Biology subjects

Sattler, J.

Publications and source records attributed to Sattler, J..

4 recordsLinked to original sources

bioRxiv2026

Machine learning-guided discovery of a conserved plasmid proteomic signature enables MALDI-TOF MS detection of pOXA-48-carrying Enterobacterales

OXA-48 carbapenemases are among the most widespread and important resistance mechanisms in Enterobacterales. Yet detecting carbapenemases by conventional workflows necessitates additional testing, thus delaying optimization of therapy and implementation of infection control measures. Here, we present a machine learning approach that identifies the conserved pOXA-48 plasmid directly from routine MALDI-TOF spectra acquired for species identification. The model detects pOXA-48 carriers with an AUROC of 0.96-0.98 across two independent hospital cohorts and instrument platforms, indicating near-perfect discrimination. Using bottom-up proteomics, plasmid conjugation, and plasmid curing, we link the discriminative MALDI-TOF spectral features to proteins encoded on pOXA-48, with DUF1496 domain-containing protein producing the most discriminative spectral feature. Our approach reframes the resistance prediction task from inferring a resistance phenotype to detecting a conserved plasmid through its expressed proteomic signature and has the potential to enable rapid MALDI-TOF MS-based diagnostics for a wide range of plasmid-based resistance determinants.

microbiology↗

bioRxiv2025

Early antifungal resistance prediction based on MALDI-TOF mass spectrometry and machine learning

Antimicrobial resistance (AMR) is a significant global health threat. Recent studies have shown that combining MALDI-TOF mass spectrometry with machine learning (ML) algorithms can accelerate AMR determination. However, these efforts have predominantly focused on bacterial pathogens. Given the significant morbidity, mortality and healthcare costs associated with fungal infections and their evolving antifungal resistance to treatment, there is a need for precision medicine approaches that enable early and accurate detection of antifungal resistance. We developed a machine-learning pipeline that integrates MALDI-TOF mass spectrometry data and drug features to predict antifungal resistance and identify spectral biomarkers for antifungal resistance. By leveraging the DRIAMS dataset, we included 658 pathogen spectra linked to 3,046 phenotypic antifungal resistance results. This dataset covered three drug classes and seven yeast species. The model was trained using categorical, phenotypic antifungal susceptibility testing results as ground truth. Our study systematically investigated the influence of various dimensionality reduction methods on MALDI-TOF mass spectra, antifungal encodings, and machine learning models using nested cross-validation to evaluate predictive performance. We identified that applying principal component analysis to MALDI-TOF mass spectra for dimensionality reduction, and training a multi-layer perceptron yielded the highest and most stable performance for the prediction of antifungal resistance. Our method achieved an AUPRC of 0.77 across the 10 highest-performing species-drug pairs and corresponding Matthews correlation coefficient of 0.69. The model demonstrated the best performance for the species-drug combinations of Candida albicans with micafungin, Candida parapsilosis with fluconazole, and Saccharomyces cerevisiae with itraconazole and fluconazole. By comparing established species-based guidelines, susceptibility test results, and machine learning predictions, we estimated that integrating our algorithm into antifungal selection could help avoid prescriptions to likely resistant pathogens in approximately 3 out of 10 patients for whom standard guidelines recommend such treatments.

bioinformatics↗

bioRxiv2025

Early detection of ampicillin susceptibility in Enterococcus faecium with MALDI-TOF MS and machine learning

BackgroundEnterococcus faecium can cause severe infections and is often resistant to the first-line antibiotic ampicillin. Consequently, clinicians usually prescribe broad-spectrum antibiotics, promoting the selection of multidrug-resistant bacteria. In this study, we investigate the application of machine learning techniques to detect ampicillin susceptibility directly from MALDI-TOF mass spectrometry. This technique could enable an earlier optimised treatment in infections with ampicillin-susceptible E. faecium. MethodsTwo datasets of clinical E. faecium MALDI-TOF spectra and their resistance phenotype were analysed: our own Technical University of Munich (TUM) dataset and the publicly available MS-UMG dataset. We tested logistic regression (LR) and LightGBM models on each dataset via nested cross-validation and explored transferability on the respective other dataset. ResultsLightGBM demonstrated slightly better performance than LR in identifying susceptible isolates in the TUM dataset (area under the precision-recall curve (AUPRC) 0.907 {+/-} 0.016 vs 0.902 {+/-} 0.030) as well as in the MS-UMG dataset (AUPRC 0.902 {+/-} 0.029 vs 0.899 {+/-} 0.054). External validation demonstrated good model transferability (AUPRC of 0.784 {+/-} 0.039 when trained on MS-UMG; 0.804 {+/-} 0.013 when trained on TUM). SHAP analysis consistently identified a top-ranked spectral feature corresponding to a peak at an m/z of 5091 in resistant isolate spectra. ConclusionThis study demonstrates that LR and LightGBM models can identify ampicillin-susceptible E. faecium isolates from MALDI-TOF spectra and generalise well to unseen datasets. While clinical implementation currently still requires confirmatory testing, the addition of larger datasets in the future will support the development of more robust machine learning models.

microbiology↗

bioRxiv2018

Cationic charge and polyspecificity of an integrin domain regulates infectivity of malaria parasites

Cell-cell and cell-substrate adhesion is critical for many functions in life. In eukaryotes, I-domains mediate functions as divergent as tissue traversal by malaria-causing Plasmodium parasites as well as cell adhesion and migration by human leucocytes. The I-domain containing protein TRAP is important for Plasmodium sporozoite motility and invasion. Here we show that the I-domain of TRAP is required to mediate adhesional properties which can be partially preserved when the native I-domain is replaced by I-domains from human integrins or from an apicomplexan parasite that does not infect insects. By putting in vivo data and structural features in perspective we conclude that polyspecificity and positive charge around the ligand binding site of the I-domain are important for TRAP function. Our data suggest a highly preserved functionality of I-domains across eukaryotic evolution that is used by apicomplexan parasites to invade a broad range of tissues in a variety of hosts.

microbiology↗