Biology subjects

Hernandez-Garcia, M.

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

8 recordsLinked to original sources

bioRxiv2026

A synthetic MALDI-TOF MS atlas for microbial classification across hospitals

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry (MS) is central to routine clinical microbiology for species identification. Its combination with machine learning is constrained by fragmented public datasets, species imbalance and acquisition-related variability. Synthetic datasets could provide more training data, but they must preserve the spectral information needed to make predictions on real samples. Here, we present an open, synthetic MALDI-TOF MS atlas comprising 4.3 million spectra across 43 clinically relevant bacterial species. To construct the atlas, we harmonised public repositories from three countries and trained a species- and institution-conditioned concept-bottleneck generative variational autoencoder (CBGM-VAE) on 179,086 measured spectra. We assessed the atlas as both a stand-alone training resource and a complement to real data, using bacterial species classification as a functional benchmark. Evaluation comprised 7,269 real spectra from more than 7 countries, 24 institutions and multiple acquisition platforms, covering: temporal, technical, biological and combined shifts. A neural network trained only on synthetic spectra achieved F1 scores ranging from 0.788 to 1.000 across external cohorts. When synthetic spectra were combined with real training data, classification performance improved or matched using real data only. These findings demonstrate that the atlas retains transferable information and can complement measured data. The openly released atlas provides a reusable resource for supervised learning and benchmarking. It can also be explored for pretraining representation-learning and foundation models in MALDI-TOF MS research.

microbiology↗

bioRxiv2026

Genomic Characterization of the RyC collection: 50 Multidrug Resistant Clinical Isolates of Escherichia coli and Klebsiella spp.

Antimicrobial resistance (AMR) is a major global public health threat, and Enterobacterales producing extended-spectrum {beta}-lactamases (ESBLs) represent some of the most common and concerning pathogens in clinical settings. Importantly, the dissemination of these resistance mechanisms is largely driven by mobile genetic elements (MGEs), particularly plasmids. Advancing our understanding of AMR evolution through experimentation requires moving beyond domesticated laboratory strains and towards clinically relevant isolates. However, despite the abundance of genomic data in public repositories, there is a lack of well-characterised clinical collections available for experimental work. Here, we characterise the RyC collection, which includes 50 multidrug-resistant, ESBL-producing Escherichia coli and Klebsiella spp. strains isolated from the gut microbiota of hospitalised patients at Hospital Universitario Ramon y Cajal (Madrid, Spain). We generated high-quality genome assemblies for all strains using a combination of short- and long-read sequencing technologies. From these data, we performed a comprehensive characterisation of the pangenome, mobilome, resistome and defensome of the collection. We present the RyC collection as a robust and experimentally tractable resource to study AMR evolution and MGEs dynamics in clinically relevant bacterial backgrounds. Impact statementAntimicrobial resistance (AMR) is a growing global health threat driven by the rapid dissemination of resistance genes among clinically relevant bacteria. A major challenge in studying AMR evolution is the reliance on domesticated laboratory strains, which poorly represent the complexity of pathogens circulating in hospitals. Here, we introduce the RyC collection, a set of well-characterised, multidrug-resistant Enterobacterales isolates obtained from hospitalised patients. By combining high-quality genome sequencing with detailed analyses of their gene content and mobile genetic elements (MGEs), this collection provides a realistic and experimentally tractable system to study how resistance evolves and spreads. The RyC collection will facilitate research on AMR dynamics, plasmid biology and host-MGEs interactions, ultimately contributing to the development of more effective strategies to combat antibiotic-resistant infections.

microbiology↗

bioRxiv2024

Plasmid-encoded insertion sequences promote rapid adaptation in clinical enterobacteria

