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pmid:40893312

Genome-Wide Identification of Microglia-Related RNA-Binding Proteins and Regulated Alternative Splicing in Spinal Cord Injury.

Wenbo Gu · Xiao Zhang · Xiaoya Yuan · Lihong Hu · Xusheng Li · Di Luo · Haifeng Yuan

ACS omega · 2025 doi:10.1021/acsomega.5c03477

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Experiment

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title
Genome-Wide Identification of Microglia-Related RNA-Binding Proteins and Regulated Alternative Splicing in Spinal Cord Injury.
doi
10.1021/acsomega.5c03477
pmid
40893312
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[0]
Wenbo Gu
[1]
Xiao Zhang
[2]
Xiaoya Yuan
[3]
Lihong Hu
[4]
Xusheng Li
[5]
Di Luo
[6]
Haifeng Yuan
journal
ACS omega
year
2025
abstract
Background: Spinal cord injury (SCI) represents one of the recognized difficulties, and its pathological mechanisms remain unclear. Aberrant regulation of the RNA-binding protein (RBP) and selective splicing are associated with SCI. Nonetheless, the mechanisms of RBP regulation and abnormal selective splicing events associated with SCI are unexplored. Methods: The Spinal Cord Injury Group (GSE185301) dataset and human peripheral blood RNA sequencing (GSE151371) dataset were obtained from the Gene Expression Omnibus (GEO) database. High-throughput sequencing data from sham-operated (Ctrl) and spinal cord injury (SCI) mice were subjected to gene expression profiling and genome-wide identification of differential selective splicing events. SCI-associated selective splicing events, differentially expressed cells, and differentially expressed RBPs underwent cellular quantification, principal component analysis, and enrichment analysis. Coexpression analysis was conducted to elucidate the regulatory associations among SCI-related variable splicing events, differentially expressed cells, and differentially expressed RBPs. Results: A total of 1643 alternative splicing events (ASEs), 3128 differentially expressed genes (DEGs), 166 differentially expressed RNA-binding proteins (RBPs), and 6 differential cellular taxa were identified, including mesangial cells, microglia, neuronal cells, oligodendrocyte precursor cells (OPCs), oligodendrocytes, and vascular cells. GO and KEGG analyses revealed that differential ASEs, RBPs, and cells were involved in regulating SCI through various biological pathways. Next, we chose to regulate alternative splicing (RAS), which is mainly enriched in the neurodevelopmental and projection neuron developmental pathways, and screened 10 SCI-associated regulated alternative splicing genes (RASGs), including App, Cdc42, Chl1, Kif2A, Nptn, Rtn4, Sema4d, Sema6d, Sema6a, and Rab13. Second, the correlation analysis between differential cellular taxa and differentially expressed RBP events identified a total of 12 RBPs significantly associated with cellular taxa and 4 RBPs associated with SCI. The construction of a cellular-RBP-RAS regulatory network revealed the regulatory mechanisms associated with RBPs post-SCI. These RBPs, including Nkrf, Marcks, NDRG4, and Ryr2, were validated in a human peripheral blood RNA sequencing dataset 3 days after SCI and may serve as molecular targets for SCI repair. Conclusion: High-throughput data analysis identified differential RAS, RBPs, and immune cells during SCI. A regulatory network of differential RBPs with RAS and cells was established. Four RBPs associated with SCI were identified: Nkrf, Marcks, NDRG4, and Ryr2. These key RBPs may serve as potential targets for the treatment of patients with SCI.
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The negative binomial distribution model was fitted using DESeq2, the hypothesis was tested by the Wald test or likelihood ratio test, and the analysis results were used to determine whether a gene is differentially expressed by fold-change (FC) and false discovery rate (FDR).
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The negative binomial distribution model was fitted using DESeq2, the hypothesis was tested by the Wald test or likelihood ratio test, and the analysis results were used to determine whether a gene is differentially expressed by fold-change (FC) and false discovery rate (FDR).
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All data related to this manuscript are available through the links and supplementary files provided in the manuscript. Should there be any questions or if further information is required, please do not hesitate to contact the corresponding author.
