Biology subjects

Hutter, S.

Publications and source records attributed to Hutter, S..

4 recordsLinked to original sources

bioRxiv2025

A Unifying Mechanism for Shared Splicing Aberrations in Splicing Factor Mutant Cancers

Cancer-associated splicing factor (SF) mutations in SF3B1, U2AF1, and SRSF2 induce distinct changes in alternative splicing (AS). Yet these mutations are strikingly mutually exclusive, pointing to a convergent downstream mechanism. We hypothesized this would be reflected in the AS transcriptome. By analyzing transcriptomes of 395 patients with clonal myeloid disorders and 64 healthy donors, we found most AS alterations to be mutation-specific. However, a robust subset, enriched in the retained intron (RI) program, was shared across mutants. These RI events were bidirectional but highly concordant, and mirrored the effects of SRSF1 loss. SF-mutant states induced hypophosphorylation of RS domains in SRSF1, reducing its function. This arose from an altered AMPK-AKT balance impairing the AKT-SRPK1-SRSF1 axis. A common upstream trigger was activation of DNA damage response (DDR) by transcriptional R-loops, which increased AMPK signaling and reduced AKT activity. Pharmacologic DDR activation recapitulated reduced AKT/SRPK1 activity and SRSF1 hypophosphorylation, while relieving DDR restored SRSF1 phosphorylation and corrected RI defects. Thus, beyond cis-acting, mutation-specific changes, SF-mutant cancers share a trans-acting, stress-driven AS signature wherein DDR signaling rewires SRSF1 activity impacting AS. Our results link replication stress, kinase signaling, and RNA processing across genetically diverse clonal states, highlighting potential therapeutic approaches at these nodes. HighlightsO_LIWhile most splicing changes differ by splicing factor (SF) mutation, certain retained introns are common across subtypes. C_LIO_LIChanges in RI are bidirectional, concordant across mutant groups, and mirrors SRSF1 loss. C_LIO_LISF mutations activate DDR, triggering an AMPK/AKT imbalance that culminates in SRSF1 hypophosphorylation. C_LIO_LIRelieving R-loop induced DDR restores SRSF1 phosphorylation and reverses RI. C_LI

cancer biology↗

bioRxiv2025

IntegrateALL: an end-to-end RNA-seq analysis pipeline for multilevel data extraction and interpretable subtype classification in B-precursor ALL

Transcriptome sequencing (RNA-seq) is emerging as a diagnostic standard for B-cell precursor acute lymphoblastic leukemia (B-ALL). Expression-based classifiers reach [~]95% accuracy, but reproducible end-to-end solutions that also integrate transcript-derived genomic drivers and quantitative virtual karyotyping are lacking. We developed IntegrateALL, a Snakemake pipeline that standardizes RNA-seq analysis from FASTQ to rule-based subtype assignment across 26 WHO-HAEM5/ICC entities by integrating expression-based subtype prediction, gene fusion- / hotspot SNV calling and virtual karyotyping. We introduce KaryALL, a machine-learning classifier that uses normalized expression and minor-allele-frequency features (RNASeqCNV) to distinguish near haploid, hypodiploid and high hyperdiploid B-ALL and chromosome-21 gains/iAMP21 (accuracy: 0.98 / F1-score: 0.96 on 615 independent test samples). SNP-array concordance supported RNA-based karyotyping. Applied to 774 unselected B-ALL cases, IntegrateALL yielded unambiguous subtype assignments in 81.5%, based on concordance of gene expression class with a defining driver (75.3% of all cases) or, in selected cases, high-confidence expression-based classification alone (6.2%); the remainder (18.5%) were flagged for manual curation. Independent validation (3 cohorts; n=436, including pediatric cases) reproduced these distributions. Across all patients (n=1,210), 2.6% harbored two subtype defining drivers, including hyperdiploidy in fusion-driven subtypes where it was not expected or subtype-defining SNVs (e.g., PAX5 P80R / IKZF1 N159Y) co-occurring with BCR::ABL1-positive/-like, KMT2A- or DUX4-fusions. In most dual-driver cases, one subtype gene expression signature predominated, indicating a hierarchy of oncogenic control and the value of systematic driver screening alongside expression-based calls. IntegrateALL provides an adaptable fully reproducible workflow for molecular B-ALL characterization by systematically integrating genomic drivers and downstream gene regulation.

cancer biology↗

bioRxiv2023

Interface-guided phenotyping of coding variants in the transcription factor RUNX1 with SEUSS

Understanding the consequences of single amino acid substitutions in cancer driver genes remains an unmet need. Perturb-seq provides a tool to investigate the effects of individual mutations on cellular programs. Here we deploy SEUSS, a Perturb-seq like approach, to generate and assay mutations at physical interfaces of the RUNX1 Runt domain. We measured the impact of 115 mutations on RNA profiles in single myelogenous leukemia cells and used the profiles to categorize mutations into three functionally distinct groups: wild-type (WT)-like, loss-of-function (LOF)-like and hypomorphic. Notably, the largest concentration of functional mutations (non-WT-like) clustered at the DNA binding site and contained many of the more frequently observed mutations in human cancers. Hypomorphic variants shared characteristics with loss of function variants but had gene expression profiles indicative of response to neural growth factor and cytokine recruitment of neutrophils. Additionally, DNA accessibility changes upon perturbations were enriched for RUNX1 binding motifs, particularly near differentially expressed genes. Overall, our work demonstrates the potential of targeting protein interaction interfaces to better define the landscape of prospective phenotypes reachable by amino acid substitutions.

bioinformatics↗

bioRxiv2017

Glycosylation Flux Analysis Reveals Dynamic Changes Of Intracellular Glycosylation Flux Distribution In Chinese Hamster Ovary Fed-Batch Cultures

N-linked glycosylation of proteins has both functional and structural significance. Importantly, the glycan structure of a therapeutic protein influences its efficacy, pharmacokinetics, pharmacodynamics and immunogenicity. In this work, we developed glycosylation flux analysis (GFA) for predicting intracellular production and consumption rates (fluxes) of glycoforms, and applied this method to CHO fed-batch monoclonal antibody (mAb) production using two different media compositions, with and without additional manganese feeding. The GFA is based on a constraint-based modelling of the glycosylation network, employing a pseudo steady state assumption. While the glycosylation fluxes in the network are balanced at each time point, the GFA allows the fluxes to vary with time by way of two scaling factors: (1) an enzyme-specific factor that captures the temporal changes among glycosylation reactions catalyzed by the same enzyme, and (2) the cell specific productivity factor that accounts for the dynamic changes in the mAb production rate. The GFA of the CHO fed-batch cultivations showed that regardless of the media composition, the fluxes of galactosylation decreased with the cultivation time in comparison to the other glycosylation reactions. Furthermore, the GFA showed that the addition of Mn, a cofactor of galactosyltransferase, has the effect of increasing the galactosylation fluxes but only during the beginning of the cultivation period. The results thus demonstrated the power of the GFA in delineating the dynamic alterations of the glycosylation fluxes by local (enzyme-specific) and global (cell specific productivity) factors.

systems biology↗