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. 2019 Jan;18(1):84-104.
doi: 10.1080/15384101.2018.1558873. Epub 2018 Dec 28.

Role of oxidative stress in Retinitis pigmentosa: new involved pathways by an RNA-Seq analysis

Luigi Donato  1   2 , Concetta Scimone  1   2 , Giacomo Nicocia  3 , Rosalia D'Angelo  1   2 , Antonina Sidoti  1   2
Affiliations

Role of oxidative stress in Retinitis pigmentosa: new involved pathways by an RNA-Seq analysis

Luigi Donato et al. Cell Cycle. 2019 Jan.

Retraction in

Abstract

Retinitis pigmentosa (RP) is a very heterogeneous inherited ocular disorder group characterized by progressive retinal disruption. Retinal pigment epithelium (RPE) degeneration, due to oxidative stress which arrests the metabolic support to photoreceptors, represents one of the principal causes of RP. Here, the role of oxidative stress in RP onset and progression was analyzed by a comparative whole transcriptome analysis of human RPE cells, treated with 100 µg/ml of oxLDL and untreated, at different time points. Experiment was thrice repeated and performed on Ion ProtonTM sequencing system. Data analysis, including low quality reads trimming and gene expression quantification, was realized by CLC Genomics Workbench software. The whole analysis highlighted 14 clustered "macro-pathways" and many sub-pathways, classified by selection of 5271 genes showing the highest alteration of expression. Among them, 23 genes were already known to be RP causative ones (15 over-expressed and 8 down-expressed), and their enrichment and intersection analyses highlighted new 77 candidate related genes (49 over-expressed and 28 down-expressed). A final filtering analysis then highlighted 29 proposed candidate genes. This data suggests that many new genes, not yet associated with RP, could influence its etiopathogenesis.

Keywords: RNA-Seq; RPE; Retinitis pigmentosa; enrichment; pathway.

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Figures

Figure 1.
Figure 1.
MTT determination of oxLDL treatment in RPE cells. Cell death was assessed at considered time points (1 h, 2 h, 4 h and 6 h) after basal one, in oxLDL treated samples compared to untreated group. Results shown as mean ± standard error of mean (n = 3). p – value < 0.05.
Figure 2.
Figure 2.
Ranking – based scores of 14 selected pathways. We used a survival percentage to rank the pathways from the most “changed” to the lowest one. A score was therefore assigned based on the ranking: for instance, if a given pathway ranked as first, since we investigate 14 pathways, it was assigned a score of 14 points.
Figure 3.
Figure 3.
Volcano plots for all genes involved in 14 “macro-pathways”. Represented volcano plots are based on p-values and fold-changes, produced by the EDGE test, for all genes involved in 14 “macro-pathways”. It is highlighted how the most of analyzed genes differ between treated and untreated samples.
Figure 4.
Figure 4.
Cytoscape and CluePedia enrichment analysis for most 20 altered genes. Based on publicly available data from STRING and IntAct, about 150 terms were connected by about70 edges, related to enrichment categories: activation (green), binding (blue), catalysis (deep purple), expression (yellow), inhibition (red), ptmod (light purple), reaction (black). Only relevant enrichment (p < 0.05) in the identified protein interactome is shown. a) Both STRING and IntAct enrichment for 1 h. b) Both STRING and IntAct enrichment for 2 h. c) Both STRING and IntAct enrichment for 4 h. d) Both STRING and IntAct enrichment for 6 h.
Figure 5.
Figure 5.
Correlation analysis of fold – change data between qRT – PCR and RNA – Seq. Expression data of 20 selected genes from qRT – PCR and RNA – Seq are means of three replicates, considering all selected time points (a, b, c, d). Scatterplots were generated by the fold – change values from RNA – Seq (x – axis) and qRT – PCR (y – axis).
Figure 6.
Figure 6.
Time – dependent trend of selected 14 pathways. The 14 candidate “macro-pathways” showed a time of exposure-related trend very variable for each one. The most of them exhibit a decrease of % of survival at 2 h and an increase at 4 h. Exceptions are represented by “Circadian rhythms” (stable until 2 h, then decreases until 4 h and it doesn’t change furthermore), “Fatty acids metabolism” (stable until 4 h and then increases after 6 h), “Phototransduction” (decreasing to 1 h, then increasing until 4 h and, finally, decreasing until 6 h) and “Inflammation” (decreases until 2 h, then increases).
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Design and data analysis of this study were realized thanks to private funding offered by Mrs. Gemelli Francesca’s family..

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