Ageing is a complex and multifactorial process. For two decades, the Human Ageing Genomic Resources (HAGR) have aided researchers in the study of various aspects of ageing and its manipulation. Here we present the key features and recent enhancements of these resources, focusing on its six main databases. One database, GenAge, focuses on genes related to ageing, featuring 307 genes linked to human ageing and 2205 genes associated with longevity and ageing in model organisms. AnAge focuses on ageing, longevity, and life-history across animal species, containing data on 4645 species. DrugAge includes information about 1097 longevity drugs and compounds in model organisms such as mice, rats, flies, worms, and yeast. GenDR provides a list of 214 genes associated with the life-extending benefits of dietary restriction in model organisms. CellAge contains a catalogue of 866 genes associated with cellular senescence. The LongevityMap serves as a repository for genetic variants associated with human longevity, encompassing 3144 variants pertaining to 884 genes. Additionally, HAGR provides various tools as well as gene expression signatures of ageing, dietary restriction, and replicative senescence based on meta-analyses. Our databases are integrated, regularly updated, and manually curated by experts. HAGR is freely available online ( https://genomics.senescence.info/ ).

Naked mole-rats (NMRs) (Heterocephalus glaber) are long-lived mammals that possess a natural resistance to cancer and other age-related pathologies, maintaining a healthy life span >30 years. In this study, using immunohistochemical and RNA-sequencing analyses, we compare skin morphology, cellular composition, and global transcriptome signatures between young and aged (aged 3‒4 vs. 19‒23 years, respectively) NMRs. We show that similar to aging in human skin, aging in NMRs is accompanied by a decrease in epidermal thickness; keratinocyte proliferation; and a decline in the number of Merkel cells, T cells, antigen-presenting cells, and melanocytes. Similar to that in human skin aging, expression levels of dermal collagens are decreased, whereas matrix metalloproteinase 9 and matrix metalloproteinase 11 levels increased in aged versus in young NMR skin. RNA-sequencing analyses reveal that in contrast to human or mouse skin aging, the transcript levels of several longevity-associated (Igfbp3, Igf2bp3, Ing2) and tumor-suppressor (Btg2, Cdkn1a, Cdkn2c, Dnmt3a, Hic1, Socs3, Sfrp1, Sfrp5, Thbs1, Tsc1, Zfp36) genes are increased in aged NMR skin. Overall, these data suggest that specific features in the NMR skin aging transcriptome might contribute to the resistance of NMRs to spontaneous skin carcinogenesis and provide a platform for further investigations of NMRs as a model organism for studying the biology and disease resistance of human skin.

271 CellAge orthogroups. Directory containing FASTA files of CellAge orthogroups.

3 Patricia R. Pitrez, Luís Estronca, Luís Monteiro, Guillem Colell, Helena Vazão, Deolinda 4 Santinha, Karim Harhouri, Daniel Thornton, Claire Navarro, Anne-Laure Egesipe, Tânia 5 Carvalho, Rodrigo L. dos Santos, Nicolas Lévy, James C. Smith, João Pedro de Magalhães, 6 Alessandro Ori, Andreia Bernardo, Annachiara De Sandre-Giovannoli, Xavier Nissan, Anna 7 Rosell, Lino Ferreira* 8 9 Affiliations: 10 Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal 11 Faculty of Medicine, University of Coimbra, 3000-548, Coimbra, Portugal 12 Neurovascular Research Laboratory. Vall d’Hebron Research institute. Passeig Vall d’Hebron 11913 129, 08035, Barcelona, Spain 14 Molecular Genetics Laboratory, Department of Medical Genetics, La Timone Children’s hospital, 15 Marseille, France 16 Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of 17 Liverpool, Liverpool L7 8TX, United Kingdom 18 Aix Marseille Univ, INSERM, GMGF, Marseille, France 19 CECS, I-STEM, AFM, Institute for Stem Cell Therapy and Exploration of Monogenic diseases, 20 Evry cedex, France 21 IMM, Instituto de Medicina Molecular, Universidade de Lisboa, Lisbon, Portugal 22 Cambridge Science Park, Mogrify Ltd, 25 Milton Road, Cambridge, CB4 0FW, UK 23 Developmental Biology Laboratory, The Francis Crick Institute, London, NW1 1AT UK 24 Leibniz Institute on Aging-Fritz Lipmann Institute, 07745 Jena, Germany 25

