Biology subjects

Stark, A.-K.

Publications and source records attributed to Stark, A.-K..

3 recordsLinked to original sources

bioRxiv2023

Single cell DNA methylation ageing in mouse blood

Ageing is the accumulation of changes and overall decline of the function of cells, organs and organisms over time. At the molecular and cellular level, the concept of biological age has been established and biomarkers of biological age have been identified, notably epigenetic DNA-methylation based clocks. With the emergence of single-cell DNA methylation profiling methods, the possibility to study biological age of individual cells has been proposed, and a first proof-of-concept study, based on limited single cell datasets mostly from early developmental origin, indicated the feasibility and relevance of this approach to better understand organismal changes and cellular ageing heterogeneity. Here we generated a large single-cell DNA methylation and matched transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages (10-101 weeks of age). We observed that the number of genes expressed increased at older ages, but gene specific changes were small. We next developed a robust single cell DNA methylation age predictor (scEpiAge), which can accurately predict age in a broad range of publicly available datasets, including very sparse data and it also predicts age in single cells. Interestingly, the DNA methylation age distribution is wider than technically expected in 19% of single cells, suggesting that epigenetic age heterogeneity is present in vivo and may relate to functional differences between cells. In addition, we observe differences in epigenetic ageing between the major blood cell types. Our work provides a foundation for better single-cell and sparse data epigenetic age predictors and highlights the significance of cellular heterogeneity during ageing. Highlights- Model to estimate DNA methylation age in single cells - Large multi-omics dataset of single cells from murine blood - Epigenetic age deviations from chronological age are greater than technical expected from technical variability - Number of genes expressed increases with chronological and epigenetic age

cell biology↗

bioRxiv2018

PI3Kδ hyper-activation promotes the development of B cells that exacerbate Streptococcus pneumoniae infection in an antibody-independent manner

Streptococcus pneumoniae is a major cause of pneumonia and a leading cause of death world-wide. Antibody-mediated immune responses can offer protection against repeated exposure to S. pneumoniae, yet vaccines only offer partial protection. Patients with Activated PI3K{delta} Syndrome (APDS) are highly susceptible to S. pneumoniae. We generated a conditional knockin mouse model of this disease and identified a CD19+B220- B cell subset that is induced by PI3K{delta} signaling, is resident in the lungs, and which promotes increased susceptibility to S. pneumoniae during the early phase of infection via an antibody-independent mechanism. We show that an inhaled PI3K{delta} inhibitor improves survival rates following S. pneumoniae infection in wild-type mice and in mice with activated PI3K{delta}. These results suggest that a subset of B cells in the lung can promote the severity of S. pneumoniae infection, representing a novel therapeutic target.

immunology↗

bioRxiv2017

Multi-Tissue DNA Methylation Age Predictor In Mouse

BackgroundDNA-methylation changes at a discrete set of sites in the human genome are predictive of chronological and biological age. However, it is not known whether these changes are causative or a consequence of an underlying ageing process. It has also not been shown whether this epigenetic clock is unique to humans or conserved in the more experimentally tractable mouse.\n\nResultsWe have generated a comprehensive set of genome-scale base-resolution methylation maps from multiple mouse tissues spanning a wide range of ages. Many CpG sites show significant tissue-independent correlations with age and allowed us to develop a multi-tissue predictor of age in the mouse. Our model, which estimates age based on DNA methylation at 329 unique CpG sites, has a median absolute error of 3.33 weeks, and has similar properties to the recently described human epigenetic clock. Using publicly available datasets, we find that the mouse clock is accurate enough to measure effects on biological age, including in the context of interventions. While females and males show no significant differences in predicted DNA methylation age, ovariectomy results in significant age acceleration in females. Furthermore, we identify significant differences in age-acceleration dependent on the lipid content of the offspring diet.\n\nConclusionsHere we identify and characterize an epigenetic predictor of age in mice, the mouse epigenetic clock. This clock will be instrumental for understanding the biology of ageing and will allow modulation of its ticking rate and resetting the clock in vivo to study the impact on biological age.

genomics↗