Global analysis of urinary extracellular vesicle small RNAs in autosomal dominant polycystic kidney disease

Autosomal dominant polycystic kidney disease (ADPKD) is the most prevalent monogenic renal disease progressing to end‐stage renal disease. There is a pressing need for the identification of early ADPKD biomarkers to enable timely intervention and the development of effective therapeutic approaches. Here, we profiled human urinary extracellular vesicles small RNAs by small RNA sequencing in patients with ADPKD and compared their differential expression considering healthy control individuals to identify dysregulated small RNAs and analyze downstream interaction to gain insight about molecular pathophysiology.


| INTRODUCTION
2][3] ADPKD is characterized by a bilateral accumulation of fluidfilled cysts in the kidneys, leading to progressive enlargement of renal volume and a gradual decline in kidney function, often resulting in end stage renal disease. 4Besides the kidney-related symptoms, individuals with ADPKD might experience extrarenal manifestations including polycystic liver disease (PLD), cerebral aneurysms, and cardiovascular abnormalities, including early-onset hypertension. 5The majority of ADPKD cases (78%) are attributed to mutations in PKD1, whereas around 15% of the cases are caused by mutations in PKD2 gene. 6[13] The most preferred method for monitoring ADPKD progression is the measurement of total kidney volume. 14However, this method is labor-intensive, which hinders its routine use in follow-ups.Mutation classification has been suggested as a means to identify patients at risk of the severe form of the disease; however, reports indicate that this cannot be done with complete certainty because of the modifying effects of other genetic and environmental factors. 15Currently, there are no reliable blood or urine biomarkers available that can accurately predict the course of the disease and identify patients at risk of the severe form of the disease.The presence of these biomarkers would enable improved management of patients and provide valuable support for clinical trials and the development of therapies.
Over the past decade, there has been a growing interest in exosomes, small extracellular vesicles with a diameter ranging between 40 and 160 nm derived from endosomes. 16These small vesicles contain various constituents including different types of nucleic acids (including microRNAs [miRNAs] and Piwi-interacting RNAs [piRNAs]), proteins, lipids, amino acids and other metabolites.Initially, extracellular vesicles were considered as cellular waste products resulting from cell damage or cell homeostasis 17 but recent advances indicated that extracellular vesicles play a key role in intercellular communication that regulate major cellular processes including signal transduction and immune response. 18,19Extracellular vesicles are involved in cell-cell communication within the nephron influencing renal physiology and pathophysiology as their role have been implicated in various renal diseases and disorders including ADPKD. 11,20,21Studies have demonstrated the presence of altered extracellular vesicles profiles, in term of their nucleic acid and protein content, in various renal diseases including ADPKD. 11,21The extracellular vesicles content, specifically small non-coding RNAs, are considered to play a role in the pathogenesis of ADPKD by influencing various signaling pathways through the post-transcriptional control of related genes. 22e pathologically modified extracellular vesicles profiles may provide potential biomarkers for disease diagnosis, prognosis and monitoring.
They can also provide valuable insights for the development of potential therapies.
In the present study, we have taken a systematic approach to comprehensively profile the small RNA content of urinary extracellular vesicles from ADPKD patients and compared them with those from healthy individuals.We have identified significantly dysregulated miRNAs and piRNAs in ADPKD patients and compared the results to an independent global miRNA database of normal renal cortical tissue and PKD1 renal cysts.Additionally, we have further analyzed the network of common target mRNAs of the significantly dysregulated miRNAs.Our findings highlight potential novel biomarkers for ADPKD, identify potential therapeutic targets for the disease, and contribute to a better understanding of its pathophysiology.

| Patient recruitment
In total, 23 ADPKD patients were included in the present study.These patients were recruited from the nephrology unit at Mubarak Al-Kabeer Hospital and Mubarak Al-Abdullah Al-Sabah Dialysis Center in Jabriya.Eligible participants were adults over 18 years of age who were capable of providing informed consent.Inclusion criteria involved having a confirmed clinical diagnosis of ADPKD with positive genetic test.All patients had PKD1 mutations that were reported previously. 6,23,24In addition, 28  Prior to participation, written informed consent was obtained from all enrolled patients in accordance with the guidelines set forth by the MOH research committee.