Plasmids are extrachromosomal genetic elements commonly found in bacteria. Plasmids are known to fuel bacterial evolution through horizontal gene transfer (HGT), but recent analyses indicate that they can also promote intragenomic adaptations. However, the role of plasmids as catalysts of bacterial evolution beyond HGT remains poorly explored. In this study, we investigate the impact of a widespread conjugative plasmid, pOXA-48, on the evolution of various multidrug-resistant clinical enterobacteria. Combining experimental and within-patient evolution analyses, we unveil that plasmid pOXA-48 promotes bacterial evolution through the transposition of plasmid-encoded insertion sequence 1 (IS1) elements. Specifically, IS1-mediated gene inactivations expedite the adaptation rate of clinical strains in vitro and foster within-patient adaptation in the gut microbiota. We decipher the mechanism underlying the plasmid-mediated surge in IS1 transposition, revealing a negative feedback loop regulated by the genomic copy number of IS1. Given the overrepresentation of IS elements in bacterial plasmids, our findings propose that plasmid-mediated IS transposition represents a crucial mechanism for swift bacterial adaptation.

microbiology↗

bioRxiv2022

Within-patient evolution of plasmid-mediated antimicrobial resistance

Antibiotic resistance (AMR) in bacteria is a major threat to public health, and one of the key elements in the spread and evolution of AMR in clinical pathogens is the transfer of conjugative plasmids. The drivers of AMR evolution have been extensively studied in vitro, but the evolution of plasmid-mediated AMR in vivo remains poorly explored. Here, we tracked the evolution of the clinically-relevant plasmid pOXA-48, which confers resistance to the last-resort antibiotics carbapenems, in a large collection of enterobacterial clones isolated from the gut of hospitalised patients. Combining genomic and experimental approaches, we first characterized plasmid diversity and the genotypic and phenotypic effects of multiple plasmid mutations on a common genetic background. Second, using cutting-edge genomic editing in wild-type multidrug resistant enterobacteria, we dissected three cases of within-patient plasmid-mediated AMR evolution. Our results revealed, for the first time, compensatory evolution of plasmid-associated fitness cost, as well as the evolution of enhanced plasmid-mediated AMR, in bacteria evolving within the gut of hospitalised patients. Crucially, we observed that the evolution of plasmid-mediated AMR in vivo involves a pivotal trade-off between resistance levels and bacterial fitness. This study highlights the need to develop new evolution-informed approaches to tackle plasmid-mediated AMR dissemination.

evolutionary biology↗

bioRxiv2021

Discrimination of species within the Enterobacter cloacae complex using MALDI-TOF Mass Spectrometry and Fourier-Transform Infrared Spectroscopy coupled with Machine Learning tools

The Enterobacter cloacae complex (ECC) encompasses heterogeneous clusters of species that have been associated with nosocomial outbreaks. These species may host different acquired antimicrobial resistance and virulence mechanisms and their identification are challenging. This study aims to develop predictive models based on MALDI-TOF MS spectral profiles and machine learning for species-level identification. A total of 198 ECC and 116 K. aerogenes clinical isolates from the University Hospital Ramon y Cajal (Spain) and the University Hospital Basel (Switzerland) were included. The capability of the proposed method to differentiate the most common ECC species (E. asburiae, E. kobei, E. hormaechei, E. roggenkampii, E. ludwigii, E. bugandensis) and K. aerogenes was demonstrated by applying unsupervised hierarchical clustering with PCA pre-processing. We observed a distinctive clustering of E. hormaechei and K. aerogenes and a clear trend for the rest of the ECC species to be differentiated over the development dataset. Thus, we developed supervised, non-linear predictive models (Support Vector Machine with Radial Basis Function and Random Forest). The external validation of these models with protein spectra from the two participating hospitals yielded 100% correct species-level assignment for E. asburiae, E. kobei, and E. roggenkampii and between 91.2% and 98.0% for the remaining ECC species. Similar results were obtained with the MSI database developed recently (https://msi.happy-dev.fr/) except in the case of E. hormaechei, which was more accurately identified by Random Forest. In short, MALDI-TOF MS combined with machine learning demonstrated to be a rapid and accurate method for the differentiation of ECC species.

microbiology↗

bioRxiv2021

Development and Validation of a MALDI-TOF-Based Model to Predict Extended-Spectrum Beta-Lactamase and/or Carbapenemase-Producing in Klebsiella pneumoniae Clinical Isolates

