Biology subjects

Rodriguez-Gonzalez, A.

Publications and source records attributed to Rodriguez-Gonzalez, A..

12 recordsLinked to original sources

bioRxiv2025

An epigenomic investigation of atrial fibrillation in a matched left and right atrial human cohort

As the most prevalent cardiac arrythmia, atrial fibrillation is an important contributor to cardiovascular morbidity and mortality. Human population findings increasingly support its complex genetic architecture, with most genetic association signals for atrial fibrillation found in the non-coding genome. In this study, we integrated genome-wide histone modification, gene expression and DNA methylation levels in a paired left and right atrial cohort comprising permanent atrial fibrillation patients and sinus rhythm controls. First, we first identified epigenomic regions enriched in histone H3 lysine 27 acetylation (H3K27ac) across left and right atria from patients and controls, and associated them with differentially expressed genes to derive a set of dysregulated candidate loci - including NPPB and SCX. Second, by incorporating an independent replication cohort, we were able to validate gene expression and epigenomic differences for a subset of these candidate loci. Third, we profiled base-resolution DNA methylation levels and identified differentially-methylated regions (DMRs) between atrial fibrillation and sinus rhythm samples. Integration of these data with histone modification levels and gene expression allowed us to propose epigenetic mechanisms underlying transcriptomic and epigenomic changes across dysregulated loci, such as disruption of transcription factor binding by DNA methylation at the LRRC4B locus. The data and analyses we report constitute a systematic investigation of gene regulatory alterations across the left and right atria in permanent atrial fibrillation.

genomics↗

bioRxiv2025

Accelerating Drug Repurposing with AI: The Role of Large Language Models in Hypothesis Validation

Drug repurposing accelerates drug discovery by identifying new therapeutic uses for existing drugs, but validating computational predictions remains a challenge. Large Language Models (LLMs) offer a potential solution by analyzing biomedical literature to assess drug-disease associations. This study evaluates four LLMs (GPT-4o, Claude-3, Gemini-2, and DeepSeek) using ten prompt strategies to validate repurposing hypotheses. The best-performing prompts and models were tested on 30 pathway-based cases and 10 benchmark cases. Results show that structured prompts enhance LLM accuracy, with GPT-4o and DeepSeek emerging as the most reliable models. Benchmark cases achieved significantly higher accuracy, precision, and F1-score (p < 0.001), while recall remained consistent across datasets. These findings highlight LLMs potential in drug repurposing validation while emphasizing the need for structured prompts and human oversight.

bioinformatics↗

bioRxiv2025

Evaluating the Influence of Disease-Gene Associations in the Significance of Disease Modules through the lens of Network Medicine

The rapid expansion of genomic and biomedical data has paved the way for constructing complex disease networks, offering new insights into disease mechanisms and therapeutic target identification. Central to these networks are disease modules, which are constructed from seed genes that are prioritized based on their relevance to a given disease. Gene prioritization aims to rank genes based on their strength of association with a disease. Resources like DisGeNET, integrated within DISNET knowledge base, assigns a Gene-Disease Association (GDA) score that reflects the confidence in the association between a gene and a disease. This study investigates how both disease module sizes and GDA scores influence the statistical significance of these modules. By characterizing disease modules, filtering the data based on their GDA score thresholds, and analysing the relationship between their size, GDA score, and significance, potential cutoffs for robust module construction are estimated. Our findings suggest that disease modules filtered to include GDA scores above 0.3 and module sizes greater than 10 tend to be significant. These insights provide guidance for optimal gene prioritization and module selection, ultimately enhancing strategies for target identification and drug repurposing in network medicine.

bioinformatics↗

bioRxiv2025

Mammalian epigenomic conservation of promoters and enhancers in the heart associates with trait-associated variation and impacts cardiomyocyte phenotypes

