A modifications within individual cells. Through our analysis, we uncovered transcripts exhibiting a dense m6A profile, notably linked to neurological disorders such as Alzheimer's disease. Our findings suggest a pivotal role of m6A-containing transcripts, particularly in the context of CAMK2A neurons. Overall, this work provides new insights into the molecular mechanisms underlying hippocampal physiology and lays the foundation for future studies investigating the dynamic nature of m6A RNA methylation in the healthy and diseased brain.

Ideafix: a decision tree-based method for the refinement of variants in FFPE DNA sequencing data.

Increasingly, treatment decisions for cancer patients are being made from next-generation sequencing results generated from formalin-fixed and paraffin-embedded (FFPE) biopsies. However, this material is prone to sequence artefacts that cannot be easily identified. In order to address this issue, we designed a machine learning-based algorithm to identify these artefacts using data from >1 600 000 variants from 27 paired FFPE and fresh-frozen breast cancer samples. Using these data, we assembled a series of variant features and evaluated the classification performance of five machine learning algorithms. Using leave-one-sample-out cross-validation, we found that XGBoost (extreme gradient boosting) and random forest obtained AUC (area under the receiver operating characteristic curve) values >0.86. Performance was further tested using two independent datasets that resulted in AUC values of 0.96, whereas a comparison with previously published tools resulted in a maximum AUC value of 0.92. The most discriminating features were read pair orientation bias, genomic context and variant allele frequency. In summary, our results show a promising future for the use of these samples in molecular testing. We built the algorithm into an R package called Ideafix (DEAmination FIXing) that is freely available at https://github.com/mmaitenat/ideafix.

Characterization of a new small-molecule inhibitor of HDAC6 in glioblastoma.

Histone deacetylase 6 (HDAC6) is an epigenetic modifier that is an attractive pharmacological target in cancer. In this work, we show that HDAC6 is elevated in glioblastoma, the most malignant and common brain tumor in adults, in which its high levels correlate with poor patient survival and is more abundant in glioma stem cell subpopulation. Moreover, we identified a new small-molecule inhibitor of HDAC6, which presents strong sensitivity for HDAC6 inhibition and exerts high cytotoxic activity, alone or in combination with temozolomide. It is also able to significantly reduce tumor growth in vivo. Transcriptomic analysis of patient-derived glioma stem cells revealed an increase in cell differentiation and cell death pathways, as well as a decrease in cell-cycle activity and cell division by the treatment with the compound. Finally, the comparison with a pan-HDAC inhibitor, Vorinostat (SAHA), or HDAC6-specific inhibitor, Tubastatin A, showed higher target specificity and antitumor activity of the new HDAC6 inhibitor. In conclusion, our data reveal the efficacy of a novel HDAC6 inhibitor in glioblastoma preclinical setting.