Calmodulin and Its Binding Proteins in Parkinson’s Disease

Bohush, Anastasiia; Leśniak, Wiesława; Weis, Serge; Filipek, Anna

doi:10.3390/ijms22063016

Cited by 21 publications

(10 citation statements)

References 103 publications

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“…Aβ, tau, and α-synuclein are all found in Lewy bodies in both PD and LBD [ 36 , 37 ]. A recent review of CaMBPs in PD focused on experimentally validated CaMBPs, including CaMKII, calcineurin, NMDAR, AchR, Adenosine A2AR, and cdk5, but failed to mention or assess the CaM-binding ability of other critical PD proteins, including the risks and biomarkers covered here [ 38 ]. The involvement of CaMKII and A2AR in psychiatric disorders was discussed above.…”

Section: Discussionmentioning

confidence: 99%

Calmodulin Binding Domains in Critical Risk Proteins Involved in Neurodegeneration

O’Day

2022

CIMB

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Neurodegeneration leads to multiple early changes in cognitive, emotional, and social behaviours and ultimately progresses to dementia. The dysregulation of calcium is one of the earliest potentially initiating events in the development of neurodegenerative diseases. A primary neuronal target of calcium is the small sensor and effector protein calmodulin that, in response to calcium levels, binds to and regulates hundreds of calmodulin binding proteins. The intimate and entangled relationship between calmodulin binding proteins and all phases of Alzheimer’s disease has been established, but the relationship to other neurodegenerative diseases is just beginning to be evaluated. Risk factors and hallmark proteins from Parkinson’s disease (PD; SNCA, Parkin, PINK1, LRRK2, PARK7), Huntington’s disease (HD; Htt, TGM1, TGM2), Lewy Body disease (LBD; TMEM175, GBA), and amyotrophic lateral sclerosis/frontotemporal disease (ALS/FTD; VCP, FUS, TDP-43, TBK1, C90rf72, SQSTM1, CHCHD10, SOD1) were scanned for the presence of calmodulin binding domains and, within them, appropriate binding motifs. Binding domains and motifs were identified in multiple risk proteins, some of which are involved in multiple neurodegenerative diseases. The potential calmodulin binding profiles for risk proteins involved in HD, PD, LBD, and ALS/FTD coupled with other studies on proven binding proteins supports the central and potentially critical role for calmodulin in neurodegenerative events.

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Section: Discussionmentioning

confidence: 99%

Calmodulin Binding Domains in Critical Risk Proteins Involved in Neurodegeneration

O’Day

2022

CIMB

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“…Calmodulin, a regulator of several pathways related to cell proliferation and survival, 94,95 displayed excellent diagnostic accuracy, and its potential use in the clinical setting deserves careful assessment. In fact, although cell cycle dysfunction has been previously associated with neurodegenerative disorders, 96 deregulation of calmodulin levels can be associated with other non-neurodegenerative diseases, like cancer.…”

Section: Discussionmentioning

confidence: 99%

Blood Biomarkers for the Diagnosis of Neurodegenerative Dementia: A Systematic Review

Santos

Cabreira

Rocha

et al. 2022

J Geriatr Psychiatry Neurol

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Importance Accurately diagnosing neurodegenerative dementia is often challenging due to overlapping clinical features. Disease specific biomarkers could enhance diagnostic accuracy. However, CSF analysis procedures and advanced imaging modalities are either invasive or high-priced, and routinely unavailable. Easily accessible disease biomarkers would be of utmost value for accurate differential diagnosis of dementia subtypes. Objective To assess the diagnostic accuracy of blood-based biomarkers for the differential diagnosis of AD from Frontotemporal Lobar Degeneration (FTLD), or AD from Dementia with Lewy Bodies (DLB). Methods Systematic review. Three databases (PubMed, Scopus, and Web of Science) were searched. Studies assessing blood-based biomarkers levels in AD versus FTLD, or AD versus DLB, and its diagnostic accuracy, were selected. When the same biomarker was assessed in three or more studies, a meta-analysis was performed. QUADAS-2 criteria were used for quality assessment. Results Twenty studies were included in this analysis. Collectively, 905 AD patients were compared to 1262 FTLD patients, and 209 AD patients were compared to 246 DLB patients. Regarding biomarkers for AD versus FTLD, excellent discriminative accuracy (AUC >0.9) was found for p-tau181, p-tau217, synaptophysin, synaptopodin, GAP43 and calmodulin. Other biomarkers also demonstrated good accuracy (AUC = 0.8-0.9). For AD versus DLB distinction, only miR-21-5p and miR-451a achieved excellent accuracy (AUC >0.9). Conclusion Encouraging results were found for several biomarkers, alone or in combination. Prospective longitudinal designs and consensual protocols, comprising larger cohorts and homogeneous testing modalities across centres, are essential to validate the clinical value of blood biomarkers for the precise etiological diagnosis of dementia.

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“…However, we found that 91 target genes were enriched only in the calcium signaling pathway ( Figures 3E , F ). Calcium signaling pathways play an important role in PD (Calì et al, 2014 ; Bohush et al, 2021 ). Therefore, our study may contribute to understanding the molecular mechanisms underlying PD.…”

Section: Discussionmentioning

confidence: 99%

Identification of hub genes of Parkinson's disease through bioinformatics analysis

Yang

Wang

et al. 2022

Front. Neurosci.

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Parkinson's disease (PD) is a common neurodegenerative disease, and there is still a lack of effective diagnostic and treatment methods. This study aimed to search for hub genes that might serve as diagnostic or therapeutic targets for PD. All the analysis was performed in R software. The expression profile data of PD (number: GSE7621) was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) associated with PD were screened by the “Limma” package of the R software. Key genes associated with PD were screened by the “WGCNA” package of the R software. Target genes were screened by merging the results of “Limma” and “WGCNA.” Enrichment analysis of target genes was performed by Gene Ontology (GO), Disease Ontology (DO), and Kyoto Enrichment of Genes and Genomes (KEGG). Machine learning algorithms were employed to screen for hub genes. Nomogram was constructed using the “rms” package. And the receiver operating characteristic curve (ROC) was plotted to detect and validate our prediction model sensitivity and specificity. Additional expression profile data of PD (number: GSE20141) was acquired from the GEO database to validate the nomogram. GSEA was used to determine the biological functions of the hub genes. Finally, RPL3L, PLEK2, PYCRL, CD99P1, LOC100133130, MELK, LINC01101, and DLG3-AS1 were identified as hub genes of PD. These findings can provide a new direction for the diagnosis and treatment of PD.

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Calmodulin and Its Binding Proteins in Parkinson’s Disease

Cited by 21 publications

References 103 publications

Calmodulin Binding Domains in Critical Risk Proteins Involved in Neurodegeneration

Calmodulin Binding Domains in Critical Risk Proteins Involved in Neurodegeneration

Blood Biomarkers for the Diagnosis of Neurodegenerative Dementia: A Systematic Review

Identification of hub genes of Parkinson's disease through bioinformatics analysis

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