Unveiling the Temporal Dynamics and Molecular Regulation Profiles of Neutrophil Extracellular Traps Following Spinal Cord Injury.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40787256", "kind": "science-record", "revision": 4, "content_hash": "sha256:d1379dfcf13faa32d534ef4ca9c943a14f89f7c6497a30552f26f158ebbc53ad", "created_at": "2026-11-01T20:30:52Z", "updated_at": "2026-11-01T22:56:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Unveiling the Temporal Dynamics and Molecular Regulation Profiles of Neutrophil Extracellular Traps Following Spinal Cord Injury.", "doi": "10.2147/JIR.S530446", "pmid": "40787256", "authors": ["Jinze Li", "Chao Chang", "Yanqiu Li", "Shengyu Cui", "Jun Bai", "Can Zhang", "Xinyu Wang", "Kang Li", "Fengzeng Jian"], "journal": "Journal of inflammation research", "year": 2025, "abstract": "Background: Spinal cord injury (SCI) initiates secondary inflammatory processes that exacerbate tissue damage, with neutrophil extracellular traps (NETs) playing a significant role in amplifying these cascades. This study aimed to explore the temporal dynamics and key regulatory genes of NET formation in SCI to identify therapeutic targets.\nMethods: We integrated two transcriptomic datasets from the GEO database to identify differentially expressed NETs-related genes (NRGs) in SCI. WGCNA identified SCI-related modules, while GSVA assessed NET formation dynamics. Publicly available single-cell RNA sequencing data from the GEO database determined cell-specific expression patterns of key NRGs. Findings were validated through immunofluorescence, Western blot, and qPCR in a mouse SCI model. Regulatory networks were constructed, and potential therapeutic compounds were predicted using DSigDB and molecular docking.\nResults: We identified seven key NRGs (Casp1, Ccl3, Fcgr2b, Itgam, Itgb2, Tlr2, Tlr4) in SCI. GSVA revealed peak NET score at day 1 post-injury, with attenuation at days 3 and 7. Single-cell transcriptome analysis demonstrated predominant expression of these key genes in neutrophils during the acute phase, most prominently at 1 day post-injury, which coincides with the most pronounced neutrophil infiltration. Immunofluorescence and Western blot analyses confirmed significantly elevated NET formation at 1 day post-SCI. qPCR verified the expression of all key NRGs. Regulatory network analysis identified CHD1 as an important transcription factor governing NET formation, while miRNA-mRNA network construction revealed sophisticated post-transcriptional regulation mechanisms. Drug prediction analysis identified atorvastatin as a promising therapeutic candidate with strong binding affinity to multiple key NET-related proteins.\nConclusion: Our study provides insights into the temporal dynamics and molecular mechanisms of NET formation after SCI, identifying potential therapeutic targets to mitigate neutrophil-mediated secondary injury and improve functional outcomes.", "claims": [{"id": "claim-method", "statement": "The limma package was utilized to conduct differential expression analysis, comparing the SCI-1d, SCI-3d, and SCI-7d groups against the Sham group.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40787256", "locator": "sections[4] \"Data Processing and the Identification of NETs-Related DEGs\" paragraph 2", "quote": "The limma package was utilized to conduct differential expression analysis, comparing the SCI-1d, SCI-3d, and SCI-7d groups against the Sham group."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data generated in this study are publicly available in the Gene Expression Omnibus (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40787256", "locator": "sections[32] \"Data Sharing Statement\" paragraph 2", "quote": "The data generated in this study are publicly available in the Gene Expression Omnibus (GEO) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Twenty-two differentially expressed NETs-related genes were shared across all three post-SCI time points.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40787256", "locator": "sections[19] \"Identification of Differentially Expressed NETs-Related Genes in SCI\" paragraph 4", "quote": "The Venn diagram analysis revealed 22 shared DENRGs that were consistently differentially expressed across all three time points (1d, 3d, and 7d) post-SCI (Figure 1G)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Twenty-two differentially expressed NETs-related genes were shared across all three post-SCI time points.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40787256"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE151371"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE151371"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40787256", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JIR.S530446", "sha256": 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Publicly available single-cell RNA sequencing data from the GEO database determined cell-s", "rapeutic targets. Methods: We integrated two transcriptomic datasets from the GEO database to identify d", "ith attenuation at days 3 and 7. Single-cell transcriptome analysis demonstrated predominant expression", "tomic analysis. Importantly, our single-cell RNA sequencing data demonstrated that these genes are predo"], "order_basis": "paper_text_order:455", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["of key NRGs. Findings were validated through immunofluorescence, Western blot, and qPCR in a mouse SCI model", "the most pronounced neutrophil infiltration. 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Immunofluorescence and Western blot analyses confirmed significantly elevated NE", "munofluorescence analysis (n = 6 per group), Western blot analysis (n = 3 per group), qPCR analysis (n"], "order_basis": "paper_text_order:837", "id": "assay:3"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["hrough immunofluorescence, Western blot, and qPCR in a mouse SCI model. Regulatory networks we", "ly elevated NET formation at 1 day post-SCI. qPCR verified the expression of all key NRGs. Reg", "p), Western blot analysis (n = 3 per group), qPCR analysis (n = 6 per group), and in vitro neu", "mental Validation of Key Genes Expression by qPCR To validate the expression of key NETs-relat"], "order_basis": "paper_text_order:855", "id": "assay:4"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["unofluorescence, Western blot, and qPCR in a mouse SCI model. Regulatory networks were construc", "estricted access to nutrition and hydration. Mice were group-housed (4–5 mice per cage) in standard", "reated neutrophils (p < 0.05) (n = 6). (C–I) In vivo qPCR validation of key NRGs in spinal cord t"], "order_basis": "paper_text_order:865", "id": "assay:5"}, {"role": "data_generation", "definition_id": "clinical_cohort", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["profile of peripheral blood samples from SCI patients were extracted for validation. The results showed", "in the gene expression profile of peripheral blood samples from SCI patients were extracted for validation.", "Validation of eight hub genes in peripheral blood samples from SCI patients compared to healthy controls. 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