Biology subjects

Rodriguez-Sanchez, B.

Publications and source records attributed to Rodriguez-Sanchez, B..

13 recordsLinked to original sources

bioRxiv2026

The route of infection shapes Rift Valley fever virus pathogenesis, humoral immune response, and horizontal transmission in sheep.

Rift Valley fever (RVF) is a zoonotic arboviral disease that causes adverse pregnancy outcomes and high mortality in domestic and wild ruminants. The disease is caused by the RVF virus (RVFV), which is transmitted by mosquitoes from several genera, mainly Aedes and Culex. However, whether ruminants can become infected by horizontal virus transmission remains unclear. In addition, how the route of RVFV inoculation may influence RVF pathogenesis and the host immune response in animals is still largely unknown. With this aim, we conducted a comparative experimental study in which young sheep were either inoculated subcutaneously (SC) or intranasally (IN) with the virulent RVFV 56/74 strain. We then evaluated disease dynamics, viremia, virus excretion, tissue damage, and the humoral immune response. We also aimed to determine whether RVFV can be transmitted from infected to in-contact animals, and to assess whether the inoculation route may influence virus excretion and the likelihood of subsequent horizontal transmission. The results showed that SC inoculated sheep had a shorter incubation period, an earlier onset of viremia, and an earlier seroconversion. In contrast, IN inoculated animals developed higher rectal temperatures, reached higher peak viremia, and developed a more robust neutralizing antibody response. They also exhibited increased concentrations of analytes indicative of moderate but more severe hepatic injury compared with the subcutaneous group, along with more pronounced histopathological damage in the central nervous system. These results demonstrate the influence of the route of inoculation on RVF pathogenesis and the host immune response. Our results also confirmed the horizontal transmission of RVFV between SC inoculated sheep and in-contact animals housed in the same room, a phenomenon not observed in the IN inoculated group. This finding underscores the influence of the inoculation route on virus transmission and the potentially significant role of horizontal transmission in RVF epidemiology and disease control. Author summaryAccording to the World Health Organization (WHO), RVFV is considered a priority pathogen due to its ability to strain animal and public health systems, especially in developing countries. RVF outbreaks have occurred across most of Africa and, since 2000, in the Arabian Peninsula. Evidence of RVFV circulation in North Africa further highlights the threat to Europe, where competent mosquito vectors are present. How the inoculation route shapes disease dynamics and hosts immunity is still largely unknown. Similarly, whether the virus can spread between infected and non-infected animals without competent vectors remains unclear. A comparative infection in which young sheep were inoculated SC or IN with the RVFV 56/74 strain showed that SC inoculated sheep had a shorter incubation period, an earlier onset of viremia, and earlier seroconversion. However, rectal temperature and peak viremia were higher in IN inoculated sheep, which also showed evidence of moderate but more severe hepatic damage, accompanied by greater central nervous system damage. Only the in-contact animals housed in the subcutaneous group became infected, demonstrating horizontal transmission. Our results show that the route of inoculation influences disease progression and that RVFV can be transmitted among sheep in the absence of mosquitoes.

immunology↗

bioRxiv2025

MARISMa: a routine MALDI-TOF MS database from 2018 to 2024

Clinical microbiology laboratories play a crucial role in identifying pathogens, guiding antibiotic treatment, and managing antimicrobial resistance (AMR). Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has become essential for rapid, accurate, and cost-effective microbial identification. Recent advances in integrating MALDI-TOF MS with Artificial Intelligence (AI) show promise in improving microbial detection and prediction of AMR. However, progress is limited by the lack of comprehensive and openly accessible datasets that restrict the validation, reproducibility, and applicability of the model. To address this gap, we introduce a publicly available MALDI-TOF MS dataset comprising 202,700 unique spectra from isolates collected between 2018 and 2024 at the Hospital General Universitario Gregorio Maranon, Spain. This dataset includes 186,213 bacteria, 16,163 fungal, and 371 mycobacterial samples, of which 29,679 contain AMR annotations. This resource is openly and freely shared, rigorously curated, and designed to support a wide range of machine learning. By ensuring unrestricted access to high-quality, standardized data, this dataset aims to promote transparency, reproducibility, comparative benchmarking, and collaborative progress in AI-driven clinical microbiology.

microbiology↗

bioRxiv2025

Applied Machine Learning for human bacteriaMALDI-TOF Mass Spectrometry: a systematicreview

