Proteomic studies associated with Parkinson’s disease

Expert Review of Proteomics ISSN: 1478-9450 (Print) 1744-8387 (Online) Journal homepage: http://www.tandfonline.com/loi/ieru20 Proteomic studies associated with Parkinson’s disease Murat Kasap, Gurler Akpinar & Aylin Kanli To cite this article: Murat Kasap, Gurler Akpinar & Aylin Kanli (2017): Proteomic studies associated with Parkinson’s disease, Expert Review of Proteomics, DOI: 10.1080/14789450.2017.1291344 To link to this article: http://dx.doi.org/10.1080/14789450.2017.1291344 Accepted author version posted online: 04 Feb 2017. Submit your article to this journal View related articles View Crossmark data Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=ieru20 Download by: [The UC San Diego Library] Date: 08 February 2017, At: 00:10 Publisher: Taylor & Francis Journal: Expert Review of Proteomics DOI: 10.1080/14789450.2017.1291344 Review Title: Proteomic studies associated with Parkinson's disease Authors: Murat Kasap*, Gurler Akpinar and Aylin Kanli Affiliation Kocaeli University Medical School Department of Medical Biology / DEKART Proteomics Laboratory 41380 Kocaeli, Turkey *Corresponding author Murat Kasap Phone: +90 2623037539 Email: [email protected] Abstract Introduction: Parkinson’s disease (PD) is an insidious disorder affecting more than 1-2% of the population over the age of 65. Understanding the etiology of PD may create opportunities for developing new treatments. Genomic and transcriptomic studies are useful, but do not provide evidence for the actual status of the disease. Conversely, proteomic studies deal with proteins, which are real time players, and can hence provide information on the dynamic nature of the affected cells. The number of publications relating to the proteomics of PD is vast. Therefore, there is a need to evaluate the current proteomics literature and establish the connections between the past and the present to foresee the future. Areas covered: PubMed and Web of Science were used to retrieve the literature associated with PD proteomics. Studies using human samples, model organisms and cell lines were selected and reviewed to highlight their contributions to PD. Expert commentary: The proteomic studies associated with PD achieved only limited success in facilitating disease diagnosis, monitoring and progression. A global system biology approach using new models is needed. Future research should integrate the findings of proteomics with other omics data to facilitate both early diagnosis and the treatment of PD. Keywords: Parkinson’s disease, Proteomics, Cellular Models, Animal Models, Neurotoxins, Neurodegeneration, Mitochondrial impairment 1. Parkinson’s disease Parkinson’s disease (PD), a neurodegenerative disease, affects 10 million people worldwide [1]. It is a major and growing public health challenge. Despite extensive research, the cause and the mechanisms underlying PD have not been established conclusively [2,3]. Every year, the number of patients with PD dramatically increases as hundreds of patients with PD are newly diagnosed. The cost for the treatment of PD has been rising, resulting in an ever increasing economic impact [4-7]. PD is the most common neurodegenerative disorder after Alzheimer’s Disease. The incidence and prevalence rates of PD vary, and the variation depends on the time of the study, the country where study occurred, and the predominant ethnicity and the demographic characteristics of the study location. Van Den Eeden et al. (2003) reported PD incidence rates in an increasing order in Hispanics, non-Hispanic whites, Asians and Blacks [8]. There are also varying incidence rates reported in studies carried out in different regions of the world. Physical examination and the use of neuroimaging are the main tools used for clinical diagnosis of PD [3,9,10]. There are numerous motor and non-motor symptoms used for the diagnosis. The main motor symptoms recognized are tremor, muscle rigidity, bradykinesia, and postural instability [11-13]. The most often encountered non-motor symptoms include loss of sense of smell, sleep disturbances, depression, pain, constipation and weight loss [14]. For a physician, the diagnosis of PD may be a challenging task because patients develop a highly heterogeneous phenotype. Up to 50% of patients diagnosed with PD are falsely diagnosed despite having the characteristic PD symptoms [15-18]. The reason behind the misdiagnosis of PD is the existence of other forms of Parkinsonian-like diseases, such as corticobasal degeneration (CBD), multiple system atrophy (MSA), dementia with Lewy bodies (LB) and progressive supranuclear palsy (PSP) [19,20]. Therefore, there is an urgent need for an ideal diagnostic biomarker that will allow for highly specific and sensitive diagnosis of PD [2]. The biomarkers of PD should address the pre-clinical or pre-motor stages or motor stages of the disease and could be based on clinical imaging, genetics, proteomics or biochemical factors or various combinations of these approaches [21,22]. Much effort has been made to identify a unique biomarker that will be sensitive, reproducible, technically feasible, noninvasive, in-expensive and valid. However, such efforts failed, although the knowledge gained from the biomarker studies helped increase understanding the molecular neuropathology of PD. Recently, more complicated but reliable approaches using multivariate signatures have been utilized that reduce the emphasis on single candidate biomarkers [23,24]. Univariate biomarkers are not sufficiently sensitive or specific for the diagnosis of complex, multifactorial disorders [25]. Multivariate approaches are needed to identify reliable biomarker signatures. In the multivariate signature analysis approach, the results of the identification and selection of candidate proteins show clear potential for detecting and correctly classifying PD, even in the presence of other neurodegenerative diseases [26]. For example, plasma concentrations of interleukin-6 (IL-6) were positively associated with PD risk in multivariate analyses [27]. In a patent application publication, Goldknopf et al. (2010) studied protein signatures of serum samples obtained from 2D gels (Patent No: US2010/0314251 A1). The researchers described 21 blood serum protein biomarkers that are useful for the diagnosis of early stage PD by taking advantage of their abnormal blood serum concentrations. More recently, the Parkinson’s Progression Markers Initiative study quantified CSF alpha-synuclein, amyloid-beta 1-42, total tau and phosphorylated tau at Thr181 in 660 subjects [28]. Although there was no suggested biomarker panel/signature, the authors suggested that the measurement of CSF biomarkers in patients with early-stage PD may reflect the heterogeneity of the disease. 2. Disease Pathology Degeneration of the dopaminergic neurons in the substantia nigra pars compacta (SNpC) of the mid-brain appears to cause the loss of control of motor movements. Although many pathogenic mechanisms have been shown to contribute to PD, the pathogenesis of PD is not exactly known. LBs present in the SNpC are considered to be the pathological hallmark of PD and are usually defined as histological lesions that are composed of protein and lipid aggregates [11,29]. Although controversial, the formation of LBs in PD is linked to abnormal cellular protein processing due to the dysfunction of the ubiquitin proteasome system (UPS) [30-32]. There is a general sense that LBs are toxic entities for dopaminergic neurons [33]. However, as some researchers have noted, LBs may not be toxic to the neurons and likely provide a buffering capacity for protection against unfolded protein stress [34-36]. This idea of “providing buffering capacity” may be supported by the earlier studies in which PD progress was rapid in Parkin mutation carriers due to the lack of LBs [37,38]. However, pathological examinations of brain samples from certain genetically confirmed Parkin mutation carriers later on demonstrated the presence of LB pathology, indicating that there seems to be no clear support for the hypothesis that LBs provide a buffering capacity against unfolded protein stress [37,39-41]. Although α-synuclein (α-SYN) and