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Review Metabolic Investigations of the Molecular Mechanisms Associated with Parkinson’s Disease

Robert Powers 1,2,*, Shulei Lei 1, Annadurai Anandhan 3,4, Darrell D. Marshall 1, Bradley Worley 1, Ronald L. Cerny 1, Eric D. Dodds 1, Yuting Huang 1, Mihalis I. Panayiotidis 5, Aglaia Pappa 6 and Rodrigo Franco 3,4,*

1 Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA; [email protected] (S.L.); [email protected] (D.D.M.); [email protected] (B.W.); [email protected] (R.L.C.); [email protected] (E.D.D.); [email protected] (Y.H.) 2 Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE 68588, USA 3 Redox Biology Center, University of Nebraska-Lincoln, Lincoln, NE 68588, USA; [email protected] 4 School of Veterinary Medicine and Biomedical Sciences, University of Nebraska-Lincoln, Lincoln, NE 68588, USA 5 Department of Applied Sciences, Northumbria University, Newcastle Upon Tyne NE1 8ST, UK; [email protected] 6 Department of Molecular Biology and Genetics, Democritus University of Thrace, Alexandroupolis 68100, Greece; [email protected] * Correspondence: [email protected] (R.P.); [email protected] (R.F.); Tel.: +01-402-472-3039 (R.P.); +01-402-472-8547 (R.F.)

Academic Editors: Daniel Raftery, James Cox, Katja Dettmer and Adrian S. Culf Received: 5 April 2017; Accepted: 16 May 2017; Published: 24 May 2017

Abstract: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by fibrillar cytoplasmic aggregates of α-synuclein (i.e., Lewy bodies) and the associated loss of dopaminergic cells in the substantia nigra. Mutations in genes such as α-synuclein (SNCA) account for only 10% of PD occurrences. Exposure to environmental toxicants including pesticides and metals (e.g., paraquat (PQ) and manganese (Mn)) is also recognized as an important PD risk factor. Thus, aging, genetic alterations, and environmental factors all contribute to the etiology of PD. In fact, both genetic and environmental factors are thought to interact in the promotion of idiopathic PD, but the mechanisms involved are still unclear. In this study, we summarize our findings to date regarding the toxic synergistic effect between α-synuclein and paraquat treatment. We identified an essential role for central carbon (glucose) metabolism in dopaminergic cell death induced by paraquat treatment that is enhanced by the overexpression of α-synuclein. PQ “hijacks” the pentose phosphate pathway (PPP) to increase NADPH reducing equivalents and stimulate paraquat redox cycling, oxidative stress, and cell death. PQ also stimulated an increase in glucose uptake, the translocation of glucose transporters to the plasma membrane, and AMP-activated protein kinase (AMPK) activation. The overexpression of α-synuclein further stimulated an increase in glucose uptake and AMPK activity, but impaired glucose metabolism, likely directing additional carbon to the PPP to supply paraquat redox cycling.

Keywords: Parkinson’s Disease; genetics; toxin synergy; molecular mechanisms; NMR; mass spectrometry

Metabolites 2017, 7, 22; doi:10.3390/metabo7020022 www.mdpi.com/journal/metabolites Metabolites 2017, 7, 22 2 of 26 Metabolites 2017, 7, 22 2 of 26

1.1. Introduction

1.1.1.1. Parkinson’s DiseaseDisease OverviewOverview Parkinson’s diseasedisease (PD)(PD) affects affects over over one one million millio individualsn individuals in thein Unitedthe United States States and moreand more than 10than million 10 million people people worldwide worldwide [1]. PD [1]. is PD a chronic is a chronic progressive progressive neurodegenerative neurodegenerative disorder disorder that leads that toleads shaking to shaking (tremors) (tremors) and difficulty and difficulty with walking, with walking, movement, movement, and coordination. and coordination. Currently, Currently, there is nothere cure is forno cure PD or for drug PD toor stopdrug the to stop progression the prog ofression the disease, of the butdisease, there but are there treatments are treatments to manage to symptomsmanage symptoms [2]. PD is [2]. associated PD is associated with the losswith of the dopaminergic loss of dopaminergic neurons from neurons the substantia from the nigrasubstantia pars compactanigra pars withincompacta the within midbrain the (Figuremidbrain1a) (Figure [ 3,4]. The1a) [3,4]. death The of thesedeath dopaminergic of these dopaminergic neurons neurons leads to aleads deficiency to a deficiency of dopamine of dopamine in the caudate in the and caudate putamen and (“striatum”),putamen (“striatum”), which results which in results an observed in an lossobserved of muscle loss of control. muscle In control. addition In toaddition neuron to loss, neur PDon isloss, also PD characterized is also characterized by the presence by the presence of Lewy bodies—proteinof Lewy bodies—protein aggregates withinaggregates neurons within [5]. Theneur exactons cause[5]. The of PD exact is currently cause unknown,of PD is butcurrently age is anunknown, important but risk age factor is an [important6]. Individuals risk factor over the [6]. age Individuals of 60 are twiceover the as likely age of to 60 develop are twice PD as relative likely to thedevelop general PD population. relative to the Only general about population. 10% of PD cases Only have about a family10% of history PD cases of thehave disease, a family and, history to date, of 18the genetic disease, mutations and, to date, (PARK1 18 ,geneticPARK2 ,mutations etc.) have ( beenPARK1 putatively, PARK2 linked, etc.) have to PD been [7,8]. putatively Genetic alterations linked to inPDα [7,8].-synuclein Genetic [9 ,alterations10], Parkin in [11 α,-synuclein12], DJ-1 [ 13[9,10],], PINK1 Parkin [11 [11,12],], and LRRK2DJ-1 [13], [14 PINK1] have [11], been and associated LRRK2 with[14] have approximately been associated 3 to 5% with of approximately PD. Environmental 3 to 5% factors of PD. have Environmental also been linked factors to have an increase also been in thelinked incidence to an increase of PD or in risk the for incidence developing of PD PD or [15 risk]. In for fact, developing sporadic orPD idiopathic [15]. In fact, PD issporadic linked toor geneticidiopathic alterations PD is linked and occupationalto genetic alterations or environmental and occupational factors. or Exposure environmenta to pesticides,l factors. heavy Exposure metals, to infectiouspesticides, agents,heavy industrialization,metals, infectious and/or agents, dietary industrialization, factors has beenand/or associated dietary withfactors an increasedhas been occurrenceassociated ofwith PD. an Recent increased studies occurrence have demonstrated of PD. thatRecent environmental studies have exposures demonstrated modify DNAthat methylationenvironmental patterns, exposures chromatin modify structure, DNA methylation and non-coding patterns, RNA chromatin signaling (epigenetics),structure, and which non-coding might contributeRNA signaling to the (epigenetics), individual’s susceptibility which might tocontribute developing to PD.the individual’s Epigenetic patterns susceptibility defined to during developing aging andPD. developmentEpigenetic patterns can be defined altered byduring environmental aging and exposuresdevelopment [16– can18]. be Paraquat altered inducesby environmental epigenetic changesexposures by [16–18]. promoting Paraquat histone induces acetylation epigenetic [19] and, chan conversely,ges by promoting paraquat histone toxicity acetylation has been reported[19] and, toconversely, be enhanced paraquat by inhibition toxicity of DNAhas methyltransferasesbeen reported to [ 20be]. Thus,enhanced PD appears by inhibition to be multifactorial of DNA wheremethyltransferases a combination [20]. of age,Thus, genetics, PD appears and environmental to be multifactorial factors where contributes a combination to disease of developmentage, genetics, (Figureand environmental1b) [21]. factors contributes to disease development (Figure 1b) [21].

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Figure 1. Parkinson’s disease results from dopaminergic neuron cell death in the substantia nigra: Figure 1. Parkinson’s disease results from dopaminergic neuron cell death in the substantia nigra: (a) (a) In vivo imaging of dopaminergic activity in the Parkinsonian basal ganglia shown by [18F] In vivo imaging of dopaminergic activity in the Parkinsonian basal ganglia shown by [18F] fluorodopa PET. The signal from striatum in a healthy control subject, a patient with symptomatic fluorodopa PET. The signal from striatum in a healthy control subject, a patient with symptomatic Parkinson’s disease and a twin who was asymptomatic at the time of scan but who subsequently Parkinson’s disease and a twin who was asymptomatic at the time of scan but who subsequently developed the disease. Reproduced with permission from [3]. (b) Schematic of the multiple factors that developed the disease. Reproduced with permission from [3]. (b) Schematic of the multiple factors contribute to the development of Parkinson’s disease. Both aging and environmental factors modify epigeneticthat contribute patterns, to the which development may also of enhanceParkinson’s an individual’sdisease. Both likelihood aging and ofenvironmental developing PD. factors modify epigenetic patterns, which may also enhance an individual’s likelihood of developing PD. 1.2. PD and Environmental Risk Factors 1.2. PD and Environmental Risk Factors The largest epidemiology study of PD in the USA identified a number of environmental factors The largest epidemiology study of PD in the USA identified a number of environmental factors correlated with an increased incidence of PD [22]. Specifically, PD was found to be more common correlated with an increased incidence of PD [22]. Specifically, PD was found to be more common in in the Midwest and the Northeast. In fact, the state of Nebraska was observed to have the highest the Midwest and the Northeast. In fact, the state of Nebraska was observed to have the highest prevalence of PD in the world (Table1). Again, this is consistent with areas associated with agriculture prevalence of PD in the world (Table 1). Again, this is consistent with areas associated with agriculture and metal processing having high rates of PD. Thus, prolonged exposure to herbicides or

Metabolites 2017, 7, 22 3 of 26 and metal processing having high rates of PD. Thus, prolonged exposure to herbicides or insecticides from farming or metals from industry likely contributes to PD. Consequently, paraquat (herbicide), rotenone (insecticide), 6-hydroxydopamine (6-OHDA, neurotoxin), 1-methyl-4-phenylpyridinium (MPP+, herbicide), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP, prodrug), and manganese have all been shown to induce PD-like symptoms. These compounds are routinely and interchangeably used to induce dopaminergic neuron cell death and as chemical mimics of PD in animal models. MPTP was discovered as a contaminant in illicit opioid synthesis, and acts as a prodrug that is converted to MPP+ in the brain and is selectively taken up by dopaminergic cells [23,24]. MPP+ inhibits mitochondrial respiratory complex I of the electron transport chain and interferes with oxidative phosphorylation in the mitochondria [25,26]. Paraquat has a structure similar to MPTP, but is a poor inhibitor of complex I. Instead, paraquat toxicity has been primarily attributed to its redox cycling that generates reactive oxygen species (ROS). Paraquat is reported to induce dopaminergic degeneration in vivo, which is one of the pathological hallmarks of PD, but contradictory results exist as well. While some environmental toxicants linked to PD such as PQ have been demonstrated to have a limited capacity to diffuse across the blood–brain barrier (BBB) [27,28], a significant increase in the permeability of the BBB in the postcommissural putamen of PD patients has been reported [29] and breakdown of the BBB has been shown to occur during aging [30]. Interestingly, α-synuclein impairs tight junction protein expression [30]. These findings again suggest the likelihood that neurodegeneration linked to environmental exposure is a consequence of genetics and or aging converging to promote dopaminergic cell loss. The chemical similarity between paraquat and MPTP initiated an investigation into an agricultural link with PD. Consequently, a correlation between paraquat agricultural usage and PD rates has been observed. The naturally occurring insecticide rotenone also inhibits complex I, which leads to energy failure and cell death [31,32]. Similarly, 6-OHDA has been proposed to induce dopaminergic neuron cell death by producing pro-oxidant capacity and selective uptake via dopamine transporters [23,33,34].

Table 1. Prevalence of PD in Nebraska [35].

Age (Years) Nebraska PD Prevalence 60–70 70–80 80+ (Rates per 100,000) Men 406 1794 4248 Women 298 991 2069

Regardless of the environmental toxin, there appears to be a common mechanism that leads to dopaminergic neuron cell death. Neurons have a very high energy demand and high glucose usage. Consequently, alterations in energy metabolism have been reported in early PD. Specifically, environmental toxins alter redox homeostasis, energy metabolism, and central carbon metabolism. Environmental toxins appear to increase ROS either through a direct redox cycling or indirectly by inhibiting the electron transport chain. Consequently, this leads to dysfunctional mitochondria and cell death. Thus, toxin-induced alterations in metabolic pathways important to central carbon metabolism, energy metabolism and redox homeostasis present a clear role for metabolomics in investigating PD.

1.3. Lewy Bodies and α-Synuclein A hallmark of PD is the formation of intracellular protein aggregates or Lewy bodies in the dopaminergic neurons within the substantia nigra (Figure2a) [ 36]. Lewy bodies are found in the cytoplasm as single or multiple spherical masses consisting of a dense protein core surrounded by a pale halo. Lewy bodies have a filamentous structure and contain over 70 different biological molecules falling within 10 distinct classes. α-Synuclein is a major component of Lewy bodies and forms the fibrils (Figure2b) [ 37,38]. In addition to α-synuclein fibrils, other components of Lewy bodies correspond to proteins involved in: (i) α-synuclein binding; (ii) synphilin-1-binding; (iii) ubiquitin-proteasome system; Metabolites 2017, 7, 22 4 of 26

(iv) cellular responses; (v) phosphorylation and signal transduction; (vi) cytoskeleton; and (vii) the cell Metabolites 2017, 7, 22 4 of 26 cycle. Importantly, Lewy bodies are correlated with neuronal loss and cognitive impairment, which suggestscognitive thatimpairment, neurons thatwhich contain suggests Lewy that bodies neurons are dying.that contain However, Lewy there bodies is no are evidence dying. thatHowever, Lewy bodiesthere is areno theevidence actual that cause Lewy of cell bodies death. areIn the fact, actual Lewy cause bodies of cell may death. be a In cytoprotective fact, Lewy bodies mechanism may be in a PD,cytoprotective while α-synuclein mechanism protofibrils in PD, mightwhile beα-synuclein cytotoxic agents.protofibrils Lewy might bodies be may cytotoxic function agents. to sequester Lewy andbodies degrade may function the α-synuclein to sequester fibrils. and degrade the α-synuclein fibrils.