Bacterial identification, antimicrobial resistance prediction, and strain typification are critical tasks in clinical microbiology, essential for guiding patient treatment and controlling the spread of infectious diseases. While Machine Learning (ML) has shown immense promise in enhancing Matrix-Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) applications for these tasks, there is currently no comprehensive review that fully addresses this from a technical ML perspective. To address this gap, we systematically reviewed 115 studies published between 2004 and 2025, focusing on key ML aspects such as data size and balance, pre-processing pipelines, model selection and evaluation, open-source data, and code availability. Our analysis highlights the predominant use of classical ML models like Random Forest and Support Vector Machines, alongside emerging interest in Deep Learning approaches for handling complex, high-dimensional data. Despite significant progress, challenges such as inconsistent pre-processing workflows, reliance on black-box models, limited external validation, and insufficient open-source resources persist, hindering transparency, reproducibility, and broader adoption. This review offers actionable insights to enhance ML-driven bacterial diagnostics, advocating for standardized methodologies, greater transparency, and improved data accessibility. In addition, we provide guidelines on how to approach ML for MALDI-TOF MS analysis, helping researchers navigate key decisions in model development and evaluation.

bioengineering↗

bioRxiv2024

Automatic surveillance of Escherichia coli bacteriological strains within clinical settings

Healthcare-associated infections (HAIs) are a significant concern within hospital environments, with the World Health Organization (WHO) identifying them as a major source of bacteriological infections. HAIs affect millions of patients annually, leading to substantial morbidity and mortality. However, a significant proportion of HAIs are preventable through early detection and appropriate intervention and isolation. Traditional methods for identifying bacterial species and strains, such as antigen tests, are often time-consuming and hamper the realtime tracking of outbreaks. The matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) technique has emerged as a more rapid and precise alternative, though it remains limited by manual identification processes and database constraints. Recent advancements have demonstrated the potential of combining MALDI-TOF MS data with machine learning (ML) to enhance in silico identification speed. In this paper we propose the first method for unsupervised Escherichia coli novel strain detection through the application of a efficient PIKE tailored to strain identification, spectral clustering techniques and a one-class support vector machine (OCSVM) novelty detection model, presenting promising results.

microbiology↗

bioRxiv2024

Overcoming Challenges of Reproducibility and Variability for the Clostridioides difficile typification

Machine learning (ML) approaches applied to Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) spectra have shown promise for the typing of Clostridioides difficile, yet their deployment in routine clinical settings remains challenging due to strong sensitivity to acquisition variability. Differences in culture media, incubation time, protein extraction protocols, and instrumentation across hospitals often lead to substantial performance degradation when models are evaluated under heterogeneous or previously unseen conditions. In this work, we systematically analyze the impact of methodological and technical variability on ML-based C. difficile typing and investigate whether data augmentation (DA) strategies can mitigate these effects. Using a dedicated dataset of 60 isolates acquired under diverse conditions, we show that DA substantially improves robustness to variability when training on spectra from selective C. difficile agar media. Importantly, models trained with DA achieve performance levels approaching those obtained using enriched Schaedler agar media, while relying exclusively on standard 24-hour incubation. Evaluation on an independent cohort of 28 newly acquired isolates confirms that DA significantly reduces performance degradation under real-world domain shift. To facilitate adoption and reproducibility, we release MAL-DIDA, an open-source Python library for DA of MALDI-TOF MS spectra.

microbiology↗

bioRxiv2024

Characterization of a nosocomial outbreak caused by VIM-1 Klebsiella michiganensis using Fourier-Transform Infrared (FT-IR) Spectroscopy

