Effect of rs3910105 in the Synuclein Gene on Dopamine Transporter Availability in Healthy Subjects

Seo, Youngduk; Pak, Kyoungjune; Nam, Hyeong Seok; Seok, Ju Won; Lee, Myung Sik; Kim, Eun Joo; Lee, Jae Meen; Kim, Seong Jang; Kim, In Joo

doi:10.3349/ymj.2018.59.6.787

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…Genetic variables included individual APOE genotype, MAPT haplotype and the SNPs rs12411216 38 , rs356181 39 and rs3910105 40 . GBA mutation status was included as a binary factor for the presence of any non-synonymous coding mutations present within the GBA region.…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease

Harvey

Reijnders

Cavill

et al. 2022

Preprint

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Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an eight-year time span were used to define two cognitive outcomes of i) cognitive impairment, and ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

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Section: Methodsmentioning

confidence: 99%

Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease

Harvey

Reijnders

Cavill

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Genetic and epigenetic variables. Genetic variables included individual APOE genotype, MAPT haplotype and the SNPs rs12411216 63 , rs356181 64 , and rs3910105 65 . GBA mutation status was included as a binary factor for the presence of any nonsynonymous coding mutations present within the GBA region.…”

Section: Baseline Datamentioning

confidence: 99%

Machine learning‐based prediction of cognitive outcomes in de novo Parkinson's disease

Harvey

Reijnders

Cavill

et al. 2022

Alzheimer's & Dementia

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Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/ genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

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“…SNCA, an important cytosolic protein, plays a critical role in restricting of synaptic vesicles. It reduces release of some neurotransmitters such as dopamine transporter, parkin (ligase), tau protein and beta amyloid (β-amyloid) [76][77][78][79][80][81]. Therefore, autophagy may affect exosomes cargo, secretion, and also the formation of hybrid autophagosome-exosome vesicles [82].…”

Section: Modulation Of Autophagy Via Exosome Vice Versamentioning

confidence: 99%

Exosomal cargos modulate autophagy in recipient cells via different signaling pathways

et al. 2020

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Vesicular system of mammalian cells is composed of two intracellular and extracellular vesicles systems, which contributes to the intra/intercellular communication and cellular homeostasis. These systems mediate transferring of biological molecules like proteins, nucleic acids, and lipids inside the cytoplasm, and between the cells. By the present study, authors describe molecular crosslink between exosome biogenesis and autophagy and take a certain focus on the autophagic cargos of exosomes and signaling pathways involved in exosome-induced autophagy in target cells and vice versa. Autophagy the generation of double-phospholipid vesicles, is a process that engulfs damaged proteins and organelles, share molecular similarity and function synergy with exosomes biogenesis for degradation or exocytosis of certain cargo. Exosomes, the smallest subtype of extracellular vesicles, originating from the membrane of the multivesicular body located inside cells demonstrate key roles in the intracellular and intercellular communication. Growing evidence demonstrates the interaction between exosome biogenesis and autophagy both at intertwined molecular pathways and crossbred vesicles known as amphisomes. Crosstalk between exosome biogenesis and autophagy contributes to maintain cellular homeostasis under external and internal stresses. Moreover, these processes can modulate each other via different signaling pathways. Exosomes contain autophagic cargos that induce autophagy via the cascade of molecular events in target cells, which called here exosome-induced autophagy. Taken together, crosstalk between exosome biogenesis and autophagy plays pivotal roles in cell homeostasis. Shedding light on the interaction between endomembrane systems may promote our knowledge about the relation between exosome and autophagy pathways in lysosome-related disorders against treatments; proposing a theoretical approach for therapy.

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Effect of rs3910105 in the Synuclein Gene on Dopamine Transporter Availability in Healthy Subjects

Cited by 6 publications

References 35 publications

Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease

Machine learning-based prediction of cognitive outcomes in de novo Parkinson’s disease

Machine learning‐based prediction of cognitive outcomes in de novo Parkinson's disease

Exosomal cargos modulate autophagy in recipient cells via different signaling pathways

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