Integrative Transcriptomics and Machine Learning Identify Temporal Stress Signatures and Astrocytic Neurocan Associated With Hypothalamic Injury
Weihao Zhu, Chenyu Li, Mengting He, Rui Shi, Xia Liu, Weibo Shi, Bin CongABSTRACT
Background
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.
Methods
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.
Results
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.
Conclusion
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.