Biology subjects

Gonzalez-Rumayor, V.

Publications and source records attributed to Gonzalez-Rumayor, V..

3 recordsLinked to original sources

bioRxiv2024

Correlation between DNA double strand breaks and cell death in peripheral blood lymphocytes from breast cancer patients

Radiotherapy is an effective treatment to fight cancer. However, it not only affects cancer cells but also healthy tissues, causing side effects. Different factors can influence the appearance of radiotoxicity, like total dose administered or patient individual characteristics, such as genetic variability. Several biomarkers have been proposed to predict radiotoxicity, especially those based on apoptosis or DNA damage, for example {gamma}-H2AX, which correlates with DNA double strand breaks. Our purpose is to analyze how apoptosis and {gamma}-H2AX correlate to each other and to link these results with selected SNPs associated with apoptosis. Blood samples from 60 breast cancer patients in remission were recruited. After mononucleated cells isolation, samples were irradiated. Then, we assessed induction and kinetics of disappearance of {gamma}-H2AX at different times after 2-Gy irradiation and apoptosis induced 24 and 48 h after 8-Gy irradiation. A negative correlation was observed between basal and residual {gamma}-H2AX and apoptosis at 48 h post-irradiation. This result supports previous studies with cancer patients showing a negative correlation between these two biomarkers. Considering the high variability of radio-induced apoptosis, we performed a genotyping study. Two SNPs located at TP53 and FAS genes were associated with apoptosis. Overall, our results indicate that individuals with less efficiency in removing damaged cells, probably due to genetic polymorphisms, presented more basal and residual levels of DNA damage.

cell biology↗

bioRxiv2020

A comprehensive and centralized database for exploring omics data in Autoimmune Diseases

Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. Here, we present ADEx (https://adex.genyo.es), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.

bioinformatics↗

bioRxiv2018

mCSEA: Detecting subtle differentially methylated regions

MotivationThe identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.\n\nResultsWe present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify differentially methylated regions from Illumina 450K and EPIC array data. It is especially useful for detecting subtle, but consistent, methylation differences in complex phenotypes. mCSEA also implements functions to integrate gene expression data and to detect genes with significant correlations among methylation and gene expression patterns. Using simulated datasets, we show that mCSEA outperforms other tools in detecting DMRs. In addition, we applied mCSEA to a previously published dataset of sibling pairs discordant for intrauterine hyperglycemia exposure. We found several differentially methylated promoters in genes related to metabolic disorders like obesity and diabetes, demonstrating the potential of mCSEA to identify differentially methylated regions not detected by other methods.\n\nAvailabilitymCSEA is freely available from the Bioconductor repository.\n\nContactpedro.carmona@genyo.es

bioinformatics↗