BackgroundAutosomal dominant polycystic kidney disease (ADPKD), a common of monogenetic disorder caused by the polycystic kidney disease‐1 (PKD1) or PKD2 genes deficiency. In this study, we have re‐analyzed a microarray dataset to generate a holistic view of this disease. MethodologyGSE7869, an expression profiling dataset was downloaded from the Gene Expression Omnibus (GEO) database. After quality control assessment, using GEO2R tool of GEO, genes with adjusted p‐value ≤ 0.05 were determined as differentially expressed (DE). The expression profiles from ADPKD samples in different sizes were compared. Using CluePedia plugin of Cytoscape software, the protein–protein interaction (PPI) networks were constructed and analyzed by Cytoscape NetworkAnalyzer tool and MCODE application. Pathway enrichment analysis of clustered genes by MCODE with the high centrality parameters in PPI networks was performed using Cytoscape ClueGO plugin. Moreover, by Enrichr database, microRNAs (miRNAs) and transcription factors (TFs) targeted DE genes were identified. ResultsIn this study to explore the molecular pathogenesis of kidney in ADPKD, mRNA expression profiles of cysts from patients in different sizes were re‐analyzed. The comparisons were performed between normal with minimally cystic tissue (MCT) samples, MCTs with small cysts, and small cysts with large cysts. 512, 7024, and 655 DE genes were determined, respectively. The top central genes, e.g. END1, EGFR, and FOXO1 were identified with topology and clustering analysis. DE genes that were significantly enriched in PPI networks are critical genes and their roles in ADPKD remain to be assessed in future experimental studies beside miRNAs and TFs predicted. Furthermore, the functional analysis resulted in which most of them are expected to be associated with ADPKD pathogenesis, such as signal pathways that involved in cell growth, inflammation, and cell polarity. ConclusionWe have here explored systematic approaches for molecular mechanisms assay of ADPKD as a monogenic disease, which may also be used for other monogenetic diseases beside complex diseases to provide suitable therapeutic targets.

The omics technologies provide an invaluable opportunity to employ a global view towards human diseases. However, the appropriate translation of big data to knowledge remains a major challenge. In this study, we have performed quality control assessments for 91 transcriptomics datasets deposited in gene expression omnibus database and also have evaluated the publications derived from these datasets. This survey shows that drawbacks in the analyses and reports of transcriptomics studies are more common than one may assume. This report is concluded with some suggestions for researchers and reviewers to enhance the minimal requirements for gene expression data generation, analysis and report.

Background:Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic cause of end-stage renal disease. Although imaging techniques are a means of accurate diagnosis when the cysts appear in the third or fourth decades of the patient's life, they are of little value for early diagnosis. Genetic tests are required for preimplantation genetic diagnosis, decision-making for kidney donation to an affected relative. Although mutation of the polycystic kidney disease (PKD1) gene is solely responsible for the most cases of ADPKD, direct genetic testing is limited by the large size of this gene and the presence of many mutations without hot spots. Therefore, indirect diagnosis with linkage analysis using informative microsatellite markers has been suggested.Materials and Methods:In this study, we assessed the informativeness of the PKD1 gene markers D16S475, D16S291, and D16S3252 in Iranian population. Using specific primers, fluorescent polymerase chain reaction (PCR) was performed on genomic DNA extracted from fifty unrelated individuals. PCR products were analyzed by the ALFexpress DNA sequencer system, and the number and frequency of alleles were determined to calculate the heterozygosity (HET) and polymorphism information content (PIC) values.Results:We found that the HET and PIC values for the D16S475 marker are 0.92 and 0.91, respectively. These two values are 0.82 and 0.80 for D16S291 and 0.50 and 0.47 for D16S3252, respectively.Conclusion:Based on this data, D16S475 and D16S291 are highly and D16S3252 is moderately informative for indirect genetic diagnosis of PKD1 mutations in this population.

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