Biology subjects

Romero-Durana, M.

Publications and source records attributed to Romero-Durana, M..

4 recordsLinked to original sources

bioRxiv2025

Airway organoids reveal patterns of Influenza A tropism and adaptation in wildlife species

Identifying animal species that are susceptible to the plethora of existing and emerging viruses is critical for predicting and containing disease outbreaks. Current efforts to assess viral tropism largely rely on experimental infection models, but such experiments are logistically and ethically infeasible for many wildlife species. To tackle this challenge, we developed a panel of airway organoids from ten taxonomically diverse wildlife and livestock species and evaluated their susceptibility to influenza viruses of mammalian (pH1N1) and avian (H5N1) origin. Our analyses revealed large species-specific differences in infection rate and cytopathogenicity that aligned with known in vivo data and field observations. Furthermore, we demonstrated that this organoid panel can serve as a powerful tool to elucidate receptor-binding mechanisms, viral dynamics, and early host adaptation in poorly characterized animal species. In summary, this work provides a robust and ethically viable approach for evaluating viral tropism and adaptation in wildlife species, and fills a critical gap in current pandemic preparedness, zoonotic disease surveillance, and wildlife conservation efforts.

microbiology↗

bioRxiv2025

A Unified Framework for TCR-pMHC Structural Model Assessment

T cell receptor (TCR) recognition of peptide-MHC complexes (pMHCs) is a central determinant of cellular immunity. Structural insights are key to understanding TCR-pMHC interactions, however progress is limited by the scarcity of experimental structures (PDB = 232 TCR-pMHC class I complexes, date 01/2025). Although protein modelling tools have rapidly advanced, their accuracy on TCR-pMHC complexes is hampered by CDR loop hypervariability, conformational flexibility, and the lack of reliable quality assessment without experimental references. To evaluate the performance of recent protein modelling algorithms on TCR-pMHC complexes, we benchmarked three general-purpose (AlphaFold3, Boltz-2, Chai-1) and two TCR-specific (tFold-TCR, TCRmodel2) modelling tools using a benchmark set of 20 experimentally determined PDB structures, demonstrating AlphaFold3 superior performance. To expand structural coverage beyond the existing experimental landscape, we present a framework that enables quality assessment of TCR-pMHC structural models without requiring experimental reference structures. To this end, we integrate multiple modelling and interface confidence metrics (pLDDT, ipTM, iPAE, pDockQ) into explainable random forest classifiers trained on 1160 models of 232 experimentally determined PDB TCR-pMHC class I complexes. This approach reliably distinguishes low-, acceptable-, medium-, and high-quality models, outperforming the combination of these metrics using literature-established thresholds. Leveraging our quality tier framework, we used AlphaFold3 to generate and evaluate the largest synthetic dataset of TCR-pMHC class I structural models to date from VDJdb-annotated sequences, comprising 33,820 complexes (169,100 models, 5 models per complex) and representing a >70-fold expansion of available structures. We demonstrate the applicability of our quality tier stratification in three settings: 1) filtering validating vs. non-validating TCR-pMHC interaction in sequence databases (VDJdb), 2) enrichment of biologically validated interactions versus synthetic negatives amongst higher quality complexes, and 3) enhancing the predictive performance of TCR-pMHC pairing models. Altogether our TCR-pMHC structural quality tier framework provides a scalable and interpretable approach for improving modelling and functional analyses of TCR-pMHC class I complexes, with translational applications in T-cell and TCR-based immunotherapies.

bioinformatics↗

bioRxiv2023

The Evolution of Local Energetic Frustration in Protein Families

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We have analyzed these signals in very well studied cases such as PDZ, SH3, and {beta} globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We have applied our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as emergent pathogens.

bioinformatics↗

bioRxiv2021

SARS-CoV-2 B.1.351 (beta) variant shows enhanced infectivity in K18-hACE2 transgenic mice and expanded tropism to wildtype mice compared to B.1 variant

SARS-CoV-2 variants display enhanced transmissibility and/or immune evasion and can be generated in humans or animals, like minks, thus generating new reservoirs. The continuous surveillance of animal susceptibility to new variants is necessary to predict pandemic evolution. In this study we demonstrate that, compared to the B.1 SARS-CoV-2 variant, K18-hACE2 transgenic mice challenged with the B.1.351 variant displayed a faster progression of infection. Furthermore, we also report that B.1.351 can establish infection in wildtype mice, while B.1 cannot. B.1.351-challenged wildtype mice showed a milder infection than transgenic mice, confirmed by detectable viral loads in oropharyngeal swabs and tissues, lung pathology, immunohistochemistry and serology. In silico models supported these findings by demonstrating that the Spike mutations in B.1.351 resulted in increased affinity for both human and murine ACE2 receptors. Overall, this study highlights the plasticity of SARS-CoV-2 animal susceptibility landscape, which may contribute to viral persistence and expansion.

microbiology↗