NAD; Humans; NAD/metabolism; Mitochondria/metabolism; Dopaminergic Neurons/metabolism; Parkinson Disease/metabolism; Neural Stem Cells/metabolism; Medicine (miscellaneous); Biochemistry, Genetics and Molecular Biology (all); Agricultural and Biological Sciences (all); General Agricultural and Biological Sciences; General Biochemistry, Genetics and Molecular Biology
Abstract :
[en] The vast majority of Parkinson's disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson's disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify differences in transcriptomic and metabolic signatures between IPD patient and healthy individual-derived midbrain neural precursor cells. Characterization of gene expression and metabolic modeling reveal pyruvate, several amino acid and lipid metabolism as the most dysregulated metabolic pathways in IPD neural precursors. Furthermore, we show that IPD neural precursors endure mitochondrial metabolism impairment and a reduced total NAD pool. Accordingly, we show that treatment with NAD precursors increases ATP yield hence demonstrating a potential to rescue early IPD-associated metabolic changes.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
ZAGARE, Alise ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Developmental and Cellular Biology
Preciat, German; Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, 2300 RA, Leiden, The Netherlands
NICKELS, Sarah Louise ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Developmental and Cellular Biology
Luo, Xi ; School of Medicine, University of Galway, University Rd, Galway, Ireland
MONZEL, Anna Sophia ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > Developmental and Cellular Biology > Team Jens Christian SCHWAMBORN
Gomez-Giro, Gemma; Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, 4362, Esch-sur-Alzette, Luxembourg
ROBERTSON, Graham ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > Developmental and Cellular Biology > Team Jens Christian SCHWAMBORN
Jaeger, Christian; Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, 4362, Esch-sur-Alzette, Luxembourg
Sharif, Jafar; Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences (IMS), Kanagawa, 230-0045, Japan
Koseki, Haruhiko ; Laboratory for Developmental Genetics, RIKEN Center for Integrative Medical Sciences (IMS), Kanagawa, 230-0045, Japan
DIEDERICH, Nico ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine > Scientific Central Services > Disease Modelling and Screening Platform ; Centre Hospitalier de Luxembourg (CHL), 4, Rue Nicolas Ernest Barblé, L-1210, Luxembourg, Luxembourg
FLEMING, Ronan MT ; University of Luxembourg ; Metabolomics and Analytics Center, Leiden Academic Centre for Drug Research, Leiden University, 2300 RA, Leiden, The Netherlands ; School of Medicine, University of Galway, University Rd, Galway, Ireland
SCHWAMBORN, Jens Christian ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Developmental and Cellular Biology
EC | Horizon 2020 Framework Programme Fonds National de la Recherche Luxembourg
Funding text :
This work was supported by the SysMedPD project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668738. Further, we acknowledge support from the National Centre of Excellence in Research on Parkinson’s Disease (NCER-PD) which is funded by the Luxembourg National Research Fund (FNR/NCER13/BM/11264123). A.Z. was supported by the Luxembourg Centre for Systems Biomedicine with an internal flagship project. We would also like to thank Dr. Nico J. Diederich and Laura Longhino from Centre Hospitalier de Luxembourg, and Thomas Rauen, Sergii Velychko, Anna-Lena Hallmann and Hans Schoeler from the Max Planck Institute in Muenster for providing iPSC lines. We thank Dr Anna Monzel for the characterization of iPSCs and the derivation of NESCs.This work was supported by the SysMedPD project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 668738. Further, we acknowledge support from the National Centre of Excellence in Research on Parkinson’s Disease (NCER-PD) which is funded by the Luxembourg National Research Fund (FNR/NCER13/BM/11264123). A.Z. was supported by the Luxembourg Centre for Systems Biomedicine with an internal flagship project. We would also like to thank Dr. Nico J. Diederich and Laura Longhino from Centre Hospitalier de Luxembourg, and Thomas Rauen, Sergii Velychko, Anna-Lena Hallmann and Hans Schoeler from the Max Planck Institute in Muenster for providing iPSC lines. We thank Dr Anna Monzel for the characterization of iPSCs and the derivation of NESCs.
