Involvement of endocytosis and alternative splicing in the formation of the pathological process in the early stages of Parkinson's disease

Itga5-PTEN signaling regulates striatal synaptic strength and motor coordination in Parkinson's disease

Background: Parkinson's disease (PD) is marked by the loss of dopaminergic neurons in the substantia nigra pars compacta, leading to motor and cognitive dysfunctions. The molecular mechanisms underlying synaptic alterations in PD remain elusive, with a focus on the role of Itga5 in synaptic integrity and motor coordination and TAT-Itga5 was designed to suppress PTEN activity in this investigation. Methods: This study utilized MPTP-induced PD animal models to investigate the expression and role of Itga5 in the striatum. Techniques included quantitative PCR, Western blotting, immunostaining, CRISPR-CasRx-mediated knockdown, electrophysiological assays, behavioral tests, and mass spectrometry. Results: Itga5 expression was significantly reduced in MPTP-induced PD models. In these models, a marked decrease in dendritic spine density and a shift towards thinner spines in striatal GABA neurons were observed, suggesting impaired synaptic integration. Knockdown of Itga5 resulted in reduced dendritic branching, decreased mushroom spines, and increased thin spines, altering synaptic architecture. Electrophysiological analyses revealed changes in action potential and spontaneous excitatory postsynaptic currents, indicating altered synaptic transmission. Motor behavior assessments showed that Itga5 deficiency led to impairments in fine motor control and coordination. Furthermore, Itga5 was found to interact with PTEN, affecting AKT signaling crucial for synaptic development and motor coordination. Conclusion: The study demonstrates that Itga5 plays a critical role in maintaining synaptic integrity and motor coordination in PD. The Itga5-PTEN-AKT pathway represents a potential therapeutic target for addressing synaptic and motor dysfunctions in PD.

Pathogenic mechanisms and potential therapeutic targets for Parkinson disease revealed by bioinformatic analysis of necroptosis and immune cell infiltration

Parkinson disease (PD) is an age-dependent neurodegenerative disease with very high prevalence by age 80 years. Necroptosis is a newly identified form of programmed cell death implicated in neurodegenerative diseases, but has not yet been conclusively associated with PD. This study examined the contributions of necroptosis to PD using bioinformatics analysis. Datasets GSE26927, GSE49036, and GSE54536 from the gene expression omnibus database were analyzed for differentially expressed genes (DEGs). These DEGs were then subjected to gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis to identify associated functions and signaling mechanisms. Necroptosis-related differentially expressed genes (NRDEGs) were then identified by the overlap of DEGs and the necroptosis gene set hsa04217. The STRING database and Cytoscape software were then used to build and visualize a protein-protein interaction network and identify hubs and key functional modules among NRDEGs. In addition, immune cell type abundance was analyzed based on DEGs using ImmuCellAI. The identified DEGs, KEGG pathway enrichment terms, and protein-protein interaction network structures of NRDEGs were validated using an independent dataset (GSE54536). The necroptosis pathway was significantly enriched and activated in PD samples. Thirteen NRDEGs were identified in the GSE26927 and GSE49036 datasets, including receptor interacting serine/threonine kinase 1, CASP8 and FADD like apoptosis regulator, TNFRSF1A associated via death domain, and interleukin 1 beta, of which 6 were validated in the GSE54536 dataset. According to gene ontology and KEGG analyses, these NRDEGs are involved in necroptosis-related processes, apoptosis, B cell receptor signaling pathways, and NOD-like receptor signaling pathways. Analysis of DEGs also revealed significant increases in CD8 + T cell and Tex cell infiltration and significant decreases in B cell and T gamma delta cell infiltration within the PD brain. Necroptosis pathways are active in PD and associated with immune cell infiltration. The factors controlling necroptotic signaling and immune infiltration identified in this study may be valuable diagnostic markers and therapeutic targets for PD.

