Biomedical subjects

Jaime Huerta-Cepas

Publications and source records attributed to Jaime Huerta-Cepas.

4 recordsLinked to original sources

PubMed2026

Penicillium melinii promotes root growth through subtle host reprogramming across model and crop species.

Root development is highly responsive to microbial interactions, yet the mechanisms by which beneficial fungi promote root growth remain incompletely understood. Here, we identified Penicillium melinii 'isolate 2' through a screen of endophytic fungi isolated from Arabidopsis and characterized it as a promoter of root development in both Arabidopsis and crop species. We combined phenotyping in vitro, rhizotron, greenhouse and field assays with reporter and mutant analyses, transcriptomics, phytohormone profiling and sequencing and annotation of the fungal genome to investigate the basis of this interaction. P. melinii consistently stimulated root growth and modified root architecture across experimental systems and host species. These effects were associated with subtle but reproducible host transcriptional reprogramming, supporting a model in which the fungus fine-tunes endogenous developmental programmes rather than broadly perturbing stress or growth pathways. Genetic and reporter analyses further suggested that this interaction modulates root branching through localized developmental reprogramming. Genomic analysis provided a framework for understanding the fungal traits associated with this beneficial interaction. The conservation of the response across model and crop species supports the relevance of P. melinii as both a useful experimental system to study beneficial plant-fungus interactions and a promising candidate for improving root traits and crop performance.

Penicillium melinii↗

PubMed2025

Root growth promotion by Penicillium melinii : mechanistic insights and agricultural applications.

This study characterizes Penicillium melinii , an endophytic fungus isolated from Arabidopsis thaliana roots, as a plant growth-promoting fungus with potential use as a model to study root development and as a biostimulant for sustainable agriculture. Although endophytes are known to promote plant growth, the underlying molecular mechanisms often remain poorly understood. Here, we aimed to elucidate how P. melinii enhances root system development and to assess its applicability across different crops. Phenotypic assays were conducted in Arabidopsis, quinoa and tomato under in vitro , greenhouse and field conditions. Root architecture and biomass were quantified using image-based phenotyping. Transcriptomic and phytohormone profiling assessed plant responses, and fungal genome sequencing coupled with secretome analysis was used to identify candidate effectors and metabolic traits. P. melinii consistently promoted root growth and increased plant biomass across species and environments, both in vitro and in the greenhouse. In tomato field trials, this translated into a significant increase in yield. The fungus colonized root surfaces without vascular penetration and triggered a mild transcriptomic response: early activation of stress-response genes followed by their attenuation and sustained upregulation of auxin-related pathways. Notably, the interaction modulates the SLR-ARF-LBD pathway and the number of pre-branch sites probably through increased auxin signalling in the oscillation zone. Additional hormonal changes were limited and mainly associated with the attenuation of the plant response to microorganisms. P. melinii enhances lateral root formation through a subtle molecular and metabolic dialogue with the host plant, underscoring its relevance as a model for studying root developmental plasticity. Its strong and reproducible growth-promoting effect, demonstrated with different fungal strains and under controlled and field conditions, supports its potential as a biostimulant for sustainable crop production.

Journal Article↗

PubMed2006

PeroxisomeDB: a database for the peroxisomal proteome, functional genomics and disease.

Peroxisomes are essential organelles of eukaryotic origin, ubiquitously distributed in cells and organisms, playing key roles in lipid and antioxidant metabolism. Loss or malfunction of peroxisomes causes more than 20 fatal inherited conditions. We have created a peroxisomal database (http://www.peroxisomeDB.org) that includes the complete peroxisomal proteome of Homo sapiens and Saccharomyces cerevisiae, by gathering, updating and integrating the available genetic and functional information on peroxisomal genes. PeroxisomeDB is structured in interrelated sections 'Genes', 'Functions', 'Metabolic pathways' and 'Diseases', that include hyperlinks to selected features of NCBI, ENSEMBL and UCSC databases. We have designed graphical depictions of the main peroxisomal metabolic routes and have included updated flow charts for diagnosis. Precomputed BLAST, PSI-BLAST, multiple sequence alignment (MUSCLE) and phylogenetic trees are provided to assist in direct multispecies comparison to study evolutionary conserved functions and pathways. Highlights of the PeroxisomeDB include new tools developed for facilitating (i) identification of novel peroxisomal proteins, by means of identifying proteins carrying peroxisome targeting signal (PTS) motifs, (ii) detection of peroxisomes in silico, particularly useful for screening the deluge of newly sequenced genomes. PeroxisomeDB should contribute to the systematic characterization of the peroxisomal proteome and facilitate system biology approaches on the organelle.

Animals↗

PubMed2006

Next station in microarray data analysis: GEPAS.

The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://www.gepas.org.

Cluster Analysis↗