Biology subjects

Stadelmaier, J.

Publications and source records attributed to Stadelmaier, J..

4 recordsLinked to original sources

bioRxiv2026

Genomic correlates of metastatic competence and progression in human melanoma

Genomic events and their timing that grant a primary tumour the competence to disseminate remain poorly defined. We performed sequencing of 247 stage I/II primary cutaneous melanomas (CMs) and 60 matched metastases without intervening therapy from a prospectively followed registry cohort with a median followup of 92 months, integrating copy-number, mutational, protein and spatial-transcriptomic analyses. Relapse was not distinguished by oncogenic point mutations, which were largely shared between primaries and metastases, but by somatic copy-number alterations (SCNAs) and global chromosomal instability. We defined OncoCycle, a six-gene copy-number signature (amplification of CDK4, MCL1 and CD276; biallelic loss of CDKN2A, CDKN2B and TP53BP1) that predicted relapse independently of established clinicopathological features in melanoma, and a pan-cancer analysis. In matched pairs, metastatic progression was driven by continued copy-number evolution and reduction in intra-tumoural heterogeneity, rather than by acquired point mutations, and OncoCycle alterations from primary tumours were preserved in metastasis seeding clones. Clonal reconstruction revealed both monoclonal and polyclonal metastasis seeding, and spatial transcriptomics resolved copy-number-defined metastatic subclones occupying and programming distinct immune and stromal niches. Thus, metastatic competence was primed early by focal SCNAs on a background of chromosomal instability, elaborated by continued copy-number evolution during dissemination and spatio-temporal interactions with the tumour-microenvironment.

genomics↗

bioRxiv2026

Deep learning enables direct HLA typing from immunopeptidomics data

The immune system eliminates malignant and infected cells through T-cell-mediated recognition of peptides presented by human leukocyte antigen molecules. Mass spectrometry-based immunopeptidomics enables unbiased identification of naturally presented HLA-restricted peptides and has become central to the development of T-cell-based immunotherapies. However, immunopeptidomics data reflects the combined peptide presentation of multiple HLA alleles, and determining which allotypes are represented in this multi-allelic complexity remains an unmet computational challenge. Here, we introduce immunotype, a deep learning-based ensemble predictor for HLA class I allotyping directly from immunopeptidomics data. Immunotype integrates peptide and HLA protein sequence information through transformer encoders and a graph neural network, complemented by a curated mono-allelic reference of known peptide-HLA binding preferences. Immunotype achieves an overall accuracy of 87.2% at protein-level resolution across diverse tissues and thereby enables rapid, cost-effective HLA typing of large-scale immunopeptidomics datasets.

bioinformatics↗

bioRxiv2026

t2pmhc: A Structure-Informed Graph Neural Network to predict TCR-pMHC Binding

Mapping of T cell receptors (TCRs) to their cognate MHC-presented peptides (pMHC) is central for the development of precision immunotherapies and vaccine design. However, accurate prediction of TCR affinity to peptide antigens remains an open challenge. Most approaches rely solely on sequence information, although increasing evidence suggests that TCR-pMHC binding is primarily determined by three-dimensional structural interactions within the entire TCR-pMHC complex. Consequently, sequence-based methods often fail to generalize to peptides not included in the training data (unseen peptides). Here we introduce t2pmhc, a structure-based graph neural network framework for predicting TCR-pMHC binding using predicted structures of the entire TCR-pMHC complex. We evaluated a Graph Convolutional Network (GCN) and a Graph Attention Network, both demonstrating improved generalization to unseen peptides compared to state-of-the-art models across a variety of public datasets. Evaluation with crystallographic structures yields high-confidence predictions, indicating that current limitations of structure-based models are largely driven by the accuracy of structure prediction. Analysis of node attention patterns in t2pmhc-GCN reveals biologically consistent patterns, assigning high attention to the peptide and the CDR3 regions. Within the peptide sequence, canonical MHC anchor residues are consistently downweighted, whereas potential TCR-binding residues are upweighted. These findings establish t2pmhc as a structure-informed framework for robust TCR-pMHC binding prediction, enabling improved generalization to unseen antigens and providing a foundation for integrating TCR repertoire sequencing into vaccine design and immunotherapy.

bioinformatics↗

bioRxiv2025

Resistance Training Reshapes the Gut Microbiome for Better Health

ObjectivesThe gut microbiome plays a critical role in metabolism, immunity, and aging. While endurance training has been shown to beneficially modulate the microbiome, the effects of resistance training remain less clear, with some studies reporting minimal changes. This project aims to investigate whether structured resistance training elicits significant changes in gut microbiome composition and diversity in sedentary, healthy adults. Methods150 participants completed an 8-week supervised resistance training program. Session-level training data, including weights and repetitions, were recorded alongside metrics like load and compliance. Fecal samples were collected throughout the study period at designated timepoints for 16S rRNA gene amplicon sequencing to assess microbiome composition and for metabolomics analyses to evaluate microbial metabolic activity. ResultsNo differences in microbial diversity were observed, and there were no significant changes in microbial community composition or fecal metabolomics across all participants post-training. However, within-individual microbial community changes significantly correlated with strength improvement, and significantly stronger shifts in beta diversity were observed in participants with high average strength gains compared to those with smaller gains. In these high responders, differential abundance analysis revealed time-dependent microbial changes, with more taxa enriched or depleted by week 8 of training. Notably, Faecalibacterium and Roseburia hominis--both associated with a healthier, anti-inflammatory microbiome--were significantly enriched. Many differentially abundant taxa belonged to the Lachnospiraceae family. ConclusionResistance training drives significant, time-dependent gut microbiome changes, particularly in those demonstrating greater improvements in strength. These shifts mirror endurance training effects and may reflect improved overall health. SummaryWhile endurance training has been consistently shown to enhance gut microbiome composition, the effects of resistance training remain less well defined, with findings to date being variable. Our results indicate that resistance training can induce meaningful, time-dependent shifts in the gut microbiome, particularly among sedentary individuals who experience substantial strength gains. Notably, we observed enrichment of key health-associated taxa, including Faecalibacterium and Roseburia hominis, both linked to anti-inflammatory effects and improved gut function. These findings suggest that resistance training may contribute to gut health in conjunction with physical fitness, supporting its broader application in health promotion strategies and future microbiome-focused research.

microbiology↗