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Figure 2. (a) Substantia nigra from patients with Parkinson’s disease (from the MRC Cambridge Figure 2. (a) Substantia nigra from patients with Parkinson’s disease (from the MRC Cambridge Brain Brain Bank) immunostained for α-synuclein. (Top) Two pigmented nerve cells, each containing an Bank) immunostained for α-synuclein. (top) Two pigmented nerve cells, each containing an α-synuclein-positive Lewy body (long arrows). Lewy neurites (short arrows) are also immunopositive. α-synuclein-positive Lewy body (long arrows). Lewy neurites (short arrows) are also immunopositive. Scale bar, 20 µm. (Bottom Left) A pigmented nerve cell with two α-synuclein-positive Lewy bodies. Scale bar, bar, 20 8 µμm.m. (Bottombottom left Right) A) pigmentedα-Synuclein-positive, nerve cell with extracellular two α-synuclein-positive Lewy body. Scale Lewy bar, bodies. 4 µm. ReproducedScale bar, 8 μ withm. ( permissionbottom right from) α-Synuclein-positive, [38]. (b) α-synuclein extracellular (α-syn) aggregation Lewy body. can takeScale placebar, 4 either μm. inReproduced the cytoplasm with orpermission in association from with[38]. the(b) cellularα-synuclein membrane. (α-syn) Inaggregation the cytosol, can unfolded take place monomers either in interactthe cytoplasm to form or initiallyin association unstable with dimers, the cellula whichr membrane. grow slowly In tothe generate cytosol, oligomers unfolded ofmonomers varying morphologies—includinginteract to form initially unstable transient dimers, spherical which and ring-likegrow slowly oligomers—that to generate eventuallyoligomers of convert varying to fibrils.morphologies—including The α-syn oligomers transient are in equilibriumspherical and with ring-like monomers oligomer and converts—that toeventually fibrils by convert monomer to additionfibrils. The via α a-syn nucleated oligomers polymerization are in equilibrium mechanism. with Themonomers accumulation and convert of these to amyloidfibrils by fibrils monomer leads toaddition the formation via a nucleated of intracellular polymerization inclusions mechanism. called Lewy The bodies. accumulation Membrane-bound of these amyloid monomeric fibrilsα leads-syn adoptsto the formation a predominantly of intracellularα-helical inclusions confirmation, called but Lewy at highbodies. concentrations Membrane-bound the protein monomeric undergoes α-syn a conformationaladopts a predominantly change either α-helical before confirmation, or coincident but with at itshigh oligomerization concentrations to the form protein membrane-bound undergoes a βconformational-sheet-rich structures change that either self-associate before or coincident to form oligomers, with its oligomerization including trans-membrane to form membrane-bound amyloid pores (theβ-sheet-rich formation structures of which maythat involveself-associate several to intermediates) form oligomers, and fibrils.including Note trans-membrane that the ring-like cytosolicamyloid oligomerspores (the formation may also associateof which withmay involve the membrane several andintermediates) form trans-membrane and fibrils. Note pores. that During the ring-likeα-syn fibrillogenesiscytosolic oligomers and aggregation,may also associate the intermediate with the membrane species (oligomers and form andtrans-membrane amyloid fibrils) pores. are During highly toxic,α-syn affectingfibrillogenesis mitochondrial and aggregation, function, endoplasmicthe intermediate reticulum–Golgi species (oligomers trafficking, and proteinamyloid degradation fibrils) are and/orhighly synaptictoxic, affecting transmission, mitochondrial and these function, intracellular endoplasmic effects are thoughtreticulum–Golgi to induce trafficking, neurodegeneration. protein Thedegradation transmembrane and/or poressynaptic disrupt transmission, membrane and integrity these asintracellular well as intracellular effects are calcium thought homeostasis to induce andneurodegeneration. signaling, and mayThe alsotransmembrane contribute to pores neuronal disrup toxicity.t membrane Interestingly, integrityα-syn as oligomerswell as intracellular and fibrils, ascalcium well ashomeostasis the monomers, and signaling, can be transferred and may also between contribute cells to and neuronal induce toxicity. disease Interestingly, spreading to α other-syn brainoligomers regions. and Spreadingfibrils, as well mechanisms as the monomers, are multiple can and be cantransferred occur via between endocytosis, cells and direct induce penetration, disease trans-synapticspreading to other transmission, brain regions. or membrane Spreading receptors. mechanisms Once are inside multiple thehost and cells,can occurα-syn via aggregates endocytosis, can nucleatedirect penetration, aggregation trans-synaptic and propagate transmission, via the mechanisms or membrane described receptors. above. Reproduced Once inside with the permissionhost cells, fromα-syn [ 39aggregates]. can nucleate aggregation and propagate via the mechanisms described above. Reproduced with permission from [39]. α-Synuclein is a 140-amino-acid (14.5 kDa) protein of unknown function that is a natively unstructuredα-Synuclein monomer is a 140-amino-acid (Figure3a) [ 39, 40(14.5]. However, kDa) prot thereein hasof unknown been some function progress that in revealing is a natively a role forunstructuredα-synuclein monomer in vesicle (Figure trafficking, 3a) [39,40]. synaptic However, vesicles there endocytosis, has been and some chaperoning progress ofin therevealing SNARE a complexrole for assemblyα-synuclein [41 –in43 vesicle]. The protein trafficking, consists synaptic of three vesicles distinct domains:endocytosis, (i) an and amphipathic chaperoning lysine-rich of the SNARE complex assembly [41–43]. The protein consists of three distinct domains: (i) an amphipathic lysine-rich N-terminus (1–60) that interacts with membranes and forms an α-helix; (ii) an acidic disordered C-terminus (96–140) that is postulated to regulate nuclear localization and is involved in interactions with metals, small molecules and other proteins; and (iii) a hydrophobic central region

Metabolites 2017, 7, 22 5 of 26

α MetabolitesN-terminus 2017, 7 (1–60), 22 that interacts with membranes and forms an -helix; (ii) an acidic disordered5 of 26 C-terminus (96–140) that is postulated to regulate nuclear localization and is involved in interactions (61–95),with metals, which small is critical molecules for protein and other aggregation proteins; and and is (iii) commonly a hydrophobic referred central to as region the non-amyloid- (61–95), whichβ componentis critical for of proteinAD amyloid aggregation plaques and (NAC) is commonly (Figure 3b). referred α-Synuclein to as theis an non-amyloid- abundant neuronalβ component protein of (~1%AD amyloidof cytosol plaques proteins) (NAC) that (Figure is expressed3b). α -Synucleinthroughout is the an abundantbrain with neuronal particularly protein high (~1% levels of cytosolin the substantiaproteins) thatnigra is. expressedα-Synuclein throughout is primarily the located brain with in the particularly presynaptic high terminal levels of in theneurons. substantia Thus, nigra. the proteinα-Synuclein may play is primarily a role in located regulating in the the presynaptic release of terminal dopamine of neurons.or the suppl Thus,y of the synaptic protein vesicles. may play Genetica role in mutations regulating in the α-synuclein release of dopamine(SNCA) [9,10] or the or supplyoverexpression of synaptic due vesicles. to multiplication Genetic mutations of SNCA in [44]α-synuclein have been (SNCA associated)[9,10 with] or overexpression familial and sporadic due to multiplicationPD (Figure 3b). of OligomerizationSNCA [44] have of been α-synuclein associated andwith the familial resulting and fibril sporadic formation PD (Figure is central3b). Oligomerization to the pathogenesis of α -synucleinof PD (Figure and the2b). resulting α-Synuclein fibril aggregatesformation ishave central been to shown the pathogenesis to bind lipid of PDmembrane (Figure2s,b). to αform-Synuclein pore-like aggregates structures, have and been to increase shown to membranebind lipid membranes,permeability to[45–48]. form pore-like The resulting structures, α-synuclein and to increase aggregates membrane cause neuroinflammation, permeability [45–48 ]. neurodegeneration,The resulting α-synuclein and neuronal aggregates cell cause death. neuroinflammation, A variety of factors neurodegeneration, including oxidative and neuronalstress [49], cell post-translationaldeath. A variety modifications of factors including [50–52], oxidative proteolysi stresss [53,54]; [49], post-translationaland fatty acids [55–57], modifications phospholipids [50–52 ], [55,58],proteolysis and [metal53,54]; ion and [59,60] fatty acids concentrations [55–57], phospholipids have been shown [55,58], to and affect metal α ion-synuclein [59,60] concentrationsaggregation. Mitochondrialhave been shown dysfunction to affect andα-synuclein energy failure aggregation. induced Mitochondrial by environmental dysfunction toxicants and may energy also lead failure to αinduced-synuclein by environmentalmisfolding and toxicants aggregation may also through lead to αimpairment-synuclein misfolding in the protein and aggregation quality control through mechanisms.impairment Importantly, in the protein there quality is a controlgrowing mechanisms. body of evidence Importantly, that indicates there is that a growing α-synuclein body is of localizedevidence to that the indicates mitochondria that α under-synuclein both is normal localized and to stress the mitochondria conditions [61]. under α-Synuclein both normal may and play stress a roleconditions in regulating [61]. α mitochondrial-Synuclein may function, play a role in inwhich regulating either mitochondrialthe overexpression function, or lose in whichof α-synuclein either the functionoverexpression may result or losein mitochondrial of α-synuclein damage function and may cell resultdeath. in So, mitochondrial α-synuclein also damage plays and a prominent cell death. roleSo, inα-synuclein idiopathic alsoPD. playsAgain, a prominentPD incidences role may in idiopathic increase PD.from Again, combined PD incidences genetic and may environmental increase from factors.combined genetic and environmental factors.

(a) (b)

Figure 3. Structure of α-synuclein: (a) Schematic representation of micelle-bound α-synuclein (α-syn; Figure 3. Structure of α-synuclein: (a) Schematic representation of micelle-bound α-synuclein (α-syn; Protein Data Bank ID: 1XQ8) [40]. The N-terminal region, the non-amyloid-β component of Alzheimer’s Protein Data Bank ID: 1XQ8) [40]. The N-terminal region, the non-amyloid-β component of disease amyloid plaques (NAC) region and the C-terminal part are colored blue, orange and red, Alzheimer’s disease amyloid plaques (NAC) region and the C-terminal part are colored blue, orange respectively. Numbers refer to amino acid residues flanking the different regions. Reproduced with and red, respectively. Numbers refer to amino acid residues flanking the different regions. permission from [39]. (b) α-Synuclein protein domain structure. α-Syn is a 140-amino-acid protein Reproduced with permission from [39]. (b) α-Synuclein protein domain structure. α-Syn is a and its sequence can be divided into three regions with distinct structural characteristics. The highly 140-amino-acidconserved N-terminal protein domain and its encodessequence for can a series be divided of imperfect into three 11 amino regions acid with repeats distinct with astructural consensus characteristics.motif of KTKEGV The highly reminiscent conserved of the N-terminal lipid-binding domain domain encodes of for apolipoproteins, a series of imperfect which 11 in amino certain acidconditions repeats formswith amphipathica consensus helicesmotif .of The KTKEGV six missense reminiscent mutations of the known lipid-binding to cause domain familial of PD apolipoproteins,(A30P, E46K, H50Q, which G51D, in certain A53E, conditions and A53T) fo lierms in amphipathic the amphipathic helices. region, The suggestingsix missense an mutations important knownfunction to forcause this familial region ofPD the (A30P, protein. E46K, The H50Q, central G51D, hydrophobic A53E, and region A53T) (non-amyloid- lie in the amphipathicβ component region,or NAC suggesting domain) ofan αimportant-synuclein function is associated for this with region an increased of the protein. propensity The ofcentral the protein hydrophobic to form regionfibrils (non-amyloid- [62]. The acidicβ component C-terminal or tail NAC contains domain) mostly of α-synuclein negatively is charged associated residues with andan increased is largely propensityunfolded. of Reproduced the protein with to form permission fibrils from[62]. The [63]. acidic C-terminal tail contains mostly negatively charged residues and is largely unfolded. Reproduced with permission from [63]. 2. NMR and MS Metabolomics Protocol to Investigate PD 2. NMR and MS Metabolomics Protocol to Investigate PD 2.1. Combining NMR and MS Improves Coverage of the Metabolome 2.1. Combining NMR and MS Improves Coverage of the Metabolome Nuclear magnetic resonance (NMR) [64] and mass spectrometry (MS) [65] have been the primary analyticalNuclear tools magnetic used to obtainresonance metabolomics (NMR) [64] datasets. and ma Historically,ss spectrometry only NMR (MS) or [65] MS hashave been been used the for primary analytical tools used to obtain metabolomics datasets. Historically, only NMR or MS has been used for a given metabolomics study, in which the choice of instrumentation has been primarily decided upon based on an investigator’s experience and expertise, instead of the needs of the study. Consequently, a suboptimal analysis of the metabolome is likely to occur. In fact, NMR

Metabolites 2017, 7, 22 6 of 26 a given metabolomics study, in which the choice of instrumentation has been primarily decided upon based on an investigator’s experience and expertise, instead of the needs of the study. Consequently, a suboptimal analysis of the metabolome is likely to occur. In fact, NMR and MS are inherently complementary and when employed together provide a broader and more accurate coverage of the metabolome [66,67]. While the number of studies is still limited, a few projects that have used both NMR and MS have observed a common trend [68–70]. A set of metabolites was only observable by NMR, while a second set of metabolites was only detected by MS. A smaller subset of metabolites was observed by both NMR and MS. Simply, NMR only observes the most abundant metabolites (≥1 µM) and MS only observes the metabolites that readily ionize. There are other important differences between NMR and MS. NMR requires minimal sample handling before data collection, is easily quantifiable, and provides multiple means of metabolite identification. In addition to the higher sensitivity, (femtomolar to attomolar), MS also has a higher resolution (~103–104) and dynamic range (~103–104). However, chromatography is commonly required for MS because of the relatively narrow nominal mass and mass defect distribution of the metabolome [71]. The use of chromatography has its own limitations and may also lead to a loss of observable metabolites for a variety of reasons [72–76]. Simply, NMR and MS have unique sets of strengths and limitations and both analytical methods beneficially contribute to a metabolomics study. In fact, a number of recent methods highlight the benefits of combining NMR and MS to improve the accuracy of metabolite identification or for identifying unknown metabolites [77–81].

2.2. Combined NMR and MS Metabolomics Methodology Towards this end, we recently optimized sample preparation, data collection, and data handling protocols to effectively integrate direct-infusion electrospray ionization mass spectrometry (DI-ESI-MS) data with 1D 1H NMR spectra (Figure4)[ 82,83]. By splitting metabolite extracts optimized for NMR acquisition and by diluting the MS-bound aliquots tenfold in H2O/methanol/formic acid (49.57 : 49.75 : 0.5), we obtained samples suitable for NMR and DI-ESI-MS while avoiding chromatographic separations. We also optimized several DI-ESI-MS ion source conditions to maximize the quality of the MS metabolomics data: sampling cone voltage (SCV) of 40 V, extraction voltage (ECV) of 4.0 V, desolvation temperature of 150 ◦C, desolvation gas flow of 500 L/h, and a cone gas flow of 0 L/h. We preprocessed the acquired mass spectra with background subtraction, followed by uniform binning with a 0.5 m/z bin size and spectral noise region removal. NMR spectra were processed with our MVAPACK [84] software and automatically phased and normalized using our phase-scatter correction (PSC) algorithm [85]. Chemical shift regions containing spectral baseline noise or solvent signals were removed based on our previously developed protocols [86,87]. Binning was performed using an adaptive intelligent binning algorithm [88] implemented in MVAPACK [84] that minimizes the splitting of signals between multiple bins. Integrating MS and NMR data clearly resulted in better class separation and tighter within-class variation than using only NMR or MS datasets (Figure5). This was accomplished by incorporating multivariate statistical techniques to properly handle multiple analytical datasets [89–91]—multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS)—into MVAPACK [84]. Multiblock methods are similar to traditional PLS and PCA, but provide a means for analyzing data from multiple analytical sources [89–91]. Simply put, the NMR and MS spectral data are placed into separate “blocks,” which allows for the generation and simultaneous usage of within-block and between-block data correlations. The inclusion of MB-PLS also led to the use of backscaled loadings to identify biologically important metabolites that contributed significantly to group separation. Importantly, these NMR and MS spectral changes are now identified as being statistically correlated. In addition to MB-PCA and MB-PLS, the data were jointly modeled using multiblock orthogonal projections to latent structures (MB-OPLS), which corroborated the MB-PLS analysis while better differentiating group separations [92]. By effectively integrating NMR and MS Metabolites 2017, 7, 22 7 of 26 datasets, we could thoroughly analyze the metabolic changes to human dopaminergic cells resulting fromMetabolites treatments 2017, 7 with, 22 toxins that were not achievable with just the NMR or MS data. 7 of 26

FigureFigure 4. A 4. flow A chartflow illustratingchart illustrating our protocol our protocol for combining for combining NMR and NMR MS datasetsand MS for datasets metabolomics. for 2.0 mLmetabolomics. of a single 2.0 metabolite mL of a single extract metabolite was split extrac intot was 1.8 mLsplit andinto 0.21.8 mL and for 0.2 NMR mL andfor NMR MS analysis,and respectively.MS analysis, Spectral respectively binning. ofSpectral the NMR binning data of used the adaptiveNMR data intelligent used adaptive binning. intelligent For MS, binning. the background For is firstMS, subtracted the background before is spectral first subtracted binning. before Spectral spectr binningal binning. of the Spectral MS data binning used fixedof the binning MS data with used a set bin widthfixed binning of 0.5 m/z with. Noise a set removalbin width and of 0.5 normalization m/z. Noise removal were separately and normalization applied to were the NMRseparately and MS datasets.applied The to NMRthe NMR and and MS datasetsMS datasets. were The then NMR modeled and MS by datasets MB-PCA wereand then MB-PLS. modeled The by resulting MB-PCA block scoresand and MB-PLS. loadings The resulting are then block analyzed scores for and significantly loadings are contributingthen analyzed metabolites.for significantly Reproduced contributing with permissionmetabolites. from Reproduced [82]. with permission from [82].

