Mechanotransduction-Aware Causal Omics on Tissue Scaffolds: A Controlled Mechanochemical Framework for Identifying Disease Genes Beyond Pure Omics Analysis

Abstract

Omics-based disease-gene discovery is typically performed as if molecular states evolve independently of tissue mechanics. Most current pipelines analyze transcriptomic or multimodal molecular data alone and identify abnormal genes using differential expressions, latent trajectories, or association-based recovery under treatment. However, in mechanically active tissues, gene expression is shaped not only by internal regulatory networks but also by mechanotransduction arising from strain, curvature, force transmission, and scaffold geometry. This raises a fundamental question: should disease-gene identification in tissues be treated as a pure omics association problem, or as a causal mechanochemical inference problem? We introduce a mechanotransduction-aware causal omics framework on a Cosserat tissue scaffold. Gene expression evolves through intrinsic regulatory dynamics, spatial diffusion, external control, and a mechanotransduction term driven by scaffold mechanics. To distinguish causation from association, we define a hidden mechano-drug rescue channel in the true data-generating system and compare predictive models that either include or omit mechanotransduction. We show that association-based rankings can incorrectly elevate downstream homeostatic or repair genes, even when the disease gene is the true direct mechanochemical target. By contrast, a causal ranking based on reconstruction of the direct mechanotransduction intervention effect correctly identifies the disease gene as the strongest beneficiary. These results argue that popular pure-omics analysis is insufficient for disease-gene discovery in mechanically structured tissues. Mechanotransduction should be modeled as part of the causal structure of tissue biology rather than treated as a secondary covariate or omitted entirely.