Research Article - Current Pediatric Research (2023) Volume 27, Issue 3
Report of novel urinary microRNAs in childhood Nephrotic syndrome: A pilot study.
Mohanapriya Chinambedu Dhandapani*, Vettriselvi Venkatesan, Sangeetha Geminiganesan, Pricilla Charmine, Yogalakshmi Venkatachalapathy, Praveen Kumar
1Department of Human Genetics Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai 600-116, Tamil Nadu, India
- Corresponding Author:
- Dr. CD Mohanapriya
Assistant Professor (Research) Department of Human Genetics SRIHER, Porur Chennai-600116, India
Received: 27 February, 2023, Manuscript No. AAJCP-23-89562; Editor assigned: 01 March, 2023, Pre QC No. AAJCP-23-89562(PQ); Reviewed: 10 March, 2023, QC No. AAJCP-23-89562; Revised: 20 March, 2023, Manuscript No. AAJCP-23-89562(R); Published: 30 March, 2023, DOI:10.35841/0971-9032.27.03.1828-1836.
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 (10 nos), SRNS (10 nos), and healthy controls (10 nos). Isolated RNA enriched for small RNA High Throughput Sequencing (HTS), and the sequence data were generated using Illumina Hiseq sequencing technology (clever gene biocorp Pvt. Ltd., Bengaluru, India). The expression profile of the differentially expressed miRNAs across the samples is presented in volcano plot and heat map. 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 down-regulated as compared to control group. Among the 19 miRNAs of SRNS patients, 8 miRNAs were found to be significantly up-regulated, while 11 were found to be down-regulated 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 study could be promising and non-invasive potential biomarker candidates for diagnosis and prognosis of pediatric NS.
microRNA profiling, Nephrotic syndrome, Next-generation sequencing, Urine biomarkers.
microRNAs (miRNAs) are small, single stranded, non-coding RNAs of 22–25 base length, regulating posttranscriptional gene expression primarily by promoting mRNA degradation or inhibiting translation and thus have a globalized role in a wide variety of biological functions, such as cellular proliferation, apoptosis, tumorigenesis, and fibrosis in various target organs [1-3]. The advent of Next-Generation Sequencing (NGS) technology has enabled the sequencing of the entire set of miRNAs present in an RNA sample. Expression profiling, identification of sequence isoforms, prediction of novel miRNAs, and prediction of possible mRNA target molecules can all be done with the data collected from such sequencing investigations to evaluate miRNA expression and function .
NGS involves simultaneous sequencing of many molecules at a time, as opposed to "first generation" sequencing, which requires sequencing of one molecule at a time [5,6]. This advancement is fast, reliable, and has lowered the time and cost per sequenced base, as well as increased the scope of sequencing applications [7,8]. MiRNAs, in view of their stability, specificity, ease of detection and quantification are commonly reported as potential biomarkers for various diseases, including Nephrotic Syndrome (NS) . In children, responsiveness to Glucocorticoid (GC) therapy has been shown to be the best indicator of prognosis. Based on the responsiveness to GC therapy, NS is classified as Steroid- Sensitive (SSNS) or Steroid-Resistant (SRNS). According to international study of kidney disease in children  80%– 90% of patients with childhood NS achieve complete remission (SSNS).
In contrast, approximately 20% of children fail to enter complete remission after 8 weeks of GC therapy and are clinically Steroid Resistant (SRNS). Even though kidney biopsy is the benchmark process to differentiate these diseases, it is an invasive method with possible complications. Therefore, a non-invasive method is required to differentiate between different etiologies of NS. Although blood is the most common specimen used for studying biomarkers, using urine specimen has several advantages which include:
• Ease and non-invasiveness of collection procedure,
• Availability in large amounts,
• If well preserved, urine samples are stable for very long time periods,
• Multiple samples can be collected without causing discomfort to the patient .
Hence, in the present study, urine specimens are opted to identify potentially useful miRNAs as biomarkers by NGS as well as to focus on the data analysis aspect, and bioinformatics analysis of miRNA-sequencing data.