The Potential of ANK1 to Predict Parkinson's Disease

The main cause of Parkinson's disease (PD) remains unknown and the pathologic changes in the brain limit rapid diagnosis. Herein, differentially expressed genes (DEGs) in the Gene Expression Omnibus (GEO) database (GSE8397 and GSE22491) were assessed using linear models for microarray analysis (limma). Ankyrin 1 (ANK1) was the only common gene differentially down-regulated in lateral substantia nigra (LSN), medial substantia nigra (MSN) and blood. Additionally, DEGs between high ANK1 and low ANK1 in GSE99039 were picked out and then uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID) for gene ontology (GO) functional annotation analysis. GO analysis displayed that these DEGs were mainly enriched in oxygen transport, myeloid cell development and gas transport (biological process (BP)); hemoglobin complex, haptoglobin-hemoglobin complex and cortical cytoskeleton (cellular component (CC)); and oxygen transporter activity, haptoglobin binding and oxygen binding (molecular function (MF)). Receiver operating characteristic (ROC) curve analysis showed ANK1 had good diagnostic accuracy and increased the area under the curve (AUC) value when combined with other biomarkers. Consistently, intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropy-ridi-ne (MPTP) in C57BL/6J mice reduced ANK1 mRNA expression in both substantia nigra and blood compared to the control group. Thus, ANK1 may serve as a candidate biomarker for PD diagnosis.

Differential Expression of Lonp1 Isoforms in Cancer Cells

Lonp1 is a mitochondrial protease that degrades oxidized and damaged proteins, assists protein folding, and contributes to the maintenance of mitochondrial DNA. A higher expression of LonP1 has been associated with higher tumour aggressiveness. Besides the full-length isoform (ISO1), we identified two other isoforms of Lonp1 in humans, resulting from alternative splicing: Isoform-2 (ISO2) lacking aa 42-105 and isoform-3 (ISO3) lacking aa 1-196. An inspection of the public database TSVdb showed that ISO1 was upregulated in lung, bladder, prostate, and breast cancer, ISO2 in all the cancers analysed (including rectum, colon, cervical, bladder, prostate, breast, head, and neck), ISO3 did not show significant changes between cancer and normal tissue. We overexpressed ISO1, ISO2, and ISO3 in SW620 cells and found that the ISO1 isoform was exclusively mitochondrial, ISO2 was present in the organelle and in the cytoplasm, and ISO3 was exclusively cytoplasmatic. The overexpression of ISO1 and, at a letter extent, of ISO2 enhanced basal, ATP-linked, and maximal respiration without altering the mitochondria number or network, mtDNA amount. or mitochondrial dynamics. A higher extracellular acidification rate was observed in ISO1 and ISO2, overexpressing cells, suggesting an increase in glycolysis. Cells overexpressing the different isoforms did not show a difference in the proliferation rate but showed a great increase in anchorage-independent growth. ISO1 and ISO2, but not ISO3, determined an upregulation of EMT-related proteins, which appeared unrelated to higher mitochondrial ROS production, nor due to the activation of the MEK ERK pathway, but rather to global metabolic reprogramming of cells.

MicroRNA Expression Profile Changes in the Leukocytes of Parkinson’s Disease Patients

Parkinson’s disease (PD) is one of the most common movement disorders. It is primarily diagnosed clinically. A correct diagnosis of PD in its early stages is important for the development of a pathogenic treatment, which necessitates a search for potential biomarkers of the disease. We evaluated the diagnostic value of several microRNAs and their relationship with the clinical characteristics of PD. The study included 70 PD patients and 40 healthy volunteers. We analyzed the expression of 15 microRNAs in blood leukocytes, which were selected based on literature data and modern concepts of molecular PD pathogenesis. All patients were evaluated using the Hoehn and Yahr scale, UPDRS, NMSQ, and PDQ-39. The data analysis revealed a statistically significant increase in the expression of miR-7-5p, miR-29c-3p, and miR-185-5p and a statistically significant decrease in the expression of miR-29a-3p and miR-30c-1-5p in leukocytes in PD. However, the altered microRNA profile was shown to have a moderate diagnostic value for PD diagnosis. MicroRNA expression changes were associated with the motor and non-motor phenotypic features of PD and administration of anti-Parkinson’s drugs. Also, a relationship between some of the microRNAs studied and the duration and severity of PD was found, which may potentially be used to monitor disease progression.

