Biology subjects

Rodriguez Gonzalez, A.

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

10 recordsLinked to original sources

bioRxiv2026

Abnormal nuclear organization impairs adipogenic lineage commitment and terminal maturation in Hutchinson-Gilford progeria syndrome (HGPS)

Hutchinson-Gilford progeria syndrome (HGPS) is a fatal premature aging disorder driven by progerin, a truncated Lamin A protein that destabilizes nuclear architecture and disproportionately affects mesenchymal-lineage tissues. One notable mesenchymal lineage defect in Progeria patients is early and severe subcutaneous lipodystrophy. We hypothesized that disorganized chromatin architecture underlies the failure of mesenchymal stem cell (MSC) lineage commitment and adipogenesis in HGPS. To test this while preserving donor age, epigenetic history, and tissue context, we induced adipogenesis in age-matched healthy and HGPS patient-derived dermal fibroblasts and profiled cells using imaging, bulk RNA-seq, and Hi-C across the differentiation time course. We found that HGPS fibroblasts commit to the adipogenic lineage at rates comparable to healthy controls but diverge downstream: induced cells display aberrant lipid-droplet morphology, mount a disorganized and delayed transcriptional response, and retain priming for alternative mesenchymal lineages (osteochondrogenic, vascular) alongside stalled adipogenic maturation. Hi-C revealed that HGPS fibroblasts exhibit some baseline spatial compartment differences from healthy cells, but more notably show differences in how they reorganize compartments during adipogenic induction. Compartment-transcription coupling was strong during adipogenesis for lipid-metabolic genes in healthy cells but was more dysregulated in HGPS. These results indicate that HGPS disrupts lineage consolidation rather than initiation, associated with disease-specific failures in chromatin reorganization during differentiation.

cell biology↗

bioRxiv2026

H3K27me3 Drives Constricted Migration Induced 3D Genome Rewiring in Melanoma

Repeated exposure of neoplastic cells to mechanical stresses during metastasis can drive stable morphological and physiological changes. We previously showed that A375 human melanoma cells subjected to 10 rounds of constricted migration ("Bottom-10 cells") became significantly more migratory and exhibited H3K9me3 relocalization, transcriptional remodeling, and chromatin compartment changes relative to naive parental A375 cells. Because these phenotypes persist across cell divisions, we hypothesized they are driven and maintained by specific epigenetic mechanisms. Here, we profiled candidate histone modifications by CUT&RUN and applied a computational model to identify marks that explain compartment switches. In parental cells, H3K9me3 and H3K4me1 strongly predict locus stability in the B and A compartments, respectively. In contrast, changes in H3K27me3 dynamically predict A-to-B compartment switching after constricted migration. Consistently, altered H3K27me3 enrichment correlates with both differentially regulated genes and chromatin compartment changes between Bottom-10 and parental populations. We therefore tested whether inhibiting H3K27me3 deposition alters migratory potential and/or the "memory" of the increased migratory phenotype. Inhibiting EZH1/2 reduced migration efficiency overall, with a stronger effect on constricted than unconstricted migration. This reduction was accompanied by decreased genomic H3K27me3 enrichment and reversal of some compartment changes in Bottom-10 cells. However, acute treatment did not durably reverse the highly migratory Bottom-10 phenotype; cells returned to their initial state after drug removal. Chronic inhibition across many sequential rounds of constricted migration dramatically reduced the fraction of cells able to migrate. Notably, cells that migrated despite chronic EZH inhibition appeared to escape treatment, acquiring many typical Bottom-10 H3K27me3 and compartment changes even though acute inhibition tends to prevent them. Overall, these results highlight H3K27me3 as a key contributor to establishing and maintaining constricted migration-induced 3D genome changes.

genomics↗

bioRxiv2025

Sharing Patterns Between Proteins and Exploring Possible Drug Interactions

Due to the nature of amino acids that make up proteins, it is possible to detect small patterns within their sequence. Some of these sequences have been identified as motifs, characterized by defined signatures that are shared across different proteins. In this study, patterns are defined as short amino acid sequences that are commonly found among proteins involved in various types of cancer. The focus of this research is on the localization of such patterns in ALDH2, which is used as a reference protein because it is known to be inhibited by disulfiram. Disulfiram, originally used to treat alcohol use disorder (AUD), has been repurposed for the treatment of lung cancer. Analyzing shared patterns can help identify common protein structures and may assist in locating potential binding cavities and interaction sites. Moreover, it can improve our understanding of how closely related or structurally similar proteins might also be affected by certain drugs. This approach could offer a new perspective on drug specificity and targeting strategies in the context of disease.

bioinformatics↗

bioRxiv2025

Prioritization of Potential Drugs Through Pathway-Based Drug Repurposing and Network Proximity Analysis

Drug repurposing is an effective strategy to identify novel therapeutic options by leveraging existing drugs with known mechanisms of action and safety profiles. This work introduces a pathway-based drug repurposing approach that combines protein-protein interaction (PPI) networks and transcriptomic data to prioritize candidate drugs. The underlying assumption is that if disease 1 and disease 2 are both associated with the same biological pathway, then drugs known to be effective for disease 2 may be potential treatments for disease 1. To explore these relationships, disease-pathway-drug triplets are constructed. Genes shared between disease 1 and the associated pathway are identified, and their proximity is calculated within the PPI network using a Z-score based on random permutations. Candidate drugs for disease 2 are then analyzed using the Connectivity Map data, assessing whether their up- or down-regulated gene signatures are enriched for the genes contributing to the proximity between disease 1 and the pathway. This integration enables the ranking of candidate drugs based on their potential biological impact on disease 1. Finally, top-ranked drug-disease associations are evaluated through manual curation of the scientific literature to assess existing evidence supporting the proposed repurposing hypothesis.

