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MicroRNAs in idiopathic childhood nephrotic syndrome

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Abstract

Background

miRNAs are non-coding RNA that are recognized as biomarkers of kidney disorders. There is limited information on the differential expression of miRNA and their target genes in idiopathic nephrotic syndrome of childhood.

Methods

We enrolled patients, 2–18 years old, with steroid-sensitive nephrotic syndrome, either at onset or during relapse, and steroid-resistant disease, at diagnosis of steroid-resistance. Patients with steroid-sensitive disease were off immunosuppressive medications, while those with steroid-resistance were on therapy with prednisolone at enrollment. Controls were healthy children attending the hospital for vaccinations or for minor non-infectious, non-kidney ailments. Following RNA extraction from whole blood, differential expression of 2549 miRNAs was examined to identify differentially expressed miRNA, defined as those with absolute log2 fold change > 2 and adjusted P < 0.05. Target genes, predicted using miRNet, were compared against the genes for nephrotic syndrome in the NCBI database, and the ontology of selected genes was examined using DAVID.

Results

Comparison of miRNA expression in 36 patients and 12 controls led to the identification of 62 and 12 differentially expressed miRNA in patients with steroid-sensitive and steroid-resistant disease, respectively. Of 76 miRNAs that were differentially regulated between the two disease categories, 26 were unique to steroid-sensitive disease and 11 to steroid-resistance. Of 5955 and 2813 genes targeted by the miRNAs specific to steroid-sensitive and steroid-resistant nephrotic syndrome, respectively, 79 were relevant in context of the disease.

Conclusion

Steroid-sensitive and steroid-resistant nephrotic syndrome have distinct miRNA expression profiles, which can be examined as biomarkers and in pathogenetic pathways.

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Acknowledgements

This work was supported by funding from the Department of Biotechnology, Government of India (BT/PR11030/MED/ 30/1644/2016). The authors acknowledge the support of the All India Institute of Medical Sciences, New Delhi, and application scientists, Dr. Ratnesh Tripathi at the Agilent Technologies, Gurugram, and Dr. Gaurav Garg, at LCGC, Life Sciences LLP, New Delhi, India, in enabling the work carried out in the study.

Funding

This work was supported by Department of Biotechnology, Government of India (BT/PR11030/MED/30/1644/2016, Arvind Bagga).

Author information

Author notes

  1. Manraj Sra and Aijaz Ahmed have contributed equally to the work.

Authors and Affiliations

  1. Division of Nephrology and ICMR Center for Advanced Research in Nephrology, Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, 110029, India

    Aditi Sinha, Manraj Sra, Aijaz Ahmed, Himanshi Saini, Kshetrimayum Ghanapriya Devi, Pankaj Hari & Arvind Bagga

  2. Department of Pathology, All India Institute of Medical Sciences, New Delhi, India

    Saumyaranjan Mallick

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  1. Aditi Sinha
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Corresponding author

Correspondence to Arvind Bagga.

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Conflict of interest

All the authors have declared that there is no conflict of interest and no competing interest.

Ethical approval

This study reports findings of research involving human participants that was conducted in compliance with ethical standards and good clinical practices, and followed written informed parental consent. All procedures performed were in accordance with the ethical standards of the institutional ethics committee (IRB approval ID: IEC/NP-334/05.09.2014; RP-10/2015) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Supplementary Information

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10157_2024_2595_MOESM1_ESM.pptx

Supplementary file1 Supplementary Figure S1. Sample electropherograms for visual assessment of RNA integrity, generated using Agilent 2100 expert software. Samples labelled as ‘pass’, based on visual assessment and RNA integrity number (RIN) >7.0, were used for differential expression of miRNAs. Sample IDs are labelled by disease (C controls; SR steroid resistant nephrotic syndrome; SS steroid sensitive nephrotic syndrome) and time-point (D diagnosis; FU follow up, i.e. remission) (PPTX 11800 KB)

10157_2024_2595_MOESM2_ESM.ppt

Supplementary file2 Supplementary Figure S2. Assessment of miRNA quality by scatter plots of normalized intensity values. Sample IDs are labelled by disease (C controls; SR steroid resistant nephrotic syndrome; SS steroid sensitive nephrotic syndrome) and time-point (D diagnosis; FU follow up, i.e. remission) (PPT 267 KB)

10157_2024_2595_MOESM3_ESM.pptx

Supplementary file3 Supplementary Figure S3. Volcano plots for the top miRNAs which were differentially expressed between patients with (a) steroid resistant nephrotic syndrome versus controls, (b) steroid sensitive nephrotic syndrome versus controls, and (c) steroid resistant versus steroid sensitive nephrotic syndrome. Top miRNAs are shown as bold black circles with leading lines, based on Euclidean distance, using threshold for log2 fold change of ±1.5 and P <0.01 (PPTX 114 KB)

10157_2024_2595_MOESM4_ESM.ppt

Supplementary file4 Supplementary Figure S4. Scatter plots of signal intensity from probes in linear models to indicate differences in miRNA expression between patients with (a) steroid resistant nephrotic syndrome versus controls, (b) steroid sensitive nephrotic syndrome versus controls, (c) steroid resistant versus steroid sensitive nephrotic syndrome, (d) steroid resistant nephrotic syndrome at diagnosis versus follow-up (remission), and (e) steroid sensitive nephrotic syndrome at diagnosis versus follow-up (remission). The X and Y axes of the scatter plot provide the log2 scaled normalized signal values for respective samples. The individual symbols, each representing an miRNA, are colored according to the signal intensity (red is high, yellow is intermediate, and blue is low). These symbols are located above or below the middle green line based on whether the respective miRNAs are over or under expressed, respectively, in Y versus X. The miRNAs whose relative expression between the two categories differs by more than two-fold fall outside the two-fold variation interval indicated by the outer diagonal green lines. The slope of the best fit regression line, shown as a black diagonal line, indicates the overall trend of miRNAs toward up or downregulation for the comparison between categories. This slope is also reflected in the coefficient of x in the equation on the top left corner (PPT 603 KB)

10157_2024_2595_MOESM5_ESM.ppt

Supplementary file5 Supplementary Figure S5. Cluster dendograms and heat maps on hierarchical clustering. Plots (a-d) show results of unsupervised clustering performed (a-b) with or (c-d) without including samples taken during follow up, to show (a, c) top 30 and (b, d) top 10 miRNAs. (e-h) Supervised clustering was similarly performed (e-f) with or (g-h) without including samples taken during follow up, to show (e, g) top 30 and (f, h) top 10 miRNAs. Sample IDs are labelled by disease (C controls; SR steroid resistant nephrotic syndrome; SS steroid sensitive nephrotic syndrome) and time-point (D diagnosis; FU follow up) (PPT 1375 KB)

10157_2024_2595_MOESM6_ESM.pptx

Supplementary file6 Supplementary Figure S6. Protein-protein interaction for genes targeted by miRNAs differentially regulated in (a) nephrotic syndrome; (b) steroid-resistant nephrotic syndrome and (c) steroid sensitive nephrotic syndrome (disconnected nodes are hidden) (PPTX 587 KB)

Supplementary file 7: Supplementary Table S1 (DOCX 19 KB)

Supplementary file 8: Supplementary Table S2 (DOCX 30 KB)

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Sinha, A., Sra, M., Ahmed, A. et al. MicroRNAs in idiopathic childhood nephrotic syndrome. Clin Exp Nephrol 29, 477–484 (2025). https://doi.org/10.1007/s10157-024-02595-3

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