Biology subjects

Hernandez-Rosales, M.

Publications and source records attributed to Hernandez-Rosales, M..

7 recordsLinked to original sources

bioRxiv2026

Strain-specific higher-order interactions break phylogenetic conservatism in microbial communities

Whether strains of the same species sharing the same ecological role are truly interchangeable has not been directly tested experimentally in communities, where higher-order interactions govern outcomes. Using 79 co-occurring Bacillaceae isolates from the Cuatrocienegas Basin, we combined whole-genome analyses with systematic strain substitutions in BARS, a three-species community in which a resistant strain protects a sensitive strain from an antagonist. Trait conservatism strongly predicted pairwise antagonistic outcomes. Yet across 34 assembled triads, 13 showed rescue beyond pairwise expectations and five showed suppression; single-strain substitutions rarely restored protection, although protection was recovered in one fully reconfigured triad. These outcomes could not be inferred from taxonomic relatedness, pangenomic content or pairwise assays; phylogenetic conservatism thus reaches its limit at the transition from pairwise to higher-order interactions. To explain this limit, we propose that strains behave as non-equivalent biological tiles whose interaction edges must align, a dynamic biological tessellation.

evolutionary biology↗

bioRxiv2025

REvolutionH-tl: A Fast and Robust Tool for Decoding Evolutionary Gene Histories

REvolutionH-tl is a fast, scalable, and integrated software platform for inferring orthology relationships, gene trees, species trees, and reconciled evolutionary scenarios directly from sequence data. Built upon the formal framework of best match graphs (BMGs), REvolutionH-tl predicts orthogroups and orthologous gene pairs with high accuracy, requiring neither precomputed trees nor multiple external tools. The software reconstructs event-labeled gene and species trees, seamlessly integrating reconciliation to produce fast, accurate, and biologically insightful evolutionary scenarios. Through extensive benchmarking on synthetic datasets with known ground truth, REvolutionH-tl outperforms or matches the accuracy of established tools such as OrthoFinder, Proteinortho, RAxML, GeneRax, and RANGER-DTL, while achieving significantly lower runtimes. A key innovation of REvolutionH-tl is its built-in support for detailed, publication-ready visualizations, which allow users to explore genome evolution dynamics, orthogroup composition, and reconciliation results with clarity and ease. These visual features position REvolutionH-tl as the first platform of its kind to combine analytical precision with intuitive interpretability. The software is open-source, cross-platform, and freely available at https://pypi.org/project/revolutionhtl/, providing a robust solution for large-scale evolutionary analyses in comparative genomics. Author summaryComparative genomics relies on understanding how genes evolve across species. This involves identifying groups of related genes, reconstructing their evolutionary trees, and aligning them with the evolutionary history of species. These steps are typically performed using multiple tools, often requiring manual integration and technical expertise. We present REvolutionH-tl, an open-source software that automates the entire evolutionary reconstruction process--starting from protein sequences and producing gene trees, species trees, orthology assignments, and reconciled evolutionary scenarios. Unlike existing tools, REvolutionH-tl also includes built-in, high-quality visualizations that help users interpret complex evolutionary events such as gene duplications and losses. We evaluated REvolutionH-tl on simulated genomes with known evolutionary histories and found that it matches or exceeds the performance of widely used tools, while being significantly faster. Its visual output makes evolutionary analysis more accessible and interpretable, offering a valuable resource for researchers studying genome evolution.

bioinformatics↗

bioRxiv2023

Dynamic co-expression modular network analysis of bHLH transcription factors regulation in the potato spindle tuber viroid-tomato pathosystem

Viroids, minimalist plant pathogens, present significant threats to crops by causing severe diseases. The use of high-throughput sequencing technologies for analyzing the transcriptomes of viroid-infected host plants has yielded informative information on gene regulation by these pathogens, however a complete understanding of the transcriptome data suffers from the inclusion of numerous genes of unknown function. Co-expression analysis addresses this by clustering genes into modules based on global gene expression levels. Our previous study emphasized basic helix-loop-helix protein (bHLH) transcriptional reprogramming in tomato in response to different potato spindle tuber viroid (PSTVd) strains. In the current research, we delve into tissue-specific gene modules, particularly in root and leaf tissues, governed by bHLH transcription factors during PSTVd infections. Utilizing public datasets that span Control (C; (mock-inoculated), PSTVd-mild (M), and PSTVd-severe (S23) strains in time-course infections, we uncovered differentially expressed gene modules. These modules were functionally characterized, identifying essential hub genes. We identified the roles of bHLH transcription factors (TFs) in managing processes like photosynthesis and rapid membrane repair in infected roots. In leaves, external layer alterations influenced photosynthesis, linking bHLH TFs to distinct metabolic functions. Expanding on these findings, we explored bipartite networks, discerning both common and unique bHLH TF regulatory roles, notably highlighting the bifan motifs significance in these interactions. Through this holistic approach, we deepen our understanding of viroid-host interactions and the intricate regulatory mechanisms underpinning them.

systems biology↗

bioRxiv2022

Deciphering the tissue-specific regulatory role of intronless genes across cancers

