Biology subjects

Guillon, J.

Publications and source records attributed to Guillon, J..

3 recordsLinked to original sources

bioRxiv2023

The YARS Tyrosyl tRNA Synthetase Regulates Senescence Induction and Escape Through the Transcriptional Control of LIN9, a Member of the DREAM Complex

Senescence is a tumor suppressor mechanism triggered by oncogene expression and chemotherapy treatment. It orchestrates a definitive cessation of cell proliferation through the activation of the p53-p21 and p16-Rb pathways, coupled with the compaction of proliferative genes within heterochromatin regions. Some cancer cells have the ability to elude this proliferative arrest but the signaling pathways involved in circumventing senescence remain to be characterized. We have recently described that malignant cells capable of evading senescence have an increased expression of specific tRNAs, such as tRNA-Leu-CAA and tRNA-Tyr-GTA, alongside the activation of their corresponding tRNA ligases, namely LARS and YARS. We have previously shown that YARS promotes senescence escape by activating proliferation and cell cycle genes but its functions during this proliferative arrest remain largely unknown. In this study, we have continued to characterize the functions of YARS, describing non-canonical transcriptional functions of the ligase. Our results show that YARS is present in the nucleus of growing and senescent cells and interacts with the Trim28 transcriptional regulator. Importantly, YARS binds to the LIN9 promoter, a critical member of the Dream complex responsible for regulating cell cycle transcription. YARS facilitates the binding and the phosphorylation of the type II RNA polymerase and promotes the deposit of activating epigenetic marks on the LIN9 promoter. Consequently, during senescence escape, YARS promotes LIN9 expression and both proteins are necessary to induce the proliferation of emergent cells. These results underscore the unconventional transcriptional functions of YARS in promoting senescence escape by activating LIN9 and likely influencing the functions of the Dream complex.

cancer biology↗

bioRxiv2020

tRNA Biogenesis and Specific Aminoacyl-tRNA Synthetases Regulate Senescence Stability Under the Control of mTOR

Oncogenes or chemotherapy treatments trigger the induction of suppressive pathways such as apoptosis or senescence. Senescence was initially defined as a definitive arrest of cell proliferation but recent results have shown that this mechanism is also associated with cancer progression and chemotherapy resistance. Senescence is therefore much more heterogeneous than initially thought. How this response varies is not really understood, it has been proposed that its outcome relies on the secretome of senescent cells and on the maintenance of their epigenetic marks. Using experimental models of senescence escape, we now described that the stability of this suppression relies on specific tRNAs and aminoacyl-tRNA synthetases. Following chemotherapy treatment, the DNA binding of the type III RNA polymerase was reduced to prevent tRNA transcription and induce a complete cell cycle arrest. By contrast, during senescence escape, specific tRNAs such as tRNA-Leu-CAA and tRNA-Tyr-GTA were up-regulated. Reducing tRNA transcription appears necessary to control the strength of senescence since RNA pol III inhibition through BRF1 depletion maintained senescence and blocked cell persistence. mTOR inhibition also prevented CIS escape in association with a reduction of tRNA-Leu-CAA and tRNA-Tyr-GTA expression. Further confirming the role of the tRNA-Leu-CAA and tRNA-Tyr-GTA, their corresponding tRNA ligases, YARS and LARS, were necessary for senescence escape. This effect was specific since the CARS ligase had no effect on persistence. YARS and LARS inactivation reduced cell emergence and proteomic analysis indicated that this effect was associated with the down-regulation of E2F1 target genes. Overall, these findings highlight a new regulation of tRNA biology during senescence and suggest that specific tRNAs and ligases contribute to the strength and heterogeneity of this suppression.

cancer biology↗

bioRxiv2018

Reproducible evaluation of classification methods in Alzheimer’s disease: framework and application to MRI and PET data

A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimers disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS, performing better than the classifiers trained and tested on each of these datasets independently. All the code of the framework and the experiments is publicly available.

neuroscience↗