Biology subjects

Schubert, B.

Publications and source records attributed to Schubert, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Germline-encoded V(D)J gene usage does not impose strict constraints on the epitope-specificity of T cell receptors

The theoretical diversity of T cell receptors (TCRs), generated through V(D)J recombination, is enormous, yet the diversity of TCRs capable of recognizing the same epitope remains unknown. Defining this TCR solution space is essential for uncovering basic principles that govern TCR specificity. Using single-cell RNA and TCR sequencing, we generated ultra-deep (more than 4000 unique TCRs per epitope) epitope-specific TCR libraries derived from 560 immunized C57BL/6 mice, identifying over 27,000 unique epitope-reactive TCRs across three distinct CD8+ T cell epitopes presented by two major histocompatibility complex (MHC) class I alleles. Saturation analyses indicated that the solution space for all studied epitopes comprises many tens of thousands of unique TCRs. Despite highly skewed and peptide-dependent VJ-usage patterns, nearly the entire set of functional germline V/ and J/ segments was detected at least once within each epitope-specific repertoire. Therefore, diversity of epitope-specific TCRs is not limited by distinct germline combinations but rather can emerge from a near-to-complete combinatorial space of - and -chain, V and J segments paired with compatible CDR3 sequences.

immunology↗

bioRxiv2026

DextraDemixer enables accurate identification of antigen-specific T cells from pMHC multimer experiments

Antigen specificity of T cells defines the adaptive immune response, yet the vast majority of known T cell receptors (TCRs) lack annotated antigen targets. Single-cell peptide-MHC (pMHC) multimer assays offer a scalable approach to map TCR-antigen interactions. Still, their utility is limited by pervasive non-specific binding and severe overlap between signal and noise, which confound the accurate identification of antigen-specific cells. To address these limitations, we present DextraDemixer, a Bayesian hierarchical mixture model that disentangles antigen-specific T cells from background noise in pMHC multimer data. The model integrates information from negative controls and clonotype structure while providing calibrated uncertainty estimates for classification. We further introduce a dynamic thresholding scheme that enables credible interval-bounded control of the false discovery rate. Extensive benchmarking on simulated datasets and antigen-specific spike-in experiments demonstrated the models robustness and improved accuracy over established methods. In a longitudinal SARS-CoV-2 vaccine study, DextraDemixer identified antigen-specific TCRs characterized by high sequence similarity, elevated antigen-specificity prediction scores, and strong clonal purity. Annotations showed high concordance with external validation data and supported the identification of antigen-specific motifs. Overall, DextraDemixer provides a principled probabilistic framework for reliable identification of antigen-specific TCRs from single-cell pMHC-multimer assays.

bioinformatics↗

bioRxiv2025

Antigen-reactive CD4+ T cells after SARS-CoV-2 vaccination show divergent phenotypic states with or without restimulation bias

Understanding antigen reactivity is crucial for characterizing CD4+ T helper (Th) cell fate, yet conventional peptide restimulation assays introduce phenotypic bias by activating cells ex vivo. However, by performing single-cell RNA and T cell receptor (TCR) sequencing on both antigen-stimulated and unstimulated samples, clonotypes can be tracked across conditions to identify antigen-reactive CD4+ T cells and simultaneously be assessed for their phenotypes in the unperturbed state. Using this reverse phenotyping strategy, complemented by DNA-barcoded peptide-HLA (pHLA) class II multimers, we here tracked SARS-CoV-2 spike-reactive CD4+ T cells longitudinally after repeated mRNA vaccination. Without stimulation, reactive clones showed more Th-neutral features and less of an activated Th1-like state than would be assessed after antigen restimulation. Furthermore, transgenic TCR re-expression guided separation of antigen-specific from bystander-activated clones. These results uncover unbiased phenotypes of antigen-reactive CD4+ T cells and highlight that cell state classification can differ fundamentally when judged by phenotype versus function.

immunology↗

bioRxiv2025

TCR activation impairs CAR-T cytotoxicity against separate target cells

Chimeric antigen receptor T cells (CAR-T) are effective therapeutics against cancer and autoimmunity, but whether the endogenous T-cell receptor (TCR) is beneficial, detrimental or irrelevant for CAR-T function and patient outcome remains unclear. We here traced anti-CD19 CAR-T clonotypes in patients with B-cell malignancies pre- and post-infusion using single-cell RNA-, TCR-, and CITE-sequencing. A cytotoxic phenotype, but not CAR-mediated in vitro reactivity to tumor cells, predicted CAR-T persistence. To test the functional impact of endogenous TCR activity on CAR-T behavior, we combined CAR transduction with orthotopic TCR replacement. This revealed that TCR signaling adds to activation of CAR-T cells, but gradually compromises CAR-mediated cytotoxicity, when TCR and CAR antigens are presented by different target cells. Therefore, spatial antigen separation alters TCR/CAR interplay with implications for therapeutic CAR-T design.