Transcriptome Profiling Reveals Differential Expression of Circadian Behavior Genes in Peripheral Blood of Monozygotic Twins Discordant for Parkinson's Disease

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases. Investigating individuals with the most identical genetic background is optimal for minimizing the genetic contribution to gene expression. These individuals include monozygotic twins discordant for PD. Monozygotic twins have the same genetic background, age, sex, and often similar environmental conditions. The aim of this study was to carry out a transcriptome analysis of the peripheral blood of three pairs of monozygotic twins discordant for PD. We identified the metabolic process “circadian behavior” as a priority process for further study. Different expression of genes included in the term “circadian behavior” confirms that this process is involved in PD pathogenesis. We found increased expression of three genes associated with circadian behavior, i.e., PTGDS, ADORA2A, and MTA1, in twins with PD. These genes can be considered as potential candidate genes for this disease.

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

Transcriptomic profiles in Parkinson's disease

Transcriptomics in Parkinson's disease offers insights into the pathogenesis of Parkinson's disease but obtaining brain tissue has limitations. In order to bypass this issue, we profile and compare differentially expressed genes and enriched pathways (KEGG) in two peripheral tissues (blood and skin) of 12 Parkinson's disease patients and 12 healthy controls using RNA-sequencing technique and validation with RT-qPCR. Furthermore, we compare our results to previous Parkinson's disease post mortem brain tissue and blood results using the robust rank aggregation method. The results show no overlapping differentially expressed genes or enriched pathways in blood vs. skin in our sample sets (25 vs. 1068 differentially expressed genes with an FDR ≤ 0.05; 1 vs. 9 pathways in blood and skin, respectively). A meta-analysis from previous transcriptomic sample sets using either microarrays or RNA-Seq yields a robust rank aggregation list of cortical gene expression changes with 43 differentially expressed genes; a list of substantia nigra changes with 2 differentially expressed genes and a list of blood changes with 1 differentially expressed gene being statistically significant at FDR ≤ 0.05. In cortex 1, KEGG pathway was enriched, four in substantia nigra and two in blood. None of the differentially expressed genes or pathways overlap between these tissues. When comparing our previously published skin transcription analysis, two differentially expressed genes between the cortex robust rank aggregation and skin overlap. In this study, for the first time a meta-analysis is applied on transcriptomic sample sets in Parkinson's disease. Simultaneously, it explores the notion that Parkinson's disease is not just a neuronal tissue disease by exploring peripheral tissues. The comparison of different Parkinson's disease tissues yields surprisingly few significant differentially expressed genes and pathways, suggesting that divergent gene expression profiles in distinct cell lineages, metabolic and possibly iatrogenic effects create too much transcriptomic noise for detecting significant signal. On the other hand, there are signs that point towards Parkinson's disease-specific changes in non-neuronal peripheral tissues in Parkinson's disease, indicating that Parkinson's disease might be a multisystem disorder.

Alternative splicing and cancer: a systematic review

The abnormal regulation of alternative splicing is usually accompanied by the occurrence and development of tumors, which would produce multiple different isoforms and diversify protein expression. The aim of the present study was to conduct a systematic review in order to describe the regulatory mechanisms of alternative splicing, as well as its functions in tumor cells, from proliferation and apoptosis to invasion and metastasis, and from angiogenesis to metabolism. The abnormal splicing events contributed to tumor progression as oncogenic drivers and/or bystander factors. The alterations in splicing factors detected in tumors and other mis-splicing events (i.e., long non-coding and circular RNAs) in tumorigenesis were also included. The findings of recent therapeutic approaches targeting splicing catalysis and splicing regulatory proteins to modulate pathogenically spliced events (including tumor-specific neo-antigens for cancer immunotherapy) were introduced. The emerging RNA-based strategies for the treatment of cancer with abnormally alternative splicing isoforms were also discussed. However, further studies are still required to address the association between alternative splicing and cancer in more detail.

