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Single-Cell Sequencing Reveals the Expression of Immune-Related Genes in Macrophages of Diabetic Kidney Disease

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Abstract

Diabetic kidney disease (DKD) is characterized by macrophage infiltration, which requires further investigation. This study aims to identify immune-related genes (IRGs) in macrophage and explore their potential as therapeutic targets. This study analyzed isolated glomerular cells from three diabetic mice and three control mice. A total of 59 glomeruli from normal kidney samples and 66 from DKD samples were acquired from four kidney transcriptomic profiling datasets. Bioinformatics analysis was conducted using both single-cell RNA (scRNA) and bulk RNA sequencing data to investigate inflammatory responses in DKD. Additionally, the “AUCell” function was used to investigate statistically different gene sets. The significance of each interaction pair was determined by assigning a probability using “CellChat.” The study also analyzed the biological diagnostic importance of immune hub genes for DKD and validated the expression of these immune genes in mice models. The top 2000 highly variable genes (HVGs) were identified after data normalization. Subsequently, a total of eight clusters were identified. It is worth mentioning that macrophages showed the highest percentage increase among all cell types in the DKD group. Furthermore, the present study observed significant differences in gene sets related to inflammatory responses and complement pathways. The study also identified several receptor-ligand pairs and co-stimulatory interactions between endothelial cells and macrophages. Notably, SYK, ITGB2, FCER1G, and VAV1 were identified as immunological markers of DKD with promising predictive ability. This study identified distinct cell clusters and four marker genes. SYK, ITGB2, FCER1G, and VAV1 may be important roles. Consequently, the present study extends our understanding regarding IRGs in DKD and provides a foundation for future investigations into the underlying mechanisms.

Graphical abstract

It shows the work flow of the study. The study comprised four sections. In the first section, the scRNA-seq data (GSE127235) from DKD were analyzed. The second section involved cell communication network analysis and AUCell scoring of cell clusters in DKD. The third section utilized bulk RNA-seq data (GSE 96804, GSE104948, GSE30122, and GSE30528) to validate and screen macrophage-specific IRGs and estimate immune cell infiltration. Finally, the fourth part involved in vivo experiments (RT-qPCR, western blot, and immunohistochemistry) to validate the expression of hub genes. Abbreviations: UMAP, uniform manifold approximation and projection; DKD, diabetic kidney disease; RNA-seq, RNA sequencing; DEGs, differentially expressed genes; ROC, receiver operating characteristic.

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Data Availability

All data generated and/or analyzed during the study are presented in this article and are available from the corresponding author upon reasonable request.

References

  1. de Boer, I.H., K. Khunti, T. Sadusky, et al. 2022. Diabetes management in chronic kidney disease: a consensus report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Diabetes Care. https://doi.org/10.2337/dci22-0027.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Deng, Y., N. Li, Y. Wu, et al. 2021. Global, regional, and national burden of diabetes-related chronic kidney disease from 1990 to 2019. Frontiers in Endocrinology 12: 672350. https://doi.org/10.3389/fendo.2021.672350.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Anders, H.J., T.B. Huber, B. Isermann, et al. 2018. CKD in diabetes: diabetic kidney disease versus nondiabetic kidney disease. Nature Reviews. Nephrology 14: 361–377. https://doi.org/10.1038/s41581-018-0001-y.

    Article  CAS  PubMed  Google Scholar 

  4. Stewart, B.J., and M.R. Clatworthy. 2020. Applying single-cell technologies to clinical pathology: progress in nephropathology. The Journal of Pathology 250: 693–704. https://doi.org/10.1002/path.5417.

    Article  PubMed  Google Scholar 

  5. Park, J., R. Shrestha, C. Qiu, et al. 2018. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360: 758–763. https://doi.org/10.1126/science.aar2131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Der, E., S. Ranabothu, H. Suryawanshi, et al. 2017. Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis. JCI Insight 2: e93009. https://doi.org/10.1172/jci.insight.93009.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wu, H., A.F. Malone, E.L. Donnelly, et al. 2018. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. Journal of the American Society of Nephrology 29: 2069–2080. https://doi.org/10.1681/ASN.2018020125.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fu, J., K.M. Akat, Z. Sun, et al. 2019. Single-cell RNA profiling of glomerular cells shows dynamic changes in experimental diabetic kidney disease. Journal of the American Society of Nephrology 30: 533–545. https://doi.org/10.1681/asn.2018090896.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Perez-Morales, R.E., M.D. Del Pino, J.M. Valdivielso, et al. 2019. Inflammation in diabetic kidney disease. Nephron 143: 12–16. https://doi.org/10.1159/000493278.

