Biology subjects

Moreno-Molina, M.

Publications and source records attributed to Moreno-Molina, M..

3 recordsLinked to original sources

bioRxiv2025

Complete genomes reveal the full extent of Mycobacterium tuberculosis complex diversity across evolutionary scales

Advances in short-read sequencing have enhanced our understanding of Mycobacterium tuberculosis complex (MTBC), but fail to capture its complete genomic diversity. We applied long-read sequencing to 216 isolates from the Valencia Region (Spain) and generated high-quality, complete genomes, revealing detailed insights into MTBC evolution across timescales. Complete genome comparisons increased the estimated evolutionary rate by 1.5-fold, resulting in a median of 312 (-1 to 792) additional SNPs per pairwise comparison. Multiple diversity hotspots were identified, mostly in the pe/ppe genes and driven by gene conversion. However, most PE/PPE epitopes were hyperconserved, with notable exceptions involving vaccine candidates. Incorporating previously undetected SNPs and indels improved resolution in transmission analyses. Furthermore, patient-specific reference mapping validates only 5-10% of within-host diversity detected by standard pipelines, indicating substantial overestimation in previous studies. These findings expand our view of MTBC diversity and have important implications for understanding host-pathogen interactions, epidemiology, and transmission dynamics.

genomics↗

bioRxiv2025

Facilitating User Interaction with the Tuberculosis Mutation Catalogue using AI Tools

The World Health Organization (WHO) 2023 Mutation Catalogue for Mycobacterium tuberculosis is a crucial knowledgebase and tool for clinical interpretation of mutations associated with drug-resistant TB. However, the documents complexity and size pose challenges for many users. This study evaluated the potential of generative artificial intelligence (AI) models to facilitate natural language user interaction with the catalogue. Four prominent AI models--Google Gemini 2.5 Pro, OpenAI ChatGPT 4.1, Perplexity AI, and DeepSeek R1--were assessed through general test questions, mutation search and retrieval tasks using both full catalogue queries and antibiotic-specific tables, and the application of additional grading rules to score novel mutations. Performance was measured based on accuracy, completeness, clarity, source citation, and the presence of hallucinations. Google Gemini 2.5 Pro consistently demonstrated superior performance in accuracy, completeness, and avoidance of hallucinations across most evaluations, especially in general queries and large dataset searches. DeepSeek R1 excelled in applying grading rules to novel mutations and showed high accuracy in focused datasets, but exhibited some hallucinations. ChatGPT 4.1 was strong in clarity but lacked proper citations, and Perplexity AI showed variable performance with a higher frequency of hallucinations. The findings highlight the potential of AI tools to enhance accessibility and utility of complex knowledgebases like the WHO Mutation Catalogue, while emphasizing the critical need for careful model selection and rigorous benchmarking to ensure accuracy and reliability. The results suggest that Google Gemini 2.5 Pro and DeepSeek R1 are strong candidates for developing a custom clinical AI agent to assist all levels of healthcare users in navigating and interpreting the complex information within the catalogue, ultimately contributing to improved TB control efforts.

bioinformatics↗

bioRxiv2020

An evolutionary functional genomics approach identifies novel candidate regions involved in isoniazid resistance in Mycobacterium tuberculosis

Efforts to eradicate tuberculosis are hampered by the rise and spread of antibiotic resistance. Several large-scale projects have aimed to specifically link clinical mutations to resistance phenotypes, but they were limited in both their explanatory and predictive powers. Here, we combine functional genomics and phylogenetic associations using clinical strain genomes to decipher the architecture of isoniazid resistance and search for new resistance determinants. This approach has allowed us to confirm the main target route of the antibiotic, determine the clinical relevance of redox metabolism as an isoniazid resistance mechanism and identify novel candidate genes harboring resistance mutations in strains with previously unexplained isoniazid resistance. This approach can be useful for characterizing how the tuberculosis bacilli acquire resistance to new antibiotics and how to forestall them.

genomics↗