Retinitis pigmentosa Cell Models for Research
Disease Burden and Research Significance
Retinitis pigmentosa (RP) is a group of inherited retinal dystrophies characterized by progressive photoreceptor degeneration, leading to night blindness, peripheral vision loss, and eventually central vision loss. The global prevalence is approximately 1 in 4,000, affecting over 1.5 million people worldwide (WHO, 2023). RP can be inherited in autosomal dominant, autosomal recessive, or X-linked patterns, with over 100 genes implicated. The disease typically manifests in early adulthood, with most patients becoming legally blind by age 40. There is currently no cure, and available treatments are limited to vitamin A supplementation and gene therapy for specific mutations (e.g., RPE65). The significant genetic heterogeneity and lack of effective therapies underscore the urgent need for research models to understand disease mechanisms and develop targeted interventions.
RP is an ideal model for studying photoreceptor biology, neurodegeneration, and gene therapy. The retina is a accessible tissue, and the disease progression is well-characterized. Public datasets, such as those from the EyeGENE network and the NEI, provide extensive genetic and clinical data. Open questions include the role of specific gene mutations in photoreceptor death, the interplay between oxidative stress and inflammation, and the development of mutation-agnostic therapies. Gene-edited cell models, such as CRISPR knockout and knock-in lines, allow researchers to dissect the function of individual genes in photoreceptor-like cells, such as Y79 or WERI-Rb1, and to test potential therapeutic strategies in a controlled environment.
Core Molecular Pathogenesis
While RP is not a cancer, the molecular pathways involved in photoreceptor degeneration share similarities with cellular stress and apoptosis pathways. Key pathways include:
- • Phototransduction cascade: Mutations in genes such as RHO, PDE6B, and CNGA1 disrupt the visual cycle, leading to accumulation of toxic intermediates and photoreceptor death.
- • Cilia and transport defects: Genes like RPGR and RP2 are involved in ciliary transport, and their dysfunction impairs protein trafficking in photoreceptors.
- • Oxidative stress and inflammation: Chronic oxidative stress and microglial activation contribute to retinal degeneration.
- • Apoptosis and autophagy: Dysregulation of these processes leads to programmed cell death of photoreceptors.
| Gene | Frequency (%) | Mutation Type | Functional Effect |
|---|---|---|---|
| RHO | 20-30 (ADRP) | Missense, nonsense | Rhodopsin misfolding, ER stress |
| RPGR | 15-20 (XLRP) | Frameshift, nonsense | Ciliary transport defect |
| USH2A | 10-15 (ARRP) | Missense, frameshift | Usherin dysfunction, structural defect |
| RP2 | 5-10 (XLRP) | Missense, nonsense | GTPase-activating protein defect |
| PDE6B | 5-10 (ARRP) | Missense | cGMP phosphodiesterase dysfunction |
Data from ClinVar and NCBI Gene (accessed 2023).
Key signaling networks in RP include:
- • cGMP-PKG signaling: Mutations in PDE6B or CNGA1 lead to elevated cGMP levels, activating PKG and causing apoptosis.
- • Unfolded protein response (UPR): RHO mutations cause ER stress, activating PERK, IRE1, and ATF6 pathways.
- • Wnt and hedgehog signaling: These pathways are involved in photoreceptor development and survival, and their dysregulation may contribute to degeneration.
- • PI3K/AKT/mTOR: This pathway regulates cell survival and autophagy, and its modulation has been explored as a therapeutic target.
Experimental Model Systems
| Cell Line | Origin | Key Mutations |
|---|---|---|
| Y79 | Retinoblastoma | RB1 deletion |
| WERI-Rb1 | Retinoblastoma | RB1 mutation |
| ARPE-19 | Retinal pigment epithelium | None (wild-type) |
| 661W | Mouse photoreceptor | None (SV40 T-antigen) |
Organoids derived from induced pluripotent stem cells (iPSCs) offer a more physiologically relevant model, recapitulating retinal development and allowing for the study of photoreceptor degeneration in a 3D context. They are particularly useful for testing gene editing and drug responses.
- • PDX (Patient-Derived Xenograft): Not commonly used for RP, as retinal tissue is not typically xenografted.
- • GEMM (Genetically Engineered Mouse Models): Examples include the P23H RHO mouse, the rd1 mouse (PDE6B mutation), and the RPGR knockout mouse. These models are widely used to study disease mechanisms and test therapies.
- • Induced models: Chemical or light-induced retinal degeneration models, such as the sodium iodate model, are used for acute injury studies.
CRISPR-Cas9 technology enables the creation of isogenic cell lines with precise genetic modifications, providing powerful tools for studying RP. Examples include:
- • RHO P23H knock-in cell lines: These lines express the most common rhodopsin mutation, allowing for the study of protein misfolding and ER stress.