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The analysis identified 1643 alternative splicing events, 3128 differentially expressed genes, 166 differentially expressed RNA-binding proteins, and 6 differential cellular taxa in spinal cord injury.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40893312", "kind": "science-record", "revision": 4, "content_hash": "sha256:5e0dae7b21fe96553a4ffa22611ec8e0aef2c000fecbd3bd6b6be50ede42d62e", "created_at": "2026-11-01T20:31:00Z", "updated_at": "2026-11-01T22:57:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Genome-Wide Identification of Microglia-Related RNA-Binding Proteins and Regulated Alternative Splicing in Spinal Cord Injury.", "doi": "10.1021/acsomega.5c03477", "pmid": "40893312", "authors": ["Wenbo Gu", "Xiao Zhang", "Xiaoya Yuan", "Lihong Hu", "Xusheng Li", "Di Luo", "Haifeng Yuan"], "journal": "ACS omega", "year": 2025, "abstract": "Background: Spinal cord injury (SCI) represents one of the recognized difficulties, and its pathological mechanisms remain unclear. Aberrant regulation of the RNA-binding protein (RBP) and selective splicing are associated with SCI. Nonetheless, the mechanisms of RBP regulation and abnormal selective splicing events associated with SCI are unexplored. Methods: The Spinal Cord Injury Group (GSE185301) dataset and human peripheral blood RNA sequencing (GSE151371) dataset were obtained from the Gene Expression Omnibus (GEO) database. High-throughput sequencing data from sham-operated (Ctrl) and spinal cord injury (SCI) mice were subjected to gene expression profiling and genome-wide identification of differential selective splicing events. SCI-associated selective splicing events, differentially expressed cells, and differentially expressed RBPs underwent cellular quantification, principal component analysis, and enrichment analysis. Coexpression analysis was conducted to elucidate the regulatory associations among SCI-related variable splicing events, differentially expressed cells, and differentially expressed RBPs. Results: A total of 1643 alternative splicing events (ASEs), 3128 differentially expressed genes (DEGs), 166 differentially expressed RNA-binding proteins (RBPs), and 6 differential cellular taxa were identified, including mesangial cells, microglia, neuronal cells, oligodendrocyte precursor cells (OPCs), oligodendrocytes, and vascular cells. GO and KEGG analyses revealed that differential ASEs, RBPs, and cells were involved in regulating SCI through various biological pathways. Next, we chose to regulate alternative splicing (RAS), which is mainly enriched in the neurodevelopmental and projection neuron developmental pathways, and screened 10 SCI-associated regulated alternative splicing genes (RASGs), including App, Cdc42, Chl1, Kif2A, Nptn, Rtn4, Sema4d, Sema6d, Sema6a, and Rab13. Second, the correlation analysis between differential cellular taxa and differentially expressed RBP events identified a total of 12 RBPs significantly associated with cellular taxa and 4 RBPs associated with SCI. The construction of a cellular-RBP-RAS regulatory network revealed the regulatory mechanisms associated with RBPs post-SCI. These RBPs, including Nkrf, Marcks, NDRG4, and Ryr2, were validated in a human peripheral blood RNA sequencing dataset 3 days after SCI and may serve as molecular targets for SCI repair. Conclusion: High-throughput data analysis identified differential RAS, RBPs, and immune cells during SCI. A regulatory network of differential RBPs with RAS and cells was established. Four RBPs associated with SCI were identified: Nkrf, Marcks, NDRG4, and Ryr2. These key RBPs may serve as potential targets for the treatment of patients with SCI.", "claims": [{"id": "claim-method", "statement": "The negative binomial distribution model was fitted using DESeq2, the hypothesis was tested by the Wald test or likelihood ratio test, and the analysis results were used to determine whether a gene is differentially expressed by fold-change (FC) and false discovery rate (FDR).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40893312", "locator": "sections[4] \"Reads Alignment and Differentially Expressed Gene (DEG) Analysis\" paragraph 2", "quote": "The negative binomial distribution model was fitted using DESeq2, the hypothesis was tested by the Wald test or likelihood ratio test, and the analysis results were used to determine whether a gene is differentially expressed by fold-change (FC) and false discovery rate (FDR)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data related to this manuscript are available through the links and supplementary files provided in the manuscript. 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