New Database Maps 3,111 Genes Across the 11 Hallmarks of Aging

Aging has long been studied one fragment at a time: a gene here, a pathway there, a disease connection somewhere else. A team at The Institute of Mathematical Sciences (IMSc) in Chennai, India, has now pulled much of that scattered knowledge into a single, searchable framework. In a paper published in the journal Biogerontology, Rahul Tiwari, Mridhula Balaji, Nikhil Chivukula, Priyotosh Sil and Areejit Samal introduce AgingHallmarksDB, an interactive web platform built around the eleven hallmarks of aging, the canonical categories that biogerontologists use to organize the molecular drivers of growing old. The resource assembles 3,111 aging hallmark-associated genes drawn from seven established databases, and it layers on tissue and cell-type information, exosome data, protein-protein interactions and gene regulatory networks so that researchers can interrogate aging as a system rather than as a list of isolated parts.

The eleven hallmarks trace their lineage to a landmark 2013 review in Cell by Carlos López-Otín and colleagues, which was expanded in 2023 to include disabled macroautophagy, chronic inflammation, dysbiosis and splicing dysregulation alongside the original nine categories such as genomic instability, telomere attrition, loss of proteostasis, epigenetic alterations, mitochondrial dysfunction and cellular senescence. The framework has been enormously influential, but it has also exposed a practical problem: the genes assigned to each hallmark live in different databases with different curation standards, and no single resource allowed researchers to ask, in one place, which genes are shared across hallmarks, which are tissue-specific, or which hallmark processes are enriched in a given disease. AgingHallmarksDB was designed to close exactly that gap.

Technically, the platform integrates gene sets from seven sources, including the Human Ageing Genomic Resources, the Aging Atlas, the Digital Ageing Atlas, Open Genes, AgingReG and CSGene, harmonizing identifiers through the HGNC gene nomenclature resources. From the merged compendium of 3,111 genes, the team derived a high-confidence consensus set of 1,089 genes supported by multiple databases, giving users a choice between broad coverage and stricter evidence. Each gene is annotated with tissue expression from the Human Protein Atlas, cell-type class information, and links to exosomal cargo catalogued in ExoRBase, reflecting the growing recognition that extracellular vesicles carry aging-related signals between tissues. Protein-protein interactions and transcriptional regulatory relationships, drawn from resources such as the HuRI reference interactome, OmniPath and Reactome, complete the molecular wiring diagram.

The analytical heart of the platform is a pair of standard but powerful statistical tools: over-representation analysis (ORA) and gene set enrichment analysis (GSEA). ORA asks whether a user-supplied list of genes contains more members of a particular aging hallmark than would be expected by chance, using a false discovery rate correction to control for multiple testing. GSEA, by contrast, takes a ranked list, for example every gene in a transcriptome ordered by how much its expression changed under some condition, and asks whether hallmark genes cluster disproportionately at the top or bottom of the ranking. Together these methods let researchers translate raw gene lists and expression datasets into statements about which aging processes are actually at work.

One of the first things the team did with their own resource was to quantify how interconnected the hallmarks really are. Measuring gene overlaps and Jaccard similarity, a metric that scores the size of the shared gene set relative to the union of two sets, they found substantial crosstalk between hallmark categories, confirming what many biologists have suspected: the hallmarks are not independent modules but overlapping facets of a shared underlying biology. Network analysis reinforced the point. When hallmark gene pairs were mapped onto the human protein-protein interaction network, the pairs turned out to be directly connected far more often than chance would predict, suggesting that hallmark genes physically cooperate in molecular machines.

Topology analysis of that interaction network also surfaced something potentially more consequential: a set of key age-associated genes that act as central hubs across multiple aging hallmarks. In network language, hubs are nodes with disproportionately many connections, and they often represent proteins whose perturbation has outsized effects on the system. The identification of such multi-hallmark hub genes provides a systems-level shortlist for experimentalists interested in interventions that might influence several aging processes at once, a long-standing goal in the search for geroprotective therapies.

