Analysis of microRNAs signatures in juvenile glomerular proteinuria
Main Article Content
Keywords
microRNA profiling, nephrotic syndrome, next-generation sequencing, urine biomarkers
Abstract
Background : Urinary microRNAs (miRNAS) are found to be as non-invasive biomarkers in many diseases, including nephrotic syndrome (NS). NS is a common kidney disorder predominant in children and has also been reported in adults. Approximately 85 to 90% of patients with NS respond to steroid treatment with complete remission of proteinuria are known as steroid sensitive nephrotic syndrome (SSNS), while 10 to 15% have partial or even no response to steroid therapy termed as steroid resistant nephrotic syndrome (SRNS). The global urinary miRNA signature in paediatric NS patients and its clinical significance have not been explored. The present study was therefore attempted to analyse the miRNA profile in urinary samples by high-throughput Illumina sequencing via synthesis (SBS) technology in control and NS patients.
Methodology: MicroRNA isolation was carried out in urine samples collected from SSNS (n=10), SRNS (n=10), and healthy controls (n=10). Isolated RNA enriched for small RNA high throughput sequencing (HTS), and the sequence data were generated using Illumina HiSeq sequencing technology (Clevergene Biocorp Pvt. Ltd., Bengaluru, India). The expression profile of the differentially expressed miRNAs across the samples is presented in volcano plot and heatmap. miRNA sequencing revealed significant increase in 4 and 19 miRNAs in SSNS and SRNS samples respectively, compared to that of normal subjects.
Results: Among the 4 miRNAs of SSNS patients, 3 miRNAs were found to be significantly elevated, while only one was found to be downregulated as compared to control group. Among the 19 miRNAs of SRNS patients, 8 miRNAs were found to be significantly upregulated, while 11 were found to be downregulated when compared to control. The GO analysis showed that the target genes were mainly involved in biological processes, molecular function and cellular components. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed pathways in Ras signaling, MAPK signaling, calcium signaling etc. The miRNA sequencing was submitted in the SRA database in NCBI, Bio Project ID :PRJNA858929 (Accession Number for submission (SSNS: SAMN29759595 , SRNS: SAMN29759596, Control: SAMN29759597).
Conclusion : Urinary miRNAs identified in this studys could be promising and non-invasive potential biomarker candidates for diagnosis and prognosis of pediatric NS.
References
1. Bartel DP(2009). MicroRNAs: target recognition and regulatory functions. Cell.;136:215–233 . https://doi.org/10.1016/j.cell.2009.01.002
2. Carthew RW, Sontheimer E(J2009)Origins and Mechanisms of miRNAs and siRNAs. Cell. ;136:642–655. https://doi.org/10.1016/j.cell.2009.01.035
3. Ambros V. (2004) The functions of animal microRNAs. Nature.;431:350–355. https://doi.org/10.1038/nature02871
4. Shendure J, Ji (H2008). Next-generation DNA sequencing. Nature Biotechnol.,26: 1135–1145. https://doi.org/10.1038/nbt1486
5. Sanger, F, Coulson AR. A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J. Mol.Biol. 1975;94:441–448. https://doi.org/10.1016/0022-2836(75)90213-2
6. Maxam AM, Gilbert W. (1977 )A new method for sequencing DNA. Proceed.Nat.Acad. Sci. United States of America, ;74:560–564. https://doi.org/10.1073/pnas.74.2.560
7. Wang, WC, Lin FM, Chang, WC, Lin KY, Huang HD, Lin NS (2009) miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinform. ;10:328. https://doi:10.1186/1471-2105-10-328
8. Slatko BE, Gardner AF, Ausubel FM(2018). Overview of next-generation sequencing technologies. Curr. Protoc.Mol.Biol. ;122:e59. https://doi.org/10.1002/cpmb.59
9. Chen T, Wang C, Yu H, Ding M, Zhang C, Lu X, Zhang CY, Zhang C(2019). Increased urinary exosomal microRNAs in children with idiopathic nephrotic syndrome. EBioMedicine,;39:552-561. https://doi.org/10.1016/j.ebiom. 2018.11.018.
