Lysophospholipids–potent candidates for brain food, protects neuronal cells against α-Synuclein aggregation

Karaki, Tatsuya; Haniu, Hisao; Matsuda, Yoshikazu; Tsukahara, Tamotsu

doi:10.1016/j.biopha.2022.113891

Cited by 7 publications

(5 citation statements)

References 36 publications

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Section: Discussionsupporting

confidence: 91%

“…41 Lysophosphatidylcholines and lysophosphatidylethanolamine have been found to strongly inhibit α-syn aggregation. 42 We also identified lower levels in two phosphatidylethanolamines (34:2 and 36:4), which are downstream metabolites of phosphatidylcholines, which confirms previous findings. 11 We detected several triglycerides to be lowered in L-Dopa negative patients, and higher H/Y stages were associated with lower levels of triglycerides.…”

Section: Lipid Metabolism and Crosslink Between Fatty Acids And Alpha...supporting

confidence: 91%

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The role of dopaminergic medication, lipid, and endocannabinoid pathway alterations in idiopathic andPRKN/PINK1-mediated Parkinson’s disease – a large-scale targeted metabolomics study

Balck,

Borsche,

Campbell

et al. 2024

Preprint

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Alterations in many metabolites and biochemical pathways have been described in Parkinson’s Disease (PD). However, only a small fraction of these have been replicated in independent studies. As almost every PD patient is treated with dopaminergic medication, it is challenging to discriminate between disease- and drug-related effects, especially in relation to dopamine metabolism. We conducted a large-scale metabolomic study in plasma from 140 idiopathic (IPD), 19PRKN/PINK1-linked PD patients, and 64 healthy controls to disentangle disease-related metabolite alterations from drug-related effects. We distinguished between L-Dopa and non-L-Dopa treated PD patients to uncover nuanced metabolic changes associated with different therapies. We demonstrate that L-Dopa treatment uniquely influences the metabolome, with methyldopa and methoxytyramine, both L-Dopa breakdown products, elevated in L-Dopa-treated IPD andPRKN/PINK-linked PD patients. These alterations were not seen in untreated IPD patients and those on agonist treatment only. Polyamine metabolism alterations, notably elevation of putrescine and ornithine, were partly caused by L-Dopa treatment but also found in non-L-Dopa treated PD patients. In non-L-Dopa-treated patients, endocannabinoid metabolites were lowered and associated with disease duration. We observed lipid metabolism alterations, highlighting potential crosslinks with alpha-synuclein and providing insights into pathophysiological mechanisms. AllPRKN/PINK1-linked PD patients received L-Dopa treatment. However, our data potentially support the well-established role of oxidative damage in these subtypes of PD. In conclusion, our study emphasizes the significant impact of L-Dopa treatment on the metabolome, which might be of relevance not only for metabolomics studies but also for PD biomarker research in general. Finally, our study highlights potential biomarkers and pathways crucial for the understanding disease mechanisms of PD.

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Section: Discussionsupporting

confidence: 91%

Section: Lipid Metabolism and Crosslink Between Fatty Acids And Alpha...supporting

confidence: 91%

The role of dopaminergic medication, lipid, and endocannabinoid pathway alterations in idiopathic andPRKN/PINK1-mediated Parkinson’s disease – a large-scale targeted metabolomics study

Balck,

Borsche,

Campbell

et al. 2024

Preprint

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“…The top contributing phospholipids lysophosphatidylethanolamine, phosphatidylethanolamine, lysophosphatidylcholine and phosphatidylcholine are also important for phospholipid metabolism [KEGG 41 pathways map00564]. Interestingly, lysophosphatidylethanolamine 16:0 and lysophosphatidylcholine 16:0, which were top predictors of PD scale scores in this study, have recently been shown to inhibit the aggregation of α-synuclein 42 . Triacylglycerol and diacylglycerol are important for several pathways including oxidative phosphorylation and thermogenesis [KEGG map04714], glycogen production, GLUT4 translocation and glucose uptake [KEGG map04931] and glycerolipid metabolism [KEGG map00561].…”

Section: Discussionmentioning

confidence: 80%

Prediction of motor and non-motor Parkinson’s disease symptoms using serum lipidomics and machine learning: a 2-year study

Galper,

Mori,

McDonald

et al. 2024

npj Parkinsons Dis.