    Article  CAS  PubMed  Google Scholar 

  10. Katherine, R., R.A. Tuttle, Charles E. Alpers, George L. Bakris, Frank C. Brosius, Peter Kolkhof, and Jaime Uribarri. 2022. Molecular mechanisms and therapeutic targets for diabetic kidney disease. Kidney International 102: 248–260. https://doi.org/10.1016/j.kint.2022.05.012.

    Article  CAS  Google Scholar 

  11. Calle, P., and G. Hotter. 2020. Macrophage phenotype and fibrosis in diabetic nephropathy. International Journal of Molecular Sciences 21: 2806. https://doi.org/10.3390/ijms21082806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fu, J., Z. Sun, X. Wang, et al. 2022. The single-cell landscape of kidney immune cells reveals transcriptional heterogeneity in early diabetic kidney disease. Kidney International. https://doi.org/10.1016/j.kint.2022.08.026.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Pan, Y., S. Jiang, Q. Hou, et al. 2018. Dissection of glomerular transcriptional profile in patients with diabetic nephropathy: SRGAP2a protects podocyte structure and function. Diabetes 67: 717–730. https://doi.org/10.2337/db17-0755.

    Article  CAS  PubMed  Google Scholar 

  14. Grayson, P.C., S. Eddy, J.N. Taroni, et al. 2018. Metabolic pathways and immunometabolism in rare kidney diseases. Annals of the Rheumatic Diseases 77: 1226–1233. https://doi.org/10.1136/annrheumdis-2017-212935.

    Article  CAS  PubMed  Google Scholar 

  15. Woroniecka, K.I., A.S. Park, D. Mohtat, et al. 2011. Transcriptome analysis of human diabetic kidney disease. Diabetes 60: 2354–2369. https://doi.org/10.2337/db10-1181.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Efremova, M., M. Vento-Tormo, S.A. Teichmann, et al. 2020. Cell PhoneDB: Inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nature Protocols 15: 1484–1506. https://doi.org/10.1038/s41596-020-0292-x.

    Article  CAS  PubMed  Google Scholar 

  17. Tang, S.C.W., and W.H. Yiu. 2020. Innate immunity in diabetic kidney disease. Nature Reviews Nephrology 16: 206–222. https://doi.org/10.1038/s41581-019-0234-4.

    Article  CAS  PubMed  Google Scholar 

  18. Chen, J., Q. Liu, J. He, et al. 2022. Immune responses in diabetic nephropathy: Pathogenic mechanisms and therapeutic target. Frontiers in Immunology 13: 958790. https://doi.org/10.3389/fimmu.2022.958790.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Li, H.-D., Y.-K. You, B.-Y. Shao, et al. 2022. Roles and crosstalks of macrophages in diabetic nephropathy. Frontiers in Immunology 13: 1015142. https://doi.org/10.3389/fimmu.2022.1015142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Boels, M.G.S., A. Koudijs, M.C. Avramut, et al. 2017. Systemic monocyte chemotactic protein-1 inhibition modifies renal macrophages and restores glomerular endothelial glycocalyx and barrier function in diabetic nephropathy. American Journal of Pathology 187: 2430–2440. https://doi.org/10.1016/j.ajpath.2017.07.020.

    Article  CAS  PubMed  Google Scholar 

  21. Lim, A.K., F.Y. Ma, D.J. Nikolic-Paterson, et al. 2009. Antibody blockade of c-fms suppresses the progression of inflammation and injury in early diabetic nephropathy in obese db/db mice. Diabetologia 52: 1669–1679. https://doi.org/10.1007/s00125-009-1399-3.

    Article  CAS  PubMed  Google Scholar 

  22. Park, M.D., A. Silvin, F. Ginhoux, et al. 2022. Macrophages in health and disease. Cell 185: 4259–4279. https://doi.org/10.1016/j.cell.2022.10.007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Loke, P., and J.D. Lin. 2022. Redefining inflammatory macrophage phenotypes across stages and tissues by single-cell transcriptomics. Science Immunology 7: eabo4652. https://doi.org/10.1126/sciimmunol.abo4652.

    Article  CAS  PubMed  Google Scholar 

  24. Li, L., H. Fu, and Y. Liu. 2022. The fibrogenic niche in kidney fibrosis: components and mechanisms. Nature Reviews. Nephrology 18: 545–557. https://doi.org/10.1038/s41581-022-00590-z.

    Article  CAS  PubMed  Google Scholar 

  25. Wu, L., C. Liu, D.Y. Chang, et al. 2021. Annexin A1 alleviates kidney injury by promoting the resolution of inflammation in diabetic nephropathy. Kidney International 100: 107–121. https://doi.org/10.1016/j.kint.2021.02.025.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ke, G., X. Chen, R. Liao, et al. 2021. Receptor activator of NF-kappaB mediates podocyte injury in diabetic nephropathy. Kidney International 100: 377–390. https://doi.org/10.1016/j.kint.2021.04.036.

