Volume 14 Issue 1
Jan.  2023
Turn off MathJax
Article Contents
Article Navigation >  ORGAN TRANSPLANTATION  > 2023  >  14(1): 83-92
Dong Boqing, Li Yang, Shi Yuting, et al. Identification of M1 macrophage-related genes in rejection after kidney transplantation based on weighted gene co-expression network analysis[J]. ORGAN TRANSPLANTATION, 2023, 14(1): 83-92. doi: 10.3969/j.issn.1674-7445.2023.01.011
Citation: Dong Boqing, Li Yang, Shi Yuting, et al. Identification of M1 macrophage-related genes in rejection after kidney transplantation based on weighted gene co-expression network analysis[J]. ORGAN TRANSPLANTATION, 2023, 14(1): 83-92. doi: 10.3969/j.issn.1674-7445.2023.01.011
shu

Identification of M1 macrophage-related genes in rejection after kidney transplantation based on weighted gene co-expression network analysis

doi: 10.3969/j.issn.1674-7445.2023.01.011

  • Department of Kidney Transplantation, the First Affiliated Hospital of Xi'an Jiaotong University, Institute of Organ Transplantation of Xi'an Jiaotong University, Xi'an 710061, China

More Information
  • Corresponding author: Xue Wujun, Email: xwujun126@xjtu.edu.cn
  • Received Date: 2022-09-14
    Available Online: 2023-01-17
  • Publish Date: 2023-01-15
    Abstract
  •   Objective  To identify M1 macrophage-related genes in rejection after kidney transplantation and construct a risk prediction model for renal allograft survival.  Methods  GSE36059 and GSE21374 datasets after kidney transplantation were downloaded from Gene Expression Omnibus (GEO) database. GSE36059 dataset included the samples from the recipients with rejection and stable allografts. Using this dataset, weighted gene co-expression network analysis (WGCNA) and differential analysis were conducted to screen the M1 macrophage-related differentially expressed gene (M1-DEG). Then, GSE21374 dataset (including the follow-up data of graft loss) was divided into the training set and validation set according to a ratio of 7∶3. In the training set, a multivariate Cox's model was constructed using the variables screened by least absolute shrinkage and selection operator (LASSO), and the ability of this model to predict allograft survival was evaluated. CIBERSORT was employed to analyze the differences of infiltrated immune cells between the high-risk group and low-risk group, and the distribution of human leukocyte antigen (HLA)-related genes was analyzed between two groups. Gene set enrichment analysis (GSEA) was used to further clarify the biological process and pathway enrichment in the high-risk group. Finally, the database was employed to predict the microRNA (miRNA) interacting with the prognostic genes.  Results  In the GSE36059 dataset, 14 M1-DEG were screened. In the GSE21374 dataset, Toll-like receptor 8 (TLR8), Fc gamma receptor 1B (FCGR1B), BCL2 related protein A1 (BCL2A1), cathepsin S (CTSS), guanylate binding protein 2(GBP2) and caspase recruitment domain family member 16 (CARD16) were screened by LASSO-Cox regression analysis, and a multivariate Cox's model was constructed based on these 6 M1-DEG. The area under curve (AUC) of receiver operating characteristic of this model for predicting the 1- and 3-year graft survival was 0.918 and 0.877 in the training set, and 0.765 and 0.736 in the validation set, respectively. Immune cell infiltration analysis showed that the infiltration of rest and activated CD4+ memory T cells, γδT cells and M1 macrophages were increased in the high-risk group (all P < 0.05). The expression level of HLA I gene was up-regulated in the high-risk group. GSEA analysis suggested that immune response and graft rejection were enriched in the high-risk group. CTSS interacted with 8 miRNA, BCL2A1 and GBP2 interacted with 3 miRNA, and FCGR1B interacted with 1 miRNA.  Conclusions  The prognostic risk model based on 6 M1-DEG has high performance in predicting graft survival, which may provide evidence for early interventions for high-risk recipients.

     