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Identifying biological factors which contribute to the clinical progression of heterogeneous motor and non-motor phenotypes in Parkinson’s disease may help to better understand the disease process. Several lipid-related genetic risk factors for Parkinson’s disease have been identified, and the serum lipid signature of Parkinson’s disease patients is significantly distinguishable from controls. However, the extent to which lipid profiles are associated with clinical outcomes remains unclear. Untargeted high-performance liquid chromatography-tandem mass spectrometry identified >900 serum lipids in Parkinson’s disease subjects at baseline (n = 122), and the potential for machine learning models using these lipids to predict motor and non-motor clinical scores after 2 years (n = 67) was assessed. Machine learning models performed best when baseline serum lipids were used to predict the 2-year future Unified Parkinson’s disease rating scale part three (UPDRS III) and Geriatric Depression Scale scores (both normalised root mean square error = 0.7). Feature analysis of machine learning models indicated that species of lysophosphatidylethanolamine, phosphatidylcholine, platelet-activating factor, sphingomyelin, diacylglycerol and triacylglycerol were top predictors of both motor and non-motor scores. Serum lipids were overall more important predictors of clinical outcomes than subject sex, age and mutation status of the Parkinson’s disease risk gene LRRK2. Furthermore, lipids were found to better predict clinical scales than a panel of 27 serum cytokines previously measured in this cohort (The Michael J. Fox Foundation LRRK2 Clinical Cohort Consortium). These results suggest that lipid changes may be associated with clinical phenotypes in Parkinson’s disease.

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“…The synthesis of structured triglycerides with DHA esterified in all sn-positions has been described in the literature, reaching a product yield higher than 80% using commercial lipases adsorbed on hydrophobic supports [71]. Other than that, lysophospholipids (LPL) are considered as important bioactive lipids because many of them are involved in a wide variety of pathological processes [72]. Garcia-Quinto et al [46] produced two phospholipids in mono-and di-substituted forms using different lipases and phospholipases immobilized via interfacial adsorption on hydrophobic supports.…”

Section: Immobilized Enzymes In the Functional Food Industrymentioning

confidence: 99%

Use of Potential Immobilized Enzymes for the Modification of Liquid Foods in the Food Industry

Garcia-Quinto,

Aranda-Cañada,

García-García

et al. 2023

Processes

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Enzymes are complex proteins that carry out biochemical reactions. Apart from being necessary for life, they are used in numerous industrial processes, especially in the textile, pharmaceutical, food and chemical sectors. One of the longest-lived industries regarding the use of enzymes is the food industry. Enzymes have always been used, mainly in their free form, to obtain new products and to improve the organoleptic qualities in different industries, such as in dairy, fruit and vegetables, and beverages. However, today, immobilized enzymes are the focus of attention in the liquid food industry, as they offer numerous advantages, such as stabilization and reuse, which enable cost reduction.

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Lysophospholipids–potent candidates for brain food, protects neuronal cells against α-Synuclein aggregation

Cited by 7 publications

References 36 publications

The role of dopaminergic medication, lipid, and endocannabinoid pathway alterations in idiopathic andPRKN/PINK1-mediated Parkinson’s disease – a large-scale targeted metabolomics study

The role of dopaminergic medication, lipid, and endocannabinoid pathway alterations in idiopathic andPRKN/PINK1-mediated Parkinson’s disease – a large-scale targeted metabolomics study

Prediction of motor and non-motor Parkinson’s disease symptoms using serum lipidomics and machine learning: a 2-year study

Use of Potential Immobilized Enzymes for the Modification of Liquid Foods in the Food Industry

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