Biology subjects

Kowald, A.

Publications and source records attributed to Kowald, A..

5 recordsLinked to original sources

bioRxiv2026

Inferring Accumulation Times of Mitochondrial DNA Deletion Mutants from Cross-Sectional Single-Cell Data: Methodological Framework and Validation

The accumulation of mitochondrial DNA (mtDNA) deletion mutants in post-mitotic cells is a hallmark of mammalian ageing and a key contributor to tissue decline in skeletal muscle and neurons. Although the occurrence of such deletions is well documented, the mechanisms by which they clonally expand to levels exceeding the threshold for respiratory chain dysfunction remain unresolved. A transcription-coupled replication model has recently been advanced, predicting that deletions affecting genes involved in a negative feedback mechanism gain a selective replication advantage. This model implies relatively short accumulation times for mutant takeover, a critical but experimentally inaccessible parameter since single-cell measurements are destructive. Here, we present a novel approach to infer such accumulation times from cross-sectional single-cell RNA sequencing (scRNAseq) data, exploiting the fact that mtDNA deletions are also reflected at the transcript level. To establish feasibility, we generated synthetic datasets using two stochastic models of the mitochondrial life cycle and used these as a gold standard. We then applied the Moran process, a classical stochastic model of birth-death dynamics, to calculate distributions of mutant accumulation times and to extract key parameters. The Moran model reproduced the distributions obtained from stochastic simulations with high fidelity, demonstrating robustness across different assumptions about mitochondrial regulation. By fitting the model to synthetic data, we successfully recovered the true values for key parameters like mutation probability, selection advantage, and the fraction of advantageous mutants. Our findings establish a methodological framework for estimating mtDNA mutant dynamics from single-cell transcriptomic data. This approach opens the way for application to large-scale datasets such as Tabula Muris Senis and Tabula Sapiens, offering new insights into the role of mtDNA deletions in ageing and age-related disease.

molecular biology↗

bioRxiv2026

Inferring Accumulation Times of Mitochondrial DNA Deletion Mutants from Cross-Sectional Single-Cell Data: Application to Tabula Muris Senis

Mitochondrial DNA (mtDNA) deletion mutants accumulate clonally in post-mitotic cells during ageing and can reach high intracellular fractions that impair oxidative phosphorylation. Despite extensive experimental documentation, the mechanisms underlying this accumulation and the timescales over which deletion mutants expand within individual cells remain poorly constrained. In a companion study, we developed a stochastic modelling framework based on a Moran birth-death process and showed that key parameters of mtDNA dynamics, mutation probability, selection advantage, and the fraction of advantageous mutations, can be inferred from cross-sectional single-cell RNA sequencing (scRNAseq) data. Here, we apply this framework to experimental scRNAseq data from the Tabula Muris Senis project, which provides a lifespan-resolved atlas of mouse tissues. We identify and quantify mtDNA deletions in limb muscle cells across multiple ages. Gene-resolved analyses reveal pronounced deletion hotspots associated with elevated heteroplasmy at ND5 and ND6, consistent with predictions that disruption of transcriptional feedback regulation confers a replication advantage. Conversely, deletions affecting ATP6 are associated with reduced heteroplasmy, suggesting an unexpected role of this gene in supporting mtDNA propagation. The distribution of unique deletion species per cell follows a Poisson distribution with a low mean, indicating limited intracellular mutant diversity, further constraining plausible accumulation mechanisms. Parameter fits yield selection advantage estimates that imply mean accumulation times of approximately 3 months from first occurrence to near-complete dominance. Our results support a model in which mtDNA deletion accumulation is driven by rare mutation events followed by rapid clonal expansion, and they demonstrate the potential of single-cell omics data to quantify fundamental parameters of mitochondrial ageing dynamics.

molecular biology↗

bioRxiv2025

Tentative evidence that aging is caused by a small number of interacting processes

Human life expectancy has increased dramatically over the past two centuries, marking a significant public health achievement. While some projections predict a future where median lifespans reach 100 years, others contend that further longevity will depend on breakthroughs targeting the biological processes of aging. Recent studies in mice have demonstrated that telomerase activation, achieved via gene therapy and transgenic approaches, can extend both median and maximum lifespans substantially without an accompanying increase in cancer risk. We analysed survival data from three such studies using the Gompertz mortality model and show that these interventions reduce the slope parameter, indicative of a slower aging rate, rather than merely lowering baseline mortality. This observation challenges traditional models that assume independent, additive damage accumulation, suggesting instead that aging is driven by a limited number of interdependent processes with significant cross-talk. Mathematical modelling indicates that only three to five processes with substantial cross-talk may account for the observed deceleration. Extrapolation using Swedish survivorship data further implies that a reduction in the aging rate, similar to that seen in mice, could elevate the median human lifespan from 85 to over 100 years. These findings provide a compelling framework for developing targeted anti-aging interventions and a new perspective on the modifiability of the aging process.

biochemistry↗

bioRxiv2021

Senolytics and the compression of late-life mortality

Senescent cells play an important role in mammalian ageing and in the etiology of age-related diseases. Treatment of mice with senolytics - drugs that selectively remove senescent cells - causes an extension of median lifespan but has little effect on maximum lifespan. Postponement of some mortality to later ages, without a corresponding increase in maximum mortality, can be termed compression of mortality. When we fit the standard Gompertz mortality model to the survival data following senolytic treatment, we find an increase in the slope parameter, commonly described as the actuarial ageing rate. These observations raise important questions about the actions of senolytic treatments and their effects on health and survival, which are not yet sufficiently understood. To explore how the survival data from senolytics experiments might be explained, we combine recent exploration of the evolutionary basis of cellular senescence with theoretical consideration of the molecular processes that might be involved. We perform numerical simulations of senescent cell accumulation and senolytic treatment in an ageing population. The simulations suggest that while senolytics diminish the burden of senescent cells, they may also impair the general repair capacity of the organism, leading to a faster accumulation post-treatment of new senescent cells. Our results suggest a framework to address the benefits and possible side effects of senolytic therapies, with the potential to aid the design of optimal treatment regimens.

biochemistry↗

bioRxiv2018

Pathway maps enable straightforward yet customized and semi-automated yet insightful analyses of omics data

To explore the molecular processes underlying some biological theme of interest based on public data, gene lists are used herein as input for the construction of annotated pathway maps, employing Cytoscape apps, and then high-throughput (\"omics\") gene expression data are overlaid onto these maps. Seeded with a published set of marker genes of the senescence-associated secretory phenotype and the genes of the cellular senescence KEGG pathway, a gene/protein interaction network and annotated clusters (a \"pathway map\") of cellular senescence are derived. The map can be amended, by adding some application-specific genes, and overlaid with gene expression data describing cellular senescence of fibroblasts and with disease-related gene expression data associated with prostate and pancreatic cancer, and with ischemic stroke, allowing insights into the role of cellular senescence in disease. Some gene expression data are derived from the \"Biomarker Benchmark repository\". The pathway map approach can be followed in principle for any biological theme of interest, fostering much-needed independence from the investigator-biased expert networks usually used for overlaying gene expression data.

genomics↗