10. International Study of Kidney Disease in Children. Report,( 1981). The primary nephrotic syndrome in children. Identification of patients with minimal change nephrotic syndrome from initial response to prednisone. J. Pediatr. 1981;98:561–564.://doi.org/10.1016/s0022-3476(81)80760-3
11. Harpole M, Davis, J, Espina, V.( 2016) Current state of the art for enhancing urine biomarker discovery. Expert Rev.Proteom. ;13:609–626. https://doi.org(/10.1080/14789450.2016.1190651
12. Andrews S(2010). Fast QC : A Quality control tool for high throughput sequence data. http://www. bioinformatics.babraham.ac.uk/projects/fastqc/
13. Ewels P, Magnusson M, Lundin S, Käller M, (2016) MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics,;32:3047–3048, https://doi.org/10.1093/bioinformatics/btw354
14. Babraham TI. (2012).Trim Galore! Cambridge Babraham Institute, https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/
15. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17,10-12. doi:https://doi.org/10.14806/ej.17.1.200.
16. Kozomara A. Griffiths-Jones S.(2014) miRBase: Annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. ;42:D68–D73. https://doi.org/10.1093/nar/gkt1181
17. Love MI, Huber W, Anders S.( 2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. ;15: 550 https://doi.org/10.1186/s13059-014-0550-8
18. Agarwal V, Bell GW, Nam J-W, Bartel DP(2015). Predicting effective microRNA target sites in mammalian mRNAs. eLife.;4:e05005. https://doi.org/10.7554/eLife.05005
19. Yu G, Wang L-G, Han Y, He Q-Y.( 2012) ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: J. Integr. Biol.;16:284–287.
20. Xiao L, Zhou X, Liu, F. Hu C, Zhu X, Luo Y, Wang M, Xu X, Yang S, Kanwar YS, Sun L. (2015) MicroRNA-129-5p modulates epithelial-to-mesenchymal transition by targeting SIP1 and SOX4 during peritoneal dialysis. Lab Invest. ;95:817–832 https://doi.org/10.1038/labinvest.2015.57
21. Maurer B, Stanczyk J, Jüngel A, Akhmetshina A, Trenkmann M, Brock M, Kowal-Bielecka O, Gay RE, Michel BA, Distler JH, Gay S, Distler O(2010). MicroRNA-29, a key regulator of collagen expression in systemic sclerosis. Arthritis Rheumat.;62:1733–1743. https://doi.org/10.1002/art.27443
22. Sun Y, Koo S, White N, Peralta E, Esau C, Dean NM, Perera RJ.( 2004) Development of a micro-array to detect human and mouse microRNAs and characterization of expression in human organs. Nucleic acids Res. ;32:e188. https://doi.org/10.1093/nar/gnh186
23. Tian Z, Greene AS, Pietrusz JL, Matus IR, Liang M(2008). MicroRNA-target pairs in the rat kidney identified by microRNA microarray, proteomic, and bioinformatic analysis. Genome Res. ;18: 404–411. https://doi.org/10.1101/gr.6587008
24. Kato M, Zhang J, Wang M, Lanting L, Yuan H, Rossi JJ, Natarajan R( 2007). MicroRNA-192 in diabetic kidney glomeruli and its function in TGF-beta-induced collagen expression via inhibition of E-box repressors. Proc Natl Acad Sci USA;104:3432– 3437. https://doi.org/10.1073/pnas.0611192104.
