Biology subjects

Chagoyen, M.

Publications and source records attributed to Chagoyen, M..

8 recordsLinked to original sources

bioRxiv2026

From expansion to consolidation: two decades ofGene Ontology evolution

The Gene Ontology (GO) is a long-standing, community-maintained knowledge resource that underpins the functional annotation of gene products across numerous biological databases. Released regularly, GO and its associated annotations form a large, continuously evolving dataset whose temporal dynamics have direct consequences for data reuse, versioning, and reproducibility. Because analytical results derived from GO are inherently tied to specific ontology and annotation releases, a systematic understanding of how GO changes over time is essential for transparent interpretation and long-term reuse of GO-based analyses. Here, we present a comprehensive temporal characterization of the Gene Ontology and its annotations spanning 21 years of publicly available releases. Treating successive ontology and annotation versions as longitudinal research data, we quantify changes in ontology structure, term composition, relationships, and annotation content across time and across three representative annotation resources. Our analysis reveals sustained growth of GO over its lifetime, accompanied by marked structural reorganization, particularly affecting high-level, general ontology terms. Notably, across multiple structural and annotation metrics, we identify a transition toward increased stability beginning around 2017, consistent with a maturation phase of the resource. This work provides a reference framework for researchers who rely on GO releases for data integration, benchmarking, and reproducible functional analysis.

bioinformatics↗

bioRxiv2025

Discovering cell types underlying rare disease phenotypes using scRNA-seq data from non-diseased tissues

Despite their low individual prevalence, rare diseases collectively pose a significant health burden, affecting millions of people worldwide. These conditions often result from single-gene mutations, yet the cellular contexts in which these alterations act remain largely unknown--information crucial for improving diagnosis and treatment. As patient-derived samples are scarce, we use single-cell RNA sequencing (scRNA-seq) data from non-diseased tissues to identify relevant cell populations. We introduce Cell4Rare, a computational framework that integrates these healthy scRNA-seq datasets with known phenotype-associated genes to map disease phenotypes to specific cell types. Applied across diverse tissues and phenotypes, Cell4Rare was validated against literature-based associations, achieving robust performance with an AUC of 0.71. These results highlight the potential of computational analyses of non-diseased scRNA-seq data to uncover the cellular basis of rare disease phenotypes, paving the way for improved diagnostics and therapeutic strategies.

systems biology↗

bioRxiv2025

Redefining Housekeeping Genes in the Context of Vertebrate Development

Housekeeping genes are typically defined as genes active in all adult tissues and essential for basic cellular functions. However, this view overlooks genes that are crucial during early development. Using zebrafish as a model system, we examined gene expression from the first hours of embryogenesis through adulthood. We found that many genes expressed throughout development remain broadly expressed in adults, but others do not; and some adult housekeeping genes are not active early on. This reveals that essential gene functions shift across life stages. Genes active throughout development are more likely to be evolutionarily ancient, shared with humans, and cause defects in many organs when disrupted. Yet some genes expressed only at specific developmental stages, especially around gastrulation, can be equally essential. This supports the developmental hourglass model, where mid-embryogenesis is the most conserved stage. Our results broaden the concept of housekeeping genes to include genes essential across time, not only across adult tissues.

systems biology↗

bioRxiv2025

Cell atlases and the Developmental Foundations of the Phenotype

It is widely acknowledged that development shapes phenotypes, yet the extent to which genes with similar expression patterns during development lead to equivalent organismal phenotypes when mutated remains unclear. Here, we propose addressing this issue, which we term the [D]evelopment-to- [P]henotype, or [D]-[P], rule, by leveraging single-cell gene expression atlases and phenotypic ontologies, using Caenorhabditis elegans as a model system. This framework quantifies the proportionality between developmental expression and phenotypic similarities, demonstrating that the relationship holds on average. Genes that strongly fulfill the rule exhibit broad "housekeeping" expression and are associated with systemic phenotypes, whereas weak similarities correspond to specific expression patterns and specialized phenotypes. Deviations from the [D]-[P] rule provide insights into developmental divergence and phenotypic degeneracy, highlighting genes with narrow functional roles but systemic phenotypic impact. Furthermore, genes that closely adhere to the rule exhibit the highest pleiotropic impact on organismal traits. Our analysis also identifies cell types, such as ASK neurons, as key mediators of phenotype-specific gene contributions, exemplified by their association with chemosensory behavior and chemotaxis. These findings validate the [D]-[P] rule and underscore the role of cells as critical mediators of the genotypephenotype map, offering a unified framework to understand the developmental origins of phenotypic complexity.

