Parkinson’s disease: dopaminergic nerve cell model is consistent with experimental finding of increased extracellular transport of α-synuclein
© Büchel et al.; licensee BioMed Central Ltd. 2013
Received: 26 February 2013
Accepted: 28 October 2013
Published: 6 November 2013
Parkinson’s disease is an age-related disease whose pathogenesis is not completely known. Animal models exist for investigating the disease but not all results can be easily transferred to humans. Therefore, mathematical or probabilistic models for the human disease are to be constructed in silico in order to predict specific processes within a cell, such as the dopamine metabolism and transport processes in a neuron.
We present a Systems Biology Markup Language (SBML) model of a whole dopaminergic nerve cell consisting of 139 reactions and 111 metabolites which includes, among others, the dopamine metabolism and transport, oxidative stress, aggregation of α-synuclein (αSYN), lysosomal and proteasomal degradation, and mitophagy. The predictive power of the model was investigated using flux balance analysis for the identification of steady model states. To this end, we performed six experiments: (i) investigation of the normal cell behavior, (ii) increase of O2, (iii) increase of ATP, (iv) influence of neurotoxins, (v) increase of αSYN in the cell, and (vi) increase of dopamine synthesis. The SBML model is available in the BioModels database with identifier MODEL1302200000.
It is possible to simulate the normal behavior of an in vivo nerve cell with the developed model. We show that the model is sensitive for neurotoxins and oxidative stress. Further, an increased level of αSYN induces apoptosis and an increased flux of αSYN to the extracellular space was observed.
KeywordsParkinson’s disease Dopaminergic nerve cell model SBML model Flux balance analysis
Parkinson’s disease (PD) is the second most frequent neurodegenerative disorder after Alzheimer’s disease. The mean age of onset is in the late 50s  and 1 % of the population older than 60 years is affected by PD . The cell death of the dopaminergic neurons in the substantia nigra is responsible for typical disease symptoms: tremor, rigidity, and akinesia. The major genetic factor for PD is α-synuclein (αSYN). Protein aggregates that are the neuropathological hallmark of PD, called Lewy bodies (LBs), confirm αSYN as the principal component . Besides αSYN, mutations in the genes encoding LRRK2 , parkin , PINK1 , and DJ-1  are genetically linked to PD.
Until now, the underlying genetic and molecular mechanisms of PD have not been completely understood. Mittag et al. showed in their study that it is not possible to predict the disease risk for PD with top-validated single-nucleotide polymorphisms, although such a prediction is possible for type 1 diabetes . Thus, in the case of PD, genetic markers alone cannot explain the disease outbreak. Therefore, more complex disease mechanisms must exist. While many animal models were developed for biological disease investigations, it is challenging to build one that elicits all aspects of the PD syndrome during aging. Some animal models exclusively reflect the symptoms of the disease or just a small fraction of them . Further, not all findings of the animal models can be easily transferred to human beings, and it is not possible to investigate the molecular mechanisms of PD in a living human being. Therefore, mathematical models were developed to obtain insights into the cellular behavior [10, 11]. A common method for the detailed investigation of these in silico models is flux balance analysis (FBA). It uses the reaction stoichiometry of the metabolic reactions to determine the most important cellular fluxes and cell steady states (where the cellular substance concentrations are in equilibrium) .
In this study, we apply a constraint-based modeling approach with the aim to derive a mathematical description of the basic dopaminergic nerve cell, whose predictions yield new insights of the disease mechanisms of PD. The quantitative computational dopaminergic nerve cell model has been stored in in the Systems Biology Markup Language (SBML). SBML is a widespread and machine-readable XML format that can be used for simulating, storing, and exchanging biological models . It allows for a detailed description of metabolic reactions and is able to connect the model’s content to peer-reviewed databases. Our dopaminergic nerve cell model includes the complete dopamine (DA) synthesis, metabolism, and transport introduced by Best et al.. Further, we model mitochondrial biogenesis and mitophagy [14, 15], degradation processes of the lysosome and the proteasome , and reactions of the cell to reactive oxygen species (ROS)  and αSYN . Additionally, the two genes HtrA2 and PINK1, both of which are involved in the mitochondrial stress response , are also integrated. The model is investigated using FBA by assigning different flux constraints and target functions. The experiments reproduce the normal behavior of an in vivo dopaminergic nerve cell at steady state and show that an increase of αSYN amplifies protein aggregation and leads to a drastically increased flux of αSYN to the extracellular space. This basic dopaminergic nerve cell model is publicly available at the BioModels database  with identifier MODEL1302200000 and can be further extended and investigated by other researchers to reveal insights of the underlying mechanism of PD.