Materials and Methods
Patient recruitment and data collection
The study group consisted of 30 children with NS (10 SSNS and 10 SRNS) and 10 normal subjects without a family history of renal disease. They were recruited from the pediatric nephrology department of Sri Ramachandra Institute of Higher Education and Research (SRIHER) Ref: 33/DOF/2020 Chennai, India. The research protocol was approved by the institutional review board/ethics committee of SRIHER . Written informed consent was obtained from the parents of both patients and healthy controls. All cases fulfilled the criteria of the international standard of kidney disease in children  for the diagnosis of the NS. Children with NS aged between 16 months and 16 years were included in the study if they present clinical symptoms of proteinuria, hypoalbuminemia, and edema. The clinical response of each patient to GC (i.e., SRNS or SSNS) was assessed approximately 6 to 10 weeks after initial presentation. Cases with secondary NS and those from consanguineous families, urinary tract infection, other immune diseases, and age more than years old were excluded .
Sample collection and processing
About 5 ml of urine samples were collected from 20 NS patients and 10 healthy control groups among which in NS, 10 subjects responded to steroid therapy (SSNS) and 10 subjects after 6 to 10 weeks of GC therapy showed steroid resistance (SRNS). The samples are processed immediately by centrifuging at 3000 × g for about 20 min and at 13,000 × g for 5 min at 4°C. The supernatant was discarded and the urinary cell pellet was stored for microRNA isolation .
MicroRNA isolation was carried out for SSNS (10 nos), SRNS (10 nos) and healthy controls (10 nos), using MiRNeasy Mini kit (Qiagen) according to the manufacturer’s instructions. About 3.5 μl miRNeasy serum/plasma spike-in controls was added to urine samples, mixed thoroughly with equal volume of chloroform with a vortex, stood at room temperature for 2–3 min and centrifuged at 12,000 × g at 4°C for 15 min. The upper aqueous phase was transferred to a sterile and RNase-free tube, and 1.5 volumes of 100% ethanol were added .
The isolated nucleic acids were quantified to determine the concentrations and purity of the samples. The concentration and quality of extracted RNA were assessed by spectrophotometry on the Nano drop 1000 (thermo scientific, Waltham, MA). The ratio of the absorbance at 260 and 280 nm (A260/280) is used to assess the purity of nucleic acids. About 2 μl of isolated sample was added to the lower pedestal of the Nano drop and the purity of nucleic acid was assessed for the entire collected urine sample using Nano drop equipment. A ratio of 1.7-2.0 was generally accepted as a good quality. All 30 samples were subjected for both qualitative and quantitative analysis .
Small RNA sequencing using NGS platform
Isolated miRNA from each subject was pooled before proceeding with small RNA High Throughput Sequencing (HTS), and the sequence data were generated using Illumina HiSeq NGS technology, (clever gene biocorp Pvt. Ltd., Bengaluru, India). The quality of the data was checked using FastQC12 and MultiQC13 software. The adapter sequences, low-quality bases, and the reads shorter than 14 bp were removed using the trim galore 14 and Cutadapt v3.415 program (Table 1). (The generated miRNA sequencing data were submitted in Sequence Read Archive (SRA) database in NCBI (Ref: bio project ID: PRJNA858929).
|Sample name||Number of reads||Read length|
|Reads after Qc filtering and trimming|
|Sample name||Number of reads||Read length|
Table 1. Filtering raw data using trim galore and cut adapt tools to remove unwanted reads.
Alignment and identification of miRNA
The QC passed reads were mapped onto indexed human reference genome (GRCh38.p7) using mapper.pl script of miRdeep2 v220.127.116.11  (Table 2). Reads mapped to the reference genome were used to identify miRNA with miRdeep2 using known and novel miRNA identification parameters. Human mature miRNAs from mirBase v22.116 were used for miRNA prediction.
Total miRNAs (Known Novel)
Table 2. Identification of known and novel miRNA using miRdeep2 tool.
Total 212 novel and 284 known miRNAs were identified; miRNAs with miRDeep score less than 1 were excluded from further analysis resulting in 205 novel and 284 known miRNAs (Table 3). Expression levels of miRNAs were estimated using miRdeep2 quatifiler.pl script.
|Total miRNA||Filtered miRNA||miRNA Predicted miRdeep2|
Table 3. Known and Novel miRNAs predicted using miRdeep2 after filtering.
Differential expression analysis was carried out using the DESeq217 package. The read counts were normalized and differential expression was tested comparing the test group with control group. MiRNAs with absolute log2 fold change ≥ 1 and p-value ≤ 0.05 were considered significant (Table 4). The expression profile of the differentially expressed miRNAs across the samples is presented heat map and as volcano plot (Figures 1-4).
