About Metabolism And Genomics in Cystic Kidney (MAGICK)

Autosomal dominant polycystic kidney disease (ADPKD) is the most common cause of inherited kidney disease; it causes the kidneys to enlarge and stop working and many times patients need either dialysis or transplantation to replace the kidneys’ function. This research program is designed to use innovative mouse models based on inactivation of the same genes as cause the human disease in combination with other recent technological advances to create large, comprehensive data sets of the specific changes in gene expression and metabolic function in the subset of cells in the kidney that will give rise to the cysts that ultimately destroy kidney function. These data sets will be made publicly available so that the maximum number of investigators can make use of them in their efforts to understand how this disease occurs and, more importantly, to develop the therapies that will slow this disease without causing unnecessary side effects to patients.

Citation

When using this data, please cite the following reference.
Zhang, C., Rehman, M., Tian, X., Pei, S.L.C., Gu, J., Bell, T.A., Dong, K., Tham, M.S., Cai, Y., Wei, Z., et al. (2024). Glis2 is an early effector of polycystin signaling and a target for therapy in polycystic kidney disease. Nat. Commun. 15, 3698. 10.1038/s41467-024-48025-6.

Transcriptomic Datasets

Our goal: To develop cell type specific in vivo translatome datasets based on mouse model systems to support discovery of mechanisms of ADPKD pathogenesis and of new therapeutic targets.

This resource has been developed with the following principles:

Multiple biologically inter-related animal models.

Based on the Cilia Dependent Cyst Activation (CDCA) pathway phenotype: The observation that while Pkd gene knockouts in mouse kidney form cysts relatively rapidly, cyst formation is suppressed when cilia are genetically removed at the time of Pkd gene knockout. Loss of cilia suppresses cyst growth in genetic models of autosomal dominant polycystic kidney disease. Ma, M. et al. Nat Genet. 2013 Sep;45(9):1004-12.

Six genotypes based on the CDCA pattern were used in the study.
Pkd1 models
Non-cystic (Pkd1): Pkd1fl/+;R26Rpl10a;Pax8rtTA;TetOCre
Pkd1_KO: Pkd1fl/fl;R26Rpl10a;Pax8rtTA;TetOCre
Pkd1_Kif3a_KO: Pkd1fl/fl;Kif3afl/fl;R26Rpl10a;Pax8rtTA;TetOCre

Pkd2 models
Non-cystic (Pkd2): Pkd2fl/+;R26Rpl10a;Pax8rtTA;TetOCre
Pkd2_KO: Pkd2fl/fl;R26Rpl10a;Pax8rtTA;TetOCre
Pkd2_Ift88_KO: Pkd2fl/fl;Ift88fl/fl;R26Rpl10a;Pax8rtTA;TetOCre

Cell type specific translatome.

Specificity only for kidney cells in which Pkd genes were inactivated was achieved using Translating Ribosome Affinity Purification (TRAP) RNASeq.

Precystic disease stage.

Pkd gene inactivation was induced in all models from postnatal days 28 to 42 (4-6 weeks age) TRAP RNASeq was performed at 7 weeks age and 10 weeks age. At these time points, polycystin proteins and cilia are gone from cells where Cre recombinase has been active, but discernible cyst formation has not yet begun.

Separate analysis of male and female mice.

Separate analyses for male and female mice were prespecified because female mice are relatively protected and progress more slowly in adult inducible models of Pkd1 and Pkd2 inactivation.

CDCA data set comparisons

The "Cilia Dependent Cyst Activation (CDCA) pattern" in the data set comparisons is premised on the biological understanding that cyst-relevant signals in Pkd-only single knockouts, which are destined to form cysts, differ in the same direction from both Non-cystic and the respective Pkd+cilia double knockout. Furthermore, signals relevant to cyst formation should not differ between the Non_cystic and Pkd+cilia double knockouts since the latter are protected from cyst formation despite the inactivation of a Pkd gene.

Overview of TRAP RNASeq procedure


Overview of the data analysis interpretation


Web development

The website was custom designed for this project and led by Monkol Lek. The open source code for this website is available on github. Please report any issues or suggestions on the issues section on the github repository.

Funding

This work was supported by the following grant from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)/National Institutes of Health (NIH): RC2 DK120534.