Pang, S. Y. et al. The interplay of aging, genetics and environmental factors in the pathogenesis of Parkinson’s disease. Transl. Neurodegener. 8, 23 (2019). DOI: 10.1186/s40035-019-0165-9
Houlden, H. & Singleton, A. B. The genetics and neuropathology of Parkinson’s disease. Acta Neuropathol. 124, 325–338 (2012). DOI: 10.1007/s00401-012-1013-5
Sulzer, D. Multiple hit hypotheses for dopamine neuron loss in Parkinson’s disease. Trends Neurosci. 30, 244–250 (2007). DOI: 10.1016/j.tins.2007.03.009
Schwamborn, J. C. Is Parkinson’s disease a neurodevelopmental disorder and will brain organoids help us to understand it? Stem Cells Dev. 27, 968–975 (2018). DOI: 10.1089/scd.2017.0289
Ashby, E. L. et al. Altered expression of human mitochondrial branched chain aminotransferase in dementia with Lewy bodies and vascular dementia. Neurochem. Res. 42, 306–319 (2017). DOI: 10.1007/s11064-016-1855-7
Anandhan, A. et al. Metabolic dysfunction in Parkinson’s disease: bioenergetics, redox homeostasis and central carbon metabolism. Brain Res. Bull. 133, 12–30 (2017). DOI: 10.1016/j.brainresbull.2017.03.009
Blaszczyk, J. W. Energy metabolism decline in the aging brain—pathogenesis of neurodegenerative disorders. Metabolites 10, 450 (2020). DOI: 10.3390/metabo10110450
Cai, R. et al. Enhancing glycolysis attenuates Parkinson’s disease progression in models and clinical databases. J. Clin. Invest. 129, 4539–4549 (2019). DOI: 10.1172/JCI129987
Geiszler, P. C. et al. Dynamic metabolic patterns tracking neurodegeneration and gliosis following 26S proteasome dysfunction in mouse forebrain neurons. Sci. Rep. 8, 4833 (2018). DOI: 10.1038/s41598-018-23155-2
Bose, A. & Beal, M. F. Mitochondrial dysfunction and oxidative stress in induced pluripotent stem cell models of Parkinson’s disease. Eur. J. Neurosci. 49, 525–532 (2019). DOI: 10.1111/ejn.14264
Gonzalez-Rodriguez, P. et al. Disruption of mitochondrial complex I induces progressive parkinsonism. Nature 599, 650–656 (2021). DOI: 10.1038/s41586-021-04059-0
Liu, X. L., Wang, Y. D., Yu, X. M., Li, D. W. & Li, G. R. Mitochondria-mediated damage to dopaminergic neurons in Parkinson’s disease (review). Int J. Mol. Med. 41, 615–623 (2018).