ubiquitin are the main constituents of LBs, there are nearly 80 known LB-associated proteins [42,43]. The deposition of α-SYN into LBs is considered to be the general feature of PD status, and thus the oligomerization of α-SYN has been proposed to be one of the causes of the selective loss of dopaminergic neurons [35,44]. Recent structural studies revealed an interesting domain organization for α-SYN [45]. The Nterminal domain is predicted to be responsible for membrane recognition and anchorage. The C-terminal domain is an intrinsically disordered domain and is partly responsible for the solubility of α-SYN. The central domain is prone to aggregation and is subjected to posttranslational modifications, such as phosphorylation and glycosylation [46-48]. The exact function of α-SYN is not known. Structural studies with α-SYN produced data that are useful for assigning a putative function. Overall, α-SYN may contribute to neurotransmission by regulating synaptic vesicle size, recycling and plasticity [49]. The involvement of α-SYN in PD pathogenesis is supported by the fact that missense mutations, such as E46K, A53T and A30P, can cause early onset familial PD with autosomal dominant inheritance [50-52]. More interestingly, duplications and triplications of the wild-type α-SYN can also produce PD, raising the possibility that α-SYN itself is sufficient to induce neurodegeneration [53-55]. However, the correlation between the biological function and cellular toxicity of α-SYN has not been firmly established. The development of PD and its progress cannot be linked to a single factor. Scientists generally agree that most cases of PD result from a combination of environmental factors and the underlying genetic make-up of the patients. The interactions between genes and the environment can be quite complex. However, a list of environmental factors, such as pesticides, herbicides and insecticides (mainly toxins), for which some evidence has been provided to designate them as risk factors for PD is available[56]. In addition, occupational exposures to various metals have been suggested to aid development of PD [57]. Additionally, many industrial solvents that can contaminate groundwater contribute to the development of PD [58,59]. Despite the identification of these risk factors, there is little to prevent the environmental contaminants from becoming the possible cause for PD, with the exception of legislating new regulations and laws to prevent their unsafe use. In contrast, to the environmental factors, genetic factors which collectively account for approximately 30% of the familial and 3%–5% of the sporadic cases, can be studied, defined and described to understand their effect on disease development and progression [60]. In fact, the molecular pathways contributing to PD have been elucidated by studying the relevant genetic mutations. So far, at least 20 genetic loci have been identified [61]. Some of the mutations detected in these genes are highly penetrant producing rare, monogenic forms of the disease. The presence of multiple PD-causing genes, including SNCA, PARK2, DJ-1, PINK-1, LRRK2 and VPS35, indicates that PD is a genetically heterogeneous disorder [62]. Recent studies keep on revealing new genes such as, TMEM230, that are associated with PD [63]. These discoveries provide genetic evidence for the existence of novel molecular pathways that were previously unknown. 3. The molecular paths to PD It is clear that more than one mechanism is involved in PD formation (Figure 1). One remaining question is how is the exact combination of these metabolic events come together. The research perspective notes that the establishment of the connections among these metabolic events will eliminate the difficulties in designing therapeutic interventions that may prevent PD progression. Below we summarize the four interconnected major metabolic events that play roles in PD development and progression. 3.1. Mitochondrial dysfunction Mitochondria are known to be the key regulator of cell survival and death and have a central role in cellular aging. There is accumulating evidence suggesting that mitochondrial dysfunction due to increased oxidative stress is involved in PD formation [64,65]. The active involvement of mitochondria in PD was first recognized when neuroblastoma cells without mitochondria were transformed with mitochondria isolated from patients with PD [66]. The cells transformed with the mitochondria displayed lower complex I activity and higher amounts of reactive oxygen species (ROS). This finding confirmed the previous observation that in the SNpC of the PD brain, the activity of the mitochondrial respiratory complex I is reduced [67]. Such observations were strengthened when neurotoxins, such as 1-methyl-4phenyl-1,2,3,6-tetrahydropyridine (MPTP) and paraquat were linked to PD and shown to implement their effects by decreasing mitochondrial function via selective inhibition of complex I activity. Further studies with damaged mitochondria revealed the importance of two proteins, specifically, Parkin and PINK-1, that were previously shown to be associated with PD in genetic studies [68,69]. Apparently, with the combined efforts of PINK-1 (a serine/threonine protein kinase) and Parkin (an E3 ubiquitin ligase), cells can remove the damaged mitochondria to enhance cellular survival [70-72]. This phenomenon, known as mitophagy, is coordinated by these two proteins. PINK1 is localized to the mitochondria, while Parkin is mainly a cytosolic protein [65,73]. A non-mutually exclusive model was proposed to explain mitophagy [74-76]. In that model, ubiquitin phosphorylation is the primary trigger responsible for Parkin activation and recruitment to mitochondria (for details see recent reviews [65,74,76]). The event of mitophagy may be explained by the following main steps. (I) When there is no damage to the mitochondria, PINK-1 is imported into the mitochondria by the TOM and TIM complexes. Upon import, the N-terminal domain of PINK1 is inserted into the inner mitochondrial membrane and cleaved by a specific protease called mitochondrial matrix protease (MMP). PINK-1 itself may also be degraded by other proteases in the mitochondria to keep the PINK-1 concentration at a steady-state. (II) When there is damage to mitochondria, PINK-1 import into the inner mitochondrial membrane fails, and PINK-1 autophosphorylates, dimerizes and accumulates at the outer mitochondrial membrane with its kinase domain facing the cytoplasm. The autophosphorylated and dimerized PINK-1 becomes fully active and starts phosphorylating ubiquitins to generate phosphorylated ubiquitin (pUb). The pUb chains act on the Parkin receptor on the mitochondria and translocate Parkin from the cytosol to the mitochondria. Then, phosphorylation of Parkin occurs, causing a conformational change resulting in a fully active form of Parkin protein that can use pUb to modify the mitochondrial outer membrane proteins and initiate mitophagy. The proteins modified by pUb are still being actively investigated. The aforementioned PINK-1/Parkin mediated mitophagy was, however, recently challenged by Bondi et al. (2016) [77]. In their study, a strong depolarization of the mitochondrial membrane was achieved in SH-SY5Y cells after dopamine (DA) exposure. However, selective elimination of dysfunctional mitochondria did not occur implying that mitophagy due to the presence of damaged mitochondria is not a universal mechanism. A similar finding was also reported by our laboratory and suggested that Parkin-induced cell death upon mitochondrial depolarization is cell type-dependent and does not occur in SH-SY5Y cells as it occurs in HEK293 and HeLa cells [78]. 