Skip to main content
SpringerLink
Log in

Development of an In Vitro Model for Inflammation Mediated Renal Toxicity Using 3D Renal Tubules and Co-Cultured Human Immune Cells

  • Original Article
  • Published:
Tissue Engineering and Regenerative Medicine Aims and scope

Abstract

Background:

The emergence of various infectious diseases and the toxic effects of hyperinflammation by biotherapeutics have highlighted the need for in vitro preclinical models mimicking the human immune system. In vitro models studying the relationship between hyperinflammation and acute renal injury mainly rely on 2D culture systems, which have shown limitations in recapitulating kidney function. Herein, we developed an in vitro kidney toxicity model by co-culturing 3D engineered kidney proximal tubules cells (RPTEC/TERT1) with human peripheral blood mononuclear cells (PBMC).

Methods:

RPTEC/TERT1 were sandwich cultured to form 3D renal tubules for 16 days. The tubules were then co-cultured with PBMC using transwell (0.4 μm pores) for 24 h. Hyperinflammation of PBMC was induced during co-culture using polyinosinic-polycytidylic acid (polyI:C) and lipopolysaccharide (LPS) to investigate the effects of the induced hyperinflammation on the renal tubules.

Results:

Encapsulated RPTEC/TERT1 cells in Matrigel exhibited elevated renal function markers compared to 2D culture. The coexistence of PBMC and polyI:C induced a strong inflammatory response in the kidney cells. This hyperinflammation significantly reduced primary cilia formation and upregulated kidney injury markers along the 3D tubules. Similarly, treating co-cultured PBMC with LPS to induce hyperinflammation resulted in comparable inflammatory responses and potential kidney injury.

Conclusion:

The model demonstrated similar changes in kidney injury markers following polyI:C and LPS treatment, indicating its suitability for detecting immune-associated kidney damage resulting from infections and biopharmaceutical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.

References

  1. Saeidnia S, Manayi A, Abdollahi M. From in vitro experiments to in vivo and clinical studies; pros and cons. Curr Drug Discov Technol. 2015;12:218–24.

    Article  CAS  PubMed  Google Scholar 

  2. Andrade EL, Bento AF, Cavalli J, Oliveira SK, Freitas CS, Marcon R, Calixto JB. Non-clinical studies required for new drug development-Part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies. Braz J Med Biol Res. 2016;49:e5644.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Akhtar A. The flaws and human harms of animal experimentation. Camb Q Healthcare Ethics. 2015;24:407–19.

    Article  Google Scholar 

  4. Irvine AR, van Berlo D, Shekhani R, Masereeuw R. A systematic review of in vitro models of drug-induced kidney injury. Curr Opin Toxicol. 2021;27:18–26.

    Article  CAS  Google Scholar 

  5. Pazhayattil GS, Shirali AC. Drug-induced impairment of renal function. Int J Nephrol Renov Dis. 2014;7:457–68.

    Google Scholar 

  6. Simon-Friedt BR, Wilson MJ, Blake DA, Yu H, Eriksson Y, Wickliffe JK. The RPTEC/TERT1 cell line as an improved tool for in vitro nephrotoxicity assessments. Biol Trace Elem Res. 2015;166:66–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Faria J, Ahmed S, Gerritsen KGF, Mihaila SM, Masereeuw R. Kidney-based in vitro models for drug-induced toxicity testing. Arch Toxicol. 2019;93:3397–418.

    Article  CAS  PubMed  Google Scholar 

  8. Tröndle K, Rizzo L, Pichler R, Koch F, Itani A, Zengerle R, Zimmermann S. Scalable fabrication of renal spheroids and nephron-like tubules by bioprinting and controlled self-assembly of epithelial cells. Biofabrication. 2021;13:035019.

    Article  Google Scholar 

  9. Secker PF, Luks L, Schlichenmaier N, Dietrich DR. RPTEC/TERT1 cells form highly differentiated tubules when cultured in a 3D matrix. Altex. 2018;35:223–34.

    Article  PubMed  Google Scholar 

  10. Shimabukuro-Vornhagen A, Gödel P, Subklewe M, Stemmler HJ, Schlößer HA, Schlaak M, et al. Cytokine release syndrome. J Immunother Cancer. 2018;6:56.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bonventre JV, Yang L. Cellular pathophysiology of ischemic acute kidney injury. J Clin Investig. 2011;121:4210–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Santomasso B, Bachier C, Westin J, Rezvani K, Shpall EJ. The other side of CAR T-cell therapy: cytokine release syndrome, neurologic toxicity, and financial Burden. Am Soc Clin Oncol Educ Book Am Soc Clin Oncol Ann Meet. 2019;39:433–44.