Inflammation and regulatory T cell genes are differentially expressed in peripheral blood mononuclear cells of Parkinson's disease patients

Our aim was to identify the differentially expressed genes (DEGs) in peripheral blood mononuclear cells (PBMC) of Parkinson’s disease (PD) patients and healthy controls by microarray technology and analysis of related molecular pathways by functional annotation. Thirty PD patients and 30 controls were enrolled. Agilent Human 8X60 K Oligo Microarray was used for gene level expression identification. Gene ontology and pathway enrichment analyses were used for functional annotation of DEGs. Protein–protein interaction analyses were performed with STRING. Expression levels of randomly selected DEGs were quantified by real time quantitative polymerase chain reaction (RT-PCR) for validation. Flow cytometry was done to determine frequency of regulatory T cells (Tregs) in PBMC. A total of 361 DEGs (143 upregulated and 218 downregulated) were identified after GeneSpring analysis. DEGs were involved in 28 biological processes, 12 cellular components and 26 molecular functions. Pathway analyses demonstrated that upregulated genes mainly enriched in p53 (CASP3, TSC2, ATR, MDM4, CCNG1) and PI3K/Akt (IL2RA, IL4R, TSC2, VEGFA, PKN2, PIK3CA, ITGA4, BCL2L11) signaling pathways. TP53 and PIK3CA were identified as most significant hub proteins. Expression profiles obtained by RT-PCR were consistent with microarray findings. PD patients showed increased proportions of CD49d⁺ Tregs, which correlated with disability scores. Survival pathway genes were upregulated putatively to compensate neuronal degeneration. Bioinformatics analysis showed an association between survival and inflammation genes. Increased CD49d⁺ Treg ratios might signify the effort of the immune system to suppress ongoing neuroinflammation.

Comprehensive analysis of core genes and key pathways in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease that occurs mostly in middle-aged and older adults. Its main pathological feature is the progressive death of substantia nigra dopaminergic neurons. As the world's population ages, the number of PD patients is increasing. In this study, we explored the relationship between PD and the cell cycle. In this study, we collected two independent PD transcriptomic datasets, GSE54536 and GSE6613, from the Gene Expression Omnibus (GEO) database. Gene set enrichment analysis (GSEA) was used to identify dysregulated pathways in PD samples. Gene expression was verified by qPCR in PD patients. Nineteen pathways were negatively enriched in both the GSE54536 and GSE6613 datasets. Seven of these 19 pathways were cell cycle-related pathways, including the M/G1 transition, S phase, G1/S transition, mitotic G1-G1/S phases, CDT1 association with the CDC6 ORC origin complex, cell cycle checkpoints and synthesis of DNA. Next, we found that eight genes (PSMA4, PSMB1, PSMC5, PSMD11, MCM4, RPA1, POLE, and PSME4) were mainly enriched in the GSE54536 and GSE6613 datasets. In GSE54536, PSMA4, PSMB1, PSMC5, and PSME4 could significantly predict the occurrence of PD, whereas, in GSE6613, RPA1 and PSME4 could significantly predict the occurrence of PD. Only PSME4 showed significant results in both datasets. Finally, we assessed blood samples from PD patients and controls. Compared with the control samples, the PD samples had lower mRNA levels of PSME4. In summary,these findings can significantly enhance our understanding of the causes and potential molecular mechanisms of PD; the cell cycle signaling pathways and PSME4 may be therapeutic targets for PD.