Walter, J. et al. Neural stem cells of Parkinson’s disease patients exhibit aberrant mitochondrial morphology and functionality. Stem Cell Rep. 12, 878–889 (2019). DOI: 10.1016/j.stemcr.2019.03.004
Alaamery, M. et al. Role of sphingolipid metabolism in neurodegeneration. J. Neurochem. 158, 25–35 (2021). DOI: 10.1111/jnc.15044
Belarbi, K. et al. Glycosphingolipids and neuroinflammation in Parkinson’s disease. Mol. Neurodegener. 15, 59 (2020). DOI: 10.1186/s13024-020-00408-1
Mashima, R. & Maekawa, M. Lipid biomarkers for the peroxisomal and lysosomal disorders: their formation, metabolism and measurement. Biomark. Med. 12, 83–95 (2018). DOI: 10.2217/bmm-2017-0225
Schommer, J., Marwarha, G., Nagamoto-Combs, K. & Ghribi, O. Palmitic acid-enriched diet increases α-synuclein and tyrosine hydroxylase expression levels in the mouse brain. Front. Neurosci. 12, 552 (2018). DOI: 10.3389/fnins.2018.00552
Macias-Garcia, D. et al. Serum lipid profile among sporadic and familial forms of Parkinson’s disease. NPJ Parkinsons Dis. 7, 59 (2021). DOI: 10.1038/s41531-021-00206-6
Figura, M. et al. Serum amino acid profile in patients with Parkinson’s disease. PLoS ONE 13, e0191670 (2018). DOI: 10.1371/journal.pone.0191670
Venkatesan, D., Iyer, M., Narayanasamy, A., Siva, K. & Vellingiri, B. Kynurenine pathway in Parkinson’s disease—an update. eNeurologicalSci 21, 100270 (2020). DOI: 10.1016/j.ensci.2020.100270
Pehar, M., Harlan, B. A., Killoy, K. M. & Vargas, M. R. Nicotinamide adenine dinucleotide metabolism and neurodegeneration. Antioxid. Redox Signal. 28, 1652–1668 (2018). DOI: 10.1089/ars.2017.7145
Ross, S. M. Nicotinamide adenine dinucleotide (NAD+) biosynthesis in the regulation of metabolism, aging, and neurodegeneration. Holist. Nurs. Pract. 35, 230–232 (2021). DOI: 10.1097/HNP.0000000000000461
Monzel, A. S. et al. Derivation of human midbrain-specific organoids from neuroepithelial stem cells. Stem Cell Rep. 8, 1144–1154 (2017). DOI: 10.1016/j.stemcr.2017.03.010
Nickels, S. L. et al. Reproducible generation of human midbrain organoids for in vitro modeling of Parkinson’s disease. Stem Cell Res. 46, 101870 (2020). DOI: 10.1016/j.scr.2020.101870
Smits, L. M. et al. Modeling Parkinson’s disease in midbrain-like organoids. NPJ Parkinson’s Dis. 5, 5 (2019). DOI: 10.1038/s41531-019-0078-4
Zagare, A., Gobin, M., Monzel, A. S. & Schwamborn, J. C. A robust protocol for the generation of human midbrain organoids. STAR Protoc. 2, 100524 (2021). DOI: 10.1016/j.xpro.2021.100524
Reinhardt, P. et al. Derivation and expansion using only small molecules of human neural progenitors for neurodegenerative disease modeling. PLoS ONE 8, e59252 (2013). DOI: 10.1371/journal.pone.0059252
Preciat, G., Wegrzyn, A. B., Thiele, I., Hankemeier, T. & Fleming, R. M. T. XomicsToModel: omics data integration and generation of thermodynamically consistent metabolic models. Preprint at bioRxiv https://doi.org/10.1101/2021.11.08.467803 (2022).
Preciat, G. et al. Mechanistic model-driven exometabolomic characterisation of human dopaminergic neuronal metabolism. Preprint at bioRxiv https://doi.org/10.1101/2021.06.30.450562 (2022).
Brunk, E. et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36, 272–281 (2018). DOI: 10.1038/nbt.4072
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016). DOI: 10.1093/nar/gkv1070
Radogna, F., Gerard, D., Dicato, M. & Diederich, M. Assessment of mitochondrial cell metabolism by respiratory chain electron flow assays. Methods Mol. Biol. 2276, 129–141 (2021). DOI: 10.1007/978-1-0716-1266-8_9
Kordus, R. J., Hossain, A., Malter, H. E. & LaVoie, H. A. Mitochondrial metabolic substrate utilization in granulosa cells reflects body mass index and total follicle stimulating hormone dosage in in vitro fertilization patients. J. Assist. Reprod. Genet. 37, 2743–2756 (2020). DOI: 10.1007/s10815-020-01946-9
Meiser, J., Weindl, D. & Hiller, K. Complexity of dopamine metabolism. Cell Commun. Signal. 11, 34 (2013). DOI: 10.1186/1478-811X-11-34
Sertbas, M., Ulgen, K. & Cakir, T. Systematic analysis of transcription-level effects of neurodegenerative diseases on human brain metabolism by a newly reconstructed brain-specific metabolic network. FEBS Open Bio 4, 542–553 (2014). DOI: 10.1016/j.fob.2014.05.006
Noronha, A. et al. The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res. 47, D614–D624 (2019). DOI: 10.1093/nar/gky992
Fleming, R., Luo, X., Liu, Y., Balck, A. Klein, C. Identification of metabolites reproducibly associated with Parkinson’s disease via meta-analysis and computational modelling. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-3209421/v1 (2023).