Interindividual genetic variation associated with cardiovascular traits and disease is commonly found in the non-coding genome, suggesting regulatory changes underlie many genetic association signals. Mammalian gene regulation is controlled by promoters and enhancers with rapidly divergent activities. However, the interplay between human non- coding genetic variation and regulatory evolution remains largely unexplored. Here, we investigated promoter and enhancer evolution in the mammalian heart using genome-wide epigenomic profiling, intersected these elements with genetic variants associated with cardiovascular traits, and experimentally tested candidate regions in human cardiomyocytes derived from pluripotent stem cells. First, we applied a comparative genomics approach to identify epigenomically-conserved promoters and enhancers in the heart, as well as elements with primate-specific or human-only epigenomic signals. Second, we evaluated the association of common genetic variation and signatures of cell-type specific gene regulation with mammalian epigenomic conservation. We report an enrichment of common genetic variants in epigenomically-conserved promoters and enhancers, which also associate with regulatory pleiotropy across cardiac cell types, and promoter-enhancer contacts in cardiomyocytes. Based on these findings, we selected candidate epigenomically-conserved elements across three cardiovascular genetics loci (KCNH2, CPEB4 and PRKCE), and investigated gene expression and cellular outcomes upon CRISPR/Cas9-mediated deletion of each region in human cardiomyocytes. These analyses inform how mammalian epigenomic conservation associates with specific gene expression contributions and downstream cellular responses, such as cardiomyocyte hypertrophy and sensitivity to hypoxia/reoxygenation. Moreover, the comparative analyses we present here contribute to ongoing efforts to prioritise and functionally characterise cardiovascular association signals from human population genetics.

genomics↗

bioRxiv2025

Decoding cell-type-specific alterations in Alzheimer's disease through scRNA-seq and network analysis

Alzheimers Disease (AD) is a neurodegenerative disorder characterized by complex, cell-type-specific molecular alterations. This study integrates single-cell RNA sequencing (scRNA-seq) with network-based methodologies to decode transcriptional changes across major brain cell types in AD. Using scRNA-seq data from 432,555 single cells, we constructed Protein-Protein Interaction (PPI) networks specific to each cell type and assessed the differential expression of genes in diseased conditions. Our findings reveal that glutamatergic neurons and inhibitory interneurons exhibit the highest transcriptional dysregulation, while pericytes and endothelial cells show limited changes. The analysis identified significant enrichment of Differentially Expressed Genes (DEGs) within the AD protein module. Network analysis highlights highly connected proteins such as HSPB1, which is implicated in proteostasis, and CXCR4, which is involved in neuroinflammation. Our results underscore the importance of cell-type-specific approaches in AD research, demonstrating that neurons experience more extensive dysregulation, while vascular-associated cells play key roles in maintaining Blood-Brain Barrier (BBB) integrity. These insights emphasize the necessity of tailored therapeutic strategies addressing the heterogeneous molecular landscape of AD.

systems biology↗

bioRxiv2023

Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenarios

BackgroundDrug repurposing plays a significant role in bringing effective treatments for certain diseases faster and more cost-effectively. Successful repurposing cases are mostly supported by a classical paradigm that stems from de novo drug development. This paradigm is based on the "one-drug-one-target-one-disease" idea. It consists of designing drugs specifically for a single disease and its drugs gene target. In this article, we investigated the use of biological pathways as potential elements to achieve effective drug repurposing. MethodsConsidering a total of 4.214 successful cases of drug repurposing, we identified cases in which biological pathways serve as the underlying basis for successful repurposing, referred to as DREBIOP. Once the repurposing cases based on pathways were identified, we studied the inherent patterns within them by considering the different biological elements associated with this dataset, as well as the pathways involved in these cases. Furthermore, we obtained gene-disease association values to demonstrate the diminished significance of the drugs gene target in these repurposing cases. To achieve this, we compared the values obtained for the DREBIOP set with the overall association values found in DISNET, as well as with the drugs target gene (DREGE) based repurposing cases using the Mann-Whitney U Test. ResultsA collection of drug repurposing cases, known as DREBIOP, was identified as a result. DREBIOP cases exhibit distinct characteristics when compared to DREGE cases. Notably, DREBIOP cases are associated with a higher number of biological pathways, with Vitamin D Metabolism and ACE inhibitor being the most prominent pathways involved. Additionally, it was observed that the association values of GDAs in DREBIOP cases are significantly lower than those in DREGE cases (p-value < 0.05). ConclusionsBiological pathways assume a pivotal role in drug repurposing cases. This investigation successfully revealed patterns that distinguish drug repurposing instances associated with biological pathways. These identified patterns can be applied to any known repurposing case, enabling the detection of pathway-based repurposing scenarios or the classical paradigm. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/572035v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@187d2ceorg.highwire.dtl.DTLVardef@210b96org.highwire.dtl.DTLVardef@17fc4b7org.highwire.dtl.DTLVardef@14c0184_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Analysis of the Confidence in the Prediction of the Protein Folding by Artificial Intelligence

