Biology subjects

Amadoz, A.

Publications and source records attributed to Amadoz, A..

3 recordsLinked to original sources

bioRxiv2024

A senescence-specific lncRNA controls metabolic rewiring of senescent cells

Despite the classical view of senescence as passive growth arrest, senescent cells remain metabolically active to be able to cope with the energetic demand of the senescence program. However, the mechanisms underlying this metabolic reprogramming remain poorly understood. We have identified sin-lncRNA, a previously uncharacterized lncRNA, that plays a pivotal role in this response. Sin-lncRNA is only expressed by senescent cells, induced by the senescence master regulator C/EBP{beta}. While strongly activated in senescence, sin-lncRNA loss reinforces the senescence program by altering oxidative phosphorylation and rewiring mitochondrial metabolism. By interacting with the TCA enzyme dihydrolipoamide S-succinyltransferase (DLST) it facilitates its localization to the mitochondria. On the other hand, sin-lncRNA depletion results in DLST nuclear translocation linked to DLST-dependent transcriptional alteration of OXPHOS genes. While in highly proliferative cancer cells, sin-lncRNA expression remains undetected, it is strongly induced upon cisplatin-induced senescence. Depletion of sin-lncRNA in ovarian cancer cells results in deficient oxygen consumption and increased extracellular acidification, sensitizing the cells to cisplatin treatment. Altogether, these results indicate that sin-lncRNA is specifically induced in cellular senescence to maintain metabolic homeostasis. Our findings reveal a new regulatory mechanism in which a lncRNA contributes to the adaptive metabolic changes in senescent cells, unveiling the existence of an RNA-dependent metabolic rewiring specific to senescent cells.

cell biology↗

bioRxiv2018

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models

Backgroundin spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling.\n\nResultswe present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway module metabolic activities that can also be used for class prediction and in silico prediction of Knock-Out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer.\n\nConclusionsMetabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.\n\nMetabolizer can be found at: http://metabolizer.babelomics.org.

bioinformatics↗

bioRxiv2017

Signaling Pathway Activities Improve Prognosis for Breast Cancer

With the advent of high-throughput technologies for genome-wide expression profiling, a large number of methods have been proposed to discover gene-based signatures as biomarkers to guide cancer prognosis. However, it is often difficult to interpret the list of genes in a prognostic signature regarding the underlying biological processes responsible for disease progression or therapeutic response. A particularly interesting alternative to gene-based biomarkers is mechanistic biomarkers, derived from signaling pathway activities, which are known to play a key role in cancer progression and thus provide more informative insights into cellular functions involved in cancer mechanism. In this study, we demonstrate that pathway-level features, such as the activity of signaling circuits, outperform conventional gene-level features in prediction performance in breast cancer prognosis. We also show that the proposed classification scheme can even suggest, in addition to relevant signaling circuits related to disease outcome, a list of genes that do not code for signaling proteins whose contribution to cancer prognosis potentially supplements the mechanisms detected by pathway analysis.

bioinformatics↗