25. Kato M, Yuan H, Xu ZG, Lanting L, Li SL, Wang M, Hu MC, Reddy MA, Natarajan R. (2006)Role of the Akt/FoxO3a pathway in TGF-beta1-mediated mesangial cell dysfunction: a novel mechanism related to diabetic kidney disease. J. Amer. Soc. Nephrol.;17:3325–3335. https://doi.org/10.1681/ASN.2006070754
26. Chung AC, Lan HY. (2015) MicroRNAs in renal fibrosis. Front. Physiol. ;6:50. https://doi.org/10.3389/fphys.2015.00050
27. Putta S, Lanting L, Sun G, Lawson G, Kato M, Natarajan R. (2012) Inhibiting microRNA-192 ameliorates renal fibrosis in diabetic nephropathy. J. Amer. Soc. Nephrol. ;23:458–469. https://doi.org/10.1681/ASN.2011050485
28. Deshpande SD, Putta S, Wang M, Lai JY, Bitzer M, Nelson RG, Linda L Lanting, Kato M, Rama N(2013). Transforming growth factor-beta induced cross talk between p53 and a microRNA in the pathogenesis of diabetic nephropathy. Diabetes ;62:3151–3162. https://doi: 10.2337/db13-0305
29. Krupa A, Jenkins R, Luo DD, Lewis A, Phillips A, Fraser D(2010). Loss of MicroRNA-192 promotes fibrogenesis in diabetic nephropathy. J. Am. Soc. Nephrol.;21:438–447. https://doi: 10.1681/ASN.2009050530
30. Gulati S, Sengupta D, Sharma RK, Sharma A, Gupta RK, Singh U, Gupta A(2006)Steroid resistant nephrotic syndrome: role of histopathology. Indian Pediat. ;43:55–60.
31. Ranganathan S (2016). Pathology of podocytopathies causing nephrotic syndrome in children. Front. Pediat.;4:32. https://doi.org/10.3389/fped.2016.00032
32. Wang G, Tam LS, Li EK, Kwan BC, Chow KM, Luk, CC, Li PK, Szeto CC(2011). Serum and urinary free microRNA level in patients with systemic lupus erythematosus. Lupus, ;20:493–500. https://doi.org/10.1177/0961203310389841
33. Wang G, Kwan BC, Lai FM, Choi PC, Chow KM, Li PK, Szeto CC (2010). Intrarenal expression of microRNAs in patients with IgA nephropathy. Lab. Invest.;90:98–103. https://doi.org/10.1038/labinvest.2009.118
34. Lorenzen JM, Kielstein JT, Hafer C, Gupta SK, Kümpers P, Faulhaber‐ Walter R, Haller H, Fliser D, Thum (2011)T. Circulating miR‐210 predicts survival in critically ill patients with acute kidney injury. Clinical Journal of the Amer. Soc. Nephrol.; 6:1540–1546. https:// doi.org/10.2215/CJN.00430111
35. Cai X, Xia Z, Zhang C, Luo Y, Gao Y, Fan Z, Liu M, Zhang (2013). Serum microRNAs levels in primary focal segmental glomerulosclerosis. Pediat. Nephrol.;28:1797–1801. https://doi.org/10.1007/s00467-013-2434-7
36. Zhang W, Zhang C, Chen H, Li L, Tu Y, Liu C, Shi S. Zen K, Liu Z2014). Evaluation of microRNAs miR-196a, miR-30a-5P, and miR-490 as biomarkers of disease activity among patients with FSGS. Clin. J. Amer. Soc. Nephrol.;9:1545–1552. https://doi.org/10.2215/CJN.11561113
37. Bao J, Li X, Li Y, Huang C, Meng X, Li (2020)J. MicroRNA-141-5p acts as a tumor suppressor via targeting RAB32 in chronic myeloid leukemia. Front. Pharmacol. ;10:1545. https://doi.org/10.3389/fphar.2019.01545
38. He H, Wang L, Zhou W, Zhang Z, Wang L, Xu S, Wang D, Dong J, Tang C, Tang H, Yi X, Ge J.. MicroRNA expression profiling in clear cell renal cell carcinoma: Identification and functional validation of key miRNAs. PloS one, 2015;10: e0125672. https://doi.org/10.1371/journal.pone.0125672
39. Dweep H, Sticht C, Kharkar A, Pandey P, Gretz N. Parallel analysis of mRNA and microRNA microarray profiles to explore functional regulatory patterns in polycystic kidney disease using PKD/Mhm rat model. PloS one, 2013; 8: e53780. https://doi.org/10.1371/journal.pone.0053780
40. Ye H, Su B, Ni H, Li, L, Chen X, You X, Zhang H(2018). microRNA-199a may be involved in the pathogenesis of lupus nephritis viamodulating the activation of NF-κB by targeting Klotho. Mol. Immunol. ;103:235–242. https://doi.org/10.1016/j.molimm.2018.10.003
41. Godwin JG, Ge X, Stephan K, Jurisch A, Tullius SG, Iacomini J(2010). Identification of a microRNA signature of renal ischemia reperfusion injury. Proc Natl Acad Sci U S A.;107:14339-4344. https://doi: 10.1073/pnas.0912701107.
