Renal Cysts and Diabetes Syndrome (RCAD) Cell Models for Research

Renal Cysts and Diabetes Syndrome (RCAD) is a rare autosomal dominant disorder caused by mutations in the HNF1B gene. The exact prevalence is unknown, but it is estimated to affect 1 in 100,000 individuals. RCAD is characterized by renal cysts (often bilateral), maturity-onset diabetes of the young type 5 (MODY5), and various other developmental anomalies. The disease presents with high clinical variability, even within families. Renal function can decline progressively, leading to end-stage renal disease in some patients. Diabetes typically develops before age 25 and is non-insulin dependent initially. There is no cure, and management focuses on treating symptoms. The disease's rarity and heterogeneity make it a valuable model for studying gene function in kidney and pancreas development.

RCAD is an ideal model for studying the role of HNF1B in organogenesis and metabolic regulation. The disease offers a clear genotype-phenotype correlation, with specific mutations leading to distinct clinical outcomes. Public datasets, such as those from the TCGA and GTEx, provide expression data for HNF1B in relevant tissues. Open questions include the molecular mechanisms underlying the variable expressivity and the potential for targeted therapies. Gene-edited cell models, such as HNF1B knockout or knock-in lines, are essential for functional studies and drug screening.

While RCAD is not a cancer predisposition syndrome, HNF1B is a transcription factor involved in several key pathways:

• • Wnt/β-catenin signaling: HNF1B regulates genes involved in cell proliferation and differentiation.
• • Hedgehog signaling: HNF1B interacts with GLI transcription factors to modulate kidney development.
• • TGF-β signaling: HNF1B influences epithelial-mesenchymal transition, which is critical in renal fibrosis.

These pathways are also implicated in various cancers, making RCAD models relevant for oncology research.

Data from ClinVar and literature. Note: RCAD is not typically associated with somatic mutations in cancer, but HNF1B is overexpressed in some cancers (e.g., ovarian, pancreatic).

HNF1B regulates a network of genes involved in:

• • Cell adhesion and polarity: e.g., E-cadherin, β-catenin.
• • Metabolic regulation: e.g., glucose transporter GLUT2, insulin genes.
• • Renal development: e.g., PAX2, WT1, and other transcription factors.

Dysregulation of these networks leads to the clinical features of RCAD.

Organoids derived from patient iPSCs or adult stem cells can recapitulate renal cyst formation and are valuable for studying HNF1B function.

• • Genetically engineered mouse models (GEMMs): Hnf1b heterozygous knockout mice exhibit renal cysts and diabetes, mimicking RCAD.
• • Induced models: CRISPR-based somatic editing in adult mice can be used to study HNF1B function in specific tissues.
• • Patient-derived xenografts (PDX): Not commonly used for RCAD due to its non-cancerous nature, but organoids can be transplanted for in vivo studies.

CRISPR-Cas9 technology enables the generation of isogenic cell lines with specific HNF1B mutations. For example:

• • HNF1B knockout cell lines: Complete loss of function, useful for studying haploinsufficiency.
• • HNF1B point mutation knock-in lines: Mimic patient-specific mutations, such as p.Arg295His.

These models are commercially available and sequence-verified, accelerating research. They are essential for functional studies, drug screening, and understanding disease mechanisms.

Knockout and knock-in lines are used to validate the role of HNF1B in gene regulation. For example, RNA-seq on HNF1B knockout cells can identify downstream targets. CRISPR screens can also identify genetic modifiers of HNF1B function.

Isogenic pairs (wild-type vs. HNF1B mutant) are used in high-throughput screens to identify compounds that rescue the mutant phenotype. These models can also be used to test drugs for diabetes and renal disease.

CRISPR synthetic lethality screens can identify genes that are essential in HNF1B-deficient cells, providing potential therapeutic targets. Additionally, secretome analysis of mutant cells can reveal biomarkers for early diagnosis.