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Re-Exploring the Inflammation-Related Core Genes and Modules in Cerebral Ischemia

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Abstract

The genetic transcription profile of brain ischemic and reperfusion injury remains elusive. To address this, we used an integrative analysis approach including differentially expressed gene (DEG) analysis, weighted-gene co-expression network analysis (WGCNA), and pathway and biological process analysis to analyze data from the microarray studies of nine mice and five rats after middle cerebral artery occlusion (MCAO) and six primary cell transcriptional datasets in the Gene Expression Omnibus (GEO). (1) We identified 58 upregulated DEGs with more than 2-fold increase, and adj. p < 0.05 in mouse datasets. Among them, Atf3, Timp1, Cd14, Lgals3, Hmox1, Ccl2, Emp1, Ch25h, Hspb1, Adamts1, Cd44, Icam1, Anxa2, Rgs1, and Vim showed significant increases in both mouse and rat datasets. (2) Ischemic treatment and reperfusion time were the main confounding factors in gene profile changes, while sampling site and ischemic time were not. (3) WGCNA identified a reperfusion-time irrelevant and inflammation-related module and a reperfusion-time relevant and thrombo-inflammation related module. Astrocytes and microglia were the main contributors of the gene changes in these two modules. (4) Forty-four module core hub genes were identified. We validated the expression of unreported stroke-associated core hubs or human stroke-associated core hubs. Zfp36 mRNA was upregulated in permanent MCAO; Rhoj, Nfkbiz, Ms4a6d, Serpina3n, Adamts-1, Lgals3, and Spp1 mRNAs were upregulated in both transient MCAO and permanent MCAO; and NFKBIZ, ZFP3636, and MAFF proteins, unreported core hubs implicated in negative regulation of inflammation, were upregulated in permanent MCAO, but not in transient MCAO. Collectively, these results expand our knowledge of the genetic profile involved in brain ischemia and reperfusion, highlighting the crucial role of inflammatory disequilibrium in brain ischemia.

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Abbreviations

DEGs:

differentially expressed genes

DE:

differential expression

WGCNA:

weighted-gene co-expression network analysis

MCAO:

middle cerebral artery occlusion

GEO:

Gene Expression Omnibus

DS1:

training datasets

DS2:

validation datasets

AD:

Alzheimer’s disease

PPI:

protein-protein interaction

GWAS:

genome-wide association studies

SNP:

single nucleotide polymorphism

MAPK:

mitogen-activated protein kinase

LAA:

large artery atherosclerosis

BBB:

blood-brain barrier

OGD:

oxygen-glucose deprivation

GSEA:

gene set enrichment analysis

GSVA:

gene set variation analysis

CCA:

common carotid artery

ICA:

internal carotid artery

ECA:

external carotid artery

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Acknowledgements

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Availability of Data and Materials

The datasets analyzed during the current study are available in the GEO dataset (https://www.ncbi.nlm.nih.gov/).

Funding

This work was supported by the National Key R&D Program of China (2019YFC0120000; 2018YFC1312300), the National Natural Science Foundation of China (NSFC: 82071385), and the Key Research and Development Project of Shandong (2019JZZY021010) to Q.W.

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Author notes

  1. Wenjing Lv and Junqi Jiang contributed equally to this work.

Authors and Affiliations

  1. Department of Geriatrics, The Affiliated Hospital of Qingdao University, No.16 Jiangsu Road, Qingdao, 266071, China

    Wenjing Lv, Zixuan Wang, Ang Xing & Xueping Zheng

  2. Department of Neurosurgery & Pathophysiology, Institute of Neuroregeneration & Neurorehabilitation, Qingdao University, 308 Ningxia Street, Qingdao, 266071, China

    Wenjing Lv, Zixuan Wang & Qi Wan

  3. Medical College, Qingdao University, Qingdao, 266071, China

    Junqi Jiang, Yi Xu & Zhiyuan Chen

  4. Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China

    Tingting Qu

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Contributions

W.L. collected and analyzed the data and was a major contributor in writing the manuscript; J.J., Y.X., and Z.C. performed the experimental validation; Q.W. reviewed and revised the manuscript and provided financial support; A.X., X.Z., and Z.W. provided experimental guidance and training; T.Q. was a major contributor to the production of the images and charts.

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Correspondence to Qi Wan.

