Biology subjects

Krauthammer, M.

Publications and source records attributed to Krauthammer, M..

9 recordsLinked to original sources

bioRxiv2025

Uncovering Cas9 PAM diversity through metagenomic mining and machine learning

Recognition of protospacer adjacent motifs (PAMs) is crucial for target site recognition by CRISPR-Cas systems. In genome editing applications, the requirement for specific PAM sequences at the target locus imposes substantial constraints, driving efforts to search for novel Cas9 orthologs with extended or alternative PAM compatibilities. Here, we present CRISPR-PAMdb, a comprehensive and publicly accessible database compiling Cas9 protein sequences from 3.8 million bacterial and archaeal genomes and PAM profiles from 7.4 million phage and plasmid sequences. Through spacer-protospacer alignment, we inferred consensus PAM preferences for 8,003 unique Cas9 clusters. To extend PAM discovery beyond traditional alignment-based approaches, we developed CICERO, a machine learning model predicting PAM preferences directly from Cas9 protein sequences. Built on the ESM2 protein language model and trained on the CRISPR-PAMdb database, CICERO achieved an average accuracy of 0.68 on test data and 0.75 on experimentally validated Cas9 orthologs. For Cas9 clusters where alignment-based predictions were infeasible, CICERO generated PAM profiles for an additional 50,308 Cas9 proteins, including 17,453 high-confidence predictions with accuracies above 0.86. CRISPR-PAMdb, alongside CICERO models, enables large-scale exploration of PAM diversity across Cas9 proteins, accelerating design of next-generation CRISPR-Cas9 tools for precise genome engineering.

synthetic biology↗

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

SAUNA - Simulated Annealing for Unique Nucleosome Arrangements from Cell-Free DNA-Sequencing Data

WithdrawalThis preprint has been withdrawn by the authors. While the preprint cites the work by Schoepflin et al. (2013) (reference [20] in the preprint), it has been brought to the authors attention that the preprint may not appropriately acknowledge the extent to which their work depends on the work by Schoepflin et al. (2013). The authors apologize for this oversight and want to emphasize that they did not intend to misattribute the work of Schoepflin et al. (2013) and want to thank them for the code of NucPosSimulator which is a core component of SAUNAs algorithm. Therefore, the authors do not wish this work to be cited as reference for the project. For any questions, please contact the corresponding author.

bioinformatics↗

bioRxiv2023

Predicting prime editing efficiency across diverse edit types and chromatin contexts with machine learning

Prime editing is a powerful genome editing technology, but its efficiency varies depending on the pegRNA design and target locus. Existing computational models for predicting prime editing rates are limited by their focus on specific edit types and by omitting the local chromatin environment. In our study, we developed machine learning models that predict prime editing efficiencies across a wide range of edit types up to 15 bp (PRIDICT2.0) and in different chromatin contexts (ePRIDICT). Both models can be accessed at www.pridict.it.

molecular biology↗

bioRxiv2023

Memory Th1 cells modulate heterologous diseases through innate function

Through immune memory, infections have a lasting effect on the host. While memory cells enable accelerated and enhanced responses upon re-challenge with the same pathogen, their impact on susceptibility to unrelated diseases is unclear. We identify a subset of memory T helper 1 (Th1) cells termed innate acting memory T (TIA) cells that originate from a viral infection and produce IFN-{gamma} with innate kinetics upon heterologous challenge in vivo. Activation of memory TIA cells is induced in response to IL-12 in combination with IL-18 or IL-33 but is TCR-independent. Rapid IFN-{gamma} production by memory TIA cells is protective in subsequent heterologous challenge with the bacterial pathogen Legionella pneumophila. In contrast, antigen-independent re-activation of CD4+ memory TIA cells accelerates disease onset in an autoimmune model of multiple sclerosis. Our findings demonstrate that memory Th1 cells can acquire additional TCR-independent functionality to mount rapid, innate-like responses that modulate susceptibility to heterologous challenges.

immunology↗

bioRxiv2022

ROS induction as a strategy to target persister cancer cells with low metabolic activity in NRAS mutated melanoma