Metabolites 2017, 7, 22 8 of 26 Metabolites 2017, 7, 22 8 of 26

(a) (b)

(c)

Figure 5. Scores generated from (a) PCA of 1D 1H NMR spectra; (b) PCA of DI-ESI-MS spectra; Figure 5. Scores generated from (a) PCA of 1D 1H NMR spectra; (b) PCA of DI-ESI-MS spectra; and and (c) MB-PCA of 1D 1H NMR and DI-ESI-MS spectra of metabolomes extracted from human (c) MB-PCA of 1D 1H NMR and DI-ESI-MS spectra of metabolomes extracted from human dopaminergic neuroblastoma cells treated with environmental/mitochondrial toxins. The ellipses dopaminergic neuroblastoma cells treated with environmental/mitochondrial toxins. The ellipses in in the PCA score plots correspond to the 95% confidence limits from a normal distribution for each the PCA score plots correspond to the 95% confidence limits from a normal distribution for each cluster. Symbols designate the following classes: Control (•); Rotenone (•); 6-OHDA (•); MPP+ (•); cluster. Symbols designate the following classes: Control (●); Rotenone (●); 6-OHDA (●); MPP+ (●); and Paraquat (•). Reproduced with permission from [82]. and Paraquat (●). Reproduced with permission from [82]. 3. PD and Mitochondrial/Environmental Toxins 3. PD and Mitochondrial/Environmental Toxins 3.1. Paraquat Induces Unique Metabolic Changes 3.1. Paraquat Induces Unique Metabolic Changes In a previous study, human dopaminergic neuroblastoma cells (SK-N-SH) were treated with sublethal In a previous study, human dopaminergic neuroblastoma cells (SK-N-SH) were treated with doses of environmental toxins known to induce dopaminergic cell death. Specifically, cells were treated sublethal doses of environmental toxins known to induce dopaminergic cell death. Specifically, cells with MPP+, 50 µM 6-OHDA, 0.5 mM paraquat, or 4.0 µM rotenone for 24 h [82,83]. The metabolome were treated with MPP+, 50 μM 6-OHDA, 0.5 mM paraquat, or 4.0 μM rotenone for 24 h [82,83]. The was extracted as described above (Figure4) and analyzed with both 1D 1H NMR and DI-ESI-MS. metabolome was extracted as described above (Figure 4) and analyzed with both 1D 1H NMR and The resulting MB-PCA model (Figure5c) was valid based on both a CV-ANOVA p value of 1.7 × 10−12 DI-ESI-MS. The resulting MB-PCA model (Figure 5c) was valid based on both a CV-ANOVA p value of and response permutation testing that yielded a p value equal to zero. The MB-PCA model yielded a 1.7 × 10−12 and response permutation testing that yielded a p value equal to zero. The MB-PCA model clear separation between the control group and the four toxin treatments. Furthermore, the control and yielded a clear separation between the control group and the four toxin treatments. Furthermore, the paraquat groups were separated from the other toxin treatments. Also, the MPP+ treatment group was control and paraquat groups were separated from the other toxin treatments. Also, the MPP+ treatment significantly separated from 6-OHDA and rotenone treatments. Again, these group separations were group was significantly separated from 6-OHDA and rotenone treatments. Again, these group not apparent if only the NMR or MS dataset was used (Figure5). More importantly, 6-OHDA, MPP+, separations were not apparent if only the NMR or MS dataset was used (Figure 5). More importantly, paraquat, and rotenone have been routinely used as experimental models of PD since they all result 6-OHDA, MPP+, paraquat, and rotenone have been routinely used as experimental models of PD since to a certain and variable extent in dopaminergic neuron cell death and PD-like symptoms in animal they all result to a certain and variable extent in dopaminergic neuron cell death and PD-like symptoms models [93]. Nevertheless, our analysis clearly indicates that the metabolic impact of these four toxins in animal models [93]. Nevertheless, our analysis clearly indicates that the metabolic impact of these four is unique and, consequently, the molecular mechanism that results in neuronal cell death must be toxins is unique and, consequently, the molecular mechanism that results in neuronal cell death must be different. Since paraquat treatment resulted in the largest metabolome changes relative to untreated different. Since paraquat treatment resulted in the largest metabolome changes relative to untreated controls, we focused our further investigation on the molecular mechanism of PD on paraquat. controls, we focused our further investigation on the molecular mechanism of PD on paraquat. A detailed analysis of the metabolic changes by NMR and MS verified that paraquat uniquely A detailed analysis of the metabolic changes by NMR and MS verified that paraquat uniquely perturbed the metabolome of dopaminergic neurons. For example, the S-plot (Figure6a) generated perturbed the metabolome of dopaminergic neurons. For example, the S-plot (Figure 6a) generated from the MB-PLS-DA model identified metabolites significantly perturbed in dopaminergic neurons from the MB-PLS-DA model identified metabolites significantly perturbed in dopaminergic neurons following paraquat treatment. Specifically, an increase in citrate, glucose 6-phosphate/fructose following paraquat treatment. Specifically, an increase in citrate, glucose 6-phosphate/fructose 6-phoshate, heptose (sedoheptulose), and hexose (glucose or myoinositol), and a decrease in lactate, glutamate, dopamine, and phospho-aspartate were clearly observed. A similar comparison was made

Metabolites 2017, 7, 22 9 of 26

6-phoshate,Metabolites 2017 heptose, 7, 22 (sedoheptulose), and hexose (glucose or myoinositol), and a decrease in lactate,9 of 26 glutamate, dopamine, and phospho-aspartate were clearly observed. A similar comparison was made betweenbetween paraquat and the otherother toxinstoxins toto determinedetermine ifif thesethese metabolomicmetabolomic changeschanges werewere uniqueunique toto paraquat.paraquat. The resulting Shared Shared and and Unique Unique Structures Structures (SUS) (SUS) plot plot (data (data not not shown), shown), which which is isa union a union of oftwo two S-plots S-plots (paraquat (paraquat vs. controls, vs. controls, and paraquat and paraquat vs. MPP+, vs. MPP+, rotenone rotenone and 6-OHDA), and 6-OHDA), verified verifiedthat the thatchanges the changesin citrate, in glucose citrate, 6-phosphate/fructose glucose 6-phosphate/fructose 6-phoshate, 6-phoshate, hexose, lactate, hexose, and lactate, dopamine and dopaminewere all a wereunique all neuronal a unique response neuronal to response paraquat. to Metabolome paraquat. Metabolome changes were changes further were characterized further characterized by monitoring by monitoringchanges in changesthe distributi in theon distribution and incorporation and incorporation of 13C-carbons of 13 C-carbonsinto metabolites into metabolites (Figure 7). (FigureThis was7). Thisaccomplished was accomplished by interrogatin by interrogatingg intensity changes intensity in 2D changes 1H-13C inHSQC 2D 1 H-experiments13C HSQC after experiments the addition after of the13C additionglucose to of the13C cell glucose culture to medium. the cell culture Paraquat medium. treatm Paraquatent resulted treatment in an increase resulted in inglucose, an increase glucose in glucose,6-phosphate, glucose fructose-6-phosphate, 6-phosphate, fructose-6-phosphate, glucose-1-phosphate, glucose-1-phosphate, and glucono-1, and5-lactone, glucono-1,5-lactone, which are whichassociated are associated with the withpentose the pentosephosphate phosphate pathway pathway (PPP). (PPP).Importantly, Importantly, these thesemetabolic metabolic changes changes are areconsistent consistent with with the theobserved observed changes changes in the in the1D 1 1DH NMR1H NMR and andDI-ESI-MS DI-ESI-MS spectr spectralal data. data. Paraquat Paraquat also alsodecreased decreased purine purine levels levels (ATP, (ATP, ADP, ADP, and and AMP) AMP) an andd metabolites metabolites associated associated the the glycolytic pathway [3-phospho[3-phospho glycerate,glycerate, dihydroxyacetone phosphat phosphatee (DHAP or glyceroneglycerone phosphate),phosphate), lactate,lactate, andand alanine].alanine]. A decrease in in extracellular glucose, glucose, lact lactate,ate, and and alanine alanine was was also also observed. observed. Furthermore, Furthermore, a alarge large increase increase in incitrate citrate and and a decrease a decrease in aspartate in aspartate were were observed observed following following paraquat paraquat treatment. treatment. This Thisis consistent is consistent with with the theinhibition inhibition of ofaconitase aconitase by by paraquat-induced paraquat-induced superoxide superoxide anion anion formation formation [[94],94], sincesince aconitaseaconitase convertsconverts citrate to isocitrateisocitrate in thethe tricarboxylictricarboxylic acidacid cyclecycle (TCA).(TCA). Also,Also, aspartateaspartate isis generatedgenerated from oxaloacetate, oxaloacetate, which which isis produced produced from from the the TCA TCA cycle. cycle. Finally, Finally, paraquat paraquat treatment treatment also alsoresulted resulted in a indecrease a decrease in intotal total reduced reduced (GSH) (GSH) an andd oxidized oxidized (GSSG) (GSSG) glutathione. Critically, GSHGSH depletiondepletion is anan importantimportant contributorcontributor to oxidativeoxidative stress and dopaminergicdopaminergic cell death [[95].95]. Also,Also, aa decreasedecrease inin GSHGSH isis one of the earliest biochemical alterationsalterations detected in incidental Lewy body disease, whichwhich isis consideredconsidered anan asymptomaticasymptomatic precursorprecursor toto PDPD [[96].96]. A flowflow cytometrycytometry experimentexperiment confirmedconfirmed aa 76.4%76.4% decreasedecrease in GSHGSH 4848 hh afterafter treatmenttreatment withwith paraquat.paraquat. Also,Also, cellcell deathdeath waswas correlatedcorrelated withwith thethe GSHGSH decrease.decrease. In total,total, the observedobserved metabolitemetabolite changeschanges by NMRNMR andand MSMS demonstratedemonstrate thatthat paraquatparaquat increasedincreased PPP metabolite accumulation accumulation while while decre decreasingasing glycolysis glycolysis and and impairing impairing the the TCA TCA cycle. cycle.

(a) (b)

Figure 6. (a) Alterations in citrate, glucose 6-phosphate/fructose 6-phosphate, lactate, and glucose Figure 6. (a) Alterations in citrate, glucose 6-phosphate/fructose 6-phosphate, lactate, and glucose content are specific for paraquat treatment. S-plot was generated from the combined MB-PLS-DA of 1D content are specific for paraquat treatment. S-plot was generated from the combined MB-PLS-DA of 1D 1H NMR spectra and DI-ESI-MS spectra. The S-plot was used to identify metabolites that significantly 1H NMR spectra and DI-ESI-MS spectra. The S-plot was used to identify metabolites that significantly contribute to the class separation between untreated controls and paraquat treatment. The metabolites contribute to the class separation between untreated controls and paraquat treatment. The metabolites located in the upper right quadrant increased significantly while those located in the lower left quadrant located in the upper right quadrant increased significantly while those located in the lower left significantly decreased after paraquat exposure. Reproduced with permission from [83]. (b) Western quadrant significantly decreased after paraquat exposure. Reproduced with permission from [83]. (b) blot analysis of changes in glucose-6-phosphate dehydrogenase (G6PD) expression induced by Western blot analysis of changes in glucose-6-phosphate dehydrogenase (G6PD) expression induced paraquat. Paraquat induces an increase in the expression levels of G6PD. Glyceraldehyde 3-phosphate by paraquat. Paraquat induces an increase in the expression levels of G6PD. Glyceraldehyde dehydrogenase (GAPDH) levels are used as loading controls for WBs. The changes observed in 3-phosphate dehydrogenase (GAPDH) levels are used as loading controls for WBs. The changes GAPDH levels might reflect of overall cell death (overall decrease in protein content) induced by PQ. Reproducedobserved in withGAPDH permission levels might from [reflect83]. of overall cell death (overall decrease in protein content) induced by PQ. Reproduced with permission from [83].

Metabolites 2017, 7, 22 10 of 26 Metabolites 2017, 7, 22 10 of 26

(a) (b)

Figure 7. Paraquat induces selective changes in glucose metabolism, TCA cycle, and the PPP pathway. FigureCells were7. Paraquat treated withinduces paraquat selective (0.5 mM), changes rotenone in gl (4ucoseµM), MPPmetabolism,+ (2.5 mM), TCA or 6-OHDAcycle, and (50 theµM) PPP + pathway.for 24 h inCells a glucose-free were treated medium with paraquat supplemented (0.5 mM), with 13 rotenoneC-glucose (4 (3.5 μM), g/L). MPP Analysis (2.5 mM), of 2D or1H- 6-OHDA13C (50HSQC μM) NMRfor 24 spectra h in a wasglucose-free used to evaluate medium changes supplemented in glucose-derived with 13C-glucose metabolites. (3.5 Bar g/L). graphs Analysis indicate of 2D 1H-the13C relative HSQC changes NMR spectra in peak was intensity used (concentration)to evaluate change for metabolitess in glucose-derived associated withmetabolites. the (a) PPP Bar and graphs indicatenucleotide the relative biosynthesis changes and (inb) peak glycolysis, intensity the TCA (concentration) cycle, and metabolites for metabolites found associated accumulated with in the the (a) PPPextracellular and nucleotide media. Databiosynthesis represent meansand (±b)SD glycolysis, of 3 independent the TCA experiments. cycle, and * p enzyme of the PPP and a major source of NADPH [97]. The proteomics data were confirmed by Western blot (Figure 6b) and indicate that G6PD expression increased proportional to an increase in paraquat dosage. Thus, the metabolomics and proteomics data indicated that alterations in PPP and G6PD activity may be a result of paraquat toxicity.

Metabolites 2017, 7, 22 11 of 26

3.3. Paraquat Hijacks the Pentose Phosphate Pathway MetabolitesHuman2017 ,dopaminergic7, 22 neuroblastoma cells were transduced with adenovirus11 ofencoding 26 for human G6PD (AdG6PD) or empty adenovirus (AdEmpty) to further investigate the role of G6PD in paraquat3.3. Paraquat toxicity. Hijacks G6PD the Pentose overexpression Phosphate Pathway increased cell death and oxidative death as a result of paraquatHuman treatment, dopaminergic but, importantly, neuroblastoma cellsno change were transduced was observed with adenovirus when cells encoding were for exposed human to other toxinsG6PD (Figure (AdG6PD) 8a). or Conversely, empty adenovirus paraquat-induced (AdEmpty) to further toxicity, investigate mitochondrial the role of G6PDROS formation, in paraquat and GSH depletiontoxicity. G6PDwere overexpression reversed when increased G6PD cell was death inhibited and oxidative with death 6-aminonicotinamide as a result of paraquat (6-AN) treatment, (Figure 8b). Again,but, importantly,6-AN had no changeeffect on was cell observed death whenor GSH cells depletion were exposed when to neuronal other toxins cells (Figure were8a). treated with Conversely, paraquat-induced toxicity, mitochondrial ROS formation, and GSH depletion were reversed rotenone, MPP+, or 6-OHDA instead of paraquat. These observations are consistent with the when G6PD was inhibited with 6-aminonicotinamide (6-AN) (Figure8b). Again, 6-AN had no effect on recycling of NADPH from NADP+ by G6PD being uniquely required for the redox cycling of cell death or GSH depletion when neuronal cells were treated with rotenone, MPP+, or 6-OHDA instead paraquatof paraquat. to produce These observations ROS. In effect, are consistent paraquat with would the recycling be expected of NADPH to outcompete from NADP NADPH-dependent+ by G6PD antioxidantbeing uniquely systems. required As forsuch, the unpublished redox cycling of results paraquat from to produce our group ROS. have In effect, demonstrated paraquat would that depletion of beGSH expected is not to prevented outcompete by NADPH-dependent overexpression antioxidantof GSH reductase. systems. As NADPH such, unpublished can also resultsbe produced by 6-phosphogluconatefrom our group have demonstrateddehydrogenase that depletion(PGD), ofmalic GSH isenzyme not prevented (malate by overexpressiondehydrogenase, of GSH MDH), and isocitratereductase. dehydrogenase NADPH can also (IDH) be produced [97]. Interestingly, by 6-phosphogluconate paraquat dehydrogenase treatment also (PGD), increased malic enzyme the expression (malate dehydrogenase, MDH), and isocitrate dehydrogenase (IDH) [97]. Interestingly, paraquat levels of malate dehydrogenase, which may also contribute to paraquat’s redox cycle in the treatment also increased the expression levels of malate dehydrogenase, which may also contribute to mitochondria.paraquat’s redox The cycle observed in the mitochondria. changes in Thethe observed metabolome changes and in theproteome metabolome for andhuman proteome dopaminergic neuroblastomafor human dopaminergic cells following neuroblastoma exposure cells to paraquat following are exposure summarized to paraquat in Figure are summarized 9. Our results in suggest thatFigure paraquat9. Our resultshijacks suggest the PPP that paraquatto increase hijacks NADP the PPPH-reducing to increase NADPH-reducingequivalents and equivalents stimulate paraquat redoxand stimulatecycling, oxidative paraquat redox stress, cycling, and cell oxidative death. stress, and cell death.

