The quantified immune-aging dysregulation index: a large-language model-powered method for annotating and quantifying systems-level dysregulation.

Abstract

Pathway enrichment analyses are widely used to interpret transcriptomic datasets; however, their outputs typically consist of lists of statistically enriched pathways that require qualitative interpretation and are difficult to compare across biological contexts. Methods of semantic classification that transform enrichment results into quantitative, mechanistically interpretable measures of system-level dysregulation remain underexplored. Here, we introduced TENSE (quanTifiEd immuNe-aging dySregulation index), a framework that summarizes pathway enrichment outputs into a quantitative estimate of immune-aging-associated dysregulation. Utilizing a Large Language Model classifier via a KNIME workflow, significantly enriched pathways are semantically classified into five mechanistic categories representing key processes implicated in immune aging, the DIRES scheme: DNA damage (D), DNA repair (R), epigenetic drift (E), inflammaging (I), and nucleic acid sensing (S). These pathway-derived signals are then aggregated into a normalized dysregulation score reflecting the magnitude (TENSE) and distribution (DIRES) of aging-associated processes across biological contexts. Application of TENSE to transcriptional modules derived from neurodegenerative, radiation-response, and immune activation datasets revealed distinct dysregulation profiles. Alzheimer's disease-associated modules were primarily characterized by inflammaging signatures, particularly within microglial transcriptional programs, whereas radiation response datasets exhibited dominant DNA damage-related signals. Sepsis-associated gene signatures showed strong inflammatory contributions, producing the highest TENSE values observed. Robustness analysis demonstrated high reproducibility of pathway classification across repeated runs and close agreement between large language model-derived annotations and human consensus scores. TENSE provides a reproducible and interpretable method for transforming pathway enrichment outputs into quantitative estimates of system-level immune-aging dysregulation. By bridging pathway enrichment analysis and mechanistic interpretation, the framework enables comparative analysis of aging-related biological processes across diverse datasets.