Tested miRNAs (Known+Novel)
Significantly expressed miRNAs
Down regulated miRNAs
Control vs. SSNS
Table 4. Number of differentially expressed miRNA comparing test with control groups.
Expression similarity between biological replicates was checked by spearman correlation (data is in 03_correlation_matrix.xlsx) and principal components analysis.
Small RNAs from Control, SSNS and SRNS
To identify miRNAs in control and NS patients, we generated total RNAs from urine specimens for high-throughput sequencing, yielding 18,447,872, 15,342,660 and 16,075,142 raw read totals from the normal, SSNS and SRNS patients, respectively. Following filtration and trimming, 4,185,183, 5,732,351 and 7,104,038 clean reads were obtained and mapped onto the NS reference genome, respectively (mature miRNAs from mirBase v22.1 and were used for miRNA prediction). The length distribution analysis of raw and filtered reads showed that the small RNA length from control and NS patients varied from 17 to 50 nt in normal subjects, SSNS and SRNS.
miRNA identification from control, SSNS and SRNS patients
The processing of filtered reads from high-throughput sequencing was carried out according to miRDeep-P workflow, and the expression levels of miRNAs were estimated using miRdeep2 quatifiler.pl script with the NS genome as the reference sequence. For the control samples, a total of 322 miRNAs were identified, out of which 293 miRNAs were expressed, resulting in 180 known and 113 novel miRNAs. In the SSNS patients, 281 expressed miRNAs including 149 known and 132 novel miRNAs were identified, and in the SRNS RNA samples, out of 230 expressed miRNAs, 79 known and 151 novel miRNAs were identified.
Sequencing and differential expression analysis revealed significant differences in the 4 miRNAs expressed (1 upregulated, and 3 down-regulated) in the SSNS vs. control samples. When SRNS samples were compared with control samples, significant difference was observed in the 19 miRNAs expressed (8 up-regulated and 11 down-regulated). The down regulated genes include the following: a) hsamir- 129-5p, b) hsa-mir-199a-5p, c) hsa-mir-199a-5p, d) hsamir- 129-5p, e) hsa-mir-320a-5p, f) hsa-mir-141-5p, g) hsamir- 200c-5p, h) hsa-mir-192-5p, i) hsa-mir-1307-5p, j) hsamir- 200a-5p, k) hsa-mir-29c-5p.
We constructed a network on the significantly up-regulated novel miRNA based on target scan (miRNA target prediction tool) consisting of 134 miRNA targeted genes with 139 nodes and 138 edges. The score 170 was adopted as the cut-off value, the more positive the score, the greater the repression. Volcano plot and heat map represented the expression profile of the differentially expressed miRNAs across the samples (Figure 1-4 respectively). 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)
The cumulative weighted context++ score <-0.4 was adopted as the cut-off value. The cumulative weighted context++ score is the sum of the contribution of 14 features for each of the four site types, the more negative the score, the greater the repression. The target prediction results are provided in “Known-miRNAs and novel miRNAs that was predicted are represented in Figures 5-7 respectively.
GO and pathway enrichment analysis of target genes
Gene ontology and pathways were enriched for target genes using the cluster profiler R bio conductor package. After identifying the Differentially Expressed Genes (DEGs) in NS patients and control group, we employed the Database for Annotation, Visualization and Integration Discovery (DAVID) to identify the functions of the identified DEGs and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg), pathway analyses . A total of 23 GO terms were significantly enriched among DEGs in samples obtained from SRNS children. To investigate the biological classification of DEGs, the overall genes in three ontologies were identified using DAVID. P-values <0.05 after adjustment by the Benjamini- Hochberg method were regarded as statistically significant. The novel microRNAs targeted genes and their chromosome location along with the functions including the disease (Figure 8). From the total data provided, top 20 significantly enriched gene ontology terms and KEGG pathways are presented in Figure 9.
GO and pathway enrichment analysis of target genes gene ontology and pathways were enriched for target genes using the cluster profiler bio conductor package. Top 20 significantly enriched gene ontology terms and KEGG pathways are presented.
KEGG pathway analysis
Enrichment analysis also showed that the glomerular and tubulointerstitial compartments underwent a wide range of unique pathological changes during chronic injury. As revealed by the GO enrichment analysis, shared DEGs in glomeruli were significantly enriched in exosomes. Notably, other diseases such as staphylococcus aureus infection and autoimmune thyroid disease were also listed as enriched terms, indicating that these disorders may share similar pathophysiological pathways with CKD.
