Anti-aging Plants: Bioactive Compounds, Gene-Centered Mechanisms, Evidence Across Models, and Bioinformatics Approaches

Abstract

Plant-derived molecules are increasingly recognized as modulators of conserved genetic and metabolic pathways that regulate lifespan and healthspan. A growing body of evidence demonstrates that phytochemicals influence central longevity regulators, including sirtuins (SIRT1/6), AMPK, mTOR, NRF2, FOXO transcription factors, and cellular senescence markers such as p16 and p21. These signaling nodes orchestrate key processes in mitochondrial quality control, antioxidant and detoxification capacity, autophagy, proteostasis, and systemic inflammatory tone. Compounds such as resveratrol, quercetin, fisetin, sulforaphane, epigallocatechin gallate (EGCG), curcumin, ginsenosides, berberine, and urolithin A have been shown to target these pathways with overlapping and sometimes synergistic effects, positioning them as promising candidates for anti-aging strategies.

In vitro studies using neuronal, hepatic, and mesenchymal stem cell cultures provide mechanistic insights into how these compounds alter gene expression, epigenetic marks, and metabolic flux. For example, resveratrol robustly induces SIRT1 activity, mimicking caloric restriction, while fisetin selectively promotes apoptosis of senescent cells. In rodent models, supplementation with flavonoids and polyphenols improves physical endurance, reduces neuroinflammation, and delays the onset of age-associated metabolic dysfunctions. Clinical studies, although more limited in scale, report measurable improvements in insulin sensitivity, vascular elasticity, cognitive resilience, and markers of systemic inflammation after supplementation with these plant-based compounds. Importantly, safety and dosage optimization remain under investigation, particularly in the context of long-term administration.

A parallel advance has been the integration of bioinformatics workflows to systematically connect plant-derived molecules with gene-centered aging networks. High-throughput transcriptomic and proteomic datasets from GEO and GTEx enable cross-tissue comparisons of compound-induced signatures, while perturbation datasets such as LINCS provide resources for linking phytochemicals with drug-like transcriptional profiles. Network analyses using STRING and STITCH map compound–protein–gene interactions, while pathway enrichment via KEGG and gene set libraries like MSigDB highlight conserved cellular processes influenced by these agents. This computational layer not only validates existing hypotheses but also predicts novel targets and synergistic compound combinations for experimental testing.

Together, evidence from molecular assays, animal studies, clinical observations, and computational models converges on the view that traditional plant compounds act through defined genetic and biochemical mechanisms that are central to aging. By bridging natural product pharmacology with systems-level data science, the field is moving toward a more predictive and mechanistically grounded understanding of how plant-derived interventions may extend healthspan. This integrative framework highlights both the promise and the challenges of translating centuries of empirical plant use into modern evidence-based anti-aging therapies.