(a) (b)

FigureFigure 8. 8. Paraquat-inducedParaquat-induced cell cell death death is selectively is selectively regulated regulated by glucose by glucose 6-phosphate 6-phosphate dehydrogenase dehydrogenase andand the the pentose pentose phosphate pathway. pathway. (a) Cell(a) Cell death death induced induced by paraquat by paraquat (0.5 mM), (0.5 rotenone mM), (4rotenoneµM), (4 μM), MPP+MPP+ (2.5 (2.5 mM) mM) or 6-OHDA6-OHDA (50 (50µM) μM) after after 48 h 48 of treatment,h of treatment, was simultaneously was simultaneously evaluated evaluated by flow by flow cytometrycytometry using using PIPI andand mBCl. mBCl. Cell Cell death death is observed is observed as an increaseas an increase in PI uptake. in PI Datauptake. is represented Data is represented as fold increase in the mean PI fluorescence and are means ± SE of three independent experiments. as fold increase in the mean PI fluorescence and are means ± SE of three independent experiments. * p * p < 0.05, Empty vs. G6PD values. Reproduced with permission from [83]. (b) Cell death induced by < 0.05, Empty vs. G6PD values. Reproduced with permission from [83]. (b) Cell death induced by paraquat was evaluated in the presence or absence of 6-aminonicotinamide (6-AN, 1 mM). Cell death paraquatis represented was evaluated as an increase in inthe the presence population or ofabsence cells (%) of with 6-aminonicotinamide increased PI fluorescence. (6-AN, Reproduced 1 mM). Cell death is withrepresented permission as from an [98 increase]. in the population of cells (%) with increased PI fluorescence. Reproduced with permission from [98].

Metabolites 2017, 7, 22 12 of 26 Metabolites 2017, 7, 22 12 of 26

FigureFigure 9. 9.Paraquat Paraquathijacks hijacks thethe pentosepentose phosphate pathwa pathwayy to to induce induce oxidative oxidative stress stress and and cell cell death. death. OurOur results results demonstrate demonstrate thatthat paraquatparaquat induces an an increase increase in in the the PPP PPP (highlighted (highlighted inin green), green), which which is is reflectedreflected by by an an increase increase in glucosein glucose uptake, uptake, and inan glucosed in glucose 6-phosphate, 6-phosphate, glucono glucono 1,5-lactone, 1,5-lactone, erythrose 4-phosphate,erythrose 4-phosphate, and fructose and 6-phosphate fructose content6-phosphate (red arrows). content In (red addition, arrows). paraquat In addition, decreases paraquat glycolysis asdecreases demonstrated glycolysis by a as decrease demonstrated in 3-phosphoglycerate, by a decrease in alanine3-phosphoglycerate, and lactate levels alanine (green and lactate arrows). levels These metabolic(green arrows). changes These were alsometabolic paralleled changes by: (1) were an increase also paralleled in G6PD (theby: rate-limiting(1) an increase enzyme in G6PD in the (the PPP), andrate-limiting the expression enzyme levels in the of citratePPP), synthase,and the expression pyruvate kinaseslevels of M1/M2; citrate synthase, and (2)a pyruvate decrease kinases in lactate dehydrogenaseM1/M2; and (2) A/B a decrease chains, in which lactate participate dehydrogenas in glycolysise A/B chains, and the which TCA participate cycle (highlighted in glycolysis in orange). and Paraquatthe TCA alsocycle induced (highlighted an increase in orange). in citrate Paraquat accumulation also induced which an increase is associated in citrate to theaccumulation well-known inhibitorywhich is effectassociated on aconitase to the well-known (highlighted inhibitory in orange). effect An abnormalon aconitase increase (highlighted in citrate in levelsorange). has An been reportedabnormal to exertincrease an inhibitoryin citrate effectlevels onhas glycolysis been reported by allosteric to exert inhibition an inhibitory of PFK effect (broken on glycolysis red line), which by explainsallosteric why inhibition an increase of PFK glucose (broken uptake red andline), impaired which explains TCA cycle why is an not increase translated glucose to an uptake upregulation and inimpaired glycolysis. TCA Modulation cycle is not of translated G6PD levels to an and upregu activitylation was in directlyglycolysis. linked Modulation to paraquat of G6PD toxicity levels and oxidativeand activity stress. 6-AN,was 6-aminonicotinamide,directly linked to ACO,paraqu aconitaseat toxicity oraconitate and oxidative hydratase (EC:4.2.1.3;stress. 6-AN, ADC, aspartate6-aminonicotinamide, 4-decarboxylase ACO, (EC:4.1.1.12); aconitase ALDO,or aconitate fructose-bisphosphate hydratase (EC:4.2.1.3; aldolase ADC, (EC:4.1.2.13); aspartate ALT, alanine4-decarboxylase transaminase (EC:4.1.1.12); (EC:2.6.1.2); ALDO, CS, citrate fructose-bis synthasephosphate (EC:2.3.3.1); aldolase FBP, (EC:4.1.2.13); fructose-1,6-bisphosphatase ALT, alanine I (EC:3.1.3.11);transaminase FUM,(EC:2.6.1.2); fumarate CS, hydratase citrate synthase (EC:4.2.1.2); (EC:2.3.3.1); G6PD, glucose-6-phosphate FBP, fructose-1,6-bisphosphatase 1-dehydrogenase I (EC:1.1.1.49);(EC:3.1.3.11); GAPDH, FUM, fumarate glyceraldehyde hydratase 3-phosphate (EC:4.2.1.2); dehydrogenaseG6PD, glucose-6-phosphate (EC:1.2.1.12); 1-dehydrogenase GLDH, glutamate dehydrogenase,(EC:1.1.1.49); GAPDH, (EC: 1.4.1.2); glyceralde GLS,hyde glutaminase 3-phosphate (EC: dehydrogenase 3.5.1.2); GOT1, (EC:1.2.1.12); aspartate GLDH, aminotransferase, glutamate cytoplasmicdehydrogenase, (EC:2.6.1.1); (EC: 1.4.1.2); GPI, glucose-6-phosphate GLS, glutaminase (EC: isomerase 3.5.1.2); (EC:5.3.1.9); GOT1, aspartate HK, hexokinase aminotransferase, (EC:2.7.1.1); IDH,cytoplasmic isocitrate dehydrogenase(EC:2.6.1.1); GPI, (EC:1.1.1.42); glucose-6-phos LDH,phate L-lactate isomerase dehydrogenase (EC:5.3.1.9); (EC:1.1.1.27); HK, MDH,hexokinase malate (EC:2.7.1.1); IDH, isocitrate dehydrogenase (EC:1.1.1.42); LDH, L-lactate dehydrogenase dehydrogenase (EC:1.1.1.37); OGDH, 2-oxoglutarate dehydrogenase, (EC:1.2.4.2); LSC, succinyl-CoA (EC:1.1.1.27); MDH, malate dehydrogenase (EC:1.1.1.37); OGDH, 2-oxoglutarate dehydrogenase, synthetase (EC:6.2.1.4 6.2.1.5); PC, pyruvate carboxylase (EC:6.4.1.1); PGD, 6-phosphogluconate (EC:1.2.4.2); LSC, succinyl-CoA synthetase (EC:6.2.1.4 6.2.1.5); PC, pyruvate carboxylase (EC:6.4.1.1); dehydrogenase (EC:1.1.1.44); PDH, pyruvate dehydrogenase (EC:1.2.4.1); PGK1, phosphoglycerate PGD, 6-phosphogluconate dehydrogenase (EC:1.1.1.44); PDH, pyruvate dehydrogenase (EC:1.2.4.1); kinase (EC:2.7.2.3); PGM, phosphoglucomutase (EC:5.4.2.2); PFK, 6-phosphofructokinase 1 (EC:2.7.1.11); PGK1, phosphoglycerate kinase (EC:2.7.2.3); PGM, phosphoglucomutase (EC:5.4.2.2); PFK, RPI, ribose 5-phosphate isomerase A (EC:5.3.1.6); SDH, succinate dehydrogenase (EC:1.3.5.1); TPI, 6-phosphofructokinase 1 (EC:2.7.1.11); RPI, ribose 5-phosphate isomerase A (EC:5.3.1.6); SDH, triosephosphate isomerase (EC:5.3.1.1); TAL, transaldolase (EC:2.2.1.2); TKT, transketolase (EC:2.2.1.1). succinate dehydrogenase (EC:1.3.5.1); TPI, triosephosphate isomerase (EC:5.3.1.1); TAL, Reproduced with permission from [83]. transaldolase (EC:2.2.1.2); TKT, transketolase (EC:2.2.1.1). Reproduced with permission from [83].

Metabolites 2017, 7, 22 13 of 26

3.4. Glucose Metabolism Regulates Paraquat Toxicity We further investigated the role of glucose metabolism on paraquat toxicity based on the prior observations that metabolites derived from 13C-glucose are uniquely perturbed by paraquat exposure and the hijacking of the PPP. Consequently, glucose availability significantly impacted the survivability of rat dopaminergic mesencephalic cell line N27 following exposure to paraquat. Specifically, glucose deprivation increased cell survival (Figure 10a). Also, replacing glucose with galactose protected cells from paraquat toxicity. Galactose directs metabolism from glycolysis into glutaminolysis and OXPHOS phosphorylation for ATP production. An inhibition in glycolysis for cells grown in glucose-free or galactose-supplemented media was verified by changes in the extracellular medium acidification (ECAR) (Figure 10b). Furthermore, treatment of cells with 2-deoxy-D-glucose (2-DG), which is a hexokinase inhibitor that prevents the production of glucose-6-phosphate, also provided protection from paraquat toxicity (Figure 10c). These results, in total,Metabolites demonstrate2017, 7, 22 that glucose metabolism contributes to the death of cells following treatment13 of 26 with paraquat at ≥100 μM. 3.4.The Glucose metabolomics Metabolism Regulates analysis Paraquat indicated Toxicity both an increase in intracellular glucose and a corresponding decrease in extracellular glucose after exposure to paraquat. These observations suggestedWe furtherthat paraquat investigated treatment the role ofincreases glucose metabolismglucose uptake. on paraquat A fluorescently toxicity based labeled on the analog prior of observations that metabolites derived from 13C-glucose are uniquely perturbed by paraquat exposure glucose (2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino]-2-deoxy-D-glucose, 2-NBDG) was used to and the hijacking of the PPP. Consequently, glucose availability significantly impacted the survivability monitor changes in glucose uptake due to paraquat exposure. Correspondingly, neuronal cells of rat dopaminergic mesencephalic cell line N27 following exposure to paraquat. Specifically, glucose treated with paraquat exhibited a >60% increase in glucose uptake in response to paraquat treatment deprivation increased cell survival (Figure 10a). Also, replacing glucose with galactose protected cells (Figure 11a), which is consistent with the metabolomics data. Glucose uptake is regulated by a from paraquat toxicity. Galactose directs metabolism from glycolysis into glutaminolysis and OXPHOS saturablephosphorylation transport for system ATP production. involving Anthe inhibitionNa+-independent in glycolysis glucose for cells transporters grown in glucose-free(GLUT), and or the Na+-dependentgalactose-supplemented glucose mediatransporters was verified (SGLT). by changes Exposure in the to extracellularparaquat resulted medium in acidification a significant (ECAR) increase in(Figure the translocation 10b). Furthermore, of SGLT1 treatment and GLUT4 of cells withtransporters 2-deoxy-D-glucose to the plasma (2-DG), membrane.which is a hexokinaseConsequently, inhibitinginhibitor thatglucose prevents uptake the with production STF-31, of a glucose-6-phosphate, GLUT inhibitor, (Figure also provided 11b) or ascorbic protection acid, from a paraquatcompetitive inhibitortoxicity (Figureof glucose, 10c). Thesedecreased results, paraquat in total, demonstratetoxicity. Again, that glucosethese results metabolism clearly contributes demonstrate to the that glucosedeath ofmetabolism cells following is an treatment important with cont paraquatributor atto≥ paraquat-induced100 µM. cell death.