3.2. Oxidative stress, DA toxicity and selective dopaminergic neuron degeneration Initial realization of the importance of oxidative stress in PD came from the analysis of postmortem PD brains [79,80]. Researchers found that oxidative damage occurs in the nigral dopaminergic neurons, in the form of lipid peroxidation, protein carbonylation and DNA damage. The cause of oxidative damage was proposed to be due to the presence of high levels of reactive oxygen species (ROS) and reactive nitrogen species (RNS) [81]. ROS can be produced via a variety of cellular processes. The production of ROS species by mitochondria is a real concern since it contributes to PD development [82]. In experiments carried out with MPTP, an increase in ROS production was demonstrated [83]. Nitric oxide (NO) is another neuronal oxidative damage-causing molecule [84]. Conjugation of NO with superoxide radicals produces a highly reactive peroxynitrite, which in turn gives rise to damaging molecules such as nitrogen dioxide, hydroxyl and carbonate radicals. Nitrogen dioxide can also conjugate with the side chains of tyrosine residues in proteins to form nitrated tyrosine residues. This type of modification can potentially harm protein function and thus cellular processes. The cause of selective dopaminergic neuron degeneration in PD has always been a topic of discussion. Although there are millions of neurons in the brain, only dopaminergic neurons are highly affected in patients with PD? Understanding the basis of selectivity may be of crucial importance for the development of targeted therapies. As it has been firmly established, dopaminergic neurons produce the neurotransmitter DA to control the motor movements. DA has interesting chemical properties. It can autoxidize at neutral pH and form quinone species plus hydrogen peroxide, which can form hydroxyl radicals in a reaction catalyzed by iron (the Fenton reaction) [85]. As it turns out, the levels of iron are much higher in the SNpC, and the higher iron concentration facilitates the production of free radical species [86]. To create cellular models that can mimic DA toxicity, several research groups increased the level of α-SYN by ectopic expression and treated cells with DA [87-91]. They found that cytosolic DA promoted the formation of toxic synuclein protofibrils, which explains the increased vulnerability of these neurons to degeneration. 3.3. Defects in the unfolded protein response Thus far, the studies carried out have unequivocally emphasized that PD is a proteinopathy disease [92]. Like PD, many other neurodegenerative diseases are characterized by the presence of aggregated misfolded proteins in cells or the extracellular environment. There is no single mechanism that can describe the causes of protein aggregation. For example, in Prion diseases, a 250-amino-acid alpha helix-rich glycoprotein (PrPc) turns into a beta sheet rich-protein (PrPsc) creating insoluble and protease-resistant protein aggregates [93]. In Huntington’s disease, Htt, a large protein with a molecular weight of 350 kDa, contains polyQ repeats [94]. Increased numbers of polyQ repeats cause the generation of beta sheet structures and increases the protein’s tendency to aggregate. In Alzheimer’s disease beta amyloid accumulation in the extracellular space and accumulation of highly phosphorylated tau is observed [95]. In PD, α-SYN creates insoluble fibrils and causes protein aggregation [96]. All these protein aggregates critically affect neuronal connections and plasticity and trigger apoptotic signaling pathways. Major factors contributing to the protein aggregation in neurodegenerative diseases may include (i) the intrinsic tendency of certain proteins to form aggregates, (ii) mutations causing changes in amino acid properties (e.g., from hydrophilic amino acids to hydrophobic), (iii) defaults in post translational modifications (PTMs) such as hyperphosphorylation and hyperglycolisation, (iv) oxidative stress, (v) the quantity of the protein synthesis in the cell, and (vi) the size of the protein. The Lewy bodies found in PD brains consist of a heterogeneous mixture of aggregated proteins and lipids. While the core of a Lewy body consists of lipids, structures around the core are mostly formed by ubiquitin, neurofilament proteins, proteasomal proteins and α-SYN [11]. In general, there are two reasons misfolded proteins form harmful aggregates. One is that the amount of misfolded proteins due to mutations or oxidative damage or any other source exceeds the amount of misfolded proteins that can normally be dealt with by the cells. The second is that mutations can cause a decrease in or loss of proteasomal activity [97-99]. There are two ways cells address protein misfolding to prevent its deleterious effects. One way is to use chaperons, which play a defensive role in cells against protein misfolding by helping proteins to fold properly. If protein misfolding cannot be dealt with in this manner, then the second mechanism, protein degradation, is initiated. One of the major players in protein degradation is the proteasome, and its absence or decreased activity is sometimes encountered in neurodegenerative diseases, especially in PD [99,100]. UPS is the principal mechanism for protein degradation, which is initiated by marking the misfolded proteins with ubiquitin, a 76-amino acid protein, to target the misfolded proteins to the proteasomes [101,102]. Three enzymes play crucial roles in the ubiquitination pathway [103]. One of the enzymes is the ubiquitin-activating enzyme (E1), which activates ubiquitin by forming a protein complex with it. The second enzyme is the ubiquitin conjugating enzyme (E2), which transfers ubiquitin residues from E1 onto itself. The conjugated ubiquitin is then transferred to the substrate via the help of a ubiquitin protein ligase (E3). Polyubiquitinylated proteins are then sent to the 20/26S proteasome for degradation [104]. A single E1, many E2s and multiple families of E3 enzymes are hosted by most eukaryotic cells that allow recognition of various cellular proteins for degradation. Defects in E3 enzymes may cause accumulation of their substrates within the cells. For example, Parkin assists in the proteasomal degradation of at least 27 proteins [105] and any mutation that effects Parkin’s E3 ubiquitin ligase activity will cause accumulation of its substrates within the cells [106,107]. The support for the involvement of UPS comes from several lines of evidence: (i) establishment of the direct link of Parkin with the UPS system [105]; (ii) establishment of the direct link of the ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL-1) with the UPS system [108]; (iii) inhibition of proteasome activity via overexpression of the wild type or the mutant α-synuclein in cultured cells and in the brains of transgenic mice [109-111]; (iv) decrease in the activity of the 20S proteasome in SNpC of PD brains [91]; and (v) decrease in the proteasome activity in protein extracts prepared from rotenone and paraquat-treated mouse brains [112,113]. However, despite the provided evidence, there is a controversy surrounding the involvement of UPS in PD because (i) mouse models lacking functional Parkin fail to exhibit Parkinsonian phenotypes [114,115]; (ii) the majority of previously identified Parkin substrates do not accumulate in Parkin knock-out mice; and (iii) inactivation of UCHL-1 in mice does not lead to neuronal cell death [107,116-118]. A better understanding of the details regarding the role of UPS in PD is needed [31]. Understanding the mechanism of protein aggregation may aid in the development of novel diagnostic and therapeutic strategies. However, one of the major limitations impeding this research area is the absence of simple protein aggregation models. A novel model utilizing nonfunctional artificial aggregation-prone proteins was used to understand why some proteins have a tendency to aggregate in cells [119]. Quantitative proteomic analysis revealed that the toxicity of these artificial proteins in human cells correlates with their ability to promote aberrant protein interactions and to deregulate the cytosolic stress response. These co-aggregating proteins share common features such as their multidomain character, the presence of excess disordered regions in their structures, their multifunctionality and key positions that they hold in the cellular network. It appears that the aggregation-prone proteins create a metastable subproteome and cause multifactorial toxicity and eventually induce the collapse of essential cellular functions. 