Proteomics analysis of mitochondrial dysfunction triggered

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http://journals.tubitak.gov.tr/biology/

Turkish Journal of Biology Turk J Biol(2017) 41: 765-784© TÜBİTAKdoi:10.3906/biy-1702-44

Proteomics analysis of mitochondrial dysfunction triggered by complex specific electron transport chain inhibitors reveals common pathways involving protein misfolding in an

SH-SY5Y in vitro cell model

Betül ŞAHİN1,2, Ahmet Tarık BAYKAL2,*1Genetic Engineering and Biotechnology Institute, TÜBİTAK Marmara Research Center, Gebze, Kocaeli, Turkey

2Department of Medical Biochemistry, School of Medicine, Acıbadem University, İstanbul, Turkey

* Correspondence: [email protected]

1. IntroductionMitochondria are specialized structures that generate energy in the form of ATP for cells to function. One of the main mechanisms of ATP production is the oxidative phosphorylation (OXPHOS) of organic compounds such as carbohydrates and fats in the cytosol and mitochondria. OXPHOS occurs in the mitochondrial electron transport system, which consists of four different complexes (I–IV) and two electron carriers that produce the proton gradient across the mitochondrial membrane to induce ATP synthase, complex V, to generate ATP. Mitochondria are also involved in maintaining calcium homeostasis and cell proliferation and play a role in development and apoptosis (Shaw and Nunnari, 2002; Honda and Hirose, 2003; Oakes and Korsmeyer, 2004). Because of mitochondria’s involvement in various biological processes, mitochondrial abnormalities are the main cause of many human diseases. Indeed, several studies have shown that mitochondrial dysfunction is evident in neurodegenerative diseases,

some autoimmune disorders, and cancers (Corrado et al., 2012; Barrera et al., 2016).

Increasing evidence suggests that mitochondrial dysfunction is more apparent in aging and neurodegenerative disorders (Mattson, 2006; Parone et al., 2013). Neurons are one of the most metabolically active cells in the body and they are required for the production of large amounts of ATP, and so it is logical to state that healthy mitochondria are necessary to maintain neuronal functions. Indeed, with an intense demand for energy and limited capacity for regeneration, impaired functioning of mitochondria has a detrimental outcome on neuronal survival (Schon and Manfredi, 2003).

Neurodegenerative diseases share many attributes at the molecular level, such as misfolding of specific proteins and the formation of protein aggregates, which leads to intercellular inclusions and cellular protein clusters (Ross and Poirier, 2004). Although involvement of mitochondrial dysfunction in the pathogenesis of

Abstract: Mitochondrial dysfunction has been previously identified in neurodegenerative diseases such as Alzheimer disease, Huntington disease, and Parkinson disease. Chemical inhibition of the mitochondrial electron transport chain (ETC) was shown to trigger symptoms in animal models similar to those observed in human neurodegenerative diseases. In order to understand the effect of mitochondrial dysfunction on the proteome level, LC-MSE–based bottom-up, label-free differential proteomics expression analysis was used to monitor protein level changes in SH-SY5Y neuroblastoma cells induced by ETC-specific inhibitors (MPTP, 3-NP, sodium azide, antimycin A, and oligomycin). A total of 379 proteins were identified across the sample set and 75 of them were found to be differentially expressed (>30% fold change). Complex-specific inhibition of the five ETS complexes were expected to result in the aberrant regulation of different molecular pathways, but the bioinformatics analysis of the LC-MSMS data showed that the differentially expressed proteins were mostly involved in similar metabolic processes. The findings suggest that the complex-specific alterations may not be directly linked to neurodegenerative pathways, but could be considered contributors. Moreover, the proteins that showed the highest protein expression difference (>60% fold change) are involved in pathways regarding protein-folding and response to unfolded proteins. The results indicate that protein misfolding pathways might have a central role in the genesis and progression of neurodegenerative diseases and that label-free LC-MSMS proteomics analysis is an invaluable approach for studying of molecular pathways in neurodegeneration.

Key words: NanoUPLC-ESI-qTOF, shotgun proteomics, mitochondrial dysfunction, neurodegeneration, protein expression, ETC complex inhibition

Received: 14.02.2017 Accepted/Published Online: 14.06.2017 Final Version: 10.11.2017

Research Article

ŞAHİN and BAYKAL / Turk J Biol

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neurodegenerative disorders has been shown (Beal, 2005; Akbar et al., 2016), the molecular mechanisms of the pathology have not been fully elucidated. Interestingly, there is ample evidence of dysfunctional mitochondria being the cause of neurodegeneration rather than its consequence (Witte et al., 2010). However, there are also opposite views suggesting that mitochondrial dysfunction is likely to take place at later stages in neurodegenerative disorders (Morais and De Strooper, 2010).