    Article  Google Scholar 

  13. Islam MN, Griffin TP, Sander E, Rocks S, Qazi J, Cabral J, et al. Human mesenchymal stromal cells broadly modulate high glucose-induced inflammatory responses of renal proximal tubular cell monolayers. Stem Cell Res Ther. 2019;10:329.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Mihajlovic M, van den Heuvel LP, Hoenderop JG, Jansen J, Wilmer MJ, Westheim AJF, et al. Allostimulatory capacity of conditionally immortalized proximal tubule cell lines for bioartificial kidney application. Sci Rep. 2017;7:7103.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Courau T, Bonnereau J, Chicoteau J, Bottois H, Remark R, Assante Miranda L, et al. Cocultures of human colorectal tumor spheroids with immune cells reveal the therapeutic potential of MICA/B and NKG2A targeting for cancer treatment. J Immunother Cancer. 2019;7:74.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Yakavets I, Francois A, Benoit A, Merlin JL, Bezdetnaya L, Vogin G. Advanced co-culture 3D breast cancer model for investigation of fibrosis induced by external stimuli: optimization study. Sci Rep. 2020;10:21273.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. André MC, Gille C, Glemser P, Woiterski J, Hsu HY, Spring B, et al. Bacterial reprogramming of PBMCs impairs monocyte phagocytosis and modulates adaptive T cell responses. J Leukoc Biol. 2012;91:977–89.

    Article  PubMed  Google Scholar 

  18. Vessillier S, Eastwood D, Fox B, Sathish J, Sethu S, Dougall T, et al. Cytokine release assays for the prediction of therapeutic mAb safety in first-in man trials–Whole blood cytokine release assays are poorly predictive for TGN1412 cytokine storm. J Immunol Methods. 2015;424:43–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ye C, Yang H, Cheng M, Shultz LD, Greiner DL, Brehm MA, et al. A rapid, sensitive, and reproducible in vivo PBMC humanized murine model for determining therapeutic-related cytokine release syndrome. FASEB J. 2020;34:12963–75.

    CAS  Google Scholar 

  20. Frank-Bertoncelj M, Pisetsky DS, Kolling C, Michel BA, Gay RE, Jüngel A, et al. TLR3 ligand poly(I:C) exerts distinct actions in synovial fibroblasts when delivered by extracellular vesicles. Front Immunol. 2018;9:28.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wang J, Wang Y, Wang H, Wang H, Liu JF, Wu Y, et al. Transcriptomic analysis identifies candidate genes and gene sets controlling the response of porcine peripheral blood mononuclear cells to poly I:C stimulation. G3 Genes Genomes Genet. 2016;6:1267–75.

    Article  CAS  Google Scholar 

  22. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    Article  CAS  PubMed  Google Scholar 

  23. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.

    Article  CAS  PubMed  Google Scholar 

  24. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    Article  CAS  PubMed  Google Scholar 

  25. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44:W90–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10:1523.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bangs F, Anderson KV. Primary cilia and mammalian hedgehog signaling. Cold Spring Harb Perspect Biol. 2017;9:a028175.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Liang H, Xu F, Zhang T, Huang J, Guan Q, Wang H, Huang Q. Inhibition of IL-18 reduces renal fibrosis after ischemia-reperfusion. Biomed Pharmacother. 2018;106:879–89.

    Article  PubMed  Google Scholar 

  29. Aksel EG, Akyüz B. Effect of LPS and LTA stimulation on the expression of TLR-pathway genes in PBMCs of Akkaraman lambs in vivo. Trop Anim Health Prod. 2021;53:65.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Pozzi A, Zent R. ZO-1 and ZONAB interact to regulate proximal tubular cell differentiation. J Am Soc Nephrol. 2010;21:388–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Rajasekaran SA, Palmer LG, Quan K, Harper JF, Ball WJ Jr, Bander NH, et al. Na, K-ATPase beta-subunit is required for epithelial polarization, suppression of invasion, and cell motility. Mol Biol Cell. 2001;12:279–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Giles RH, Ajzenberg H, Jackson PK. 3D spheroid model of mIMCD3 cells for studying ciliopathies and renal epithelial disorders. Nat Protoc. 2014;9:2725–31.