MicroRNAs have been widely reported as promising biomarkers for many diseases, including NS as they not only help in the diagnosis of specific diseases, but also for understanding the disease pathogenesis and even for establishing novel therapeutics . The present study was attempted to investigate the urinary miRNA profile in a cohort of children with NS using Illumina SBS technology and bioinformatics.
The present study revealed that the signature of urinary miRNAs from paediatric NS was markedly different from that of healthy children, with 4 and 19 miRNAs being significantly increased in SSNS and SRNS samples, respectively, compared to that of normal subjects. Among the 4 miRNAs of SSNS patients, 3 miRNAs were found to be significantly elevated, while only one was found to be low as compared control. Among the 19 miRNAs of SRNS children, 8 miRNAs were found to be significantly upregulated, while 11 were found to be low as compared control.
The down regulated genes include the following: a) hsamir- 129-5p, b) hsa-mir-199a-5p, c) hsa-mir-199a-5p, d) hsamir- 129-5p, e) hsa-mir-320a-5p, f) hsa-mir-141-5p, g) hsamir- 200c-5p, h) hsa-mir-192-5p, i) hsa-mir-1307-5p, j) hsamir- 200a-5p, k) hsa-mir-29c-5p.
Since microRNA exerts its biological effects through suppression of target genes, it is necessary to identify microRNA–target pairs to understand the biological significance of specific microRNAs. miRNA-129-5p is one of the miRNAS identified in the present study. MiRNA-129-5p is believed to be a potent downstream inhibitor of TGF-β1 in renal fibrosis . According to them, decreased expression of miR-129-5p was accompanied with alterations in EMT-related genes and the expression of respective proteins in vivo. In addition, in vitro studies revealed reduced expression of Ecadherin and claudin-1 with increased cell migration in HMrSV5, a Human Peritoneal Mesothelial Cell line (HPMC), treated with TGF-β1 and increased expression of vimentin, fibronectin and transcription factors, Smad-Interacting Protein-1 (SIP-1) and SOX4. Furthermore, alteration in EMTrelated genes and proteins were reversed by overexpression of miR-129-5p. In systemic sclerosis a disease characterized by widespread fibrosis expression of the mmu-miR-29 family is decreased. mmu-miR-29a-5p suppresses the expression of collagen type I and III . Furthermore, TGF-β suppressed miR-29a-5p which resulted in feed forward up-regulation of TGF-β.
Another miR-192 was shown to be highly expressed in the kidney, as compared with other organs [22,23]. In the context of renal fibrosis, expression levels of miR-192 increased significantly in glomeruli isolated from diabetic mice . Evidence shows that the levels of Transforming Growth Factor β1 (TGF-β1), a major mediator of renal fibrosis are increased under diabetic conditions in renal cells, including Mesangial Cells (MC), can up-regulate ECM proteins such as collagens, and also can promote MC survival and oxidant stress . To impair normal kidney function. A number of studies have shown that TGF-β regulates expression of several microRNAs, such as miR-21, miR-192, miR-200, miR-433, and miR-29. miR-21, and miR-433 which are positively induced by TGF-β signaling play a pathological role in kidney diseases.
In contrast, members in both miR-29 and miR-200 families which are inhibited by TGF-β signaling protect kidneys from renal fibrosis by suppressing the deposition of ECM and preventing epithelial-to-mesenchymal transition, respectively . miR-192 was proved to repress zinc finger E-box binding homeobox 1/2 (Zeb1/2) and mediates TGF-β–induced collagen expression in MCs in vitro [24,27]. Further supportive evidence can be drawn from the findings in miR-192 KO mice . Deletion of miR-192 gene in type I diabetic mice reduced proteinuria, albuminuria, renal fibrosis, and hypertrophy as compared to diabetic wild-type mice. Taken together, these studies demonstrate a pro-fibrotic role of miR-192 in TGF-β- dependent renal fibrosis observed in animal models of diabetic and obstructive nephropathy. In contrast, the reverse is true in human nephropathy. Krupa, et al.  have shown that TGF-β1 suppresses miR-192 expression in human Tubular Epithelial Cells (TEC) and loss of miR-192 promotes fibro genesis in diabetic nephropathy.
