Construction of Parkinson's disease marker-based weighted protein-protein interaction network for prioritization of co-expressed genes

Genome-Wide DNA Methylation in Early-Onset-Dementia Patients Brain Tissue and Lymphoblastoid Cell Lines

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer's disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.

Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.

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.

MicroRNA-532-5p upregulation protects neurological deficits after ischemic stroke through inhibition of BTB and CNC homology 1

OBJECTIVE

MicroRNA (miR)-532-5p has been reported to protect against ischemic stroke (IS), while the underlying mechanism of miR-532-5p targeting BTB and CNC homology 1 (BACH1) in IS remains unknown. Thus, we aim to detect the role of miR-532-5p in IS via targeting BACH1.

METHODS

Blood samples were collected from IS patients and healthy controls. Rat middle cerebral artery occlusion (MCAO) models were established and intracerebrally injected with altered miR-532-5p or BACH1 plasmid vectors to reveal their roles in neurological function, brain tissue pathology and inflammation in MCAO. Expression of miR-532-5p and BACH1 in patients' blood samples and rat brain tissues was assessed, and the targeting relationship between miR-532-5p and BACH1 was confirmed.

RESULTS

MiR-532-5p was downregulated and BACH1 was upregulated in IS. BACH1 was targeted by miR-532-5p. Restored miR-532-5p or inhibited BACH1 improved neurological function and inhibited inflammation and apoptosis in MCAO rats. On the contrary, miR-532-5p reduction or BACH1 overexpression had totally opposite effects on MCAO rats. The protective role of miR-532-5p for MCAO rats was reversed by upregulated BACH1.

CONCLUSION

MiR-532-5p upregulation protects against neurological deficits after IS through inhibition of BACH1.

Proteomic characterization of secretory granules in dopaminergic neurons indicates chromogranin/secretogranin-mediated protein processing impairment in Parkinson's disease

Parkinson’s disease (PD) is an aging disorder related to vesicle transport dysfunctions and neurotransmitter secretion. Secretory granules (SGs) are large dense-core vesicles for the biosynthesis of neuropeptides and hormones. At present, the involvement of SGs impairment in PD remains unclear. In the current study, we found that the number of SGs in tyrosine hydroxylase-positive neurons and the marker proteins secretogranin III (Scg3) significantly decreased in the substantia nigra and striatum regions of 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP) exposed mice. Proteomic study of SGs purified from the dopaminergic SH-sy5Y cells under 1-methyl-4-phenylpyridinium (MPP+) treatments (ProteomeXchange PXD023937) identified 536 significantly differentially expressed proteins. The result indicated that disabled lysosome and peroxisome, lipid and energy metabolism disorders are three characteristic features. Protein-protein interaction analysis of 56 secretory proteins and 140 secreted proteins suggested that the peptide processing mediated by chromogranin/secretogranin in SGs was remarkably compromised, accompanied by decreased candidate proteins and peptides neurosecretory protein (VGF), neuropeptide Y, apolipoprotein E, and an increased level of proenkephalin. The current study provided an extensive proteinogram of SGs in PD. It is helpful to understand the molecular mechanisms in the disease.

Unraveling the role of salt-sensitivity genes in obesity with integrated network biology and co-expression analysis

Obesity is a multifactorial disease caused by complex interactions between genes and dietary factors. Salt-rich diet is related to the development and progression of several chronic diseases including obesity. However, the molecular basis of how salt sensitivity genes (SSG) contribute to adiposity in obesity patients remains unexplored. In this study, we used the microarray expression data of visceral adipose tissue samples and constructed a complex protein-interaction network of salt sensitivity genes and their co-expressed genes to trace the molecular pathways connected to obesity. The Salt Sensitivity Protein Interaction Network (SSPIN) of 2691 differentially expressed genes and their 15474 interactions has shown that adipose tissues are enriched with the expression of 23 SSGs, 16 hubs and 84 bottlenecks (p = 2.52 x 10-16) involved in diverse molecular pathways connected to adiposity. Fifteen of these 23 SSGs along with 8 other SSGs showed a co-expression with enriched obesity-related genes (r ≥ 0.8). These SSGs and their co-expression partners are involved in diverse metabolic pathways including adipogenesis, adipocytokine signaling pathway, renin-angiotensin system, etc. This study concludes that SSGs could act as molecular signatures for tracing the basis of adipogenesis among obese patients. Integrated network centered methods may accelerate the identification of new molecular targets from the complex obesity genomics data.

