Pharmacological interventions in human aging

Pharmacological interventions are emerging as potential avenues of alleviating age-related disease. However, the knowledge of ongoing clinical trials as they relate to aging and pharmacological interventions is dispersed across a variety of mediums. In this review we summarize 136 age-related clinical trials that have been completed or are ongoing. Furthermore, we establish a database that describe the trials (AgingDB, www.agingdb.com) keeping track of the previous and ongoing clinical trials, alongside their outcomes. The aim of this review and database is to give people the ability to easily query for their trial of interest and stay up to date on the latest results. In sum, herein we give an overview of the current pharmacological strategies that have been applied to target human aging.

Nuclear morphology is a deep learning biomarker of cellular senescence

Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack of exclusive or universal markers. Using neural networks, we predict senescence from the nuclear morphology of human fibroblasts with up to 95% accuracy, and investigate murine astrocytes, murine neurons, and fibroblasts with premature aging in culture. After generalizing our approach, the predictor recognizes higher rates of senescence in p21-positive and ethynyl-2'-deoxyuridine (EdU)-negative nuclei in tissues and shows an increasing rate of senescent cells with age in H&E-stained murine liver tissue and human dermal biopsies. Evaluating medical records reveals that higher rates of senescent cells correspond to decreased rates of malignant neoplasms and increased rates of osteoporosis, osteoarthritis, hypertension and cerebral infarction. In sum, we show that morphological alterations of the nucleus can serve as a deep learning predictor of senescence that is applicable across tissues and species and is associated with health outcomes in humans.

New methodologies in ageing research

Ageing is arguably the most complex phenotype that occurs in humans. To understand and treat ageing as well as associated diseases, highly specialised technologies are emerging that reveal critical insight into the underlying mechanisms and provide new hope for previously untreated diseases. Herein, we describe the latest developments in cutting edge technologies applied across the field of ageing research. We cover emerging model organisms, high-throughput methodologies and machine-driven approaches. In all, this review will give you a glimpse of what will be pushing the field onwards and upwards.

Lamin A/C promotes DNA base excision repair

The A-type lamins (lamin A/C), encoded by the LMNA gene, are important structural components of the nuclear lamina. LMNA mutations lead to degenerative disorders known as laminopathies, including the premature aging disease Hutchinson-Gilford progeria syndrome. In addition, altered lamin A/C expression is found in various cancers. Reports indicate that lamin A/C plays a role in DNA double strand break repair, but a role in DNA base excision repair (BER) has not been described. We provide evidence for reduced BER efficiency in lamin A/C-depleted cells (Lmna null MEFs and lamin A/C-knockdown U2OS). The mechanism involves impairment of the APE1 and POLβ BER activities, partly effectuated by associated reduction in poly-ADP-ribose chain formation. Also, Lmna null MEFs displayed reduced expression of several core BER enzymes (PARP1, LIG3 and POLβ). Absence of Lmna led to accumulation of 8-oxoguanine (8-oxoG) lesions, and to an increased frequency of substitution mutations induced by chronic oxidative stress including GC>TA transversions (a fingerprint of 8-oxoG:A mismatches). Collectively, our results provide novel insights into the functional interplay between the nuclear lamina and cellular defenses against oxidative DNA damage, with implications for cancer and aging.

A defined human aging phenome

Aging is among the most complex phenotypes that occur in humans. Identifying the interplay between different age-associated features is undoubtedly critical to our understanding of aging and thus age-associated diseases. Nevertheless, what constitutes human aging is not well characterized. Towards this end, we mined millions of PubMed abstracts for age-associated terms, enabling us to generate a detailed description of the human aging phenotype. We discovered age-associated features in clusters that can be broadly associated with previously defined hallmarks of aging, consequently identifying areas where interventions could be pursued. Importantly, we validated the newly discovered features by manually verifying the prevalence of these features in combined cohorts describing 76 million individuals, allowing us to stratify features in aging that appear to be the most prominent. In conclusion, we propose a comprehensive landscape of human aging: the human aging phenome.