42. Butz H, Szabo PM, Nofech-Mozes R, Rotondo F, Kovacs K, Mirham L, Girgis H, Boles D, Patocs A, Yousef (2014)GM. Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma. Clin. Chem.;60:1314-1326. https://doi.org/10.1373/clinchem.2014.225854.
43. Wang WN, Zhang WL, Zhou GY, Ma FZ, Sun T, Su SS, Xu ZG( 2016)Prediction of the molecular mechanisms and potential therapeutic targets for diabetic nephropathy by bioinformatics methods. Int. J. Mol. Med.;37:1181-1188. https://doi.org/10.3892/ijmm.2016.2527
44. Teng J, Sun F, Yu PF, Li, JX, Yuan D, Chang J, Lin SH. (2015)Differential microRNA expression in the serum of patients with nephrotic syndrome and clinical correlation analysis. Int. J. Clin. Exp Pathol. ;8:7282–7286.
45. Busse M, Feta A, Presto J, Wilén M, Grønning M, Kjellén L, Kusche-Gullberg M. Contribution of EXT1, EXT2, and EXTL3 to heparan sulfate chain elongation. J Biol Chem. 2007 Nov 9;282(45):32802-10. doi: 10.1074/jbc.M703560200. Epub 2007 Aug 29. PMID: 17761672.
DOI: https://doi.org/10.1681/ASN.2018080852
46. .Heller, S., Li, Z., Lin, Q., Geusz, R., Breunig, M., Hohwieler, M., Zhang, X., Nair, G. G., Seufferlein, T., Hebrok, M., Sander, M., Julier, C., Kleger, A., & Costa, I. G. (2021).
Transcriptional changes and the role of ONECUT1 in hPSC pancreatic differentiation. Communications biology, 4(1), 1298. https://doi.org/10.1038/s42003-021-02818-3
47. Philippi A, Heller S, Costa IG, Senée V, Breunig M, Li Z, Kwon G, Russell R, Illing A, Lin Q, Hohwieler M, Degavre A, Zalloua P, Liebau S, Schuster M, Krumm J, Zhang X, Geusz R, Benthuysen JR, Wang A, Chiou J, Gaulton K, Neubauer H, Simon E, Klein T, Wagner M, Nair G, Besse C, Dandine-Roulland C, Olaso R, Deleuze JF, Kuster B, Hebrok M, Seufferlein T, Sander M, Boehm BO, Oswald F, Nicolino M, Julier C, Kleger A. Mutations and variants of ONECUT1 in diabetes. Nat Med. 2021 Nov;27(11):1928-1940. doi: 10.1038/s41591-021-01502-7. Epub 2021 Oct 18. PMID: 34663987; PMCID: PMC9356324.
