Biology subjects

Veith, T.

Publications and source records attributed to Veith, T..

9 recordsLinked to original sources

bioRxiv2026

Endogenous mutational mechanisms and metabolic context shape endometrial cancer

Uterine corpus endometrial carcinoma (UCEC) is a common gynecologic malignancy with rising mortality, yet its genome-wide mutational architecture remains incompletely understood. Here we analyze deep whole-genome sequencing data from 440 UCEC tumors from The Cancer Genome Atlas, integrated with transcriptomic and clinical data, to define subtype-specific mutational processes and genomic architectures. We uncover pronounced molecular subtype-specific differences in endogenous mutational mechanisms, retrotransposition activity, structural variation, and chromosomal instability. LINE-1 retrotransposition emerges as a key contributor to genome instability in UCEC, acting as a prominent source of structural variation in copy-number stable tumors and associating with chromothripsis and ecDNA-mediated oncogene amplification in copy-number high tumors. Mutational signature SBS28 contributes significantly to mutations in POLE-deficient tumors and is strongly correlated with SBS10a and SBS10b. Mismatch repair-deficient tumors exhibit a previously unrecognized doublet base substitution signature and a defining imbalance between ID2 and ID1 indel processes, reflecting pervasive DNA template-strand replication slippage and associated with increased tumor immunogenicity. Copy-number low tumors follow a distinct low-mutagenesis evolutionary trajectory characterized by reduced replication stress, low proliferative activity, genomic stability and enrichment of SBS18 associated with oxidative damage. Notably, we identify a consistent inverse association between body mass index and tumor mutational burden in UCEC, suggesting that host metabolic state may influence fundamental cellular processes governing mutation accumulation, promoting tumor development through non-mutagenic mechanisms rather than elevated genomic instability. Together, these findings establish a mutagenesis-centric framework for UCEC that links endogenous mutational mechanisms to tumor architecture and host metabolic context, uncovering previously unrecognized subtype-specific genomic features with important implications for refined risk stratification and therapeutic strategies.

genomics↗

bioRxiv2025

A global serosurvey of RNA virus-reactive antibodies in bats and rodents reveals the enzootic presence of flavi- and paramyxoviruses in African Pteropodidae

Bats and rodents have been identified as reservoir hosts of diverse RNA viruses that pose potential risks to humans. However, overall virus prevalence in wildlife and underlying factors determining the zoonotic potential of reservoir-borne viruses remain poorly characterized. Virus detection often relies on the identification of viral nucleotide sequences and may therefore be limited by variable virus concentration, tissue tropism, sample quality, and duration of infection. In this study, we applied a multiplex immunofluorescence assay to examine bat and rodent seroprevalence against 14 different human pathogenic RNA viruses spanning seven virus families (Coronaviridae, Flaviviridae, Hantaviridae, Paramyxoviridae, Phenuiviridae, Pneumoviridae, Togaviridae). We retrospectively analyzed 1,135 bat (16 species) and 454 rodent (15 species) blood or transudate specimens collected at 63 sites in seven countries. Tropical bats exhibited notably high rates of seropositivity against certain virus families (Flaviviridae: 42.8%; Paramyxoviridae: 60.4%; Togaviridae: 7.5%). Multivariable logistic regression models were created to evaluate the association of tropical bat characteristics with seropositivity. Preliminary findings indicate that large colony size, found among species such as Eidolon helvum and Rousettus aegyptiacus, is a risk factor for orthoflavi- and orthorubulavirus infection, consistent with the communal spread of bat-adapted viruses. Author SummaryMany animal populations carry viruses that have the potential to spill over to humans and cause severe disease. Despite surveillance efforts, it is logistically challenging to detect virus infections in wildlife, as viruses may have a short duration of infection or unknown routes of transmission. In this study, we examined blood and transudate samples from rodents and bats around the world for antibodies against 14 different viruses known to cause disease in humans. We analyzed the samples via a mosaic chip-based immunofluorescence test, which has the advantage of detecting antibodies against whole-virus antigen. This method allows for broad surveillance, as we would also expect to uncover evidence of past infection with bat- and rodent-adapted viruses that are related, but not identical, to known human pathogens. We detected antibodies against all seven virus families we examined, with especially high detection rates of antibodies in tropical bats. We used the results to construct multivariable logistic regression models of tropical bat seroprevalence to examine potential associations between tropical bat characteristics and previous infection by certain virus families. Bats with large colony sizes, such as Eidolon helvum and Rousettus aegyptiacus, were apparently at increased risk of infection with orthoflavi- and orthorubulaviruses compared to other tropical bat species.