    Article  CAS  PubMed  Google Scholar 

  27. Zhou, T., L. Sun, S. Yang, et al. 2020. 20(S)-ginsenoside Rg3 protects kidney from diabetic kidney disease via renal inflammation depression in diabetic rats. Journal of Diabetes Research 2020: 7152176. https://doi.org/10.1155/2020/7152176.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Xu, H., X. Wu, H. Qin, et al. 2015. Myocardin-related transcription factor A epigenetically regulates renal fibrosis in diabetic nephropathy. Journal of the American Society of Nephrology 26: 1648–1660. https://doi.org/10.1681/ASN.2014070678.

    Article  CAS  PubMed  Google Scholar 

  29. Wang, L., D. Aschenbrenner, Z. Zeng, et al. 2021. Gain-of-function variants in SYK cause immune dysregulation and systemic inflammation in humans and mice. Nature Genetics 53: 500–510. https://doi.org/10.1038/s41588-021-00803-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Qiao, Y., X. Tian, L. Men, et al. 2018. Spleen tyrosine kinase promotes NLR family pyrin domain containing 3 inflammasome-mediated IL-1beta secretion via c-Jun N-terminal kinase activation and cell apoptosis during diabetic nephropathy. Molecular Medicine Reports 18: 1995–2008. https://doi.org/10.3892/mmr.2018.9164.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Liu, L., X. Pang, W. Shang, et al. 2020. miR-136 improves renal fibrosis in diabetic rats by targeting down-regulation of tyrosine kinase SYK and inhibition of TGF-beta1/Smad3 signaling pathway. Renal Failure 42: 513–522. https://doi.org/10.1080/0886022X.2020.1764854.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yang, W.S., J.W. Chang, N.J. Han, et al. 2012. Spleen tyrosine kinase mediates high glucose-induced transforming growth factor-beta1 up-regulation in proximal tubular epithelial cells. Experimental Cell Research 318: 1867–1876. https://doi.org/10.1016/j.yexcr.2012.05.016.

    Article  CAS  PubMed  Google Scholar 

  33. Yang, W.S., J.W. Seo, N.J. Han, et al. 2008. High glucose-induced NF-kappaB activation occurs via tyrosine phosphorylation of IkappaBalpha in human glomerular endothelial cells: involvement of Syk tyrosine kinase. American Journal of Physiology. Renal Physiology 294: F1065–F1075. https://doi.org/10.1152/ajprenal.00381.2007.

    Article  CAS  PubMed  Google Scholar 

  34. Yang, W.S., J.S. Kim, N.J. Han, et al. 2015. Toll-like receptor 4/spleen tyrosine kinase complex in high glucose signal transduction of proximal tubular epithelial cells. Cellular Physiology and Biochemistry 35: 2309–2319. https://doi.org/10.1159/000374034.

    Article  CAS  PubMed  Google Scholar 

  35. Song, X., M. Gong, Y. Chen, et al. 2019. Nine hub genes as the potential indicator for the clinical outcome of diabetic nephropathy. Journal of Cellular Physiology 234: 1461–1468. https://doi.org/10.1002/jcp.26958.