48. Mehrabian, M., Hildebrandt, H., & Schmitt-Ulms, G. (2016). NCAM1 Polysialylation: The Prion Protein's Elusive Reason for Being?. ASN neuro, 8(6), 1759091416679074. https://doi.org/10.1177/1759091416679074
49. Hochheiser, K., Tittel, A., & Kurts, C. (2011). Kidney dendritic cells in acute and chronic renal disease. International journal of experimental pathology, 92(3), 193–201. https://doi.org/10.1111/j.1365-2613.2010.00728.x
50. Yao J, Le TC, Kos CH, Henderson JM, Allen PG, Denker BM, et al. (2004) α-Actinin-4-Mediated FSGS: An Inherited Kidney Disease Caused by an Aggregated and Rapidly Degraded Cytoskeletal Protein. PLoS Biol 2(6): e167. https://doi.org/10.1371/journal.pbio.0020167
51. Feng, D., DuMontier, C., & Pollak, M. R. (2015). The role of alpha-actinin-4 in human kidney disease. Cell & bioscience, 5, 44. https://doi.org/10.1186/s13578-015-0036-8
2. Carthew RW, Sontheimer E(J2009)Origins and Mechanisms of miRNAs and siRNAs. Cell. ;136:642–655. https://doi.org/10.1016/j.cell.2009.01.035
3. Ambros V. (2004) The functions of animal microRNAs. Nature.;431:350–355. https://doi.org/10.1038/nature02871
4. Shendure J, Ji (H2008). Next-generation DNA sequencing. Nature Biotechnol.,26: 1135–1145. https://doi.org/10.1038/nbt1486
5. Sanger, F, Coulson AR. A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J. Mol.Biol. 1975;94:441–448. https://doi.org/10.1016/0022-2836(75)90213-2
6. Maxam AM, Gilbert W. (1977 )A new method for sequencing DNA. Proceed.Nat.Acad. Sci. United States of America, ;74:560–564. https://doi.org/10.1073/pnas.74.2.560
7. Wang, WC, Lin FM, Chang, WC, Lin KY, Huang HD, Lin NS (2009) miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinform. ;10:328. https://doi:10.1186/1471-2105-10-328
8. Slatko BE, Gardner AF, Ausubel FM(2018). Overview of next-generation sequencing technologies. Curr. Protoc.Mol.Biol. ;122:e59. https://doi.org/10.1002/cpmb.59
9. Chen T, Wang C, Yu H, Ding M, Zhang C, Lu X, Zhang CY, Zhang C(2019). Increased urinary exosomal microRNAs in children with idiopathic nephrotic syndrome. EBioMedicine,;39:552-561. https://doi.org/10.1016/j.ebiom. 2018.11.018.
10. International Study of Kidney Disease in Children. Report,( 1981). The primary nephrotic syndrome in children. Identification of patients with minimal change nephrotic syndrome from initial response to prednisone. J. Pediatr. 1981;98:561–564.://doi.org/10.1016/s0022-3476(81)80760-3
11. Harpole M, Davis, J, Espina, V.( 2016) Current state of the art for enhancing urine biomarker discovery. Expert Rev.Proteom. ;13:609–626. https://doi.org(/10.1080/14789450.2016.1190651
12. Andrews S(2010). Fast QC : A Quality control tool for high throughput sequence data. http://www. bioinformatics.babraham.ac.uk/projects/fastqc/
13. Ewels P, Magnusson M, Lundin S, Käller M, (2016) MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics,;32:3047–3048, https://doi.org/10.1093/bioinformatics/btw354
14. Babraham TI. (2012).Trim Galore! Cambridge Babraham Institute, https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/
15. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17,10-12. doi:https://doi.org/10.14806/ej.17.1.200.
