Gene expression profiling analysis of the putamen for the investigation of compensatory mechanisms in Parkinson's disease

Unraveling the regulatory landscape of Parkinson disease: A molecular symphony of miRNAs, transcription factors, and high-risk genes

MicroRNAs (miRNAs) have emerged as critical regulators of post-transcriptional gene expression, impacting various biological processes (development, differentiation, and progression). In medicine, miRNAs are promising diagnostic biomarkers for neurodegenerative diseases, including Parkinson's disease (PD). The current study aims at exploring the role of miRNAs and transcription factors (TFs) in regulating genes-associated with PD. Deploying bioinformatics tools, the study identifies specific miRNAs and TFs involved in PD and their potential connections to the organ-disease junction. Notably, certain miRNAs are found to be highly expressed in brain, than compared to blood. Furthermore, the study explores the expression patterns of PD-related genes in different regions of the brain and attempts to construct complex network of interactions contributing to PD pathogenesis. Additionally, the regulatory relationship of two miRNAs namely hsa-miR-375-3p and hsa-miR-423-3p with TFs are well examined. Overall, the study provides a comprehensive moon-shot view of the molecular aspects of PD and their potential therapeutic targets which could be further used as diagnostic biomarkers in early detection, drug design and development attributing towards precision medicine.

Weighted gene co-expression network analysis identifies specific modules and hub genes related to Parkinson's disease

OBJECTIVE

Parkinson's disease (PD) is one of the most common neurodegenerative diseases. This study aims to screen specific modules and key genes related to PD.

METHODS

Gene expression profile data GSE6613 and GSE22491 were downloaded from the Gene Expression Omnibus database. The significantly differentially expressed genes (DEGs) in different datasets were screened, followed by gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The Weighted Gene Co-expression Network Analysis (WGCNA) was used to screen disease-related modules that are significantly stable across datasets. The protein-protein interaction network was constructed using the DEGs in the stable module obtained and preservation modules. Finally, the hub genes directly related to PD were screened.

RESULTS

A total of 179 DEGs with the same significant difference direction were screened. The enrichment analysis of GO and KEGG pathways showed that 20 significantly related GO biological processes and 9 KEGG signaling pathways were screened. A total of three highly conservative modules were detected in the WGCNA network. Finally, three significant PD-related KEGG pathways screened from the Comparative Toxicogenomics Database were identified, including neuroactive ligand-receptor interaction (CRHR2, CTSG, GRIN1, GRIN2D, LPAR4 and P2RX3), amyotrophic lateral sclerosis (BCL2, GRIN1 and GRIN2D) and alcoholism (CAMKK2, GRIN1, GRIN2D and SLC18A2). Key genes, such as SLC18A2, GRIN1 and GRIN2D, may be potential candidate genes for PD progression.

CONCLUSIONS

Our findings indicate that SLC18A2, GRIN1 and GRIN2D may play an important role in the pathogenesis of PD.

Genome-wide epigenetic analyses in Japanese immigrant plantation workers with Parkinson's disease and exposure to organochlorines reveal possible involvement of glial genes and pathways involved in neurotoxicity

BACKGROUND

Parkinson's disease (PD) is a disease of the central nervous system that progressively affects the motor system. Epidemiological studies have provided evidence that exposure to agriculture-related occupations or agrichemicals elevate a person's risk for PD. Here, we sought to examine the possible epigenetic changes associated with working on a plantation on Oahu, HI and/or exposure to organochlorines (OGC) in PD cases.

RESULTS

We measured genome-wide DNA methylation using the Illumina Infinium HumanMethylation450K BeadChip array in matched peripheral blood and postmortem brain biospecimens in PD cases (n = 20) assessed for years of plantation work and presence of organochlorines in brain tissue. The comparison of 10+ to 0 years of plantation work exposure detected 7 and 123 differentially methylated loci (DML) in brain and blood DNA, respectively (p < 0.0001). The comparison of cases with 4+ to 0-2 detectable levels of OGCs, identified 8 and 18 DML in brain and blood DNA, respectively (p < 0.0001). Pathway analyses revealed links to key neurotoxic and neuropathologic pathways related to impaired immune and proinflammatory responses as well as impaired clearance of damaged proteins, as found in the predominantly glial cell population in these environmental exposure-related PD cases.

CONCLUSIONS

These results suggest that distinct DNA methylation biomarker profiles related to environmental exposures in PD cases with previous exposure can be found in both brain and blood.