| Total RNA extraction from urinary extracellular vesicles, library preparation and sequencing
RNA was extracted from urinary extracellular vesicles using a Urine Exosome Purification and RNA Isolation Maxi Kit (catalog.no, 58800; Norgen Biotech Corp.) in accordance with the manufacturer's instructions.

| Small RNA library preparation and sequencing
A total of 10 ng of purified small RNA samples was used for library preparation using a QIAseq miRNA Library Kit (331502; Qiagen, Hilden, Germany) in accordance with the manufacturer's instructions.
The protocol involves sequential ligation of 3 0 and 5 0 end adapters followed by universal cDNA synthesis with unique molecular index assignment.Synthesized cDNA libraries were further amplified by PCR cycles and purified using Qiagen QMN beads.Prepared final libraries were validated and quantified using bioanalyzer (Agilent, Santa Clara, CA, USA) and qubit fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) respectively.Sequencing was carried out on MiSeq system using Miseq 150 cycle version 3 kit (MS-102-3001; Illumina Inc.San Diego, CA, USA).The resulting Fastq files were analyzed using Gene globe data analysis webtools (Qiagen).
Normalization was carried out using the trimmed mean of M (edgeR) method.A linear scaling factor for each sample was calculated from a weighted mean after reducing the dataset by log fold-changes relative to the control samples and by absolute intensity. 25ndidate miRNAs were shortlisted using the criteria of log 2 fold change > 2 and p < 0.05.

| Annotation of small RNAs from sequencing data
FastQC software (Illumina, San Diego, California) was used to check the quality of Fastq files.Reads shorter than 14 nucleotides were discarded.Cutadapt v3.7 (https://cutadapt.readthedocs.io/en/v3.7/guide.html)was used to clip the reads that passed the quality control from the adapter sequences by imposing a maximum error rate in terms of mismatches, insertions and deletions equal to 0.15.The average length of the raw sRNA-seq reads was 22 bp.Trimmed reads were mapped against an in-house reference of human miRNA sequences composed of 1917 precursor miRNAs from miRBase v2274 (https://mirbase.org/).Using these settings, the seeding was not performed for reads shorter than 32 bp, and the reads were entirely evaluated for the alignment.miRNAs were annotated and quantified using two methods called the "knowledge-based" and "position-based" methods.In the "knowledge-based" method, the arm starting positions of the miRNA precursors were well defined.
Based on the position of the mapped reads, it was possible to quantify the mature miRNAs.Alternatively, the "position-based" method was implemented for those miRNA precursors in which the positions of the 5p and 3p arms are not defined in miRBase.With the second method, based on the position of the read, it was assigned a "-5p" or "-3p" suffix to the name of miRNAs written in italics (to distinguish from the other miRNAs with assigned arms derived from miRBase).
The quantification of mature miRNA annotations was performed by counting the read alignment reported by BWA output and an overall of 3524 miRNAs were analyzed.The results generated by the annotation and quantification methods were merged into a unique mature miRNA count matrix, composed of 3524 rows (miRNAs) and 335 columns (samples).

| Identification of differentially expressed small RNA (DE-sRNAs) and functional enrichment analysis of the critical miRNA
The validated miRNA-target mRNAs were retrieved using miRNet (www.mirnet.ca),an interactive web tool.The tool fetches miRNAtarget interaction data (that are computationally predicted and experimentally validated) from miRTarBase, 26 miRDB 27 and TargetScan 28 databases.We considered the study miRNAs that are seen in these databases as candidate miRNAs in intersection with the mRNAs. 29gnificantly correlated pairs of such miRNA-mRNA interactions were utilized to generate the coexpression network using Cytoscape 3.6.1 (https://cytoscape.org).Significant biological pathways of significant candidate miRNAs were identified using the DIANA-microT web server. 30The most statistically enriched Gene Ontology (GO) (http://geneontology.org) terms were visualized using the ggplot2 visualization package, 31 The heatmap was plotted using SRPLOT (https://www.bioinformatics.com.cn).