(a) (b) (c)

FigureFigure 10. 10. InhibitionInhibition of of glucose glucose metabolism protectsprotects against against paraquat paraquat toxicity. toxicity. Rat Rat dopaminergic dopaminergic N27 N27 cellscells were were growngrown in culturein culture media media with or with without or glucose,without or glucose, in a glucose-free or in mediuma glucose-free supplemented medium supplementedwith galactose. with When galactose. indicated, When cells indicated, were treated cells were with treated PQ or MPPwith+ PQ(2.5 or mM) MPP for+ (2.5 48 mM) h in for the 48 h inpresence the presence or absence or absence of 2-DG. of 2-DG. (a,c) Cell(a,c) survivalCell survival was determined was determined by the by simultaneous the simultaneous analysis analysis of ofplasma plasma membrane membrane integrity integrity (PI uptake)(PI uptake) and intracellular and intracellular GSH content GSH (mBClcontent fluorescence). (mBCl fluorescence). Bar graphs Bar graphsrepresent represent % s of viable% s of cells viable (cell cells survival) (cell and survival) data are and means data± areSE ofmeans at least ± nSE= 3of independent at least n = 3 independentexperiments. experiments. (b) Glycolysis rates(b) Glycolysis and glycolytic rates reserve and capacityglycolytic of cellsreserve were evaluatedcapacity byof changescells were evaluatedin the ECAR by changes sensitive in to the 2-DG. ECAR Glycolysis sensitive is observedto 2-DG. asGlycolysis an increase is observed in ECAR whenas an switchingincrease in cells ECAR whenfrom switching a glucose-free cells environment from a glucose-free (NG) to a medium environment containing (NG) 10 to mM a glucose.medium Glycolytic containing reserve 10 mM capacity is determined by addition of oligomycin. Data are means ± SE of at least n = 3 independent glucose. Glycolytic reserve capacity is determined by addition of oligomycin. Data are means ± SE experiments and are represented with respect to control (+glucose). Two-way ANOVA Holm–Sidak of at least n = 3 independent experiments and are represented with respect to control (+ glucose). post hoc test: a, p < 0.05 vs. no PQ or MPP+ within the corresponding category of ±glucose, galactose Two-way ANOVA Holm–Sidak post hoc test: a, p < 0.05 vs. no PQ or MPP+ within the or 2-DG; b, p < 0.05, vs. +glucose, within the corresponding toxicant treatment. t-test: * p < 0.05, vs. corresponding category of ± glucose, galactose or 2-DG; b, p < 0.05, vs. + glucose, within the +glucose. Reproduced with permission from [98]. corresponding toxicant treatment. t-test: * p 0.05, vs. + glucose. Reproduced with permission from [98]. The metabolomics analysis indicated both an increase in intracellular glucose and a corresponding decrease in extracellular glucose after exposure to paraquat. These observations suggested that paraquat treatment increases glucose uptake. A fluorescently labeled analog of glucose (2-[N-(7- nitrobenz-2-oxa-1,3-diazol-4-yl) amino]-2-deoxy-D-glucose, 2-NBDG) was used to monitor changes in glucose uptake due to paraquat exposure. Correspondingly, neuronal cells treated with paraquat exhibited a >60% increase in glucose uptake in response to paraquat treatment (Figure 11a), which is consistent with the metabolomics data. Glucose uptake is regulated by a saturable transport system involving the Na+-independent glucose transporters (GLUT), and the Na+-dependent glucose transporters (SGLT). Exposure to paraquat resulted in a significant increase in the translocation of SGLT1 and GLUT4 transporters to the plasma membrane. Consequently, inhibiting glucose uptake with STF-31, a GLUT inhibitor, (Figure 11b) or ascorbic acid, a competitive inhibitor of glucose, Metabolites 2017, 7, 22 14 of 26

decreased paraquat toxicity. Again, these results clearly demonstrate that glucose metabolism is an Metabolitesimportant 2017 contributor, 7, 22 to paraquat-induced cell death. 14 of 26

(a) (b)

FigureFigure 11. 11. ParaquatParaquat increases increases glucoseglucose transport transport and and the the translocation translocation of glucose of glucose transporters. transporters. Cells wereCells treated were treated with withPQ PQfor for48 48h. h.(a) (Glucosea) Glucose transport transport was evaluatedevaluated by by the the uptake uptake 2-NBDG; 2-NBDG; (b) the survival(b) the survival of cells of treated cells treated with withPQ in PQ the in presence the presence or orabsence absence of of STF-31 STF-31 was was determined as as explained inexplained Figure 10. in Figure The bar10. Thegraph bar graphrepresents represents %s of %s viable of viable cells cells (cell (cell survival). Data Data in allin graphsall graphs are are means means ± SE of at least n = 3 independent experiments. Two-way ANOVA Holm–Sidak post hoc test: ± SE of at least n = 3 independent experiments. Two-way ANOVA Holm–Sidak post hoc test: c, p < c, p < 0.05 vs. no PQ within the corresponding ±STF-31 or phlorizin category; d, p < 0.05 vs. control (no 0.05glucose vs. transportno PQ within inhibitor) the within corresponding the corresponding ± STF-31 PQ concentration. or phlorizin Reproduced category; withd, p permission< 0.05 vs. control (no glucosefrom [98 ].transport inhibitor) within the corresponding PQ concentration. Reproduced with permission from [98]. 3.5. Paraquat Induced Metabolic Dysfunction in the Mice Midbrain and Striatum 3.5. ParaquatTo further Induced substantiate Metabolic the validity Dysfunct of ourion in vitrothe Micefindings Midbrain that paraquat and Striatum uniquely perturbs the metabolomeTo further of dopaminergic substantiate neurons, the validity we evaluated of our thein vitro metabolic findings dysfunction that paraquat induced by uniquely chronically perturbs the metabolometreating C57BI/6 of micedopaminergic with paraquat. neurons, We observed we that ev paraquataluated onlythe inducedmetabolic statistically dysfunction significant induced by changes to the metabolomes of the midbrain, which is the location of the substantia nigra and the chronically treating C57BI/6 mice with paraquat. We observed that paraquat only induced loss of dopaminergic neurons associated with PD; and to the striatum, which receive dopaminergic statisticallysignals from significant midbrain and changes controls motorto the function. metabolomes Importantly, of the we observedmidbrain, changes which to is metabolites the location of the substantiaassociated nigra with glycolysisand the (e.g.,loss of lactate), dopaminergic the TCA cycle neurons (e.g., glutamate),associated andwith GSH PD; metabolismand to the striatum, which(e.g., glutathione),receive dopaminergic consistent with signals our in from vivo midbrainresults (Figure and 12 controlsa). Paraquat motor was function. again observed Importantly, we observedto induce changes a large accumulation to metabolites of citrate associated consistent with with gl theycolysis proposed (e.g., inactivation lactate), ofthe aconitase. TCA cycle (e.g., glutamate),AMP-activated and protein GSH kinasemetabolism (AMPK) (e.g., is a metabolic glutathione), master consistent regulator that with includes our in regulating vivo results glucose (Figure 12a). uptake and, like other kinases, is regulated by phosphorylation (pAMPK) [99]. Consequently, chronic Paraquat was again observed to induce a large accumulation of citrate consistent with the proposed paraquat treatment also resulted in a significant increase in pAMPK and its substrate acetyl-CoA inactivationcarboxylase (pACC)of aconitase. only in AMP-activated the midbrain and protein striatum ki ofnase C57BI/6 (AMPK) mice is (Figure a metabolic 12b). These master results regulator that includesfurther corroborate regulating the observationglucose uptake that paraquat and, modulateslike other glucose kinases, metabolism is regula and thatted the by midbrain phosphorylation (pAMPK)and striatum [99]. are Consequently, selectively sensitive chronic to paraquat paraquat toxicity. treatment also resulted in a significant increase in pAMPK and its substrate acetyl-CoA carboxylase (pACC) only in the midbrain and striatum of C57BI/6 mice (Figure 12b). These results further corroborate the observation that paraquat modulates glucose metabolism and that the midbrain and striatum are selectively sensitive to paraquat toxicity.

Metabolites 2017, 7, 22 15 of 26 Metabolites 2017, 7, 22 15 of 26

3.0 Striatum

1 Midbrain PBS) α 2.5 Cerebellum vs Cortex 2.0

1.5

1.0 pAMPK / AMPK

(Fold change PQ (a) (b)

Figure 12. C57Bl/6J mice were exposed chronically to paraquat (PQ). One week after the final Figureinjection 12. C57Bl/6J of PQ or PBS,mice animals were wereexposed euthanized chronically to isolate to metabolites paraquat from (PQ). the midbrain, One week striatum after the final 1 injectionand of cortex PQ regions.or PBS, Integrated animals positivewere euthanized and negative-ion to isolate DI-ESI-MS metabolites and 1D fromH NMR the were midbrain, used striatum to characterize the alterations in the metabolic profiles of midbrain, striatum, and cortex regions and cortex regions. Integrated positive and negative-ion DI-ESI-MS and 1D 1H NMR were used to from control and PQ-treated mice. (a) The percent fold-change for metabolites contributing to class characterizeseparation the as alterations identified from in the OPLS-DA metabolic back-scaled profile loadingss of midbrain, plots are striatum, plotted. The and percent cortex fold regions from controlchanges and arePQ-treated all significant mice. (p < 0.05)(a) basedThe onpercent a paired fold-change t-test. The green for bars meta indicatebolites metabolites contributing with to class separationa fold-increase as identified after PQ from treatment, OPLS-DA whereas back-scale red bars indicated loadings that a metaboliteplots are decreased plotted.after The PQ percent fold changestreatment. are all Reproducedsignificant with(p < permission0.05) based from on [a98 paired]. (b) Changes t-test. inThe the green levels bars of phosphorylated indicate metabolites (p) with AMPKα1 induced by PQ were evaluated by western-bot (WB). Bar graphs represent the densitometry a fold-increase after PQ treatment, whereas red bars indicate that a metabolite decreased after PQ analysis of the corresponding WBs from three independent replicas. Data are represented as fold treatment.change Reproduced vs. the indicated with control. permission Reproduced from with [98]. permission (b) Changes from [98 ].in the levels of phosphorylated (p) AMPKα1 induced by PQ were evaluated by western-bot (WB). Bar graphs represent the densitometry4. Synergy of αanalysis-Synuclein of Geneticthe corresponding Mutations and WB Paraquats from Toxicity three independent replicas. Data are represented as fold change vs. the indicated control. Reproduced with permission from [98]. 4.1. α-Synuclein Potentiates Paraquat Toxicity and Metabolic Dysfunction

4. SynergyPD of is α a-Synuclein probable multifactorial Genetic Mutations disease, in which and exposureParaquat to Toxicity an environmental toxin is likely one component in the development and progression of the disease. Overexpression and aggregation of α-synuclein and the subsequent formation of Lewy bodies are hallmarks of PD. Consequently, is 4.1. α-Synucleinthere a synergistic Potentiates relationship Paraquat between Toxicityα-synuclein and Metabolic and exposure Dysfunction to paraquat? The overexpression PDof wild-typeis a probable (WT) αmultifactorial-synuclein or an disease, A53T mutant in whic (familialh exposure PD mutant to an [9 ,10environmental]) did not result toxin in is likely any significant change in viability (Figure 13a). Conversely, treatment with paraquat resulted in a one component in the development and progression of the disease. Overexpression and aggregation significant increase in cell death and a major perturbation in the metabolome (Figure 13). Specifically, of α-synucleinthe combination and the of paraquat subsequent and α -synucleinformation overexpression of Lewy bodies produced are hallmarks a more dramatic of PD. change Consequently, in is there thea synergistic metabolome relationship than either paraquat between treatment α-synuclein or α-synuclein and exposure overexpression to paraquat? alone. Importantly, The overexpression the of wild-typesame results (WT) were α-synuclein obtained if either or an WT A53T or A53T mutantα-synuclein (familial was overexpressed.PD mutant [9,10]) A detailed did analysis not result in any 1 13 significantof the metabolicchange in changes viability was then(Figure made 13a). using Conv 2D H-ersely,C HSQC treatment experiments with and paraquat following theresulted in a 13C-carbon distribution resulting from 13C-glucose supplemented media (Figure 14a). Overexpression significant increase in cell death and a major perturbation in the metabolome (Figure 13). of α-synuclein and paraquat exposure resulted in an enhanced glucose accumulation, an impairment Specifically,in glycolysis, the andcombination a reduction in of glycolytic paraquat capacity and and α mitochondrial-synuclein respirationoverexpression (OCR/ECAR produced ratio) a more dramatic(Figure change 14b). in the metabolome than either paraquat treatment or α-synuclein overexpression alone. Importantly, the same results were obtained if either WT or A53T α-synuclein was overexpressed. A detailed analysis of the metabolic changes was then made using 2D 1H-13C HSQC experiments and following the 13C-carbon distribution resulting from 13C-glucose supplemented media (Figure 14a). Overexpression of α-synuclein and paraquat exposure resulted in an enhanced glucose accumulation, an impairment in glycolysis, and a reduction in glycolytic capacity and mitochondrial respiration (OCR/ECAR ratio) (Figure 14b).

Metabolites 2017, 7, 22 16 of 26 Metabolites 2017, 7, 22 16 of 26 Metabolites 2017, 7, 22 16 of 26 Empty α-synuclein 100 EmptyA53T α-synuclein 80 100 A53T 60 80 40 60 20 40 Cell survival (%) Cell survival 0 20 050 PQ [μM] Cell survival (%) Cell survival 0 050(a) (b) μ PQ [ M] (a) (b) Figure 13. α-Synuclein potentiates the metabolic dysfunction and toxicity induced by paraquat (PQ). FigureN27 13.dopaminergicα-Synuclein potentiatescells were thetransduced metabolic for dysfunction 24 h with and empty toxicity adenoviruses induced by paraquator adenoviruses (PQ). FigureN27encoding dopaminergic 13. α-Synuclein either cellsWT potentiatesor were mutant transduced A53T the metabolicα for-synuclein 24 h with dysfunct (6 empty MOI).ion adenoviruses (a) andCell survivaltoxicity or adenoviruses inducedafter exposure by encodingparaquat to PQ for (PQ). N27either 48dopaminergic h WT was or determined mutant cells A53T as were explainedα-synuclein transduced in (6Figure MOI). for10. (a Bar)24 Cell graphh survivalwith represents empty after exposure percentageadenoviruses to PQof viable foror 48 adenovirusescells h was (cell survival) and data are means ± SE of at least n = 3 independent experiments. (b) Cells were treated encodingdetermined either as explained WT or mutant in Figure A53T 10. Bar α-synuclein graph represents (6 MOI). percentage (a) Cell of survival viable cells after (cell exposure survival) and to PQ for with 25 μM PQ for 24 h. Metabolites were extracted for NMR/MS metabolomics. 3D MB-PCA scores 48data h was are determined means ± SE ofas at explained least n = 3 in independent Figure 10. experiments. Bar graph represents (b) Cells were percentage treated with of 25viableµM PQcells (cell forplot 24 h. shows Metabolites the changes were extracted in the metabolome for NMR/MS base metabolomics.d on distances 3D MB-PCAbetween scoresgroups. plot The shows ellipsoids the survival) and data are means ± SE of at least n = 3 independent experiments. (b) Cells were treated changescorrespond in the to metabolome the 95% confidence based on limits distances from between a normal groups. distribution The ellipsoids for each correspondcluster. Six toindependent the 95% with 25 μM PQ for 24 h. Metabolites were extracted for NMR/MS metabolomics. 3D MB-PCA scores confidencesamples limitsof metabolic from a normalextract distributionwere used for for the each MB-PCA cluster. Sixmultivariate independent analysis. samples Reproduced of metabolic with plotextract permissionshows were the used from changes for [98]. the MB-PCAin the metabolome multivariate analysis. based on Reproduced distances with between permission groups. from [The98]. ellipsoids correspond to the 95% confidence limits from a normal distribution for each cluster. Six independent samples of metabolic extract were used for the MB-PCA multivariate analysis. Reproduced with permission from [98].

4 Empty α-synuclein 3

2 4 Empty 1 α-synuclein OCR / ECAR / OCR 3 0 2 - PQ + PQ

(a) 1 (b) OCR / ECAR / OCR Figure 14. α-Synuclein potentiates paraquat induced metabolic changes. (a0) Paraquat N27 dopaminergic Figure 14. α-Synuclein potentiates paraquat induced metabolic changes. (a) Paraquat N27 cells were transduced for 24 h with empty adenoviruses or adenoviruses encoding- eitherPQ WT or mutant + PQ A53Tdopaminergicα-synuclein cells (6 MOI). were Cells transduced were treated for 24 with h with 25 µ Mempty PQ for adenoviruses 24 h. Metabolites or adenoviruses were extracted encoding for NMReither metabolomics. WT or mutant 2D(a) 1A53TH −13 Cα-synuclein HSQC NMR (6 spectraMOI). Cells from 13wereC glucose treated labeling with 25 experiments μ(b)M PQ for were 24 h. 1 13 13 usedMetabolites to evaluate were the intracellularextracted for metabolic NMR metabolomics. changes shown 2D byH− theC MB-PCA HSQC NMR multivariate spectra from analysis. C glucose Data representlabeling the experiments mean of 3 independent were used replicates.to evaluate * p

Metabolites 2017, 7, 22 17 of 26 Metabolites 2017, 7, 22 17 of 26 provide protection against paraquat toxicity (Figure 11). Finally, the inhibition of PPP by 6-AN (Figureto provide 8b) significantly protection against increased paraquat cell survival toxicity (Figurefollowing 11). exposure Finally, the to inhibitionparaquat. ofThese PPP results by 6-AN raise the(Figure question:8b) significantly does glucose increased metabolism cell survival play a following similar role exposure and contribute to paraquat. to Thesethe observed results raise synergistic the toxicityquestion: between does glucose paraquat metabolism and α-synuclein? play a similar To role address and contribute this question, to the observedwe repeated synergistic all of toxicitythe above experimentsbetween paraquat with the and additionα-synuclein? of the To overexpression address this question, of WT weα-synuclein repeated all or of an the A53T above mutant. experiments Glucose deprivation,with the addition the inhibition of the overexpression of glucose uptake, of WT α -synucleinand the inhibition or an A53T of mutant.the PPP Glucose were all deprivation, observed to eliminatethe inhibition the stimulatory of glucose uptake,effect of and α-synuclein the inhibition on paraquat of the PPP toxicity were all (Figure observed 15). to These eliminate results, the in total,stimulatory provide effect strong of evidenceα-synuclein that on glucose paraquat metabo toxicitylism (Figure and signaling 15). These are results, critically in total, involved provide in the toxicstrong synergism evidence thatof glucoseparaquat metabolism exposure and and signaling the overexpression are critically involved of α-synuclein. in the toxic synergismSpecifically, α-synucleinof paraquat exposurepotentiates and paraquat the overexpression toxicity by of αimpairing-synuclein. energy Specifically, metabolism,α-synuclein glycolysis, potentiates and mitochondrialparaquat toxicity respiration. by impairing energy metabolism, glycolysis, and mitochondrial respiration.