Intronless genes (IGs) or single-exon genes lacking an intron are found across most Eukaryotes. Notably, IGs display a higher transcriptional fidelity as they are not regulated through alternative splicing, suggesting better predictability biomarkers and easier regulation as targets for therapy. Cancer is a complex disease that relies on progressive uncontrolled cell division linked with multiple dysfunctional biological processes. Tumor heterogeneity remains the most challenging feature in cancer diagnosis and treatment. Given the clinical relevance of IGs, we aim to identify their unique expression profiles and interactome, that may act as functional signatures across eight different cancers. We identified 940 protein-coding IGs in the human genome, of which about 35% were differentially expressed across the analyzed cancer datasets. Specifically, [~]78% of differentially expressed IGs were undergoing transcriptional reprogramming with elevated expression in tumor cells. Remarkably, in all the studied tumors, a highly conserved induction of a group of deacetylase-histones located in a region of chromosome 6 enriched in nucleosome and chromatin condensation processes. This study highlights that differentially expressed human intronless genes across cancer types are prevalent in epigenetic regulatory roles participating in specific PPI networks for ESCA, GBM, and LUAD tumors. We determine that IGs play a key role in the tumor phenotype at transcriptional and post-transcriptional levels, with important mechanisms such as interactomics rewiring.

genomics↗

bioRxiv2022

An integrative approach: using transcriptomic data and network analysis of transcriptional reprogramming in tomato response to PSTVd variants

Viroids are minimal pathogens of angiosperms, consisting of non-coding RNAs that cause severe diseases in agronomic crops. Symptoms associated with viroid infection are linked to developmental alterations due to genetic regulation. To understand the global molecular mechanisms of host response, we implemented an omics approach to identify master transcription regulators (MTRs) and their differentially expressed targets in tomato infected with mild and severe variants of PSTVd. Our approach integrates root and leaf transcriptomic data, gene regulatory network analysis, and identification of affected biological processes. Overall, our results reveal that specific bHLH, MYB, and ERF transcription factors regulate genes involved in molecular mechanisms underlying critical signaling pathways. Functional enrichment of regulons shows that bHLH-MTRs are linked to metabolism and plant defense, while MYB-MTRs are involved in signaling regulation and hormone-related processes. Strikingly, a member of the bHLH-TF family have a potential specific role as a microprotein involved in the post-translational regulation of hormone signaling events. For the severe variant, ERF-MTRs are characteristic, while ZNF-TF, tf3a-TF, BZIP-TFs, and NAC-TF act as unique MTRs. Altogether, our results lay a foundation for further research on the PSTVd and host genome interaction, providing evidence for identifying potential key genes that influence symptom development in tomato plants.

systems biology↗

bioRxiv2021

Evolutionary Perspective And Expression Analysis Of Intronless Genes Highlight The Conservation On Their Regulatory Role

Eukaryotic gene structure is a combination of exons generally interrupted by intragenic non-coding DNA regions termed introns removed by RNA splicing to generate the mature mRNA. Thus, eukaryotic genes can be either single exon genes (SEGs) or multiple exon genes (MEGs). Among SEGs, intronless genes (IGs) are a subgroup that additionally lacks introns at their UTRs, and code for proteins essentially involved in development, growth, and cell proliferation. Gene expression of IGs has been proposed to be highly specialized for neuro-specific functions and linked to cancer, neuropathies, and developmental disorders. The abundant presence of introns in eukaryotic genomes is pivotal for the precise control of gene expression. Notwithstanding, IGs exempting splicing events entail a higher transcriptional fidelity, making them even more valuable for regulatory roles. This work aimed to infer the functional role and evolutionary history of IGs using the mouse genome. Intronless protein-coding genes consist of a subgroup of ~6 % of a total of 21,527 genes with one exon. To understand the prevalence, biological relevance, and evolution, we identified and studied their 1,116 functional proteins. We validated differential expression in transcriptomics data of early embryo stages using mouse telencephalon tissue. Our results showed that expression levels of IGs are lower compared to MEGs. However, strongly upregulated IGs include transcription factors (TFs) such as the class 3 of POU (HMG Box), Neurog1, Olig1, and BHLHe22, BHLHe23, among other essential genes including the beta cluster of protocadherins. Most striking was the finding that IG-encoded BHLH TFs qualify the criteria to be referred to as microprotein candidates. Finally, predicted protein orthologs in other six genomes confirmed a high conservancy of IGs associated with regulating neurobiological processes and with chromatin organization and epigenetic regulation in Vertebrata. Moreover, this study highlights that IGs are essential modulators of regulatory processes, as Wnt signaling pathway and biological processes as pivotal as sensory organs developing at a transcriptional and post-translational level. Overall, our results suggest that IG proteins have specialized, prevalent, and unique biological roles and that functional divergence between IGs and MEGs is likely to be the result of specific evolutionary constraints.

evolutionary biology↗

bioRxiv2018

Model for breast cancer diversity and spatial heterogeneity

We present and analyze a growth model of an avascular tumor that considers the basic biological principles of proliferation, motility, death and genetic mutations of the cell. From a regulatory network analysis and an analysis of genomic data we identify two sets of genes-a set of six genes and a set of sixteen genes-that are believed to play an important role in the evolution of breast cancer. Considering that cancer cells shape the tissue microenvironment and niches to their competitive advantage, the model assumes that cancer and normal cells compete for essential nutrients and that the rate of the \"driver\" mutations depends on nutrient availability. To this end, we propose a coupling between the transport of nutrients and gene mutations dynamics. Gene mutation dynamics are modeled as a Yule-Furry Markovian process, while transport of nutrients is described with a system of reaction-diffusion equations. For each representative tumor we calculate its diversity, represented by the Shannon index, and its spatial heterogeneity, measured by its fractal dimension. These quantities are important in the clinical diagnosis of tumor malignancy. A tumor malignancy diagram, obtained by plotting diversity versus fractal dimension, is calculated for different values of a parameter {beta}, which is related to the occurrence of driver mutations. It is found that when {beta} < 1, tumors show greater diversity and more spatial heterogeneity as compared with {beta} > 1. More importantly, it is found that the results and conclusions are similar when we use the six-gene set versus sixteen-gene set.

cancer biology↗