Transcriptome analysis reveals manifold mechanisms of cyst development in ADPKD

• Autor(a) principal: Almeida, Rita Maria Cunha de
• Data de Publicação: 2016
• Outros Autores: Clendenon, Sherry G., Richards, William Graham, Boedigheimer, Michael J., Damore, Michael A., Rossetti, Sandro, Harris, Peter C., Herbert, Brittney Shea, Xu, Wei Min, Wandinger-Ness, Angela, Ward, Heather H., Glazier, James Alexander, Bacallao, Robert L.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/159798
• topic: Rim policístico autossômico dominante; Transcriptoma; Bioinformática; Expressão gênica; Kidney; Transcriptogram; Cystic kidney disease; Autosomal dominant polycystic kidney disease; Bioinformatics; Pathway identification

Background: Autosomal dominant polycystic kidney disease (ADPKD) causes progressive loss of renal function in adults as a consequence of the accumulation of cysts. ADPKD is the most common genetic cause of end-stage renal disease. Mutations in polycystin-1 occur in 87% of cases of ADPKD and mutations in polycystin-2 are found in 12% of ADPKD patients. The complexity of ADPKD has hampered efforts to identify the mechanisms underlying its pathogenesis. No current FDA (Federal Drug Administration)-approved therapies ameliorate ADPKD progression. Results: We used the de Almeida laboratory’s sensitive new transcriptogram method for whole-genome gene expression data analysis to analyze microarray data from cell lines developed from cell isolates of normal kidney and of both non-cystic nephrons and cysts from the kidney of a patient with ADPKD. We compared results obtained using standard Ingenuity Volcano plot analysis, Gene Set Enrichment Analysis (GSEA) and transcriptogram analysis. Transcriptogram analysis confirmed the findings of Ingenuity, GSEA, and published analysis of ADPKD kidney data and also identified multiple new expression changes in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways related to cell growth, cell death, genetic information processing, nucleotide metabolism, signal transduction, immune response, response to stimulus, cellular processes, ion homeostasis and transport and cofactors, vitamins, amino acids, energy, carbohydrates, drugs, lipids, and glycans. Transcriptogram analysis also provides significance metrics which allow us to prioritize further study of these pathways. Conclusions: Transcriptogram analysis identifies novel pathways altered in ADPKD, providing new avenues to identify both ADPKD’s mechanisms of pathogenesis and pharmaceutical targets to ameliorate the progression of the disease.