    Article  CAS  PubMed  Google Scholar 

  33. Desrochers TM, Palma E, Kaplan DL. Tissue-engineered kidney disease models. Adv Drug Deliv Rev. 2014;69–70:67–80.

    Article  PubMed  Google Scholar 

  34. Liu ZM, Yang MH, Yu K, Lian ZX, Deng SL. Toll-like receptor (TLRs) agonists and antagonists for COVID-19 treatments. Front Pharmacol. 2022;13:989664.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Li X, Shao M, Zeng X, Qian P, Huang H. Signaling pathways in the regulation of cytokine release syndrome in human diseases and intervention therapy. Signal Transduct Target Ther. 2021;6:367.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Khalil BA, Elemam NM, Maghazachi AA. Chemokines and chemokine receptors during COVID-19 infection. Comput Struct Biotechnol J. 2021;19:976–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Huang CC, Duffy KE, San Mateo LR, Amegadzie BY, Sarisky RT, Mbow ML. A pathway analysis of poly(I:C)-induced global gene expression change in human peripheral blood mononuclear cells. Physiol Genomics. 2006;26:125–33.

    Article  PubMed  Google Scholar 

  38. LaFavers K. Disruption of kidney-immune system crosstalk in sepsis with acute kidney injury: lessons learned from animal models and their application to human health. Int J Mol Sci. 2022;23:1702.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Anders HJ. Of Inflammasomes and alarmins: IL-1β and IL-1α in kidney disease. J Am Soc Nephrol. 2016;27:2564–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jassinskaja M, Johansson E, Kristiansen TA, Åkerstrand H, Sjöholm K, Hauri S, et al. Comprehensive proteomic characterization of ontogenic changes in hematopoietic stem and progenitor cells. Cell Rep. 2017;21:3285–97.

    Article  CAS  PubMed  Google Scholar 

  41. Alfaro E, Diaz-Garcia E, Garcia-Tovar S, Zamarrón E, Mangas A, Galera R, et al. Upregulated proteasome subunits in COVID-19 patients: a link with hypoxemia, lymphopenia and inflammation. Biomolecules. 2022;12:442.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gerhardt C, Leu T, Lier JM, Rüther U. The cilia-regulated proteasome and its role in the development of ciliopathies and cancer. Cilia. 2016;5:14.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Boukhalfa A, Miceli C, Ávalos Y, Morel E, Dupont N. Interplay between primary cilia, ubiquitin-proteasome system and autophagy. Biochimie. 2019;166:286–92.

    Article  CAS  PubMed  Google Scholar 

  44. Iriana S, Asha K, Repak M, Sharma-Walia N. Hedgehog signaling: implications in cancers and viral infections. Int J Mol Sci. 2021;22:1042.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Liu KJ. Craniofacial ciliopathies and the interpretation of hedgehog signal transduction. PLoS Genet. 2016;12:e1006460.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Schrader EK, Harstad KG, Holmgren RA, Matouschek A. A three-part signal governs differential processing of Gli1 and Gli3 proteins by the proteasome. J Biol Chem. 2011;286:39051–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Tran PV. Dysfunction of intraflagellar transport proteins beyond the primary cilium. J Am Soc Nephrol. 2014;25:2385–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tran PV, Haycraft CJ, Besschetnova TY, Turbe-Doan A, Stottmann RW, Herron BJ, et al. THM1 negatively modulates mouse sonic hedgehog signal transduction and affects retrograde intraflagellar transport in cilia. Nat Genet. 2008;40:403–10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Gerhardt C, Wiegering A, Leu T, Rüther U. Control of hedgehog signalling by the cilia-regulated proteasome. J Develop Biol. 2016;4:27.

    Article  Google Scholar 

  50. Houde C, Dickinson RJ, Houtzager VM, Cullum R, Montpetit R, Metzler M, et al. Hippi is essential for node cilia assembly and sonic hedgehog signaling. Dev Biol. 2006;300:523–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Huangfu D, Liu A, Rakeman AS, Murcia NS, Niswander L, Anderson KV. Hedgehog signalling in the mouse requires intraflagellar transport proteins. Nature. 2003;426:83–7.

    Article  CAS  PubMed  Google Scholar 

  52. Liu A, Wang B, Niswander LA. Mouse intraflagellar transport proteins regulate both the activator and repressor functions of Gli transcription factors. Development. 2005;132:3103–11.

    Article  CAS  PubMed  Google Scholar 

  53. Dummer A, Rol N, Szulcek R, Kurakula K, Pan X, Visser BI, et al. Endothelial dysfunction in pulmonary arterial hypertension: loss of cilia length regulation upon cytokine stimulation. Pulmonary circulation. 2018;8:2045894018764629.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Cassidy H, Slyne J, Higgins M, Radford R, Conlon PJ, Watson AJ, et al. Neutrophil gelatinase-associated lipocalin (NGAL) is localised to the primary cilium in renal tubular epithelial cells - a novel source of urinary biomarkers of renal injury. Biochim Biophys Acta. 2019;1865:165532.