Healthcare-associated infections (HAIs) are a significant concern worldwide due to their impact on patient safety and healthcare costs. Klebsiella spp., particularly Klebsiella pneumoniae and Klebsiella oxytoca, are frequently implicated in HAIs and often exhibit multidrug resistance mechanisms, posing challenges for infection control. In this study, we evaluated Fourier-transform Infrared (FT-IR) spectroscopy as a rapid method for characterizing a nosocomial outbreak caused by VIM-1-producing K. oxytoca. A total of 47 isolates, including outbreak strains and controls, were collected from Hospital Universitario Gregorio Maranon, Spain and the University Hospital Basel, Switzerland. FT-IR spectroscopy was employed for bacterial typing, offering rapid and accurate results compared to conventional methods like pulsed-field gel electrophoresis (PFGE) and correlating with whole-genome sequencing (WGS) results. The FT-IR spectra analysis revealed distinct clusters corresponding to outbreak strains, suggesting a common origin. Subsequent WGS analysis identified Klebsiella michiganensis as the causative agent of the outbreak, challenging the initial assumption based on FT-IR results. However, both FT-IR and WGS methods showed high concordance, with an Adjusted Rand index (AR) of 0.882 and an Adjusted Wallace coefficient (AW) of 0.937, indicating the reliability of FT-IR in outbreak characterization. Furthermore, FT-IR spectra visualization highlighted discriminatory features between outbreak and non-outbreak isolates, facilitating rapid screening in case and outbreak is suspected. In conclusion, FT-IR spectroscopy offers a rapid and cost-effective alternative to traditional typing methods, enabling timely intervention and effective management of nosocomial outbreaks. Its integration with WGS enhances the accuracy of outbreak investigations, demonstrating its utility in clinical microbiology and infection control practices.

microbiology↗

bioRxiv2024

Analysis of high-molecular-weight proteins using MALDI-TOF MS and Machine Learning for the differentiation of clinically relevant Clostridioides difficile ribotypes

Clostridioides difficile is the main cause of antibiotic related diarrhea and some ribotypes (RT), such as RT027, RT181 or RT078, are considered high risk clones. A fast and reliable approach for C. difficile ribotyping is needed for a correct clinical approach. This study analyses high-molecular-weight proteins for C. difficile ribotyping with MALDI-TOF MS. Sixty-nine isolates representative of the most common ribotypes in Europe were analyzed in the 17,000-65,000 m/z region and classified into 4 categories (RT027, RT181, RT078 and Other RTs). Five supervised Machine Learning algorithms were tested for this purpose: K-Nearest Neighbors, Support Vector Machine, Partial Least Squares-Discriminant Analysis, Random Forest and Light-Gradient Boosting Machine. All algorithms yielded cross-validation results >70%, being RF and Light-GBM the best performing, with 88% of agreement. Area under the ROC curve of these two algorithms was >0.9. RT078 was correctly classified with 100% accuracy and isolates from the RT181 category could not be differentiated from RT027.

microbiology↗

bioRxiv2022

Identification of Mycobacterium abscessus subspecies by MALDI-TOF Mass Spectrometry and Machine Learning

Mycobacterium abscessus complex is one of the most common and pathogenic nontuberculous mycobacteria (NTM) isolated in clinical laboratories. It consists of three subspecies: M. abscessus subsp. abscessus, M. abscessus subsp. bolletii and M. abscessus subsp. massiliense. Due to their different antibiotic susceptibility pattern, a rapid and accurate identification method is necessary for their differentiation. Although matrix assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) has proven useful for NTM identification, the differentiation of M. abscessus subspecies is challenging. In this study, a collection of 244 clinical isolates of M. abscessus complex was used for MALDI-TOF MS analysis and for the development of machine learning predictive models. Overall, using a Random Forest model with several confidence criteria (samples by triplicate and similarity values >60%), a total of 95.8% of isolates were correctly identified at subspecies level. In addition, differences in culture media, colony morphology and geographic origin of the strains were evaluated, showing that the latter most affected the mass spectra of isolates. Finally, after studying all protein peaks previously reported for this complex, two novel peaks with potential for subspecies differentiation were found. Therefore, machine learning methodology has proven to be a promising approach for rapid and accurate identification of subspecies of the M. abscessus complex using MALDI-TOF MS.

microbiology↗

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↗

bioRxiv2021

Rapid and reproducible MALDI-TOF-based method for detection Vancomycin-resistant Enterococcus faecium using classifying algorithms

