Nutraceuticals: An integrative approach to starve Parkinson's disease.
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ABSTRACT:
SUBMITTER: Lama A
PROVIDER: S-EPMC8474522 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Parkinson's disease (PD) has had six genome-wide association studies (GWAS) conducted as well as several gene expression studies. However, only variants in MAPT and SNCA have been consistently replicated. To improve the utility of these approaches, we applied pathway analyses integrating both GWAS and gene expression. The top 5000 SNPs (p<0.01) from a joint analysis of three existing PD GWAS were identified and each assigned to a gene. For gene expression, rather than the traditional comparison of one anatomical region between sets of patients and controls, we identified differentially expressed genes between adjacent Braak regions in each individual and adjusted using average control expression profiles. Over-represented pathways were calculated using a hyper-geometric statistical comparison. An integrated, systems meta-analysis of the over-represented pathways combined the expression and GWAS results using a Fisher's combined probability test. Four of the top seven pathways from each approach were identical. The top three pathways in the meta-analysis, with their corrected p-values, were axonal guidance (p = 2.8E-07), focal adhesion (p = 7.7E-06) and calcium signaling (p = 2.9E-05). These results support that a systems biology (pathway) approach will provide additional insight into the genetic etiology of PD and that these pathways have both biological and statistical support to be important in PD.
| S-EPMC3043094 | biostudies-literature
Project description:BackgroundPD is a progressive neurodegenerative disorder commonly treated by levodopa. The findings from genetic studies on adverse effects (ADRs) and levodopa efficacy are mostly inconclusive. Here, we aim to identify predictive genetic biomarkers for levodopa response (LR) and determine common molecular link with disease susceptibility. A systematic review for LR was conducted for ADR, and drug efficacy, independently. All included articles were assessed for methodological quality on 14 parameters. GWAS of PD were also reviewed. Protein-protein interaction (PPI) analysis using STRING and functional enrichment using WebGestalt was performed to explore the common link between LR and PD.ResultsFrom 37 candidate studies on levodopa toxicity, 18 genes were found associated, of which, CAn STR 13, 14 (DRD2) was most significantly associated with dyskinesia, followed by rs1801133 (MTHFR) with hyper-homocysteinemia, and rs474559 (HOMER1) with hallucination. Similarly, 8 studies on efficacy resulted in 4 genes in which rs28363170, rs3836790 (SLC6A3) and rs4680 (COMT), were significant. To establish the molecular connection between LR with PD, we identified 35 genes significantly associated with PD. With 19 proteins associated with LR and 35 with PD, two independent PPI networks were constructed. Among the 67 nodes (263 edges) in LR, and 62 nodes (190 edges) in PD pathophysiology, UBC, SNCA, FYN, SRC, CAMK2A, and SLC6A3 were identified as common potential candidates.ConclusionOur study revealed the genetically significant polymorphism concerning the ADRs and levodopa efficacy. The six common genes may be used as predictive markers for therapy optimization and as putative drug target candidates.
| S-EPMC5606117 | biostudies-literature
Project description:The discovery of novel disease-associated variations in genes is often a daunting task in highly heterogeneous disease classes. We seek a generalizable algorithm that integrates multiple publicly available genomic data sources in a machine-learning model for the prioritization of candidates identified in patients with retinal disease. To approach this problem, we generate a set of feature vectors from publicly available microarray, RNA-seq, and ChIP-seq datasets of biological relevance to retinal disease, to observe patterns in gene expression specificity among tissues of the body and the eye, in addition to photoreceptor-specific signals by the CRX transcription factor. Using these features, we describe a novel algorithm, positive and unlabeled learning for prioritization (PULP). This article compares several popular supervised learning techniques as the regression function for PULP. The results demonstrate a highly significant enrichment for previously characterized disease genes using a logistic regression method. Finally, a comparison of PULP with the popular gene prioritization tool ENDEAVOUR shows superior prioritization of retinal disease genes from previous studies. The java source code, compiled binary, assembled feature vectors, and instructions are available online at https://github.com/ahwagner/PULP.
| S-EPMC4509594 | biostudies-literature
Project description:Pathogens pose serious threats to human health, agricultural investment, and biodiversity conservation through the emergence of zoonoses, spillover to domestic livestock, and epizootic outbreaks. As such, wildlife managers are often tasked with mitigating the negative effects of disease. Yet, parasites form a major component of biodiversity that often persist. This is due to logistical challenges of implementing management strategies and to insufficient understanding of host-parasite dynamics. We advocate for an inclusive understanding of molecular diversity in driving parasite infection and variable host disease states in wildlife systems. More specifically, we examine the roles of genetic, epigenetic, and commensal microbial variation in disease pathogenesis. These include mechanisms underlying parasite virulence and host resistance and tolerance, and the development, regulation, and parasite subversion of immune pathways, among other processes. Case studies of devil facial tumor disease in Tasmanian devils (Sarcophilus harrisii) and chytridiomycosis in globally distributed amphibians exemplify the broad range of questions that can be addressed by examining different facets of molecular diversity. For particularly complex systems, integrative molecular analyses present a promising frontier that can provide critical insights necessary to elucidate disease dynamics operating across scales. These insights enable more accurate risk assessment, reconstruction of transmission pathways, discernment of optimal intervention strategies, and development of more effective and ecologically sound treatments that minimize damage to the host population and environment. Such measures are crucial when mitigating threats posed by wildlife disease to humans, domestic animals, and species of conservation concern.