Therefore, in the present study, the differential expression of miR-192, miR-129-5p, miR-29a-5p, miR-200a, miR-200b, miR-200c and miR-141 observed may be due to the participation of these miRNAs in the pathogenesis of SRNS and was a clear manifestation of renal fibrosis. Supportive evidence can be drawn from studies showing significant tubulointerstitial fibrosis and/or glomerulosclerosis in the biopsy of SRNS children [30,31]. TGF-β and miR-192 increased the expression of miR-200 family members (miR-200a, miR-200b, miR-200c and miR-141) which can also target Zeb1/2.24. The levels of many miRNAs, such as miR-200a, miR-200c, miR-141, miR-429 and miR-192, are lower in patients with active lupus nephritis than those in healthy controls . Studies of the intra-renal expression of miRNAs in IgAN patients revealed that miR-200c was downregulated while miR-141, miR-192 and miR-205 were upregulated [33,34].
These authors have also reported that the downregulation of intrarenal miR-200c is inversely correlated with proteinuria. Another study reported that patients with Minimal Change Disease (MCD) or Focal Glomerulo Sclerosis (FSGS) had higher levels of urinary miR-200c than those with other causes of NS.34 Cai, et al.  reported that serum miR-192 and miR-205 levels were higher in FSGS patients compared to MCNS patients. Levels of serum miR192 and miR-205 were correlated with proteinuria in FSGS patients while miR-192 levels, not miR-205 levels, were correlated with proteinuria in MCNS. In addition, serum miR-192 levels were correlated with interstitial fibrosis in FSGS patients.
miR-320 was also reported to be higher in FSGS patients relative to control and patients with FSGS remission . miR-320 expression level was also significantly elevated in patients with acute kidney disease . microRNA-141-5p, an important member of the miR-200 family, has been reported to be involved in cellular proliferation, migration, invasion, and drug resistance in different kinds of human malignant tumors. However, the role and function of miR-141-5p in SSNS or SRNS are not known. Bioinformatics analysis predicted that RAB32 was the potential target of miR-141-5p. miR-141 plays a dual role of an oncogene or tumor suppressor by regulating target genes . miR-199a was shown to be involved in the renal development and disease.
miR-199a expression is down-regulated in human renal cell carcinoma and deregulated in rodent genetic models of polycystic kidney disease, in lupus nephritis, and in ischemia/ reperfusion injury [38-41]. Bioinformatics has emerged as a powerful tool for providing comprehensive insights into the molecular mechanisms of disease and identifying potential biomarkers and therapeutic targets.42.43 The GO function annotation is divided into three functional groups, Biological Process (BP), Molecular Function (MF), and Cell Component (CC), (Figure 2; Biological Process (BP), n=401, Molecular Function (MF), n=89; Cellular Component (CC), n=66) as determined by adjusted p-values (Benjamini-Hochberg). The top 20 enriched pathway terms are shown in Figures 4 and 5 [42-49].
For BP, several signaling pathways (e.g., membrane potential, SMAD1-mediated signal transduction, cell-cell adhesion via plasma membrane adhesion molecule, neuron projection and development, sodium ion transmembrane transport, axonogenesis etc) were found to be dysregulated. Molecular functions include calcium ion transmembrane transporter activity, ligand gated ion channel activity, microtubule binding, actin binding, phosphatase binding, protein serine/threonine kinase activity . Cellular components include microtubule, neuronal cell body, focal adhesion, glutaminergic synapse, synaptic membrane, cell-cell communication, KEGG includes RAP1 signaling pathway, cAMP signaling pathway, RAS signaling pathway, calcium signaling pathway, MAPK pathway, PI3K-AKT signaling pathway .
Notably, other diseases such as prion disease, Huntington disease, amyotrophic lateral sclerosis, Alzheimer disease, human T-cell leukemia virus-1 infection and salmonella infection, were also listed as enriched terms, indicating that these disorders may share similar pathophysiological pathways with NS. To sum up, the present study explored the role of urinary miRNAs as non-invasive biomarkers for the diagnosis of children with NS using high-throughput sequencing technology. The present study is unique in reporting few novel miRNAs associated with steroid resistance of nephrotic children. Bio-informatics analysis further shows some target genes of these miRNAs were associated with neuronal and renal pathological changes. Although studies have shown that the characteristic expression changes in MiR-29a, miR-192 and miR-200c, can be used as markers for disease diagnosis and severity evaluation in the urine of adult patients with NS44, studies on the direct role of miRNAs in children with NS are limited. Therefore, inference can be drawn based on the previous studies on the expression profiles of miRNAs and their targets in various kidney diseases.