Results and discussion
The dopaminergic nerve cell model
The developed model consists of eleven sub-models, which are described here in more detail:
Sub-model 1, 2, and 3 - DA synthesis, metabolism, and transport: DA is synthesized from L-DOPA which is synthesized from L-tyrosine with the enzymes aromatic L-amino acid decarboxylase and tyrosine-hydroxylase (TH). The degradation of DA to homovanillic acid is catalyzed by monoamine oxidase (MAO) and the catechol-O-methyl transferase . During this process, toxic side products can be built, such as salsolinol, which inhibits MAO and TH . Besides these reactions, the model includes the release of DA vesicles as well as the re-uptake via the dopamine transporter (DAT). The interaction of DA with αSYN, which enhances the protein aggregation and reaction with ROS, is also included in the SBML model.
Sub-model 4 - 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP): The neurotoxin MPTP is used to elicit PD symptoms in animal models (mouse or monkey). MPTP is the precursor of MPP+, which is imported into nigrostriatal DA neurons via DAT and irreversibly inhibits the mitochondrial respiration chain. Next, the uptake of MPTP causes neuronal cell death. We included MPTP in our model to represent several drugs that lead to drug-induced Parkinsonism .
Sub-model 5 - Apoptosis: The model includes two simple mechanisms that induce apoptosis, the first of which is the release of cytochrome C by defective mitochondria, which activates caspase-9. Second, we included the assumption that protein aggregates enhance the ROS production and induce apoptosis [3, 29]. The process of apoptosis itself is not modeled in detail because the model focuses on the procedures that induce apoptosis.
Sub-model 6 - Degradation: The lysosome and the ubiquitin-proteasome-system (UPS) are responsible for the degradation of proteins or foreign substances in the cell. The basic degradation processes of nucleic acids, polysaccharides, proteins, and lipids by the lysosome are contained in the model. The UPS is modeled in detail to investigate the influence of the E3-ligase Parkin on the ubiquitylation of misfolded proteins .
Sub-model 7 - α SYN and LB formation: αSYN, ubiquitin, neurofilaments, and other proteins build the PD characteristic protein aggregates . The nerve cell model includes the αSYN aggregation by the interaction of αSYN with DA and the aggregation enhanced by the interaction with ROS. Further, the aggregation inhibition by DJ-1 (see sub-model 8) is modeled as well as the inhibition of the mitochondrial fusion by DJ-1 .
Sub-model 8 - The chaperone DJ-1: DJ-1 is the product of the PARK7 gene. Defects in this protein are causal for an autosomal-recessive form of PD  and DJ-1 knock-out leads to increased ROS production and the elongation of the mitochondria . DJ-1 has a redox-sensitive chaperone function, is an indicator for oxidative stress, and is able to protect neurons against this form of stress . The model includes these protection effects against oxidative stress as well as the function to inhibit the aggregation and toxic influence of αSYN [14, 15].
Sub-model 9 - Mitochondrial quality control: Steady fusion and fission processes maintain the morphology and the membrane potential of mitochondria. If a mitochondrion is defective it can be repaired through three different quality control processes: (i) molecular quality control, (ii) organella quality control, and (iii) cellular quality control . The first mechanism involves HtrA1/Omi, PINK1, and TRAP1 and leads to mitochondrial fission and conversely OPA1 mediated fusion. In the second mechanism, the mitochondrion is removed by autophagy involving PINK1 and Parkin, and VDAC1 or mitofusin . The third mechanism starts if the other mechanisms fail. Then, the mitochondrion releases pro-apoptotic factors and the cell dies. The model includes the respiration chain with ATP production, the three repair mechanisms, and the production of ROS by defective mitochondria.
Sub-model 10 - Protein phosphorylation by PINK1: The model also represents the phosphorylation of Parkin, HtrA2/Omi, and TRAP1 by PINK1 . Parkin inhibits, similar to phosphorylated TRAP1, the release of cytochrome C and consequently, Parkin and TRAP prevent apoptosis [35–37]. Additionally, Parkin stimulates the repair of defective mitochondria  and TRAP1 inhibits mitochondrial protein misfolding (see sub-model 9) .
