Identification and clustering analysis of drug-responsive temporally varying genes through high-frequency longitudinal RNA sequencing

High-frequency longitudinal RNA sequencing has emerged as a powerful approach for capturing dynamic transcriptional responses to therapeutic interventions, yet traditional differential expression analysis fails to identify genes with temporal variability that lack static expression differences. We used our previously developed computational framework for identifying Temporally Varying Genes (TVGs) from daily blood samples collected over 10-21 days in Sprague-Dawley rats treated with hepatotoxic compounds including tetracycline, isoniazid, carbon tetrachloride, and valproate. Our methodology employs variance-based scoring to detect genes exhibiting significant temporal fluctuations under treatment conditions. Unsupervised hierarchical clustering of TVGs identified three distinct temporal patterns: early-transient responses, sustained activation, and late-phase upregulation, each enriched for specific biological processes. Principal component analysis demonstrated clear treatment-induced transcriptomic shifts from baseline "Healthy Region" clusters to treatment-adapted "Response Region" states, with sample trajectories reflecting dose-dependent temporal dynamics. Cross-compound analysis revealed 186 commonly regulated genes across all treatments, representing conserved hepatotoxicity signatures, while compound-specific responses highlighted distinct mechanistic pathways. This approach enables kinetic-pharmacodynamic modeling that distinguishes primary drug targets from secondary adaptive responses, advancing precision medicine applications through dynamic molecular portraits of drug action and individual treatment variability.