Classification algorithms applied to blood-based transcriptome meta-analysis to predict idiopathic Parkinson's disease

Diagnosis of Parkinson's disease (PD) remains a challenge in clinical practice, mostly due to lack of peripheral blood markers. Transcriptomic analysis of blood samples has emerged as a potential means to identify biomarkers and gene signatures of PD. In this context, classification algorithms can assist in detecting data patterns such as phenotypes and transcriptional signatures with potential diagnostic application. In this study, we performed gene expression meta-analysis of blood transcriptome from PD and control patients in order to identify a gene-set capable of predicting PD using classification algorithms. We examined microarray data from public repositories and, after systematic review, 4 independent cohorts (GSE6613, GSE57475, GSE72267 and GSE99039) comprising 711 samples (388 idiopathic PD and 323 healthy individuals) were selected. Initially, analysis of differentially expressed genes resulted in minimal overlap among datasets. To circumvent this, we carried out meta-analysis of 17,712 genes across datasets, and calculated weighted mean Hedges' g effect sizes. From the top-100- positive and negative gene effect sizes, algorithms of collinearity recognition and recursive feature elimination were used to generate a 59-gene signature of idiopathic PD. This signature was evaluated by 9 classification algorithms and 4 sample size-adjusted training groups to create 36 models. Of these, 33 showed accuracy higher than the non-information rate, and 2 models built on Support Vector Machine Regression bestowed best accuracy to predict PD and healthy control samples. In summary, the gene meta-analysis followed by machine learning methodology employed herein identified a gene-set capable of accurately predicting idiopathic PD in blood samples.

Weighted gene co-expression network analysis reveals specific modules and biomarkers in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) ranks as the second most frequently occurring neurodegenerative disease. The precise pathogenic mechanism of this disease remains unknown. The aim of the present study was to identify the biomarkers in PD and classify the primary differentially expressed genes (DEGs).

METHODS

The present study searched for and downloaded mRNA expression data from the Gene Expression Omnibus database to identify differences in mRNA expression in the substantia nigra (SN) and blood of patients with PD and healthy controls. In addition, in order to investigate the biological functions of the classified dysregulated genes, the present study utilized Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), reverse transcription-quantitative PCR (RT-qPCR), gene co-expression network analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A receiver operating characteristic (ROC) curve was applied to assay TMEM243 as a diagnostic marker.

RESULTS

Between PD and controls in GSE20292, the present study identified 1862 DEGs. Using the weighted gene co-expression network analysis, the present study identified 15 modules in PD. The module preservation analysis revealed that the tan, blue and green-yellow modules were the most stable. KEGG pathway analysis revealed that five DEGs in the black module were significantly enriched in the ubiquitin-mediated proteolysis pathway, nucleotide excision repair pathway, mismatch repair pathway. The present study selected 303 genes with high connectivity in blue, green-yellow and tan modules as hub genes, where 58 were differentially expressed in both the GSE20292 and GSE54536 datasets. In the SN and blood, 11 genes exhibited the same trend of expression. Furthermore, in the blood samples of patients with PD, the results displayed a significant upregulation of TMEM243. The expression levels of CCR4, CAMK1D, ACTR1B and SPSB3 increased, while both the levels of INA and PSMD4 decreased. These findings are consistent with the bioinformatics analysis results but are not statistically significant. TMEM243 can be considered as a diagnostic biomarker (area under the curve = 0.694; sensitivity, 80 %; specificity, 56 %; P < 0.018).

CONCLUSION

TMEM243 was distinctly upregulated in the blood samples of patients with PD, as validated via RT-qPCR, and was highly sensitive, revealing its potential as a biomarker for the future diagnosis of PD.

Identification of key genes and upstream regulators in ischemic stroke

INTRODUCTION

Ischemic stroke (IS) causes severe neurological impairments and physical disabilities and has a high economic burden. Our study aims to identify the key genes and upstream regulators in IS by integrated microarray analysis.