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The rights and interests of mice met the requirements of the Laboratory Animal Welfare Ethics Committee of Qingdao University (No. 20210401C572420210831043).

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Supplementary Information

ESM 1:

Table S1. Characteristics of studies in DS1, DS2 and primary cells.

ESM 2:

Table S2. Information of top 5% upregulated and downregulated DEGs in DS1.

ESM 3:

Table S3. DEGs that were obvious in both DS1 and DS2

ESM 4:

Table S4. Module genes overlapped with cell type-specific markers and DEGs of primary

ESM 5:

Table S5. Pathways and process of modules identified by metascape, GSEA and GSVA analysis.

ESM 6:

Table S6. Expression of core hub-genes of ischemia related modules.

ESM 7:

Table S7. Expression of core hub-genes of blue and turquoise modules in primary astrocyte, microglia, epithelium, macrophages from GEO datasets.

ESM 8:

Table S8. Primers in RT-qPCR analysis.

ESM 9:

Table S9. Previously-identified genes implicated in ischemic stroke and their distribution in modules or DS1-DEGs.

ESM 10:

Figure S1. (a) Clustering analysis and traits of all samples in DS1, bases on overlapped DEGs of DS1 and DS2. Colors range from light to dark red: Group – control, MCAO; Sample sites – cortex, penumbra, CA1 region of hippocampus, hemisphere, core, and striatum; ischemia time – 0 h, 0.17 h, 0.5 h, 0.75 h, 1 h, 2 h, 6 h; reperfusion time – 0 h, 2 h, 3 h, 8 h, 24 h, 48 h, 72 h. (b) Clustering analysis in 101 rat samples, bases on DEGs of rat samples. (c) Clustering analysis in DS2, bases on DS2 - DEGs, and on overlapped DEGs of DS1 and DS2. (d) Clustering analysis of the modules on eigengenes-based dissimilarity in DS1, the red line indicates the cutline, modules with dissimilarity <0.2 are merged for further analysis. (e) Correlations of Gene significance for group (A value of correlation between the group and gene expression) and Module Membership in modules (A value of correlation between a specific module and gene expression). The dots concentrated in the upper right corner represent a high correlation between modules and the group. For group in (b) and (c): white bar - control, red bar - MCAO. Figure S2. (a) Clustering analysis of MCAO samples in DS1 on ‘TOM’-based dissimilarity. (b) Heatmap of relationships between modules and reperfusion time, the values are set as the ‘correlation coefficient (p-value)’. (c) Clustering analysis of the reperfusion time related modules on eigengenes-based dissimilarity in DS1 - MCAO samples, the red line indicates the cutline; all dissimilarities of modules > 0.8. (d) Correlations of Gene significance for ischemia time (A value of correlation between the ischemia time and gene expression) and Module Membership in modules (A value of correlation between a specific module and gene expression). The dots concentrated in the upper right corner represent a high correlation between modules and the reperfusion time. Figure S3. (a) The overlap of upregulated and downregulated DEGs in DS1 and the AD dataset GSE122063 (hypergeometric test), respectively. (b) The overlap of DS1-module genes and the AD frontal cortex DEGs in GSE122063 (hypergeometric test). (c) Heatmaps and bar-plots of eigengenes expression in the group-related-blue, turquoise, brown and purple module, respectively. (d) Venn diagrams of upregulated DEGs-DS1-DS2 with primary inflammatory cells’ DEGs. (e) Venn diagrams of upregulated DEGs-DS1-DS2 with blue module core-hubs and turquoise module core-hubs. Figure S4. (a-d) Metascape network plots of enriched terms in the blue, turquoise, brown, and purple modules, respectively. Each node represents an enriched term and is colored first by its cluster ID. Term labels are only shown for one term per cluster. Nodes marked with red border present overlapped terms of GSEA, GSVA, and metascape analysis. Figure S5. Expression of unreported core hubs in DS1. P value ≥ 0.05 is ns; 0.05>p value≥0.01 is *; 0.01>p value≥0.001 is **; 0.001>p value≥0.0001 is ***; p value<0. 0001 is ****.

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Lv, W., Jiang, J., Xu, Y. et al. Re-Exploring the Inflammation-Related Core Genes and Modules in Cerebral Ischemia. Mol Neurobiol 60, 3439–3451 (2023). https://doi.org/10.1007/s12035-023-03275-1

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