microbiology↗

bioRxiv2025

CLONEID: A Framework for Monitoring and Steering Subclonal Dynamics

Molecular assays can resolve clonal structure, but they are expensive and typically sparse in time, whereas phenotypic observations such as imaging can be collected frequently but often are not preserved in the context needed for later interpretation. We present CLONEID, an event-based framework for organizing clone-resolved phenotypic, molecular, and specimen-context records so that genotype-to-phenotype interpretation can be maintained across time. CLONEID links time-stamped Events, assay-specific Perspectives, and reconciled Identities through structured ingestion, provenance-aware retrieval, and reproducible export, complementing upstream clone-calling methods. In a long-term gastric cancer density-selection experiment, CLONEID linked repeated culture events, growth measurements, and late karyotypic profiling within a shared record, supporting longitudinal interpretation of phenotypic adaptation together with underlying chromosomal state.

bioinformatics↗

bioRxiv2025

Inverse Game Theory characterizes Frequency-Dependent Selection Driven by Karyotypic Diversity in Triple Negative Breast Cancer

Chromosomal instability (CIN), characterized by pervasive copy number alterations (CNAs), significantly contributes to cancer progression and therapeutic resistance. CNAs drive intratumoral genetic heterogeneity, creating distinct subpopulations whose interactions shape tumor evolution through frequency-dependent selection. Here, we introduce, ECO-K (Ecological-Karyotypes), an inverse game theory framework that infers subpopulation interactions from longitudinal single-cell whole genome sequencing data. Applying this approach to serially-passaged, triple-negative breast cancer (TNBC) cell lines and patient-derived xenografts (PDXs), we systematically identified frequency-dependent selection dynamics governed by karyotypic diversity. Notably, in one PDX lineage, we found consistent evidence of karyotypically defined subpopulations acting as interaction hubs, associated specifically with chromosome 1 loss and chromosome 14p gain. Our framework provides testable predictions of intratumoral ecological dynamics, highlighting opportunities to strategically target key subpopulations to disrupt tumor evolution.

cancer biology↗

bioRxiv2023

Interactions between ploidy and resource availability shape clonal interference at initiation and recurrence of glioblastoma

Glioblastoma (GBM) is the most aggressive form of primary brain tumor. Complete surgical resection of GBM is almost impossible due to the infiltrative nature of the cancer. While no evidence for recent selection events have been found after diagnosis, the selective forces that govern gliomagenesis are strong, shaping the tumors cell composition during the initial progression to malignancy with late consequences for invasiveness and therapy response. We present a mathematical model that simulates the growth and invasion of a glioma, given its ploidy level and the nature of its brain tissue micro-environment (TME), and use it to make inferences about GBM initiation and response to standard-of-care treatment. We approximate the spatial distribution of resource access in the TME through integration of in-silico modelling, multi-omics data and image analysis of primary and recurrent GBM. In the pre-malignant setting, our in-silico results suggest that low ploidy cancer cells are more resistant to starvation-induced cell death. In the malignant setting, between first and second surgery, simulated tumors with different ploidy compositions progressed at different rates. Whether higher ploidy predicted fast recurrence, however, depended on the TME. Historical data supports this dependence on TME resources, as shown by a significant correlation between the median glucose uptake rates in human tissues and the median ploidy of cancer types that arise in the respective tissues (Spearman r = -0.70; P = 0.026). Taken together our findings suggest that availability of metabolic substrates in the TME drives different cell fate decisions for cancer cells with different ploidy and shapes GBM disease initiation and relapse characteristics.

cancer biology↗

bioRxiv2023

Mathematical modeling of clonal interference by density-dependent selection in heterogeneous cancer cell lines

Many cancer cell lines are aneuploid and heterogeneous, with multiple karyotypes co-existing within the same cell line. Karyotype heterogeneity has been shown to manifest phenotypically, affecting how cells respond to drugs or to minor differences in culture media. Knowing how to interpret karyotype heterogeneity phenotypically, would give insights into cellular phenotypes before they unfold temporally. Here we reanalyze single cell RNA (scRNA)- and scDNA sequencing data from eight stomach cancer cell lines by placing gene expression programs into a phenotypic context. We quantify differences in growth rate and contact inhibition between the eight cell lines using live-cell imaging, and use these differences to prioritize transcriptomic biomarkers of growth rate and carrying capacity. Using these biomarkers, we find significant differences in the predicted growth rate or carrying capacity between multiple karyotypes detected within the same cell line. We use these predictions to simulate how the clonal composition of a cell line will change depending on the timing of splitting cells. Once validated, these models can aid the design of experiments that steer evolution with density dependent selection.