METHODS

An integrated analysis of microarray studies of IS was performed to identify the differentially expressed genes (DEGs) in IS compared to normal control. Based on these DEGs, we performed the functional annotation and transcriptional regulatory network constructions. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the expression of DEGs.

RESULTS

From two Gene Expression Omnibus datasets obtained, we obtained 1526 DEGs (534 up-regulated and 992 down-regulated genes) between IS and normal control. The results of functional annotation showed that Oxidative phosphorylation and Alzheimer's disease were significantly enriched pathways in IS. Top four transcription factors (TFs) with the most downstream genes including PAX4, POU2F1, ELK1, and NKX2-5. The expression of six genes (ID3, ICAM2, DCTPP1, ANTXR2, DUSP1, and RGS2) was detected by qRT-PCR. Except for DUSP1 and RGS2, the other four genes in qRT-PCR played the same pattern with that in our integrated analysis.

CONCLUSIONS

The dysregulation of these six genes may involve with the process of ischemic stroke (IS). Four TFs (PAX4, POU2F1, ELK1 and NKX2-5) were concluded to play a role in IS. Our finding provided clues for exploring mechanism and developing novel diagnostic and therapeutic strategies for IS.

Evaluation of RNA Blood Biomarkers in the Parkinson's Disease Biomarkers Program

There is a high misdiagnosis rate between Parkinson’s disease (PD) and atypical parkinsonian disorders (APD), such as progressive supranuclear palsy (PSP), the second most common parkinsonian syndrome. In our earlier studies, we identified and replicated RNA blood biomarkers in several independent cohorts, however, replication in a cohort that includes PSP patients has not yet been performed. To this end, we evaluated the diagnostic potential of nine previously identified RNA biomarkers using quantitative PCR assays in 138 blood samples at baseline from PD, PSP and healthy controls (HCs) nested in the PD Biomarkers Program. Linear discriminant analysis showed that COPZ1 and PTPN1 distinguished PD from PSP patients with 62.5% accuracy. Five biomarkers, PTPN1, COPZ1, FAXDC2, SLC14A1s and NAMPT were useful for distinguishing PSP from controls with 69% accuracy. Several biomarkers correlated with clinical features in PD patients. SLC14A1-s correlated with Unified Parkinson’s Disease Rating Scale total and part III scores. In addition, COPZ1, PTPN1 and MLST8, correlated with Montreal Cognitive Assessment (MoCA). Interestingly, COPZ1, EFTUD2 and PTPN1 were downregulated in cognitively impaired (CI) compared to normal subjects. Linear discriminant analysis showed that age, PTPN1, COPZ1, FAXDC2, EFTUD2 and MLST8 distinguished CI from normal subjects with 65.9% accuracy. These results suggest that COPZ1 and PTPN1 are useful for distinguishing PD from PSP patients. In addition, the combination of PTPN1, COPZ1, FAXDC2, EFTUD2 and MLST8 is a useful signature for cognitive impairment. Evaluation of these biomarkers in a larger study will be a key to advancing these biomarkers into the clinic.

Identification of key genes and pathways in Parkinson's disease through integrated analysis

