Biology subjects

Romero, Y.

Publications and source records attributed to Romero, Y..

4 recordsLinked to original sources

bioRxiv2026

Lung progenitors exhaustion in response to microenvironment stress during aging

Progenitor cells in aged tissues undergo changes in their microenvironment that may impact their functionality during regeneration. Despite recent advances in understanding the role of adult lung progenitors, the impact of aging on these cells remains unclear. To analyze aging modifications, we used aged wild-type mice of 18-24 months old, and Zmpste24-/- deficient mice, which exhibit an accelerated aging phenotype. A three-dimensional organoid culture system was employed to assess the lung regeneration capacity. Additionally, mouse epithelial cells and fibroblasts were isolated and characterized with senescence and autophagy markers. Our findings revealed that lung epithelial cells from aged mice and Zmpste24-/- mice hold their regeneration capacity, maintaining their phenotype and a healthy cellular state through an increase in autophagy, particularly when co-cultured with healthy fibroblasts. Conversely, cultured fibroblasts from Zmpste24-/- mice show nuclear defects and acquire a senescent phenotype, characterized by mTORC1 activation and reduced autophagy, which in turn impairs organoid formation. Moreover, these progenitor cells become increasingly susceptible to mechanical stress with aging due to reduced nuclear lamins and the Zmpste24 defect. This vulnerability is illustrated by FACS sorting, which can further compromise their regenerative potential. Our results indicate that, in aging, progenitor cells and their fibroblast niche integrate microenvironmental signals that shape cell-cell interactions essential for lung regeneration.

cell biology↗

bioRxiv2025

Connectome-based predictive modelling predicts frailty levels in older adults

Frailty is characterized by a persistent and progressive decline in physiological reserves, leading to increased vulnerability to stressors and a heightened risk of adverse health outcomes, both physically and mentally. Despite frailtys prevalence in older adults, there is limited research on its neural substrates, especially using task-based brain functional connectivity. In this study, we used connectome-based predictive modelling (CPM) to find a linear relationship between task-based connectomes -- taken from tasks that involved similar handgrip manipulations -- and a separate measure of frailty: the maximum grip strength in older adults. We observed that the task-based connectomes were able to explain individual differences in grip strength, with the Subcortical and Cerebellum network, particularly the caudate nucleus, functional connectivity being the strongest predictor. These findings demonstrate that task-based functional connectomes can serve as personalized markers that can predict individual behavioral measures, including handgrip strength, and point to involvement of the caudate nucleus in frailty. Key pointsO_LIWe used connectome-based predictive modeling on task-based fMRI to predict grip strength in older adults, a key marker of physical frailty. C_LIO_LIThe model significantly explained inter-individual differences in contraction strength using functional connectivity patterns. C_LIO_LISubcortical regions, especially the caudate nucleus, played a major role in prediction, highlighting their relevance in frailty assessment and potential interventions. C_LI

neuroscience↗

bioRxiv2025

Using Monoids for the Integration of Genomic and Metabolic Parameters in the Prediction of Phenotypes in Regulatory Cascades.

Molecular network modeling requires the use of mathematical and computational formalisms for a robust and accurate prediction of phenotypes. Furthermore, there is a need to extend these formalisms to be applied to large-scale molecular networks, thus helping in the understanding of biological complexity. In this work, we propose an extension to the modeling framework known as design principles, developed by M.A. Savageau, which is based on the Power Law Modeling. While valuable to understand several properties of molecular networks, the power law modeling cannot be used to infer kinetic orders, which are typically related to the number of binding sites present in molecules. We modified the traditional approach by incorporating the use of monoids generating a new methodological approach that we call Genotype Arithmetic. This approach solves local geometry, solving networks through monoids and varieties, reducing the computational complexity. The resulting combinatorial object contains a family of geometric points, whose fixed points in the exponent space define a set of constraints determining the correct kinetic order to be used in the power law modeling. This characteristic constitutes a prediction of the binding strength and/or the number of DNA binding sites in regulatory sequences, as well as the reaction orders in enzymatic kinetics. To show the applicability of the present approach, we show how the number of binding sites can be approximated in metabolic pathways formed by 3 to 9 reactions, in allosteric systems of end-product inhibition with intermediate catalytic reactions and one gene inhibition. Author summaryIn this work, was revised the Power Law Modeling to analyze and predict kinetic and genomic parameters in molecular networks. It was developed a new approach, called Genotype Arithmetic, which takes advantage from the properties of Monoids found within this formalism. Using this algebraic and geometric technique, were predicted the number of binding sites for various cases associated with the pathway length in the gene allosteric system for end-product inhibition.

bioinformatics↗

bioRxiv2019

PulmonDB: a curated lung disease gene expression database

Chronic Obstructive Pulmonary Disease (COPD) and Idiopathic Pulmonary Fibrosis (IPF) have contrasting clinical and pathological characteristics, and interesting whole-genome transcriptomic profiles. However, data from public repositories are difficult to reprocess and reanalyze. Here we present PulmonDB, a web-based database (http://pulmondb.liigh.unam.mx/) and R library that facilitates exploration of gene expression profiles for these diseases by integrating transcriptomic data and curated annotation from different sources. We demonstrated the value of this resource by presenting the expression of already well-known genes of COPD and IPF across multiple experiments and the results of two differential expression analyses in which we successfully identified differences and similarities. With this first version of PulmonDB, we create a new hypothesis and compare the two diseases from a transcriptomics perspective.

bioinformatics↗