bioinformatics↗

bioRxiv2025

Exploring Protein Patterns, Cavity Interactions, and Therapeutic Insights in Cancer

Protein sequence alignments are essential for identifying proteins shared structural and functional features. Detecting short amino acid sequences, termed patterns, across lung cancer and other related datasets facilitates the identification of relevant features. This study builds on previous findings by exploring proteins that share common patterns already identified. Using sequence matching at 5% and 10% occurrence thresholds, we identified 2,368 and 47 patterns, respectively. To reduce complexity and refine the dataset, shorter patterns from the 10% occurrence streamlined the analysis by isolating highly relevant patterns while reducing redundancy among proteins sharing sequence segments. Subsequent analyses integrated structural predictions for protein folding comparison, enabling the detection of patterns in different proteins and the identification of potential key residues. During cavity detection prediction, some amino acids were inspected in detail to assess their impact on protein function and their relevance in drug-target interactions. These insights were considered during docking studies, focusing on proteins used in treatments with pre-described ligands. By connecting raw sequence data to folding structures and functional features, we identified critical protein cavities that underscore the role of mutations in altering protein behavior and influencing drug-target interactions. These findings highlight protein activitys structural foundations and their importance in understanding cancer biology. By uncovering conserved sequence patterns and their structural implications, this study provides insights into potential biomarkers and therapeutic targets, that could aid in developing more effective cancer treatments.

bioinformatics↗

bioRxiv2025

Benchmarking Docking Tools on Experimental and Artificial Intelligence-Predicted Protein Structures

In silico analysis provides valuable insights into studying macromolecules, particularly proteins. Protein structure prediction models, like AlphaFold (AF), offer a cost-effective and time-efficient alternative to traditional methods like X-ray crystallography, NMR spectroscopy, and cryo-EM for determining protein structures. These models are increasingly used in protein-ligand interaction studies, a key aspect of drug discovery. Docking and molecular dynamics simulations facilitate this process, and researchers are continuously developing open-access tools for cavity detection and docking to accelerate protein-ligand interaction studies. However, while many of these tools perform well in specific cases, their strengths and weaknesses in analyzing predicted protein structures remain largely unknown. Therefore, it is crucial to compare docking analyses using experimentally determined protein structures and deep learning-based models. In this study, two well-characterized proteins, dopamine D3 receptor with its ligand ETQ and neprilysin with its ligand sacubitrilat, are used to evaluate docking predictions. The docking tools CB-Dock 2 and COACH-D are applied to both X-ray crystallography-derived structures and five different AF-generated models. The objective is to assess the accuracy of these docking approaches and determine whether this strategy can effectively simulate macromolecular behavior in their microenvironment. By doing so, this study aims to generate new insights and contribute to accelerating research in protein-ligand interactions.

bioinformatics↗

bioRxiv2020

Analysis of new nosological models from disease similarities using clustering

While classical disease nosology is based on phenotypical characteristics, the increasing availability of biological and molecular data is providing new understanding of diseases and their underlying relationships, that could lead to a more comprehensive paradigm for modern medicine. In the present work, similarities between diseases are used to study the generation of new possible disease nosologic models that include both phenotypical and biological information. To this aim, disease similarity is measured in terms of disease feature vectors, that stood for genes, proteins, metabolic pathways and PPIs in the case of biological similarity, and for symptoms in the case of phenotypical similarity. An improvement in similarity computation is proposed, considering weighted instead of Booleans feature vectors. Unsupervised learning methods were applied to these data, specifically, density-based DBSCAN clustering algorithm. As evaluation metric silhouette coefficient was chosen, even though the number of clusters and the number of outliers were also considered. As a results validation, a comparison with randomly distributed data was performed. Results suggest that weighted biological similarities based on proteins, and computed according to cosine index, may provide a good starting point to rearrange disease taxonomy and nosology.

bioinformatics↗

bioRxiv2019

Completing missing MeSH code mappings in UMLS through alternative expert-curated sources

The increasing availability of biological, clinical and literary sources enables the study of diseases from a more comprehensive approach. However, the interoperability of these sources, particularly of the codes used to identify diseases, poses a major challenge. Because of its role as a hub of multiple medical vocabularies, the Unified Medical Language System (UMLS) has become one of the most widely used resources for mapping diseases from different classification systems. The coverage of these mappings, nevertheless, is still limited, so researchers must resort to other methods to fill these gaps. In this article we analyze the limitations in UMLS mappings for the main disease vocabularies and propose the exploitation of some alternative expert-curated sources to complete it. As a result, we demonstrate that this approach allows resolving more than 50% of the missing mappings for some vocabularies. All the findings are shared for validation and reuse.

bioinformatics↗

bioRxiv2018

DISNET: Disease understanding through complex networks creation and analysis

Within the global endeavour of improving population health, one major challenge is the increasingly high cost associated with drug development. Drug repositioning, i.e. finding new uses for existing drugs, is a promising alternative; yet, its effectiveness has hitherto been hindered by our limited knowledge about diseases and their relationships. In this paper we present DISNET (Drug repositioning and disease understanding through complex networks creation and analysis), a web-based system designed to extract knowledge from signs and symptoms retrieved from medical data bases, and to enable the creation of customisable disease networks. We here present the main functionalities of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia, PubMed and MayoClinic; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. We further present a validation of the processing performed by the system; and describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses.\n\nDatabase URL: http://disnet.ctb.upm.es

bioinformatics↗

bioRxiv2018

Disease networks and their contribution to disease understanding and drug repurposing. A survey of the state of the art

Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes. One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks.

bioinformatics↗