The determination of protein structure has been facilitated using deep learning models, which can predict protein folding from protein sequences. In some cases, the predicted structure can be compared to the already-known distribution if there is information from classic methods such as nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography, or electron microscopy (EM). However, challenges arise when the proteins are not abundant, their structure is heterogeneous, and protein sample preparation is difficult. To determine the level of confidence that supports the prediction, different metrics are provided. These values are important in two ways: they offer information about the strength of the result and can supply an overall picture of the structure when different models are combined. This work provides an overview of the different deep-learning methods used to predict protein folding and the metrics that support their outputs. The confidence of the model is evaluated in detail using two proteins that contain four domains of unknown function.

bioinformatics↗

bioRxiv2023

Orphan Drugs and Rare Diseases: Unveiling Biological Patterns through Drug Repurposing

Rare diseases are a collection of unusual pathologies that afflict millions of individuals globally. However, the creation of treatments for these conditions is frequently limited due to the high expenses and lack of profitability associated with drug development. Orphan drugs, which are medications specifically designed for rare diseases, have played a pivotal role in treating these diseases over the past several years. Nevertheless, their creation remains challenging, and many rare diseases lack approved therapies. Therefore, drug repurposing has emerged as a viable strategy for identifying potential new treatments for these pathologies, a technique consists in using existing drugs to treat a new disease different from the one that they were developed for. This approach can significantly reduce the time and cost of drug development while increasing the likelihood of success. In this paper, we examined the temporal progression of orphan drugs since their introduction and assess the impact of drug repositioning on treatments for rare diseases. Additionally, we aim to identify biological patterns that may be unique to rare diseases treated with repurposed orphan drugs. To this end, we analyzed various biological components associated with these diseases, categorized linked diseases, and obtained the type of orphan drug associated with them. Lastly, we evaluated the phenotypic similarity between diseases treated with an orphan drug through repurposing. Through these findings, we have gained insight into the evolution of orphan drug development in recent years and identified specific patterns that characterize rare diseases associated with them.

bioinformatics↗

bioRxiv2023

Enhancing drug repurposing on graphs by integrating drug molecular structure as feature

Drug repurposing has become increasingly important, particularly in light of the COVID-19 pandemic. This process involves identifying new therapeutic uses for existing drugs, which can significantly reduce the cost, risk, and time associated with developing new drugs, de novo development. A previous conducted study proved that Deep Learning can be used to streamline this process by identifying drug repurposing hypotheses. The study presented a model called REDIRECTION, which utilized the rich biomedical information available in graph form and combined it with Geometric Deep Learning to find new indications for existing drugs. The reported metrics for this model were 0.87 for AUROC and 0.83 for AUPRC. In this current study, the importance of node features in GNNs is explored. Specifically, the study used GNNs to embed two-dimensional drug molecular structures and obtain corresponding features. These features were incorporated into the drug repurposing graph, along with some other enhancements, resulting in an improved model called DMSR. Performance score for the reported metrics values raised by 0.0448 in AUROC and 0.0919 in AUPRC. Based on these findings, we believe that the method used for embedding drug molecular structures is interesting and captures valuable information about drugs. Its incorporation in the graph for drug repurposing can significantly benefit the process, leading to improved performance evaluation metrics.

bioinformatics↗

bioRxiv2022

Genetic perturbation of MAPK11 (p38β) promotes radiosensitivity by enhancing IR-associated senescence.