3.4. Post-translational modifications The association between protein modifications, such as phosphorylation, ubiquitination and carbonylation, with PD has been studied to some extent. Protein phosphorylation and ubiquitination are especially two interconnected phenomena that control signaling networks [120]. Phosphorylation often serves as a marker to trigger protein ubiquitination [121,122]. In some cases, ubiquitination provides a switching mechanism that can turn on/off the kinase activity of certain proteins [123]. In fact, ubiquitination has been shown to regulate global phosphorylation machinery [124]. Thus far, a phosphoproteome study targeting the global changes occurring in PD has not been carried out. In our laboratory, by using the ProQ diamond phosphoprotein stain, we were able to show some changes in the phosphorylation status of some proteins upon expression of a mutant Parkin protein [125] (Özgül S, PhD thesis, Unpublished data). However, the types of signaling events that are affected on a global scale have not yet been elucidated. Ubiquitination is a targeting signal that directs proteins for proteasomal degradation. Beyond this well-known function, the involvement of ubiquitination has also been implicated in the non-degradative regulation of cellular processes including signal transduction, enzymatic activation, endocytosis/trafficking and DNA repair [126-128]. Over the past couple of years, the interplay between ubiquitination and phosphorylation has been investigated and the crosstalk between these two metabolic events was marked as the key principle in eukaryotic signaling [129]. The crosstalk of PINK1 with Parkin may be the best-described example to demonstrate the interplay between ubiquitination and phosphorylation [65]. α-SYN is also phosphorylated under pathological conditions. However, the contribution of this phosphorylation to the aforementioned crosstalk has not been elucidated [130]. Protein oxidation via carbonylation is another type of PTM that is observed in PD pathogenesis [131,132]. Studies demonstrated that carbonyl groups on proteins may be indicators of oxidative damage and monitoring such protein carbonylation may allow for the measurement of the extent of the damage. In PD, due to the production of reactive radical species, protein carbonylation is expected to occur and may be linked to the pathogenesis. In a study by Oikawa et al. (2014), a monkey model system was used in which ROS levels were increased to mimic the oxidative stress in PD to monitor changes in protein carbonylation [133]. The researchers found carbonyl modification in several mitochondrial proteins and predicted that deciphering the modified carbonylated proteins may provide new insights into PD. The autoxidation of DA results in the formation of DA quinone (DAQ), which readily participates in nucleophilic addition reactions with sulfhydryl groups on cysteine [134]. These additions covalently modify proteins, prevent proteins from performing their functions and interfere with downstream metabolic processes. There have been several important studies performed to clarify the effect of DA in PD. Some of these studies were focused on revealing the changes in protein abundance in vivo and in vitro upon DAQ exposure [135]. A subset of proteins exhibited changes in their abundance. The proteins modified by DAQ have been identified in another study carried out by the same group [136]. In that study, the researchers used 14C-DA to treat isolated rat brain mitochondria and differentiated SH-SY5Y cells. Their findings shed light on cellular protein alterations due to DA toxicity and provided implications for PD pathogenesis. Here, we discussed the well-characterized examples of PTMs to demonstrate their existence in PD. The ever growing number of PTMs suggests their importance in the initiation and progression of PD [137]. 4. PD and Proteomics The proteomics literature concerning PD encompasses two main areas of research. The first area of research addresses the metabolic events leading to PD, and the second area of research addresses the biomarkers for diagnosis and monitoring of PD. Both areas benefit from proteomic approaches. The biological materials used in these studies included human samples, model organisms and cellular models (Figure 2). 4.1. Proteomics of Human Samples 4.1.1. Human serum Serum is the most easily accessible biological material that is used for the measurement of biomarkers. Its usefulness as a biomarker source has been known for decades. For neurodegenerative diseases, serum is not considered to be as useful. This assumption prevented scientists from carrying out intense research to look for biomarkers of PD in the serum. However, there is an equal chance of finding a biomarker protein in the serum as finding it in the cerebral spinal fluid (CSF). In fact, brain tissue-derived proteins may enter into the serum more readily than into the CSF by passing through the blood-brain barrier. If the technical difficulties of working with serum are overcome, then novel biomarkers for the early detection of PD may be discovered. Among the technical difficulties of working with serum is the presence of abundant, unrelated proteins that may prevent the detection of potential biomarkers. Different approaches have been developed to reduce abundant, unrelated proteins and increase the chance of discovering potential biomarkers in the serum [138]. However, each approach has its own limitations that prevent the discovery of ideal biomarkers. There have been several targeted studies performed to investigate serum levels of some proteins in PD. One of those proteins was α-SYN, which was detected in the saliva, serum, urine and gastrointestinal tract of patients with PD [139]. The results of assaying α-SYN in the plasma have not been as promising and produced conflicting data. Both increases and decreases of the serum α-SYN levels in PD patients in comparison to controls have been reported [140]. Another interesting protein that was studied in the serum samples of patients with PD in comparison to controls was DJ-1. DJ-1 levels showed no difference between patients PD and matched controls [141-143]. In conclusion, α-SYN and DJ-1 have been shown to be non-ideal biomarkers for the early detection or monitoring of PD. Because more than 95% of the serum α-SYN and DJ-1 in these studies came from the red blood cells, the minute changes occurring in the levels of these two proteins cannot be measured to asses the disease status. Another serum protein proposed to have potential as a biomarker in PD is Apo A1, a major component of the high-density lipoproteins. A reduction in Apo A1 serum levels has been reported in patients with PD, but this finding needs to be verified by other laboratories [144]. Apo A1 also appears to be an unsuitable biomarker for PD because its plasma levels can change with drug treatment, such as statins [145]. Finally, lower EGF plasma levels were proposed to be a marker for increased cognitive impairment in PD, but this finding only achieved limited acceptance among the clinics [146]. In our laboratory, we measured Parkin protein levels in serum samples and tested its potential use as a biomarker [147]. However, we were not able to show a difference between the serum samples from patients with PD and the controls. When studies are carried out for biomarker discovery in the serum, the assays must be validated. Independent cohorts and cross-sectional studies should also be carried out to ensure the usefulness of the potential biomarker. Thus far, the only potential biomarker with a candidacy supported by multiple large cohort studies is uric acid [148-150]. High serum uric acid levels are associated with reduced risk of developing PD and slower disease progression [16]. 