Biology subjects

Schmitz, U.

Publications and source records attributed to Schmitz, U..

11 recordsLinked to original sources

bioRxiv2026

A comprehensive benchmark of transcriptome-wide fusion detection using long-read RNA sequencing

Fusion transcripts contribute to cancer, inherited diseases, developmental disorders, and evolution. Long-read RNA sequencing enables direct sequencing of full-length transcripts, creating new opportunities to detect complex fusion architectures, including previously inaccessible multi-segmented fusion transcripts. However, accurate transcriptome-wide fusion detection remains challenging because existing methods struggle to distinguish genuine fusion events from technical artefacts. Here, we present a comprehensive benchmark of transcriptome-wide fusion detection using simulated datasets and transcriptomes from three cancer cell lines across Oxford Nanopore Technologies (ONT) cDNA, PCR-cDNA, and direct RNA sequencing, Pacific Biosciences (PacBio) Kinnex sequencing, Illumina short-read RNA sequencing, six long-read fusion callers, and multiple analysis strategies. False-positive fusion calls remained the dominant limitation across sequencing platforms and algorithms. Increasing sequencing depth improved recall but also amplified spurious fusion calls, whereas higher read-support thresholds improved precision at the expense of sensitivity. ONT PCR-cDNA sequencing combined with CTAT-LR-Fusion achieved the best overall balance between precision and recall, whereas JAFFAL was the only caller to reliably identify simulated tri-gene fusions. Consensus calling reduced false positives but markedly reduced sensitivity, with only one of 400 simulated fusions detected by all six callers. Breakpoint localisation emerged as a major limitation across all methods. Long-read sequencing consistently recovered more validated fusion transcripts than short-read sequencing, enabled detection of complex tri-gene fusions, and produced more biologically plausible fusion landscapes with fewer promiscuous gene partners. Collectively, our results establish the first comprehensive benchmarking framework for transcriptome-wide fusion detection, using long-read RNA sequencing, and provide practical guidance for selecting sequencing workflows and computational strategies, while identifying key priorities for future algorithm development.

bioinformatics↗

bioRxiv2026

Advancing Liver Cancer Precision Medicine with TARGET-SL

Background and AimsA major goal of personalised liver oncology is the ability to make targeted predictions about cancer-specific toxicity, however there are limited methods available. To address this, we validated the performance of our bioinformatics framework, TARGET-SL, through ex vivo drug screening. MethodsUsing TARGET-SL we predicted gain of function (GOF), loss of function (LOF) and synthetic lethal (SL) genetic events, and corresponding drug candidates. We validated drug predictions across hepatocellular carcinoma (HCC) cell lines, and a cohort of HCC and cholangiocarcinoma (CCA) patient-derived organoids (PDOs). ResultsFor HCC cells and PDOs we found 37.5% and 25% of the respective selected compounds induced unique target-specific growth inhibition based on genetic biomarkers, suggesting novel biomarker-driven drug sensitivities. ConclusionsOur analyses demonstrate TARGET-SLs potential to enhance personalized drug screening for liver cancer, by focusing on genetically informed targets. This will reduce experimental costs and accelerate the pace of therapeutic discovery. Impact and ImplicationsPrimary liver cancer (PLC) is a cancer with poor prognosis, and current therapies increase survival only for a minority of patients. Through the application of TARGET-SL we can predict, for each patient, the essential genes and corresponding small molecule inhibitors. These data support further investigation in larger patient cohorts and offer the possibility to specify new small molecule inhibitors and to repurpose current drugs for PLC treatment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/725819v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@10cb252org.highwire.dtl.DTLVardef@8f3049org.highwire.dtl.DTLVardef@ab4467org.highwire.dtl.DTLVardef@17f9d3_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITARGET-SL can predict gene and drug sensitivities for cell lines and patient-derived organoids C_LIO_LIThis may reduce drug screening costs and accelerate the pace of therapeutic discovery. C_LIO_LITARGET-SL may assist in the repurposing of current drugs and their rapid translation for primary liver cancer C_LIO_LITARGET-SL is tumour-type agnostic, and therefore may have application in other cancers with poor prognosis C_LI

cancer biology↗

bioRxiv2026

ICON: An isoform-aware hierarchical random forest model for cell type classification