Phosphorylation by PINK1 was suggested to activate the proteolytic activity of HtrA2/Omi and confer some resistance to mitochondrial stress .
Sub-model 11 - molecular units of currency: This part includes the molecular units of currency, namely O2, H2O, NAD+, NADH, , , ADP + Pi, and ATP. For the FBA, these substances are allowed to flow in and out of the model within the defined ranges.
We restricted our model to autonomous cell events. The LRRK2 gene, which is the most common autosomal-dominant gene and an important genetic cause for sporadic PD, was not included in the core model because its complete function remains unknown . It is recently believed that LRKK2 is involved in immune processes .
The nerve cell model was further annotated using methods from the application KEGGtranslator [43, 44] and published in the BioModels database. It is available with identifier MODEL1302200000 and as Additional file 1.
Parameters and target functions of the flux balance analyses
Performed FBA with the
corresponding target function
Normal nerve cell
minApo, maxApo, minDeg, maxDeg
Drug influence (MPTP)
Increased αSYN flux
Increased tyrosine flux
Conversion of the healthy cell to a diseased cell
In the first experiment, we investigated the model of a healthy dopaminergic nerve cell to determine the normal flux ranges. Compared to the model by Best et al. the resulting model from our study contains more reactions with cytosolic dopamine as a substrate. Thus, it was necessary to adapt the suggested initial flux bounds from Best et al. (see Additional file 4).
Fluxes of the basic model
Fluxes in μM·h−1
ROS production (DA)
ROS production (mitochondria, complex I)
ROS production (mitochondria, complex III)
ROS elimination (DJ-1)
ROS damage (protein)
ROS damage (mitochondria)
ROS damage (DNA)
Biogenesis of mitochondria
αSYN output reaction
Toxic effect of αSYN aggregates
Inhibition of αSYN aggregation by DJ-1
In conclusion, there is no essential contribution of DJ-1 towards inhibiting αSYN aggregation in the two FBA experiments. For an αSYN input flux higher than 78 μM·h−1, the FBA, with respect to the target function minApo, shows unexpected fluctuations in the ROS production flux of the DA synthesis and in the αSYN aggregation flux (see Figure 6C). This observation can be explained by the defined flux ranges of the DA synthesis reactions. Compared to all other model constraints, these bounds are defined in a very narrow range due to the knowledge that some reactions proceed slower than others. Therefore, reactions with larger flux ranges need to be divided, and this separation causes the observed behavior. Another interesting effect is that the αSYN output flux that transports αSYN to the extracellular space increases simultaneously with the rise of the αSYN input flux with a small peak at a flux of 87 μM·h−1. This behavior stabilizes at an αSYN input flux of 65 μM·h−1 for maxDeg and at a flux of 75 μM·h−1 for minApo FBA. Apoptosis is only observed in the maxDeg FBA and is initiated by the aggregation of αSYN.
In summary, we observed that a higher DA cell concentration is not causal for PD but it can positively stimulate its development by producing ROS.
In this study, a basic dopaminergic nerve cell model was generated and made available in the BioModels database. This model contains DA synthesis, metabolism and release, the respiration chain and anti-oxidative defense mechanisms, the degradation system represented by mitophagy, the lysosome and the proteasome, as well as the apoptosis pathway. Besides these basic cell functions, the influence of ROS, DJ-1, Parkin, and neurotoxins were modeled. The behavior of the model was investigated using FBA. We showed that an increased DA concentration in the cell produces more ROS but does not induce cell death. An increased αSYN input flux induces ROS production, mitophagy, and finally apoptosis. Additionally, we observed that αSYN was permanently transported out of the cell. The cellular death induced by the neurotoxin MPTP was also investigated. The results confirmed that the cell model shows the same behavior as a normal nerve cell.
The developed SBML model is a core model of a dopaminergic cell, which contains the essential cellular processes. It can be easily extended with further experimental knowledge and assumptions. Thus, the model presented in this work can be used as a basis to conduct further in silico studies and to investigate PD in more detail.