Kim, A. et al. Upgraded methodology for the development of early diagnosis of Parkinson’s disease based on searching blood markers in patients and experimental models. Mol. Neurobiol. 56, 3437–3450 (2019). DOI: 10.1007/s12035-018-1315-2
D’Andrea, G. et al. Different circulating trace amine profiles in de novo and treated Parkinson’s disease patients. Sci. Rep. 9, 6151 (2019). DOI: 10.1038/s41598-019-42535-w
Calvani, R. et al. A novel multi-marker discovery approach identifies new serum biomarkers for Parkinson’s disease in older people: an EXosomes in PArkiNson Disease (EXPAND) ancillary study. Geroscience 42, 1323–1334 (2020). DOI: 10.1007/s11357-020-00192-2
Picca, A. et al. Circulating amino acid signature in older people with Parkinson’s disease: a metabolic complement to the EXosomes in PArkiNson Disease (EXPAND) study. Exp. Gerontol. 128, 110766 (2019). DOI: 10.1016/j.exger.2019.110766
Han, W., Sapkota, S., Camicioli, R., Dixon, R. A. & Li, L. Profiling novel metabolic biomarkers for Parkinson’s disease using in-depth metabolomic analysis. Mov. Disord. 32, 1720–1728 (2017). DOI: 10.1002/mds.27173
Lewitt, P. A. et al. 3-hydroxykynurenine and other Parkinson’s disease biomarkers discovered by metabolomic analysis. Mov. Disord. 28, 1653–1660 (2013). DOI: 10.1002/mds.25555
Schulte, E. C. et al. Alterations in lipid and inositol metabolisms in two dopaminergic disorders. PLoS ONE 11, e0147129 (2016). DOI: 10.1371/journal.pone.0147129
Luan, H. et al. Comprehensive urinary metabolomic profiling and identification of potential noninvasive marker for idiopathic Parkinson’s disease. Sci. Rep. 5, 13888 (2015). DOI: 10.1038/srep13888
Luan, H. et al. LC-MS-based urinary metabolite signatures in idiopathic Parkinson’s disease. J. Proteome Res. 14, 467–478 (2015). DOI: 10.1021/pr500807t
Kumari, S. et al. Quantitative metabolomics of saliva using proton NMR spectroscopy in patients with Parkinson’s disease and healthy controls. Neurol. Sci. 41, 1201–1210 (2020). DOI: 10.1007/s10072-019-04143-4
Chang, K. H. et al. Alterations of sphingolipid and phospholipid pathways and ornithine level in the plasma as biomarkers of Parkinson’s disease. Cells 11, 395 (2022). DOI: 10.3390/cells11030395
Hertel, J. et al. Integrated analyses of microbiome and longitudinal metabolome data reveal microbial-host interactions on sulfur metabolism in Parkinson’s disease. Cell Rep. 29, 1767–1777.e1768 (2019). DOI: 10.1016/j.celrep.2019.10.035
Valvona, C. J., Fillmore, H. L., Nunn, P. B. & Pilkington, G. J. The regulation and function of lactate dehydrogenase A: therapeutic potential in brain tumor. Brain Pathol. 26, 3–17 (2016). DOI: 10.1111/bpa.12299
Alvarez, Z., Hyrossova, P., Perales, J. C. & Alcantara, S. Neuronal progenitor maintenance requires lactate metabolism and PEPCK-M-directed cataplerosis. Cereb. Cortex 26, 1046–1058 (2016). DOI: 10.1093/cercor/bhu281
Stubbs, D. et al. Neurovascular congruence during cerebral cortical development. Cereb. Cortex 19, i32–i41 (2009). DOI: 10.1093/cercor/bhp040
Goldman, S. A. & Chen, Z. Perivascular instruction of cell genesis and fate in the adult brain. Nat. Neurosci. 14, 1382–1389 (2011). DOI: 10.1038/nn.2963