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve cellular heterogeneity across complex biological systems. However, conventional short-read scRNA-seq is inherently limited in its inability to capture full-length transcripts. Isoform profiles, arising from alternative splicing, provide a deeper layer of resolution, enabling finer discrimination of cellular subtypes and dynamic states, particularly in heterogenous tissues. Long-read RNA sequencing technologies enable accurate transcript-level profiling and more comprehensive characterisation of isoform diversity. Despite these advances, existing cell type annotation methods remain largely tailored to gene-level data, thereby limiting fidelity and leaving isoform-level information an untapped reservoir of biological insight. Here, we present a hierarchical random forest (HRF) framework, ICON, for isoform-aware cell classification in scRNA-seq data. By jointly modelling gene- and isoform-level expression the framework captures both abundance and useage patterns, enabling classification beyond gene-level resolution. A two-stage strategy first assigns cell identities using highly variable gene and isoform features, followed by targeted reclassification of ambiguous cells based on relative isoform and gene usage, thereby resolving conflicts that arise from transcriptional heterogeneity. Importantly, ICON provides interpretable outputs by identifying key genes and isoforms that drive cell type discrimination, linking classification to underlying regulatory mechanisms. Benchmarking on long-read scRNA-seq datasets demonstrates consistent improvements over conventional gene-based approaches. With increasing adoption of long-read sequencing, our framework provides a robust, interpretable foundation for isoform-aware cell type annotation, improving resolution and insight.

bioinformatics↗

bioRxiv2025

The ZNF512B-NuRD complex co-ordinates a neural-specific gene expression program

The nucleosome remodelling and deacetylase complex (NuRD) plays a key role in chromatin regulation and a wide range of biological processes including development, haemopoiesis, immunity and neurogenesis. Its interaction with tissue-enriched and sequence-specific transcription factors (TFs) leads to distinct functional outputs in the given tissue by targeting a specific set of genes. However, how NuRD dynamically and specifically regulates gene expression in a tissue-specific manner is poorly understood. Here, we refine an N-terminal specific NuRD interaction motif which enables direct engagement with many transcriptional regulatory proteins. Using a series of structural modelling and biochemical techniques, we show that ZNF512B, a poorly characterised neuronal-expressed zinc finger protein, directly binds to the RBBP4 subunit of the NuRD complex. Subsequent knockdown of ZNF512B results in the downregulation of several neural-related molecular pathways suggesting that ZNF512B may play a regulatory role during neurogenesis. We also show that in NTERA-2 neural cells, the expression of ZNF512B is necessary for cell growth and survival, and is markedly enhanced during neural progenitor cell (NPC) differentiation. In summary, our data suggest that ZNF512B might regulate neural-specific transcriptional programs via engagement with the NuRD complex.

molecular biology↗

bioRxiv2025

ScIsoX: A Multidimensional Framework for Measuring Transcriptomic Complexity in Single-Cell Long-Read Sequencing Data

Single-cell isoform sequencing enables high-resolution characterisation of transcript isoform expression, yet analytical frameworks to systematically measure transcriptomic complexity are lacking. Here, we introduce ScIsoX, a computational framework that integrates a novel hierarchical data structure, a suite of complexity metrics, and dedicated visualisation tools for isoform-level analysis. ScIsoX supports systematic exploration of global and cell-type-specific isoform expression patterns arising from alternative splicing, revealing multidimensional complexity signatures across diverse datasets - insights often missed by conventional gene-level approaches.

