A Parkinson's disease Circ RNA s Resource reveals a link between circ SLC 8A1 and oxidative stress
2020; Springer Nature; Volume: 12; Issue: 9 Linguagem: Inglês
10.15252/emmm.201911942
ISSN1757-4684
AutoresMor Hanan, Alon Simchovitz, Nadav Yayon, Shani Vaknine, Roni Cohen‐Fultheim, Miriam Karmon, Nimrod Madrer, Talia Miriam Rohrlich, Moria Maman, Estelle R. Bennett, David Greenberg, Eran Meshorer, Erez Y. Levanon, Hermona Soreq, Sebastián Kadener,
Tópico(s)Nuclear Receptors and Signaling
ResumoResource26 July 2020Open Access Transparent process A Parkinson's disease CircRNAs Resource reveals a link between circSLC8A1 and oxidative stress Mor Hanan Mor Hanan Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Alon Simchovitz Alon Simchovitz Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Nadav Yayon Nadav Yayon Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Shani Vaknine Shani Vaknine Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Roni Cohen-Fultheim Roni Cohen-Fultheim Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel Search for more papers by this author Miriam Karmon Miriam Karmon Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel Search for more papers by this author Nimrod Madrer Nimrod Madrer Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Talia Miriam Rohrlich Talia Miriam Rohrlich The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Moria Maman Moria Maman The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Estelle R Bennett Estelle R Bennett Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author David S Greenberg David S Greenberg Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Eran Meshorer Eran Meshorer The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Erez Y Levanon Erez Y Levanon orcid.org/0000-0002-3641-4198 Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel Search for more papers by this author Hermona Soreq Corresponding Author Hermona Soreq [email protected] orcid.org/0000-0002-0955-526X Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Sebastian Kadener Corresponding Author Sebastian Kadener [email protected] orcid.org/0000-0003-0080-5987 Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Biology Department, Brandeis University, Waltham, MA, USA Search for more papers by this author Mor Hanan Mor Hanan Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Alon Simchovitz Alon Simchovitz Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Nadav Yayon Nadav Yayon Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Shani Vaknine Shani Vaknine Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Roni Cohen-Fultheim Roni Cohen-Fultheim Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel Search for more papers by this author Miriam Karmon Miriam Karmon Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel Search for more papers by this author Nimrod Madrer Nimrod Madrer Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Talia Miriam Rohrlich Talia Miriam Rohrlich The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Moria Maman Moria Maman The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Estelle R Bennett Estelle R Bennett Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author David S Greenberg David S Greenberg Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Eran Meshorer Eran Meshorer The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Erez Y Levanon Erez Y Levanon orcid.org/0000-0002-3641-4198 Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel Search for more papers by this author Hermona Soreq Corresponding Author Hermona Soreq [email protected] orcid.org/0000-0002-0955-526X Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Search for more papers by this author Sebastian Kadener Corresponding Author Sebastian Kadener [email protected] orcid.org/0000-0003-0080-5987 Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel Biology Department, Brandeis University, Waltham, MA, USA Search for more papers by this author Author Information Mor Hanan1,2, Alon Simchovitz1,2, Nadav Yayon1,2, Shani Vaknine1,2, Roni Cohen-Fultheim3, Miriam Karmon3, Nimrod Madrer1,2, Talia Miriam Rohrlich2,4, Moria Maman2,4, Estelle R Bennett1,2, David S Greenberg1,2, Eran Meshorer2,4, Erez Y Levanon3, Hermona Soreq *,1,2 and Sebastian Kadener *,1,5 1Department of Biological Chemistry, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel 2The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel 3Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel 4Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel 5Biology Department, Brandeis University, Waltham, MA, USA *Corresponding author. Tel: +972 548820629; E-mail: [email protected] *Corresponding author. Tel: +972 548820629; E-mail: [email protected] EMBO Mol Med (2020)12:e11942https://doi.org/10.15252/emmm.201911942 Correction(s) for this article A Parkinson's disease CircRNAs Resource reveals a link between circSLC8A1 and oxidative stress06 November 2020 