We developed six different experiments for the identification of steady model states and applied several FBAs with different target functions. Each experiment simulated different cell states and was performed using the simulation tool VANTED (version 2.01, ) and the add-on FBA-Sim Vis . VANTED is an application for visualizing and investigating graphs and biological networks. Further, VANTED offers the possibility to investigate these networks statistically . The VANTED add-on FBA-Sim Vis enables the performance of FBAs and the dynamic visualization of the resulting fluxes. The fluxes are calculated using the COBRA toolbox  and an adapted version of the Clp simplex solver .
Flux balance analyses
We performed six different experiments with the application VANTED including the add-on FBA-Sim Vis: (i) investigation of the normal cell behavior, (ii) increase of O2, (iii) increase of ATP, (iv) influence of neurotoxins, (v) increase of αSYN in the cell, and (vi) increase of dopamine synthesis (see Table 1).
Bounds for each flux must be defined prior to performing an FBA. We took the flux values from Best et al. with a range of ± 10 % of the original values for the DA synthesis, metabolism, and transport  (see Additional files 3 and 4). Since the complete dopaminergic nerve cell model contains more reactions than the model by Best et al., the lower bound values of the reaction OMSynthesis1, OMSynthesis2, HVASynthesis3, and HVASynthesis4 need to be set to 0 in order to enable the performance of a complete FBA.
If it is well-known from experiments and literature that some reactions appear rarely or proceed slowly, the upper bound can be restricted to a maximum of 30 μM·h−1. For all other reactions, a flux range between 0 and 100 μM·h−1 was defined.
Besides the flux bounds, it is necessary to define those reactions that should be minimized or maximized. These fluxes are also called target functions. In our experiments, we always minimized or maximized the apoptosis or degradation fluxes. The experiments and the corresponding fluxes are listed in Table 1.
Flux balance analysis
Reactive oxygen species
Systems Biology Markup Language
The authors thank Marc Abrams, UCSD, for proof-reading the manuscript.
This project was funded by the Federal Ministry of Education and Research (BMBF, Germany) in the National Genome Research Network (NGFN-Plus) under grant number 01GS08134 and a Marie Curie International Outgoing Fellowship within the EU 7th Framework Program for Research and Technological Development (project AMBiCon, 332020) to AD. The authors acknowledge support by the German Research Foundation (DFG) and the Open Access Publishing Fund of the University of Tuebingen.
- Samii A, Nutt JG, Ransom BR: Parkinson’s disease. Lancet. 2004, 363 (9423): 1783-1793. 10.1016/S0140-6736(04)16305-8. http://dx.doi.org/10.1016/S0140-6736(04)16305-8.View ArticlePubMedGoogle Scholar
- de Lau LML, Breteler MMB: Epidemiology of Parkinson’s disease. Lancet Neurol. 2006, 5 (6): 525-535. 10.1016/S1474-4422(06)70471-9. http://dx.doi.org/10.1016/S1474-4422(06)70471-9.View ArticlePubMedGoogle Scholar
- Zhou W, Schaack J, Zawada WM, Freed CR: Overexpression of human alpha-synuclein causes dopamine neuron death in primary human mesencephalic culture. Brain Res. 2002, 926 (1-2): 42-50. 10.1016/S0006-8993(01)03292-9.View ArticlePubMedGoogle Scholar