4.1.2. CSF Because of its direct contact with brain structures, CSF, as a biological material is more reflective of the changes in brain protein patterns. Brain structures undergoing degeneration may release pathogenically relevant molecules into the CSF. In human CSF, over 2500 proteins have been identified, and extensive two-dimensional (2D) gel maps have been published [151,152]. Promising results have been obtained with CSF to potentially diagnose PD at an early stage. DJ-1 immunoblotting with CSF has been shown to differentiate patients with PD from controls [153]. Additionally, decreased α-SYN levels in CSF have been observed in individuals with PD [154]. An association was established between the α-SYN levels and the rate of progression of motor symptoms and cognitive decline in patients with PD [155]. However, the use of α-SYN alone is not sufficient as a single biomarker, and other reliable markers are needed [156]. To identify such markers, Sinha et al. (2009) performed a comparative proteomic study in which CSF obtained from patients with PD were compared with the case and the controls using 2D analysis [151]. The researchers identified six differentially expressed proteins, including, serum albumin chain-A, hemoglobin beta fragment, mutant globulin, PRP 14 and serum transferring N-terminal lobe. Their results suggested that the differential expression of proteins in the CSF could be associated with neuronal dysfunction in PD. In a similar study, up to 14 differentially expressed proteins were identified by LC-MS/MS. Alterations in the levels of Apo E, autotoxin, complement C4 alpha and SOD1 were found to be associated with PD [157]. These studies reported different biomarker candidates for PD. In addition to α-SYN, eight CSF proteins (tau, amyloid beta, beta 2 microglobulin, vitamin D binding protein, APOA2, APOE, BNDF and IL-8) were considered as a useful panel for differential diagnosis of PD [158]. However, none of these biomarkers in the suggested panel has been validated, and they are not currently used in clinics. Considering that CSF is an ideal source for biomarker discovery, further research should be carried out. 4.1.3. Human brain tissue The first trial study with human brain tissue samples from patients with PD used the mesencephalic brain region and reported increases in Mn-SOD and dihydropteridine reductase levels with respect to controls [159]. In a follow-up study, a comparative proteome analysis of hSNpC samples from patients with PD and controls was conducted [160]. Nine proteins with changed levels were identified. The findings were consistent with the view of oxidative stress involvement in PD pathogenesis and demonstrated for the first time that proteomics approaches are useful for the elucidation of the molecular basis of neurodegeneration. A putative link between neuromelanin (NM) and neuronal cell death in PD was proposed [161]. The proteomic analysis of NM granules from hSNpC was performed, and 72 proteins were identified [162]. Their molecular classification established that NM-containing granules originate from the endosome-lysosome lineage. Subsequent studies of the same group showed that NM is structurally and functionally different from the melanin synthesized in peripheral cells, and this difference may underlie the vulnerability of NM-containing neurons to cell death. When iron is released from NM, it elevates oxidative stress in mitochondria and causes mitochondrial dysfunction [163]. The changes in protein levels in the SNpC may become a hallmark for PD. Thus, the interest in identifying the protein content of the SNpC led scientists to perform more focused studies. For example, differences between patients with PD and matched controls were studied in mitochondria-enriched fractions isolated from dissected SNpC brain regions [164]. The researchers identified 842 proteins and showed changes in the levels of 119 proteins. The results revealed that levels of mortalin, a mitochondrial stress protein, were substantially decreased in PD brains. However, neither overexpression nor silencing of mortalin had any significant effect on the viability of MES cells, suggesting that mortalin alone is not sufficient in to cause neurodegeneration. Werner et al. (2008) examined SNpC tissue samples isolated from human brains and detected changes in proteins regulating redox and iron metabolism [29]. They also detected changes in the levels of structural proteins. Two independent studies were also aimed at creating a proteome map of the hSNpC. In the first study, 1263 proteins were identified using MALDI-TOF/TOF as well as iontrap MS platforms [165]. In the second study, nearly 1800 proteins were identified, which provided the largest nigral proteome map published thus far [166]. The proteins identified in both studies provided an inventory that may be linked to neurodegenerative processes. 4.2. Proteomics in Animal Samples A major challenge of working with human brain samples is the limited availability of postmortem tissue, which prevents designing statistically powerful case-controlled proteomic studies. Contrary to human-based studies, animal model studies provide more statistically powerful data. Various strains of mice, rats and non-human primates have been used as animal models of PD. In addition to these mammalian models, Caenorhabditis elegans (roundworm), Drosophila melanogaster (fruit fly) and Danio rerio (zebrafish) were used, although these species are phylogenetically distantly related to humans. Two types of animal models have been created to imitate neurodegeneration in humans. In the first model, the animal is exposed to a neurotoxin to damage the neurons in specific parts of the brain. In the second model, neurodegeneration is achieved by genetically modifying the animal to create transgenic animals. 4.2.1. Neurodegeneration via neurotoxins 4.2.1.1. MPTP-induced neurodegeneration MPTP is an accidentally discovered neurotoxin that is commonly used to create PD models in animals. The toxin inhibits complex I of the electron transfer chain and can replicate many of the pathological hallmarks of PD in mice [167]. Jin et al. (2005) used MPTP to treat mice for five weeks and then compared mitochondrial protein profiles of SNpC between MPTPtreated mice and the controls. Although changes were observed in the levels of more than 100 proteins, the researchers focused on DJ-1, as mutations in DJ-1 have been suggested to cause familial PD [168]. The level of PEP-19, a neuronal calmodulin binding protein, has been shown to be decreased in MPTP-treated mice [169]. Nine regulated proteins were identified in SNpC and striatal tissue. The proteins are involved in energy metabolism and protein degradation [167]. Proteins playing functional roles in the mitochondria, glycolysis and cytoskeleton were down regulated [170]. When striatal mitochondrial proteins were investigated in MPTP-treated mice, the levels of four proteins were reported as decreased, while the levels of α-SYN level increased [171]. MPTP-treated mice were used to investigate the healing effect of electropuncture at the proteome level [172]. Some of the mitochondrial protein levels which showed decreases upon MPTP-treatment were restored after electropuncture. The first comprehensive study which analyzed the overall effect of MPTP on the mouse brain proteome used four different brain regions (striatum, cerebellum, cortex and the rest) with the objective of identifying potential nigrostriatal-specific changes [173]. They identified 4895 non-redundant proteins of which 518 changed their abundance upon MPTP treatment. Of these 518 proteins, 270 displayed changes, indicating their association with nigrostriatal pathways. Mice MPTP models suffer from limitations in replicating the complexity of PD in humans. In an attempt to replicate the complexity of PD, Lin et al. (2015) carried out a study in which an MPTP-treated monkey model was used [174]. The model reproduced the same features of idiopathic PD observed in humans including the loss of dopaminergic neurons and motor abnormalities. In addition, this model provided another advantage in terms of distinguishing between asymptomatic and symptomatic PD stages due to slow MPTP intoxication. This study is the first most comprehensive quantitative proteomic study in which changes in glycoproteins, phosphoproteins and the global protein profile were reported. The results demonstrated that the regulated proteins are involved in energy production, pathways of oxidative damage, protein clearance, transport and preservation of synaptic integrity. A recent study used an alternative model system, zebrafish, to demonstrate MPTP-induced changes at the proteome and the transcriptome levels [175]. After MPTP induction, the fish displayed erratic swimming patterns and increased freezing bouts. This study reported 73 proteins as differentially regulated in response to MPTP in the zebrafish brain. The proteins are involved in several cellular events including cytoskeleton remodeling, neuronal development and differentiation, synaptic vesicle fusion, GABA-B receptor signaling, cellular growth and migration, and endocytosis. 