REP1 deficiency causes systemic dysfunction of lipid metabolism and oxidative stress in choroideremia

Abstract

Choroideremia (CHM) is an X-linked recessive chorioretinal dystrophy caused by mutations in CHM, encoding for Rab escort protein 1 (REP1). Loss of functional REP1 leads to the accumulation of unprenylated Rab proteins and defective intracellular protein trafficking, the putative cause for photoreceptor, retinal pigment epithelium (RPE), and choroidal degeneration. CHM is ubiquitously expressed, but adequate prenylation is considered to be achieved, outside the retina, through the isoform REP2. Recently, the possibility of systemic features in CHM has been debated; therefore, in this study, whole metabolomic analysis of plasma samples from 25 CHM patients versus age- and sex-matched controls was performed. Results showed plasma alterations in oxidative stress-related metabolites, coupled with alterations in tryptophan metabolism, leading to significantly raised serotonin levels. Lipid metabolism was disrupted with decreased branched fatty acids and acylcarnitines, suggestive of dysfunctional lipid oxidation, as well as imbalances of several sphingolipids and glycerophospholipids. Targeted lipidomics of the chmru848 zebrafish provided further evidence for dysfunction, with the use of fenofibrate over simvastatin circumventing the prenylation pathway to improve the lipid profile and increase survival. This study provides strong evidence for systemic manifestations of CHM and proposes potentially novel pathomechanisms and targets for therapeutic consideration.

Keywords: Genetic diseases; Metabolism; Ophthalmology.

Figure 1. Global metabolomic analysis of choroideremia (CHM) patients versus age- and sex-matched controls.

(A) Principal component analysis (PCA). Control and CHM samples are represented as blue and red circles, respectively (n = 25 each group). (B) Cluster analysis of control and CHM samples show no clear separation between groups. (C) Top 30 metabolites detected by random forest analysis based on importance to group separation. (D) Pathway analysis calculated using MetaboLync pathway analysis (MPA) software. Pathways with MPA score higher than 1 were considered.

Figure 2. CHM patients exhibit evidence of increased oxidative stress.

(A) Schematic of the glutathione metabolism pathway, where several compounds were found to be increased (red) or decreased (green) in CHM patients compared with controls. Metabolites trending to significance (0.05 < P ≤ 0.1) are represented in a lighter shade to distinguish from those significantly altered (P ≤ 0.05). (B–I) Scatter dot plots of specific metabolites indicating the mean ± SD levels in CHM patient samples (red) and control samples (blue) (n = 25). P value was determined using matched pair t tests. ^#0.05 < P ≤ 0.1, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

Figure 3. Alterations in tryptophan and hemoglobin metabolism pathways in CHM patients.

(A) Pathway schematics and altered metabolites in tryptophan metabolism with decreased metabolites in green and increased in red. (B–E) Scatter dot plots of altered metabolites showing control (blue) and CHM (red) groups with mean ± SD (n = 25). (F) Schematic representation in the hemoglobin/heme metabolism pathway with decreased metabolites shown in green and increased metabolites in red. (G–J) Scatter dot plots of altered metabolites showing control (blue) and CHM (red) groups with mean ± SD (n = 25). P value was determined using matched pair t tests. ^#0.05 < P ≤ 0.1, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001.

Figure 4. Disturbance of sphingolipid metabolism in CHM patients.

(A) General sphingolipid metabolism pathway with compounds differentially detected in CHM patients highlighted in red (increased) or green (decreased) compared with control levels. (B–I) Scatter dot plots of key metabolite levels in both control (blue) and choroideremia (red) plasma samples. Lines indicate mean ± SD (n = 25). P value was determined using matched pair t tests. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. SM, sphingomyelin.

Figure 5. Metabolites involved in lipid metabolism subclasses differentially detected in CHM patients.

(A–E) Bars represent mean ± SD of control (blue) and choroideremia (red) plasma samples (n = 25). P value was determined using matched pair t tests. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. ^#0.05 < P ≤ 0.1. GPC, glycerophosphocholine; GPE, gylcerophosphoethanolamine; GPS, glycerophosphoserine; 3-Cmpfp, 3-carboxy-4-methyl-5-pentyl-2-furanpropionate; CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate.

Figure 6. Lipidomic profiles of zebrafish.

(A) PCA of day 6 chm ^ru848 mutant fish untreated (red squares) or treated with 0.3 nM simvastatin (black squares) or 700 nM fenofibrate (gray squares), compared with WT fish (blue circles). (B–K) Scatter dot plots with key metabolites shared with human plasma metabolites and respective levels detected in all groups. Lines indicate mean ± SD (n = 4, 10 fish per group). P value was determined using 1-way ANOVA. *P ≤ 0.05, **P ≤ 0.01. a.u., arbitrary units; Lyso-PS, lysophosphoserine/1-stearoyl-GPS; Sph(d18:1/22:0), lactosyl-N-behenoyl-sphingosine; S1P, sphingosine-1-phosphate; CAR, carnitine; PC, phosphatidylcholine; SM, sphingomyelin.

Figure 7. Characterization of chm^ru848 zebrafish treated daily with 0.3 nM simvastatin or 700 nM fenofibrate from 24 hours after fertilization.

(A) Survival days of chm^ru848 fish untreated (red) or treated with simvastatin (black) or fenofibrate (gray) (n = 4, 50 fish per group). (B) Average levels of cholesterol (μM per μg of protein) in WT fish (blue circles), and chm^ru848 zebrafish untreated (red squares) or treated with simvastatin (black squares) or with fenofibrate (gray squares) at 6 dpf (n ≥ 2, 5 fish per condition). Data represent mean ± SD. (C) Retinal sections and wholemount morphology of WT, untreated chm^ru848 fish, and chm^ru848 fish treated with simvastatin or fenofibrate at 6dpf. Scale bar: 100 μm. P value was determined using 1-way ANOVA. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001.