    • FullText(HTML)
  • loading
    • References(36)
  • [1]
    FLETCHER BR, DAMERY S, AIYEGBUSI OL, et al. Symptom burden and health-related quality of life in chronic kidney disease: a global systematic review and meta-analysis[J]. PLoS Med, 2022, 19(4): e1003954. DOI: 10.1371/journal.pmed.1003954.
    [2]
    THONGPRAYOON C, HANSRIVIJIT P, LEEAPHORN N, et al. Recent advances and clinical outcomes of kidney transplantation[J]. J Clin Med, 2020, 9(4): 1193. DOI: 10.3390/jcm9041193.
    [3]
    LAI X, ZHENG X, MATHEW JM, et al. Tackling chronic kidney transplant rejection: challenges and promises[J]. Front Immunol, 2021, 12: 661643. DOI: 10.3389/fimmu.2021.661643.
    [4]
    ORDIKHANI F, POTHULA V, SANCHEZ-TARJUELO R, et al. Macrophages in organ transplantation[J]. Front Immunol, 2020, 11: 582939. DOI: 10.3389/fimmu.2020.582939.
    [5]
    WANG J, LUO P, ZHAO J, et al. Profiling the resident and infiltrating monocyte/macrophages during rejection following kidney transplantation[J]. J Immunol Res, 2020: 5746832. DOI: 10.1155/2020/5746832.
    [6]
    LOCATI M, CURTALE G, MANTOVANI A. Diversity, mechanisms, and significance of macrophage plasticity[J]. Annu Rev Pathol, 2020, 15: 123-147. DOI: 10.1146/annurev-pathmechdis-012418-012718.
    [7]
    DEVRAJ VM, KALIDINDI K, GUDITI S, et al. Macrophage polarization in kidney transplant patients[J]. Transpl Immunol, 2022, 75: 101717. DOI: 10.1016/j.trim.2022.101717.
    [8]
    DOU M, DING C, ZHENG B, et al. Immune-related genes for predicting future kidney graft loss: a study based on GEO database[J]. Front Immunol, 2022, 13: 859693. DOI: 10.3389/fimmu.2022.859693.
    [9]
    XU J, HU J, XU H, et al. Long non-coding RNA expression profiling in biopsy to identify renal allograft at risk of chronic damage and future graft loss[J]. Appl Biochem Biotechnol, 2020, 190(2): 660-673. DOI: 10.1007/s12010-019-03082-2.
    [10]
    LIU S, WANG Z, ZHU R, et al. Three differential expression analysis methods for RNA sequencing: limma, EdgeR, DESeq2[J]. J Vis Exp, 2021(175). DOI: 10.3791/62528.
    [11]
    TIBSHIRANI R. The LASSO method for variable selection in the Cox model[J]. Stat Med, 1997, 16(4): 385-395.
    [12]
    RU Y, KECHRIS KJ, TABAKOFF B, et al. The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations[J]. Nucleic Acids Res, 2014, 42(17): e133. DOI: 10.1093/nar/gku631.
    [13]
    ZHOU H, LU H, SUN L, et al. Diagnostic biomarkers and immune infiltration in patients with T cell-mediated rejection after kidney transplantation[J]. Front Immunol, 2022, 12: 774321. DOI: 10.3389/fimmu.2021.774321.
    [14]
    SHEN Q, WANG Y, CHEN J, et al. Single-cell RNA sequencing reveals the immunological profiles of renal allograft rejection in mice[J]. Front Immunol, 2021, 12: 693608. DOI: 10.3389/fimmu.2021.693608.
    [15]
    LU J, ZHANG Y, SUN J, et al. The immune cell landscape in renal allografts[J]. Cell Transplant, 2021, 30: 963689721995458. DOI: 10.1177/0963689721995458.
    [16]
    HOLÁN V, KRULOVÁ M, ZAJÍCOVÁ A, et al. Nitric oxide as a regulatory and effector molecule in the immune system[J]. Mol Immunol, 2002, 38(12/13): 989-995. DOI: 10.1016/s0161-5890(02)00027-5.
    [17]
    BRAZA F, BROUARD S, CHADBAN S, et al. Role of TLRs and DAMPs in allograft inflammation and transplant outcomes[J]. Nat Rev Nephrol, 2016, 12(5): 281-290. DOI: 10.1038/nrneph.2016.41.
    [18]
    DESSING MC, BEMELMAN FJ, CLAESSEN N, et al. Intragraft Toll-like receptor profiling in acute renal allograft rejection[J]. Nephrol Dial Transplant, 2010, 25(12): 4087-4092. DOI: 10.1093/ndt/gfq589.
    [19]
    WANG N, LIANG H, ZEN K. Molecular mechanisms that influence the macrophage M1-M2 polarization balance[J]. Front Immunol, 2014, 5: 614. DOI: 10.3389/fimmu.2014.00614.
    [20]
    VAN LOON E, GAZUT S, YAZDANI S, et al. Development and validation of a peripheral blood mRNA assay for the assessment of antibody-mediated kidney allograft rejection: a multicentre, prospective study[J]. EBioMedicine, 2019, 46: 463-472. DOI: 10.1016/j.ebiom.2019.07.028.
    [21]