- Zimprich A, Biskup S, Leitner P, Lichtner P, Farrer M, Lincoln S, Kachergus J, Hulihan M, Uitti RJ, Calne DB, Stoessl AJ, Pfeiffer RF, Patenge N, Carbajal IC, Vieregge P, Asmus F, Müller-Myhsok B, Dickson DW, Meitinger T, Strom TM, Wszolek ZK, Gasser T: Mutations in LRRK2 cause autosomal-dominant parkinsonism with pleomorphic pathology. Neuron. 2004, 44 (4): 601-607. 10.1016/j.neuron.2004.11.005. http://dx.doi.org/10.1016/j.neuron.2004.11.005.View ArticlePubMedGoogle Scholar
- 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 (6676): 605-608. 10.1038/33416. http://dx.doi.org/10.1038/33416.View ArticlePubMedGoogle Scholar
- Valente EM, Abou-Sleiman PM, Caputo V, Muqit MMK, Harvey K, Gispert S, Ali Z, Del Turco D, Bentivoglio AR, Healy DG, Albanese A, Nussbaum R, González-Maldonado R, Deller T, Salvi S, Cortelli P, Gilks WP, Latchman DS, Harvey RJ, Dallapiccola B, Auburger G, Wood NW: Hereditary early-onset Parkinson’s disease caused by mutations in PINK1. Science. 2004, 304 (5674): 1158-1160. 10.1126/science.1096284. http://dx.doi.org/10.1126/science.1096284.View ArticlePubMedGoogle Scholar
- Bonifati V, Rizzu P, van Baren MJ, Schaap O, Breedveld GJ, Krieger E, Dekker MCJ, Squitieri F, Ibanez P, Joosse M, van Dongen JW, Vanacore N, van Swieten JC, Brice A, Meco G, van Duijn CM, Oostra BA, Heutink P: Mutations in the DJ-1 gene associated with autosomal recessive early-onset parkinsonism. Science. 2003, 299 (5604): 256-259. 10.1126/science.1077209. http://dx.doi.org/10.1126/science.1077209.View ArticlePubMedGoogle Scholar
- Mittag F, Büchel F, Saad M, Jahn A, Schulte C, Bochdanovits Z, Simón-Sánchez J, Nalls MA, Keller M, Hernandez DG, Gibbs JR, Lesage S, Brice A, Heutink P, Martinez M, Wood NW, Hardy J, Singleton AB, Zell A, Gasser T, Sharma M, IPDGC: Use of support vector machines for disease risk prediction in genome-wide association studies: concerns and opportunities. Hum Mutat. 2012, 33 (12): 1708-1718. 10.1002/humu.22161. http://dx.doi.org/10.1002/humu.22161.View ArticlePubMedGoogle Scholar
- Beal MF: Parkinson’s disease: a model dilemma. Nature. 2010, 466 (7310): S8-S10. 10.1038/466S8a. http://dx.doi.org/10.1038/466S8a.View ArticlePubMedGoogle Scholar
- de la Fuente-Fernández R, Schulzer M, Mak E, Calne DB, Stoessl AJ: Presynaptic mechanisms of motor fluctuations in Parkinson’s disease: a probabilistic model. Brain. Fuente2004, 127 (Pt 4): 888-899. http://dx.doi.org/10.1093/brain/awh102.View ArticleGoogle Scholar
- Best JA, Nijhout HF, Reed MC: Homeostatic mechanisms in dopamine synthesis and release: a mathematical model. Theor Biol Med Model. 2009, 6: 21-10.1186/1742-4682-6-21. http://dx.doi.org/10.1186/1742-4682-6-21.PubMed CentralView ArticlePubMedGoogle Scholar
- Orth JD, Thiele I, Palsson BØ: What is flux balance analysis?. Nat Biotechnol. 2010, 28 (3): 245-248. 10.1038/nbt.1614. http://dx.doi.org/10.1038/nbt.1614.PubMed CentralView ArticlePubMedGoogle Scholar
- Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Novère NL, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, et al: The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics. 2003, 19 (4): 524-531. 10.1093/bioinformatics/btg015.View ArticlePubMedGoogle Scholar
- Henchcliffe C, Beal MF: Mitochondrial biology and oxidative stress in Parkinson disease pathogenesis. Nat Clin Pract Neurol. 2008, 4 (11): 600-609. http://dx.doi.org/10.1038/ncpneuro0924.View ArticlePubMedGoogle Scholar
