npj aging最新文献

During prostate aging collagen-IV is modified by advanced glycation end-products, inducing crosslinking of the basement membrane surrounding glandular acini. Basement membrane stiffness sensed by Endo180 disrupts its suppressor complex with CD147, triggering epithelial-to-mesenchymal transition and limiting survival in prostate cancer patients. Here we report basement membrane stiffness and Endo180 cooperate in rewiring epithelia for mitochondrial oxidative phosphorylation (OXPHOS) and growth suppressor evasion, cell proliferation promotion, cell death resistance, inflammation, invasion and metastasis without affecting genome instability, highlighting a non-oncogenic biomechanical event in age-related neoplasia. Endo180-CD147 complex coupled to OXPHOS in Gleason 6 and in Gleason ≥7 tumors Endo180 uncoupled from CD147-OXPHOS, identifying a bio-switch for invasive cancer. Endo180 correlated with patient age in normal tissue, Gleason 6 and 7, but not Gleason 8 tumors, suggesting biological age unleashes its suppression of tumorigenesis. Application of Endo180-based diagnostics in age-related glandular cancers could inform treatment decisions, improving quality of life and survival.

In this pilot study, a subset of CALERIE Phase 2 (No. NCT00427193, registered 25th Jan 2007) participants (n = 26) were evaluated for the effects of 2 years of 25% calorie restriction (CR) on N-glycosylation of IgG, plasma, and complement C3, as well as IgG-based biological age (GlycAge). Plasma samples were collected at baseline (BL), 12 (12mo), and 24 months (24mo). IgG galactosylation was higher at 24mo compared to BL (p = 0.051) and increased from 12mo to 24mo (p = 0.016); GlycAge decreased over the same period (p = 0.027). GlycAge was positively associated with TNF-α (p = 0.030) and ICAM-1 (p = 0.017). Between BL and 24mo, plasma high-branched glycans declined (p = 0.013), bisecting GlcNAcs increased in both plasma (p < 0.001) and IgG (p = 0.01), complement C3 protein (p < 0.001), C3-Man9 (p < 0.001), and C3-Man9Glc1C3 (p = 0.046) were reduced. The absence of a control group warrants cautious interpretation.

Physiological brain aging is associated with cognitive impairment and neuroanatomical changes. Brain age prediction of routine clinical 2D brain MRI scans were understudied and often unsuccessful. We developed a novel brain age prediction framework for clinical 2D T1-weighted MRI scans using a deep learning-based model trained with research grade 3D MRI scans mostly from publicly available datasets (N = 8681; age = 51.76 ± 21.74). Our model showed accurate and fast brain age prediction on clinical 2D MRI scans from cognitively unimpaired (CU) subjects (N = 175) with MAE of 2.73 years after age bias correction (Pearson's r = 0.918). Brain age gap of Alzheimer's disease (AD) subjects was significantly greater than CU subjects (p < 0.001) and increase in brain age gap was associated with disease progression in both AD (p < 0.05) and Parkinson's disease (p < 0.01). Our framework can be extended to other MRI modalities and potentially applied to routine clinical examinations, enabling early detection of structural anomalies and improve patient outcome.

Mosaic loss of chromosome Y (mLOY) is the most common somatic mutation in hematopoietic cells of aging men and is linked to cancer risk and mortality. However, its relationship with treatment modalities remains unclear. In 348 prostate cancer patients at Juntendo University Hospital, local radiotherapy was associated with a higher prevalence of mLOY (OR = 2.55, 95% CI: 1.08-6.50, P = 0.04), whereas surgery and endocrine therapy were not. We then examined BioBank Japan data from over 30,000 patients with prostate, lung, colorectal, and gastric cancers. Fixed-effect meta-analysis across these sites showed a significant association between radiotherapy and mLOY (OR = 1.48, 95% CI: 1.11-1.98, P = 0.01). No significant effect heterogeneity was observed across cancer types (Q = 0.36, I² = 0%, P = 0.95). Our findings suggest that radiotherapy may exacerbate genomic instability, indicating a potential vulnerability in certain cancer patients to DNA damage induced by radiation therapy.

Natural language processing (NLP) can expand the utility of clinical records data in dementia research. We deployed NLP algorithms to detect core features of dementia with Lewy bodies (DLB) and applied those to a large database of patients diagnosed with dementia in Alzheimer's disease (AD) or DLB. Of 14,329 patients identified, 4.3% had a diagnosis of DLB and 95.7% of dementia in AD. All core features were significantly commoner in DLB than in dementia in AD, although 18.7% of patients with dementia in AD had two or more DLB core features. In conclusion, NLP applications can identify core features of DLB in routinely collected data. Nearly one in five patients with dementia in AD have two or more DLB core features and potentially qualify for a diagnosis of probable DLB. NLP may be helpful to identify patients who may fulfil criteria for DLB but have not yet been diagnosed.

Given the growing global elderly population and the accelerating decrease in grey matter volume (GMV) with age, understanding healthy brain aging is increasingly important. This study investigates whether variations in modifiable traits can account for differences in GMV and whether these traits can inform strategies to mitigate risks of future brain disorders. We identified 66 traits significantly associated with total GMV. Further, we examined the joint contributions of different domain traits to the GMV variance, finding that blood biomarkers and physical measurements accounted for the largest proportion of GMV variance. Some traits mediated the relationship between the genetic risk for brain disorders and GMV. Moreover, the identified traits divided the population into two subgroups, with significant differences in GMV and incidences of brain disorders. Our findings underscore the importance of modifiable traits in supporting healthy brain aging and reducing the risk of brain disorders, suggesting potential targets for intervention.