A workflow for the integrative transcriptomic description of molecular pathology and the suggestion of normalizing compounds, exemplified by Parkinson's disease

The volume of molecular observations on human diseases in public databases is continuously increasing at accelerating rates. A bottleneck is their computational integration into a coherent description, from which researchers may derive new well-founded hypotheses. Also, the need to integrate data from different technologies (genetics, coding and regulatory RNA, proteomics) emerged in order to identify biomarkers for early diagnosis and prognosis of complex diseases and therefore facilitating the development of novel treatment approaches. We propose here a workflow for the integrative transcriptomic description of the molecular pathology in Parkinsons’s Disease (PD), including suggestions of compounds normalizing disease-induced transcriptional changes as a paradigmatic example. We integrated gene expression profiles, miRNA signatures, and publicly available regulatory databases to specify a partial model of the molecular pathophysiology of PD. Six genetic driver elements (2 genes and 4 miRNAs) and several functional network modules that are associated with PD were identified. Functional modules were assessed for their statistical significance, cellular functional homogeneity, literature evidence, and normalizing small molecules. In summary, our workflow for the joint regulatory analysis of coding and non-coding RNA, has the potential to yield clinically as well as biologically relevant information, as demonstrated here on PD data.

Biological networks in Parkinson's disease: an insight into the epigenetic mechanisms associated with this disease

BACKGROUND

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorders in the world. Studying PD from systems biology perspective involving genes and their regulators might provide deeper insights into the complex molecular interactions associated with this disease.

RESULT

We have studied gene co-expression network obtained from a PD-specific microarray data. The co-expression network identified 11 hub genes, of which eight genes are not previously known to be associated with PD. Further study on the functionality of these eight novel hub genes revealed that these genes play important roles in several neurodegenerative diseases. Furthermore, we have studied the tissue-specific expression and histone modification patterns of the novel hub genes. Most of these genes possess several histone modification sites those are already known to be associated with neurodegenerative diseases. Regulatory network namely mTF-miRNA-gene-gTF involves microRNA Transcription Factor (mTF), microRNA (miRNA), gene and gene Transcription Factor (gTF). Whereas long noncoding RNA (lncRNA) mediated regulatory network involves miRNA, gene, mTF and lncRNA. mTF-miRNA-gene-gTF regulatory network identified a novel feed-forward loop. lncRNA-mediated regulatory network identified novel lncRNAs of PD and revealed the two-way regulatory pattern of PD-specific miRNAs where miRNAs can be regulated by both the TFs and lncRNAs. SNP analysis of the most significant genes of the co-expression network identified 20 SNPs. These SNPs are present in the 3' UTR of known PD genes and are controlled by those miRNAs which are also involved in PD.

CONCLUSION

Our study identified eight novel hub genes which can be considered as possible candidates for future biomarker identification studies for PD. The two regulatory networks studied in our work provide a detailed overview of the cellular regulatory mechanisms where the non-coding RNAs namely miRNA and lncRNA, can act as epigenetic regulators of PD. SNPs identified in our study can be helpful for identifying PD at an earlier stage. Overall, this study may impart a better comprehension of the complex molecular interactions associated with PD from systems biology perspective.

Genome-wide pathway-based association analysis identifies risk pathways associated with Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disease. It is generally believed that it is influenced by both genetic and environmental factors, but the precise pathogenesis of PD is unknown to date. In this study, we performed a pathway analysis based on genome-wide association study (GWAS) to detect risk pathways of PD in three GWAS datasets. We first mapped all SNP markers to autosomal genes in each GWAS dataset. Then, we evaluated gene risk values using the minimum P-value of the tagSNPs. We took a pathway as a unit to identify the risk pathways based on the cumulative risks of the genes in the pathway. Finally, we combine the analysis results of the three datasets to detect the high risk pathways associated with PD. We found there were five same pathways in the three datasets. Besides, we also found there were five pathways which were shared in two datasets. Most of these pathways are associated with nervoussystem. Five pathways had been reported to be PD-related pathways in the previous literature. Our findings also implied that there was a close association between immune response and PD. Continued investigation of these pathways will further help us explain the pathogenesis of PD.

PARP-1 involvement in neurodegeneration: A focus on Alzheimer's and Parkinson's diseases

DNA damage is the prime activator of the enzyme poly(ADP-ribose)polymerase1 (PARP-1) whose overactivation has been proven to be associated with the pathogenesis of numerous central nervous system disorders, such as ischemia, neuroinflammation, and neurodegenerative diseases. Under oxidative stress conditions PARP-1 activity increases, leading to an accumulation of ADP-ribose polymers and NAD(+) depletion, that induces energy crisis and finally cell death. This review aims to explain the contribution of PARP-1 in neurodegenerative diseases, focusing on Alzheimer's and Parkinson's disease, to stimulate further studies on this issue and thereby engage a new perspective regarding the design of possible therapeutic agents or the identification of biomarkers.

Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease

Background

Parkinson's Disease (PD) is one of the most prevailing neurodegenerative diseases. Improving diagnoses and treatments of this disease is essential, as currently there exists no cure for this disease. Microarray and proteomics data have revealed abnormal expression of several genes and proteins responsible for PD. Nevertheless, few studies have been reported involving PD-specific protein-protein interactions.

