Does Background Matter? A Comparative Characterization of Mouse Models of Autosomal Retinitis Pigmentosa rd1 and Pde6b-KO.
Angelina V ChirinskaiteAlexander Yu RotovMariia E ErmolaevaLyubov A TkachenkoAnastasia N VaganovaLavrentii G DanilovKsenia N FedoseevaNicolay A KostinJulia V SopovaMichael L FirsovElena I LeonovaPublished in: International journal of molecular sciences (2023)
Many retinal degenerative diseases result in vision impairment or permanent blindness due to photoreceptor loss or dysfunction. It has been observed that Pde6b rd1 mice (rd1), which carry a spontaneous nonsense mutation in the pde6b gene, have a strong phenotypic similarity to patients suffering from autosomal recessive retinitis pigmentosa. In this study, we present a novel mouse model of retinitis pigmentosa generated through pde6b gene knockout using CRISPR/Cas9 technology. We compare this Pde6b-KO mouse model to the rd1 mouse model to gain insights into the progression of retinal degeneration. The functional assessment of the mouse retina and the tracking of degeneration dynamics were performed using electrophysiological methods, while retinal morphology was analyzed through histology techniques. Interestingly, the Pde6b-KO mouse model demonstrated a higher amplitude of photoresponse than the rd1 model of the same age. At postnatal day 12, the thickness of the photoreceptor layer in both mouse models did not significantly differ from that of control animals; however, by day 15, a substantial reduction was observed. Notably, the decline in the number of photoreceptors in the rd1 model occurred at a significantly faster rate. These findings suggest that the C3H background may play a significant role in the early stages of retinal degeneration.
Keyphrases
- mouse model
- optical coherence tomography
- diabetic retinopathy
- optic nerve
- crispr cas
- end stage renal disease
- chronic kidney disease
- genome editing
- genome wide
- ejection fraction
- preterm infants
- gene expression
- peritoneal dialysis
- metabolic syndrome
- resting state
- functional connectivity
- genome wide identification
- insulin resistance
- transcription factor