| Comparison of miRNA expression in extracellular vesicles and PKD1 cystic tissues
miRNA expression data (GSE133530) from renal cysts of ADPKD patients is available in the database of Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo)under accession number of GSE133530.The GSE133530 dataset comprises miRNA expression data from normal renal cortical tissue (n = 4), minimally cystic tissue (n = 7) and ADPKD renal cyst (n = 18) kidney samples.We retrieved the dataset and compared miRNA expression in exosome tissue from our ADPKD patients with miRNA expression in renal cyst tissues from the GSE133530 dataset.We obtained DE-miRNA from GSE133530 dataset using the GEO2R tool provided by the GEO database (https://www.ncbi.nlm.nih.gov/geo/geo2r).Only miRNAs implicated in cystic tissues and our ADPKD samples were considered for subsequent analysis to identify driver target genes.

| Identification of driver target genes of key miRNA
The cytoHubba v.0.1 plug-in of Cytoscape was used to select potential hub genes from the identified DE-miRNAs. 32,33The bottleneck centrality analysis of the miRNA-mRNA regulatory network was used to determine the driver genes that are targeted by the key miRNA. 34,35

| Statistical analysis
We calculated receiver operating characteristic (ROC) curves.These curves compare sensitivity versus specificity across a range of values for the ability to predict a dichotomous outcome.Area under the ROC curve (AUC) is another measure of test performance.
We calculated the AUC, confidence intervals, p values of all calculated ROC curves and the ROC curves for each individual piRNA and miRNA, and displayed them along their corresponding sensitivity and specificity.

| Expression profiles of miRNAs isolated from urinary extracellular vesicles of ADPKD patients
Characterization of purified urinary extracellular vesicles based on size using different microscopic modalities showed a diameter in the range of 40-150 nm (see Supporting information, Figure S1A-C).Flow cytometry analysis indicated that extracellular vesicles exhibited positive expression of CD63 and TSC-101 and negative expression of GRP94 (see Supporting information, Figure S1D).Urinary extracellular vesicles small RNAs were extracted from individuals (n = 51).The cohort included 28 healthy controls with normal kidney function and 23 ADPKD patients with known PKD1 mutations.The baseline and clinical characteristics of the ADPKD patients and controls are shown in Table 1.The expression profiles of urinary extracellular vesicles small RNAs from the 23 ADPKD patients were compared with the expression profiles from the 28 healthy individuals.Principal component analysis of the small RNA expression data demonstrated clustering of ADPKD patients and healthy controls with a separation between the two clusters (Figure 1A).Results of heatmap clustering based on the expression pattern of all 318 detectable miRNAs and piRNAs of healthy and ADPKD groups are presented in the Supporting information (Table S1).Transcript cluster heatmaps (Figure 1B) indicated that the optimal number of clusters was eight; the larger cluster (n = 52 miRNAs and piRNA) was designated C2 and the smaller cluster (n = 23 miRNAs and piRNA) was designated C1 (see Supporting information, Table S1).Clusters C3, C6 and C8 revealed the same changes in the expression of miRNA/piRNAs in healthy and ADPKD.Clusters C1 and C4 revealed higher expression of miRNA/piRNAs in ADPKD patients, whereas clusters C2, C5 and C7 revealed lower expression of miRNA/piRNAs in ADPKD patients (see Supporting information, Table S1).

