Network Model to Predict Age-Related Transcriptional Reprogramming

Understanding how secreted factors from aged tissue, often referred to as the senescence-associated secretome, reshape cellular phenotypes remains a major challenge due to the complexity of downstream molecular cascades. Here, we present a computational framework for in silico perturbation modeling designed to predict distinct transcriptional responses to age-specific extracellular environmental cues. We exemplify applications of this framework using articular chondrocytes exposed to secretomes derived from infrapatellar fat pads-an integral component of the cartilage microenvironment-excised from the knee joints of young and aged animals. First, we accessed public transcriptomic data of cartilage from healthy and osteoarthritic knee joints and constructed a cartilage-specific co-expression network using topological overlap matrices, which measure network interconnectedness. We then implemented a Random Walk with Restart to simulate the downstream signal propagation of differentially expressed ligands secreted from young and aged infrapatellar fat pads. We benchmarked predicted perturbation signatures against RNA-seq data from aged chondrocytes treated in vitro with either young or aged infrapatellar fat pad-conditioned medium. Our evaluation pipeline included functional enrichment comparison and receiver operating characteristic analysis. These analyses confirmed that simulated perturbations recapitulated chondrocyte signaling pathways modulated by young and aged infrapatellar fat pad secretomes, including primary effects on mitochondrial respiration, a central hallmark of aging. The network paradigm introduced here provides a data-driven strategy to disentangle how complex, age-dependent extracellular environments influence cellular fate. Ultimately, we anticipate that this pipeline can be extended to diverse tissues and age-related diseases to guide the development of interventions that restore youthful cellular phenotypes.