Biology subjects

Flossdorf, M.

Publications and source records attributed to Flossdorf, M..

3 recordsLinked to original sources

bioRxiv2025

Decoding Helicobacter pylori Resistance: Machine Learning Enhanced Prediction of Antibiotic Susceptibility using Whole-Genome Sequencing

BackgroundHelicobacter pylori is a significant risk factor for gastric cancer, peptic ulcers, and MALT lymphoma. Rising antibiotic resistance rates complicate treatment strategies. While nucleotide sequence based assays are reliable in predicting clarithromycin and levofloxacin resistance, predicting metronidazole resistance is more challenging due to diverse metabolic pathways contributing to resistance, and high genomic variability. MethodsWe assembled a cohort of 483 H. pylori clinical isolates, combining whole-genome sequencing with phenotypic susceptibility testing. Machine learning models (SVM, XGBoost, FNN) were trained on genomic variants to predict resistance phenotypes. A sliding-window approach and SHAP-based importance scoring were used for feature selection to identify biologically relevant mutations, improving prediction accuracy, particularly for metronidazole resistance. ResultsThe best-performing FNN model improved metronidazole resistance prediction by 16% compared to conventional (non-ML, single polymorphisms) sequence-based detection methods applied to the same strain collection. Feature selection identified 32 feature sets, with 11 sets significantly improving F1-scores over the baseline. Combining 2-4 feature sets revealed 53 synergistic combinations across all models. Validation showed that 87% of these combinations significantly outperformed non-ML molecular testing, with 16 combinations achieving F1-scores above 0.65. ConclusionMachine-learning can significantly improve the performance of sequence-based susceptibility testing for metronidazole in H. pylori. Novel candidate predictive markers identified from whole-genome data offer testable hypotheses about yet unexplored mechanisms of metronidazole resistance. These findings support the potential for ML-based approaches to enable more accurate susceptibility-guided therapies.

microbiology↗

bioRxiv2022

High-resolution kinetic gene expression analysis of T helper cell differentiation reveals a STAT-dependent, unique transcriptional program in Th1/2 hybrid cells

Selective differentiation of CD4+ T helper (Th) cells into specialized subsets such as Th1 and Th2 cells is a key element of the adaptive immune system driving appropriate immune responses. Besides those canonical Th cell lineages, hybrid phenotypes such as Th1/2 cells arise in vivo, and their generation could be reproduced in vitro. While master-regulator transcription factors like T-bet for Th1 and GATA-3 for Th2 cells drive and maintain differentiation into the canonical lineages, the transcriptional architecture of hybrid phenotypes is less well understood. In particular, it has remained unclear whether a hybrid phenotype implies a mixture of the effects of several canonical lineages for each gene, or rather a bimodal behavior across genes. Th cell differentiation is a dynamic process in which the regulatory factors are modulated over time, but longitudinal studies of Th cell differentiation are sparse. Here, we present a dynamic transcriptome analysis following Th cell differentiation into Th1, Th2 and Th1/2 hybrid cells. We identified an early bifurcation point in gene expression programs, and we found that only a minority of [~]20% of Th cell-specific genes showed mixed effects from both Th1 and Th2 cells on Th1/2 hybrid cells. While most genes followed either Th1 or Th2 cell gene expression, another fraction of [~]20% of genes followed a Th1 and Th2 cell-independent transcriptional program under control of the transcription factors STAT1 and STAT4. Overall, our results emphasize the key role of high-resolution longitudinal data for the characterization of cellular phenotypes.

immunology↗

bioRxiv2021

Heritable changes in division speed accompany the diversification of single T cell fate

Rapid clonal expansion of antigen specific T cells is a fundamental feature of adaptive immune responses. It enables the outgrowth of an individual T cell into thousands of clonal descendants that diversify into short-lived effectors and long-lived memory cells. Clonal expansion is thought to be programmed upon priming of a single naive T cell and then executed by homogenously fast divisions of all of its descendants. However, the actual speed of cell divisions in such an emerging T cell family has never been measured with single-cell resolution. Here, we utilize continuous live-cell imaging in vitro to track the division speed and genealogical connections of all descendants derived from a single naive CD8+ T cell throughout up to ten divisions of activation-induced proliferation. This comprehensive mapping of T cell family trees identifies a short burst phase, in which division speed is homogenously fast and maintained independent of external cytokine availability or continued T cell receptor stimulation. Thereafter, however, division speed diversifies and model-based computational analysis using a novel Bayesian inference framework for tree-structured data reveals a segregation into heritably fast and slow dividing branches. This diversification of division speed is preceded already during the burst phase by variable expression of the interleukin-2 receptor alpha chain. Later it is accompanied by selective expression of memory marker CD62L in slower dividing branches. Taken together, these data demonstrate that T cell clonal expansion is structured into subsequent burst and diversification phases the latter of which coincides with specification of memory vs. effector fate. SignificanceRapid clonal expansion of antigen-specific T cells is a fundamental feature of adaptive immune responses. Here, we utilize continuous live-cell imaging in vitro to track the division speed and genealogical connections of all descendants derived from a single naive CD8+ T cell throughout up to ten divisions of activation-induced proliferation. Bayesian inference of tree-structured data reveals that clonal expansion is divided into a homogenously fast burst phase encompassing two to three divisions and a subsequent diversification phase during which T cells segregate into quickly dividing effector T cells and more slowly cycling memory precursors. Our work highlights cell cycle speed as a major heritable property that is regulated in parallel to key lineage decisions of activated T cells.

immunology↗