- • RPGR knockout cell lines: These lines lack functional RPGR, enabling investigation of ciliary transport defects.
- • Reporter cell lines: For example, a cell line with a GFP-tagged rhodopsin can be used to monitor protein trafficking in real time.
Commercially available, sequence-verified gene-edited cell lines accelerate research by providing consistent and reproducible models. These models are essential for drug screening, functional genomics, and target validation.
Related Disease
| Disease name | Disease type |
|---|
Related Services
Related Products
| Product name | Cat.No. | Species | Gene ID | |
|---|---|---|---|---|
| KIF3A Knockout HEK293 Cell Line | EDJ-KQ904 | Human | 11127 | Details Get a Quote |
| GNG2 Knockout HEK293 Cell Line | EDJ-KQ1211 | Human | 54331 | Details Get a Quote |
| PATJ Knockout HEK293 Cell Line | EDJ-KQ1357 | Human | 10207 | Details Get a Quote |
| PPIH Knockout HEK293 Cell Line | EDJ-KQ2325 | Human | 10465 | Details Get a Quote |
| ATF6 Knockout HEK293 Cell Line | EDJ-KQ2861 | Human | 22926 | Details Get a Quote |
| ALPK1 Knockout HEK293 Cell Line | EDJ-KQ3058 | Human | 80216 | Details Get a Quote |
| TEDC2 Knockout HEK293 Cell Line | EDJ-KQ3591 | Human | 80178 | Details Get a Quote |
| ARF4 Knockout HEK293 Cell Line | EDJ-KQ4079 | Human | 378 | Details Get a Quote |
| SHROOM2 Knockout HEK293 Cell Line | EDJ-KQ4080 | Human | 357 | Details Get a Quote |
| CFAP410 Knockout HEK293 Cell Line | EDJ-KQ4173 | Human | 755 | Details Get a Quote |
| KIF3C Knockout HEK293 Cell Line | EDJ-KQ4267 | Human | 3797 | Details Get a Quote |
| GABRR1 Knockout HEK293 Cell Line | EDJ-KQ4660 | Human | 2569 | Details Get a Quote |
| RGS16 Knockout HEK293 Cell Line | EDJ-KQ4914 | Human | 6004 | Details Get a Quote |
| MPP3 Knockout HEK293 Cell Line | EDJ-KQ5237 | Human | 4356 | Details Get a Quote |
| TULP3 Knockout HEK293 Cell Line | EDJ-KQ5248 | Human | 7289 | Details Get a Quote |
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Applications of Gene-Edited Cells
Gene-edited cell lines are used to validate the function of genes implicated in RP. For example, a CRISPR knockout of the USH2A gene in ARPE-19 cells can reveal its role in cell adhesion and polarity. Similarly, a knock-in of the RHO P23H mutation can be used to study the downstream effects on the unfolded protein response and apoptosis. These models allow for high-throughput screening of genetic modifiers and therapeutic targets.
Isogenic pairs, where the only difference is the presence or absence of a specific mutation, are ideal for drug screening. For instance, a wild-type and RHO P23H knock-in cell line can be used to identify compounds that reduce ER stress or prevent photoreceptor death. Additionally, gene-edited cells can be used to model resistance to therapies, such as resistance to gene therapy vectors, and to optimize treatment regimens.
CRISPR screens can identify genes that, when knocked out, confer resistance to oxidative stress or other insults, revealing potential therapeutic targets. For example, a genome-wide CRISPR knockout screen in photoreceptor-like cells treated with a stressor can identify genes whose loss protects against cell death. These genes may serve as biomarkers or drug targets for RP.
Public Data Resources
| Database | URL | Description |
|---|---|---|
| TCGA | https://www.cancer.gov/tcga | The Cancer Genome Atlas, provides genomic data for various cancers, but not directly for RP. |
| cBioPortal | https://www.cbioportal.org/ | Integrates genomic data from TCGA and other sources, useful for exploring mutations in RP-related genes. |
| DepMap | https://depmap.org/ | The Dependency Map, provides CRISPR screen data for cancer cell lines, but can be used to explore gene dependencies relevant to RP. |
| GEO | https://www.ncbi.nlm.nih.gov/geo/ | Gene Expression Omnibus, contains transcriptomic data from RP patient samples and models. |
| ClinVar | https://www.ncbi.nlm.nih.gov/clinvar/ | Database of clinically relevant genetic variants, including RP mutations. |
| NCBI Gene | https://www.ncbi.nlm.nih.gov/gene/ | Gene information, including RP-related genes. |