    TAN PS, GAVIN AL, BARNES N, et al. Unique monoclonal antibodies define expression of Fc gamma RI on macrophages and mast cell lines and demonstrate heterogeneity among subcutaneous and other dendritic cells[J]. J Immunol, 2003, 170(5): 2549-2556. DOI: 10.4049/jimmunol.170.5.2549.
    [22]
    MANNAM VK, LEWIS RE, CRUSE JM. The fate of renal allografts hinges on responses of the microvascular endothelium[J]. Exp Mol Pathol, 2013, 94(2): 398-411. DOI: 10.1016/j.yexmp.2012.06.002.
    [23]
    SHAATH H, VISHNUBALAJI R, ELKORD E, et al. Single-cell transcriptome analysis highlights a role for neutrophils and inflammatory macrophages in the pathogenesis of severe COVID-19[J]. Cells, 2020, 9(11): 2374. DOI: 10.3390/cells9112374.
    [24]
    KUBO K, KAWATO Y, NAKAMURA K, et al. Effective suppression of donor specific antibody production by cathepsin S inhibitors in a mouse transplantation model[J]. Eur J Pharmacol, 2018, 838: 145-152. DOI: 10.1016/j.ejphar.2018.09.007.
    [25]
    FUJⅡ H, IVISON SM, SHIMIZU H, et al. Inhibition of cathepsin S reduces allogeneic T cell priming but not graft-versus-host disease against minor histocompatibility antigens[J]. Biol Blood Marrow Transplant, 2012, 18(4): 546-556. DOI: 10.1016/j.bbmt.2011.11.027.
    [26]
    HUGHES CS, COLHOUN LM, BAINS BK, et al. Extracellular cathepsin S and intracellular caspase 1 activation are surrogate biomarkers of particulate-induced lysosomal disruption in macrophages[J]. Part Fibre Toxicol, 2016, 13: 19. DOI: 10.1186/s12989-016-0129-5.
    [27]
    REEVE J, EINECKE G, MENGEL M, et al. Diagnosing rejection in renal transplants: a comparison of molecular- and histopathology-based approaches[J]. Am J Transplant, 2009, 9(8): 1802-1810. DOI: 10.1111/j.1600-6143.2009.02694.x.
    [28]
    KOBAYASHI S, NAGANO H, MARUBASHI S, et al. Guanylate-binding protein 2 mRNA in peripheral blood leukocytes of liver transplant recipients as a marker for acute cellular rejection[J]. Transpl Int, 2010, 23(4): 390-396. DOI: 10.1111/j.1432-2277.2009.00991.x.
    [29]
    LARA-REYNA S, CASELEY EA, TOPPING J, et al. Inflammasome activation: from molecular mechanisms to autoinflammation[J]. Clin Transl Immunology, 2022, 11(7): e1404. DOI: 10.1002/cti2.1404.
    [30]
    KARASAWA T, KAWASHIMA A, USUI F, et al. Oligomerized CARD16 promotes Caspase-1 assembly and IL-1β processing[J]. FEBS Open Bio, 2015, 5: 348-356. DOI: 10.1016/j.fob.2015.04.011.
    [31]
    NANKIVELL BJ, ALEXANDER SI. Rejection of the kidney allograft[J]. N Engl J Med, 2010, 363(15): 1451-1462. DOI: 10.1056/NEJMra0902927.
    [32]
    KAMINSKI H, MARSÈRES G, COSENTINO A, et al. Understanding human γδ T cell biology toward a better management of cytomegalovirus infection[J]. Immunol Rev, 2020, 298(1): 264-288. DOI: 10.1111/imr.12922.
    [33]
    KIRK AD, IBRAHIM S, DAWSON DV, et al. Characterization of T cells expressing the gamma/delta antigen receptor in human renal allografts[J]. Hum Immunol, 1993, 36(1): 11-19. DOI: 10.1016/0198-8859(93)90003-j.
    [34]
    KAMINSKI H, GARRIGUE I, COUZI L, et al. Surveillance of γδ T cells predicts cytomegalovirus infection resolution in kidney transplants[J]. J Am Soc Nephrol, 2016, 27(2): 637-645. DOI: 10.1681/ASN.2014100985.
    [35]
    BACHELET T, COUZI L, PITARD V, et al. Cytomegalovirus-responsive γδ T cells: novel effector cells in antibody-mediated kidney allograft microcirculation lesions[J]. J Am Soc Nephrol, 2014, 25(11): 2471-2482. DOI: 10.1681/ASN.2013101052.
    [36]
    JENKINS SJ, ALLEN JE. The expanding world of tissue-resident macrophages[J]. Eur J Immunol, 2021, 51(8): 1882-1896. DOI: 10.1002/eji.202048881.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (306) PDF downloads(84) Cited by()
    Proportional views
    Related

    WeChat Subscription Account

    Wechat Service Account

    Contact Us
    • Tel：020-38736410
    • Email: organtranspl@163.com
    • Address：The Third Affiliated Hospital of Sun Yat-sen University, No. 600, Tianhe Road, Tianhe District, Guangzhou
    • Editorial Office of Organ Transplantation  粤ICP备2021030844号
    Links

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return