{
  "id": 16179459,
  "title": "Inference and validation of causal genetic interactions in aging human fibroblasts",
  "url": "https://urgent.news/2026/10/23/inference-and-validation-of-causal-genetic-interactions-in-aging",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-23T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.21.760791v1?rss=1"
  },
  "original_language": "en",
  "account": "Over the past century, mortality from age-related diseases has surged to become the leading cause of death and economic burden in developed nations. Despite advancements in understanding aging, its intricate nature has hindered significant therapeutic breakthroughs. Aging manifests across all biological systems and scales, creating a complex web of interrelationships that are challenging to decipher. Furthermore, human aging is predominantly studied through cross-sectional data rather than longitudinal observations, raising questions about whether such data contain enough dynamic information to uncover causal interactions.\n\nTo address this issue, researchers employed convergent cross mapping (CCM), a technique for identifying causal relationships within nonlinear dynamical systems. By reconstructing directed genetic interactions from age-ordered, cross-sectional transcriptomic data, they were able to recover population-level dynamics rather than individual variations. This approach was applied to dermal fibroblasts from 490 GTEx donors, leading to the identification of a genome-scale causal network whose topology, along with age-dependent gene expression changes, predicted potential drivers of aging.\n\nTo validate these predictions, the researchers conducted single-gene overexpression experiments in fibroblasts from 55-year-old donors. Single-cell RNA sequencing confirmed that 53.8% of testable CCM-predicted causal interactions could be experimentally verified, demonstrating that dynamics reconstructed from cross-sectional populations contain experimentally recoverable information about gene-gene interactions. This percentage surpassed that produced by other methods and a random null hypothesis.\n\nThe researchers then explored whether the information obtained could identify interventions that reverse cellular aging. Through independent transcriptomic and high-dimensional morphological assays, they found that CCM-selected perturbations shifted old fibroblasts towards states characteristic of young cells. These rejuvenation measures were strongly correlated, and the most effective interventions caused a majority of 55-year-old cells to resemble cells from 25-year-old donors.\n\nIn conclusion, the study demonstrates that causal dynamics can be recovered from cross-sectional population data and used to identify interventions in aging. Moreover, the findings suggest a method to distinguish genes that age with age from genes that drive those age-related changes, offering experimentally testable targets for altering the aging phenotype.",
  "summary": "In the past hundred years, the fraction of fatalities due to age-related disease has risen from an envied minority of cases to the leading cause of death and economic morbidity in the developed world. Despite notable advances in understanding aging, its intrinsic complexity has prevented major therapeutic breakthroughs. This complexity extends across all systems and scales in an organism, leaving…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}