systems biology↗

bioRxiv2022

Genetic variability, including gene duplication and deletion, in early sequences from the 2022 European monkeypox outbreak

Genome sequences from 47 monkeypox virus infections detected in a German university virology laboratory were analyzed in context of other sequences from the 2022 outbreak and earlier monkeypox genomes. Identical non-synonymous amino acid changes in six genes and the signature of APOBEC editing match other sequences from the European outbreak. Non-synonymous changes that were present in one to three sequences were found in 34 other genes. In sequences from two lesions of one patient, an 856 nucleotide translocation between genome termini resulted in the duplication of an initial 5 gene, and the disruption or complete deletion of four genes near the 3 genome end. Orthopoxvirus genome rearrangements of this nature are known to confer fitness advantages in the face of selection pressure. This change may therefore represent an early virus adaptation in the novel widespread and sustained human-to-human context of the current monkeypox outbreak.

bioinformatics↗

bioRxiv2021

Heterogeneity, turn-over rate and karyotype space shape susceptibility to missegregation-induced extinction

The phenotypic efficacy of somatic copy number alterations (SCNAs) stems from their incidence per base pair of the genome, which is orders of magnitudes greater than that of point mutations. One mitotic event stands out in its potential to significantly change a cells SCNA burden-a chromosome missegregation. We present a general deterministic framework for modeling chromosome missegregations and use it to evaluate the possibility of missegregation-induced population extinction (MIE). The model predicts critical curves that separate viable from non-viable populations as a function of their turnover- and missegregation rates. Missegregation- and turnover rates estimated from a PAN-cancer scRNA-seq dataset of 15,464 cells are then compared to predictions. The majority of tumors across all cancer types had missegregation- and turnover rates that were within viable regions of the parameter space. When a dependency of missegregation rate on karyotype was introduced, karyotypes associated with low missegregation rates acted as a stabilizing refuge, rendering MIE impossible unless turnover rates are exceedingly high. Intra-tumor heterogeneity, including heterogeneity in missegregation rates, increases as tumors progress, rendering MIE unlikely. Author SummaryWhen a cell missegregates a chromosome while dividing, the chance is high that its two daughter cells will behave drastically different from each other and from their parental cell. Chromosome missegregations are therefore one of the most powerful forces of phenotypic diversity. We developed a mathematical model of chromosome missegregations that allows for this cell-to-cell diversity to be accounted for. The model serves to help understand how selection acts upon cells with versatile chromosome contents, as a tool for genotype-to-phenotype mapping in various microenvironments. As a first application example we used the model to address whether there exists an upper limit on missegregation rate, beyond which cancer populations collapse. Chromosome missegregations are common. They occur in 1.2-2.3% per mitosis in normal cells [1] and in cancer cells their rate is between one and two orders of magnitudes higher [2]. The model revealed that the upper limit of missegregation rate is a function of the tumors turnover rate (i.e. how fast the tumor renews itself). In heterogenous populations however, cells with low missegregation rates protect the population from collapse. Intra-tumor heterogeneity, including heterogeneity in missegregation rates, increases as tumors progress, rendering missegregation-induced extinction unlikely.

cancer biology↗

bioRxiv2021

Post-entry, spike-dependent replication advantage of B.1.1.7 and B.1.617.2 over B.1 SARS-CoV-2 in an ACE2-deficient human lung cell line

Epidemiological data demonstrate that SARS-CoV-2 variants of concern (VOC) B.1.1.7 and B.1.617.2 are more transmissible and infections are associated with a higher mortality than non-VOC virus infections. Phenotypic properties underlying their enhanced spread in the human population remain unknown. B.1.1.7 virus isolates displayed inferior or equivalent spread in most cell lines and primary cells compared to an ancestral B.1 SARS-CoV-2, and were outcompeted by the latter. Lower infectivity and delayed entry kinetics of B.1.1.7 viruses were accompanied by inefficient proteolytic processing of spike. B.1.1.7 viruses failed to escape from neutralizing antibodies, but slightly dampened induction of innate immunity. The bronchial cell line NCI-H1299 supported 24- and 595-fold increased growth of B.1.1.7 and B.1.617.2 viruses, respectively, in the absence of detectable ACE2 expression and in a spike-determined fashion. Superior spread in NCI-H1299 cells suggests that VOCs employ a distinct set of cellular cofactors that may be unavailable in standard cell lines.

microbiology↗