16. Kozomara A. Griffiths-Jones S.(2014) miRBase: Annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. ;42:D68–D73. https://doi.org/10.1093/nar/gkt1181
17. Love MI, Huber W, Anders S.( 2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. ;15: 550 https://doi.org/10.1186/s13059-014-0550-8
18. Agarwal V, Bell GW, Nam J-W, Bartel DP(2015). Predicting effective microRNA target sites in mammalian mRNAs. eLife.;4:e05005. https://doi.org/10.7554/eLife.05005
19. Yu G, Wang L-G, Han Y, He Q-Y.( 2012) ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: J. Integr. Biol.;16:284–287.
20. Xiao L, Zhou X, Liu, F. Hu C, Zhu X, Luo Y, Wang M, Xu X, Yang S, Kanwar YS, Sun L. (2015) MicroRNA-129-5p modulates epithelial-to-mesenchymal transition by targeting SIP1 and SOX4 during peritoneal dialysis. Lab Invest. ;95:817–832 https://doi.org/10.1038/labinvest.2015.57
21. Maurer B, Stanczyk J, Jüngel A, Akhmetshina A, Trenkmann M, Brock M, Kowal-Bielecka O, Gay RE, Michel BA, Distler JH, Gay S, Distler O(2010). MicroRNA-29, a key regulator of collagen expression in systemic sclerosis. Arthritis Rheumat.;62:1733–1743. https://doi.org/10.1002/art.27443
22. Sun Y, Koo S, White N, Peralta E, Esau C, Dean NM, Perera RJ.( 2004) Development of a micro-array to detect human and mouse microRNAs and characterization of expression in human organs. Nucleic acids Res. ;32:e188. https://doi.org/10.1093/nar/gnh186
23. Tian Z, Greene AS, Pietrusz JL, Matus IR, Liang M(2008). MicroRNA-target pairs in the rat kidney identified by microRNA microarray, proteomic, and bioinformatic analysis. Genome Res. ;18: 404–411. https://doi.org/10.1101/gr.6587008
24. Kato M, Zhang J, Wang M, Lanting L, Yuan H, Rossi JJ, Natarajan R( 2007). MicroRNA-192 in diabetic kidney glomeruli and its function in TGF-beta-induced collagen expression via inhibition of E-box repressors. Proc Natl Acad Sci USA;104:3432– 3437. https://doi.org/10.1073/pnas.0611192104.
25. Kato M, Yuan H, Xu ZG, Lanting L, Li SL, Wang M, Hu MC, Reddy MA, Natarajan R. (2006)Role of the Akt/FoxO3a pathway in TGF-beta1-mediated mesangial cell dysfunction: a novel mechanism related to diabetic kidney disease. J. Amer. Soc. Nephrol.;17:3325–3335. https://doi.org/10.1681/ASN.2006070754
26. Chung AC, Lan HY. (2015) MicroRNAs in renal fibrosis. Front. Physiol. ;6:50. https://doi.org/10.3389/fphys.2015.00050
27. Putta S, Lanting L, Sun G, Lawson G, Kato M, Natarajan R. (2012) Inhibiting microRNA-192 ameliorates renal fibrosis in diabetic nephropathy. J. Amer. Soc. Nephrol. ;23:458–469. https://doi.org/10.1681/ASN.2011050485
28. Deshpande SD, Putta S, Wang M, Lai JY, Bitzer M, Nelson RG, Linda L Lanting, Kato M, Rama N(2013). Transforming growth factor-beta induced cross talk between p53 and a microRNA in the pathogenesis of diabetic nephropathy. Diabetes ;62:3151–3162. https://doi: 10.2337/db13-0305
29. Krupa A, Jenkins R, Luo DD, Lewis A, Phillips A, Fraser D(2010). Loss of MicroRNA-192 promotes fibrogenesis in diabetic nephropathy. J. Am. Soc. Nephrol.;21:438–447. https://doi: 10.1681/ASN.2009050530
30. Gulati S, Sengupta D, Sharma RK, Sharma A, Gupta RK, Singh U, Gupta A(2006)Steroid resistant nephrotic syndrome: role of histopathology. Indian Pediat. ;43:55–60.
