Remarkable progress in ageing research has been achieved over the past decades. General perceptions and experimental evidence pinpoint that the decline of physical function often initiates by cell senescence and organ ageing. Epigenetic dynamics and immunometabolic reprogramming link to the alterations of cellular response to intrinsic and extrinsic stimuli, representing current hotspots as they not only (re-)shape the individual cell identity, but also involve in cell fate decision. This review focuses on the present findings and emerging concepts in epigenetic, inflammatory, and metabolic regulations and the consequences of the ageing process. Potential therapeutic interventions targeting cell senescence and regulatory mechanisms, using state-of-the-art techniques are also discussed.

Objectives: Though type 2 diabetes (T2D) has been known as a metabolic disease caused by multiple factors, the etiology remains insufficiently understood. Here, we aimed to figure out whether circulating immune cell profiles causally impact T2D liability.Methods: We applied one genome-wide association study (GWAS) summary statistics of blood traits in 563,085 participants from the Blood Cell Consortium and another GWAS of flow cytometric profile of lymphocyte subsets comprising 3,757 Sardinians to identify genetically predicted blood immune cells. We also obtained GWAS summary statistics in 898,130 individuals from the DIAGRAM Consortium to evaluate genetically predicted T2D. We primarily used inverse variance weighted (IVW) and weighted median methods to perform Mendelian randomization analyses and sensitivity analyses to evaluate heterogeneity and pleiotropy.Results: For circulating blood leukocyte and its subpopulations, the increase of genetically predicted circulating monocyte count was causally correlated with a higher risk of T2D [odds ratio (OR) = 1.06, 95% confidence interval (CI) = 1.02–1.10, p = 0.0048]. For lymphocyte subsets, CD8⁺ T cell and CD4⁺ CD8^dim T cell count were identified with causal effect on T2D susceptibility (CD8⁺ T cell: OR = 1.09, 95% CI = 1.03–1.17, p = 0.0053; CD4⁺ CD8^dim T cell: OR = 1.04, 95% CI = 1.01–1.08, p = 0.0070). No pleiotropy was determined.Conclusions: These findings demonstrated that higher circulating monocyte and T-lymphocyte subpopulation predicted increased T2D susceptibility, which confirmed the immunity predisposition for T2D. Our results may have the potential to provide new therapeutic targets for the diagnosis and treatment of T2D.

Haematopoietic stem cells (HSCs) possess unique physiological adaptations to sustain blood cell production and cope with stress responses throughout life. To maintain these adaptations, HSCs rely on maintaining a tightly controlled protein translation rate. However, the mechanism of how HSCs regulate protein translation remains to be fully elucidated. In this study, we investigate the role of transfer RNA (tRNA) m¹A58 ‘writer’ proteins TRMT6 and TRMT61A in regulating HSCs function. Trmt6 deletion promoted HSC proliferation through aberrant activation of mTORC1 signaling. TRMT6-deficient HSCs exhibited an impaired self-renewal ability in competitive transplantation assay. Mechanistically, single cell RNA-seq analysis reveals that the mTORC1 signaling pathway is highly upregulated in HSC-enriched cell populations after Trmt6 deletion. m¹A-tRNA-seq and Western blot analysis suggest that TRMT6 promotes methylation modification of specific tRNA and expression of TSC1, fine-tuning mTORC1 signaling levels. Furthermore, Pharmacological inhibition of the mTORC1 pathway rescued functional defect in TRMT6-deficient HSCs. To our knowledge, this study is the first to elucidate a mechanism by which TRMT6-TRMT61A complex-mediated tRNA-m¹A58 modification regulates HSC homeostasis.

Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually annotated with clean labels. However, given the high cost of such manual annotation, new medical imaging classification problems may need to rely on machine-generated noisy labels extracted from radiology reports. Indeed, many Chest X-Ray (CXR) classifiers have been modelled from datasets with noisy labels, but their training procedure is in general not robust to noisy-label samples, leading to sub-optimal models. Furthermore, CXR datasets are mostly multi-label, so current multi-class noisy-label learning methods cannot be easily adapted. In this paper, we propose a new method designed for noisy multi-label CXR learning, which detects and smoothly re-labels noisy samples from the dataset to be used in the training of common multi-label classifiers. The proposed method optimises a bag of multi-label descriptors (BoMD) to promote their similarity with the semantic descriptors produced by language models from multi-label image annotations. Our experiments on noisy multi-label training sets and clean testing sets show that our model has state-of-the-art accuracy and robustness in many CXR multi-label classification benchmarks, including a new benchmark that we propose to systematically assess noisy multi-label methods. Code is available at https://github.com/cyh-0/BoMD.