Hutchinson-Gilford Progeria Syndrome (HGPS) is a premature aging disease in children that leads to early death. Smooth muscle cells (SMCs) are the most affected cells in HGPS individuals, although the reason for such vulnerability remains poorly understood. In this work, we develop a microfluidic chip formed by HGPS-SMCs generated from induced pluripotent stem cells (iPSCs), to study their vulnerability to flow shear stress. HGPS-iPSC SMCs cultured under arterial flow conditions detach from the chip after a few days of culture; this process is mediated by the upregulation of metalloprotease 13 (MMP13). Importantly, double-mutant Lmna G609G/G609G Mmp13 −/− mice or Lmna G609G/G609G Mmp13 +/+ mice treated with a MMP inhibitor show lower SMC loss in the aortic arch than controls. MMP13 upregulation appears to be mediated, at least in part, by the upregulation of glycocalyx. Our HGPS-SMCs chip represents a platform for developing treatments for HGPS individuals that may complement previous pre-clinical and clinical treatments.

Background Cellular senescence, a permanent state of replicative arrest in otherwise proliferating cells, is a hallmark of aging and has been linked to aging-related diseases. Many genes play a role in cellular senescence, yet a comprehensive understanding of its pathways is still lacking. Results We develop CellAge ( http://genomics.senescence.info/cells ), a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses. Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes. Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates. We also build cellular senescence protein-protein interaction and co-expression networks. Clusters in the networks are enriched for cell cycle and immunological processes. Network topological parameters also reveal novel potential cellular senescence regulators. Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes ( C9orf40 , CDC25A , CDCA4 , CKAP2 , GTF3C4 , HAUS4 , IMMT , MCM7 , MTHFD2 , MYBL2 , NEK2 , NIPA2 , and TCEB3 ) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence. Conclusions Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence.

In spite of a growing body of research and data, human ageing remains a poorly understood process. Over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), a collection of databases and tools for studying the biology and genetics of ageing. Here, we present HAGR'smain functionalities, highlighting new additions and improvements. HAGR consists of six core databases: (i) the GenAge database of ageing-related genes, in turn composed of a dataset of> 300 human ageing-related genes and a dataset with > 2000 genes associated with ageing or longevity in model organisms; (ii) the AnAge database of animal ageing and longevity, featuring > 4000 species; (iii) the GenDR database with > 200 genes associated with the life-extending effects of dietary restriction; (iv) the LongevityMap database of human genetic association studies of longevity with > 500 entries; (v) the DrugAge database with > 400 ageing or longevity-associated drugs or compounds; (vi) the CellAge database with > 200 genes associated with cell senescence. All our databases are manually curated by experts and regularly updated to ensure a high quality data. Cross-links across our databases and to external resources help researchers locate and integrate relevant information. HAGRis freely available online (http://genomics.senescence.info/).

Many studies have reported genetic interventions that have an effect on mouse life span; however, it is crucial to discriminate between manipulations of aging and aging-independent causes of life extension. Here, we used the Gompertz equation to determine whether previously reported aging-related mouse genes statistically affect the demographic rate of aging. Of 30 genetic manipulations previously reported to extend life span, for only two we found evidence of retarding demographic aging: Cisd2 and hMTH1. Of 24 genetic manipulations reported to shorten life span and induce premature aging features, we found evidence of five accelerating demographic aging: Casp2, Fn1, IKK-β, JunD, and Stub1. Overall, our reassessment found that only 15% of the genetic manipulations analyzed significantly affected the demographic rate of aging as predicted, suggesting that a relatively small proportion of interventions affecting longevity do so by regulating the rate of aging. By contrast, genetic manipulations affecting longevity tend to impact on aging-independent mortality. Our meta-analysis of multiple mouse longevity studies also reveals substantial variation in the controls used across experiments, suggesting that a short life span of controls is a potential source of bias. Overall, the present work leads to a reassessment of genes affecting the aging process in mice, with broad implications for our understanding of the genetics of mammalian aging and which genes may be more promising targets for drug discovery.