(a) (b) (c)

FigureFigure 15. 15. GlucoseGlucose metabolism metabolism and and the PPPthe regulatePPP regulate the toxic the synergism toxic synergism of α-synuclein of α and-synuclein paraquat and paraquat(PQ). N27 (PQ). dopaminergic N27 dopaminergic cells were transducedcells were fortran 24sduced h with for empty 24 viralh with particles empty or viral adenoviruses particles or adenovirusesencoding either encoding WT or mutanteither A53TWT orα -synucleinmutant A53T (6 MOI). α-synuclein After transduction, (6 MOI). cellsAfter were transduction, treated with cells werePQ (50treatedµM in with b) for PQ 48 h(50 in theμM presence in b) for or 48 absence h in the of glucose.presence (a or) STF-31 absence (b, 0.5of mM),glucose. or 6-AN(a) STF-31 (c, 1 mM). (b, 0.5 mM),Cell survivalor 6-AN was (c, determined1 mM). Cell as explainedsurvival inwas Figure dete 10rmined. Bar graphs as explained represent in % Figure s of viable 10. cellsBar (cellgraphs representsurvival) % and s of data viable are meanscells (cell± S.E.M. survival) of at and least datan = 3are independent means ± S.E.M. experiments. of at least Two-way n = 3 independent ANOVA experiments.Holm–Sidak Two-way post hoc test ANOVA was done Holm–Sidak for each PQ post concentration hoc test independently:was done for a,eachp < 0.05,PQ concentration vs. empty, independently:within the corresponding a, p < 0.05, category vs. empty, of ± withinglucose the (a) corre±STF31;sponding (b) ±6-AN category (c); b, ofp ±< glucose 0.05, vs. ( +glucosea) ± STF31; (a), (b) ±-STF31 6-AN ( (cb);), b, or p -6-AN < 0.05, (c vs.), within + glucose the corresponding (a), -STF31 (b category), or -6-AN of Empty(c), within or α-synuclein. the corresponding Reproduced category with of Emptypermission or α-synuclein. from [98]. Reproduced with permission from [98].

5.5. Conclusions Conclusions PDPD is currentlycurrently believed believed to be to a multifactorialbe a multifactorial disease, in disease, which age, in genetics, which andage, environmental genetics, and α environmentaltoxins are all considered toxins are significant all considered risk factors. significant The overexpression risk factors. The or mutation overexpression of -synuclein or mutation [9,10] of α α-synucleinhas been identified [9,10] has as a been major identified genetic factor as a associated major genetic with PD. factor In fact, associated the formation with ofPD.-synuclein In fact, the aggregates or Lewy bodies in dopaminergic neurons are the primary clinical sign of the disease. formation of α-synuclein aggregates or Lewy bodies in dopaminergic neurons are the primary Lewy bodies are correlated with neuronal loss and cognitive impairment, which are key symptoms of clinical sign of the disease. Lewy bodies are correlated with neuronal loss and cognitive impairment, PD. The biological function of α-synuclein is currently unknown, and while a variety of factors which are key symptoms of PD. The biological function of α-synuclein is currently unknown, and have been shown to induce fibril formation, oxidative stress [49] is an important contributor. while a variety of factors have been shown to induce fibril formation, oxidative stress [49] is an Thus, mitochondrial dysfunction, energy failure, and redox imbalance induced by environmental importanttoxicants maycontributor. be the primary Thus, mechanism mitochondrial leading dysf to αunction,-synuclein energy misfolding failure, and and aggregation redox imbalance in PD. inducedExposure by environmental to environmental toxicants toxins, may such be as herbicidesthe primary or pesticides,mechanism has leading been correlated to α-synuclein with misfoldingan increase and in theaggregation incidence in of PD. PD and has been shown to induce PD-like symptoms. Specifically, environmentalExposure to toxins environmental have been shown toxins, to such induce as cellherbic deathides of or dopaminergic pesticides, has neurons been primarycorrelated through with an increasean increase in the in ROS.incidence Consequently, of PD and the has loss been of neurons shown in to the induce substantia PD-like nigra symptoms. of the midbrain Specifically, is a environmentalhallmark of PD. toxins We have have demonstrated been shown that to paraquat,induce cell rotenone, death 6-OHDA,of dopaminergic and MPP neurons+ have distinct primary throughmolecular an mechanismsincrease in leadingROS. Consequently, to dopaminergic the neuron loss of cell neurons death. Wein havethe substantia shown that nigra paraquat of the midbrainhijacks the is NADPHa hallmark from of thePD. PPP We to have redox demonstrated cycle, induces that oxidative paraquat, damage, rotenone, and impairs 6-OHDA, antioxidant and MPP+ havedefenses. distinct Furthermore, molecular paraquatmechanisms increases leading glucose to dopaminergic transport and neuron carbon fluxcell todeath. the PPP We (Figure have shown16). that paraquat hijacks the NADPH from the PPP to redox cycle, induces oxidative damage, and impairs antioxidant defenses. Furthermore, paraquat increases glucose transport and carbon flux to the PPP (Figure 16). Finally, paraquat impairs the TCA cycle, which leads to the accumulation of

Metabolites 2017, 7, 22 18 of 26

Metabolites 2017, 7, 22 18 of 26 Finally, paraquat impairs the TCA cycle, which leads to the accumulation of citrate and impairment of glycolysis.citrate and Critically,impairment we of have glycolysis. demonstrated Critically, a clear we have toxic demonstrated synergy between a clear paraquat toxic synergy and α-synuclein. between αparaquat-Synuclein and impairs α-synuclein. glycolysis α-Synuclein and upregulates impairs glucose glycolysis transport. and up Thisregulates channels glucose carbon transport. flux to the PPPThis tochannels increase carbon paraquat’s flux to redox the PPP cycling to increase and ROS paraqu formation.at’s redox In effect, cyclingα-synuclein and ROS feeds formation. the production In effect, ofα-synuclein ROS that leadsfeeds to the its ownproduction misfolding of ROS and aggregation.that leads to Consequently, its own misfolding glucose metabolismand aggregation. plays anConsequently, important role glucose in the metabolism molecular mechanismplays an important of PD since role in it regulatesthe molecular the effect mechanism of α-synuclein of PD since on paraquatit regulates toxicity. the effect The inhibition of α-synuclein of glucose on paraquat transporters toxicity. prevents The the inhibition potentiation of glucose of paraquat transporters toxicity bypreventsα-synuclein. the potentiation Also, the of inhibition paraquat of toxicity the PPP by protects α-synuclein. against Also, this the synergistic inhibition toxicity. of the PPP Consistent protects withagainst the cell-basedthis synergistic assays, toxicity. we also observedConsistent that with paraquat the cell-based selectively assays, induced we metabolic also observed dysfunction that inparaquat the midbrain selectively and striatum induced ofmetabolic C57Bl/6 dysfunction mice. Overall, in glucosethe midbrain metabolism and striatum and AMPK of C57Bl/6 contribute mice. to dopaminergicOverall, glucose cell metabolism death, induced and byAMPK PQ and contributeα-synuclein to dopaminergic interactions. Centralcell death, carbon induced metabolism by PQ andand metabolicα-synuclein dysfunction/signaling interactions. Central are carbon important metabolism contributors andto metabolic dopaminergic dysfunction/signaling cell death induced are by gene–environmentimportant contributors interactions. to dopaminergic cell death induced by gene–environment interactions.

Figure 16. Glucose metabolism and AMPK signaling regulate the toxicity of paraquat + α-synuclein. Figure 16. Glucose metabolism and AMPK signaling regulate the toxicity of paraquat + α-synuclein. We have demonstrated that PQ hijacks the PPP to use NADPH electrons for redox cycling and to induceWe have cell demonstrated death. (a) We that also PQ revealed hijacks that the glucose PPP to metabolism/transport use NADPH electrons contributes for redox tocycling PQ-induced and to dopaminergicinduce cell death. cell ( death,a) We asalso evidenced revealed bythat the glucose protective metaboli effectssm/transport of STF-31, contributes AA (GLUT-like to PQ-induced transport inhibitors)dopaminergic and cell 2-DG death, (glucose as evidenced metabolism by the inhibitor). protective (b) Furthermore,effects of STF-31, we presentAA (GLUT-like evidence transport that the stimulationinhibitors) and of glutamine 2-DG (glucose metabolism metabolism via the inhibitor). TCA cycle (b) by Furthermore, galactose supplementation we present evidence also protectsthat the againststimulation PQ; (cof) inglutamine contrast, metabolism glucose metabolism via the TCA protected cycle against by galactose the mitochondrial supplementation complex also I inhibitor protects MPPagainst+ while PQ; sole(c) reliancein contrast, on glutamine glucose metabolism metabolism inducedprotected by against galactose the supplementation mitochondrial complex sensitized I + cellsinhibitor to MPP MPP+-induced while sole cell reliance death; (d on) PQ-induced glutamine metabo AMPKlism signaling induced is dependent by galactose on iNOS;supplementation (e) AMPK + signalingsensitized activated cells to MPP in response-induced to cell PQ death; or glucose (d) PQ-induced deprivation AMPK exerted signaling a protective is dependent effect against on iNOS; PQ; ((fe)) overexpressionAMPK signaling of αactivated-synuclein in stimulatedresponse to PQ PQ toxicity or glucose (gene-environment deprivation exerted interaction), a protective metabolic effect dysfunctionagainst PQ; ( andf) overexpression AMPK activation; of α-synuclein (g) reproduced stimulated with permission PQ toxicity from (gene-environment [98]. interaction), metabolic dysfunction and AMPK activation; (g) reproduced with permission from [98].

6. Future Perspectives The work presented herein provides some insights into potential underlying molecular mechanisms that lead to PD, specifically with regards to the herbicide paraquat. It also provides valuable evidence in support of a gene–environment interaction that may increase the likelihood of

Metabolites 2017, 7, 22 19 of 26

6. Future Perspectives The work presented herein provides some insights into potential underlying molecular mechanisms that lead to PD, specifically with regards to the herbicide paraquat. It also provides valuable evidence in support of a gene–environment interaction that may increase the likelihood of developing PD. Nevertheless, the molecular mechanism attributed to other environmental toxins is still currently unknown. Additionally, the biological role in PD for other genetic alterations (i.e., Parkin, DJ-1, PINK1, and LRRK2) requires further investigation. Even in the case of α-synuclein, the mechanism by which α-synuclein interacts with paraquat to induce cell death is still unclear. For example, it is still uncertain if α-synuclein aggregates are a direct cause of neuronal cell death or if the formation of Lewy bodies is a protective mechanism. Similarly, while some evidence was provided that cell-based results are consistent with in vivo studies, substantial effort is still required to verify if any of these processes are related to the development of PD in human patients. Consequently, there is more that we still do not know about the molecular processes that lead to the development of PD. However, this study clearly demonstrated the inherent value of combining metabolic studies with traditional cell biology to explore the molecular mechanisms associated with Parkinson’s disease. Metabolomics is expected to continue to play an important role in further PD studies. While avoiding environmental toxins is obvious advice, it is particularly pertinent for individuals with a genetic predisposition to developing PD. However, we are still far from identifying the environmental agents that are a current concern to the public and are also linked to an increased risk in developing PD. For example, most of the research to date has been focused on pesticides (paraquat and rotenone) with a strong epidemiological association with PD, but whose usage is restricted and thus are not currently a major risk for human populations. More importantly, it is clear that a single environmental exposure will not (at least by itself) cause PD, which complicates the further identification of gene–environment interactions, which, together with aging, may trigger progressive neurodegeneration. Since our research strongly suggests that glucose metabolism contributes to PQ toxicity and to the synergistic effect between PQ and α-synuclein overexpression, a low-carbohydrate diet may be beneficial to preventing the development and progression of PD [100]. In fact, a ketogenic diet (low carbohydrate/high fat) has been reported to exert protective effects in PD [101]. To date, there are no successful therapies to treat or prevent PD. As a result, α-synuclein has garnered a tremendous amount of attention as a therapeutic target with a number of potential treatments in development or in clinical trials [102]. These potential drugs are designed to increase α-synuclein clearance, prevent its aggregation, or inhibit post-translational modifications, but no clinical successes have been observed as of yet. Correspondingly, an alternative strategy may be to directly target the metabolic processes “hijacked” by toxins associated with PD. FDA-approved metabolic inhibitors have been safely used as drugs in humans for decades [103]. While broad-based antioxidant therapies have not had much success in treating PD [104], a focused effort on developing drugs that inhibit a specific target of a toxin, such as G6PD in the case of PQ, may be a viable alternative. Furthermore, a personalized therapy based on a patient’s history of toxin exposure coupled with a ketogenic diet may prove to be a successful approach to prevent the progression of the disease.

