Metabolome-based signature of disease pathology in MS

Andersen, Stacy L.; Briggs, Farren; Winnike, Jason H.; Natanzon, Yanina; Maichle, S.; Knagge, Kevin; Newby, L. Kristin; Gregory, Simon G.

doi:10.1016/j.msard.2019.03.006

Cited by 43 publications

(41 citation statements)

References 58 publications

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“…Random forests, an ensemble learning method, were used to identify lipids relevant for disease/gender classification. Similar to a previous study [41], the classification at each node in a tree was done with 100 randomly selected metabolites and the forest consisted of 5000 trees. The process was repeated 50 times, and the model with the highest area under the curve (AUC) was kept.…”

Section: Experimental Design and Statistical Analysesmentioning

confidence: 99%

Explorative Combined Lipid and Transcriptomic Profiling of Substantia Nigra and Putamen in Parkinson’s Disease

Brouwers

Wieringa

Martens³

2020

Cells

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Parkinson’s disease (PD) is characterized by the loss of dopaminergic neurons from the substantia nigra (SN) that project to the dorsal striatum (caudate-putamen). To better understand the molecular mechanisms underlying PD, we performed combined lipid profiling and RNA sequencing of SN and putamen samples from PD patients and age-matched controls. SN lipid analysis pointed to a neuroinflammatory component and included elevated levels of the endosomal lipid Bis (Monoacylglycero)Phosphate 42:8, while two of the three depleted putamen lipids were saturated sphingomyelin species. Remarkably, we observed gender-related differences in the SN and putamen lipid profiles. Transcriptome analysis revealed that the top-enriched pathways among the 354 differentially expressed genes (DEGs) in the SN were “protein folding” and “neurotransmitter transport”, and among the 261 DEGs from putamen “synapse organization”. Furthermore, we identified pathways, e.g., “glutamate signaling”, and genes, encoding, e.g., an angiotensin receptor subtype or a proprotein convertase, that have not been previously linked to PD. The identification of 33 genes that were common among the SN and putamen DEGs, which included the α-synuclein paralog β-synuclein, may contribute to the understanding of general PD mechanisms. Thus, our proof-of-concept data highlights new genes, pathways and lipids that have not been explored before in the context of PD.

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Section: Experimental Design and Statistical Analysesmentioning

confidence: 99%

Explorative Combined Lipid and Transcriptomic Profiling of Substantia Nigra and Putamen in Parkinson’s Disease

Brouwers

Wieringa

Martens³

2020

Cells

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“…Pyroglutamate, laurate, N-methylmaleimide, acylcarnitine C14:1, and phosphatidylcholine were among these metabolites. The authors also found metabolites involved in biological pathways like glutathione metabolism, cellular membrane composition, FA metabolism, and oxidation [ 109 ].…”

Section: Metabolites In Specific Neurological Diseasesmentioning

confidence: 99%

“…Many studies have already demonstrated the importance of some phospholipids such as lysophosphatidylethanolamine and sphingomyelin to assess the degree of neuronal damage and changes in the myelin sheath potential predictors of the stage of diseases such as AD, PD, and MuS [ 70 , 109 , 111 ]. In addition, it is also possible to observe the involvement of harmful oxidative processes, with the potential to affect membranes, as has already been proposed for Lysophosphatidic acid C18: 2 in patients with AD [ 71 ].…”

Section: Metabolites In Specific Neurological Diseasesmentioning

confidence: 99%

“…In addition, it is also possible to observe the involvement of harmful oxidative processes, with the potential to affect membranes, as has already been proposed for Lysophosphatidic acid C18: 2 in patients with AD [ 71 ]. Furthermore, carnitine and the related metabolite acetylcarnitine, an intermediary of fatty acid oxidation, were found in studies on AD, ALS, MS, and stroke and related to impairment mitochondrial and energy metabolisms and fatty acids transport during the catabolism of lipids [ 9 , 68 , 83 , 109 ]. Because of the acetyl group, the acetylcarnitine can cross the blood–brain barrier more easily and is the preferred form in the central nervous system [ 9 ].…”

Section: Metabolites In Specific Neurological Diseasesmentioning

confidence: 99%

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Circulating Metabolites as Potential Biomarkers for Neurological Disorders—Metabolites in Neurological Disorders

et al. 2020

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There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

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“…As metabolites are the biological end products of upstream processes involving gene and protein expression, the metabolome closely reflects the clinical phenotype and, thus, can provide valuable insight in to underlying pathological processes and identify novel biomarkers of disease. The majority of metabolomics studies in MS focus on the identification of blood-borne metabolite perturbations in MS patients relative to healthy controls using both nuclear magnetic resonance (NMR)-based [6][7][8] and mass spectrometry-based metabolomics [9][10][11][12] . While this is useful in aiding our understanding of MS disease activity as a whole, the distinction between MS and controls is not a clinical diagnostic challenge.…”

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confidence: 99%

A blood-based metabolomics test to distinguish relapsing–remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application

Yeo

Sealey

Zhou

et al. 2020

Sci Rep

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The transition from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive MS (SPMS) represents a huge clinical challenge. We previously demonstrated that serum metabolomics could distinguish RRMS from SPMS with high diagnostic accuracy. As differing sample-handling protocols can affect the blood metabolite profile, it is vital to understand which factors may influence the accuracy of this metabolomics-based test in a clinical setting. Herein, we aim to further validate the high accuracy of this metabolomics test and to determine if this is maintained in a 'real-life' clinical environment. Blood from 31 RRMS and 28 SPMS patients was subjected to different sample-handling protocols representing variations encountered in clinics. The effect of freeze-thaw cycles (0 or 1) and time to erythrocyte removal (30, 120, or 240 min) on the accuracy of the test was investigated. For test development, samples from the optimised protocol (30 min standing time, 0 freeze-thaw) were used, resulting in high diagnostic accuracy (mean ± SD, 91.0 ± 3.0%). This test remained able to discriminate RRMS and SPMS samples that had experienced additional freeze-thaw, and increased standing times of 120 and 240 min with accuracies ranging from 85.5 to 88.0%, because the top discriminatory metabolite biomarkers from the optimised protocol remained discriminatory between RRMS and SPMS despite these sample-handling variations. In conclusion, while strict sample-handling is essential for the development of metabolomics-based blood tests, the results confirmed that the RRMS vs. SPMS test is resistant to sample-handling variations and can distinguish these two MS stages in the clinics.

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Metabolome-based signature of disease pathology in MS

Cited by 43 publications

References 58 publications

Explorative Combined Lipid and Transcriptomic Profiling of Substantia Nigra and Putamen in Parkinson’s Disease

Explorative Combined Lipid and Transcriptomic Profiling of Substantia Nigra and Putamen in Parkinson’s Disease

Circulating Metabolites as Potential Biomarkers for Neurological Disorders—Metabolites in Neurological Disorders

A blood-based metabolomics test to distinguish relapsing–remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application

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