    Article  CAS  Google Scholar 

  55. Zhao J, Jiang P, Guo S, Schrodi SJ, He D. Apoptosis, autophagy, NETosis, necroptosis, and pyroptosis mediated programmed cell death as targets for innovative therapy in rheumatoid arthritis. Front Immunol. 2021;12:809806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bisgrove BW, Yost HJ. The roles of cilia in developmental disorders and disease. Development (Cambridge, England). 2006;133:4131–43.

    Article  CAS  PubMed  Google Scholar 

  57. Yoder BK. Role of primary cilia in the pathogenesis of polycystic kidney disease. J Am Soc Nephrol. 2007;18:1381–8.

    Article  CAS  PubMed  Google Scholar 

  58. López-Contreras AK, Martínez-Ruiz MG, Olvera-Montaño C, Robles-Rivera RR, Arévalo-Simental DE, Castellanos-González JA, Rodríguez-Carrizalez AD. Importance of the use of oxidative stress biomarkers and inflammatory profile in aqueous and vitreous humor in diabetic retinopathy. Antioxidants. 2020;9:891.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Yamanari T, Sugiyama H, Tanaka K, Morinaga H, Kitagawa M, Onishi A, et al. Urine trefoil factors as prognostic biomarkers in chronic kidney disease. Biomed Res Int. 2018;2018:3024698.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Bülow RD, Boor P. Extracellular matrix in kidney fibrosis: more than just a scaffold. J Histochem Cytochem. 2019;67:643–61.

    Article  Google Scholar 

  61. Nakanishi Y, Horimasu Y, Yamaguchi K, Sakamoto S, Masuda T, Nakashima T, et al. IL-18 binding protein can be a prognostic biomarker for idiopathic pulmonary fibrosis. PLoS One. 2021;16:e0252594.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Choi YH, Choi YS, Kim YK, Rahman MS, Pradeep GC, Yoo JC, et al. A multifunctional alanine-rich anti-inflammatory peptide BCP61 showed potent inhibitory effects by inhibiting both NF-κB and MAPK expression. Inflammation. 2017;40:688–96.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Korea Institute of Toxicology, Republic of Korea (KK-2313, Grant No. 1711195891) and under the framework of Global Joint Research Promotion Program managed by the National Research Council of Science and Technology (NST, Grant No. Global-22-011) of the Ministry of Science and ICT (MSIT).

Author information

Author notes

  1. Mi-lang Kyun and Tamina Park have contributed equally to this study as the first authors.

  2. Yu Bin Lee and Kyoung-Sik Moon have contributed equally to this study.

Authors and Affiliations

  1. Department of Advanced Toxicology Research, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea

    Mi-lang Kyun, Hyewon Jung, Inhye Kim, Ji-In Kwon, Seo Yule Jeong, Myeongjin Choi, Yu Bin Lee & Kyoung-Sik Moon

  2. Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea

    Tamina Park & Daeui Park

Authors
  1. Mi-lang Kyun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tamina Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyewon Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Inhye Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ji-In Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Seo Yule Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Myeongjin Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Daeui Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yu Bin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Kyoung-Sik Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MlK: Data curation, Writing-original draft, Investigation, Methology, Formal analysis. TP: Data curation, Writing-original draft, Formal analysis. HJ, IK and JIK: Data curation, Methology, Formal analysis. SYJ and MC: Methology, Formal analysis. DP: Supervision, Data curation. YBL: Supervision, Investigation, Writing-review & editing. KSM: Supervision, Funding acquisition, Project administration. All the authors read and approved the manuscript. MlK and TP contributed equally to this study as the first authors. YBL and KSM contributed equally to this study as corresponding authors.

Corresponding authors

Correspondence to Yu Bin Lee or Kyoung-Sik Moon.

Ethics declarations

Conflict of interest

There are no conflicts to declare.

Ethical statement

There are no animal experiments carried out for this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOC 2449 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kyun, Ml., Park, T., Jung, H. et al. Development of an In Vitro Model for Inflammation Mediated Renal Toxicity Using 3D Renal Tubules and Co-Cultured Human Immune Cells. Tissue Eng Regen Med 20, 1173–1190 (2023). https://doi.org/10.1007/s13770-023-00602-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13770-023-00602-4

Keywords

Search

Navigation