Age-related conditions are the leading causes of death and health-care costs. Reducing the rate of aging would have enormous medical and financial benefits. Myriad genes and pathways are known to regulate aging in model organisms, fostering a new crop of anti-aging companies. Approaches range from drug discovery efforts to big-data methods and direct-to-consumer (DTC) strategies. Challenges and pitfalls of commercialization include reliance on findings from short-lived model organisms, poor biological understanding of aging, and hurdles in performing clinical trials for aging. A large number of potential aging-associated interventions and targets exist, but given the long validation times only a small fraction can be explored for clinical applications. If even one company succeeds, however, the impact will be huge.

Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age-related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenorhabditis elegans' lifespan, using as features Gene Ontology (GO) terms annotated for proteins targeted by the compounds and chemical descriptors calculated from each compound's chemical structure. The model with the best predictive accuracy used both biological and chemical features, achieving a prediction accuracy of 80%. The top 20 most important GO terms include those related to mitochondrial processes, to enzymatic and immunological processes, and terms related to metabolic and transport processes. We applied our best model to predict compounds which are more likely to increase C. elegans' lifespan in the DGIdb database, where the effect of the compounds on an organism's lifespan is unknown. The top hit compounds can be broadly divided into four groups: compounds affecting mitochondria, compounds for cancer treatment, anti-inflammatories, and compounds for gonadotropin-releasing hormone therapies.

Aging is a major worldwide medical challenge. Not surprisingly, identifying drugs and compounds that extend lifespan in model organisms is a growing research area. Here, we present DrugAge (http://genomics. senescence. info/drugs/), a curated database of lifespan-extending drugs and compounds. At the time of writing, DrugAge contains 1316 entries featuring 418 different compounds from studies across 27 model organisms, including worms, flies, yeast and mice. Data were manually curated from 324 publications. Using drug-gene interaction data, we also performed a functional enrichment analysis of targets of lifespan-extending drugs. Enriched terms include various functional categories related to glutathione and antioxidant activity, ion transport and metabolic processes. In addition, we found a modest but significant overlap between targets of lifespan-extending drugs and known aging-related genes, suggesting that some but not most aging-related pathways have been targeted pharmacologically in longevity studies. DrugAge is freely available online for the scientific community and will be an important resource for biogerontologists.

In spite of a growing body of research and data, human ageing remains a poorly understood process. To facilitate studies of ageing, over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), which are now the leading online resource for biogerontologists. In this update, we present HAGR’s main functionalities, including new additions and improvements to HAGR. HAGR consists of five databases: 1) the GenAge database of ageing-related genes, in turn composed of a dataset of >300 human ageing-related genes and a dataset with >2000 genes associated with ageing or longevity in model organisms; 2) the AnAge database of animal ageing and longevity, featuring >4000 species; 3) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; 4) the LongevityMap database of human genetic association studies of longevity with >500 entries; 5) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; 6) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts to ensure a high quality data and presented in an intuitive and clear interface that includes cross-links across our databases and to external resources. HAGR is freely available online (<http://genomics.senescence.info/>).

In model organisms, over 2,000 genes have been shown to modulate aging, the collection of which we call the ‘gerontome’. Although some individual aging-related genes have been the subject of intense scrutiny, their analysis as a whole has been limited. In particular, the genetic interaction of aging and age-related pathologies remain a subject of debate. In this work, we perform a systematic analysis of the gerontome across species, including human aging-related genes. First, by classifying aging-related genes as pro- or anti-longevity, we define distinct pathways and genes that modulate aging in different ways. Our subsequent comparison of aging-related genes with age-related disease genes reveals species-specific effects with strong overlaps between aging and age-related diseases in mice, yet surprisingly few overlaps in lower model organisms. We discover that genetic links between aging and age-related diseases are due to a small fraction of aging-related genes which also tend to have a high network connectivity. Other insights from our systematic analysis include assessing how using datasets with genes more or less studied than average may result in biases, showing that age-related disease genes have faster molecular evolution rates and predicting new aging-related drugs based on drug-gene interaction data. Overall, this is the largest systems-level analysis of the genetics of aging to date and the first to discriminate anti- and pro-longevity genes, revealing new insights on aging-related genes as a whole and their interactions with age-related diseases.