The disturbance of intercellular communication is one of the hallmarks of aging. The goal of this study is to clarify the impact of chronological aging on extracellular vesicles (EVs), a key mode of communication in mammalian tissues. We focused on epidermal keratinocytes, the main cells of the outer protective layer of the skin which is strongly impaired in the skin of elderly. EVs were purified from conditioned medium of primary keratinocytes isolated from infant or aged adult skin. A significant increase of the relative number of EVs released from aged keratinocytes was observed whereas their size distribution was not modified. By small RNA sequencing, we described a specific microRNA (miRNA) signature of aged EVs with an increase abundance of miR-30a, a key regulator of barrier function in human epidermis. EVs from aged keratinocytes were found to be able to reduce the proliferation of young keratinocytes, to impact their organogenesis properties in a reconstructed epidermis model and to slow down the early steps of skin wound healing in mice, three features observed in aged epidermis. This work reveals that intercellular communication mediated by EVs is modulated during aging process in keratinocytes and might be involved in the functional defects observed in aged skin.
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http://dx.doi.org/10.18632/aging.205245 | DOI Listing |
Neuron
January 2025
Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel. Electronic address:
The contemporary understanding that the immune response significantly supports higher brain functions has emphasized the notion that the brain's condition is linked in a complex manner to the state of the immune system. It is therefore not surprising that immunity is a key factor in shaping brain aging. In this perspective article, we propose amending the Latin phrase "mens sana in corpore sano" ("a healthy mind in a healthy body") to "a healthy mind in a healthy immune system.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.
Background: While some memory decline in old age is "normal", there are some older individuals with maintained high cognitive performance. Using a multimodal approach including neuroimaging, fitness, genetic and questionnaire data (Figure 1A), we aimed to identify factors that are related to successful cognitive aging and whether these differ between sexes.
Method: We analyzed 165 cognitively normal older adults age ≥ 60 years from an ongoing study (SFB1436) (age=71±8years, 43% female).
Alzheimers Dement
December 2024
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
Background: There is increasing need for noninvasive biomarkers of Alzheimer's Disease (AD) neuropathologic change for early detection and intervention through risk-factor modification and disease-modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discriminate between observed biological age from chronological age for healthy aging. Deviation of true age from predicted age has been associated with heart failure, hypertension, and coronary heart disease.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Centre for Brain Research (CBR), Indian Institute of Science, Bengaluru, Karnataka, India.
Background: Data-driven methods, particularly deep learning, are transforming neuroimaging by accurately estimating Brain Age using diverse modalities. Discrepan- cies between predicted and actual age unveil potential health risks. Utilizing a training set of healthy subjects, a regression algorithm correlates brain features to age, allowing inference for unseen patients.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Background: Brain age (BA) prediction models have emerged as valuable tools for understanding individual differences in trajectories of brain aging. These models aim to estimate overall brain health by predicting BA based on structural MRI data. To enhance the specificity of existing BA models, we introduce a deep learning-based BA prediction model.
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