Parkinson's disease (PD) is a progressive, degene-rative neurological disease, typically characterized by tremors and muscle rigidity. The present study aimed to identify differe-ntially expressed genes (DEGs) between patients with PD and healthy patients, and clarify their association with additional biological processes that may regulate factors that lead to PD. An integrated analysis of publicly available Gene Expression Omnibus datasets of PD was performed. DEGs were identified between PD and normal blood samples. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses, as well as protein‑protein interaction (PPI) networks were used to predict the functions of identified DEGs. Reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) was performed to validate the predicted expression levels of identified DEGs in whole blood samples obtained from patients with PD and normal healthy controls. A total of 292DEGs were identified between the PD and normal blood samples. Of these, 156 genes were significantly upregulated and 136 genes were significantly downregulated in PD samples following integrated analysis of four PD expression datasets. The 10 most upregulated and downregulated genes were used to construct a PPI network, where ubiquitin C‑terminal hydrolase L1 (UCHL1), 3‑phosphoinositide dependent protein kinase 1 (PDPK1) and protein kinase cAMP‑activated catalytic subunit β (PRKACB) demonstrated the highest connectivity in the network. DEGs were significantly enriched in amoebiasis, vascular smooth muscle contraction, and the Wnt and calcium signaling pathways. The expression levels of significant DEGs, UCHL1, PDPK1 and PRKACB were validated using RT‑qPCR analysis. The findings revealed that UCHL1 and PDPK1 were upregulated and PRKACB was downregulated in patients with PD when compared with normal healthy controls. In conclusion, the results indicate that the significant DEGs, including UCHL1, PDPK1 and PRKACB may be associated with the development of PD. In addition, these factors may be involved in various signaling pathways, including amoebiasis, vascular smooth muscle contraction and the Wnt and calcium signaling pathways.

Blood Transcriptomic Meta-analysis Identifies Dysregulation of Hemoglobin and Iron Metabolism in Parkinson' Disease

Disrupted iron metabolism has been implicated in the pathogenesis of Parkinson’s disease (PD), a progressive neurodegenerative disorder that severely affects movement and coordination, yet the molecular mechanisms underlying this association remain unknown. To this end, we performed a transcriptomic meta-analysis of four blood microarrays in PD. We observed a significant downregulation of genes related to hemoglobin including, hemoglobin delta (HBD), alpha hemoglobin stabilizing protein (ASHP), genes implicated in iron metabolism including, solute carrier family 11 member 2 (SLC11A2), ferrochelatase (FECH), and erythrocyte-specific genes including erythrocyte membrane protein (EPB42), and 5′-aminolevulinate synthase 2 (ALAS2). Pathway and network analysis identified enrichment in processes related to mitochondrial membrane, oxygen transport, oxygen and heme binding, hemoglobin complex, erythrocyte development, tetrapyrrole metabolism and the spliceosome. Collectively, we identified a subnetwork of genes in blood that may provide a molecular explanation for the disrupted hemoglobin and iron metabolism in the pathogenesis of PD.

Cholesterol in the Pathogenesis of Alzheimer's, Parkinson's Diseases and Autism: Link to Synaptic Dysfunction

In our previous review, we described brain cholesterol metabolism in control conditions and in the case of some rare neurological pathologies linked to defects in the genes which are directly involved in the synthesis and/or traffic of cholesterol. Here, we have analyzed disruptions in cholesterol homeostasis in widespread neurodegenerative diseases (Alzheimer's and Parkinson's diseases) and autism spectrum disorders. We particularly focused on the synaptic dysfunctions that could arise from changes in both membrane cholesterol availability and oxysterol production. Notably, alterations in the brain cholesterol metabolism and neurotransmission occur in the early stages of these pathologies and the polymorphism of the genes associated with cholesterol homeostasis and synaptic communication affects the risk of onset and severity of these diseases. In addition, pharmacological and genetic manipulations of brain cholesterol homeostasis in animal models frequently have marked effects on the progression of neurodegenerative diseases. Thus, the development of Alzheimer's, Parkinson's and autism spectrum disorders may be partially associated with an imbalance of cholesterol homeostasis that leads to changes in the membrane cholesterol and oxysterol levels that, in turn, modulates key steps in the synaptic transmission.

