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

Area of Science:

• Computational biology
• Bioinformatics
• Systems biology

Background:

• Pathway enrichment analysis is crucial for interpreting transcriptomic data but yields qualitative results difficult to compare.
• Quantitative methods for semantic classification of enrichment results into mechanistically interpretable measures are needed.

Purpose of the Study:

• To introduce TENSE (quanTifiEd immuNe-aging dySregulation index), a novel framework for quantifying immune-aging dysregulation.
• To enable reproducible and comparative analysis of aging-associated biological processes across diverse datasets.

Main Methods:

• Developed TENSE, a framework utilizing a Large Language Model classifier within a KNIME workflow.
• Semantically classified significantly enriched pathways into five mechanistic categories (DIRES: DNA damage, DNA repair, epigenetic drift, inflammaging, nucleic acid sensing).
• Aggregated pathway-derived signals into a normalized dysregulation score (TENSE) and distribution (DIRES).

Main Results:

• Applied TENSE to neurodegenerative, radiation-response, and immune activation datasets, revealing distinct dysregulation profiles.
• Alzheimer's disease modules showed inflammaging signatures; radiation response datasets displayed DNA damage signals.
• Sepsis signatures yielded high TENSE values due to strong inflammatory contributions; high reproducibility was confirmed.

Conclusions:

• TENSE offers a reproducible and interpretable method to quantify system-level immune-aging dysregulation from pathway enrichment outputs.
• The framework bridges pathway enrichment analysis with mechanistic interpretation, facilitating cross-dataset comparisons of aging processes.