Identification of molecular pathways and protein-protein interactions in adipose tissue-derived mesenchymal stromal cells (ASCs) under physiological oxygen concentration in a diabetic rat model

Document Type : Original Article

Authors

1 Maimonides Institute of Biomedical Research in Cordoba (IMIBIC), Spain. Avenida Menéndez Pidal s/n, CP 14004 Córdoba, Spain

2 Cellular Therapy Unit, Reina Sofia University Hospital, Cordoba, Spain. Avenida Menéndez Pidal s/n, CP 14004 Córdoba, Spain

3 University of Cordoba, Spain. Avenida Menéndez Pidal s/n, CP 14004 Córdoba, Spain

4 Bio-Knowledge Lab, Glorieta de los Países Bálticos, s/n. Edificio Baobab 1, Oficina 15, Polígono Tecnocórdoba, 14014 Córdoba, Spain

Abstract

Objective(s): Adipose tissue-derived mesenchymal stromal cells (ASCs) are useful in cell-based therapy.  However, it is well known that diabetes mellitus (DM) alters ASCs’ functionality. The majority of in vitro studies related to ASCs are developed under non-physiological oxygen conditions. Therefore, they may not reflect the full effects of DM on ASCs, in vivo. The main aim of the current study is to identify molecular pathways and underlying biological mechanisms affected by diabetes on ASCs in physiological oxygen conditions.
Materials and Methods: ASCs derived from healthy (ASCs-C) and diabetic (ASCs-D) rats were expanded under standard culture conditions (21% O2) or cultured in physiological oxygen conditions (3% O2) and characterized. Differential gene expressions (DEGs) of ASCs-D with respect to ASCs-C were identified and analyzed with bioinformatic tools. Protein-protein interaction (PPI) networks, from up- and down-regulated DEGs, were also constructed.
Results: The bioinformatic analysis revealed 1354 up-regulated and 859 down-regulated DEGs in ASCs-D, with 21 and 78 terms over and under-represented, respectively. Terms linked with glycosylation and ribosomes were over-represented and terms related to the activity of RNA-polymerase II and transcription regulation were under-represented. PPI network disclosed RPL11-RPS5 and KDR-VEGFA as the main interactions from up- and down-regulated DEGs, respectively.
Conclusion: These results provide valuable information about gene pathways and underlying molecular mechanisms by which diabetes disturbs ASCs biology in physiological oxygen conditions. Furthermore, they reveal, molecular targets to improve the use of ASCs in autologous transplantation.