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Circular RNAs (circRNAs) are brain-abundant RNAs of mostly unknown functions. To seek their roles in Parkinson's disease (PD), we generated an RNA sequencing resource of several brain region tissues from dozens of PD and control donors. In the healthy substantia nigra (SN), circRNAs accumulate in an age-dependent manner, but in the PD SN this correlation is lost and the total number of circRNAs reduced. In contrast, the levels of circRNAs are increased in the other studied brain regions of PD patients. We also found circSLC8A1 to increase in the SN of PD individuals. CircSLC8A1 carries 7 binding sites for miR-128 and is strongly bound to the microRNA effector protein Ago2. Indeed, RNA targets of miR-128 are also increased in PD individuals, suggesting that circSLC8A1 regulates miR-128 function and/or activity. CircSLC8A1 levels also increased in cultured cells exposed to the oxidative stress-inducing agent paraquat but were decreased in cells treated with the neuroprotective antioxidant regulator drug Simvastatin. Together, our work links circSLC8A1 to oxidative stress-related Parkinsonism and suggests further exploration of its molecular function in PD. Synopsis In human brains, circRNA levels are region specific and inversely correlate to editing. In PD circRNAs are reduced in SN and accumulation correlation with age is lost. CircSLC8A1 increases in PD and oxidation, is bound to Ago2 and sponges miR128 targets, modulating neuronal survival and aging. CircRNA levels to be brain region-specific and are inversely correlate to the editing of Alu repeats. In the healthy substantia nigra (SN), circRNAs accumulate in an age-dependent manner, but in the Parkinson's Disease (PD) SN, this correlation is lost and the total number of circRNAs is reduced. CircSLC8A1 levels increase in the SN of PD individuals and in cultured cells exposed to the oxidative stress-inducing agent Paraquat. CircSLC8A1 carries 7 binding sites for miR-128 and is strongly bound to Ago2. Indeed, RNA targets of miR-128 are also increased in PD individuals, suggesting that circSLC8A1 regulates miR-128 function and/or activity. The paper explained Problem Parkinson's disease (PD) is the leading neurodegenerative movement disorder; however, the molecular mechanisms underlying cellular degeneration in PD brains remain poorly understood. Circular RNAs (circRNAs) are a recently discovered class of RNAs but their roles and expression patterns in the healthy and diseased human brain are largely unknown. Results We used very deep RNA sequencing to explore the expression patterns of circRNAs in three different brain regions from PD patients, including the substantia nigra (SN) where dopaminergic neurons die in the diseased brain, the amygdala which responds to stressful situations at large and the temporal gyrus harboring the routes from deep brain nuclei to the cortex. We found CircRNA levels to be brain region-specific and inversely correlated to RNA editing. Also, we identified age-dependent accumulation of circRNAs in the healthy SN but not in the PD SN, where this correlation is lost and the total number of circRNAs is reduced. We focused our experiments on CircSLC8A1, which originates from the Ca2+ regulating SLC8A1 gene. We found CircSLC8A1 increases in the SN of PD individuals and in cultured cells exposed to the oxidative stress-inducing agent Paraquat. Notably, CircSLC8A1 carries 7 binding sites for one microRNA, miR-128, and is strongly bound to Ago2. Correspondingly, we found increases in RNA targets of miR-128 in PD brains, suggesting that circSLC8A1 regulates the function and/or activity of miR-128. Impact Our findings establish a resource of circRNAs expressed in the brain of PD patients, reveal previously unknown links between circRNA expression, oxidative stress and PD pathology, and call for exploring the implications of circSLC8A1 accumulation for addressing the initiation of PD neurodegenerative processes. Introduction Parkinson's disease (PD) is the second most common neurodegenerative disease and the most common movement disorder, affecting 1–2% of the population over 65 (Farrer, 2006; Farrer et al, 2006). Parkinson's disease is characterized by a progressive loss of dopaminergic neurons in the SN, which is attributed to multi-leveled and elusive complex interactions between genetic susceptibilities, male sex, environmental toxins, mitochondrial dysfunction, and imbalanced signaling processes (Farrer, 2006). Consequent depletion of the nigrostriatal pathway and of striatal dopamine then lead to the most prominent motor symptoms of the patients, including bradykinesia, hypokinesia, rigidity, resting tremor, and postural stability. The leading risk factors for PD include age and environmental exposure to herbicides, pesticides, and metal-derived substances (Collier et al, 2011). The major molecular hallmark of PD is intracellular accumulation of protein deposits (named Lewy bodies) which are