systems biology↗

bioRxiv2024

Differential expression and co-expression reveal cell types relevant to rare disease phenotypes

Disease phenotypes, serving as valuable descriptors for delineating the spectrum of human pathologies, play a critical role in understanding disease mechanisms. Integration of these phenotypes with single-cell RNA sequencing (scRNA-seq) data facilitates the elucidation of potential associations between phenotypes and specific cell types underlying them, which sheds light on the underlying physiological processes related to these phenotypes. In this study, we utilized scRNA-seq data to infer potential associations between rare disease phenotypes and cell types. Differential expression and co-expression analyses of genes linked to abnormal phenotypes were employed as metrics to identify the involved cell types. Comparative assessments were made against existing phenotype-cell type associations documented in the literature. Our findings underscore the utility of differential expression and co-expression analyses in identifying significant relationships. Moreover, co-expression analysis unveils cell types potentially linked to abnormal phenotypes not extensively characterized in prior studies. Key points- Cell types underling rare disease phenotypes remain largely unknown - Single-cell RNA-seq data from healthy tissues can be analyzed to reveal these cell types - We employed differential expression and co-expression analysis to identify cell types associated with rare disease phenotypes - We validated our results with known relations described in the literature

bioinformatics↗

bioRxiv2023

Disentangling protein metabolic costs in human cells and tissues.

Many intrinsic functional characteristics of cells and tissues shape their genome-wide expression patterns. But what other factors might also modulate these patterns are not fully known. Here, we revisit the general model of costs in which the protein products of highly expressed genes should be short and made up of biosynthetically cheap amino acids. We first use single-cell expression data from a collection of human cell types to confirm this model with a twist: the most highly expressed proteins tend to be particularly short and use expensive amino acids. By clustering how these two factors change with expression across all cell types, we identified a set of archetypal profiles that uniquely balance costs and that occur at different proportion across cell types. Similar profiles were also found by examining the expression data of tissues, which allowed us to recognize those following a more or less costly strategy. We then asked how this model might delineate the expression changes seen in a tumor relative to its normal solid tissue, as it has been argued that energy constraints determine cancer progression. We discovered a strong signal for the overexpression of biosynthetically cheap compact genes in cancer tissues. Our work highlights how both aspects of the metabolic cost of a protein, length and amino acid biosynthesis, represent valuable measures for understanding the different levels of biological organization and also the differences between health and disease.

systems biology↗

bioRxiv2021

Predicting biological pathways of chemical compounds with a profile-inspired aproach

Assignment of chemical compounds to biological pathways is a crucial step to understand the relationship between the chemical repertory of an organism and its biology. Protein sequence profiles are very successful in capturing the main structural and functional features of a protein family, and can be used to assign new members to it based on matching of their sequences against these profiles. In this work, we extend this idea to chemical compounds, constructing a profile-inspired model for a set of related metabolites (those in the same biological pathway), based on a fragment-based vectorial representation of their chemical structures. We use this representation to predict the biological pathway of a chemical compound with good overall accuracy (AUC 0.74-0.90 depending on the database tested), and analyzed some factors that affect performance. The approach, which is compared with equivalent methods, can in addition detect those molecular fragments characteristic of a pathway. The method is available as a graphical interactive web server http://csbg.cnb.csic.es/iFragMent

systems biology↗

bioRxiv2018

Complex genetic and epigenetic regulation deviates gene expression from a global transcriptional program

Environmental or genetic perturbations lead to gene expression changes. While most analyses of these changes emphasize the presence of qualitative differences on just a few genes, we now know that changes are widespread. This large-scale variation has been linked to the exclusive influence of a global transcriptional program determined by the new physiological state of the cell. However, given the sophistication of eukaryotic regulation, we expect to have a complex architecture of specific control affecting this program. Here, we examine this architecture. Using data of Saccharomyces cerevisiae expression in different nutrient conditions, we first propose a five-sector genome partition, which integrates earlier models of resource allocation, as a framework to examine the deviations from the global control. In this scheme, we recognize invariant genes, whose regulation is dominated by physiology, specific genes, which substantially depart from it, and two additional classes that contain the frequently assumed growth-dependent genes. Whereas the invariant class shows a considerable absence of specific regulation, the rest is enriched by regulation at the level of transcription factors (TFs) and epigenetic modulators. We nevertheless find markedly different strategies in how these classes deviate. On the one hand, there are TFs that act in a unique way between partition constituents, and on the other, the action of chromatin modifiers is significantly diverse. The balance between regulatory strategies ultimately modulates the action of the general transcription machinery and therefore limits the possibility of establishing a unifying program of expression change at a genomic scale.

systems biology↗