Biology subjects

Candela, A.

Publications and source records attributed to Candela, A..

6 recordsLinked to original sources

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↗

bioRxiv2024

Rapid, automatic typing of Clostridioides difficile Ribotypes Using MALDI-TOF MS

Clostridioides difficile is a major cause of hospital-acquired diarrhea, posing significant clinical challenges due to its high morbi-mortality rates and its involvement in nosocomial outbreaks. Detecting its toxigenic ribotypes rapidly and accurately is crucial for effective outbreak control. This study aimed to create a rapid diagnostic methodology based on MALDI-TOF MS and Machine Learning algorithms to differentiate toxigenic C. difficile RTs. MALDI-TOF spectra were acquired from 379 clinical isolates sourcing from 10 Spanish hospitals and analysed using Clover MSDAS, a specific software for MALDI-TOF spectra analysis, considered as the state-of-the-art tool for this purpose, and AutoCdiff, an ad hoc software developed in this study. Seven biomarker peaks were found to differentiate epidemic RT027 and RT181 strains from other RTs (2463, 3353, 4933, 4993, 6187, 6651 and 6710 m/z). Two peaks (2463 and 4993 m/z) were specifically found in RT027 isolates while combinations of the other 5 peaks allowed the differentiation of RT181 from other ribotypes. Automatic classification tools developed in Clover MSDAS and AutoCdiff using the specific peaks and the entire protein spectra, respectively, showed up to 100% balanced accuracy. Both methods allowed correct ribotype assignment for isolates sourcing from real-time outbreaks. The developed models, available from Clover MSDAS and the AutoCdiff website -https://bacteria.id-offer researchers a valuable tool for quick C. difficile ribotype determination based on MALDI-TOF spectra analysis. Although further validation of the models is still required, they represent rapid and cost-effective methods for standardized C. difficile ribotype assignment.

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↗

bioRxiv2017

Glutamatergic drive along the septo-temporal axis of hippocampus boosts prelimbic oscillations in the neonatal mouse

The long-range coupling within prefrontal-hippocampal networks that account for cognitive performance emerges early in life. The discontinuous hippocampal theta bursts have been proposed to drive the generation of neonatal prefrontal oscillations, yet the cellular substrate of these early interactions is still unresolved. Here, we selectively target optogenetic manipulation of glutamatergic projection neurons in the CA1 area of either dorsal or intermediate/ventral hippocampus at neonatal age to elucidate their contribution to the emergence of prefrontal oscillatory entrainment. We show that despite stronger theta and ripples power in dorsal hippocampus, the prefrontal cortex is mainly coupled with intermediate/ventral hippocampus by phase-locking of neuronal firing via dense direct axonal projections. Theta band-confined activation by light of pyramidal neurons in intermediate/ventral but not dorsal CA1 that were transfected by in utero electroporation with high-efficiency channelrhodopsin boosts prefrontal oscillations. Our data causally elucidates the cellular origin of the long-range coupling in the developing brain.\n\nHighlightsO_LINeonatal theta bursts, sharp waves and ripples vary along septo-temporal axis\nC_LIO_LIHippocampal activity times prefrontal oscillations via direct axonal projections\nC_LIO_LISelective hippocampal targeting along septo-temporal axis causes precise firing\nC_LIO_LILight stimulation of hippocampal neurons at 8 Hz boosts prefrontal oscillations\nC_LI

neuroscience↗