Keywords

References

1. Zang L, Hao H, Liu J, Li Y, Han W, Mu Y. Mesenchymal stem cell therapy in type 2 diabetes mellitus. Diabetol Metab Syndr 2017; 9:36. 
2.  Cho J, D’Antuono M, Glicksman M, Wang J, Jonklaas J. A review of clinical trials: mesenchymal stem cell transplant therapy in type 1 and type 2 diabetes mellitus. Am J Stem Cells 2018; 7:82-93. 
3. Kornicka K, Houston J, Marycz K. Dysfunction of mesenchymal stem cells isolated from metabolic syndrome and type 2 diabetic patients as result of oxidative stress and autophagy may limit their potential therapeutic use. Stem Cell Rev Rep 2018; 14:337-345. 
4. Zhang J, Liu Y, Chen Y, Yuan L, Liu H, Wang J, et al. Adipose-derived stem cells: current applications and future directions in the regeneration of multiple tissues. Stem Cells Int 2020; 2020:8810813. 
5. Choi JR, Yong KW, Wan Safwani WKZ. Effect of hypoxia on human adipose-derived mesenchymal stem cells and its potential clinical applications. Cell Mol Life Sci. 2017; 74:2587-2600. 
6.  Kim H, Han JW, Lee JY, Choi YJ, Sohn YD, Song M, et al. Diabetic mesenchymal stem cells are ineffective for improving limb ischemia due to their impaired angiogenic capability. Cell Transplant 2015; 24:1571-1584.
7. Mohamed-Ahmed S, Fristad I, Lie SA, Suliman S, Mustafa K, Vindenes H, et al. Adipose-derived and bone marrow mesenchymal stem cells: a donor-matched comparison. Stem Cell Res Ther 2018; 9:168. 
8. Saki N, Jalalifar MA, Soleimani M, Hajizamani S, Rahim F. Adverse effect of high glucose concentration on stem cell therapy. Int J Hematol Oncol Stem Cell Res 2013; 7:34-40. 
9. Samal JRK, Rangasami VK, Samanta S, Varghese OP, Oommen OP. Discrepancies on the role of oxygen gradient and culture condition on mesenchymal stem cell fate. Adv Healthc Mater 2021; 10:e2002058. 
10. Wu Y, Yang J, Ai Z, Yu M, Li J, Li S. Identification of key genes and transcription factors in aging mesenchymal stem cells by DNA microarray data. Gene 2019; 692:79-87.
11.  Ko KI, Coimbra LS, Tian C, Alblowi J, Kayal RA, Einhorn TA, et al. Diabetes reduces mesenchymal stem cells in fracture healing through a TNFα-mediated mechanism. Diabetologia 2015; 58:633-642. 
12. Li L, Pan Z, Yang S, Shan W, Yang Y. Identification of key gene pathways and coexpression networks of islets in human type 2 diabetes. Diabetes Metab Syndr Obes 2018; 11:553-563.
13. Liu Y, Tang SC. Recent progress in stem cell therapy for diabetic nephropathy. kidney dis (Basel) 2016; 2:20-27.
14. Roberto Pontarolo, Andréia Cristina Conegero Sanches, Astrid Wiens, Cássio Marques Perlin, Fernanda Stumpf Tonin (April 1st 2015). Pharmacological Treatments for Type 2 Diabetes, Treatment of Type 2 Diabetes, Colleen Croniger, IntechOpen. Available from: https://www.intechopen.com/chapters/47557
15. Mahmoud M, Abu-Shahba N, Azmy O, El-Badri N. Impact of diabetes mellitus on human mesenchymal stromal cell biology and functionality: implications for autologous transplantation. Stem Cell Rev Rep 2019; 15:194-217.
16. Rodrigues M, Wong VW, Rennert RC, Davis CR, Longaker MT, Gurtner GC. Progenitor cell dysfunctions underlie some diabetic complications. Am J Pathol. 2015; 185:2607-2618.
17. de Lima KA, de Oliveira GL, Yaochite JN, Pinheiro DG, de Azevedo JT, Silva WA Jr, et al. Transcriptional profiling reveals intrinsic mRNA alterations in multipotent mesenchymal stromal cells isolated from bone marrow of newly-diagnosed type 1 diabetes patients. Stem Cell Res Ther 2016; 7:92. 
18. Qi Y, Ma J, Li S, Liu W. Applicability of adipose-derived mesenchymal stem cells in treatment of patients with type 2 diabetes. Stem Cell Res Ther 2019; 10:274. 
19. Nyengaard JR, Ido Y, Kilo C, Williamson JR. Interactions between hyperglycemia and hypoxia: implications for diabetic retinopathy. Diabetes. 2004; 53:2931-2938. 
20. Balakumar P, Chakkarwar VA, Kumar V, Jain A, Reddy J, Singh M. Experimental models for nephropathy. J Renin Angiotensin Aldosterone Syst 2008; 9:189-195.
21. Jayaprasad, B., P.S. Sharavanan, and R. Sivaraj, Antidiabetic effect of Chloroxylon swietenia bark extracts on streptozotocin induced diabetic rats. Beni-Suef University J Basic Appl Sci 2016; 5: 61-69.
22. Subio. 2008 Accessed Dec 2016; Available from: https://www.subioplatform.com/.
23. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, et al. DAVID bioinformatics resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 2007; 35:W169-175. 
24. The Database for Annotation, Visualization and Integrated Discovery (DAVID).  2003-2020 Accessed Jan 2019; Available from: https://david.ncifcrf.gov/.
25. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000; 25:25-29.