immunology↗

bioRxiv2025

Iron-sulfur cluster proteins present the weak spot in plasma-treated Escherichia coli

Non-thermal atmospheric pressure plasmas have an antiseptic activity beneficial in different medical applications. In a genome-wide screening, hydrogen peroxide and superoxide were identified as key species contributing to the antibacterial effects of plasma while [FeS] cluster proteins emerged as potential cellular targets. We investigated the impact of plasma treatment on [FeS] cluster homeostasis in Escherichia coli treated for 1 min with the effluent of a microscale atmospheric pressure plasma jet ({micro}APPJ). Mutants defective in [FeS] cluster synthesis and maintenance lacking the SufBC2D scaffold protein complex or desulfurase IscS were hypersensitive to plasma treatment. Monitoring the activity of [FeS] cluster proteins of the tricarboxylic acid cycle (aconitase, fumarase, succinate dehydrogenase) and malate dehydrogenase (no [FeS] clusters), we identified cysteine, iron, superoxide dismutase, and catalase as determinants of plasma sensitivity. Survival rates, enzyme activity, and restoration of enzyme activity after plasma treatment were superior in mutants with elevated cysteine levels and in the wildtype under iron replete conditions. Mutants with elevated hydrogen peroxide and superoxide detoxification capacity over-expressing sodA and katE showed full protection from plasma-induced enzyme inactivation and survival rates increased from 34% (controls) to 87%. Our study indicates that metabolic and genetic adaptation of bacteria may result in plasma tolerance and resistance, respectively. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/631878v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@119626eorg.highwire.dtl.DTLVardef@18d2973org.highwire.dtl.DTLVardef@9c5568org.highwire.dtl.DTLVardef@1ab4e58_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG HighlightsO_LIEnzymes with [FeS] clusters are rapidly inactivated by plasma C_LIO_LIClusters damaged by plasma are repaired in vivo using iron and cysteine C_LIO_LIOver-expression of sodA and katE completely prevents disruption of [FeS] clusters by plasma C_LIO_LIPlasma resistance is increased threefold by SodA and KatE over-production C_LIO_LIPre-adaptation of E. coli to O2- increases plasma tolerance C_LI

microbiology↗

bioRxiv2024

Benchmarking of T-Cell Receptor - Epitope Predictors with ePytope-TCR

Understanding the recognition of disease-derived epitopes through T-cell receptors (TCRs) has the potential to serve as a stepping stone for the development of efficient immunotherapies and vaccines. While a plethora of sequence-based prediction methods for TCR-epitope binding exists, their available pre-trained models have not been comparatively evaluated on standardized datasets and evaluation settings. Furthermore, technical problems such as non-standardized input and output formats of these prediction tools hinder interoperability and broad usage in applied research. To alleviate these shortcomings, we introduce ePytope-TCR, an extension of the vaccine design and immuno-prediction framework ePytope. We integrated 18 TCR-epitope prediction methods into this common framework offering interoperable interfaces with standard TCR repertoire data formats. We showcase the applicability of ePytope-TCR by evaluating the performance of the prediction methods on two challenging datasets for annotating single-cell repertoires and predicting TCR cross-reactivity towards mutated epitopes. While novel predictors successfully predicted binding to frequently observed epitopes, all methods failed for less observed epitopes. Further, we detected a strong bias in the prediction scores between different epitope classes. We envision this benchmark to guide researchers in their choice of a predictor for a given setting. Further, we aspire to accelerate the development of novel prediction models by allowing fast benchmarking against existing approaches through common interfaces and defining standardized evaluation settings.

bioinformatics↗

bioRxiv2024

Quality of vaccination-induced T cell responses is conveyed by polyclonality and high, but not maximum, antigen receptor avidity