primarily composed of precipitates of the alpha-synuclein (SNCA) protein. Much of the knowledge of genes and pathways involved in PD derives from studies of inherited forms of the disease. Those have been associated with mutations in alpha-synuclein (SNCA), a regulator of synaptic function, as well as Parkin and Pink1, which are involved in mitochondria quality control, and DJ-1, associated with oxidative stress response and the mitochondrial protein kinase LRRK2 [4]. Disease age of onset in families with mutations in these genes is between 20 and 50, considerably earlier than 65 years of age, the typical onset age for the sporadic forms of the disease. Additionally, PD involves massive changes in RNA metabolism (La Cognata et al, 2015), both in brain neurons (Liu et al, 2017) and in patients' leukocytes (Soreq et al, 2008, 2012, 2014; Simchovitz et al, 2020), but contributions of circular RNAs (circRNAs) to PD risks have so far remained unexplored. CircRNAs are a recently rediscovered type of RNA generated by co-transcriptional circularization of specific exons by the spliceosome in a process named back-splicing (Memczak et al, 2013; Ashwal-Fluss et al, 2014; Jeck & Sharpless, 2014; Li et al, 2018; Kristensen et al, 2019). They accumulate with age in the brains of flies, nematodes, and mice (Westholm et al, 2014; Gruner et al, 2016; Cortes-Lopez et al, 2018), and some of them are enriched in synapses and dendrites (Rybak-Wolf et al, 2015; Veno et al, 2015; You et al, 2015). Most of the known circRNAs are derived from RNA-polymerase II transcription of protein coding genes, and their circular structure makes them resistant to cellular exonucleases and hence exceptionally stable RNA molecules compared to canonical mRNA (Jeck et al, 2013). Hence, circRNAs can potentially become biomarkers for disease and/or the follow-up of treatment efficacy or targets for new therapeutics (Zhang et al, 2018). While thousands of circRNAs have been described, the function of only very few has been elucidated. These include circCDR1as, the most abundant mammalian circRNA. CircCDR1as originates from the antisense strand of the cerebellar degeneration-related protein 1 (CDR1) gene which is highly specific to neurons (Hansen et al, 2013). It binds the miR effector protein Argonaute2 (Ago2) and harbors 73 binding sites for miR-7 (Hansen et al, 2013; Memczak et al, 2013). Initially, it was though that CDR1as sponges and degrades miR-7. However, knockout of CDR1as results in lower, not higher levels of this miRNA, suggesting that CDR1as regulates the function and/or stability of miR-7 in a more complex way (Piwecka et al, 2017). This process likely involves the non-coding RNA Cyrano that regulates miR-7 levels and activity (Guo et al, 2014; Kleaveland et al, 2018). Notably, several circRNAs have been shown to be functional in vivo in mice and flies (Holdt et al, 2016; Chen et al, 2017; Li et al, 2017a,2017b; Pamudurti et al, 2017). Those circRNAs have been proposed to have a variety of molecular functions including RBP binding and transport (Ashwal-Fluss et al, 2014), rRNA maturation, and miRNA stabilization (Holdt et al, 2016). Moreover, a subset of circRNAs is translated but no function has been found for their protein products (Legnini et al, 2017; Pamudurti et al, 2017; Yang et al, 2017; Liang et al, 2019). Interestingly, several reports have suggested a link between circRNAs and neurodegenerative disorders including Alzheimer's disease and amyotrophic lateral sclerosis (Lukiw, 2013; Errichelli et al, 2017; Shi et al, 2017; Dube et al, 2019). Supporting this notion, many circRNA features, including their age-dependent accumulation in the brain, suggest potential roles of these RNAs in neuronal demise. Together, this called for establishing a resource of circRNAs in the healthy and PD brain regions where the potential role of these intriguing transcripts in disease could be addressed. CircRNAs production has been shown to be regulated by RNA editing or hyper-editing events—higher levels of editing beyond transcriptome alignment (Porath et al, 2014; Ivanov et al, 2015; Rybak-Wolf et al, 2015). As 99% of the A-to-I RNA editing in humans occurs in Alu elements, those elements may serve as a measure of global editing levels (Levanon et al, 2004). Alu elements are the most abundant transposable elements in the human genome (over one million copies), and RNA originated from Alu elements inserted in opposite orientations can form dsRNA structures, providing a substrate for RNA editing by adenosine deaminases (ADAR proteins). Interestingly, many circRNAs are flanked by introns that contain Alu or other repeats which have been postulated to mediate the generation of some circRNAs (Ivanov et al, 2015). Multiple Alu pairing events in introns suggest a role of competition between Alu pairing and the formation of alternative circRNAs from the same locus (Zhang et al, 2014a,2014b). Supporting this notion, ADAR expression negatively correlates with circRNA levels in neural tissue in flies as well as in mammalian cell lines (Ivanov et al, 2015; Rybak-Wolf et al, 2015). However, the regulation is more complex. On one hand, the relative location of the Alu elements (or other repeats) is key for determining how ADAR modulates circRNA biogenesis (Ivanov et al, 2015). On the other hand, this regulation also involves DHX9, an RNA helicase which binds inverted-repeat Alu elements, unwinds their secondary structure and represses circRNA abundance (Aktas et al, 2017). However, whether such editing events relate to circRNA abundance in human healthy or diseased brain tissues has not yet been investigated. To address potential roles of circRNAs in PD and determine whether these molecules could biomark this disease, we comprehensively profiled the circRNAs in three brain regions from dozens of PD and healthy individuals: The substantia nigra (SN), where much of the PD-related demise of dopaminergic neurons takes place, the medial temporalis gyrus (MTG), and the amygdala (AMG), which controls acute stress responses that are known to accompany this disease. Comparing brain tissues from human PD patients to matched apparently healthy aged individuals identified a PD-related loss of the age-dependent increase of SN circRNAs and specific changes in RNA editing of Alu elements. Having found that circSLC8A1 is significantly upregulated in the SN of PD individuals, we selected this circRNA as an in-depth example for addressing the role of circRNAs in the PD brain as it relates to the main PD-inducing risks such as oxidative stress. In sum, our work provides an important resource for studying circRNAs in PD and demonstrates that these RNAs are miss-regulated in the brain of PD individuals. Results A genomic resource for studying circular RNAs in PD brains To determine how gene expression at large and circRNA profiles in particular are changed in specific brain regions from individuals with PD, we generated and sequenced dozens of rRNA-depleted RNA-seq libraries from the AMG, SN, and MTG of PD or healthy individuals (Fig EV1A–D; see Dataset EV1 for clinical data for the donors). The sequenced and analyzed tissues included 27 control and 42 PD samples (libraries were only prepared from samples with RIN > 6.5, and we excluded samples with low sequencing number of circRNAs; Appendix Fig S1E–H). This relatively large set assisted in dealing with the individual heterogeneity characteristic of human brain tissues (Barbash et al, 2017). We sequenced these libraries at a deep level (50 M reads per sample on average; Fig EV1I, Dataset EV2), allowing reliable and simultaneous detection and quantification of mRNAs, long non-coding RNAs (lncRNAs) and circRNAs. We then used a bioinformatics pipeline to identify and annotate circRNAs and mRNAs (Memczak et al, 2013). Click here to expand this figure. Figure EV1. Tissues retrieved and experimental in-house RNA sequencing methodology A. Brain region origin of tissues retrieved from the NBB detailing each brain area. B. Average RIN plot of RNA prepared from the frozen tissues. C. Library preparation key steps (see details in Materials and Methods). D. Library plot in a TapeStation run. E–G. Number of circRNAs detected in each sample of all 3 regions. Samples with < 5,000 circRNAs were removed from the analysis. H. Number of samples from PD and control from each tissue that were included in the analysis. I. Number of total reads and circRNAs from each tissue. Download figure Download PowerPoint An initial analysis of the heterogeneity of the samples by non-supervised hierarchical clustering indicated substantial clustering of the MTG or AMG samples by brain region but failed to distinguish the PD from the control samples (Figs 11A and B, and EV2A). This likely reflected heterogeneity between individuals; the pronounced brain region specificity of gene expression patterns that dominate this analysis over other factors such as gender, age, Braak stage differences, disease symptoms severity, and possibly the impact of treatment by diverse medications may also be relevant (Braak et al, 2003). Unlike the MTG or AMG samples, non-supervised clustering analysis of the SN enabled better separation between the gene expression patterns of PD and control individuals (Figs 11C and EV2B–D). This might reflect the bigger differences in gene expression due to the loss of dopaminergic neurons in the SN. Correspondingly, tyrosine hydroxylase (TH) levels were significantly decreased in the PD SN samples (t-test P = 0.025; Fig 11D), compatible with the major decrease in dopaminergic metabolism in this brain region upon PD. Figure 1. Brain region-specific mRNA profiles indicate multicellular PD-related processes A. Brain region origin of tissues: SN (blue), MTG (green), and AMG (red). B. Non-supervised clustering heatmap plot indicates substantial clustering of transcripts from each tissue but not of transcripts from patients compared to controls. PD samples indicated in red. C. PCA clustering of all RNA molecules in the SN control (green) and PD samples (blue). D. qPCR validation of TH levels in SN samples of PD and controls, normalized to beta-actin mRNA. t-test *P = 0.025. Data presented as mean ± SD. n = 18 for CT and 24 for PD. E. Top enriched GO pathways of DE genes in the SN of PD vs controls and the corresponding logP values, Wald test (DEseq2 analysis). F, G. Venn diagrams demonstrating cell type-specific DE genes in the SN of PD vs control tissues, up/down arrows indicate gene groups that were up/down regulated. H. Module–trait relationship of WGCNA analysis showing correlation and corrected P values (in brackets) for each gene module (indicated by colors) as related to the different external traits selected (disease condition, brain region, age, and sex). Correlation test p value calculated by WGCNA package. I. Module membership vs. gene significance in selected modules as reflecting external sample traits: disease condition (reflected in the brown and red modules), brain region (the blue module), and sex (the cyan module), regression-based p value calculated by WGCNA analysis. n = 8 for amygdala control, 15 for amygdala PD, 8 for MTG control, and 13 for MTG PD, 10 for SN control and 15 for SN PD. J. Significant GO terms that emerged as associated with the blue, red, and brown modules and the corresponding log P values, Fisher's exact P value. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. PCA analysis of all tissues and of PD vs control samples, WGCNA analysis QC, clustering of the samples and modules A. PCA of all mRNA samples from all tissues. B. Substantia nigra sample heatmap, relative expression of each gene is represented by colors, marked by the color key: Reduction in expression is represented by light green and elevation is represented by blue. C, D. PCA plot of control vs PD samples in the amygdala and MTG. E. Sample dendrogram showing sample clustering and the different classification patterns of external traits for each sample. F. Cluster dendrogram of all genes. G, H. log2 fold change and −log P value of known DE splicing factors in PD SN vs control SN. n = 10 for SN control and 15 for SN PD. Data presented as mean ± SD, Walt test (DEseq2 analysis). Download figure Download PowerPoint Global gene expression following correction for cell composition (for microglia, astrocytes, and neurons, see Materials and Methods) indicated that the differences in the SN of PD individuals included genes associated with PD (e.g., SNCA, TH, DNAJC6, SYNJ1, GBA, DNAJC6, and SLC6A3) (corrected P = 0.00048), synapse (corrected P = 2.06E-05) and neurodegeneration (corrected P = 0.0017; Miller et al, 2004; Zhang et al, 2005; Moran et al, 2006; Simunovic et al, 2009; Lewis & Cookson, 2012). Gene Ontology (GO) enrichment analysis of the mRNAs differentially expressed (DE) between PD and control individuals indicated that the DE mRNAs in the SN of PD patients are enriched for genes involved in cell junctions, synaptic vesicle (corrected P = 8.52E-04), ion transport (corrected P = 1.37E-04), and dopamine biosynthetic processes (corrected P = 0.0198), (Fig 11E, Dataset EV3 for all DE genes, FDR < 0.01 that were used for the GO term analysis, Dataset EV4 shows detailed the identified GO terms). We next utilized single-cell RNA sequencing data from human brains to determine the cell types expressing these DE genes (McKenzie et al, 2018). We found most of the DE transcripts (Fig 11F and G) to be expressed in several cell types, with endothelial cells, oligodendrocytes, and astrocytes expressing a higher proportion of them than neurons and microglia. Also, the SN neuron-specific DE genes predictably showed pronounced decreases in PD (63 vs. 14 genes, chi-square test, P = 3.5E-12), whereas oligodendrocytes and microglia-expressed genes showed a tendency for increases (24 and 12 vs. 2 and no genes in healthy brains), possibly reflecting their increased fractions and/or elevated inflammation. To seek putative co-regulation of specific DE mRNAs, we performed weighted gene correlation network analysis (WGCNA; Langfelder & Horvath, 2008). This analysis identified transcripts with closely altered expression patterns, creating gene modules with intra-related variability scores (Fig EV2E and F and Dataset EV5). Genes were clustered into 15 modules indicated by different colors (Fig 11H, left color scale), and we assessed their relationships to sample traits such as sex, age, brain region, and condition by calculating the correlation and P value for each module and trait (Fig 11H). We also measured correlations between module memberships and the closeness of each gene's significance in particular modules. Significant P values emerged in certain module–trait relationships, including the brown and red modules with the sample condition (PD or control), the blue module with tissue classification, a
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