Association of <scp><i>PICALM</i></scp> with Cognitive Impairment in Parkinson's Disease

Periñán, María Teresa; Macías‐García, Daniel; Labrador‐Espinosa, Miguel A.; Jesús, Silvia; Buiza‐Rueda, Dolores; Gómez, A.; Muñoz‐Delgado, Laura; Gómez‐Garre, Pilar; Mir, Pablo

doi:10.1002/mds.28283

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…According to previous studies, the motor function of idiopathic PD patients were associated with variants in SNCA 36,37 , BST1 38 , ATP8B2 39 , PARK16 40 , and APOE 41 . Additionally, variants of APOE 31,32 , SNCA 42 , TMEM106B 43 , COMT 44 , MAPT 23,44 , PICALM 45 have been found to be associated with variability of cognitive function in idiopathic PD patients. Nevertheless, the associations between genetic variations and clinical features in sporadic PD remain largely elusive and deserved to be further explored.…”

Section: Introductionmentioning

confidence: 99%

BIN3rs2280104 T allele is associated with excessive daytime sleepiness and altered network topology in Parkinson’s disease

Chen

et al. 2023

Preprint

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Background Excessive daytime sleepiness (EDS) is one of the most common non-motor symptoms in Parkinson's disease (PD). Previous studies showed that PD patients with EDS exhibited more severe motor and non-motor symptoms. Our recent studies revealed that BIN3 rs2280104 was negatively associated with scores of Epworth Sleepiness Scale (ESS) in PD patients. The objective of this study is to examine whether BIN3 rs2280104 shapes brain networks of PD patients and whether network metrics associated with BIN3 rs2280104 mediate the effects of BIN3 rs2280104 on EDS. Methods PD patients (n = 144) receiving functional magnetic resonance imaging in Parkinson's Progression Markers Initiative (PPMI) database were investigated. The clinical manifestations and graphical metrics of structural and functional network were compared among different genotype groups of BIN3 rs2280104. The mediation analysis was used to explore the causal associations between network metrics modified by BIN3 rs2280104 and EDS of PD patients. Results ESS scores were associated with more severe motor and non-motor symptoms. BIN3 rs2280104 T allele was negatively associated with ESS scores in PD patients. Additionally, BIN3 rs2280104 significantly shaped structural and functional network metrics of PD patients. The nodal Cp of left superior temporal pole in functional network and the degree centrality of left calcarine in structural network were negatively associated with ESS scores, however, only the degree centrality of left calcarine in structural network mediated the effects of BIN3 rs2280104 on EDS of PD patients. Conclusions To summarize, BIN3 rs2280104 is significantly associated with EDS and network topology of PD patients. Additionally, the degree centrality of left calcarine in structural network mediated the effects of BIN3 rs2280104 on EDS. Future studies were required to identify the molecular mechanisms underlying the effects of BIN3 rs2280104 on EDS and brain network metrics of PD patients.

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Section: Introductionmentioning

confidence: 99%

BIN3rs2280104 T allele is associated with excessive daytime sleepiness and altered network topology in Parkinson’s disease

Chen

et al. 2023

Preprint

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“…Genes and variants initially identified for testing association with clinical parameters were selected based on a prior demonstrated association with PD/parkinsonism or disease progression [MDSgene.org; (10)(11)(12)(34)(35)(36)(37)(38)], or because the gene harbors pathogenic variants that cause PD/parkinsonism [for review, see (39, 40)]. Of 168 variants with a previously reported association and a MAF > 1%, 138 variants (Supplementary Table 2) were present in our data after filtering as described above.…”

Section: Association Testsmentioning

confidence: 99%

“…Iwaki et al (11) demonstrated sex-specific SNP associations with features of the PD phenotype: female patients had a higher risk of developing dyskinesias and a lower risk of developing cognitive impairment. Periñán et al (12) reported an association of the TT genotype at the PICALM SNP rs3851179 with a decreased risk of cognitive impairment in PD. GBA variants have been associated with PD and generally are associated with faster progression and more severe phenotypes (13,14).…”