While the quantity of vaccination-induced T cells represents a routine immunogenicity parameter, the quality of such responses is poorly understood. Here, we report on a clinical cohort of 29 human healthy individuals who received three mRNA vaccinations against SARS-CoV-2 before any breakthrough infection. We characterized the magnitude, phenotype and clonal composition of CD8 T cell responses against 16 epitope specificities by ELISpot, flow cytometry as well as single-cell RNA, TCR and surface protein sequencing. To test the functionality of identified clonotypes, 106 T cell receptors (TCR) from five epitope-specific repertoires were re-expressed and tested for peptide sensitivity. While recruited repertoires were overall enriched for high-avidity TCRs, differential clonal expansion was not linked to fine avidity differences. Instead, maintenance of polyclonality ensured robustness in counteracting mutational escape of epitopes. Our findings on the induction and maintenance of high-functionality polyclonal T cell repertoires shed light on T cell quality as a neglected criterion in the assessment of vaccine immunogenicity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/620795v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@f48079org.highwire.dtl.DTLVardef@1eaa46org.highwire.dtl.DTLVardef@13cfb93org.highwire.dtl.DTLVardef@a8a6e5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

Proteasomal cleavage prediction: state-of-the-art and future directions

Epitope vaccines are a promising approach for precision treatment of pathogens, cancer, autoimmune diseases, and allergies. Effectively designing such vaccines requires accurate proteasomal cleavage prediction to ensure that the epitopes included in the vaccine trigger an immune response. The performance of proteasomal cleavage predictors has been steadily improving over the past decades owing to increasing data availability and methodological advances. In this review, we summarize the current proteasomal cleavage prediction landscape and, in light of recent progress in the field of deep learning, develop and compare a wide range of recent architectures and techniques, including long short-term memory (LSTM), transformers, and convolutional neural networks (CNN), as well as four different denoising techniques. All open-source cleavage predictors re-trained on our dataset performed within two AUC percentage points. Our comprehensive deep learning architecture benchmark improved performance by 1.7 AUC percentage points, while closed-source predictors performed considerably worse. We found that a wide range of architectures and training regimes all result in very similar performance, suggesting that the specific modeling approach employed has a limited impact on predictive performance compared to the specifics of the dataset employed. We speculate that the noise and implicit nature of data acquisition techniques used for training proteasomal cleavage prediction models and the complexity of biological processes of the antigen processing pathway are the major limiting factors. While biological complexity can be tackled by more data and, to a lesser extent, better models, noise and randomness inherently limit the maximum achievable predictive performance. All our datasets and experiments are available at https://github.com/ziegler-ingo/cleavage_benchmark.

bioinformatics↗

bioRxiv2023

Predicting T Cell Receptor Functionality against Mutant Epitopes

Cancer cells or pathogens can escape recognition by T cell receptors (TCRs) through mutations of immunogenic epitopes. TCR cross-reactivity, i.e., recognition of multiple epitopes with sequence similarities, can be a factor to counteract such mutational escape. However, cross-reactivity of cell-based immunotherapies may also cause severe side effects when self-antigens are targeted. Therefore, the ability to predict the effect of mutations in the epitope sequence on T cell functionality in silico would greatly benefit the safety and effectiveness of newly-developed immunotherapies and vaccines. We here present "Predicting T cell Epitope-specific Activation against Mutant versions" (P-TEAM), a Random Forest-based model which predicts the effect of point mutations of an epitope on T cell functionality. We first trained and tested P-TEAM on a comprehensive dataset of 36 unique murine TCRs in response to systematic single-amino acid mutations of their target epitope (representing 5.472 unique TCR-epitope interactions). The model was able to classify T cell reactivities, corresponding to in vivo recruitment of T cells, and quantitatively predict T cell functionalities for unobserved single-point mutated altered peptide ligands (APLs), or even unseen TCRs, with consistently high performance. Further, we present an active learning framework to guide experimental design for assessing TCR functionality against novel epitopes, minimizing primary data acquisition costs. Finally, we applied P-TEAM to a novel dataset of 7 human TCRs reactive to the tumor neoantigen VPSVWRSSL. We observed a similarly robust performance for these human TCRs as for the murine TCRs recognizing SIINFEKL, thus providing evidence that our approach is applicable to therapeutically relevant TCRs as well as across species. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.