bioinformatics↗

bioRxiv2022

SpliceWiz: easy, optimized, and accurate alternative splicing analysis in R

Despite an abundance of publicly available RNA sequencing datasets, a lack of integrated user-friendly tools hinder exploration of alternative splicing. SpliceWiz is an innovative, ultra-fast graphical R application that accurately quantifies splicing events using isoform-specific alignments. It is designed to accommodate hundreds of samples typically seen in clinical datasets. Novel event filters remove low-confidence measurements from analysis, enhancing accuracy over existing methods. Group-averaged strand-specific sequencing coverage plots enable clear visualization of group differences in alternative splicing, using a new file format with demonstrable performance improvements over the current BigWig standard. ompBAM, a C++ library upon which SpliceWiz is built, automates multi-threaded alignment file processing for R package developers. SpliceWiz is a powerful platform for diverse users to explore alternative splicing in large datasets.

bioinformatics↗

bioRxiv2022

Towards resolution of the intron retention paradox in breast cancer

After many years of neglect in the field of alternative splicing, the importance of intron retention (IR) in cancer has come into focus following landmark discoveries of aberrant IR patterns in cancer. Many solid and liquid tumours are associated with drastic increases in IR and such patterns have been pursued as both biomarkers and therapeutic targets. Paradoxically, breast cancer (BrCa) is the only tumour type in which IR is reduced compared to adjacent normal breast tissue. In this study, we have conducted a pan-cancer analysis of IR with emphasis on BrCa and its subtypes. We explored mechanisms that could cause aberrant and pathological IR and clarified why normal breast tissue has unusually high IR. Strikingly, we found that reduced IR in BrCa can be largely attributed to normal breast tissue having the highest occurrence of IR events compared to other healthy tissues. Our analyses suggest that low numbers of IR events in breast tumours are associated with poor prognosis, particularly in the luminal B subtype. Interestingly, we found that IR frequencies negatively correlate with cell proliferation in BrCa cells, i.e. rapidly dividing tumour cells have the lowest number of IR events. Aberrant RNA binding protein (RBP) expression and changes in tissue composition are among the causes of low IR in BrCa. Our results suggest that IR should be considered for therapeutic manipulation in BrCa patients with aberrantly low IR levels and that further work is needed to understand the cause and impact of high IR in other tumour types.

cancer biology↗

bioRxiv2021

PPAR-delta acts as a metabolic master checkpoint for metastasis in pancreatic cancer

SO_SCPLOWUMMARYC_SCPLOWIn pancreatic cancer, emerging evidence suggests that PPAR-{delta} overexpression is associated with tumor progression and metastasis, but a mechanistic link is still missing. Here we now show that PPAR-{delta} acts as the integrating upstream regulator for the metabolic rewiring, which is preceding the subsequent initiation of an invasive/metastatic program. Specifically, paracrine and metabolic cues regularly found in the metastasis-promoting tumor stroma consistently enhance, via induction of PPAR-{delta} activity, the glycolytic capacity and reserve of pancreatic cancer cells, respectively, accompanied by decreased mitochondrial oxygen consumption. Consequently, genetic or pharmacological inhibition of PPAR-{delta} results in reduced invasiveness and metastasis. Mechanistically, PPAR-{delta} acts by shifting the MYC/PGC1A balance towards MYC, enhancing metabolic plasticity. Targeting MYC similarly prevents the metabolic switch and subsequent initiation of invasiveness. Therefore, our data demonstrate that PPAR-{delta} is a key initiator for the metabolic reprogramming in pancreatic cancer, thereby acting as a checkpoint for the phenotypic change towards invasiveness. These findings provide compelling evidence for a novel treatment strategy to combat pancreatic cancer progression.

cancer biology↗

bioRxiv2021

Chromatin accessibility regulates intron retention in a cell type-specific manner

Dynamic intron retention (IR) in vertebrate cells is of widespread biological importance. Aberrant IR is associated with numerous human diseases including cancer. Despite consistent reports demonstrating intrinsic sequence features that predispose introns to become retained, conflicting findings about cell type-specific IR regulation demand a systematic analysis in a controlled experimental setting. We integrated matched transcriptomics and epigenetics data (including DNA methylation, nucleosome occupancy, histone modifications) from primary human myeloid and lymphoid cells. Using machine learning we trained two complementary models to determine the role of epigenetic factors in the regulation of IR. We show that increased chromatin accessibility contributes substantially to the retention of introns in a cell-specific manner. We also confirm that intrinsic characteristics of introns are key for them to evade splicing. With mounting reports linking pathogenic alterations to RNA processing, our findings may have profound implications for the design of therapeutic approaches targeting aberrant splicing.