Matrix-Assisted Laser Desorption Ionization Time-Of-Flight (MALDI-TOF) Mass Spectrometry (MS) is a reference method for microbial identification and it can be used to predict Antibiotic Resistance (AR) when combined with artificial intelligence methods. However, current solutions need time-costly preprocessing steps, are difficult to reproduce due to hyperparameter tuning, are hardly interpretable, and do not pay attention to epidemiological differences inherent to data coming from different centres, which can be critical. We propose using a multi-view heterogeneous Bayesian model (KSSHIBA) for the prediction of AR using MALDI-TOF MS data together with their epidemiological differences. KSSHIBA is the first model that removes the ad-hoc preprocessing steps that work with raw MALDI-TOF data. In addition, due to its Bayesian probabilistic nature, it does not require hyperparameter tuning, provides interpretable results, and allows exploiting local epidemiological differences between data sources. To test the proposal, we used data from 402 Klebsiella pneumoniae isolates coming from two different domains and 20 different hospitals located in Spain and Portugal. KSSHIBA outperforms current state-of-the-art approaches in antibiotic susceptibility prediction, obtaining a 0.78 AUC score in Wild Type classification and a 0.90 AUC score in Extended-Spectrum Beta-Lactamases (ESBL)+Carbapenemases (CP)-producers. The proposal consistently removes the need for ad-hoc preprocessing by working with raw MALDI-TOF data, which, in turn, reduces the time needed to obtain the results of the resistance mechanism in microbiological laboratories. The proposed model implementation as well as both data domains are publicly available.

microbiology↗

bioRxiv2020

The distribution of plasmid fitness effects explains plasmid persistence in bacterial communities

Introductory paragraphPlasmid persistence in bacterial populations is strongly influenced by the fitness effects associated with plasmid carriage. However, plasmid fitness effects in wild-type bacterial hosts remain largely unexplored. In this study, we determined the distribution of fitness effects (DFE) for the major antibiotic resistance plasmid pOXA-48 in wild-type, ecologically compatible enterobacterial isolates from the human gut microbiota. Our results show that although pOXA-48 produced an overall reduction in bacterial fitness, the DFE was dominated by quasi-neutral effects, and beneficial effects were observed in several isolates. Incorporating these data into a simple population dynamics model revealed a new set of conditions for plasmid stability in bacterial communities, with plasmid persistence increasing with bacterial diversity and becoming less dependent on conjugation. Moreover, genomic results showed a link between plasmid fitness effects and bacterial phylogeny, helping to explain pOXA-48 epidemiology. Our results provide a simple and general explanation for plasmid persistence in natural bacterial communities.

microbiology↗

bioRxiv2020

Dissemination routes of the carbapenem resistance plasmid pOXA-48 in a hospital setting

Introductory paragraphInfections caused by carbapenemase-producing enterobacteria (CPE) are a major concern in clinical settings worldwide. Two fundamentally different processes shape the epidemiology of CPE in hospitals: the dissemination of CPE clones from patient to patient (between-patient transfer), and the transfer of carbapenemase-encoding plasmids between enterobacteria in the gut microbiota of individual patients (within-patient transfer). The relative contribution of each process to the overall dissemination of carbapenem resistance in hospitals remains poorly understood. Here, we used mechanistic models combining epidemiological data from more than 9,000 patients with whole genome sequence information from 250 enterobacteria clones to characterise the dissemination routes of the carbapenemase-encoding plasmid pOXA-48 in a hospital setting over a two-year period. Our results revealed frequent between-patient transmission of high-risk pOXA-48-carrying clones, mostly of Klebsiella pneumoniae and sporadically Escherichia coli. The results also identified pOXA-48 dissemination hotspots within the hospital, such as specific wards and individual rooms within wards. Using high-resolution plasmid sequence analysis, we uncovered the pervasive within-patient transfer of pOXA-48, suggesting that horizontal plasmid transfer occurs in the gut of virtually every colonised patient. The complex and multifaceted epidemiological scenario exposed by this study provides new insights for the development of intervention strategies to control the in-hospital spread of CPE.

microbiology↗