13 research outputs found
Somatic mutations in human ageing: New insights from DNA sequencing and inherited mutations
The accumulation of somatic mutations is a driver of cancer and has long been
associated with ageing. Due to limitations in quantifying mutation burden with
age in non-cancerous tissues, the impact of somatic mutations in other ageing
phenotypes is unclear. Recent advances in DNA sequencing technologies have
allowed the large-scale quantification of somatic mutations in ageing. These
studies have revealed a gradual accumulation of mutations in most normal
tissues with age as well as a substantial clonal expansion driven mostly by
cancer-related mutations. Nevertheless, because of the relatively modest burden
of age-related somatic mutations identified so far and their stochastic nature,
it is difficult to envision how somatic mutation accumulation alone can explain
most ageing phenotypes that develop gradually. Studies across species have also
found that longer-lived species have lower somatic mutation rates, though these
could be explained by selective pressures to reduce or postpone cancer as
longevity increases. Overall, with a few exceptions like cancer, results from
recent DNA sequencing studies do not add weight to the idea that somatic
mutations with age drive ageing phenotypes and the phenotypic role, if any, of
somatic mutations in ageing remains unclear. Recent studies in patients with
somatic mutation burden and no signs of accelerated ageing further question the
role of somatic mutations in ageing
Human Ageing Genomic Resources:updates on key databases in ageing research
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/).</p
Human Ageing Genomic Resources:updates on key databases in ageing research
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/).</p
Age-associated differences in the cancer molecular landscape.
Cancer is an age-related disease, as incidence and mortality for most types of cancer increase with age. However, how molecular alterations in tumors differ among patients of different ages remains poorly understood. Recent studies have shed light on the age-associated molecular landscapes in cancer. Here, we summarize the main findings of these current studies, highlighting major differences in the genomic, transcriptomic, epigenetic, and immunological landscapes between cancer in younger and older patients. Importantly, some cancer driver genes are mutated more frequently in younger or older patients. We discuss the potential roles of aging-related processes in shaping these age-related differences in cancer. We further emphasize the remaining unsolved questions that could provide important insights that will have implications in personalized medicine
Ageing-associated changes in the expression of lncRNAs in human tissues reflect a transcriptional modulation in ageing pathways
Intercellular communication analysis of the human retinal pigment epithelial and choroidal cells predicts pathways associated with aging, cellular senescence and age-related macular degeneration
A human tissue-specific transcriptomic analysis reveals a complex relationship between aging, cancer, and cellular senescence
Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer
Abstract Introduction Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx. Results We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns. Conclusion We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis