Unknown,Transcriptomics,Genomics,Proteomics

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A transcriptome analysis study on mRNA and long non-coding RNAs in patients with Parkinson’s disease

ABSTRACT: Parkinson’s disease (PD) is the second most common neurodegenerative disorder, affecting ~2–3% of people over 65 years of age. Recent studies have revealed that the molecular pathogenesis of PD involves a series of pathways and neurobiological processes, such as protein degradation, mitochondrial dysfunction, oxidative stress, autophagy, calcium homeostasis, axonal transport, and neuroinflammation, all suggesting that both the onset and course of PD is a complex multilevel and systematic process. Accumulating evidence demonstrates that long non-coding RNAs (lncRNAs) affect the pathogenesis of several diseases, such as cancers, immunological diseases, and neurodegenerative disorders, including Alzheimer’s disease and PD. Next generation sequencing provides a high-throughput method for exploring the diverse polyadenylated RNA populations. This approach allows accurate identification and quantitation of mRNAs and other non-coding RNAs, such as lncRNAs. The present study explored mRNAs and lncRNAs expression by using the next generation sequencing analysis (NGS) and the quantitative real-time PCR (qRT-PCR) assay for the confirmation of the NGS results, followed by functional analysis of the results.

INSTRUMENT(S): NextSeq 550

ORGANISM(S): Homo sapiens

SUBMITTER: Genomix4Life Genomix4Life

PROVIDER: E-MTAB-11326 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:The full complement of molecular pathways contributing to Parkinson’s disease (PD) pathogenesis remains unknown. Here, to address this issue, we began by using a high-resolution variant of functional magnetic resonance imaging (fMRI) to pinpoint brainstem regions differentially affected by, and resistant to, the disease. Then, relying on the imaging information as a guide, we profiled gene expression levels of postmortem brain samples and used a factorial statistical model to identify a disease related decrease in the expression of the polyamine enzyme spermidine/spermine N1-acetyltransferase 1 (SAT1). Next, a series of studies were performed to confirm the pathogenic relevance of this finding. First, to test for a causal link between polyamines and α-synuclein toxicity, we investigated a yeast model expressing α-synuclein. Polyamines were found to enhance the toxicity of α-synuclein, and an unbiased genome-wide screen for modifiers of α-synuclein toxicity identified Tpo4, a member of a family of proteins responsible for polyamine transport. Second, to test for a causal link between SAT1 activity and PD histopathology we investigated a mouse model expressing α-synuclein. DENSPM (N1, N11-diethylnorspermine), a polyamine analog that increases SAT1 activity, was found to reduce PD histopathology, while Berenil (diminazene aceturate), a pharmacological agent that reduces SAT1 activity, worsened the histopathology. Third, we genotyped PD patients and controls and isolated a rare but novel variant in the SAT1 gene, although the functional significance of this genetic variant was not identified. Taken together, the results suggest that the polyamine pathway contributes to PD pathogenesis. Imaging-guided microarray In principle, gene expression profiling techniques like microarray are well suited to identify molecular pathways contributing to the pathogenesis of complex diseases. In practice, however, microarray applied to diseases of the brain present a number of analytic challenges. By identifying regions within the same brain structure that are differentially targeted by and resistant to a disease, imaging-guided microarray is an approach designed to address these limitations. Specifically, guided by the spatial information generated from high resolution functional imaging, a 2x2 factorial analysis-of-variance can be designed, including both within and between group factors, and this “double subtraction” model is effective in improving signal-to-noise in a microarray experiment. Relying on imaging findings, we harvested the DMNV from 6 postmortem brains with evidence of PD and from 5 control brains. The postmortem PD cases were evaluated for pathological changes (Lewy body-containing neurons and Lewy neurites evidenced with antibodies directed against α-synuclein aggregates) that matched the pattern proposed by Braak. We relied on the imaging results to identify a neighboring medullary region relatively unaffected by the disease to be used as a within-brain control. We decided on the inferior olivary nucleus (ION), because it is histologically identifiable, and harvested the ION from each of the 6 PD cases and 5 controls. Microarray techniques were used to generate gene expression profiles for each of the 22 tissue samples. A repeated-measures 2x2 factorial ANOVA model constructed for the imaging study was applied to the expression dataset, in which expression levels from two regions of the medulla (DMNV vs. ION) were included as the first within group factor, diagnosis (PD vs. controls) was the between group factor, and age and sex were included as covariates. Based on current literature, one of the top hits (SAT1) was investigated further to determine if it played a role in PD pathogenesis.

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A transcriptome analysis study on mRNA and long non-coding RNAs in patients with Parkinson’s disease

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