Lautrup, S., Sinclair, D. A., Mattson, M. P. & Fang, E. F. NAD+ in brain aging and neurodegenerative disorders. Cell Metab. 30, 630–655 (2019). DOI: 10.1016/j.cmet.2019.09.001
Sison, S. L. & Ebert, A. D. Decreased NAD+ in dopaminergic neurons. Aging 10, 526–527 (2018). DOI: 10.18632/aging.101433
Gomes, A. P. et al. Declining NAD+ induces a pseudohypoxic state disrupting nuclear-mitochondrial communication during aging. Cell 155, 1624–1638 (2013). DOI: 10.1016/j.cell.2013.11.037
Tarabichi, M. et al. Systems biology of cancer: entropy, disorder, and selection-driven evolution to independence, invasion and “swarm intelligence”. Cancer Metastasis Rev. 32, 403–421 (2013). DOI: 10.1007/s10555-013-9431-y
Schwartz, L., Henry, M., Alfarouk, K. O., Reshkin, S. J. & Radman, M. Metabolic shifts as the hallmark of most common diseases: the quest for the underlying unity. Int J. Mol. Sci. 22, 3972 (2021). DOI: 10.3390/ijms22083972
Canto, C., Menzies, K. J. & Auwerx, J. NAD+ metabolism and the control of energy homeostasis: a balancing act between mitochondria and the nucleus. Cell Metab. 22, 31–53 (2015). DOI: 10.1016/j.cmet.2015.05.023
Hu, Q. et al. Genetically encoded biosensors for evaluating NAD+/NADH ratio in cytosolic and mitochondrial compartments. Cell Rep. Methods 1, 100116 (2021). DOI: 10.1016/j.crmeth.2021.100116
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). DOI: 10.1093/bioinformatics/btp616
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289–300 (1995).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005). DOI: 10.1073/pnas.0506580102
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015). DOI: 10.1016/j.cels.2015.12.004
Hiller, K. et al. MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. Anal. Chem. 81, 3429–3439 (2009). DOI: 10.1021/ac802689c
Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat. Protoc. 14, 639–702 (2019). DOI: 10.1038/s41596-018-0098-2
Norton, W. T., Abe, T., Poduslo, S. E. & DeVries, G. H. The lipid composition of isolated brain cells and axons. J. Neurosci. Res 1, 57–75 (1975). DOI: 10.1002/jnr.490010106
Singh, A. et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics 35, 3055–3062 (2019). DOI: 10.1093/bioinformatics/bty1054
Chang, K. H. et al. Alternations of metabolic profile and kynurenine metabolism in the plasma of Parkinson’s disease. Mol. Neurobiol. 55, 6319–6328 (2018). DOI: 10.1007/s12035-017-0845-3
Yilmaz, A. et al. Metabolic profiling of CSF from people suffering from sporadic and LRRK2 Parkinson’s disease: a pilot study. Cells 9, 2394 (2020). DOI: 10.3390/cells9112394
Dong, M. X., Hu, L., Wei, Y. D. & Chen, G. H. Metabolomics profiling reveals altered lipid metabolism and identifies a panel of lipid metabolites as biomarkers for Parkinson’s disease related anxiety disorder. Neurosci. Lett. 745, 135626 (2021). DOI: 10.1016/j.neulet.2021.135626
Wuolikainen, A. et al. Multi-platform mass spectrometry analysis of the CSF and plasma metabolomes of rigorously matched amyotrophic lateral sclerosis, Parkinson’s disease and control subjects. Mol. Biosyst. 12, 1287–1298 (2016). DOI: 10.1039/C5MB00711A