| S-EPMC7162296 | biostudies-literature
Project description:Cognitive dysfunction is common in Parkinson's disease (PD) and predicts poor clinical outcomes. It is associated primarily with pathologic involvement of basal forebrain cholinergic and prefrontal dopaminergic systems. Impairments in executive functions, attention, and visuospatial abilities are its hallmark features with eventual involvement of memory and other domains. Subtle symptoms in the premotor and early phases of PD progress to mild cognitive impairment (MCI) which may be present at the time of diagnosis. Eventually, a large majority of PD patients develop dementia with advancing age and longer disease duration, which is usually accompanied by immobility, hallucinations/psychosis, and dysautonomia. Dopaminergic medications and deep brain stimulation help motor dysfunction, but may have potential cognitive side effects. Central acetylcholinesterase inhibitors, and possibly memantine, provide modest and temporary symptomatic relief for dementia, although there is no evidence-based treatment for MCI. There is no proven disease-modifying treatment for cognitive impairment in PD. The symptomatic and disease-modifying role of physical exercise, cognitive training, and neuromodulation on cognitive impairment in PD is under investigation. Multidisciplinary approaches to cognitive impairment with effective treatment of comorbidities, proper rehabilitation, and maintenance of good support systems in addition to pharmaceutical treatment may improve the quality of life of the patients and caregivers.
| S-EPMC7851260 | biostudies-literature
Project description:Parkinson's disease (PD), a neurodegenerative disorder, is the second most common neurological illness in United States. Neurologically, it is characterized by the selective degeneration of a unique population of cells, the nigrostriatal dopamine neurons. The current treatment is symptomatic and mainly involves replacement of dopamine deficiency. This therapy improves only motor symptoms of Parkinson's disease and is associated with a number of adverse effects including dyskinesia. Therefore, there is unmet need for more comprehensive approach in the management of PD. Cannabis and related compounds have created significant research interest as a promising therapy in neurodegenerative and movement disorders. In this review we examine the potential benefits of medical marijuana and related compounds in the treatment of both motor and nonmotor symptoms as well as in slowing the progression of the disease. The potential for cannabis to enhance the quality of life of Parkinson's patients is explored.
| S-EPMC5165161 | biostudies-other
Project description:Mutations in MeCP2 result in a crippling neurological disease, but we lack a lucid picture of MeCP2's molecular role. Individual transcriptomic studies yield inconsistent differentially expressed genes. To overcome these issues, we demonstrate a methodology to analyze all modern public data. We obtained relevant raw public transcriptomic data from GEO and ENA, then homogeneously processed it (QC, alignment to reference, differential expression analysis). We present a web portal to interactively access the mouse data, and we discovered a commonly perturbed core set of genes that transcends the limitations of any individual study. We then found functionally distinct, consistently up- and downregulated subsets within these genes and some bias to their location. We present this common core of genes as well as focused cores for up, down, cell fraction models, and some tissues. We observed enrichment for this mouse core in other species MeCP2 models and observed overlap with ASD models. By integrating and examining transcriptomic data at scale, we have uncovered the true picture of this dysregulation. The vast scale of these data enables us to analyze signal-to-noise, evaluate a molecular signature in an unbiased manner, and demonstrate a framework for future disease focused informatics work.
| S-EPMC10049497 | biostudies-literature
Project description:BackgroundShared dysregulated pathways may contribute to Parkinson's disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson's disease and type 2 diabetes are linked at the molecular level.Methods and findingsUsing a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson's disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson's disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP), previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Prognostic Biomarker Study (PROBE), revealed that expression of APP is significantly upregulated in Parkinson's disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson's disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients.ConclusionsThese results provide the first evidence that Parkinson's disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first time that increased expression of APP in blood may modulate the neurodegenerative phenotype in type 2 diabetes patients.
| S-EPMC3869818 | biostudies-literature
Project description:BackgroundParkinson's disease (PD) is a prevalent neurodegenerative disease that is pathologically described as a six-stage α-synucleinopathy. In stage 4, α-synuclein reaches the hippocampus, inducing cognitive deficits, from which it progresses to the isocortex, leading to dementia. Among hippocampal fields, cornu ammonis 2 is particularly affected by this α-synucleinopathy and critical for cognitive decline. Volumetric studies using magnetic resonance imaging have produced controversial results, with only some reporting volume loss, whereas stereological data obtained using nonspecific markers do not reveal volume changes, neural or glial loss. Proteomic analysis has not been carried out in the hippocampus of patients with PD.ObjectiveThis study aims to explain hippocampal changes in patients with PD at the cellular and proteomic levels.Methodsα-Synuclein inclusions, volume and neural (NeuN), microglial (Iba-1) and astroglial (GFAP) populations were stereologically analyzed. SWATH-MS quantitative proteomic analysis was also conducted.ResultsArea fraction fractionator probe revealed a higher area fraction α-synucleinopathy in cornu ammonis 2. No volume change, neurodegeneration, microgliosis or astrogliosis was detected. Proteomic analysis identified 1,634 proteins, of which 83 were particularly useful for defining differences among PD and non-PD groups. Among them, upregulated (PHYIP, CTND2, AHSA1 and SNTA1) and downregulated (TM163, REEP2 and CSKI1) proteins were related to synaptic structures in the diseased hippocampus.ConclusionThe distribution of α-synuclein in the hippocampus is not associated with volumetric, neural or glial changes. Proteomic analysis, however, reveals a series of changes in proteins associated with synaptic structures, suggesting that hippocampal changes occur at the synapse level during PD.
| S-EPMC8461741 | biostudies-literature
Project description: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.
| S-EPMC5041099 | biostudies-literature