| Validation of urinary exosome miRNA expression via comparison to PKD1 renal cystic tissues
To validate our cohort findings, we analyzed the data available in the GEO database on miRNA expression from PKD1 renal cystic tissues obtained from ADPKD patients.Thirty-one differentially expressed miRNAs that we found shared between our urinary extracellular vesicles miRNAs and miRNAs from PKD1 cystic tissues (Figure 4A).Heat maps further displayed expression pattern of these 31 miRNAs in normal, minimally cystic tissue (MCT) and PKD1 renal cystic tissue groups (Figure 4B).We found that seven dysregulated urinary extracellular vesicles miRNAs (viz.miR-29c-3p, miR-671-5p, miR-320c, miR-320b, miR-199b-3p, miR-146a-5p and miR-1246) are also significantly expressed in cystic tissues of PKD1 (Figure 4C).miR-199b-3p is significantly overexpressed in MCT and PKD1 renal cystic tissue, but, in urinary exosome, the expressed was non-significant.We also observed that miR-8485 and miR-3656 are significant unique urinary exosomal miRNA (Figure 4C).Significant increases in the expression levels of miR-1246, miR-320b and miR-320c were found in MCT, PKD1 renal cystic tissue and urinary exosome compared to normal tissue (Figure 4D).Additionally, a significant decrease in the expression levels of miR-29c-3p was observed in MCT, PKD1 renal cystic tissue and urinary exosome compared to normal tissue.We also observed, in two of the miRNAs (i.e.miR-146a-5p and miR-671) that showed no dysregulation in ADPKD urinary extracellular vesicles, a similar pattern across the MCT cystic tissues and PKD1 renal cystic tissue compared to healthy cyst (Figure 4D).

| Prediction of target genes of extracellular vesicles miRNAs
miRNAs play a significant role in modulating mRNA expression levels within cells.They achieve this by binding to target mRNAs and leading to translational repression and gene silencing.The detected 318 differentially expressed miRNAs (including the identified nine dysregulated miRNAs in urinary extracellular vesicles from ADPKD patients) were found to map to 846 target genes in the miRNet database (see Supporting information, Table S2).The protein-protein interaction network built using the differentially expressed miRNA-mRNA pairs revealed that the interaction network comprises 846 interacting nodes and 949 edges.The regulatory network of four significant dysregulated miRNAs that is expressed in urinary extracellular vesicles as well as cystic tissues of PKD1 (i.e.miR-29c-3p, miR-671-5p, miR-320c and miR-1246) and predicted genes is given in Figure 5A.expressed genes (Figure 5A).A large number of target genes mapped to each of these four DE-miRNAs (Figure 5B): miR-29c-3p targets 254, miR-671-5p targets 157 genes and miR-320c targets 108 genes.
Thus, these four miRNAs could be considered as key miRNAs.We next set upon building a key miRNA-driver-gene network.Furthermore, we found that four significantly dysregulated urinary extracellular vesicles miRNAs (i.e.miR-29c-3p, miR-671-5p, miR-320c and miR-1246) are also significantly expressed in cystic tissues of PKD1.The bottleneck centrality analysis of the key miRNA-mRNA regulatory network was used to identify the driver genes for key DE-miRNAs.

| Predicted functions and pathways of differentially expressed miRNAs in ADPKD
We performed functional enrichment analysis of the predicted target genes from key DE-miRNA analysis.Biological pathways and Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.genome.jp/kegg)pathway enrichment analysis were successively identified by DAVID (https://david.ncifcrf.gov/tools.jsp) to validate that these miRNAs are involved in ADPKD.Enrichment analysis revealed that the targeted genes were enriched mainly in mitogen-activated protein kinase signaling (mTOR), Wnt signaling, phosphoinositide 3-kinase (PI3K)-Akt signaling, apoptosis, MAPK signaling, p53 signaling, calcium signaling and ErbB signaling pathways (Figure 6A; see also Supporting information, Table S3).Of interest, the top ADPKD-associated pathways of most of the extracellular vesicles key miRNAs families were related to Wnt signaling, mTOR, ErbB signaling, PI3K-Akt and calcium signaling (Figure 6B).In addition, more specific participation in APKD-related biological process analysis showed that the enriched processes were involved in cell proliferation, hypoxia, nephrogenesis, cell migration, cell differentiation, fibrosis and DNA damage (Figure 5C). Figure 6D shows the PI3K-Akt signaling pathway associated with target genes of extracellular vesicles miRNAs.