- Büeler H: Impaired mitochondrial dynamics and function in the pathogenesis of Parkinson’s disease. Exp Neurol. 2009, 218 (2): 235-246. 10.1016/j.expneurol.2009.03.006. http://dx.doi.org/10.1016/j.expneurol.2009.03.006.View ArticlePubMedGoogle Scholar
- Lim KL, Tan JMM: Role of the ubiquitin proteasome system in Parkinson’s disease. BMC Biochem. 2007, 8 (Suppl 1): S13,-10.1186/1471-2091-8-S1-S13. http://dx.doi.org/10.1186/1471-2091-8-S1-S13.View ArticleGoogle Scholar
- Friguet B: Oxidized protein degradation and repair in ageing and oxidative stress. FEBS Lett. 2006, 580 (12): 2910-2916. 10.1016/j.febslet.2006.03.028. http://dx.doi.org/10.1016/j.febslet.2006.03.028.View ArticlePubMedGoogle Scholar
- Cookson MR: α-Synuclein and neuronal cell death. Mol Neurodegener. 2009, 4: 9-10.1186/1750-1326-4-9. http://dx.doi.org/10.1186/1750-1326n-4-9n.PubMed CentralView ArticlePubMedGoogle Scholar
- Alnemri ES: HtrA2 and Parkinson’s disease: think PINK?. Nat Cell Biol. 2007, 9 (11): 1227-1229. 10.1038/ncb1107-1227. http://dx.doi.org/10.1038/ncb1107-1227n.View ArticlePubMedGoogle Scholar
- Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol. 2010, 4: 92-10.1186/1752-0509-4-92. http://dx.doi.org/10.1186/1752-0509n-4-92n.PubMed CentralView ArticlePubMedGoogle Scholar
- Thomas PD, Kejariwal A, Campbell MJ, Mi H, Diemer K, Guo N, Ladunga I, Ulitsky-Lazareva B, Muruganujan A, Rabkin S, Vandergriff JA, Doremieux O: PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 2003, 31: 334-341. 10.1093/nar/gkg115.PubMed CentralView ArticlePubMedGoogle Scholar
- Krull M, Pistor S, Voss N, Kel A, Reuter I, Kronenberg D, Michael H, Schwarzer K, Potapov A, Choi C, Kel-Margoulis O, Wingender E: TRANSPATH: an information resource for storing and visualizing signaling pathways and their pathological aberrations. Nucleic Acids Res. 2006, 34 (Database issue): D546-D551. http://dx.doi.org/10.1093/nar/gkj107.PubMed CentralView ArticlePubMedGoogle Scholar
- Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011, 39 (Database issue): D561-D568. http://dx.doi.org/10.1093/nar/gkq973.PubMed CentralView ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25-29. 10.1038/75556. http://dx.doi.org/10.1038/75556.PubMed CentralView ArticlePubMedGoogle Scholar
- Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH: PID: the Pathway Interaction Database. Nucleic Acids Res. 2009, 37 (Database issue): D674-D679. http://dx.doi.org/10.1093/nar/gkn653.PubMed CentralView ArticlePubMedGoogle Scholar
- Elsworth JD, Roth RH: Dopamine synthesis, uptake, metabolism, and receptors: relevance to gene therapy of Parkinson’s disease. Exp Neurol. 1997, 144: 4-9. 10.1006/exnr.1996.6379. http://dx.doi.org/10.1006/exnr.1996.6379.View ArticlePubMedGoogle Scholar
- Naoi M, Maruyama W, Akao Y, Yi H: Dopamine-derived endogenous N-methyl-(R)-salsolinol: its role in Parkinson’s disease. Neurotoxicol Teratol. 2002, 24 (5): 579-591. 10.1016/S0892-0362(02)00211-8.View ArticlePubMedGoogle Scholar
- Shin HW, Chung SJ: Drug-induced parkinsonism. J Clin Neurol. 2012, 8: 15-21. 10.3988/jcn.2012.8.1.15. http://dx.doi.org/10.3988/jcn.2012.8.1.15.PubMed CentralView ArticlePubMedGoogle Scholar
- Tompkins MM, Basgall EJ, Zamrini E, Hill WD: Apoptotic-like changes in Lewy-body-associated disorders and normal aging in substantia nigral neurons. Am J Pathol. 1997, 150: 119-131.PubMed CentralPubMedGoogle Scholar
