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Differential Expression of PKD2-Associated Genes in Autosomal Dominant Polycystic Kidney Disease.

Yook YJ, Woo YM, Yang MH, Ko JY, Kim BH, Lee EJ, Chang ES, Lee MJ, Lee S, Park JH - Genomics Inform (2012)

Bottom Line: Autosomal dominant polycystic kidney disease (ADPKD) is characterized by formation of multiple fluid-filled cysts that expand over time and destroy renal architecture.The majority of identified mutations was involved in critical biological processes, such as metabolism, transcription, cell adhesion, cell cycle, and signal transduction.These data may be helpful in PKD2-related mechanisms of ADPKD pathogenesis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Science, Sookmyung Women's University, Seoul 140-742, Korea.

ABSTRACT
Autosomal dominant polycystic kidney disease (ADPKD) is characterized by formation of multiple fluid-filled cysts that expand over time and destroy renal architecture. The proteins encoded by the PKD1 and PKD2 genes, mutations in which account for nearly all cases of ADPKD, may help guard against cystogenesis. Previously developed mouse models of PKD1 and PKD2 demonstrated an embryonic lethal phenotype and massive cyst formation in the kidney, indicating that PKD1 and PKD2 probably play important roles during normal renal tubular development. However, their precise role in development and the cellular mechanisms of cyst formation induced by PKD1 and PKD2 mutations are not fully understood. To address this question, we presently created Pkd2 knockout and PKD2 transgenic mouse embryo fibroblasts. We used a mouse oligonucleotide microarray to identify messenger RNAs whose expression was altered by the overexpression of the PKD2 or knockout of the Pkd2. The majority of identified mutations was involved in critical biological processes, such as metabolism, transcription, cell adhesion, cell cycle, and signal transduction. Herein, we confirmed differential expressions of several genes including aquaporin-1, according to different PKD2 expression levels in ADPKD mouse models, through microarray analysis. These data may be helpful in PKD2-related mechanisms of ADPKD pathogenesis.

No MeSH data available.


Related in: MedlinePlus

Clustering of gene expression patterns in Pkd2 mutant MEF cell lines. We performed clustering analysis microarray data between KO set ratio data and TG set ratio data using the graphical user interface (GUI) system. (A) Gene expression profiles of 2,306 probe sets distributed in a dendrogram. (B) Line plots of each cluster. KO set, ratio between KO MEF and KOWT MEF; TG set, ratio between TG MEF and TGWT MEF. MEFs, mouse embryo fibroblasts; KO, knockout (MEF); TG, transgenic (MEF); KOWT, knockout wild-type (MEF); TGWT, transgenic wild-type (MEF).
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Figure 2: Clustering of gene expression patterns in Pkd2 mutant MEF cell lines. We performed clustering analysis microarray data between KO set ratio data and TG set ratio data using the graphical user interface (GUI) system. (A) Gene expression profiles of 2,306 probe sets distributed in a dendrogram. (B) Line plots of each cluster. KO set, ratio between KO MEF and KOWT MEF; TG set, ratio between TG MEF and TGWT MEF. MEFs, mouse embryo fibroblasts; KO, knockout (MEF); TG, transgenic (MEF); KOWT, knockout wild-type (MEF); TGWT, transgenic wild-type (MEF).

Mentions: We used a mouse 30 K whole gene oligonucleotide microarray to identify mRNAs whose expression was altered by the Pkd2 KO and PKD2 TG MEF cells. The normalized log ratios corresponding to each time point were exported to the software for clustering algorithm. We used Axon's Acuity 3.1 (Axon Instruments, Union City, CA, USA) for using the 'gene shaving' algorithm. To estimate the number of clusters in a dataset, we used 'Gap Statistics' in Acuity 3.1. The cluster number estimated by gap statistics was used for an input parameter in gene shaving. After gene shaving, we used a single linkage hierarchical clustering. The single linkage hierarchical clustering method divides 8 'gene shaving' clusters (Fig. 2). The majority of genes whose expression was appreciably altered encoded proteins involved in critical biological processes, such as metabolism, transcription, cell adhesion, cell cycle, and signal transcription. Forty-five genes whose expression was changed by 2-fold or greater were identified (Table 1).