4.2.1.2. 6-hydroxy DA (6OH-DA)-induced neurodegeneration The 6OH-DA model is often called a “hemiparkinsonian model”. Since the discovery of its effects in 1968 by Ungerstedt, 6OH-DA has been used as the gold standard model to study PD [176]. The major advantage of 6OH-DA is that it can be introduced unilaterally and delivered into each subcomponent of the SNpC. Therefore, in this model, each animal can serve as its own “within-subject” and control. In 2005, De Iuliis et al. (2005) created a hemiparkinsonian mouse model and, then removed the SNpC to perform a comparative proteomic study [177]. The hemiparkinsonian animals exhibited different expression patterns of alpha-enolase and beta-actin. Peirson et al. (2004) reported a list of peptides and proteins identified in brain tissue sections of 6OH-DA-treated rats and found that cytochrome C, cytochrome C oxidase and calmodulin levels were regulated [178]. Hemiparkinsonian rats treated with either L-Dopa or the DA receptor agonist bromocriptine displayed changes in striatal proteins the play roles in energy metabolism [179]. Proteome changes of the striatum were analyzed in 6OH-DA-treated rats, and changes were observed in mitochondriadependent energy metabolism in addition to calcium metabolism, antioxidant mechanisms and the cytoskeleton [180]. The authors of this study associated up-regulation of cytoskeletal proteins with remodeling of dendrites, axons and synapses indicating that the adult rat brain has high plasticity and regeneration potential. Changes in the expression levels of neuropeptides are as significant as the changes in the expression levels of proteins. In a state-of-the-art proteomics study, the expression profiles of neuropeptides in a 6OH-DA model were elucidated using a peptidomics approach [181]. Several novel biologically active peptides were identified. These neuropeptides may be important in preventing the failure of the central nervous system in PD. In another study, a 6OH-DA rat model was used to monitor the changes in protein abundance involved in synaptic plasticity. Twenty-two down-regulated proteins were detected. Ten of these proteins are involved in neuronal transmission and recycling across synapses [182]. 4.2.1.3. Methamphetamine-induced neurodegeneration There are other toxins that can be used to treat mice to create PD models. Methamphetamine (MATH), for example, causes significant generation of ROS and decreased complex I activity, leading to DA depletion [183,184]. The protein expression profile in the striatum of acute MATH-treated rats revealed the presence of 36 differentially regulated proteins [185]. Those proteins are associated with mitochondrial dysfunction, oxidative damage and lysosomal degradation. Chin et al. (2008) published their findings with MATH- and MPTP-treated mice [186]. The abundance of 86 proteins was changed following the neurotoxin treatments. Functional classification of the identified proteins revealed problems with mitochondrial dysfunction, the oxidative stress response and apoptosis. In another study, 13 differentially expressed proteins which might be important for understanding MATH-induced neurotoxic effects were identified by performing differential phosphoproteome analysis of transgenic mice lacking pleiotrophin and midkine proteins [187]. 4.2.2. Samples from transgenic animal models The pathogenesis of PD is not easy to understand because majority of the cases are idiopathic and caused by complex interactions between genes and environmental factors. On the other hand, some of the PD cases are due to genetic mutations leading to familial forms of the disease. So far, at least 20 PD risk loci have been identified [2]. Among the mutations studied, the mutations in α-SYN, Parkin, PINK1, DJ-1 and LRRK-2 are clearly associated with PD. Transgenic mice lacking PD-linked genes have been used to elucidate the pathways contributing to PD pathology. Several groups have generated a Parkin knockout mouse model to facilitate the investigation of the functional consequences of gene inactivation [114,115,188]. These studies failed to show neuronal loss in the nigrostriatal region. However, proteomic studies using the Parkin deficient mice demonstrated mitochondrial dysfunction, oxidative damage, defects in protein handling and synaptic function [189,190]. A proteomic study was undertaken with transgenic mice expressing a mutant form of α-SYN (A30P mutant) to understand how mutations in αSYN contribute to pathophysiology [191]. Analysis of the results revealed impaired energy metabolism associated with the mitochondria. Similarly, transgenic Drosophila expressing αSYN (A30P mutant) displayed dysregulation of a group of proteins associated with the actin cytoskeleton and the mitochondria [192,193]. It has been known that the overexpression of α-SYN leads to neurodegeneration and, cause PD. To study changes in wild-type α-SYN expression, a C. elegans model was used in a proteomic study [194]. Actin and several ribosomal proteins were identified as negative markers at early PD stages. In a recent study, a transgenic PINK1 negative mouse was used to monitor changes at the proteome level [195]. Twenty-nine proteins displaying changes in either abundance or phosphorylation status were identified. Functional categorization of the identified proteins underlined the importance of neuronal plasticity, neurotransmission, energy metabolism, oxidative stress, proteostasis networks, cellular signaling and structure. 4.2.3. Cellular models Cellular models confirmed the existing theories on PD pathogenesis and allowed for the discovery of metabolic events involved in PD by way of combining simpler molecular mechanisms into complex networks [196-200]. In addition, the effects of PD-associated mutations can often be reproduced using cellular models. Cellular models are also used to study the effects of PD-related toxins. Among the cellular models, human SH-SY5Y cells are the most frequently used model in neurodegeneration research due to their catecholaminergic neuroblastoma origin. Most of the genes belonging to the major PD pathways and modules are intact in the SH-SY5Y genome [201]. These cells can express DA transporters/receptors and are able to form storage vesicles [35,202]. An SH-SY5Y cell line that was developed in our laboratory allows for tetracycline-regulated gene expression (TET-ON) [125]. We recently used this cell line to characterize a compound heterozygous Parkin mutant (Q311R and A371T) using proteomic and molecular approaches [73]. Comparative DIGE analysis of the mutant and wild-type Parkin-expressing cells showed changes in the abundance of thirteen proteins. Some of these proteins were not previously associated with PD and could be novel therapeutic targets. In addition to SH-SY5Y cells, other catecholaminergic human neuroblastoma cell lines that are used in PD research include SK-N-MC, SK-N-BE and BEZ-M17 [199]. Cell lines such as PC-12 and MES are also used in PD-associated studies [135,164]. Cellular models reflect pathologies more rapidly, require less financial support, need no ethical approval, provide better imaging opportunities, are easy to replicate, are more prone to genetic manipulation and offer various selections of cell types. Despite these advantages, cellular models fail to reproduce the complexity of PD and any finding generated using a cellular model should be cautiously evaluated and needs to be validated in animal models or human subjects. 