bioRxiv Science⌕ Search

Biology subjects

Mooijaart, S. P.

Publications and source records attributed to Mooijaart, S. P..

2 recordsLinked to original sources

Ageing of the Upper Airway Epithelial Niche Limits Tissue-Resident T Cell Immunity

Respiratory tract infections are a major cause of morbidity and mortality among older adults worldwide. Yet, how ageing shapes protective immunity within the upper respiratory tract (URT) remains poorly understood. Here, we profiled the nasal immune landscape of young adults and older adults with and without frailty, using high-dimensional cytometry, proteomics, single-cell transcriptomics and T cell receptor (TCR) sequencing. Ageing was associated with a selective reduction of tissue-resident memory T (Trm) cells, independent of effector T cell numbers, inflammageing levels or frailty and was accompanied by a decline in T cell receptor repertoire stability. Trm cells from older adults also showed decreased steady-state IFN{gamma} expression, accompanied by reduced antiviral transcriptional programs in the mucosa. Mechanistically, in vitro PBMC-epithelium co-culture models revealed that older adults have a reduced capacity for Trm differentiation, which was associated with a diminished epithelial TGF-{beta} secretion, driven by reduced expression in ciliated epithelial cells. Together, these findings reveal that URT immunity deteriorates with age due to disrupted epithelial-immune crosstalk. This identifies the epithelial niche as a key regulator of mucosal immune ageing and suggests that enhancing TGF{beta} signaling may improve the effectiveness of mucosal airway vaccination strategies in older populations.

immunology↗

Predicting biological age based on the BBMRI-NL 1H-NMR metabolomics repository

The blood metabolome incorporates cues from the environment as well as the hosts genetic background, potentially offering a holistic view of an individuals health status. We have compiled a vast resource of 1H-NMR metabolomics and phenotypic data encompassing over 25,000 samples derived from 26 community and hospital-based cohorts. Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individuals biological age. Exploration in independent cohorts demonstrates that being judged older by ones metabolome, as compared to ones chronological age, confers an increased risk on future cardiovascular disease, mortality and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data. In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardio-metabolic health.

epidemiology↗