{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:05:08Z","timestamp":1789707908239,"version":"4.0.1"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T00:00:00Z","timestamp":1789171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Background\/Objectives: Sleep and circadian disturbances are common in neurodevelopmental conditions, yet the developmental cortical programs linking clock-related transcriptional regulators to disease vulnerability remain unclear. Methods: Here, we integrated human developmental brain transcriptomes, weighted gene co-expression network analysis (WGCNA), human and mouse cortical single-cell atlases, prenatal immune activation transcriptomes, and ASD postmortem brain datasets to characterize the developmental architecture of BrainSpan-derived cortical programs and examine their behavior in perturbational and disease contexts. Results: In the BrainSpan frontal cortex, canonical clock-related genes followed structured but heterogeneous developmental trajectories rather than behaving as a coordinated oscillator-like unit. WGCNA identified a postnatal-rising BrainSpan-derived primary developmental module that was strongly associated with developmental age and enriched for synaptic signaling, neurotransmitter transport, ion transport, membrane excitability, cellular respiration, metabolic regulation, and proteostatic processes. Network analysis placed multiple canonical clock-related and clock-regulatory genes, including NPAS2, BHLHE40, BHLHE41, PER family members, RORA, NR1D1\/2, and CLOCK, within a broader neuronal and homeostatic co-expression architecture, although their module-membership strengths varied substantially. Projection onto a human cortical developmental single-cell atlas revealed a non-uniform distribution of the corrected BrainSpan-derived developmental signature, with relatively higher scores in excitatory and inhibitory neuronal populations and lower scores in neuroblast and radial glial populations. A mouse cortical developmental single-cell atlas provided a comparative view of the stage- and cell-type-dependent expression of clock-related genes and the transferred developmental signature during corticogenesis. In a Poly(I:C)-based maternal immune activation dataset, litter-aware reanalysis identified stage-resolved genome-wide transcriptional responses following E12.5 exposure. However, neither the aggregate core clock-gene expression score nor the independently transferred BrainSpan-derived developmental signature showed a significant overall treatment effect or collection-stage-by-treatment interaction, indicating that this bulk dataset provides a perturbational context rather than evidence for selective disruption of the developmental program. An exploratory region-stratified analysis of GSE28521 yielded near-null effects for the BrainSpan-derived developmental signature, with confidence intervals crossing zero across all examined regions. These ASD postmortem findings were therefore treated as a boundary assessment rather than evidence of ASD-specific convergence. Conclusions: Collectively, these findings position clock-related genes as components of a developmentally regulated cortical maturation program enriched for neuronal signaling, synaptic maturation, metabolic regulation, and stress-response processes.<\/jats:p>","DOI":"10.3390\/genes17091107","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T08:10:23Z","timestamp":1789373423000},"page":"1107","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clock-Related Genes Mark a Developmental Cortical Maturation Program Associated with Stage-Resolved Responses to Prenatal Immune Activation"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2407-738X","authenticated-orcid":false,"given":"Yilin","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Medicine, Nankai University, Tianjin 300071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7271-7655","authenticated-orcid":false,"given":"Xin","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Medicine, Nankai University, Tianjin 300071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1038\/s41583-022-00576-7","article-title":"Genomics, convergent neuroscience and progress in understanding autism spectrum disorder","volume":"23","author":"Willsey","year":"2022","journal-title":"Nat. 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