Integrative Transcriptomics and Machine Learning Identify Temporal Stress Signatures and Astrocytic Neurocan...

Stress-related neurological dysfunction evolves dynamically, but molecular markers distinguishing stress stages remain limited. We aimed to characterize temporal hypothalamic transcriptomic changes and identify molecular signatures for stress-stage discrimination.

Whole-transcriptome sequencing was performed in mouse hypothalami after stress. Differential expression, temporal clustering, co-expression network analysis, and machine learning were integrated to identify stress-associated mRNA and miRNA signatures. Candidate markers were validated by RT-qPCR and single-nucleus RNA sequencing. Ncan function was further evaluated by protein supplementation, perineuronal net analysis, behavioral testing, and regulatory network analysis.

mRNAs exhibited strong temporal sensitivity and captured stage-specific transcriptional remodeling, whereas miRNAs primarily reflected stress regardless of duration. A six-mRNA signature (Ncan, Mt2, Nab2, Ptch1, and Catspere1/Catspere2) accurately distinguished control, acute stress, and chronic stress (accuracy, 1.000), while an 11-miRNA signature robustly classified stress states (accuracy, 0.944). Both signatures were validated by RT-qPCR and nested cross-validation, with accuracies of 0.898 and 0.890, respectively. Ncan was consistently top-ranked, predominantly expressed in astrocytes, progressively downregulated by stress, and its supplementation restored PNNs and alleviated depressive-like behaviors. Virtual Ncan knockout disrupted the stress network, with Ctcf predicted as its upstream regulator.

Our study identifies distinct stress-stage and stress-state transcriptomic signatures and highlights astrocytic Ncan as a candidate biomarker that may contribute to stress-induced neural injury by regulating PNN homeostasis.