Metabolic reprogramming is an emerging hallmark of resistance to cancer therapy but may generate vulnerabilities that can be targeted with small molecules. Multi-omics analysis revealed that NRAS-mutated melanoma cells with a mesenchymal transcriptional profile adopt a quiescent metabolic program to resist cellular stress response induced by MEK-inhibitor resistance. However, as a result of elevated baseline ROS levels, these cells become highly sensitive to ROS induction. In vivo xenograft experiments and single-cell RNA sequencing demonstrated that intra-tumor heterogeneity requires the combination of a ROS-inducer and a MEK-inhibitor to target both tumor growth and metastasis. By ex vivo pharmacoscopy of 62 human metastatic melanomas, we found that MEK-inhibitor resistant tumors significantly benefitted from the combination therapy. Finally, we profiled 486 cancer cell lines and revealed that oxidative stress responses and translational suppression are biomarkers of ROS-inducer sensitivity, independent of cancer indication. These findings link transcriptional plasticity to a metabolic phenotype that can be inhibited by ROS-inducers in melanoma and other cancers. Statement of SignificanceTargeted-therapy resistance in cancer arises from genetic selection and both transcriptional and metabolic adaptation. We show that metabolic reprogramming sensitizes resistant cells to ROS-induction in combination with pathway inhibitors. Predictive biomarkers of metabolic sensitivity to ROS-inducing agents were identified in many cancer entities, highlighting the generalizability of this treatment approach. Graphical summary O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

cancer biology↗

bioRxiv2020

Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens

Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable conversion of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have vast potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on thousands of lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2020

Chromatin-Bound PARP1 Correlates with Upregulation of Inflammatory Genes in Response to Long-Term Treatment with Veliparib

Poly-ADP-ribose polymerase (PARP) inhibitors are active against cells and tumors with defects in homology-directed repair as a result of synthetic lethality. PARP inhibitors have been suggested to act by either catalytic inhibition or by PARP localization in chromatin. In this study, we treat human HCC1937 BRCA1 mutant and isogenic BRCA1-complemented cells for three weeks with veliparib, a PARP inhibitor. We show that long-term treatment with veliparib results in chromatin-bound PARP1 in the BRCA1 mutant cells, and that this correlates with significant upregulation of inflammatory genes and activation of the cyclic GMP-AMP synthase (cGAS)/ signalling effector stimulator of interferon genes (STING) pathway. In contrast, long-term treatment of isogenic BRCA1-complemented cells with veliparib does not result in chromatin-associated PARP or significant upregulation of the inflammatory response. Our results suggest that long-term veliparib treatment may prime BRCA1 mutant tumors for positive responses to immune checkpoint blockade.

cancer biology↗

bioRxiv2020

The Gene Expression Profile of Uropathogenic Escherichia coli in Women with Uncomplicated Urinary Tract Infections Is Recapitulated in the Mouse Model

Uropathogenic Escherichia coli (UPEC) is the primary causative agent of uncomplicated urinary tract infections (UTIs). UPEC fitness and virulence determinants have been evaluated in a variety of laboratory settings that include a well-established mouse model of UTI. However, the extent to which bacterial physiology differs between experimental models and human infections remains largely understudied. To address this important question, we compared the transcriptomes of three different UPEC isolates in human infection and a variety of laboratory conditions including LB culture, filter-sterilized urine culture, and the UTI mouse model. We observed high correlation in gene expression between the mouse model and human infection in all three strains examined (Pearson correlation coefficient of 0.86-0.87). Only 175 of 3,266 (5.4%) genes shared by all three strains had significantly different expression levels, with the majority of them (145 genes) down-regulated in patients. Importantly, gene expression of both canonical virulence factors and metabolic machinery were highly similar between the mouse model and human infection, while the in vitro conditions displayed more substantial differences. Interestingly, comparison of gene expression between the mouse model and human infection hint at differences in bladder oxygenation as well as nutrient composition. In summary, our work strongly validates the continued use of this mouse model for the study of the pathogenesis of human UTI. ImportanceDifferent experimental models have been used to study UPEC pathogenesis including in vitro cultures in different media, tissue culture, as well as mouse models of infection. The latter is especially important since it allows evaluation of mechanisms of pathogenesis and potential therapeutic strategies against UPEC. Bacterial physiology is greatly shaped by environment and it is therefore critical to understand how closely bacterial physiology in any experimental model relates to human infection. In this study, we found a very strong correlation in bacterial gene expression between the mouse model and human UTI using identical strains, suggesting that the mouse model accurately mimics human infection, definitively supporting its continued use in UTI research.

microbiology↗