RNA editing alterations define manifestation of prion diseases

Abstract

Prion diseases are fatal neurodegenerative disorders caused by misfolding of the normal prion protein into an infectious cellular pathogen. Clinically characterized by rapidly progressive dementia and accounting for 85% of human prion disease cases, sporadic Creutzfeldt-Jakob disease (sCJD) is the prevalent human prion disease. Although sCJD neuropathological hallmarks are well-known, associated molecular alterations are elusive due to rapid progression and absence of preclinical stages. To investigate transcriptome alterations during disease progression, we utilized tg340-PRNP129MM mice infected with postmortem material from sCJD patients of the most susceptible genotype (MM1 subtype), a sCJD model that faithfully recapitulates the molecular and pathological alterations of the human disease. Here we report that transcriptomic analyses from brain cortex in the context of disease progression, reveal epitranscriptomic alterations (specifically altered RNA edited pathway profiles, eg., ER stress, lysosome) that are characteristic and possibly protective mainly for preclinical and clinical disease stages. Our results implicate regulatory epitranscriptomic mechanisms in prion disease neuropathogenesis, whereby RNA-editing targets in a humanized sCJD mouse model were confirmed in pathological human autopsy material.

Keywords: ER-stress; RNA editing; RNA-sequencing; prion diseases; sporadic Creutzfeldt–Jakob disease.

Fig. 1.

Correlation of sCJD-mice gene-expression alterations at preclinical and clinical disease stages with corresponding alterations determined in scrapie-murine models. Venn diagrams displaying the overlap of gene-expression alterations at preclinical (A) and clinical (B) stages among different models of prion diseases. Significant overlaps are observed between sCJD-mice and the tested scrapie models. For a complete list of overlapping genes, please refer to SI Appendix, Tables S2 and S3 and Dataset S1.

Fig. 2.

Correlation of RNA-seq data with RT-qPCR experimental validations in sCJD (tg340) mice. Correlation of gene-expression values between RNA-seq and RT-qPCR. R² and Pearson correlation coefficients (r) for each time point were as follows: (A) 120 dpi: R² = 0.4567, r = 0.6758; (B) 180 dpi: R² = 0.5817, r = 0.7627. Statistically significant correlation between in silico and experimental analyses was detected for both time points (***P < 0.0001).

Fig. 3.

Experimental cross-validation of RNA-seq data in postmortem sCJD MM1 subtype cases by RT-qPCR. RNAs from control (n = 12) and postmortem sCJD patients of the MM1 subtype (frontal cortex region n = 8) were retrotranscribed and tested using Taqman probes to determine human gene expression; human genes were selected based on the corresponding analysis in the sCJD (tg340) mouse model (clinical disease, 180 dpi). GAPDH was used for normalization. Following a D’Agostino and Pearson test to verify the normality of the distribution, mean FC values were compared in disease and control cases using the Mann–Whitney U test. *P < 0.05, **P < 0.01, and ***P < 0.001.

Fig. 4.

Experimental cross-validation of RNA-seq data in postmortem sCJD MM1 subtype cases by Western blot. Western blot analysis of Desmoplakin, c-Jun, CD44, Aldh1a1, vimentin, IGF1, EGR-1, PRDX6, HMOX-1, IBA-1, and cystatin C in sCJD patient postmortem brain tissue. GAPDH was used as loading control. Densitometries derived from the quantification of 6 cases per group are shown. Following a D’Agostino and Pearson test to verify the normality of the distribution, mean FC values were compared in disease and control cases using the Mann–Whitney U test. *P < 0.05, **P < 0.01, and ***P < 0.001.

Fig. 5.

Experimental validation of RNA-seq data in sCJD (tg340) mice at protein level. Western blot analysis of clusterin, desmoplakin, c-Jun, cathepsin D, CD44, Aldh1a1, vimentin, Hmox-1, Egr-1, Prdx6, and Iba-1at preclinical (120 dpi) at clinical (180 dpi) disease stages in the tg340 mice inoculated with brain homogenates from control and postmortem sCJD cases of the MM1 disease subtype. Following a D’Agostino and Pearson test to verify the normality of the distribution, mean FC values were compared between diseased and control animals for each time point using the Mann–Whitney U test. GAPDH was used as a loading control protein. Densitometries derived from the quantification of 3 animals per group are shown. *P < 0.05, **P < 0.01, and ***P < 0.001.

Fig. 6.

Differential editing profiles of control and sCJD-tg340 mice at preclinical and clinical disease stages. Differential editing was identified as indicated in the text. Ring A: Bars indicate the absolute number of RNA editing sites displaying differential editing for each phenotype (cntr, sCJD), RNA-editing mediating enzyme (ADAR, APOBEC), and disease time point (preclinical: 120 dpi, clinical: 180 dpi). Ring B: Genomic distribution (color code legend on top of the graph) of differential editing events for each phenotype, RNA-editing mediating enzyme and disease time point. The bars indicate the absolute number of positions identified. Ring C: Percent genomic distribution (color code legend on top of the graph) of differential editing events for each phenotype, RNA-editing mediating enzyme and disease time-point. The bars visualize the distribution of RNA-editing positions across functional gene regions calculated as a percentage of the total RNA-editing positions identified in each phenotype. For a detailed list of differentially editing events between the studied animal groups and time points, refer to Dataset S3.