- Kamp F, Exner N, Lutz AK, Wender N, Hegermann J, Brunner B, Nuscher B, Bartels T, Giese A, Beyer K, Eimer S, Winklhofer KF, Haass C: Inhibition of mitochondrial fusion byα-synuclein is rescued by PINK1, Parkin and DJ-1. EMBO J. 2010, 29 (20): 3571-3589. 10.1038/emboj.2010.223. http://www.hubmed.org/display.cgi?uids=20842103.PubMed CentralView ArticlePubMedGoogle Scholar
- Krebiehl G, Ruckerbauer S, Burbulla LF, Kieper N, Maurer B, Waak J, Wolburg H, Gizatullina Z, Gellerich FN, Woitalla D, Riess O, Kahle PJ, Proikas-Cezanne T, Krüger R: Reduced basal autophagy and impaired mitochondrial dynamics due to loss of Parkinson’s disease-associated protein DJ-1. PLoS One. 2010, 5 (2): e9367-10.1371/journal.pone.0009367. http://dx.doi.org/10.1371/journal.pone.0009367.PubMed CentralView ArticlePubMedGoogle Scholar
- Kahle PJ, Waak J, Gasser T: DJ-1 and prevention of oxidative stress in Parkinson’s disease and other age-related disorders. Free Radic Biol Med. 2009, 47 (10): 1354-1361. 10.1016/j.freeradbiomed.2009.08.003. http://dx.doi.org/10.1016/j.freeradbiomed.2009.08.003.View ArticlePubMedGoogle Scholar
- Westermann B: Mitochondrial fusion and fission in cell life and death. Nat Rev Mol Cell Biol. 2010, 11 (12): 872-884. 10.1038/nrm3013. http://dx.doi.org/10.1038/nrm3013.View ArticlePubMedGoogle Scholar
- Springer W, Kahle PJ: Regulation of PINK1-Parkin-mediated mitophagy. Autophagy. 2011, 7 (3): 266-278. 10.4161/auto.7.3.14348.View ArticlePubMedGoogle Scholar
- Johnson BN, Berger AK, Cortese GP, Lavoie MJ: The ubiquitin E3 ligase parkin regulates the proapoptotic function of Bax. Proc Natl Acad Sci U S A. 2012, 109 (16): 6283-6288. 10.1073/pnas.1113248109. http://dx.doi.org/10.1073/pnas.1113248109.PubMed CentralView ArticlePubMedGoogle Scholar
- Berger AK, Cortese GP, Amodeo KD, Weihofen A, Letai A, LaVoie MJ: Parkin selectively alters the intrinsic threshold for mitochondrial cytochrome c release. Hum Mol Genet. 2009, 18 (22): 4317-4328. 10.1093/hmg/ddp384. http://dx.doi.org/10.1093/hmg/ddp384.PubMed CentralView ArticlePubMedGoogle Scholar
- Pridgeon JW, Olzmann JA, Chin LS, Li L: PINK1 protects against oxidative stress by phosphorylating mitochondrial chaperone TRAP1. PLoS Biol. 2007, 5 (7): e172-10.1371/journal.pbio.0050172. http://dx.doi.org/10.1371/journal.pbio.0050172.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhu J, Wang KZQ, Chu CT: After the banquet: Mitochondrial biogenesis, mitophagy and cell survival. Autophagy. 2013, 9 (9): 1-14.Google Scholar
- Altieri DC, Stein GS, Lian JB, Languino LR: TRAP-1, the mitochondrial Hsp90. Biochim Biophys Acta. 2012, 1823 (3): 767-773. 10.1016/j.bbamcr.2011.08.007. http://dx.doi.org/10.1016/j.bbamcr.2011.08.007.PubMed CentralView ArticlePubMedGoogle Scholar
- Plun-Favreau H, Klupsch K, Moisoi N, Gandhi S, Kjaer S, Frith D, Harvey K, Deas E, Harvey RJ, McDonald N, Wood NW, Martins LM, Downward J: The mitochondrial protease HtrA2 is regulated by Parkinson’s disease-associated kinase PINK1. Nat Cell Biol. 2007, 9 (11): 1243-1252. 10.1038/ncb1644. http://dx.doi.org/10.1038/ncb1644.View ArticlePubMedGoogle Scholar
- Healy DG, Falchi M, O’Sullivan SS, Bonifati V, Durr A, Bressman S, Brice A, Aasly J, Zabetian CP, Goldwurm S, Ferreira JJ, Tolosa E, Kay DM, Klein C, Williams DR, Marras C, Lang AE, Wszolek ZK, Berciano J, Schapira AHV, Lynch T, Bhatia KP, Gasser T, Lees AJ, Wood NW, ILRRKC: Phenotype, genotype, and worldwide genetic penetrance of LRRK2-associated Parkinson’s disease: a case-control study. Lancet Neurol. 2008, 7 (7): 583-590. 10.1016/S1474-4422(08)70117-0. http://dx.doi.org/10.1016/S1474-4422n(08)70117-0nn.PubMed CentralView ArticlePubMedGoogle Scholar