Acknowledgments: This work was supported by the National Institutes of Health Grants P20RR17675, R01AI087668, R21AI087561, and R01CA163649, Centers of Biomedical Research Excellence (COBRE), P30 GM103335 and P20GM113126, the Scientist Development Grant of the American Heart Association (12SDG12090015), the Nebraska Tobacco Settlement Biomedical Research Development Fund, the Nebraska Center for Integrated Biomolecular Communication Systems Biology Core (NIH National Institutes of General Medical Sciences P20-GM113126), and the Office of Research of the University of Nebraska-Lincoln. Part of this research was performed in facilities renovated with support from the NIH under Grant RR015468. We would like to thank the Flow Cytometry Core Facility at the Nebraska Center for Virology for the access to flow cytometry instrumentation (NIGMS grant number P30 GM103509). Author Contributions: All authors have read and approved the final version of the manuscript. Conflicts of Interest: The authors declare no conflict of interest. Metabolites 2017, 7, 22 20 of 26

Abbreviations The following abbreviations are used in this manuscript:

1D one-dimensional 2D two-dimensional 3D three-dimensional 2-DG 2-deoxy-D-glucose 2-NBDG 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino]-2-deoxy-D-glucose 6-AN 6-aminonicotinamide 6-OHDA 6-hydroxydopamine α-syn α-synuclein AA ascorbic acid ACO aconitase AD Alzheimer’s disease ADC aspartate 4-decarboxylase AdEmpty empty adenovirus AdG6PD adenovirus encoding for human G6PD ADP adenosine diphosphate ALDO fructose-bisphosphate aldolase ALT Alanine transaminase AMP adenosine monophosphate AMPK AMP-activated protein kinase AMPKα1 AMP-activated protein kinase α1 ANOVA analysis of variance ATP adenosine triphosphate BBB blood brain barrier CS ATP-citrate synthase CV-ANOVA ANOVA of the cross-validated residuals DHAP dihydroxyacetone phosphate DI-ESI-MS direct infusion-electrospray ionization mass spectrometry DNA deoxyribonucleic acid ECAR extracellular acidification rate ECV extraction voltage FBP fructose-1,6-bisphosphatase I FUM fumarate hydratase G6PD glucose-6-phosphate dehydrogenase GAPDH Glyceraldehyde 3-phosphate dehydrogenase GLDH Glutamate dehydrogenase GLS Glutaminase GLUT Na+-independent glucose transporters GOT1 Aspartate aminotransferase, cytoplasmic GPI Glucose-6-phosphate isomerase GSH reduced glutathione GSSH oxidized glutathione HK hexokinase HSQC heteronuclear single quantum coherence IDH isocitrate dehydrogenase iNOS inducible nitric oxide LDH L-lactate dehydrogenase LRRK2 leucine-rich repeat kinase 2 LSC succinyl-CoA synthetase mBCl monochlorobimane MB-PCA multiblock principal component analysis Metabolites 2017, 7, 22 21 of 26

MB-PLS multiblock partial least squares MDH malate dehydrogenase Mn manganese MOI multiplicity of infection MPP+ 1-methyl-4-phenylpyridinium MPTP 1-methyl-4-phenyl-1,2, 3, 6-tetrahydropyridine MS mass spectrometry NAC non-amyloid-β component of AD amyloid plaques NADP nicotinamide adenine dinucleotide phosphate NADPH nicotinamide adenine dinucleotide phosphate hydrogen NG glucose free environment NMR nuclear magnetic resonance spectroscopy OCR oxygen consumption rate OGDH 2-oxoglutarate dehydrogenase OPLS orthogonal projection to latent structures OXPHOS oxidative phosphorylation pACC phosphorylated acetyl-CoA carboxylase pAMPK phosphorylated AMP-activated protein kinase PBS phosphate buffered saline PC pyruvate carboxylase PCA principal component analysis PD Parkinson’s disease PDH pyruvate dehydrogenase PET positron emission tomography PFK 6-phosphofructokinase 1 PGD 6-phosphogluconate dehydrogenase PGK1 phosphoglycerate kinase 1 PGM phosphoglucomutase PI propidium iodide PINK1 phosphatase and tensin homolog (PTEN)-induced kinase 1 PLS partial least squares PPP pentose phosphate pathway PQ paraquat PSC phase-scatter correction RNA ribonucleic acid ROS reactive oxygen species RPI ribose 5-phosphate isomerase A SCV sampling cone voltage SDH succinate dehydrogenase SE standard error SGLT Na+-dependent glucose transporters SNARE soluble N-ethylmaleimide-sensitive factor (NSF) attachment protein receptor STF-31 4-[[[[4-(1,1-dimethylethyl)phenyl]sulfonyl]amino]methyl]-N-3-pyridinyl-benzamide SUS shared and unique structures TAL transaldolase TCA tricarboxylic acid TKT transketolase TPI triosephosphate isomerase US United States UDP uridine diphosphate UMP uridine monophosphate WB Western blot WT wild-type Metabolites 2017, 7, 22 22 of 26

References

1. Pringsheim, T.; Jette, N.; Frolkis, A.; Steeves, T.D.L. The prevalence of Parkinson’s disease: A systematic review and meta-analysis. Mov. Disord. 2014, 29, 1583–1590. [CrossRef][PubMed] 2. Olanow, C.W. The scientific basis for the current treatment of Parkinson’s disease. Annu. Rev. Med. 2004, 55, 41–60. [CrossRef][PubMed] 3. Dunnett, S.B.; Bjorklund, A. Prospects for new restorative and neuroprotective treatments in Parkinson’s disease. Nature 1999, 399, A32–A39. [CrossRef][PubMed] 4. Dauer, W.; Przedborski, S. Parkinson’s disease: Mechanisms and models. Neuron 2003, 39, 889–909. [CrossRef] 5. Olanow, C.W.; Tatton, W.G. Etiology and pathogenesis of Parkinson’s disease. Annu. Rev. Neurosci. 1999, 22, 123–144. [CrossRef][PubMed] 6. Reeve, A.; Simcox, E.; Turnbull, D. Ageing and Parkinson’s disease: Why is advancing age the biggest risk factor? Ageing Res. Rev. 2014, 14, 19–30. [CrossRef][PubMed] 7. Klein, C.; Westenberger, A. Genetics of Parkinson’s disease. Cold Spring Harb. Perspect. Med. 2012, 2, a008888. [CrossRef][PubMed] 8. Simon-Sanchez, J.; Schulte, C.; Bras, J.M.; Sharma, M.; Gibbs, J.R.; Berg, D.; Paisan-Ruiz, C.; Lichtner, P.; Scholz, S.W.; Hernandez, D.G.; et al. Genome-wide association study reveals genetic risk underlying Parkinson’s disease. Nat. Genet. 2009, 41, 1308–1312. [CrossRef][PubMed] 9. Stefanis, L. A-synuclein in Parkinson’s disease. Cold Spring Harb Perspect. Med. 2012, 2, a009399. [CrossRef] [PubMed] 10. Conway, K.A.; Harper, J.D.; Lansbury, P.T. Accelerated in vitro fibril formation by a mutant α-synuclein linked to early-onset Parkinson disease. Nat. Med. 1998, 4, 1318–1320. [CrossRef][PubMed] 11. Valente, E.M.; Abou-Sleiman, P.M.; Caputo, V.; Muqit, M.M.K.; Harvey, K.; Gispert, S.; Ali, Z.; del Turco, D.; Bentivoglio, A.R.; Healy, D.G.; et al. Hereditary early-onset Parkinson’s disease caused by mutations in pink1. Science 2004, 304, 1158–1160. [CrossRef][PubMed] 12. Kitada, T.; Asakawa, S.; Hattori, N.; Matsumine, H.; Yamamura, Y.; Minoshima, S.; Yokochi, M.; Mizuno, Y.; Shimizu, N. Mutations in the parkin gene cause autosomal recessive juvenile parkinsonism. Nature 1998, 392, 605–608. [PubMed] 13. Bonifati, V.; Rizzu, P.; van Baren, M.J.; Schaap, O.; Breedveld, G.J.; Krieger, E.; Dekker, M.C.J.; Squitieri, F.; Ibanez, P.; Joosse, M.; et al. Mutations in the DJ-1 gene associated with autosomal recessive early-onset parkinsonism. Science 2003, 299, 256–259. [CrossRef][PubMed] 14. Zimprich, A.; Biskup, S.; Leitner, P.; Lichtner, P.; Farrer, M.; Lincoln, S.; Kachergus, J.; Hulihan, M.; Uitti, R.J.; Calne, D.B.; et al. Mutations in LRRK2 cause autosomal-dominant parkinsonism with pleomorphic pathology. Neuron 2004, 44, 601–607. [CrossRef][PubMed] 15. Tanner, C.M. The role of environmental toxins in the etiology of Parkinson’s disease. Trends NeuroSci. 1989, 12, 49–54. [CrossRef] 16. Wuellner, U.; Kaut, O.; de Boni, L.; Piston, D.; Schmitt, I. DNA methylation in Parkinson’s disease. J. Neurochem. 2016, 139, 108–120. [CrossRef][PubMed] 17. Feng, Y.; Jankovic, J.; Wu, Y.-C. Epigenetic mechanisms in Parkinson’s disease. J. Neurol. Sci. 2015, 349, 3–9. [CrossRef][PubMed] 18. Hegarty, S.V.; Sullivan, A.M.; O’Keeffe, G.W. The epigenome as a therapeutic target for Parkinson’s disease. Neural Regen. Res. 2016, 11, 1735–1738. [CrossRef][PubMed] 19. Song, C.; Kanthasamy, A.; Jin, H.; Anantharam, V.; Kanthasamy, A.G. Paraquat induces epigenetic changes by promoting histone acetylation in cell culture models of dopaminergic degeneration. Neurotoxicology 2011, 32, 586–595. [CrossRef][PubMed] 20. Kong, M.; Ba, M.; Liang, H.; Ma, L.; Yu, Q.; Yu, T.; Wang, Y. 5’-aza-dc sensitizes paraquat toxic effects on PC12 cell. Neurosci. Lett. 2012, 524, 35–39. [CrossRef][PubMed] 21. Migliore, L.; Coppede, F. Genetics, environmental factors and the emerging role of epigenetics in neurodegenerative diseases. Mutat. Res. Fundam. Mol. Mech. Mutagen. 2009, 667, 82–97. [CrossRef] [PubMed] 22. Wright, W.A.; Evanoff, B.A.; Lian, M.; Criswell, S.R.; Racette, B.A. Geographic and ethnic variation in Parkinson disease: A population-based study of us medicare beneficiaries. Neuroepidemiology 2010, 34, 143–151. [CrossRef][PubMed] Metabolites 2017, 7, 22 23 of 26

23. Blum, D.; Torch, S.; Lambeng, N.; Nissou, M.F.; Benabid, A.L.; Sadoul, R.; Verna, J.M. Molecular pathways involved in the neurotoxicity of 6-ohda, dopamine and mptp: Contribution to the apoptotic theory in parkinson’s disease. Prog. Neurobiol. 2001, 65, 135–172. [CrossRef] 24. Smeyne, R.J.; Jackson-Lewis, V. The MPTP model of Parkinson’s disease. Mol. Brain Res. 2005, 134, 57–66. [CrossRef][PubMed] 25. Gerlach, M.; Riederer, P.; Przuntek, H.; Youdim, M.B.H. Mptp mechanisms of neurotoxicity and their implications for Parkinson’s disease. Eur. J. Pharmacol. Mol. Pharmacol. Sect. 1991, 208, 273–286. [CrossRef] 26. Przedborski, S.; Jackson-Lewis, V.; Djaldetti, R.; Liberatore, G.; Vila, M.; Vukosavic, S.; Almer, G. The parkinsonian toxin mptp: Action and mechanism. Restor. Neurol. Neurosci. 2000, 16, 135–142. [PubMed] 27. Bartlett, R.M.; Holden, J.E.; Nickles, R.J.; Murali, D.; Barbee, D.L.; Barnhart, T.E.; Christian, B.T.; DeJesus, O.T. Paraquat is excluded by the blood brain barrier in rhesus macaque: An in vivo pet study. Brain Res. 2009, 1259, 74–79. [CrossRef][PubMed] 28. Bartlett, R.M.; Murali, D.; Nickles, R.J.; Barnhart, T.E.; Holden, J.E.; DeJesus, O.T. Assessment of fetal brain uptake of paraquat in utero using in vivo PET/CT imaging. Toxicol. Sci. 2011, 122, 551–556. [CrossRef] [PubMed] 29. Gray,M.T.; Woulfe, J.M. Striatal blood-brain barrier permeability in Parkinson’s disease. J. Cereb. Blood Flow Metab. 2015, 35, 747–750. [CrossRef][PubMed] 30. Montagne, A.; Barnes, S.R.; Sweeney, M.D.; Halliday, M.R.; Sagare, A.P.; Zhao, Z.; Toga, A.W.; Jacobs, R.E.; Liu, C.Y.; Amezcua, L.; et al. Blood-brain barrier breakdown in the aging human hippocampus. Neuron 2015, 85, 296–302. [CrossRef][PubMed] 31. Betarbet, R.; Sherer, T.B.; MacKenzie, G.; Garcia-Osuna, M.; Panov, A.V.; Greenamyre, J.T. Chronic systemic pesticide exposure reproduces features of Parkinson’s disease. Nat. Neurosci. 2000, 3, 1301–1306. [PubMed] 32. Sherer, T.B.; Betarbet, R.; Testa, C.M.; Seo, B.B.; Richardson, J.R.; Kim, J.H.; Miller, G.W.; Yagi, T.; Matsuno-yagi, A.; Greenamyre, T. Mechanism of toxicity in rotenone models of Parkinson’s disease. J. Neurosci. 2003, 23, 10756–10764. [PubMed] 33. Glinka, Y.; Gassen, M.; Youdim, M.B. Mechanism of 6-hydroxydopamine neurotoxicity. J Neural Transm. Suppl. 1997, 50, 55–66. [PubMed] 34. Rodriguez-Pallares, J.; Parga, J.A.; Munoz, A.; Rey, P.; Guerra, M.J.; Labandeira-Garcia, J.L. Mechanism of 6-hydroxydopamine neurotoxicity: The role of nadph oxidase and microglial activation in 6-hydroxydopamine- induced degeneration of dopaminergic neurons. J. Neurochem. 2007, 103, 145–156. [CrossRef] [PubMed] 35. Strickland, D.; Bertoni, J.M. Parkinson’s prevalence estimated by a state registry. Mov. Disord. 2004, 19, 318–323. [CrossRef][PubMed] 36. Wakabayashi, K.; Tanji, K.; Mori, F.; Takahashi, H. The lewy body in Parkinson’s disease: Molecules implicated in the formation and degradation of alpha-synuclein aggregates. Neuropathology 2007, 27, 494–506. [CrossRef][PubMed] 37. Kim, W.S.; Kaagedal, K.; Halliday, G.M. Alpha-synuclein biology in lewy body diseases. Alzheimer’s Res. Ther. 2014, 6, 73. [CrossRef][PubMed] 38. Spillantini, M.G.; Schmidt, M.L.; Lee, V.M.Y.; Trojanowski, J.Q.; Jakes, R.; Goedert, M. A-synuclein in lewy bodies. Nature 1997, 388, 839–840. [CrossRef][PubMed] 39. Lashuel, H.A.; Overk, C.R.; Oueslati, A.; Masliah, E. The many faces of α-synuclein: From structure and toxicity to therapeutic target. Nat. Rev. Neurosci. 2013, 14, 38–48. [CrossRef][PubMed] 40. Ulmer, T.S.; Bax, A.; Cole, N.B.; Nussbaum, R.L. Structure and dynamics of micelle-bound human α-synuclein. J. Biol. Chem. 2005, 280, 9595–9603. [CrossRef][PubMed] 41. Burre, J.; Sharma, M.; Tsetsenis, T.; Buchman, V.; Etherton, M.R.; Suedhof, T.C. A-synuclein promotes snare-complex assembly in vivo and in vitro. Science 2010, 329, 1663–1667. [CrossRef][PubMed] 42. Lee, H.J.; Kang, S.J.; Lee, K.; Im, H. Human α-synuclein modulates vesicle trafficking through its interaction with prenylated rab acceptor protein 1. Biochem. Biophys. Res. Commun. 2011, 412, 526–531. [CrossRef] [PubMed] 43. Nemani, V.M.; Lu, W.; Berge, V.; Nakamura, K.; Onoa, B.; Lee, M.K.; Chaudhry, F.A.; Nicoll, R.A.; Edwards, R.H. Increased expression of α-synuclein reduces neurotransmitter release by inhibiting synaptic vesicle reclustering after endocytosis. Neuron 2010, 65, 66–79. [CrossRef][PubMed] 44. Chartier-Harlin, M.C.; Kachergus, J.; Roumier, C.; Mouroux, V.; Douay, X.; Lincoln, S.; Levecque, C.; Larvor, L.; Andrieux, J.; Hulihan, M.; et al. A-synuclein locus duplication as a cause of familial Parkinson’s disease. Lancet 2004, 364, 1167–1169. [CrossRef] Metabolites 2017, 7, 22 24 of 26

