Browsing by Author "Borrageiro, Genevie"

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    Investigation of differential gene expression in Parkinson's disease patients: A whole transcriptome approach
    (Stellenbosch : Stellenbosch University, 2016-12) Borrageiro, Genevie; Bardien, Soraya; Hemmings, Sian; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences: Molecular Biology and Human Genetics.
    ENGLISH SUMMARY: Parkinson’s disease (PD) is the most common neurodegenerative movement disorder and is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. Dopaminergic neuronal loss results in motor symptoms such as resting tremor, bradykinesia, rigidity and postural instability. Although several PD-causing genes have been identified, the process(es) that lead to progressive neuronal loss is poorly understood. Therefore, PD research efforts have turned to genome-wide next generation sequencing technologies to provide clues to the etiology. The focus of this study was to investigate the entire transcriptome in South African patients with PD (particularly in the under-studied mixed ancestry population) to identify biological pathways that may shed light on the pathobiology of PD. A total of 40 study participants (20 patients and 20 controls) were recruited for the study. The PD patients were recruited from the Movement Disorders clinic at Tygerberg Hospital in Cape Town, South Africa and had to meet the UK Parkinson’s disease Society Brain Bank Diagnostic Criteria for PD. All individuals were from the South African mixed ancestry ethnic group which is a unique admixture of Khoisan, Black, Caucasian, and Asian populations. Copy number variation (CNV) detection in known PD genes (SNCA, PARK2 (Parkin), UCHL1, PINK1, PARK7, LRRK2, GCH1 and ATP13A2 and two point mutations (SNCA A30P and LRRK2 G2019S) was determined using the Multiplex Ligation-dependent Probe Amplification (MLPA) technique. Total RNA was extracted from whole blood samples and RNA-Sequencing was performed at NXT-Dx (Gent, Belgium) on the Illumina HiSeq® 4000. Bioinformatic analysis was performed using Partek® Flow® software. DAVID and Ingenuity Pathway Analysis (IPA) were used for enrichment analysis and to identify possible biological pathways involved. One candidate gene of interest was selected for further verification by quantitative real-time PCR (qPCR). MLPA analysis revealed a heterozygous exon 2 deletion in PARK2 in one patient however a second mutation in this gene was not identified. Therefore, all patients potentially have idiopathic PD and were analysed as one group for RNA-Seq. All samples produced good quality reads, as determined using FastQC (scores > 39) and on average 95.3% of the transcripts generated for each sample could be aligned to the reference genome (hg19). Bioinformatics analysis resulted in a candidate gene list of 132 differentially expressed genes. Analysis of these genes using IPA identified five significant dysregulated canonical pathways which included regulation of eIF4 and p70S6K signaling, EIF2 signaling, LPS/IL-1 mediated inhibition of RXR function, xenobiotic metabolism signaling and maturity onset diabetes of young (MODY) signaling which contribute to PD pathogenesis. A possible link between CEBPA, PGC-1α and PD was also highlighted as both genes were in the prioritized candidate list and IPA gene network. CEBPA has been shown to interact with known PD genes and PGC-1 is a transcriptional regulator of mitochondrial biogenesis a key pathway linked to PD. CEBPA was prioritized for replication studies using qPCR and was found to be significantly down-regulated in patients which contrasted with the RNA-Seq results. The present study revealed candidate genes, CEBPA and PGC-1α which could potentially be involved in the development of PD and a possible link to diabetes through mitochondrial mechanisms. We speculate that the increased PGC-1α levels observed is in response to loss of mitochondria, which leads to increased levels of reactive oxygen species that ultimately result in death of dopaminergic neurons. Our study illustrates that the use of RNA-Seq in combination with IPA is a powerful approach that may reveal candidate genes and biological pathways involved in PD. A better understanding of the molecular mechanisms underlying PD is critical for development of therapeutic modalities in order to prevent, stop or reverse dopaminergic neuronal loss in PD patients.