31. Ranganathan S (2016). Pathology of podocytopathies causing nephrotic syndrome in children. Front. Pediat.;4:32. https://doi.org/10.3389/fped.2016.00032
32. Wang G, Tam LS, Li EK, Kwan BC, Chow KM, Luk, CC, Li PK, Szeto CC(2011). Serum and urinary free microRNA level in patients with systemic lupus erythematosus. Lupus, ;20:493–500. https://doi.org/10.1177/0961203310389841
33. Wang G, Kwan BC, Lai FM, Choi PC, Chow KM, Li PK, Szeto CC (2010). Intrarenal expression of microRNAs in patients with IgA nephropathy. Lab. Invest.;90:98–103. https://doi.org/10.1038/labinvest.2009.118
34. Lorenzen JM, Kielstein JT, Hafer C, Gupta SK, Kümpers P, Faulhaber‐ Walter R, Haller H, Fliser D, Thum (2011)T. Circulating miR‐210 predicts survival in critically ill patients with acute kidney injury. Clinical Journal of the Amer. Soc. Nephrol.; 6:1540–1546. https:// doi.org/10.2215/CJN.00430111
35. Cai X, Xia Z, Zhang C, Luo Y, Gao Y, Fan Z, Liu M, Zhang (2013). Serum microRNAs levels in primary focal segmental glomerulosclerosis. Pediat. Nephrol.;28:1797–1801. https://doi.org/10.1007/s00467-013-2434-7
36. Zhang W, Zhang C, Chen H, Li L, Tu Y, Liu C, Shi S. Zen K, Liu Z2014). Evaluation of microRNAs miR-196a, miR-30a-5P, and miR-490 as biomarkers of disease activity among patients with FSGS. Clin. J. Amer. Soc. Nephrol.;9:1545–1552. https://doi.org/10.2215/CJN.11561113
37. Bao J, Li X, Li Y, Huang C, Meng X, Li (2020)J. MicroRNA-141-5p acts as a tumor suppressor via targeting RAB32 in chronic myeloid leukemia. Front. Pharmacol. ;10:1545. https://doi.org/10.3389/fphar.2019.01545
38. He H, Wang L, Zhou W, Zhang Z, Wang L, Xu S, Wang D, Dong J, Tang C, Tang H, Yi X, Ge J.. MicroRNA expression profiling in clear cell renal cell carcinoma: Identification and functional validation of key miRNAs. PloS one, 2015;10: e0125672. https://doi.org/10.1371/journal.pone.0125672
39. Dweep H, Sticht C, Kharkar A, Pandey P, Gretz N. Parallel analysis of mRNA and microRNA microarray profiles to explore functional regulatory patterns in polycystic kidney disease using PKD/Mhm rat model. PloS one, 2013; 8: e53780. https://doi.org/10.1371/journal.pone.0053780
40. Ye H, Su B, Ni H, Li, L, Chen X, You X, Zhang H(2018). microRNA-199a may be involved in the pathogenesis of lupus nephritis viamodulating the activation of NF-κB by targeting Klotho. Mol. Immunol. ;103:235–242. https://doi.org/10.1016/j.molimm.2018.10.003
41. Godwin JG, Ge X, Stephan K, Jurisch A, Tullius SG, Iacomini J(2010). Identification of a microRNA signature of renal ischemia reperfusion injury. Proc Natl Acad Sci U S A.;107:14339-4344. https://doi: 10.1073/pnas.0912701107.
42. Butz H, Szabo PM, Nofech-Mozes R, Rotondo F, Kovacs K, Mirham L, Girgis H, Boles D, Patocs A, Yousef (2014)GM. Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma. Clin. Chem.;60:1314-1326. https://doi.org/10.1373/clinchem.2014.225854.