Integrative transcriptomic meta-analysis of Parkinson's disease and depression identifies NAMPT as a potential blood biomarker for de novo Parkinson's disease

Emerging research indicates that depression could be one of the earliest prodromal symptoms or risk factors associated with the pathogenesis of Parkinson’s disease (PD), the second most common neurodegenerative disorder worldwide, but the mechanisms underlying the association between both diseases remains unknown. Understanding the molecular networks linking these diseases could facilitate the discovery of novel diagnostic and therapeutics. Transcriptomic meta-analysis and network analysis of blood microarrays from untreated patients with PD and depression identified genes enriched in pathways related to the immune system, metabolism of lipids, glucose, fatty acids, nicotinamide, lysosome, insulin signaling and type 1 diabetes. Nicotinamide phosphoribosyltransferase (NAMPT), an adipokine that plays a role in lipid and glucose metabolism, was identified as the most significant dysregulated gene. Relative abundance of NAMPT was upregulated in blood of 99 early stage and drug-naïve PD patients compared to 101 healthy controls (HC) nested in the cross-sectional Parkinson’s Progression Markers Initiative (PPMI). Thus, here we demonstrate that shared molecular networks between PD and depression provide an additional source of biologically relevant biomarkers. Evaluation of NAMPT in a larger prospective longitudinal study including samples from other neurodegenerative diseases, and patients at risk of PD is warranted.

Blood Biomarkers Associated with Cognitive Decline in Early Stage and Drug-Naive Parkinson's Disease Patients

Early diagnosis of Parkinson's disease (PD) continues to be a major challenge in the field. The lack of a robust biomarker to detect early stage PD patients has considerably slowed the progress toward the development of potential therapeutic agents. We have previously evaluated several RNA biomarkers in whole blood from participants enrolled in two independent clinical studies. In these studies, PD patients were medicated, thus, expression of these biomarkers in de novo patients remains unknown. To this end, we tested ten RNA biomarkers in blood samples from 99 untreated PD patients and 101 HC nested in the cross-sectional Parkinson's Progression Markers Initiative by quantitative real-time PCR. One biomarker out of ten, COPZ1 trended toward significance (nominal p = 0.009) when adjusting for age, sex, and educational level. Further, COPZ1, EFTUD2 and PTBP1 mRNAs correlated with clinical features in PD patients including the Hoehn and Yahr scale, Movement Disorder Society revision of Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Montreal Cognitive Assessment (MoCA) score. Levels of EFTUD2 and PTBP1 were significantly higher in cognitively normal PD patients (PD-CN) compared to cognitively impaired PD patients (PD-MCI). Interestingly, blood glucose levels were significantly higher in PD and PD-MCI patients (≥ 100 mg/dL, pre-diabetes) compared to HC. Collectively, we report the association of three RNA biomarkers, COPZ1, EFTUD2 and PTBP1 with clinical features including cognitive decline in early drug-naïve PD patients. Further, our results show that drug-naïve PD and PD-MCI patients have glucose levels characteristic of pre-diabetes patients, suggesting that impaired glucose metabolism is an early event in PD. Evaluation of these potential biomarkers in a larger longitudinal study is warranted.

Splicing Regulation of Pro-Inflammatory Cytokines and Chemokines: At the Interface of the Neuroendocrine and Immune Systems

Alternative splicing plays a key role in posttranscriptional regulation of gene expression, allowing a single gene to encode multiple protein isoforms. As such, alternative splicing amplifies the coding capacity of the genome enormously, generates protein diversity, and alters protein function. More than 90% of human genes undergo alternative splicing, and alternative splicing is especially prevalent in the nervous and immune systems, tissues where cells need to react swiftly and adapt to changes in the environment through carefully regulated mechanisms of cell differentiation, migration, targeting, and activation. Given its prevalence and complexity, this highly regulated mode of gene expression is prone to be affected by disease. In the following review, we look at how alternative splicing of signaling molecules—cytokines and their receptors—changes in different pathological conditions, from chronic inflammation to neurologic disorders, providing means of functional interaction between the immune and neuroendocrine systems. Switches in alternative splicing patterns can be very dynamic and can produce signaling molecules with distinct or antagonistic functions and localization to different subcellular compartments. This newly discovered link expands our understanding of the biology of immune and neuroendocrine cells, and has the potential to open new windows of opportunity for treatment of neurodegenerative disorders.