    Article  CAS  PubMed  Google Scholar 

  36. Liu, S., C. Wang, H. Yang, et al. 2020. Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease. Annals of Translational Medicine 8: 1427. https://doi.org/10.21037/atm-20-1087.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Chou, C., X. Zhang, C. Krishna, et al. 2022. Programme of self-reactive innate-like T cell-mediated cancer immunity. Nature 605: 139–145. https://doi.org/10.1038/s41586-022-04632-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Morrish, E., and J. Ruland. 2022. Cytotoxic FCER1G(+) innate-like T cells: New potential for tumour immunotherapy. Signal Transduction and Targeted Therapy 7: 204. https://doi.org/10.1038/s41392-022-01061-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Huang, R., G. Guo, L. Lu, et al. 2019. The three members of the Vav family proteins form complexes that concur to foam cell formation and atherosclerosis. Journal of Lipid Research 60: 2006–2019. https://doi.org/10.1194/jlr.M094771.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Fukumoto, K., M. Sakata-Yanagimoto, M. Fujisawa, et al. 2020. VAV1 mutations contribute to development of T-cell neoplasms in mice. Blood 136: 3018–3032. https://doi.org/10.1182/blood.2020006513.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ma, J., W. Hu, D. Zhang, et al. 2021. CD226 knockout alleviates high-fat diet induced obesity by suppressing proinflammatory macrophage phenotype. Journal of Translational Medicine 19: 477. https://doi.org/10.1186/s12967-021-03150-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Zhou, H., Z. Yang, L. Mu, et al. 2022. Integrated analysis of multiple microarray studies to identify core gene-expression signatures involved in tubulointerstitial injury in diabetic nephropathy. BioMed Research International 2022: 9554658. https://doi.org/10.1155/2022/9554658.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Xu, B., L. Wang, H. Zhan, et al. 2021. Investigation of the mechanism of complement system in diabetic nephropathy via bioinformatics analysis. Journal of Diabetes Research 2021: 5546199. https://doi.org/10.1155/2021/5546199.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Hu, Y., S. Liu, W. Liu, et al. 2021. Bioinformatics analysis of genes related to iron death in diabetic nephropathy through network and pathway levels based approaches. PLoS One 16: e0259436. https://doi.org/10.1371/journal.pone.0259436.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Geng, X.D., W.W. Wang, Z. Feng, et al. 2019. Identification of key genes and pathways in diabetic nephropathy by bioinformatics analysis. Journal of Diabetes Investigation 10: 972–984. https://doi.org/10.1111/jdi.12986.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Huang, J., and Q. Zhou. 2022. CD8+T cell-related gene biomarkers in macular edema of diabetic retinopathy. Frontiers in Endocrinology 13: 907396. https://doi.org/10.3389/fendo.2022.907396.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Liu, L., Q. Zhao, M. Kong, et al. 2022. Myocardin-related transcription factor A regulates integrin beta 2 transcription to promote macrophage infiltration and cardiac hypertrophy in mice. Cardiovascular Research 118: 844–858. https://doi.org/10.1093/cvr/cvab110.

    Article  CAS  PubMed  Google Scholar 

  48. Herb, M., A. Gluschko, and M. Schramm. 2018. LC3-associated phagocytosis initiated by integrin ITGAM-ITGB2/Mac-1 enhances immunity to Listeria monocytogenes. Autophagy 14: 1462–1464. https://doi.org/10.1080/15548627.2018.1475816.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zheng, J.M., Z.H. Jiang, D.J. Chen, et al. 2019. Pathological significance of urinary complement activation in diabetic nephropathy: a full view from the development of the disease. Journal of Diabetes Investigation 10: 738–744. https://doi.org/10.1111/jdi.12934.

    Article  PubMed  Google Scholar 

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Funding

This work was supported by financial support from Tianjin Science and Technology Major Special Project and Engineering Public Health Science and Technology Major Special Project (No. 21ZXGWSY00100), Tianjin Natural Science Foundation Key Project (No. 22JCZDJC00590), Tianjin Key Medical Discipline (Specialty) Construct Project (No. TJYXZDXK-032A), and Scientific Research Funding of Tianjin Medical University Chu Hsien-I Memorial Hospital (No. ZXY-ZDSYSZD-1).

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Author notes

  1. Xian Shao, Yueyue Shi and Yao Wang contributed equally to this work and share first authorship.

Authors and Affiliations

  1. NHC Key Laboratory of Hormones and Development, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China

    Xian Shao, Li Zhang, Pufei Bai, JunMei Wang, Ashanjiang Aniwan, Yao Lin, Saijun Zhou & Pei Yu

  2. Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University, Tianjin, 300134, China

    Xian Shao, Li Zhang, Pufei Bai, JunMei Wang, Ashanjiang Aniwan, Yao Lin, Saijun Zhou & Pei Yu

  3. Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300134, China

    Yueyue Shi

  4. Clinical Medical College & Affiliated Hospital of Chengdu University, Chengdu University, Chengdu, Sichuan, 610081, People’s Republic of China

    Yao Wang

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  1. Xian Shao
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Contributions

Xian Shao: conceptualization, methodology, writing—original draft, investigation, validation, experiments, data curation, and visualization. Yueyue Shi and Yao Wang: conceptualization, validation, experiments, and review and editing. Li Zhang, Pufei Bai, JunMei Wang, Ashanjiang aniwan, and Yao Lin: validation, investigation, and experiments. Saijun Zhou: supervision and conceptualization. Pei Yu: conceptualization, resources, methodology, funding acquisition, supervision, and project administration. All authors reviewed and approved the manuscript.

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Correspondence to Pei Yu.

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Animal experiments were performed with the approval of the Tianjin Medical University Animal Ethics Committee (No. 220916002).

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Shao, X., Shi, Y., Wang, Y. et al. Single-Cell Sequencing Reveals the Expression of Immune-Related Genes in Macrophages of Diabetic Kidney Disease. Inflammation 47, 227–243 (2024). https://doi.org/10.1007/s10753-023-01906-2

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