| DISCUSSION
In the present study, we employed a systematic approach to comprehensively profile small RNAs from ADPKD patients' urinary extracellular vesicles using the NGS platform.The dysregulated miRNAs and piRNA were analyzed to explore their potential as disease biomarkers.Furthermore, we validated our findings by comparing them with miRNA expression data (GSE133530) from renal cysts of ADPKD patients that were retrieved from the NCBI GEO database.To identify interaction networks and driver target genes of the key dysregulated miRNAs, we utilized both computationally predicted and experimentally validated databases.These analyses provided valuable insights into ADPKD pathophysiology and potential therapeutic directions.
Overall, our research sheds light on the regulatory role of miRNAs in ADPKD and highlights their relevance as potential biomarkers and targets for potential therapeutic interventions.
piRNAs comprise small non-coding silencing RNAs with a length range from 21 to 35 nucleotides and have been shown to have distinct protein-interaction dynamics when compared to miRNAs. 36They are known to be involved in regulation of genomic stability in animal cells 37 and their profiles have been shown to be dysregulated renal cell carcinomas. 38To our knowledge, this is the first study evaluating piRNAs profiles and their diagnostic potential in ADPKD.We have We have identified nine miRNAs (i.e.miR-8485, miR-671-5p, miR-3656, miR-320c, miR-320b, miR-199b-3p, miR-146a-5p, miR-1246 and miR-29c-3p) isolated from urinary extracellular vesicles that are significantly dysregulated between healthy controls and ADPKD.
None of these identified DE-miRNAs were reported previously to be linked directly to ADPKD.However, a few of them have shown involvement in other renal pathological processes, wheras the remainder were implicated in certain types of cancer.Two of the detected DE-miRNAs were associated with diabetic nephropathy.miR-8,485, isolated from circulating extracellular vesicles, was previously shown to be downregulated in diabetic nephropathy patients. 39Meanwhile, miR-320c was found to be overexpressed in urinary extracellular vesicles of diabetic nephropathy patients. 40miR-199b-3p has been shown to have a protective role in acute kidney injury because it inhibits oxidative stress, 41 whereas miR-146a-5p has been shown to act as a mediator of renal tubular response to ischemia-reperfusion injury, thereby suppressing the accompanied inflammation. 424][45] This is not surprising because the PI3K/Akt/mTOR pathway, which regulates cell growth and survival, is implicated in both ADPKD-related renal cyst growth and tumor growth in various types of cancer. 46R-29c-3p, in particular, showed an interesting pattern where it was significantly downregulated in ADPKD urinary extracellular vesicles compared to the healthy individuals.This particular miRNA have been shown to be downregulated in renal fibrosis, 47 which is one of the physiological contributors to ADPKD progression. 48This particular marker was also shown to be downregulated in PKD1 cystic tissue (Figure 6C) and its potential diagnostic was implicated the ROC curve analysis (Figure 3).Furthermore, functional enrichment analysis indicated the miR-29c-3p to target a large set of 254 genes that participate in ADPKD-related pathways, including PI3K-Akt and mTOR 49 (Figure 5; see also Supporting information, Figure S5 and Table S3).
Similar functional enrichment was noted for miRNAs (miR-199b-3p, miR-29c-3p, miR-320b, miR-320c and miR-146a-5p) (see Supporting information, Table S3).Magayr et al. 21previously explored the dysregulated miRNA in ADPKD urinary extracellular vesicles.Compared with their results, we found that three of our nine extracellular vesicles miRNAs were differentially expressed in a their study; specifically, hsa-miR-320b (eGFR late stage, log 2 FC 1.02, p = 0.037) and hsa-miR-1246 (eGFR early stage, log 2 FC 2.26, p = 0.014) were increased, whereas hsa-miR-199b-3p (eGFR late stage, log 2 FC -3.75, p = 0.007) was decreased in ADPKD patients compared to healthy volunteers.A similar strong link to PI3K-Akt was also identified though it was linked to different set of miRNAs. 21The observed differences can be attributed to the heterogenous nature of ADPKD itself, disparities in laboratory methodologies and the nature of the specimen.The influence of a patient's ethnicity might have a limited impact, but it cannot be entirely ruled out.
Furthermore, by examining the miRNet database, we were able to identify potential mRNA targets from kidney tissue recognized by differentially expressed extracellular vesicles miRNAs and to identify pathways enriched by the target mRNAs.The constructed regulatory networks of miRNA-mRNA (target genes) revealed a total of four key DE-miRNAs targeting five driver genes; namely, FMNL3, KMT2A, EDC3, FBRS and CTNNBIP1; these five driver genes corresponded to the top nodes having highest degree and bottleneck in the constructed network.We found that miR-29c-3p, which showed significant downregulation in the ADPKD exosome, interacted with four of the five driver genes; namely, FMNL3, EDC3, FBRS and CTNNBIP1.
EDC3 is involved in regulation of gene expression, and it has been shown that suppressing the phosphorylation of EDC3 reduces tumor growth and invasion, and thereby drives cancer progression. 50KMT2A is also involved in regulation of gene expression and is implicated in different types of leukemia. 51FMNL3 plays a significant role in actin polymerization, 52,53 regulating cell migration, metastasis and tumor progression. 54CTNNBIP1 encodes a negative regulator of the Wnt signaling pathway 55 and as the Wnt signaling pathway has been shown to be disrupted in cystic kidney, a potential connection could be hypothesized.
The study has the following limitations.A significant challenge lies in the computational prediction of miRNA-target genes because the miRNet, TargetScan and miRbase tools are prone to predicting notable proportion of false-positive interactions.This issue arises partly as a result of the context-dependent nature of posttranscriptional regulation.To address this, researchers have proposed integrating target predictions from multiple tools because increasing experimental evidence supports the hypothesis that miRNAs can act through target degradation.This approach may help identify miRNA/mRNA relationships more accurately.The findings of the present study were reached by applying an integrative system biology approach; hence, the results need to be interpreted with caution and future experimental studies are required to fully understand the molecular mechanisms underlying ADPKD.