Differential Expression of PKD2-Associated Genes in Autosomal Dominant Polycystic Kidney Disease.

Yook YJ, Woo YM, Yang MH, Ko JY, Kim BH, Lee EJ, Chang ES, Lee MJ, Lee S, Park JH - Genomics Inform (2012)

Clustering of gene expression patterns in Pkd2 mutant MEF cell lines. We performed clustering analysis microarray data between KO set ratio data and TG set ratio data using the graphical user interface (GUI) system. (A) Gene expression profiles of 2,306 probe sets distributed in a dendrogram. (B) Line plots of each cluster. KO set, ratio between KO MEF and KOWT MEF; TG set, ratio between TG MEF and TGWT MEF. MEFs, mouse embryo fibroblasts; KO, knockout (MEF); TG, transgenic (MEF); KOWT, knockout wild-type (MEF); TGWT, transgenic wild-type (MEF).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3475485&req=5

Figure 2: Clustering of gene expression patterns in Pkd2 mutant MEF cell lines. We performed clustering analysis microarray data between KO set ratio data and TG set ratio data using the graphical user interface (GUI) system. (A) Gene expression profiles of 2,306 probe sets distributed in a dendrogram. (B) Line plots of each cluster. KO set, ratio between KO MEF and KOWT MEF; TG set, ratio between TG MEF and TGWT MEF. MEFs, mouse embryo fibroblasts; KO, knockout (MEF); TG, transgenic (MEF); KOWT, knockout wild-type (MEF); TGWT, transgenic wild-type (MEF).
Mentions: We used a mouse 30 K whole gene oligonucleotide microarray to identify mRNAs whose expression was altered by the Pkd2 KO and PKD2 TG MEF cells. The normalized log ratios corresponding to each time point were exported to the software for clustering algorithm. We used Axon's Acuity 3.1 (Axon Instruments, Union City, CA, USA) for using the 'gene shaving' algorithm. To estimate the number of clusters in a dataset, we used 'Gap Statistics' in Acuity 3.1. The cluster number estimated by gap statistics was used for an input parameter in gene shaving. After gene shaving, we used a single linkage hierarchical clustering. The single linkage hierarchical clustering method divides 8 'gene shaving' clusters (Fig. 2). The majority of genes whose expression was appreciably altered encoded proteins involved in critical biological processes, such as metabolism, transcription, cell adhesion, cell cycle, and signal transcription. Forty-five genes whose expression was changed by 2-fold or greater were identified (Table 1).

Bottom Line: Autosomal dominant polycystic kidney disease (ADPKD) is characterized by formation of multiple fluid-filled cysts that expand over time and destroy renal architecture.The majority of identified mutations was involved in critical biological processes, such as metabolism, transcription, cell adhesion, cell cycle, and signal transduction.These data may be helpful in PKD2-related mechanisms of ADPKD pathogenesis.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Science, Sookmyung Women's University, Seoul 140-742, Korea.

ABSTRACT
Autosomal dominant polycystic kidney disease (ADPKD) is characterized by formation of multiple fluid-filled cysts that expand over time and destroy renal architecture. The proteins encoded by the PKD1 and PKD2 genes, mutations in which account for nearly all cases of ADPKD, may help guard against cystogenesis. Previously developed mouse models of PKD1 and PKD2 demonstrated an embryonic lethal phenotype and massive cyst formation in the kidney, indicating that PKD1 and PKD2 probably play important roles during normal renal tubular development. However, their precise role in development and the cellular mechanisms of cyst formation induced by PKD1 and PKD2 mutations are not fully understood. To address this question, we presently created Pkd2 knockout and PKD2 transgenic mouse embryo fibroblasts. We used a mouse oligonucleotide microarray to identify messenger RNAs whose expression was altered by the overexpression of the PKD2 or knockout of the Pkd2. The majority of identified mutations was involved in critical biological processes, such as metabolism, transcription, cell adhesion, cell cycle, and signal transduction. Herein, we confirmed differential expressions of several genes including aquaporin-1, according to different PKD2 expression levels in ADPKD mouse models, through microarray analysis. These data may be helpful in PKD2-related mechanisms of ADPKD pathogenesis.

No MeSH data available.


Related in: MedlinePlus