5. Expert commentary The central nervous system of the human body is highly complicated machinery with approximately 100 billion neurons, 1020 glial cells and 1015 synaptic connections. Experimental models to understand this complex system and associated diseases often fall short due to insufficient sample sizes, improper sample collection methods, intrinsic problems with the experimental approaches used and technical limitations of the work carried out. However, despite all the difficulties, pieces of this complex system have gradually been revealed, and the connections among these components were identified. In this sense, proteomic studies carried out to understand PD have provided crucial information about the actual disease status and uncovered the important molecular details. Proteomic approaches have clearly demonstrated that more than a single biological mechanism is involved in the process of PD development. During the preparation of this review, we prepared a list of regulated proteins that have been identified by proteomic studies (Table 1). Our aim was to create a Venn diagram to reveal an intersection point that may simplify the perspective for looking at PD. However, we realized that the effort to create such a diagram is not easy, because the list of regulated proteins identified by proteomic studies is considerably vast, and there are conflicting results in terms of regulation trends. We then created five different Venn diagrams by taking into consideration the sample types with which the proteomics studies were carried out (Figure 3). After analysis of the Venn diagrams, a Table was generated using the proteins that were found to be regulated in at least in two different proteomics studies (Table 2). The links between some of the proteins listed in Table 2 with PD have already been established (e.g., VDAC2 and UCHL-1). We predict that the other proteins may also be associated with PDlinked molecular events. The conflicting results are likely caused by differences among the sample types, sample storage conditions, protein isolation/preparation methods, pre-analytical sample processing techniques, protein separation approaches, quantification and identification methods, algorithms used for statistical analysis and the bioinformatics software used for analysis of the data. Despite the conflicting data, these proteomic studies were successful in identifying potential components of the major pathways and LBs. The model systems used in the proteomic studies included human samples, monkey, mouse, rat, zebrafish, fruit flies, roundworms and various cell lines. Unfortunately, none of these model systems are ideal for PD proteomic studies. Human samples seem to be the best answer, but studies demonstrated that the background variation in human samples is enormous, and the data produced by these studies lacked statistical power due to limited sample size. In addition, human tissue samples generally have heterogeneous cellular makeup that complicates the analysis of the results. Cell culture studies provide controlled environments but fail to imitate the niche that is needed for disease development. Animal models are very useful for monitoring the changes in proteome profiles, but none of them recapitulate all clinical and neuropathological properties of PD, as they fail to produce LBs and replicate the neurodegeneration pattern. Zebrafish, fruit flies and roundworms do not have complex brain structures and have limited use. Consequently, a slowly progressing model that could reflect all the cardinal signs of PD is needed. Even with that model, careful consideration would have to be given to the data produced to answer a particular question. 6. Five-year view The purpose of proteomics studies associated with PD is to facilitate disease diagnosis, monitor disease progression and provide target molecules for the development of novel therapies. However, only limited success has been obtained so far. Future proteomics experiments will focus on more targeted studies and eliminate data complexity by examining the subcellular proteomes. Such studies will bring more power and detail to discriminate between the molecular factors that are “the cause but not the consequence” for PD. Proteomic approaches have a lot to offer in this regard as they can offer identification of novel proteins leading to the elucidation of affected pathways that underlie PD. As opposed to the conventional model systems, new model systems are needed to provide novel perspectives of PD. Indeed, the scientific community should make some effort to use dopaminergic neurons derived from pluripotent stem cells to understand PD at the proteome level. Despite this extensive research, the etiology of PD is still unknown. The patients develop a highly heterogeneous phenotype mainly because PD is a multifactorial disease. Thus, a global approach such as proteomics is well-suited to study PD at the molecular level. During interpretation of the results, careful consideration of the complex nature of the disease is needed. This implies that the analysis should be carried out at the systems level using a systems biology approach. 7. Key issues • Qualitative and quantitative proteomic studies revealed mitochondrial dysfunction as one of the key metabolic events in PD. • Comparative proteomic studies using neurotoxins highlighted major contributing metabolic pathways to PD. • Human brain samples underlined the importance of oxidative stress in PD. • The contents of LBs demonstrated that PD is a proteinopathy disease. • Inter-laboratory variations in proteomic studies showed the need for development of standardized and reproducible methods. • Inter-laboratory variations in proteomic studies showed the need for better model systems. • Proteomic studies underline the need for a biomarker panel that can reflect the complex nature of PD. Table 1. Some of the up- and down-regulated proteins observed in selected proteomic studies associated with PD. Table 2. Regulated proteins that were detected at least in two different proteomic studies in which the same sample type was used. The arrows facing upward indicate up-regulation while the arrows facing downward indicate down-regulation of the listed protein. Figure Legends Figure 1. A schematic diagram showing the molecular paths leading to PD. Figure 2. A schematic diagram of the sample types used in proteomics studies. Figure 3. Venn diagrams created to depict the number of common proteins that are regulated in biological materials used in PD proteomics studies. Selected proteomic studies that used (A) human cerebrospinal fluid, (B) human SH-SY5Y cells, (C) human tissue samples from SNpC, (D) enriched murine mitochondria and (E) rat striatum. The numbers in diagrams are the number of proteins identified in each study. Percentages given in bracelets represent the fraction of total proteins identified. The numbers given in overlaps represent the proteins identified by more than one study. Funding This paper was not funded. Declaration of Interest The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. Acknowledgments We wish to thank Prof. Dr. Jiann-Shin Chen (Virginia Tech, Biochemistry Department) and Prof. Dr. Pervin Iseri (Kocaeli University, Neurology Department) for their critical comments during preparation of the manuscript. We also thank graphic designer, Aydın Ozon for his help in improving the figures. The number of publications relating to the proteomics of PD is vast, and excellent reviews with different emphases are published periodically. 