Dissecting the Role of NF-κb Protein Family and Its Regulators in Rheumatoid Arthritis Using Weighted Gene Co-Expression Network

Rheumatoid arthritis (RA) is a chronic synovial autoinflammatory disease that destructs the cartilage and bone, leading to disability. The functional regulation of major immunity-related pathways like nuclear factor kappa B (NF-κB), which is involved in the chronic inflammatory reactions underlying the development of RA, remains to be explored. Therefore, this study has adopted statistical and knowledge-based systemic investigations (like gene correlation, semantic similarity, and topological parameters based on graph theory) to study the gene expression status of NF-κB protein family (NK^PF) and its regulators in synovial tissues to trace the molecular pathways through which these regulators contribute to RA. A complex protein–protein interaction map (PPIM) of 2,742 genes and 37,032 interactions was constructed from differentially expressed genes (p ≤ 0.05). PPIM was further decomposed into a Regulator Allied Protein Interaction Network (RA^PIN) based on the interaction between genes (5 NK^PF, 31 seeds, 131 hubs, and 652 bottlenecks). Pathway network analysis has shown the RA-specific disturbances in the functional connectivity between seed genes (RIPK1, ATG7, TLR4, TNFRSF1A, KPNA1, CFLAR, SNW1, FOSB, PARVA, CX3CL1, and TRPC6) and NK^PF members (RELA, RELB, NFKB2, and REL). Interestingly, these genes are known for their involvement in inflammation and immune system (signaling by interleukins, cytokine signaling in immune system, NOD-like receptor signaling, MAPK signaling, Toll-like receptor signaling, and TNF signaling) pathways connected to RA. This study, for the first time, reports that SNW1, along with other NK regulatory genes, plays an important role in RA pathogenesis and might act as potential biomarker for RA. Additionally, these genes might play important roles in RA pathogenesis, as well as facilitate the development of effective targeted therapies. Our integrative data analysis and network-based methods could accelerate the identification of novel drug targets for RA from high-throughput genomic data.

Neuropathology and pathogenesis of extrapyramidal movement disorders: a critical update-I. Hypokinetic-rigid movement disorders

Extrapyramidal movement disorders include hypokinetic rigid and hyperkinetic or mixed forms, most of them originating from dysfunction of the basal ganglia (BG) and their information circuits. The functional anatomy of the BG, the cortico-BG-thalamocortical, and BG-cerebellar circuit connections are briefly reviewed. Pathophysiologic classification of extrapyramidal movement disorder mechanisms distinguish (1) parkinsonian syndromes, (2) chorea and related syndromes, (3) dystonias, (4) myoclonic syndromes, (5) ballism, (6) tics, and (7) tremor syndromes. Recent genetic and molecular-biologic classifications distinguish (1) synucleinopathies (Parkinson's disease, dementia with Lewy bodies, Parkinson's disease-dementia, and multiple system atrophy); (2) tauopathies (progressive supranuclear palsy, corticobasal degeneration, FTLD-17; Guamian Parkinson-dementia; Pick's disease, and others); (3) polyglutamine disorders (Huntington's disease and related disorders); (4) pantothenate kinase-associated neurodegeneration; (5) Wilson's disease; and (6) other hereditary neurodegenerations without hitherto detected genetic or specific markers. The diversity of phenotypes is related to the deposition of pathologic proteins in distinct cell populations, causing neurodegeneration due to genetic and environmental factors, but there is frequent overlap between various disorders. Their etiopathogenesis is still poorly understood, but is suggested to result from an interaction between genetic and environmental factors. Multiple etiologies and noxious factors (protein mishandling, mitochondrial dysfunction, oxidative stress, excitotoxicity, energy failure, and chronic neuroinflammation) are more likely than a single factor. Current clinical consensus criteria have increased the diagnostic accuracy of most neurodegenerative movement disorders, but for their definite diagnosis, histopathological confirmation is required. We present a timely overview of the neuropathology and pathogenesis of the major extrapyramidal movement disorders in two parts, the first one dedicated to hypokinetic-rigid forms and the second to hyperkinetic disorders.

Plasma VCAM1 levels correlate with disease severity in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by motor and non-motor symptoms. There is increasing evidence that PD pathology is accompanied by an inflammatory response. This is highly relevant for understanding disease progression and the development of novel neuroprotective therapies.

OBJECTIVE

Assessing potential dysregulation of a panel of inflammatory mediators in the peripheral blood mononuclear cells (PBMCs) and plasma of PD patients and in the context of clinical outcome metrics.

METHODS

We performed a screening of selected cell-surface chemokine receptors and adhesion molecules in PBMCs from PD patients and age-matched healthy controls in a flow cytometry-based assay. ELISA was used to quantify VCAM1 levels in the plasma of PD patients. Lymphocytic chemotactic ability was assessed using a modified Boyden chamber assay.

RESULTS

VLA4 expression was significantly downregulated on CD3+ T cells, CD56+ NK cells, and CD3+/CD56+ NK-T cells from PD patients; further, an increase of the soluble VLA4 ligand VCAM1 in patient plasma was noted. sVCAM1 in PD patients was even higher than reported for patients with multiple sclerosis, neuromyelitis optica, and rheumatoid arthritis. sVCAM1 levels correlated with the disease stage (Hoehn and Yahr scale) and motor impairment. Chemoattraction with SDF-1α revealed impaired motility of lymphocytes from PD patients relative to controls.

CONCLUSION

Our data provides evidence for a functional dysregulation of the sVCAM1-VLA4 axis in PD. Further studies evaluating the therapeutic potential of this axis are warranted.

Potential two-step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study

Introduction

We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases.

Methods

Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms.

Results

The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal.

Discussion

These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.