| CONCLUSIONS
Our comprehensive approach revealed a number of differentially expressed urinary extracellular vesicles piRNA miRNAs and their tar- differentially expressed miRNAs and five 'driver' target genes regulating the progression of ADPKD in humans, and these have the potential to serve as biomarkers for disease progression and as therapeutic targets for ADPKD.Our findings significantly contribute to the understanding of ADPKD's pathogenesis and to the identification of novel biomarkers and potential drug targets aimed at slowing disease progression in ADPKD.
healthy individuals were included in the study without any record of chronic health condition.The study received ethical approval from both the Ministry of Health (MOH) research ethics committee (Reference: 1139/2019) and the Joint Committee for The Protection of Human Subjects in Research of the Health Sciences Center (HSC) at Kuwait University and the Kuwait Institute for Medical Specialization (KIMS) (Reference: VDR/JC/690).
Urine collection involved obtaining the first urine sample (50 ml) in urine collection and preservation tubes (50 ml) (18111) from Norgen Biotech Corp. (Thorold, ON, Canada).The collected tubes were subjected to a series of centrifugation steps.First, the tubes were centrifuged at 200 g for 20 min at 40 C to separate the supernatant.The supernatant was then transferred to a new 50-ml Falcon tube and centrifuged at 2000 g for 20 min at 40 C. Subsequently, the resulting supernatant was collected, and another centrifugation step was performed at 16,000 g for 20 min at 40 C. The supernatant obtained was stored at 40 C. The remaining pellet was treated with 500 μl of dithiothreitol and subjected to vortexing and incubation at 37 C for 10 min.After incubation, the sample was centrifuged at 16,000 g for 20 min at 40 C. The supernatant from this step was combined with the stored supernatant from the previous step and filtered using a 0.2-μm filter.Equilibration of Amicon-15K filters was achieved by passing 15 ml of phosphate-buffered saline (PBS) and centrifuging at 3000 g for 25 min at 40 C. The filtered urine supernatant was loaded into the Amicon filter tubes and centrifuged at 3000 g for 45 min at 40 C, discarding the flow-through and repeating the process until the entire volume passed through the filter.This concentrated the sample and reduced its volume.The concentrated urine (approximately 500 μl) was transferred to ultracentrifuge tubes and centrifuged at 120 000 g for 70 min at 40 C. The supernatant was discarded, and the pellet was resuspended in 2 ml of PBS and centrifuged again at 120,000 g for 70 min at 40 C. Finally, the pellet was resuspended in 500 μl of PBS and stored overnight at 40 C.