Background and purposeMAPKs are among the most relevant signalling pathways involved in coordinating cell responses to different stimuli. This group includes p38MAPKs, constituted by 4 different proteins with a high sequence homology: MAPK14 (p38), MAPK11 (p38{beta}), MAPK12 (p38{gamma}) and MAPK13 (p38{delta}). Despite their high similarity, each member shows unique expression patterns and even exclusive functions. Thus, analysing protein-specific functions of MAPK members is necessary to unequivocally uncover the roles of this signalling pathway. Here, we investigate the possible role of MAPK11 in the cell response to ionizing radiation (IR). Materials and methodsWe developed MAPK11/14 knockdown through shRNA and CRISPR interference gene perturbation approaches, and analysed the downstream effects on cell responses to ionizing radiation in A549, HCT-116 and MCF-7 cancer cell lines. Specifically, we assessed IR toxicity by clonogenic assays; DNA damage response activity by immunocytochemistry; apoptosis and cell cycle by flow cytometry (Annexin V and propidium iodide, respectively); DNA repair by comet assay; and senescence induction by both X-Gal staining and gene expression of senescence-associated genes by RT-qPCR. ResultsOur findings demonstrate a critical role of MAPK11 in the cellular response to IR by controlling the associated senescent phenotype, and without observable effects on DDR, apoptosis, cell cycle or DNA damage repair. ConclusionOur results highlight MAPK11 as a novel mediator of the cellular response to ionising radiation through the control exerted onto IR-associated senescence. HighlightsO_LIGenetic perturbation of MAPK11, but not MAPK14, promotes radiosensitivity in a panel of tumor cell lines. C_LIO_LIAbrogation of MAPK11 did not modify DNA damage response, proliferation, apoptosis or cell cycle in response to ionizing radiation C_LIO_LIMAPK11 controls ionizing radiation-induced senescence C_LIO_LIMAPK11 expression could be a novel target and biomarker for radiosensitivity C_LI

cancer biology↗

bioRxiv2022

REDIRECTION: Generating drug repurposing hypotheses using link prediction with DISNET data

In the recent years and due to COVID-19 pandemic, drug repurposing or repositioning has been placed in the spotlight. Giving new therapeutic uses to already existing drugs, this discipline allows to streamline the drug discovery process, reducing the costs and risks inherent to de novo development. Computational approaches have gained momentum, and emerging techniques from the machine learning domain have proved themselves as highly exploitable means for repurposing prediction. Against this backdrop, one can find that biomedical data can be represented in terms of graphs, which allow depicting in a very expressive manner the underlying structure of the information. Combining these graph data structures with deep learning models enhances the prediction of new links, such as potential disease-drug connections. In this paper, we present a new model named REDIRECTION, which aim is to predict new disease-drug links in the context of drug repurposing. It has been trained with a part of the DISNET biomedical graph, formed by diseases, symptoms, drugs, and their relationships. The reserved testing graph for the evaluation has yielded to an AUROC of 0.93 and an AUPRC of 0.90. We have performed a secondary validation of REDIRECTION using RepoDB data as the testing set, which has led to an AUROC of 0.87 and a AUPRC of 0.83. In the light of these results, we believe that REDIRECTION can be a meaningful and promising tool to generate drug repurposing hypotheses.

bioinformatics↗

bioRxiv2022

Drug repositioning with gender perspective focused on Adverse Drug Reactions

Drug repositioning is a novel, useful, and crucial technique to find new uses for existing drugs. In this field of study, when the clinical trials necessary to obtain successful drug repositioning have been carried out, the female gender has not been given much consideration. Thus far, the participation of women in clinical trials has been very limited. There were several argued reasons to exclude them from trials, like the likelihood of pregnancy or sudden hormonal changes. This has meant that for a long time the adverse effects of a drug on women were unknown. Scientifically, it was known that due to the biological processes of pharmacokinetics and pharmacodynamics, the response to drugs was not the same in both genders, but despite this evidence, there is still no difference in the dosage or form of using a drug between men and women. In this study, we made a preliminary analysis where the main goal is to investigate gender differences within the drug repositioning field through the adverse effects produced by such treatments. A special section on specific cases of drug repositioning in rare diseases will also be considered to carry out the same verification previously mentioned in the text.

bioinformatics↗