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Gene set enrichment analysis and protein-protein interaction network analysis after sciatic nerve injury.

ABSTRACT: Background:Peripheral nerves are able to regenerate spontaneously after injury. An increasing number of studies have investigated the mechanism of peripheral nerve regeneration and attempted to find potential therapeutic targets. The various bioinformatics analysis tools available, gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) networks can effectively screen the crucial targets of neuroregeneration. Methods:GSEA and PPI networks were constructed through ingenuity pathway analysis and sequential gene expression validation ex vitro to investigate the molecular processes at 1, 4, 7, and 14 days following sciatic nerve transection in rats. Results:Immune response and the activation of related canonical pathways were classified as crucial biological events. Additionally, neural precursor cell expressed developmentally downregulated 4-like (NEDD4L), neuregulin 1 (NRG1), nuclear factor of activated T cells 2 (NFATC2), midline 1 (MID1), GLI family zinc finger 2 (GLI2), and ventral anterior homeobox 1 (VAX1), which were jointly involved in both immune response and axonal regeneration, were screened and their mRNA and protein expressions following nerve injury were validated. Among them, the expression of VAX1 continuously increased following nerve injury, and it was considered to be a potential therapeutic target. Conclusions:The combined use of GSEA and PPI networks serves as a valuable way to identify potential therapeutic targets for neuroregeneration.

SUBMITTER: Yang X

PROVIDER: S-EPMC7475449 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Gene set enrichment analysis and protein-protein interaction network analysis after sciatic nerve injury.

Yang Xiaoming X Xu Xi X Cai Xiaodong X He Jin J Lu Panjian P Guo Qi Q Wang Gang G Zhu Hui H Wang Hongkui H Xue Chengbin C

Annals of translational medicine 20200801 16

<h4>Background</h4>Peripheral nerves are able to regenerate spontaneously after injury. An increasing number of studies have investigated the mechanism of peripheral nerve regeneration and attempted to find potential therapeutic targets. The various bioinformatics analysis tools available, gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) networks can effectively screen the crucial targets of neuroregeneration.<h4>Methods</h4>GSEA and PPI networks were constructed through ...[more]

PMID: 32953788

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Project description:BackgroundSciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.MethodsSciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein-protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug-Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents.Results14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine-cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment.ConclusionsThe gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

Project description:Purpose: To demonstrate an interaction-based method for the refinement of Gene Set Enrichment Analysis (GSEA) results. Method: Intravitreal injection of miR-124-3p antagomir was used to knockdown the expression of miR-124-3p in mouse retina at postnatal day 3 (P3). Whole retinal RNA was extracted for mRNA transcriptome sequencing at P9. After preprocessing the dataset, GSEA was performed, and the leading-edge subsets were obtained. The Apriori algorithm was used to identify the frequent genes or gene sets from the union of the leading-edge subsets. A new statistic d was introduced to evaluate the frequent genes or gene sets. Reverse transcription quantitative PCR (RT-qPCR) was performed to validate the expression trend of candidate genes after the knockdown of miR-124-3p. Results: A total of 115,140 assembled transcript sequences were obtained from the clean data. With GSEA, the NOD-like receptor signaling pathway, C-type-like lectin receptor signaling pathway, phagosome, necroptosis, JAK-STAT signaling pathway, Toll-like receptor signaling pathway, leukocyte transendothelial migration, chemokine signaling pathway, NF-kappa B signaling pathway and RIG-I-like signaling pathway were identified as the top 10 enriched pathways, and their leading-edge subsets were obtained. After being refined by the Apriori algorithm and sorted by the value of the modulus of d , Prkcd, Irf9, Stat3, Cxcl12, Stat1, Stat2, Isg15, Eif2ak2, Il6st, Pdgfra, Socs4 and Csf2ra had the significant number of interactions and the greatest value of d to downstream genes among all frequent transactions. Results of RT-qPCR validation for the expression of candidate genes after the knockdown of miR-124-3p showed a similar trend to the RNA-Seq results. Conclusion: This study indicated that using the Apriori algorithm and defining the statistic d was a novel way to refine the GSEA results. We hope to convey the intricacies from the computational results to the low-throughput experiments, and to plan experimental investigations specifically.

Dataset Information

Gene set enrichment analysis and protein-protein interaction network analysis after sciatic nerve injury.

Publications

Gene set enrichment analysis and protein-protein interaction network analysis after sciatic nerve injury.

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