43. Wang WN, Zhang WL, Zhou GY, Ma FZ, Sun T, Su SS, Xu ZG( 2016)Prediction of the molecular mechanisms and potential therapeutic targets for diabetic nephropathy by bioinformatics methods. Int. J. Mol. Med.;37:1181-1188. https://doi.org/10.3892/ijmm.2016.2527
44. Teng J, Sun F, Yu PF, Li, JX, Yuan D, Chang J, Lin SH. (2015)Differential microRNA expression in the serum of patients with nephrotic syndrome and clinical correlation analysis. Int. J. Clin. Exp Pathol. ;8:7282–7286.
45. Busse M, Feta A, Presto J, Wilén M, Grønning M, Kjellén L, Kusche-Gullberg M. Contribution of EXT1, EXT2, and EXTL3 to heparan sulfate chain elongation. J Biol Chem. 2007 Nov 9;282(45):32802-10. doi: 10.1074/jbc.M703560200. Epub 2007 Aug 29. PMID: 17761672.
DOI: https://doi.org/10.1681/ASN.2018080852
46. .Heller, S., Li, Z., Lin, Q., Geusz, R., Breunig, M., Hohwieler, M., Zhang, X., Nair, G. G., Seufferlein, T., Hebrok, M., Sander, M., Julier, C., Kleger, A., & Costa, I. G. (2021).
Transcriptional changes and the role of ONECUT1 in hPSC pancreatic differentiation. Communications biology, 4(1), 1298. https://doi.org/10.1038/s42003-021-02818-3
47. Philippi A, Heller S, Costa IG, Senée V, Breunig M, Li Z, Kwon G, Russell R, Illing A, Lin Q, Hohwieler M, Degavre A, Zalloua P, Liebau S, Schuster M, Krumm J, Zhang X, Geusz R, Benthuysen JR, Wang A, Chiou J, Gaulton K, Neubauer H, Simon E, Klein T, Wagner M, Nair G, Besse C, Dandine-Roulland C, Olaso R, Deleuze JF, Kuster B, Hebrok M, Seufferlein T, Sander M, Boehm BO, Oswald F, Nicolino M, Julier C, Kleger A. Mutations and variants of ONECUT1 in diabetes. Nat Med. 2021 Nov;27(11):1928-1940. doi: 10.1038/s41591-021-01502-7. Epub 2021 Oct 18. PMID: 34663987; PMCID: PMC9356324.
48. Mehrabian, M., Hildebrandt, H., & Schmitt-Ulms, G. (2016). NCAM1 Polysialylation: The Prion Protein's Elusive Reason for Being?. ASN neuro, 8(6), 1759091416679074. https://doi.org/10.1177/1759091416679074
49. Hochheiser, K., Tittel, A., & Kurts, C. (2011). Kidney dendritic cells in acute and chronic renal disease. International journal of experimental pathology, 92(3), 193–201. https://doi.org/10.1111/j.1365-2613.2010.00728.x
50. Yao J, Le TC, Kos CH, Henderson JM, Allen PG, Denker BM, et al. (2004) α-Actinin-4-Mediated FSGS: An Inherited Kidney Disease Caused by an Aggregated and Rapidly Degraded Cytoskeletal Protein. PLoS Biol 2(6): e167. https://doi.org/10.1371/journal.pbio.0020167
51. Feng, D., DuMontier, C., & Pollak, M. R. (2015). The role of alpha-actinin-4 in human kidney disease. Cell & bioscience, 5, 44. https://doi.org/10.1186/s13578-015-0036-8
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Jun 30, 2023
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C.D.Mohanapriya, Vettriselvi Venkatesan, Sangeetha Geminiganesan, Pricilla charmine, Yogalakshmi Venkatachalapathy, & Praveen Kumar. (2023). Analysis of microRNAs signatures in juvenile glomerular proteinuria. Journal of Population Therapeutics and Clinical Pharmacology, 30(16), 342–353. https://doi.org/10.47750/jptcp.2023.30.16.046
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