- Moehle MS, Webber PJ, Tse T, Sukar N, Standaert DG, DeSilva TM, Cowell RM, West AB: LRRK2 inhibition attenuates microglial inflammatory responses. J Neurosci. 2012, 32 (5): 1602-1611. 10.1523/JNEUROSCI.5601-11.2012. http://dx.doi.org/10.1523/JNEUROSCI.5601-11nnn.2012.PubMed CentralView ArticlePubMedGoogle Scholar
- Wrzodek C, Büchel F, Ruff M, Dräger A, Zell A: Precise generation of systems biology models from KEGG pathways. BMC Syst Biol. 2013, 7: 15-10.1186/1752-0509-7-15.PubMed CentralView ArticlePubMedGoogle Scholar
- Wrzodek C, Dräger A, Zell A: KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats. Bioinformatics. 2011, 27 (16): 2314-2315. 10.1093/bioinformatics/btr377. http://dx.doi.org/10.1093/bioinformatics/btr377.PubMed CentralView ArticlePubMedGoogle Scholar
- Elmore S: Apoptosis: a review of programmed cell death. Toxicol Pathol. 2007, 35 (4): 495-516. 10.1080/01926230701320337. http://dx.doi.org/10.1080/01926230701320337.PubMed CentralView ArticlePubMedGoogle Scholar
- Hengartner MO: The biochemistry of apoptosis. Nature. 2000, 407 (6805): 770-776. 10.1038/35037710. http://dx.doi.org/10.1038/35037710.View ArticlePubMedGoogle Scholar
- Bredesen DE, Rao RV, Mehlen P: Cell death in the nervous system. Nature. 2006, 443 (7113): 796-802. 10.1038/nature05293. http://dx.doi.org/10.1038/nature05293.PubMed CentralView ArticlePubMedGoogle Scholar
- Jackson-Lewis V, Przedborski S: Protocol for the MPTP mouse model of Parkinson’s disease. Nat Protoc. 2007, 2: 141-151. http://dx.doi.org/10.1038/nprot.2006.342.View ArticlePubMedGoogle Scholar
- Li J, Uversky VN, Fink AL: Effect of Familial Parkinson’s Disease Point Mutations A30P and A53T on the Structural Properties, Aggregation, and Fibrillation of Human α-Synuclein. Biochem. 2001, 40 (38): 11604-11613. 10.1021/bi010616g. http://dx.doi.org/10.1021/bi010616g.View ArticleGoogle Scholar
- Zarranz JJ, Alegre J, Gómez-Esteban JC, Lezcano E, Ros R, Ampuero I, Vidal L, Hoenicka J, Rodriguez O, Atarés Bn, Llorens V, Tortosa EG, del Ser T, Muñoz DG, de Yebenes JG: The new mutation, E46K, of α-synuclein causes parkinson and Lewy body dementia. Ann Neurol. 2004, 55 (2): 164-173. 10.1002/ana.10795. http://dx.doi.org/10.1002/ana.10795.View ArticlePubMedGoogle Scholar
- Galvin JE: Interaction of alpha-synuclein and dopamine metabolites in the pathogenesis of Parkinson’s disease: a case for the selective vulnerability of the substantia nigra. Acta Neuropathol. 2006, 112 (2): 115-126. 10.1007/s00401-006-0096-2. http://dx.doi.org/10.1007/s00401-006nn-0096-2nnn.View ArticlePubMedGoogle Scholar
- Junker BH, Klukas C, Schreiber F: VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics. 2006, 7: 109-10.1186/1471-2105-7-109. http://dx.doi.org/10.1186/1471-2105n-7-109n.PubMed CentralView ArticlePubMedGoogle Scholar
- Grafahrend-Belau E, Klukas C, Junker BH, Schreiber F: FBA-SimVis: interactive visualization of constraint-based metabolic models. Bioinformatics. 2009, 25 (20): 2755-2757. 10.1093/bioinformatics/btp408. http://dx.doi.org/10.1093/bioinformatics/btp408.PubMed CentralView ArticlePubMedGoogle Scholar
- Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S, Kang J, Hyduke DR, Palsson B: Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc. 2011, 6 (9): 1290-1307. 10.1038/nprot.2011.308. http://dx.doi.org/10.1038/nprot.2011.308.PubMed CentralView ArticlePubMedGoogle Scholar