26. The Gene Ontology (GO).  1999-2020 Accessed Jan 2019; Available from: http://geneontology.org/.
27. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 2017; 45:D353-D361.
28. KEGG: Kyoto Encyclopedia of Genes and Genomes.  1995-2020 Accessed Jan 2019; Available from: https://www.genome.jp/kegg/.
29. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res 2017; 45:D158-D169.
30. The UniProt Consortium.  2019 Accessed Jan 2019; Available from: https://www.uniprot.org/.
31.  Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16:284-287. 
32. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 2017; 45:D362-D368. 
33. STRING CONSORTIUM.  2020 Accessed Jan 2019; Available from: https://string-db.org/.
34. Bermingham ML, Colombo M, McGurnaghan SJ, Blackbourn LAK, Vučković F, PučićBaković M, et al. N-Glycan profile and kidney disease in type 1 diabetes. Diabetes Care 2018; 41:79-87. 
35. Hamouda H, Ullah M, Berger M, Sittinger M, Tauber R, Ringe J, et al. N-glycosylation profile of undifferentiated and adipogenically differentiated human bone marrow mesenchymal stem cells: towards a next generation of stem cell     markers. Stem Cells Dev 2013; 22:3100-3113. 
36. Wilson KM, Jagger AM, Walker M, Seinkmane E, Fox JM, Kröger R, et al. Glycans modify mesenchymal stem cell differentiation to impact on the function of resulting osteoblasts. J Cell Sci 2018; 131:jcs209452.
37. Triantis V, Saeland E, Bijl N, Oude-Elferink RP, Jansen PL. Glycosylation of fibroblast growth factor receptor 4 is a key regulator of fibroblast growth factor 19-mediated down-regulation of cytochrome P450 7A1. Hepatology 2010; 52:656-66. 
38. Wang D, Zhang S, Li L, Liu X, Mei K, Wang X. Structural insights into the assembly and activation of IL-1β with its receptors. Nat Immunol 2010;11:905-911. 
39. Xu M, Chen S, Yang W, Cheng X, Ye Y, Mao J, et al. FGFR4 Links Glucose Metabolism and Chemotherapy Resistance in Breast Cancer. Cell PhysiolBiochem 2018;47:151-160.
40. Peters VA, Joesting JJ, Freund GG. IL-1 receptor 2 (IL-1R2) and its role in immune regulation. Brain Behav Immun. 2013; 32:1-8. 
41.  Redondo-Castro E, Cunningham C, Miller J, Martuscelli L, Aoulad-Ali S, Rothwell NJ, et al. Interleukin-1 primes human mesenchymal stem cells towards ananti-inflammatory and pro-trophic phenotype in vitro. Stem Cell Res Ther 2017; 8:79. 
42.  Fan H, Zhao G, Liu L, Liu F, Gong W, Liu X, et al. Pre-treatment with IL-1β enhances the efficacy of MSC transplantation in DSS-induced colitis. Cell Mol Immunol 2012; 9:473-481.
43. Mariappan MM, D’Silva K, Lee MJ, Sataranatarajan K, Barnes JL, Choudhury GG, et al. Ribosomal biogenesis induction by high glucose requires activation of upstream binding factor in kidney glomerular epithelial cells. Am J Physiol RenalPhysiol 2011; 300:F219-230. 
44. Mariappan MM. Signaling mechanisms in the regulation of renal matrix metabolism in diabetes. Exp Diabetes Res 2012; 2012:749812. 
45.  Meugnier E, Faraj M, Rome S, Beauregard G, Michaut A, Pelloux V, et al. Acute hyperglycemia induces a global downregulation of gene expression in adipose tissue and skeletal muscle of healthy subjects. Diabetes 2007; 56:992-999.
46. Zeng H, Qin L, Zhao D, Tan X, Manseau EJ, Van Hoang M, et al. Orphan nuclear receptor TR3/Nur77 regulates VEGF-A-induced angiogenesis through its transcriptional activity. J Exp Med 2006; 203:719-729.
47. Yoo YG, Yeo MG, Kim DK, Park H, Lee MO. Novel function of orphan nuclear receptor Nur77 in stabilizing hypoxia-inducible factor-1alpha. J Biol Chem 2004; 279:53365-53373.
48. Kim YS, Kwon JS, Hong MH, Kang WS, Jeong HY, Kang HJ, et al. Restoration of angiogenic capacity of diabetes-insulted mesenchymal stem cells by oxytocin. BMC Cell Biol 2013; 14:38. 
49. El-Saghir J, Nassar F, Tawil N, El-Sabban M. ATL-derived exosomes modulate mesenchymal stem cells: potential role in leukemia progression. Retrovirology 2016;13:73.
50. Min J, Jin YM, Moon JS, Sung MS, Jo SA, Jo I. Hypoxia-induced endothelial NO synthase gene transcriptional activation is mediated through the tax-responsive element in endothelial cells. Hypertension 2006;47:1189-1196. 
51.   Kim TH, Leslie P, Zhang Y. Ribosomal proteins as unrevealed caretakers for cellular stress and genomic instability. Oncotarget 2014;5:860-871. 
52. Turi Z, Lacey M, Mistrik M, Moudry P. Impaired ribosome biogenesis: mechanisms and relevance to cancer and aging. Aging (Albany NY) 2019; 11:2512-2540. 
53. Abhinand CS, Raju R, Soumya SJ, Arya PS, Sudhakaran PR. VEGF-A/VEGFR2 signaling network in endothelial cells relevant to angiogenesis. J Cell Commun Signal 2016; 10:347-354.
Iranian Journal of Basic Medical Sciences
Volume 25, Issue 2
February 2022
Pages 155-163

Files

Share

How to cite

Statistics