Section: Introductionmentioning

confidence: 99%

Variable Effects of PD-Risk Associated SNPs and Variants in Parkinsonism-Associated Genes on Disease Phenotype in a Community-Based Cohort

et al. 2021

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Genetic risk factors for Parkinson's disease (PD) risk and progression have been identified from genome-wide association studies (GWAS), as well as studies of familial forms of PD, implicating common variants at more than 90 loci and pathogenic or likely pathogenic variants at 16 loci. With the goal of understanding whether genetic variants at these PD-risk loci/genes differentially contribute to individual clinical phenotypic characteristics of PD, we used structured clinical documentation tools within the electronic medical record in an effort to provide a standardized and detailed clinical phenotypic characterization at the point of care in a cohort of 856 PD patients. We analyzed common SNPs identified in previous GWAS studies, as well as low-frequency and rare variants at parkinsonism-associated genes in the MDSgene database for their association with individual clinical characteristics and test scores at baseline assessment in our community-based PD patient cohort: age at onset, disease duration, Unified Parkinson's Disease Rating Scale I-VI, cognitive status, initial and baseline motor and non-motor symptoms, complications of levodopa therapy, comorbidities and family history of neurological disease with one or more than one affected family members. We find that in most cases an individual common PD-risk SNP identified in GWAS is associated with only a single clinical feature or test score, while gene-level tests assessing low-frequency and rare variants reveal genes associated in either a unique or partially overlapping manner with the different clinical features and test scores. Protein-protein interaction network analysis of the identified genes reveals that while some of these genes are members of already identified protein networks others are not. These findings indicate that genetic risk factors for PD differentially affect the phenotypic presentation and that genes associated with PD risk are also differentially associated with individual disease phenotypic characteristics at baseline. These findings raise the intriguing possibility that different SNPs/gene effects impact discrete phenotypic characteristics. Furthermore, they support the hypothesis that different gene and protein-protein interaction networks that underlie PD risk, the PD phenotype, and the neurodegenerative process leading to the disease phenotype, and point to the significance of the genetic background on disease phenotype.

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“…Molecular biology investigations have identified numerous biomarkers and signaling pathways that contribute to PD, including the brain-derived neurotrophic factor (BDNF) [7], histone deacetylase 4 (HDAC4) [8], vacuolar protein sorting 35 (VPS35) [9], leucine-rich repeat kinase 2 (LRRK2) [10], phosphatidylinositol binding clathrin assembly protein (PICALM) [11], RhoA-ROCK signaling pathways [12], Nrf2 signaling pathways [13], GTPase-p38 MAPK signaling pathways [14], JAK/STAT signaling pathway [15] and PI3K/Akt signaling pathway [16]. Further investigation into the molecular events linked with PD is required.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics and next generation sequencing data analysis to identify key genes and pathways influencing in Parkinson’s disease

Vastrad¹,

Vastrad²

2022

Preprint

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Parkinson's disease (PD) is the most commonly diagnosed neurodegenerative disorder Identification of novel prognostic and pathogenesis biomarkers plays a pivotal role in the management of the PD. Next generation sequencing (NGS) dataset from the GEO (Gene Expression Omnibus) database were used to identify differentially expressed genes (DEGs) in PD. Gene Ontology (GO) and REACTOME pathway enrichmental analyses were performed to elucidate the functional roles of the DEGs. Protein-protein interaction (PPI), modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were established. The receiver operating characteristic curve (ROC) analysis was used to explore the diagnostic values of hub genes in PD. In total, 957 DEGs were identified, of which 478 were up regulated genes and 479 were down regulated genes. GO and pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in nervous system development, cell junction, transporter activity and neuronal system, whereas down regulated genes were mainly enriched in response to stimulus, cell periphery, identical protein binding and immune system. The top hub genes in the constructed PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were OTUB1, PPP2R1A, AP2M1, PIN1, USP11, CDK2, IQGAP1, NEDD4, VIM and CDK1. Furthermore, ROC analysis showed that hub genes were having good diagnostic values. We identified a series of essential genes along with the pathways that were most closely related with PD initiation and progression. Our results provide a more detailed molecular mechanism for the advancement of PD, shedding light on the potential biomarkers and therapeutic targets.

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Association of PICALM with Cognitive Impairment in Parkinson's Disease

Cited by 7 publications

References 36 publications

BIN3rs2280104 T allele is associated with excessive daytime sleepiness and altered network topology in Parkinson’s disease

BIN3rs2280104 T allele is associated with excessive daytime sleepiness and altered network topology in Parkinson’s disease

Variable Effects of PD-Risk Associated SNPs and Variants in Parkinsonism-Associated Genes on Disease Phenotype in a Community-Based Cohort

Bioinformatics and next generation sequencing data analysis to identify key genes and pathways influencing in Parkinson’s disease

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