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:Parkinson's disease (PD) is the second most common neurodegenerative disease; it mainly occurs in the elderly population. Cuproptosis is a newly discovered form of regulated cell death involved in the progression of various diseases. Combining multiple GEO datasets, we analyzed the expression profile and immunity of cuproptosis-related genes (CRGs) in PD. Dysregulated CRGs and differential immune responses were identified between PD and non-PD substantia nigra. Two CRG clusters were defined in PD. Immune analysis suggested that CRG cluster 1 was characterized by a high immune response. The enrichment analysis showed that CRG cluster 1 was significantly enriched in immune activation pathways, such as the Notch pathway and the JAK-STAT pathway. KIAA0319, AGTR1, and SLC18A2 were selected as core genes based on the LASSO analysis. We built a nomogram that can predict the occurrence of PD based on the core genes. Further analysis found that the core genes were significantly correlated with tyrosine hydroxylase activity. This study systematically evaluated the relationship between cuproptosis and PD and established a predictive model for assessing the risk of cuproptosis subtypes and the outcome of PD patients. This study provides a new understanding of PD-related molecular mechanisms and provides new insights into the treatment of PD.
| S-EPMC10377639 | 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:The loss of dopaminergic neurons in the substantia nigra and the abnormal accumulation of synuclein proteins and neurotransmitters in Lewy bodies constitute the primary symptoms of Parkinson's disease (PD). Besides environmental factors, scholars are in the early stages of comprehending the genetic factors involved in the pathogenic mechanism of PD. Although genome-wide association studies (GWAS) have unveiled numerous genetic variants associated with PD, precisely pinpointing the causal variants remains challenging due to strong linkage disequilibrium (LD) among them. Addressing this issue, expression quantitative trait locus (eQTL) cohorts were employed in a transcriptome-wide association study (TWAS) to infer the genetic correlation between gene expression and a particular trait. Utilizing the TWAS theory alongside the enhanced Joint-Tissue Imputation (JTI) technique and Mendelian Randomization (MR) framework (MR-JTI), we identified a total of 159 PD-associated genes by amalgamating LD score, GTEx eQTL data, and GWAS summary statistic data from a substantial cohort. Subsequently, Fisher's exact test was conducted on these PD-associated genes using 5,152 differentially expressed genes sourced from 12 PD-related datasets. Ultimately, 29 highly credible PD-associated genes, including CTX1B, SCNA, and ARSA, were uncovered. Furthermore, GO and KEGG enrichment analyses indicated that these genes primarily function in tissue synthesis, regulation of neuron projection development, vesicle organization and transportation, and lysosomal impact. The potential PD-associated genes identified in this study not only offer fresh insights into the disease's pathophysiology but also suggest potential biomarkers for early disease detection.
| S-EPMC10991821 | 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