Nagesh Babu, G. et al. Serum metabolomics study in a group of Parkinson’s disease patients from northern India. Clin. Chim. Acta 480, 214–219 (2018). DOI: 10.1016/j.cca.2018.02.022
Toczylowska, B., Zieminska, E., Michalowska, M., Chalimoniuk, M. & Fiszer, U. Changes in the metabolic profiles of the serum and putamen in Parkinson’s disease patients—in vitro and in vivo NMR spectroscopy studies. Brain Res. 1748, 147118 (2020). DOI: 10.1016/j.brainres.2020.147118
Kumari, S. et al. Identification of potential urine biomarkers in idiopathic Parkinson’s disease using NMR. Clin. Chim. Acta 510, 442–449 (2020). DOI: 10.1016/j.cca.2020.08.005
Trupp, M. et al. Metabolite and peptide levels in plasma and CSF differentiating healthy controls from patients with newly diagnosed Parkinson’s disease. J. Parkinsons Dis. 4, 549–560 (2014). DOI: 10.3233/JPD-140389
Klatt, S. et al. A six-metabolite panel as potential blood-based biomarkers for Parkinson’s disease. NPJ Parkinsons Dis. 7, 94 (2021). DOI: 10.1038/s41531-021-00239-x
Vascellari, S. et al. Gut microbiota and metabolome alterations associated with Parkinson’s disease. mSystems 5, e00561–20 (2020). DOI: 10.1128/mSystems.00561-20
Yan, Z. et al. Alterations of gut microbiota and metabolome with Parkinson’s disease. Microb. Pathog. 160, 105187 (2021). DOI: 10.1016/j.micpath.2021.105187
Okuzumi, A. et al. Metabolomics-based identification of metabolic alterations in PARK2. Ann. Clin. Transl. Neurol. 6, 525–536 (2019). DOI: 10.1002/acn3.724
Shao, Y. et al. Comprehensive metabolic profiling of Parkinson’s disease by liquid chromatography-mass spectrometry. Mol. Neurodegener. 16, 4 (2021). DOI: 10.1186/s13024-021-00425-8
Plewa, S. et al. The metabolomic approach reveals the alteration in human serum and cerebrospinal fluid composition in Parkinson’s disease patients. Pharmaceuticals 14, 935 (2021). DOI: 10.3390/ph14090935
Tan, A. H. et al. Gut microbial ecosystem in Parkinson disease: new clinicobiological insights from multi-omics. Ann. Neurol. 89, 546–559 (2021). DOI: 10.1002/ana.25982
Dong, C. et al. Plasma metabolite signature classifies male LRRK2 Parkinson’s disease patients. Metabolites 12, 149 (2022). DOI: 10.3390/metabo12020149
Saiki, S. et al. Decreased long-chain acylcarnitines from insufficient beta-oxidation as potential early diagnostic markers for Parkinson’s disease. Sci. Rep. 7, 7328 (2017). DOI: 10.1038/s41598-017-06767-y
Kumari, S. et al. Metabolomic analysis of serum using proton NMR in 6-OHDA experimental PD model and patients with PD. Neurochem. Int. 134, 104670 (2020). DOI: 10.1016/j.neuint.2020.104670
Ahmed, S. S., Santosh, W., Kumar, S. & Christlet, H. T. Metabolic profiling of Parkinson’s disease: evidence of biomarker from gene expression analysis and rapid neural network detection. J. Biomed. Sci. 16, 63 (2009). DOI: 10.1186/1423-0127-16-63
Pathan, M., Wu, J., Lakso, H. A., Forsgren, L. & Ohman, A. Plasma metabolite markers of Parkinson’s disease and atypical parkinsonism. Metabolites 11, 860 (2021). DOI: 10.3390/metabo11120860
Zhao, H. et al. Potential biomarkers of Parkinson’s disease revealed by plasma metabolic profiling. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 1081–1082, 101–108 (2018). DOI: 10.1016/j.jchromb.2018.01.025