45. Lashuel, H.A.; Hartley, D.; Petre, B.M.; Walz, T.; Lansbury, P.T. Neurodegenerative disease: Amyloid pores from pathogenic mutations. Nature 2002, 418, 291. [CrossRef][PubMed] 46. Fink, A.L. The aggregation and fibrillation of α-synuclein. Acc. Chem. Res. 2006, 39, 628–634. [CrossRef] [PubMed] 47. Lashuel, H.A.; Petre, B.M.; Wall, J.; Simon, M.; Nowak, R.J.; Walz, T.; Lansbury, P.T., Jr. A-synuclein, especially the Parkinson’s disease-associated mutants, forms pore-like annular and tubular protofibrils. J. Mol. Biol. 2002, 322, 1089–1102. [CrossRef] 48. Volles, M.J.; Lansbury, P.T., Jr. Vesicle permeabilization by protofibrillar α-synuclein is sensitive to Parkinson’s disease-linked mutations and occurs by a pore-like mechanism. Biochemistry. 2002, 41, 4595–4602. [CrossRef] [PubMed] 49. Hashimoto, M.; Hsu, L.J.; Xia, Y.; Takeda, A.; Sisk, A.; Sundsmo, M.; Masliah, E. Oxidative stress induces amyloid-like aggregate formation of nacp/α-synuclein in vitro. Neuroreport 1999, 10, 717–721. [CrossRef] [PubMed] 50. Andringa, G.; Lam, K.Y.; Chegary, M.; Wang, X.; Chase, T.N.; Bennett, M.C. Tissue transglutaminase catalyzes the formation of α-synuclein crosslinks in Parkinson’s disease. FASEB J. 2004, 18, 932–934. [CrossRef] [PubMed] 51. Segers-Nolten, I.M.J.; Wilhelmus, M.M.M.; Veldhuis, G.; Van Rooijen, B.D.; Drukarch, B.; Subramaniam, V. Tissue transglutaminase modulates α-synuclein oligomerization. Protein Sci. 2008, 17, 1395–1402. [CrossRef] [PubMed] 52. Paleologou, K.E.; Oueslati, A.; Shakked, G.; Rospigliosi, C.C.; Kim, H.-Y.; Lamberto, G.R.; Fernandez, C.O.; Schmid, A.; Chegini, F.; Gai, W.P.; et al. Phosphorylation at s87 is enhanced in synucleinopathies, inhibits α-synuclein oligomerization, and influences synuclein-membrane interactions. J. Neurosci. 2010, 30, 3184–3198. [CrossRef][PubMed] 53. Li, W.; West, N.; Colla, E.; Pletnikova, O.; Troncoso, J.C.; Marsh, L.; Dawson, T.M.; Jaekaelae, P.; Hartmann, T.; Price, D.L.; et al. Aggregation promoting c-terminal truncation of α-synuclein is a normal cellular process and is enhanced by the familial Parkinson’s disease-linked mutations. Proc. Natl. Acad. Sci. USA 2005, 102, 2162–2167. [CrossRef][PubMed] 54. Dufty, B.M.; Warner, L.R.; Hou, S.T.; Jiang, S.X.; Gomez-Isla, T.; Leenhouts, K.M.; Oxford, J.T.; Feany, M.B.; Masliah, E.; Rohn, T.T. Calpain-cleavage of α-synuclein connecting proteolytic processing to disease-linked aggregation. Am. J. Pathol. 2007, 170, 1725–1738. [CrossRef][PubMed] 55. Perrin, R.J.; Woods, W.S.; Clayton, D.F.; George, J.M. Exposure to long chain polyunsaturated fatty acids triggers rapid multimerization of synucleins. J. Biol. Chem. 2001, 276, 41958–41962. [CrossRef][PubMed] 56. Sharon, R.; Bar-Joseph, I.; Frosch, M.P.; Walsh, D.M.; Hamilton, J.A.; Selkoe, D.J. The formation of highly soluble oligomers of α-synuclein is regulated by fatty acids and enhanced in Parkinson’s disease. Neuron 2003, 37, 583–595. [CrossRef] 57. Karube, H.; Sakamoto, M.; Arawaka, S.; Hara, S.; Sato, H.; Ren, C.H.; Goto, S.; Koyama, S.; Wada, M.; Kawanami, T.; et al. N-terminal region of α-synuclein is essential for the fatty acid-induced oligomerization of the molecules. FEBS Lett. 2008, 582, 3693–3700. [CrossRef][PubMed] 58. Jo, E.; McLaurin, J.; Yip, C.M.; St. George-Hyslop, P.; Fraser, P.E. A-synuclein membrane interactions and lipid specificity. J. Biol. Chem. 2000, 275, 34328–34334. [CrossRef][PubMed] 59. Binolfi, A.; Rasia, R.M.; Bertoncini, C.W.; Ceolin, M.; Zweckstetter, M.; Griesinger, C.; Jovin, T.M.; Fernandez, C.O. Interaction of α-synuclein with divalent metal ions reveals key differences: A link between structure, binding specificity and fibrillation enhancement. J. Am. Chem. Soc. 2006, 128, 9893–9901. [CrossRef] [PubMed] 60. Bisaglia, M.; Tessari, I.; Mammi, S.; Bubacco, L. Interaction between α-synuclein and metal ions, still looking for a role in the pathogenesis of Parkinson’s disease. NeuroMol. Med. 2009, 11, 239–251. [CrossRef][PubMed] 61. Surguchov, A. Intracellular dynamics of synucleins: “Here, there and everywhere”. Int. Rev. Cell Mol. Biol. 2015, 320, 103–169. [PubMed] 62. Jethva, P.N.; Kardani, J.R.; Roy, I. Modulation of α-synuclein aggregation by dopamine in the presence of mptp and its metabolite. FEBS J. 2011, 278, 1688–1698. [CrossRef][PubMed] 63. Xu, S.; Chan, P. Interaction between neuromelanin and alpha-synuclein in Parkinson’s disease. Biomolecules 2015, 5, 1122–1142. [CrossRef][PubMed] Metabolites 2017, 7, 22 25 of 26

64. Nicholson, J.K.; Lindon, J.C.; Holmes, E. “Metabonomics”: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29, 1181–1189. [CrossRef][PubMed] 65. Dettmer, K.; Aronov, P.A.; Hammock, B.D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 2007, 26, 51–78. [CrossRef][PubMed] 66. Marshall, D.D.; Powers, R. Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics. Prog. Nucl. Magn. Reson. Spectrosc. 2017, 100, 1–16. [CrossRef] 67. Markley, J.L.; Bruschweiler, R.; Edison, A.S.; Eghbalnia, H.R.; Powers, R.; Raftery, D.; Wishart, D.S. The future of nmr-based metabolomics. Curr. Opin. Biotechnol. 2017, 43, 34–40. [CrossRef][PubMed] 68. Psychogios, N.; Hau, D.D.; Peng, J.; Guo, A.C.; Mandal, R.; Bouatra, S.; Sinelnikov, I.; Krishnamurthy, R.; Eisner, R.; Gautam, B.; et al. The human serum metabolome. PLoS ONE 2011, 6, e16957. [CrossRef][PubMed] 69. Moco, S.; Forshed, J.; Vos, R.C.H.; Bino, R.J.; Vervoort, J. Intra- and inter-metabolite correlation spectroscopy of tomato metabolomics data obtained by liquid chromatography-mass spectrometry and nuclear magnetic resonance. Metabolomics 2008, 4, 202–215. [CrossRef] 70. Lanza, I.R.; Zhang, S.; Ward, L.E.; Karakelides, H.; Raftery, D.; Nair, K.S. Quantitative metabolomics by 1h-nmr and lc-ms/ms confirms altered metabolic pathways in diabetes. PLoS ONE 2010, 5, e10538. [CrossRef] [PubMed] 71. Kell, D.B. Metabolomics and systems biology: Making sense of the soup. Curr. Opin. Microbiol. 2004, 7, 296–307. [CrossRef][PubMed] 72. Canelas, A.B.; ten Pierick, A.; Ras, C.; Seifar, R.M.; van Dam, J.C.; van Gulik, W.M.; Heijnen, J.J. Quantitative evaluation of intracellular metabolite extraction techniques for yeast metabolomics. Analy. Chem. 2009, 81, 7379–7389. [CrossRef][PubMed] 73. Kanani, H.; Chrysanthopoulos, P.K.; Klapa, M.I. Standardizing gc-ms metabolomics. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2008, 871, 191–201. [CrossRef][PubMed] 74. Xu, F.; Zou, L.; Ong, C.N. Multiorigination of chromatographic peaks in derivatized GC/MS metabolomics: A confounder that influences metabolic pathway interpretation. J. Proteom. Res. 2009, 8, 5657–5665.[CrossRef] [PubMed] 75. Taylor, P.J. Matrix effects: The achilles heel of quantitative high-performance liquid chromatography- electrospray-tandem mass spectrometry. Clin. Biochem. 2005, 38, 328–334. [CrossRef][PubMed] 76. Kopka, J. Current challenges and developments in GC-MS based metabolite profiling technology. J. Biotechnol. 2006, 124, 312–322. [CrossRef][PubMed] 77. Hao, J.; Liebeke, M.; Sommer, U.; Viant, M.R.; Bundy, J.G.; Ebbels, T.M.D. Statistical correlations between nmr spectroscopy and direct infusion ft-icr mass spectrometry aid annotation of unknowns in metabolomics. Anal. Chem. 2016, 88, 2583–2589. [CrossRef][PubMed] 78. Bingol, K.; Brueschweiler, R. Two elephants in the room: New hybrid nuclear magnetic resonance and mass spectrometry approaches for metabolomics. Curr. Opin. Clin. Nutr. Metab. Care 2015, 18, 471–477. [CrossRef] [PubMed] 79. Bingol, K.; Bruschweiler, R. NMR/MS translator for the enhanced simultaneous analysis of metabolomics mixtures by nmr spectroscopy and mass spectrometry: Application to human urine. J. Proteom. Res. 2015, 14, 2642–2648. [CrossRef][PubMed] 80. Bingol, K.; Bruschweiler-Li, L.; Li, D.; Zhang, B.; Xie, M.; Brueschweiler, R. Emerging new strategies for successful metabolite identification in metabolomics. Bioanalysis. 2016, 8, 557–573. [CrossRef][PubMed] 81. Bingol, K.; Bruschweiler, L.; Yu, C.; Somogyi, A.; Zhang, F.; Bruschweiler, R. Metabolomics beyond spectroscopic databases: A combined MS/NMR strategy for the rapid identification of new metabolites in complex mixtures. Anal. Chem. 2015, 87, 3864–3870. [CrossRef][PubMed] 82. Marshall, D.D.; Lei, S.; Worley, B.; Huang, Y.; Garcia-Garcia, A.; Franco, R.; Dodds, E.D.; Powers, R. Combining DI-ESI–MS and NMR datasets for metabolic profiling. Metabolomics 2015, 11, 391–402. [CrossRef] [PubMed] 83. Lei, S.; Zavala-Flores, L.; Garcia-Garcia, A.; Nandakumar, R.; Huang, Y.; Madayiputhiya, N.; Stanton, R.C.; Dodds, E.D.; Powers, R.; Franco, R. Alterations in energy/redox metabolism induced by mitochondrial and environmental toxins: A specific role for glucose-6-phosphate-dehydrogenase and the pentose phosphate pathway in paraquat toxicity. ACS Chem. Biol. 2014, 9, 2032–2048. [CrossRef][PubMed] Metabolites 2017, 7, 22 26 of 26

84. Worley, B.; Powers, R. Mvapack: A complete data handling package for nmr metabolomics. ACS Chem. Biol. 2014, 9, 1138–1144. [CrossRef][PubMed] 85. Worley, B.; Powers, R. Simultaneous phase and scatter correction for nmr datasets. Chemom. Intell. Lab. Syst. 2014, 131, 1–6. [CrossRef][PubMed] 86. Halouska, S.; Powers, R. Negative impact of noise on the principal component analysis of nmr data. J. Magn. Reson. 2006, 178, 88–95. [CrossRef][PubMed] 87. Halouska, S.; Zhang, B.; Gaupp, R.; Lei, S.; Snell, E.; Fenton, R.J.; Barletta, R.G.; Somerville, G.A.; Powers, R. Revisiting protocols for the nmr analysis of bacterial metabolomes. J. Integr. OMICS 2013, 2, 120–137. 88. De Meyer, T.; Sinnaeve, D.; Van Gasse, B.; Tsiporkova, E.; Rietzschel, E.R.; De Buyzere, M.L.; Gillebert, T.C.; Bekaert, S.; Martins, J.C.; Van Criekinge, W. NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal. Chem. 2008, 80, 3783–3790. [CrossRef] [PubMed] 89. Wold, S.; Hellbern, S.; Lundstedt, T.; Siostrom, M. PLS Modeling with Latent Variables in Two or More Dimensions; PLS Model Building, Theory and Applications Symposium: Frankfurt, Germany, 1987. 90. Smilde, A.K.; Westerhuis, J.A.; de Jong, S. A framework for sequential multiblock component methods. J. Chemom. 2003, 17, 323–337. [CrossRef] 91. Westerhuis, J.A.; Kourti, T.; Macgregor, J.F. Analysis of multiblock and hierarchical PCA and PLS models. J. Chemom. 1998, 12, 301–321. [CrossRef] 92. Worley, B.; Powers, R. A sequential algorithm for multiblock orthogonal projections to latent structures. Chemometr. Intell. Lab. Syst. 2015, 149, 33–39. [CrossRef][PubMed] 93. Martinez, T.N.; Greenamyre, J.T. Toxin models of mitochondrial dysfunction in Parkinson’s disease. Antioxid. Redox Signal. 2012, 16, 920–934. [CrossRef][PubMed] 94. Cantu, D.; Fulton, R.E.; Drechsel, D.A.; Patel, M. Mitochondrial aconitase knockdown attenuates paraquat-induced dopaminergic cell death via decreased cellular metabolism and release of iron and

H2O2. J. Neurochem. 2011, 118, 79–92. [CrossRef][PubMed] 95. Garcia-Garcia, A.; Zavala-Flores, L.; Rodriguez-Rocha, H.; Franco, R. Thiol-redox signaling, dopaminergic cell death, and Parkinson’s disease. Antioxid. Redox Signal. 2012, 17, 1764–1784. [CrossRef][PubMed] 96. Perry, T.L.; Yong, V.W. Idiopathic Parkinson’s disease, progressive supranuclear palsy and glutathione metabolism in the substantia nigra of patients. Neurosci. Lett. 1986, 67, 269–274. [CrossRef] 97. Stanton, R.C. Glucose-6-phosphate dehydrogenase, NADPH, and cell survival. IUBMB Life 2012, 64, 362–369. [CrossRef][PubMed] 98. Anandhan, A.; Lei, S.; Levytskyy, R.; Pappa, A.; Panayiotidis, M.I.; Cerny, R.L.; Kalimonchuk, O.; Powers, R.; Franco, R. Glucose metabolism and ampk signaling regulate dopaminergic cell death induced by gene (α-synuclein)-envronment(paraquat) interactions. Mol. Neurobiol. 2016.[CrossRef][PubMed] 99. Cardaci, S.; Filomeni, G.; Ciriolo, M.R. Redox implications of AMPK-mediated signal transduction beyond energetic clues. J. Cell Sci. 2012, 125, 2115–2125. [CrossRef][PubMed] 100. Gasior, M.; Rogawski, M.A.; Hartman, A.L. Neuroprotective and disease-modifying effects of the ketogenic diet. Behav. Pharmacol. 2006, 17, 431–439. [CrossRef][PubMed] 101. VanItallie, T.B.; Nonas, C.; Di Rocco, A.; Boyar, K.; Hyams, K.; Heymsfield, S.B. Treatment of Parkinson disease with diet-induced hyperketonemia: A feasibility study. Neurology 2005, 64, 728–730. [CrossRef] [PubMed] 102. Dehay, B.; Bourdenx, M.; Gorry, P.; Przedborski, S.; Vila, M.; Hunot, S.; Singleton, A.; Olanow, C.W.; Merchant, K.M.; Bezard, E.; et al. Targeting α-synuclein for treatment of Parkinson’s disease: Mechanistic and therapeutic considerations. Lancet Neurol. 2015, 14, 855–866. [CrossRef] 103. Overington, J.P.; Al-Lazikani, B.; Hopkins, A.L. How many drug targets are there? Nat. Rev. Drug Discov. 2006, 5, 993–996. [CrossRef][PubMed] 104. Dias, V.; Junn, E.; Mouradian, M.M. The role of oxidative stress in Parkinson’s disease. J. Parkinson’s Dis. 2013, 3, 461–491.

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