We would like to acknowledge Young Faculty Grant Intramurals GATE for supporting to carry out this work.
Funding was provided by Young Faculty Grant Intramurals GATE project (Ref No: Ref: 33/DOF/2020)
Compliance with Ethical Standards
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from the entire individual participant included in the study.
Statement and Declarations
Conflict of interest
The authors report no conflicts of interest and they are responsible for the content and writing of this article
Availability of data and materials’ statement
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
- Bartel DP. MicroRNAs: Target recognition and regulatory functions. Cell 2009; 136(2): 215–233.
- Carthew RW, Sontheimer E. Origins and mechanisms of miRNAs and siRNAs. Cell 2009; 136(4): 642–655.
- Ambros V. The functions of animal microRNAs. Nature 2004; 431(7006): 350–355.
- Shendure J, Hanlee J. Next-generation DNA sequencing. Nature Biotechnol 2008; 26: 1135–1145.
- Sanger, F, Coulson AR. A rapid method for determining sequences in DNA by primed synthesis with DNA polymerase. J Mol Biol 1975; 94(3): 441–448.
- Maxam AM, Gilbert W. A new method for sequencing DNA. Proc Natl Acad Sci USA 1977; 74(2): 560–564.
- Wang WC, Lin FM, Chang WC, et al. miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression. BMC Bioinformatics 2009; 10: 328.
- Slatko BE, Gardner AF, Ausubel FM. Overview of next-generation sequencing technologies. Curr Protoc Mol Biol 2018; 122(1): e59.
- Chen T, Wang C, Yu H, et al. Increased urinary exosomal microRNAs in children with idiopathic nephrotic syndrome. EBio Medicine 2019; 39: 552-561.
- International study of kidney disease in children. The primary nephrotic syndrome in children. Identification of patients with minimal change nephrotic syndrome from initial response to prednisone. J Pediatr 1981; 98(4): 561–564.
- Harpole M, Davis J, Espina V. Current state of the art for enhancing urine biomarker discovery. Expert Rev Proteom 2016; 13(6): 609–626.
- Andrews S. Fast QC: A Quality control tool for high throughput sequence data.
- Ewels P, Magnusson M, Lundin S, et al. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016; 32: 3047–3048.
- Babraham TI. Trim Galore! Babraham Bioinformatics.
- Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011; 17: 10-12.
- Kozomara A, Griffiths-Jones S. miRBase: Annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 2014; 42(Database issue): D68–D73.
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15: 550
- Agarwal V, Bell GW, Nam J-W. Predicting effective microRNA target sites in mammalian mRNAs. Elife 2015; 4: e05005.
- Yu G, Wang L-G, Han Y, et al. Clusterprofiler: An R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284–287.
- Xiao L, Zhou X, Liu F. et al. MicroRNA-129-5p modulates epithelial-to-mesenchymal transition by targeting SIP1 and SOX4 during peritoneal dialysis. Lab Invest 2015; 95(7): 817–832.
- Maurer B, Stanczyk J, Jüngel A, et al. MicroRNA-29, a key regulator of collagen expression in systemic sclerosis. Arthritis Rheum 2010; 62(6): 1733–1743.
- Sun Y, Koo S, White N, et al. Development of a micro-array to detect human and mouse microRNAs and characterization of expression in human organs. Nucleic acids Res 2004; 32(22): e188.
- Tian Z, Greene AS, Pietrusz JL, et al. MicroRNA-target pairs in the rat kidney identified by microRNA microarray, proteomic, and bioinformatics analysis. Genome Res 2008; 18(3): 404–411.
- Kato M, Zhang J, Wang M, et al. 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 2007; 104(9): 3432–3437.
- Kato M, Yuan H, Xu ZG, et al. Role of the Akt/FoxO3a pathway in TGF-beta1-mediated mesangial cell dysfunction: A novel mechanism related to diabetic kidney disease. J Am Soc Nephrol 2006; 17(12): 3325–3335.
- Chung AC, Lan HY. MicroRNAs in renal fibrosis. Front Physiol 2015; 6: 50.
- Putta S, Lanting L, Sun G, et al. Inhibiting microRNA-192 ameliorates renal fibrosis in diabetic nephropathy. J Amer Soc Nephrol 2012; 23(3): 458–469.
