Hypothesis

Persistent homology features capturing increased H1 loop lifetimes in bulk aging transcriptomes are early, causative indicators of transcriptional noise that drive functional decline, rather than mere correlates.

Mechanistic Basis

Aging remodels chromatin architecture, leading to altered topologically associating domain (TAD) boundaries and heightened stochastic transcription initiation. These alterations produce spurious, transient co‑expression relationships that manifest as short‑lived loops (H1 features) in persistence diagrams of gene expression manifolds. As stochasticity accumulates, certain loops persist longer because they reflect stable, aberrant transcriptional programs that sequester transcription factors and RNA polymerase II away from productive loci. This mechanistic link predicts that rising H1 persistence precedes measurable declines in tissue function (e.g., grip strength, frailty) by providing a topological signature of transcriptional noise that directly reduces effective gene regulatory capacity.

Testable Predictions

1. In longitudinal bulk RNA‑seq cohorts (e.g., GTEx, GEO/SRA aging samples), the average lifetime (persistence) of H1 features will increase significantly with age before detectable declines in clinical functional metrics.
2. Individuals exhibiting a rapid rise in H1 persistence will show accelerated functional decline over follow‑up intervals, even after adjusting for cell‑type composition changes.
3. Experimental perturbation that reduces transcriptional noise (e.g., treatment with low‑dose HDAC inhibitors or overexpression of chromatin remodelers such as CTCF) will shorten H1 lifetimes and delay functional decline in aged mouse models.
4. Multi‑omic integration will reveal a correlation between increased H1 persistence, weakened TAD insulation (measured by Hi‑C), and elevated nascent RNA variance (measured by PRO‑seq or scRNA‑seq).

Experimental Design

• Cohort: Use publicly available longitudinal bulk RNA‑seq from GTEx (ages 20‑70, repeated biopsies where available) and validate in an independent mouse aging muscle dataset with quarterly sampling.
• Analysis: For each sample, construct a Vietoris–Rips complex on the gene expression matrix (genes as points, Euclidean distance after variance‑stabilizing transformation). Compute persistence diagrams; extract H1 lifetimes (birth–death intervals). Summarize via landscape functions or summary statistics (mean lifetime, entropy).
• Outcome Measures: Correlate H1 summary stats with subsequent changes in grip strength, gait speed, and frailty index collected at matched time points.
• Intervention: In aged mice, administer a chromatin‑stabilizing compound (e.g., butyrate) for 8 weeks; repeat RNA‑seq and functional testing.
• Statistical Test: Mixed‑effects models with H1 persistence as predictor, time and random intercepts for subject; test whether H1 adds predictive value beyond age and cell‑type proportions (likelihood‑ratio test).

Potential Confounds and Falsifiability

If H1 persistence does not improve prediction of future functional decline after accounting for age, cell‑type shifts, and technical noise, or if chromatin‑targeted interventions fail to alter H1 lifetimes despite improving function, the hypothesis would be falsified. Conversely, a consistent predictive lead time and mechanistic rescue would support the claim that topological H1 features capture a causal, noise‑driven dimension of aging.

Key References [1] https://pmc.ncbi.nlm.nih.gov/articles/PMC10277839/ [2] https://pmc.ncbi.nlm.nih.gov/articles/PMC12436302/ [3] https://pmc.ncbi.nlm.nih.gov/articles/PMC12157388/ [4] https://arxiv.org/html/2505.04360v2