Figure 2A .
Figure 2A.Clustering indicated that the difference in miRNA and piRNA expression levels between control individuals and ADPKD patients is the primary discriminator, overriding differences as a result of gender, age and body mass index (BMI).The nine extracellular vesicles miRNAs, which were shown to be differentially expressed between control and ADPKD individuals, were further analyzed using Wilcoxon rank sum tests.The results of Wilcoxon tests pointed to a relationship between BMI and miRNA expression in patients with ADPKD (see Supporting information, Figure S2A).For example, the expressions of miR-199b, miR-320c and miR-1246 were significantly and negatively correlated with BMI with a correlation coefficient of À0.34 ( p = 0.014), À0.29 (p = 0.04) and À0.33 (p = 0.018), respectively (see Supporting information,

3. 3 |
Figure S4B).The AUC values indicate that individuals with differing eGFR in ADPKD and control groups can be distinguished by the expression analysis of the markers.These data indicate that these miRNAs are valuable potential urinary-based miRNA biomarkers not only for ADPKD, but also for detecting future renal impairment in ADPKD patients.

F I G U R E 5
Predicted regulatory networks of differential miRNAs and their target genes associated with ADPKD.(A) Regulatory network of key DE-miRNA-mRNA.Red triangles represent miRNAs and ellipses represent target genes for ADPKD.(B) Bar graph shows the number of targeted genes for key DE-miRNA.(C) Regulatory network of driver genes and key DE-miRNAs.
identified 23 dysregulated piRNAs in ADPKD patients with only three of them showing statistical significance (i.e.piR_020497, piR_016271 and piR_020496).The AUC-ROC curves for these piRNAs showed F I G U R E 6 Functional enrichment analysis of DE-miRNA.(A) Bar plot of significant biological pathways of targeted genes to DE-miRNA.X-axes represent count with significance (p < 0.05) indicated by order and color trend.(B) The functional enrichment findings for the targets of the significant DE-miRNAs are shown as dot plots, with the y-axis revealing the annotation classifications, the x-axis presenting the miRNAs, and the number of identified targets in a circle also shown.The size of the dots corresponds to the gene ratio, whereas the colored dots indicate adjusted p-values.(C) Significantly enriched biological process of targeted genes.Dot size indicates the count.The count represents the number of genes associated with each process.The dot color denotes the p-values of the process, and the x-axis represents the fold enrichment score.(D) PI3K-Akt signaling pathways associated with target genes of extracellular vesicles miRNAs.similar outcomes with limited potential in predicting the ADPKD cases.Further studies are required to fully evaluate their potential in clinical practice.
get genes participating in the progression of ADPKD in humans.The present study provides an interpretation of the biological processes and pathways enriched by these markers.The study indicated that multiple miRNAs may regulate a single mRNA, and a single miRNA may target hundreds of different genes.Further integration of these data into gene regulatory networks may reveal the cooperative translational control mechanisms in cells.The study shortlisted nine 'key' demographic and clinical characteristics of the study cohort.