Vancomycin-resistant Enterococcus faecium has become a health threat over the last 20 years due to its ability to rapidly spread and cause outbreaks in hospital settings. Although MALDI-TOF MS has already demonstrated its usefulness for accurate identification of E. faecium, its implementation for antimicrobial resistance detection is still under evaluation. The reproducibility of MALDI-TOF MS for peak analysis and its performance for correct discrimination of vancomycin susceptible isolates (VSE) from those hosting the VanA and VanB resistance mechanisms was evaluated in this study. For the first goal, intra-spot, inter-spot -technical- and inter-day -biological- reproducibility was assayed. The capability of MALDI-TOF to discriminate VSE isolates from VanA VRE and VanB VRE strains was carried out on protein spectra from 178 E. faecium unique clinical isolates -92 VSE, 31 VanA VRE, 55 VanB VRE-, processed with Clover MS Data Analysis software. Unsupervised (Principal Component Analysis -PCA-) and supervised algorithms (Support Vector Machine -SVM-, Random Forest -RF- and Partial Least Squares-Discriminant Analysis -PLS-DA-) were applied. The reproducibility assay showed lower variability for normalized data (p<0.0001) and for the peaks within the 3000-9000 m/z range. Besides, 80.9%, 79.21% and 77.53% VSE vs VRE (VanA + VanB) discrimination was achieved by applying SVM, RF and PLS-DA, respectively. Correct differentiation of VanA from VanB VRE isolates was obtained by SVM in 86.65% cases. The implementation MALDI-TOF MS and peak analysis could represent a rapid and effective tool for VRE screening. However, further improvements are needed to increase the accuracy of this approach.

microbiology↗

bioRxiv2021

Evaluation of the rapidBACpro(R) II kit for the rapid identification of microorganisms directly from blood cultures using MALDI-TOF MS

ObjectivesIdentification of microorganisms directly from blood cultures (BCs) using MALDI-TOF MS has shown to be the application with most impact in this methodology. In this study, a novel commercial method, the rapidBACpro(R) II, was evaluated in four clinical microbiology laboratories. MethodsPositive blood culture samples (n=801) were processed using the rapidBACpro(R) II kit and then compared with routine gold standard. A subset of monomicrobial BCs (n=560) were analyzed in parallel with the Sepsityper(R) kit (Bruker Daltonics, Bremen, Germany) and compared with the rapidBACpro(R) II kit. In addition, the rapidBACpro(R) II kit was also compared with two different in-house methods. ResultsOverall, 80.0% of the monomicrobial isolates (609/761) were correctly identified by the rapidBACpro(R) II kit at the species level (92.3% of the Gram negative and 72.4% of the Gram positive bacteria). The comparison with the Sepsityper(R) kit yielded higher rates of correct species-level identification provided by the rapidBACpro(R) II kit for all categories (p>0.0001) except for yeasts identified with score values >1.7. It also proved superior to the ammonium chloride method (p>0.0001) but the differential centrifugation method allowed higher rates of correct identification for Gram negative bacteria (p>0.1). ConclusionsThe rapidBACpro(R) II kit allowed a high rate of microorganisms correctly identified. The percentage of accurate species-level identification of Gram positive bacteria was particularly noteworthy in comparison with other commercial and in-house methods. This fact was especially interesting in the case of Staphylococcus sp. and Streptococcus sp. in order to elucidate their clinical impact, for example in device-associated bacteremia.

microbiology↗

bioRxiv2020

Discrimination among Cryptococcus deneoformans, C. neoformans and interspecies hybrids using MALDI-TOF Mass Spectrometry

BackgroundDifferentiation of the species within the Cryptococcus neoformans complex (C. deneoformans, C. neoformans and C. neoformans interspecies hybrids -C. deneoformans x C. neoformans-) is important to define the epidemiology of the infection. ObjectivesIn this study we attempted the discrimination of three C. neoformans species using MALDI-TOF MS coupled with an in-house library. MethodsAll Cryptococcus spp. isolates were identified by AFLP markers. An in-house database was constructed 26 well characterized C. deneoformans, C. neoformans and interspecies hybrids. Forty-four Cryptococcus spp. isolates were blindly identified using MALDI-TOF MS (Bruker Daltonics) and the expanded library. Their protein spectra were also submitted to hierarchical clustering and the resulting species were verified via Partial Least Squares Differential Analysis (PLS-DA) and Support-Vector Machine (SVM). ResultsMALDI-TOF MS coupled with the in-house library allowed 100% correct identification of C. deneoformans and C. neoformans but misidentified the interspecies hybrids. The same level of discrimination among C. deneoformans and C. neoformans was achieved applying SVM. The application of the PLS-DA and SVM algorithms in a two-step analysis allowed 96.95% and 96.55% correct discrimination of C. neoformans from the interspecies hybrids, respectively. Besides, PCA analysis prior to SVM provided 98.45% correct discrimination of the 3 species analysed in a one-step analysis. ConclusionsOur results indicate that MALDI-TOF MS could be a rapid and reliable tool for the correct discrimination of C. deneoformans and C. neoformans. The correct identification of the interspecies hybrids could only be achieved by hierarchical clustering with other protein spectra from the same species.

microbiology↗