bioinformatics↗

bioRxiv2022

meTCRs - Learning a metric for T-cell receptors

T cell receptors (TCRs) bind to pathogen- or self-derived epitopes to elicit a T cell response as part of the adaptive immune system. Determining the specificity of TCRs provides context for immunological studies and can be used to identify candidates for novel immunotherapies. To avoid costly experiments, large-scale TCR-epitope databases are queried for similar sequences via various distance functions. Here, we developed the deep-learning based distance meTCRs. Contrary to most previous approaches, the method avoids computational expansive pairwise string operations by comparing TCRs in a numeric embedding. In contrast to models which are trained specificity-agnostic, we directly utilize epitope information by applying deep metric learning to guide the training. Summarizing, we present meTCRs as a scalable alternative to embed TCR repertoires for clustering, visualisation, and querying against the ever-increasing amount TCR-epitope pairs in publicly available databases.

bioinformatics↗

bioRxiv2022

Acute resistance to BET inhibitors remodels compensatory transcriptional programs via p300 co-activation

Initial clinical trials with drugs targeting epigenetic modulators - such as bromodomain and extraterminal (BET) inhibitors - demonstrate modest results in acute myeloid leukemia (AML). The main reason for this involves an increased transcriptional plasticity within AML, which allows cells to escape the therapeutic pressure. To study mechanisms of resistance, we investigated immediate epigenetic and transcriptional responses following BET inhibition, and could demonstrate that BET inhibitor-mediated release of BRD4 from chromatin is accompanied by an acute compensatory feedback loop that attenuates inhibition, or even increases expression, of specific transcriptional modules. This adaptation is most marked at key AML maintenance genes and is mediated by p300, suggesting a rational therapeutic opportunity by combining BET- and p300- inhibition. p300 activity is required during all steps of adaptation. However, the transcriptional programs that p300 regulates to induce resistance to BETi differ between AML subtypes. Remarkably, in some AMLs, p300 regulates a series of transitional transcriptional patterns that allow homeostatic adjustments during earlier stages of resistance to BET-inhibitors. In consequence, p300 remains crucial throughout all stages of resistance in sensitive AML-subtypes, although its importance declines following the development of chronic resistance to BET inhibitors in some other AMLs. Altogether, our study elucidates the mechanisms that underlie an "acute" state of resistance to BET inhibition, achieved through p300 activity, and how these mechanisms remodel to become "chronic". Importantly, however, our data also suggest that a sequential treatment with BET- and p300 inhibition may prevent resistance development, thereby improving outcomes. Key pointsO_LIA mechanistic feedback to p300 enables acute tolerance to BET inhibition. C_LIO_LIp300 regulates transcriptional networks that lead to chronic resistance to BET inhibition. C_LIO_LISequential BET-, followed by p300-inhibition, is synthetically lethal in AML, and is optimally deployed during earlier stages of resistance to BET inhibitors. C_LI

cancer biology↗

bioRxiv2022

Multi-Omics Regulatory Network Inference in the Presence of Missing Data

A key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet. In this study, we integrated regression-based methods that can handle missingness into KiMONo, a Knowledge guIded Multi-Omics Network inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent. Juan Henao is a 3rd year PhD candidate at Computational Health Center at Helmholtz Center Munich working on multi-omics and clinical data integration using both, bulk and single-cell data. Michael Lauber is a PhD Candidate at the Chair of Experimental Bioinformatics at the Technical University Munich. Currently, he is working on an approach for inference of reprogramming transcription factors for trans-differentiation. Manuel Azevedo is a Masters student at the Technical University of Munich in Mathematics with a focus on Biomathematics and Biostatistics. Currently, he is working as a Student Assistant at Helmholtz Munich, where he is also doing his masters thesis. Anastasiia Grekova is a Masters student of bioinformatics at the Technical University of Munich and the Ludwig-Maximilians-University Munich, working on multi-omics data integration in Marsico Lab at HMGU. Fabian Theis is the Head of the Institute of Computational Biology and leading the group for Machine Learning at Helmholtz Center Munich. He also holds the chair of Mathematical modelling of biological systems, Department of Mathematics, Technical University of Munich as an Associate Professor. Markus List obtained his PhD at the University of Southern Denmark and worked as a postdoctoral fellow at the Max Planck Institute for Informatics before starting his group Big Data in BioMedicine at the Technical University of Munich. Christoph Ogris holds a PostDoc position in the Marsico Lab at Helmholtz-Center Munich. His research focuses on predicting and exploiting multi-modal biological networks to identify disease-specific cross-omic interactions. Benjamin Schubert obtained his PhD at the University of Tubingen and worked as a postdoctoral fellow at Harvard Medical School and Dana-Farber Cancer Institute USA before starting his group for Translational Immmunomics at the Helmholtz Center Munich.

