Computer prediction and genetic analysis identifies retinoic acid modulation as a driver of conserved longevity

Stephen A Banse1, Christine A Sedore1, Anna L Coleman-Hulbert1

  • 1Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA.

Insights

Computational predictions identified five compounds that extend lifespan in C. elegans, including all-trans retinoic acid. This study highlights the potential of combining computational screening with functional genetics for discovering anti-aging interventions.

Area of Science:

  • Gerontology and aging research
  • Computational biology and bioinformatics
  • Genetics and molecular biology

Background:

  • Aging is a universal biological process with significant health and societal implications.
  • Discovering interventions to mitigate aging's negative health impacts is crucial for age-related comorbidities.
  • Computational prediction offers a method to prioritize potential anti-aging compounds from a vast chemical universe.

Purpose of the Study:

  • To evaluate the longevity effects of 16 compounds predicted computationally for anti-aging capacity.
  • To validate computational predictions using the Caenorhabditis Intervention Testing Program (CITP) multi-species approach.
  • To perform deeper genetic characterization of effective longevity compounds, specifically all-trans retinoic acid.

Main Methods:

  • Surveyed longevity effects of 16 computationally predicted compounds in Caenorhabditis elegans.
  • Utilized the comprehensive, multi-species approach of the Caenorhabditis Intervention Testing Program (CITP).
  • Conducted genetic analyses to identify pathways mediating the longevity effects of all-trans retinoic acid (atRA), focusing on kinases AKT-1/2, DAF-16, SKN-1, HSF-1, and AAK-2.

Main Results:

  • Five out of 16 tested compounds (all-trans retinoic acid, berberine, fisetin, propranolol, ritonavir) extended median lifespan in C. elegans.
  • Eleven compounds showed no lifespan extension or were toxic.
  • Computational predictions demonstrated a 30% positive hit rate.
  • Longevity extension by all-trans retinoic acid required regulatory kinases AKT-1 and AKT-2, but not the canonical FOXO/DAF-16 target; however, other Akt-targets (SKN-1, HSF-1) and AMPK subunit AAK-2 were necessary.

Conclusions:

  • Combining computational prediction with nematode functional genetics is a powerful strategy for discovering aging interventions.
  • The all-trans retinoic acid pathway, an ancient signaling system, can modulate lifespan.
  • Manipulating conserved metabolic regulatory circuits using endogenous signaling molecules is a promising approach for anti-aging therapies.

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