The utilization of artificial intelligence in studying the dysregulation of gene expression in Alzheimer's disease (AD) affected brain tissues remains underexplored, particularly in delineating common and specific transcriptomic signatures across different brain regions implicated in AD-related cellular and molecular processes, which could help illuminate novel disease biology for biomarker and target discovery. Herein we developed a deep learning framework, which consisted of multi-layer perceptron (MLP) models to classify neuropathologically confirmed AD versus controls, using bulk tissue RNA-seq data from the RNAseq Harmonization Study of the Accelerating Medicines Project for Alzheimer's Disease (AMP-AD) consortium. The models were trained based on data from three distinct brain regions, including dorsolateral prefrontal cortex (DLPFC), posterior cingulate cortex (PCC), and head of the caudate nucleus (HCN), obtained from the Religious Orders Study/Memory and Aging Project (ROSMAP). Subsequently, we inferred a disease progression trajectory for each brain region by applying unsupervised dimensionality transformation to the distribution of the subjects' expression profiles. To interpret the MLP models, we employed an interpretable method for deep neural network models, obtaining SHapley Additive exPlanations (SHAP) values and identified the most significantly AD-implicated genes for gene co-expression network analysis. Our models demonstrated robust performance in classification and prediction across two other external datasets from the Mayo RNA-seq (MAYO) cohort and the Mount Sinai Brain Bank (MSBB) cohort of AMP-AD. By interpreting the models both mechanistically and biologically, our study elucidated subtle molecular alterations in various brain regions, uncovering shared transcriptomic signatures activated in microglia and sex-specific modules in neurons relevant to AD. Notably, we identified, for the first time, a sex-linked transcription factor pair (ZFX/ZFY) associated with more pronounced neuronal loss in AD females, shedding light on a novel mechanism for sex dimorphism in AD. This study lays the groundwork for leveraging artificial intelligence methodologies to investigate AD at the molecular level, which is not readily achievable from conventional analysis approaches such as differential gene expression (DGE) analysis. The transcription factor implicated in sex difference also underpins a new molecular mechanistic basis of women's greater neurodegeneration in AD warranting further study.

The pursuit of understanding early genetic or protein markers for ovarian aging has garnered considerable attention in the realm of reproductive medicine. Sirtuins (SIRTs) are a group of proteins that are NAD⁺-dependent, and thanks to their properties, they are able to change the acetylation profile of proteins and post-translationally modify their functions, too. Previous research provided evidence that SIRTs influence fibrosis levels in several organs. With regard to ovaries, fibrosis is one of the features of aged ovaries and also creates a metastasis-friendly environment, thus can also be a seedbed for the development of primary cancerous lesions. Ovarian cancer remains a formidable challenge in oncology due to its high prevalence, insidious onset, and frequent recurrence. Noteworthy, ovarian cancer is the seventh most common cancer among women and the eighth leading cause of cancer death worldwide. Ovarian fibrosis runs concurrently with the activation of TGF-β/Smads signaling, as well as inflammasome (NLRP3), nuclear factor kB (NFkB) and forkhead box O (FOXO) attenuation. Reduced levels of certain sirtuins resulting from decreased nicotinamide adenine dinucleotide (NAD + ) may underlie the dysregulation of the aforementioned signaling pathways and therefore represent a potential therapeutic target. This review elucidates the role of SIRTs in ovarian aging-related fibrosis as a process that predisposes to tumorigenesis.

The Rab3 protein family is composed of a series of small GTP-binding proteins, including Rab3a, Rab3b, Rab3c, and Rab3d, termed Rab3s. They play crucial roles in health, including in brain function, such as through the regulation of synaptic transmission and neuronal activities. In the high-energy-demanding and high-traffic neurons, the Rab3s regulate essential cellular processes, including trafficking of synaptic vesicles and lysosomal positioning, which are pivotal for the maintenance of synaptic integrity and neuronal physiology. Emerging findings suggest that alterations in Rab3s expression are associated with age-related neurodegenerative pathologies, including Alzheimer's disease, Parkinson's disease, and Huntington's disease, among others. Here, we provide an overview of how Rab3s dysregulation disrupts neuronal homeostasis, contributing to impaired autophagy, synaptic dysfunction, and eventually leading to neuronal death. We highlight emerging questions on how Rab3s safeguards the brain and how their dysfunction contributes to the different neurodegenerative diseases. We propose fine-tuning the Rab3s signaling directly or indirectly, such as via targeting their upstream protein AMPK, holding therapeutic potential.

African turquoise killifish (Nothobranchius furzeri) is the shortest-lived vertebrate that can be bred in captivity, making it an ideal model organism for aging studies. However, whether the animal can be used for studying cardiac aging and whether cellular senescence contribute to this ageing process remain unclear. Here, we conducted a longitudinal study on the GRZ strain, aiming to identify phenotypic and functional markers for cardiac aging. We found that cardiac ageing in GRZ fish can be measured by comparing fish at 16 weeks to 8 weeks of age, using systemic markers such as body/fin coloration, body weight, BMI, cardiac ageing markers such as EF, E/A ratio, and swimming capacity, and cellular senescence markers such as SA-β-gal staining, p15/p16, γ-H2A.X, and SASP markers. Senolytic treatment with D (Dasatinib) and Q (Quercetin) from 12 to 16 weeks mitigated senescence and decelerated cardiac ageing. Together, our findings established GRZ as a useful vertebrate model for studying cardiac ageing and related cardiac senescence.