To demonstrate the platform’s disease relevance, the team performed hallmark enrichment analyses on gene sets associated with aging-related and non-aging-related diseases. The contrast was striking. Diseases known to be age-associated showed extensive enrichment across the hallmarks, while developmental and congenital diseases, which are not aging-related, showed only limited enrichment, an internal validation that the hallmark gene sets capture something specific about aging biology rather than disease generally. Within individual diseases, the analyses recovered biologically meaningful associations: loss of proteostasis emerged as a highly significant hallmark in Alzheimer’s disease, consistent with the well-documented accumulation of misfolded and aggregated proteins in the Alzheimer’s brain, and chronic inflammation ranked as a top hallmark in atherosclerosis, echoing a large body of work implicating inflammatory pathways in plaque formation and rupture.

These disease-hallmark associations were then stress-tested with network proximity analysis, a technique from network medicine that measures how close two sets of genes sit within the human interactome. If the hallmark genes for a given process and the genes associated with a given disease occupy nearby positions in the interaction network, that offers evidence of shared molecular machinery beyond simple gene overlap. The proximity results supported the enrichment findings, adding an independent line of evidence that the disease-hallmark links recovered by AgingHallmarksDB reflect real biological relationships. The code for this analysis is publicly available through the project’s GitHub repository, and the database itself is freely accessible at cb.imsc.res.in/aginghallmarksdb.

Perhaps the most topical demonstration involves air pollution. The team applied GSEA to a skin transcriptome associated with exposure to PM2.5, the fine particulate matter under 2.5 micrometers in diameter that is a major component of urban air pollution and an established driver of skin injury and premature skin aging. The analysis found significant enrichment of aging hallmarks in the polluted-skin gene expression signature, with epigenetic alterations standing out as particularly significant. That result aligns with earlier experimental work showing that particulate matter induces senescence in skin keratinocytes through oxidative stress-dependent epigenetic modifications, and it shows how the database can reframe an environmental exposure dataset in the language of aging biology, a capability with obvious implications for dermatology, toxicology and the growing market for interventions against extrinsic skin aging.

The broader significance of AgingHallmarksDB lies in what it makes routine. A researcher with a gene list from a CRISPR screen, a proteomics experiment or a patient cohort can now, in minutes, ask which hallmarks of aging are over-represented, which of their genes sit in the high-confidence consensus set, in which tissues and cell types those genes act, and how far their disease genes lie from hallmark genes in the interactome. The authors position the resource as a framework for investigating hallmark-associated functionality and for potential therapeutic discovery in aging and longevity, and the timing is apt: the field has been actively debating its foundational principles, with recent surveys revealing both broad engagement with the hallmark framework and genuine disagreement about its boundaries. Tools that make the framework computationally explicit, testable and comparable across datasets are exactly what such debates need. As populations worldwide age and the World Health Organization pushes healthy-aging agendas, resources that connect genes, hallmarks, tissues and diseases into one navigable map are likely to become standard infrastructure for the next generation of geroscience.

Subject of Research: An integrated database resource for systems-level analysis of aging hallmarks and associated genes

Article Title: An integrated resource for systems-level analysis of aging hallmarks and associated genes

Article References: Tiwari, R., Balaji, M., Chivukula, N., Sil, P., & Samal, A. (2026). An integrated resource for systems-level analysis of aging hallmarks and associated genes. Biogerontology, 27(5), Article 173. https://doi.org/10.1007/s10522-026-10520-2

Image Credits: AI Generated

DOI: 10.1007/s10522-026-10520-2

Keywords: aging hallmarks, AgingHallmarksDB, biogerontology, gene set enrichment analysis, over-representation analysis, protein-protein interaction network, network medicine, cellular senescence, epigenetic alterations, longevity, PM2.5, disease enrichment