bioinformatics↗

bioRxiv2021

Somatic mutations in CTCF zinc fingers produce cellular phenotypes explained by structure-function relationships

BackgroundHuman cancers commonly contain mutations in transcription factors that lead to aberrant DNA binding or altered effector function at target sites. One such factor significantly mutated in cancer is the evolutionarily-conserved CCCTC-binding factor (CTCF), which has fundamental roles in maintaining chromatin architecture and transcriptional regulation. Numerous cancer genome sequencing and functional studies have revealed CTCFs role as a haploinsufficient tumour suppressor gene. However, to date, structure-function relationships of somatic CTCF mutations have not been examined. MethodsWe collated somatic CTCF mutations from cancer genome portals and published studies to determine their nature, frequency, distribution and potential functional impact. We undertook an in-depth examination of 5 CTCF missense zinc finger (ZF) mutations occurring within key intra- and inter-ZF residues. We performed functional analyses including cell growth, colony-formation, chromatin immunoprecipitation and transcriptional reporter assays. Based on their homology, each ZF mutation was then modelled on CTCFs ZF domain crystal structure and its structural impact analysed using molecular dynamics simulations. ResultsWe observed an enrichment of somatic missense mutations occurring in the ZF region of CTCF, compared to the unstructured N- and C-termini. Functional characterisation of CTCF ZF mutations revealed a complete (L309P, R339W, R377H) or intermediate (R339Q) abrogation as well as an enhancement (G420D) of the anti-proliferative effects of CTCF. DNA binding at select sites was disrupted and transcriptional regulatory activities abrogated. In silico mutagenesis revealed that L309P had the highest mutation energy and thus most severe impact on protein stability. Molecular docking and molecular dynamics simulations confirmed that mutations in residues specifically contacting DNA bases or backbone exhibited loss of DNA binding (R339Q, R339W, R377H). Remarkably, R339Q and G420D were stabilised by the formation of new primary DNA bonds. All mutations exhibited some loss or gain of bonds at neighbouring residues, often in adjacent zinc fingers. ConclusionsOur data confirm the significant negative impact haploinsufficient CTCF ZF mutations have on its tumour suppressor function. A spectrum of loss-, change- and gain-of-function impacts in CTCF zinc fingers are observed in cell growth regulation and gene regulatory activities. We have established that diverse cellular phenotypes in CTCF are explained by examining structure-function relationships.

cancer biology↗

bioRxiv2019

Ctcf Haploinsufficiency Mediates Intron Retention in A Tissue-specific Manner

CTCF is a master regulator of gene transcription and chromatin organization with occupancy at thousands of DNA target sites. CTCF is essential for embryonic development and somatic cell viability and has been characterized as a haploinsufficient tumor suppressor. Increasing evidence demonstrates CTCF as a key player in several alternative splicing (AS) regulatory mechanisms, including transcription elongation, regulation of splicing factors, and epigenetic regulation. However, the genome-wide impact of Ctcf dosage on AS has not been investigated. We examined the effect of Ctcf haploinsufficiency on gene expression and AS in multiple tissues from Ctcf hemizygous (Ctcf+/-) mice. Distinct tissue-specific differences in gene expression and AS were observed in Ctcf+/- mice compared to wildtype mice. We observed a surprisingly large number of increased intron retention (IR) events in Ctcf+/- liver and kidney, specifically in genes associated with cytoskeletal organization, splicing and metabolism. This study provides further evidence for Ctcf dose-dependent and tissue-specific regulation of gene expression and AS. Our data provide a strong foundation for elucidating the mechanistic role of CTCF in AS regulation and its biological consequences.

molecular biology↗