systems biology↗

bioRxiv2021

Jointly learning T-cell receptor and transcriptomic information to decipher the immune response

Recent advancements in single-cell immune profiling that enable the measurement of the transcriptome and T-cell receptor (TCR) sequences simultaneously have emerged as a promising approach to study immune responses at cellular resolution. Yet, combining these different types of information from multiple datasets into a joint representation is complicated by the unique characteristics of each modality and the technical effects between datasets. Here, we present mvTCR, a multimodal generative model to learn a unified representation across modalities and datasets for joint analysis of single-cell immune profiling data. We show that mvTCR allows the construction of large-scale and multimodal T-cell atlases by distilling modality-specific properties into a shared view, enabling unique and improved data analysis. Specifically, we demonstrated mvTCRs potential by revealing and separating SARS-CoV-2-specific T-cell clusters from bystanders that would have been missed in individual unimodal data analysis. Finally, mvTCR can enable automated analysis of new datasets when combined with transfer-learning approaches. Overall, mvTCR provides a principled solution for standard analysis tasks such as multimodal integration, clustering, specificity analysis, and batch correction for single-cell immune profiling data.

bioinformatics↗

bioRxiv2020

scCODA: A Bayesian model for compositional single-cell data analysis

Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA (https://github.com/theislab/scCODA), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance and identified experimentally verified cell type changes that were missed in original analyses.

bioinformatics↗

bioRxiv2020

Joint epitope selection and spacer design for string-of-beads vaccines

MotivationConceptually, epitope-based vaccine design poses two distinct problems: (1) selecting the best epitopes eliciting the strongest possible immune response, and (2) arranging and linking the selected epitopes through short spacer sequences to string-of-beads vaccines so as to increase the recovery likelihood of each epitope during antigen processing. Current state-of-the-art approaches solve this design problem sequentially. Consequently, such approaches are unable to capture the inter-dependencies between the two design steps, usually emphasizing theoretical immunogenicity over correct vaccine processing and resulting in vaccines with less effective immunogencity. ResultsIn this work, we present a computational approach based on linear programming that solves both design steps simultaneously, allowing to weigh the selection of a set of epitopes that have great immunogenic potential against their assembly into a string-of-beads construct that provides a high chance of recovery. We conducted Monte-Carlo cleavage simulations to show that, indeed, a fixed set of epitopes often cannot be assembled adequately, whereas selecting epitopes to accommodate proper cleavage requirements substantially improves their recovery probability and thus the effective immunogenicity, pathogen, and population coverage of the resulting vaccines by at least two fold. AvailabilityThe software and the data analyzed are available at https://github.com/SchubertLab/JessEV

bioinformatics↗

bioRxiv2019

Graph-Theoretical Formulation of the Generalized Epitope-based Vaccine Design Problem

Epitope-based vaccines have revolutionized vaccine research in the last decades. Due to their complex nature, bioinformatics plays a pivotal role in their development. However, existing algorithms address only specific parts of the design process or are unable to provide formal guarantees on the quality of the solution. Here we present a unifying formalism of the general epitope vaccine design problem that tackles all phases of the design process simultaneously and combines all prevalent design principles. We then demonstrate how to formulate the developed formalism as an integer linear program which guarantees optimality of the designs. This makes it possible to explore new regions of the vaccine design space, analyze the trade-offs between the design phases, and balance the many requirements of vaccines.

bioinformatics↗

bioRxiv2019

Predicting antigen-specificity of single T-cells based on TCR CDR3 regions

It has recently become possible to assay T-cell specificity with respect to large sets of antigens as well as T-cell receptor sequence in high-throughput single-cell experiments. We propose multiple sequence-data specific deep learning approaches to impute TCR to epitope specificity to reduce the complexity of new experiments. We found that models that treat antigens as categorical variables outperform those which model the TCR and epitope sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates.

bioinformatics↗

bioRxiv2018

The EVcouplings Python framework for coevolutionary sequence analysis

SummaryCoevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. This approach requires extensive computational pipelines that integrate multiple tools, databases, and data processing steps. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The application has an easy to use command line interface to run workflows with user control over all analysis parameters, while the underlying modular Python package allows interactive data analysis and rapid development of new workflows. Through this multi-layered approach, the EVcouplings framework makes the full power of coevolutionary analyses available to entry-level and advanced users.\n\nAvailabilityhttps://github.com/debbiemarkslab/evcouplings\n\nContactsander.research@gmail.com, debbie@hms.harvard.edu

bioinformatics↗