Biology subjects

Stuewe, I.

Publications and source records attributed to Stuewe, I..

3 recordsLinked to original sources

bioRxiv2025

Multi-omics profiling reveals microenvironmental remodeling as a key driver of house dust mite-induced lung cancer progression

Chronic exposure to the common aeroallergen house dust mite (HDM) induces lung inflammation and DNA damage, but its impact on lung cancer development remains largely unexplored. Using whole-genome sequencing, RNA-seq, and DNA methylation profiling, we assessed HDM effects in lung epithelial cell lines and a mouse orthotopic lung cancer model. HDM accelerated tumor growth without altering mutational burden. Transcriptomic and epigenetic analyses revealed tissue-specific effects: in normal lung, HDM enhanced pro-inflammatory and immune activation programs, whereas in tumors it suppressed T cell responses, antigen presentation, and chemokine signaling. Immune deconvolution showed a shift toward myeloid enrichment and lymphoid suppression, with reduced cytotoxic T and NK signatures. Notably, HDM-driven tumor promotion was abolished in Il17a-/- but not Il1b-/-mice, identifying IL-17A as a critical mediator. These findings demonstrate that chronic aeroallergen exposure reshapes the lung microenvironment to promote immune suppression and accelerate lung cancer progression. HighlightsChronic house dust mite (HDM) exposure accelerates lung tumor growth through non-mutagenic, immune-mediated mechanisms. HDM activates pro-inflammatory and immune programs in normal lung tissue but suppresses antitumor T cell responses in tumors. Multi-omics profiling reveals epigenetic silencing of immune genes and a myeloid-enriched, lymphoid-deficient tumor microenvironment. HDM-driven tumor promotion depends on IL-17A but not IL-1{beta}, establishing IL-17A as a central driver of lung tumor promotion.

cancer biology↗

bioRxiv2025

A Universal Duplex Sequencing Approach for Accurate Detection of Somatic Mutations

Ultra-accurate detection of rare somatic mutations is critical for understanding mutational processes in human disease, aging, and environmental exposures, yet current methods are limited by error rates, restricted genome coverage, and high DNA input. We present UDSeq, a duplex sequencing protocol combining random fragmentation, efficient UMI ligation, and quantitative input control to achieve near-complete genome/exome representation from as little as 100 pg DNA. Benchmarking in human sperm estimates a UDSeq error rate of [~]2.5x10-9 per base pair. UDSeq captures mutational signatures from heterogeneous populations without clonal expansion, reproduces exposure-specific patterns in cell lines and rodent models, and enables cross-species profiling. Compared with prior duplex methods, UDSeq yields up to fourfold more usable duplex molecules, improves library conversion, and remains cost-effective. We include a step-by-step protocol with quality-control checkpoints for fragment size, ligation yield, library conversion, and duplication rate. UDSeq provides a scalable, low-input platform for accurate profiling of somatic mutagenesis.

genomics↗

bioRxiv2025

Improved Mutation Detection in Duplex Sequencing Data with Sample-Specific Error Profiles

Duplex sequencing enables highly accurate detection of rare somatic mutations, but existing variant callers often rely on protocol-specific heuristics that limit sensitivity, reproducibility, and cross-study comparability. We present DupCaller, a probabilistic variant caller that builds sample-specific error profiles and applies a strand-aware statistical model for mutation detection. Across 50 synthetic datasets, DupCaller identified 1.25-fold more single-base substitutions (SBSs) and 1.41-fold more indels than a state-of-the-art method, while exhibiting equal or better precision. In three duplex-sequenced cell lines treated with aristolochic acid, it recovered expected mutational signatures while detecting 3.5-fold more SBSs and 2.8-fold more indels. In 93 tissue samples-- including neurons, cord blood, sperm, saliva, and blood--DupCaller showed consistent gains, detecting 1.21- to 2.7-fold more mutations. Sensitivity scaled with sample duplication rate, yielding approximately 1.5-fold more mutations under optimal conditions and over 3-fold more in low-duplication samples where other tools falter. These results establish DupCaller as a robust and scalable solution for somatic mutation profiling in duplex sequencing across diverse biological and technical contexts.

bioinformatics↗