Biology subjects

Gimeno, M.

Publications and source records attributed to Gimeno, M..

3 recordsLinked to original sources

bioRxiv2023

A Systematic Identification of RBPs Driving Aberrant Splicing in Cancer

Alternative Splicing (AS) is a post-transcriptional process by which a single RNA can lead to different mRNA and, in some cases, several proteins. Various processes (probably many of them yet to be discovered) are involved in the regulation of alternative splicing. This work focuses on the regulation by RNA-binding proteins (RBPs). In addition to splicing regulation, these proteins are related to cancer prognosis and are emerging therapeutic targets for cancer treatment. CLIP-seq experiments target selected RBPs and result in uncovering the loci of the nascent transcriptome to where the RBP binds to. The presence of changes in the splicing status surrounding these loci is a good starting point to establishing a causal relationship. The selection of the specific RBP(s) to target in the CLIP-seq experiment is not straightforward; in many cases, this selection is driven by apriori hypotheses. In this work, we have developed an algorithm to detect RBPs that are likely related to the splicing changes between conditions. To do this we have integrated several databases of CLIP-seq experiments with an algorithm that detects differential splicing events to discover RBPs that are especially enriched in these events. This is a follow-up of a previous work that is refined by 1) improving the algorithm to predict the splicing events and 2) testing different enrichment statistics, and 3) performing additional validation experiments. As a result, the new method provides more accurate predictions, and it is also included in the Bioconductor package EventPointer. We tested the algorithm in four different experiments where seven different RBPs were knocked down. The algorithm accurately states the statistical significance of these RBPs using only the alterations in splicing. We also applied the algorithm to study sixteen cancer types from The Cancer Genome Atlas (TCGA). We found relationships between RBPs and several cancer types like CREBBP and MBNL2 alterations in adenocarcinomas of the lung, liver, prostate, rectum, stomach, and colon cancer. Some of these relationships have been validated in the literature but other ones are novel. AvailabilityThis method is integrated EventPointer, an available Bioconductor R package.

bioinformatics↗

bioRxiv2021

Identifying Lethal Dependencies with HUGE Predictive Power from Large-Scale Functional Genomic Screens

Recent functional genomic screens -such as CRISPR-Cas9 or RNAi screening-have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem related to the large number of gene knockouts limits the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the "HUb effect in Genetic Essentiality" (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens - Project Score, CERES score and DEMETER score-identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. HUGE shows an increased enrichment in a recent harmonized knowledgebase of clinical interpretations of somatic genomic variants in cancer (with an AUROC up to 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer.

genomics↗

bioRxiv2020

Language network connectivity increases in prodromal Alzheimer's disease

Language production deficits occur early in the course of Alzheimers disease (AD); however, only few studies have focused on language functional networks in prodromal AD. The current study aims to uncover the extent of language alteration at a prodromal stage, on a behavioral, structural and functional level, using univariate and multivariate analyses. Twenty-four AD participants and 24 matched healthy controls underwent a comprehensive language evaluation, a structural T1-3D MRI and resting-state fMRI. We performed seed-based analyses, using the left inferior frontal gyrus and left posterior temporal gyrus as seeds. Then, we analyzed connectivity between executive control networks and language network in each group. Finally, we used multivariate pattern analyses to test whether the two groups could be distinguished based on the pattern of atrophy within the language network; atrophy within the executive control networks, as well as the pattern of functional connectivity within the language network; and functional connectivity within executive control networks. AD participants had language impairment during standardized language tasks and connected-speech production. Univariate analyses were not able to discriminate participants at this stage, while multivariate pattern analyses could significantly predict the group membership of prodromal patients and healthy controls, both when classifying atrophy patterns or connectivity patterns of the language network. Language functional networks could discriminate AD participants better than executive control networks. Most notably, they revealed an increased connectivity at a prodromal stage. Multivariate analyses represent a useful tool for investigating the functional and structural (re-)organization of the neural bases of language. HighlightsLanguage network connectivity discriminates prodromal AD from healthy controls Language network connectivity increases in prodromal AD Atrophy patterns in the language network do not correlate with connectivity patterns in AD

neuroscience↗