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Table 1 Some of the up and down regulated proteins observed in selected proteomic studies associated with PD. Purpose of the study Sample type To investigate the effect of 6OH-DA on the proteome of MN9D cells To perform a comparative proteome analysis of mesencephalic tissue from PD patients To monitoring the effect of oxidative stress and mitochondrial dysfunction on neurodegeneration Cultured MN9D cells hSNpC To perform proteome analysis of parkin-deficient mice To perform comparative proteome analysis of hSNpC of PD patients To perform comparative proteome analysis of the transgenic mice over expressing A30P α-synuclein To compare the mitochondrial protein profiles of MPTP treated mice with the controls To identify the proteins involved in Method of study 2DE+ MALDI TOF 2DE+ MALDI TOF Regulated proteins identified Ref. CRT ↑ [203] MNSOD ↑, DHPR ↑ [159] 2DE+ MALDI TOF TPI ↑, GST ↓, PRX5 ↓, OGDC ↓ SQR ↓, MST ↓ [204] 2DE+ MALDI TOF PDHE1 ↓, NDUV2 ↑, NDUS3 ↓, COX5B ↓, PRDX2 ↓, PRDX6 ↓, PRDX1 ↓, LGUL ↓, PROF2 ↓, PLPP ↓, VPS29 ↓, CRYAB ↓, ROA1 ↓, LASP1 ↓ [189] 2DE+ MALDI TOF 2DE+ MALDI TOF NFM ↓, NFL ↓, PRDX2 ↑, ATP5H ↑, CPLX1 ↑, PROF2 ↑, CA2D1 ↑, FABPH ↑, UCRI ↑ CAR2 ↓, ENO1 ↓, LDH2 ↓ [160] Mitochondria enriched SNpC from MPTPtreated mice ICAT+ LCMS/MS SNpC and 2DE+ MALDI NECT1↑, ADA22↑, ADT1↑, AP2B1↑, PDE1B↑,KCC2B↑, AP2M1 ↑, A1ILN2↑, KCRB↑, DOPD↑, DPYL5↑, DLG1↑, FZD7↑, GSTM3↑, GBG2↑, GBG12↑, HXK1↑, LGUL↑, TOM70↑, NFASC↑, PACN1↑, KPCG↑, AT2A2↑, PP1G↑, TPIS↑, AP2A1↑, 1433T↑, CN37↑, PURA1↑, ADDA↑, DNJA2↑, NDST1↑, DX39B↑, KPYM↑, SHLB2, STXB1, ZN318, ACPM↑, CX6B1↓, Q9QZ38↓, HSP7C↓, KCC2A↓, NDUS1↓, 2A5A↓, ODPA↓, 1433E↓, 1433Z↓, ADA23↓, A4↓, ATPG↓, AT1A2↓, AT1A1↓, AT1A3↓, CLD11↓, CPSF2↓, COF1↓, COR1A↓, COX5B↓, CY1↓, DLDH↓, DJC10↓, ALDOC↓, GAB1↓, GBB1↓, HPCA↓, MIF↓, TIM13↓, MB↓, NDUA9↓, RAC1↓, ADT1↓, S6A11↓, Sptbn1↓, MYPR↓, SYPH↓, TBA4A↓, QCR6↓, VDAC2↓, PRS4↓, LYPA2↓, CD166, CPLX2↓, EAA2↓, GLNA↓, GPDM↓, MTAP2↓, NDUA8↓, MPCP↓, RL18A↓, ACTZ↓, PPIA↓, TCPE↓, GLRX3↓, VAS1↓ ENOA ↑, ACTB ↑ Mitochondria isolated of SOD2-null mice brain tissue Dissected ventral midbrain of mice hSNpC Mice whole brain [191] [168] [177] dopaminergic neuronal death TOF striatum tissues of 6-OHDA treated rats Striatum and cortex of Parkin knockout mice hSNpC ICAT + LC-MS To study the effect of expressing wild type α-synuclein on SH-SY5Y cells treated with dopamine To characterize hSNpC proteome (a non-comparative proteomic study) Cultured SHSY5Y cells Western blotting hSNpC To monitor the changes in mitochondrial proteome following in vitro DAQ exposure To analyse the proteomic changes in αsynuclein expressing transgenic C. elegans To compare proteome analysis of SNpC of PD patients with the controls Mitochondria enriched from rat brain C. elegans MALDI TOF/TOF + LC-MS 2D-DIGE+ MALDI TOF hSNpC 2DE+ MALDI TOF To investigate the alterations in protein profiles associated with PD To identify the differentially displayed proteins in CSF of PD patients To investigate the effects of increased Parkin levels on protein expression To determine the changes occurring in mitochondrial proteome in α-synuclein Human CSF 2DE+ LCMS/MS 2DE+ MALDI TOF + LC/MS 2DE+ MALDI TOF/TOF 2DE+ LCMS/MS To provide inside into the pathogenic mechanisms underlying the preclinical stages of Parkin related Parkinsonism To compare proteome profiles of nigral mitochondrial proteins of PD patients with the controls Human CSF HEK293 SH-SY5Y 2D-DIGE+ MALDI TOF FD-LC-MS/MS ATPA ↑, DHSA ↑, G3P ↑, LDHA ↑, MDHC ↑, PGK1 ↑, UCR1 ↑, DPY2 ↑, NSF ↑, SEP7 ↑, GR75 ↑, HS7C ↑, GTP2 ↑, POR1 ↑ ** 1433E ↓, ACYP2 ↓, AL1A1 ↓, ARP1A ↓, ARP1B ↓, CtBP1↓, DRP-1↓, DVLP↓, ETFD ↓, DICER ↓, GAD-67↓, MMA(V)↓, GBG4↓, GNA13↓, MMSA↓, NDUAA↓, NDUAB↓, SC23A↓, ODPX↓, MPP2↓, GRP75↓, TPD53↓ , TARBP-B↑, PFK-A↑, W8C0K0↑, PEA15↑, U-MtCK↑, EF-1-delta↑, HEMH↑, ADF↑, hOTU1↑, IGHM↑, MGL↑, PTN5↑, RADI↑, RAB14↑, RAB21↑, HSP60↑, NCKX2↑, MYPR↑, SYNPR↑, IFM10↑, TBA8↑, UCRI↑ DJ-1 ↑, HSP70 ↓, 14-3-3 ↑ [190] 1263 proteins were identified [165] GPD2 ↑, GRP75 ↓, MIC60 ↓, NDUFS2 ↓, SUCA ↑, IDH2 ↑, TUFM ↑, PCB ↑, OGDH ↑, MTCK ↓ , ALDHB1 ↑, VDAC2 ↓, FAHD1 ↑, SOD2 ↓ ACT-2↓, RPL-13↓, RPL-23↓, RPL-30↓, RPL-43↓ [135] GFAP ↑, LEG1 ↑, CRBP1↑, SORCN ↑, ANXA5 ↑ , TBCA↑ , SH3BGRL, COTL1↑, GMFB ↑, FRIH ↑, GSTM3↑, VPP1 ↓, GSTP1↑, GSTO1↑, SAHH ↓, AL1A1↓ ENPP2 ↑, CO3 ↓, HPT ↓, APOE ↑, CO4↓, SOD↓, PEDF ↑ [29] ALB ↑, ALBU ↑, HBB ↓, PRR 14↑, TRFE↑ [151] ACAT2 ↓ , HNRNPK ↓, HSPD1 ↓, PGK1↓, PRDX6 ↓, VCL ↓, VIM ↓, TPI1↑, IMPDH2↑ HSPA9↑ , NDUFS1↑, DLAT↑, ATP5A1↑ [205] [164] [85] [194] [157] [206] mutant and the wild-type expressing cells To compare proteome analysis of 6OHDA treated rats To determine the changes in proteome of SH-SY5Y cells overexpressing αsynuclein after DA exposure Rat striatum SH-SY5Y 2DE+ MALDI TOF/TOF 2DE+ LCMS/MS To create a comparative proteome profile of CSF in PD To quantitatively identify differentially expressed proteins in PD To find a marker which would allowed detection of dementia in PD patients Human CSF To compare proteome profile of SNpC from PD patients and controls Human SNpC 2DE+ MALDITOF/TOF To identify therapy induced proteome chances in T-cells of PD patients To identify proteins differentially expressed in the retina of MPTP-treated monkeys To determine differentially expressed synaptic proteins in 6-OHDA treated rats Human T-cells 2DE+ LCMS/MS 2D-DIGE + MALDI-TOF + LC-MS 18 O-labeling + 2D + LCMS/MS Human serum Human CSF Monkey retina Rat striatum SELDITOF/MS iTRAQ + LCMS/MS iTRAQ + LCMS/MS ANXA3 ↑, ANXA7 ↑, CALB1 ↑, CALM ↑, CALR ↑, RCN1 ↑, SOD↑, PDIA1↑, PDIA3↑, PRDX3↑, UCHL1↑ RPLP2↓, PTMS↓, IF5A1↓, MRP-L12↓, PRDX1↑, ANXA5↓, ANXA2↑, ALDOA↑, FSCN1↑, PKM↑, VDAC-2↓, STMN1↓, RAN1BP↓, GSTP1↑, C1QBP↑, PFN1 ↑, ENO1 ↑, RUVBL1 ↑, CRMP4 ↑, LMNA ↑, IMMT ↑, GAPDH↑, ATP5A1↑ CHGB ↑, UBB ↑, B2M ↑ [180] [207] [208] TRFE ↑, CLUS ↑, CO4B↓, APOA1↓, A2AP↓, FA5↓ [209] ALMS1↑, APOB ↑, B2GPI↓, CCD57↑, DYH5↑, EFCB6↑, GCP160↑, LONF3↑, NTNG1↑, NID1↑, PRAX-1↑, FAT2↑, RNF213↑, TICN2↑, TITIN↓, PTN13↑ GNB1↑, CNDP2↑, PHPT1↑, hVPS29↑ , UbcH13↑, SODM↑, FTL↑, CKB↑, C20orf27↑, NDUFA5↑, S100-A9↑, DRP-2↑, NF-66↑, HSP-60↑, ANXA6↓, HSPA8↓, HSP70↓,C3↓, DP↓, ATP5H↓, OAT↓, ALDOC↓, PKM↓, MAOB↓, ALDH4↓, UQCRC2↓, DLG2↓, IMMT↓, AGEL↓, STXBP1↓, NAPG↓ SNAG↑, ATPB↑, ARP2↑, CAPZB↑, TPM3↓, PSME1↑, PRDX6↑, GAPDH↓, PSMB2↑ ENOA↓, ATP5B↓, SAG↓, CRYBB2↑, HSC70↓, STMN1↓, DDAH1↓, ENOG↓ , GRP78↓, GAPDH↓, PPA1↓, CALB1↓, NDPK B↑, COX5A↓, SYUG↓ AATC↓, DHE3↓, SERA↓, GSTM1↓, PRDX6↓, Q6PCU3↑, HXK1↑, ACON↓, ODPB↓, CISY↓, DLDH↑, B2RZ24↓, D3ZG43↓, Q63080↓, ATP5J↓, ATPA↓, COX5B↓, AT1A1↓, KCRU↓, CN37↓, UCHL1↑, CYTC↓, TRY1↓, Q5XI34↓, F1LQ05↓, PALM↓, SIR2↓, CAP1↓, PHB↓, HSP7C↓, GFAP↓, PEBP1↓, SPTN1↓, NCAM1↓, PPIA↓, AAK1↓, GRP78↓, ALBU↓, Q8R2H0↓, PDE10↓, 1433F↓, GNAO↓, BASI↓, KCC2A↓, KCC2D↓, NCDN↓, 1433E↓, DPYL3↓, M0RCL5↑, D3ZQL7↓, SPTN2↓, SEPT5↓, DPYL1↓, TBA4A↓, DYN1↓, [210] [211] [212] [213] [182] To compare proteome profiles of postmortem nigral tissues dissected from PD cases hSNpC LC-MS/MS To explore the possible role of UPS dysfunction in PD, changes monitored at the proteome level in lactacystintreated PC-12 cells To investigate the effect of expressing both the wild type and the mutant Parkin proteins on the overall proteome of SH-SY5Y cells PC-12 cells 2D-DIGE +MALDI-TOF SH-SY5Y 2DDIGE+MALDITOF/TOF AMPH↓, TPM3↓, TBB3↓, ROA2↓, QCR1↓, AP2B1↓, STXB1↓, STX1B↓, SNP25↓, TERA↓, STX1A↓, SYN1↓, VAMP2↓, CLH1↓, MYO5B↓, M0R6I3↓, M0RBP6↓, D3ZC50↓, D3ZK69↓ NEBL↑, GPD1↑, TJP2↑, GFAP↑, ANXA1↑, FTL↑, ASSY↑, AKR1C3↑, SORD↑, AKR1C2↑, TTC9C↑, IGHM↓, GGH↓, SIRPB1↓, BSCL2↓, NOMO3↓, TUBA8↓, TM35A↓, EF1A2↓, NCAN↓, ERP29↓ PERI, TY3H, HSP7C, GRP78,SERPINH1, PTALR PDCD5↑, TBCA↑, ENO1↑, UCHL1↓, STMN1↑, TCP1↑, YWHAB, PPA1↑, ALDH1B1↑, DNAJB11↓, ATP5H↑, PARK7↑, OAT↑, HNRNPAB↑, RBM4↑, JUP↑, ACADM↑, VDAC1↑, GT335↑, MCCC2↑, MYDGF↑, LGUL↑ [166] [214] [73] Table 2 Regulated proteins that were detected at least in two different proteomic studies in which the same sample type was used. The arrows facing upward indicate up-regulation while the arrows facing downward indicate down-regulation of the listed protein. Accession number P00352 P14136 P01871 O75947 P47985 P81155 Protein name Sample type Reference Retinal dehydrogenase 1 (AL1A1) Glial fibrillary acidic protein (GFAP) Ig mu chain C region (IGHM) ATP synthase subunit d, mitochondrial (ATP5H) Cytochrome b-c1 complex subunit Rieske, mitochondrial (UCRI) Voltage-dependent anion-selective channel protein 2 (VDAC2) Jin J. et al., 2006↓ Werner C.J. et al., 2008↓ [164, 29] Werner C.J. et al., 2008↑ Licker V. et al., 2014↑ [29, 166] Jin J. et al., 2006↑ Licker V. et al., 2014↓ [164, 166] Basso M. et al., 2004↑ Licker V. et al., 2012↓ [160, 211] Basso M. et al., 2004 ↑ Jin J. et al., 2006 ↑ [160, 164] Jin J. et al., 2005 ↓ Van Laar V.S. et al., 2008 ↓ [168, 135] P25705 P16949 P06733 Q00981 ATP synthase subunit alpha, mitochondrial (ATP5A1) Stathmin (STMN1) Alpha-enolase (ENO1) Ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) hSNpC hSNpC hSNpC hSNpC hSNpC Enriched mitochondria SH-SY5Y SH-SY5Y SH-SY5Y Rat striatum Pennington K. et al., 2010 ↑ Alberiao T. et al., 2010 ↑ [206, 207] Alberiao T. et al., 2010 ↓ Ozgul S., et al., 2015 ↑ [207, 73] Alberiao T. et al., 2010 ↑ Ozgul S., et al., 2015 ↑ [207, 73] Lessner G. et al., 2010 ↑ Xiong Y. et al., 2014 ↑ [180, 182]