Results

Microarray based gene expression data and protein-protein interaction (PPI) databases were combined to construct the PPI networks of differentially expressed (DE) genes in post mortem brain tissue samples of patients with Parkinson's disease. Samples were collected from the substantia nigra and the frontal cerebral cortex. From the microarray data, two sets of DE genes were selected by 2-tailed t-tests and Significance Analysis of Microarrays (SAM), run separately to construct two Query-Query PPI (QQPPI) networks. Several topological properties of these networks were studied. Nodes with High Connectivity (hubs) and High Betweenness Low Connectivity (bottlenecks) were identified to be the most significant nodes of the networks. Three and four-cliques were identified in the QQPPI networks. These cliques contain most of the topologically significant nodes of the networks which form core functional modules consisting of tightly knitted sub-networks. Hitherto unreported 37 PD disease markers were identified based on their topological significance in the networks. Of these 37 markers, eight were significantly involved in the core functional modules and showed significant change in co-expression levels. Four (ARRB2, STX1A, TFRC and MARCKS) out of the 37 markers were found to be associated with several neurotransmitters including dopamine.

Conclusion

This study represents a novel investigation of the PPI networks for PD, a complex disease. 37 proteins identified in our study can be considered as PD network biomarkers. These network biomarkers may provide as potential therapeutic targets for PD applications development.

Long non-coding RNA and alternative splicing modulations in Parkinson's leukocytes identified by RNA sequencing

The continuously prolonged human lifespan is accompanied by increase in neurodegenerative diseases incidence, calling for the development of inexpensive blood-based diagnostics. Analyzing blood cell transcripts by RNA-Seq is a robust means to identify novel biomarkers that rapidly becomes a commonplace. However, there is lack of tools to discover novel exons, junctions and splicing events and to precisely and sensitively assess differential splicing through RNA-Seq data analysis and across RNA-Seq platforms. Here, we present a new and comprehensive computational workflow for whole-transcriptome RNA-Seq analysis, using an updated version of the software AltAnalyze, to identify both known and novel high-confidence alternative splicing events, and to integrate them with both protein-domains and microRNA binding annotations. We applied the novel workflow on RNA-Seq data from Parkinson's disease (PD) patients' leukocytes pre- and post- Deep Brain Stimulation (DBS) treatment and compared to healthy controls. Disease-mediated changes included decreased usage of alternative promoters and N-termini, 5′-end variations and mutually-exclusive exons. The PD regulated FUS and HNRNP A/B included prion-like domains regulated regions. We also present here a workflow to identify and analyze long non-coding RNAs (lncRNAs) via RNA-Seq data. We identified reduced lncRNA expression and selective PD-induced changes in 13 of over 6,000 detected leukocyte lncRNAs, four of which were inversely altered post-DBS. These included the U1 spliceosomal lncRNA and RP11-462G22.1, each entailing sequence complementarity to numerous microRNAs. Analysis of RNA-Seq from PD and unaffected controls brains revealed over 7,000 brain-expressed lncRNAs, of which 3,495 were co-expressed in the leukocytes including U1, which showed both leukocyte and brain increases. Furthermore, qRT-PCR validations confirmed these co-increases in PD leukocytes and two brain regions, the amygdala and substantia-nigra, compared to controls. This novel workflow allows deep multi-level inspection of RNA-Seq datasets and provides a comprehensive new resource for understanding disease transcriptome modifications in PD and other neurodegenerative diseases.

Long non-coding RNAs (lncRNAs) comprise a novel, fascinating class of RNAs with largely unknown biological functions. Parkinson's-disease (PD) is the most frequent motor disorder, and Deep-brain-stimulation (DBS) treatment alleviates the symptoms, but early disease biomarkers are still unknown and new future genetic interference targets are urgently needed. Using RNA-sequencing technology and a novel computational workflow for in-depth exploration of whole-transcriptome RNA-seq datasets, we detected and analyzed lncRNAs in sequenced libraries from PD patients' leukocytes pre and post-treatment and the brain, adding this full profile resource of over 7,000 lncRNAs to the few human tissues-derived lncRNA datasets that are currently available. Our study includes sample-specific database construction, detecting disease-derived changes in known and novel lncRNAs, exons and junctions and predicting corresponding changes in Polyadenylation choices, protein domains and miRNA binding sites. We report widespread transcript structure variations at the splice junction and exons levels, including novel exons and junctions and alteration of lncRNAs followed by experimental validation in PD leukocytes and two PD brain regions compared with controls. Our results suggest lncRNAs involvement in neurodegenerative diseases, and specifically PD. This comprehensive workflow will be of use to the increasing number of laboratories producing RNA-Seq data in a wide range of biomedical studies.