Mutations in PRDM15 Are a Novel Cause of Galloway-Mowat Syndrome

BACKGROUND

Galloway-Mowat syndrome (GAMOS) is characterized by neurodevelopmental defects and a progressive nephropathy, which typically manifests as steroid-resistant nephrotic syndrome. The prognosis of GAMOS is poor, and the majority of children progress to renal failure. The discovery of monogenic causes of GAMOS has uncovered molecular pathways involved in the pathogenesis of disease.

METHODS

Homozygosity mapping, whole-exome sequencing, and linkage analysis were used to identify mutations in four families with a GAMOS-like phenotype, and high-throughput PCR technology was applied to 91 individuals with GAMOS and 816 individuals with isolated nephrotic syndrome. In vitro and in vivo studies determined the functional significance of the mutations identified.

RESULTS

Three biallelic variants of the transcriptional regulator PRDM15 were detected in six families with proteinuric kidney disease. Four families with a variant in the protein's zinc-finger (ZNF) domain have additional GAMOS-like features, including brain anomalies, cardiac defects, and skeletal defects. All variants destabilize the PRDM15 protein, and the ZNF variant additionally interferes with transcriptional activation. Morpholino oligonucleotide-mediated knockdown of Prdm15 in Xenopus embryos disrupted pronephric development. Human wild-type PRDM15 RNA rescued the disruption, but the three PRDM15 variants did not. Finally, CRISPR-mediated knockout of PRDM15 in human podocytes led to dysregulation of several renal developmental genes.

CONCLUSIONS

Variants in PRDM15 can cause either isolated nephrotic syndrome or a GAMOS-type syndrome on an allelic basis. PRDM15 regulates multiple developmental kidney genes, and is likely to play an essential role in renal development in humans.

Galgo: a bi-objective evolutionary meta-heuristic identifies robust transcriptomic classifiers associated with patient outcome across multiple cancer types

MOTIVATION

Statistical and machine-learning analyses of tumor transcriptomic profiles offer a powerful resource to gain deeper understanding of tumor subtypes and disease prognosis. Currently, prognostic gene-expression signatures do not exist for all cancer types, and most developed to date have been optimized for individual tumor types. In Galgo, we implement a bi-objective optimization approach that prioritizes gene signature cohesiveness and patient survival in parallel, which provides greater power to identify tumor transcriptomic phenotypes strongly associated with patient survival.

RESULTS

To compare the predictive power of the signatures obtained by Galgo with previously studied subtyping methods, we used a meta-analytic approach testing a total of 35 large population-based transcriptomic biobanks of four different cancer types. Galgo-generated colorectal and lung adenocarcinoma signatures were stronger predictors of patient survival compared to published molecular classification schemes. One Galgo-generated breast cancer signature outperformed PAM50, AIMS, SCMGENE and IntClust subtyping predictors. In high-grade serous ovarian cancer, Galgo signatures obtained similar predictive power to a consensus classification method. In all cases, Galgo subtypes reflected enrichment of gene sets related to the hallmarks of the disease, which highlights the biological relevance of the partitions found.

AVAILABILITY AND IMPLEMENTATION

The open-source R package is available on www.github.com/harpomaxx/galgo.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.