- Deshpande SD, Putta S, Wang M, et al. Transforming growth factor-beta induced cross talk between p53 and a microRNA in the pathogenesis of diabetic nephropathy. Diabetes 2013; 62(9): 3151–3162.
- Krupa A, Jenkins R, Luo DD, et al. Loss of MicroRNA-192 promotes fibrogenesis in diabetic nephropathy. J Am Soc Nephrol 2010; 21(3): 438–447.
- Gulati S, Sengupta D, Sharma RK, et al. Steroid resistant nephrotic syndrome: Role of histopathology. Indian Pediat 2006; 43: 55–60.
- Ranganathan S. Pathology of podocytopathies causing nephrotic syndrome in children. Front Pediat 2016; 4(6): 32.
- Wang G, Tam LS, Li EK, et al. Serum and urinary free microRNA level in patients with systemic lupus erythematosus. Lupus 2011; 20(9): 493–500.
- Wang G, Kwan BC, Lai FM, et al. Intrarenal expression of microRNAs in patients with IgA nephropathy. Lab Invest 2010; 90: 98–103.
- Lorenzen JM, Kielstein JT, Hafer C, et al. Circulating miR‐210 predicts survival in critically ill patients with acute kidney injury. Clin J Amer Soc Nephrol 2011; 6: 1540–1546.
- Cai X, Xia Z, Zhang C, et al. Serum microRNAs levels in primary focal segmental glomerulosclerosis. Pediat Nephrol 2013; 28: 1797–1801.
- Zhang W, Zhang C, Chen H, et al. 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 2014; 9: 1545–1552.
- Bao J, Li X, Li Y, et al. microRNA-141-5p acts as a tumor suppressor via targeting RAB32 in chronic myeloid leukemia. Front Pharmacol 2020; 10: 1545.
- He H, Wang L, Zhou W, et al. MicroRNA expression profiling in clear cell renal cell carcinoma: Identification and functional validation of key miRNAs. Plos One 2015; 10: e0125672.
- Dweep H, Sticht C, Kharkar A, et al. 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(4): e53780.
- Ye H, Su B, Ni H, et al. microRNA-199a may be involved in the pathogenesis of lupus nephritis via modulating the activation of NF-κB by targeting Klotho. Mol Immunol 2018; 103: 235–242.
- Godwin JG, Ge X, Stephan K, et al. Identification of a microRNA signature of renal ischemia reperfusion injury. Proc Natl Acad Sci USA 2010; 107: 14339-4344.
- Butz H, Szabo PM, Nofech-Mozes R, et al. Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma. Clin Chem 2014; 60: 1314-1326.
- Wang WN, Zhang WL, Zhou GY, et al. Prediction of the molecular mechanisms and potential therapeutic targets for diabetic nephropathy by bioinformatics methods. Int J Mol Med 2016; 37: 1181-1188.
- Teng J, Sun F, Yu PF, et al. Differential microRNA expression in the serum of patients with nephrotic syndrome and clinical correlation analysis. Int J Clin Exp Pathol 2015; 8: 7282–7286.
- Busse M, Feta A, Presto J, et al. Contribution of EXT1, EXT2, and EXTL3 to heparan sulfate chain elongation. J Biol Chem 2007; 282(45): 32802-32810.
- Heller S, Li Z, Lin Q, et al. Transcriptional changes and the role of ONECUT1 in hPSC pancreatic differentiation. Commun Biol 2021; 4(1): 1298.
[Crossref] [Google Scholar] [Indexed]
- Philippi A, Heller S, Costa IG, et al. Mutations and variants of ONECUT1 in diabetes. Nat Med 2021; 27(11): 1928-1940.
[Crossref] [Google Scholar] [Indexed]
- Mehrabian M, Hildebrandt H, Schmitt-Ulms G. NCAM1 Polysialylation: The Prion Protein's Elusive Reason for Being?. ASN neuro, 2016; 8(6): 1759091416679074.
- Hochheiser K, Tittel A, Kurts C. Kidney dendritic cells in acute and chronic renal disease. International journal of experimental pathology, 2011; 92(3): 193–201.
- Yao J, Le TC, Kos CH, et al. α-Actinin-4-Mediated FSGS: An inherited kidney disease caused by an aggregated and rapidly degraded cytoskeletal protein. PLoS Biol 2004; 2(6): e167.
- Feng D, DuMontier C, Pollak M. The role of alpha-actinin-4 in human kidney disease. Cell Biosci 2015; 5(6): 44.