Many studies support the idea that mitochondrial dysfunction and oxidative stress have a strong impact on Alzheimer disease (AD), Huntington disease (HD), and Parkinson disease (PD) pathogenesis (Bhat et al., 2015). It has been suggested that in sporadic AD, mitochondrial dysfunction is the primary event that leads to amyloid-β protein deposition, formation of neurofibrillary tangles, and synaptic degeneration (Swerdlow et al., 2010). Energy deficiency was found to be the fundamental characteristic feature of AD brains and peripheral cells derived from the patients with AD (Gibson et al., 1998; Manczak et al., 2004; Beal, 2005). Moreover, pyruvate dehydrogenase, isocitrate dehydrogenase, and α-ketoglutarate dehydrogenase activities were impaired in postmortem AD brains and fibroblasts derived from the patients with AD (Bubber et al., 2005). Impairment in complex I, III, and IV activities have been reported in postmortem AD brain tissue and in platelets and lymphocytes derived from the patients with AD (Kish et al., 1992; Bosetti et al., 2002). However, a major change often associated with AD is the impairment of the electron transport chain at complex IV, cytochrome c oxidase (COX) (Castellani et al., 2002).

Mitochondrial dysfunction in HD follows prominent weight loss despite sustained caloric intake (Quintanilla and Johnson, 2009). Decreased OXPHOS complexes II and III activity, reduced aconitase activity in basal ganglia, and abnormally structured mitochondria were found in the onset of HD (Browne and Beal, 2004; Polyzos and McMurray, 2017).

PD, which is clinically characterized by tremors, bradykinesia, and rigidity, is another neurodegenerative disorder that has a connection with mitochondrial dysfunction (Moon, 2015). Patients with PD demonstrate mild deficiency in the mitochondrial electron transport chain complex I and NADH dehydrogenase activity (Marella, 2009). Complex I deficiency was also found in platelets, lymphocytes, and muscle tissue in PD patients (Yoshino et al., 1992). Moreover, studies have also shown that there is direct involvement of PINK1 and parkin in the abnormal mitochondrial dynamics in the fly, rat, and mouse models of PD (Reddy et al., 2011; Wang et al., 2011; Morais et al., 2014;).

Since mitochondrial dysfunction has been shown to be involved in neurodegenerative diseases, ETC-

complex–specific inhibitors have been investigated for their effect on neurons in several different studies and some of them have been suggested as a tool for in vitro models for neurodegenerative diseases (Johri and Beal, 2012; Martinez and Greenamyre, 2012). Complex-specific inhibitors of the ETC system have become important tools for modeling neurodegenerative diseases (Baykal et al., 2008).

Complex I inhibitors such as rotenone and MPTP cause behavioral and neuropathological symptoms similar to human PD (Moon, 2015). Discovery of the neurotoxic activities of compounds like MPTP helped identify complex I deficiency in the pathogenesis of PD (Ramsay et al., 1986). Deficiency in complex I activity in the substantia nigra of PD patients was one of the first findings to be reported (Schapira et al., 1990).

Phenotypic and neuropathological changes of HD were modeled in rodents and primates with the mitochondrial ETC inhibitor 3-NP (Brouillet et al., 1999), a complex II inhibitor that causes preferential striatal degeneration, aberrant movements, and cognitive deficits in rats (Brouillet et al., 1999, 2005) and in primates (Brouillet et al., 1995) that highly resemble HD. It also causes putaminal necrosis, dystonia, and delayed dyskinesia in humans (Ludolph et al., 1991; He et al., 1995)3-nitropropionic acid (3-NPA. Models of 3-NP indicate that complex II deficits could be associated with cell death in HD (Brouillet, 1999).

Sodium azide causes mitochondrial energy impairment and neuronal death (Zhang et al., 2000). It has been found that sodium azide selectively inhibits complex IV. Thus, it can be suggested that inhibition of complex IV can be a good model to study mitochondrial dysfunctions in cells to understand AD pathology. This idea has been further supported by the creation of complex IV deficiencies in animal models (Bennett et al., 2002).

Beside these inhibitors, antimycin-A (a complex III inhibitor (Ma et al., 2011)) and oligomycin (a complex V inhibitor (Galante et al., 1979; Smalley, 2016)) were used in vitro to model mitochondrial dysfunction. Antimycin-A causes complex III impairment, which has been observed in HD patients (Ma et al., 2011).

For our study, 5 different ETC complex inhibitors (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), 3-nitropropionic acid (3-NP), sodium azide, antimycin-A, and oligomycin) were used and proteomics analysis was performed to elucidate the effect of mitochondrial dysfunction. Proteomics is a large-scale study of proteins, including structure, function, and acquired modifications (Anderson and Anderson, 1998). Proteomics has become a powerful tool in a variety of molecular studies, as it provides direct information regarding the dynamics of the cell.


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The main goal of comparative proteomics is the identification and quantification of differentially expressed proteins in cells, tissues, or other biological samples. There are two general techniques in quantitative proteomics: labeled and label-free methods. Unlike labeled methods, label-free quantification does not use stable isotopes (Patel et al., 2009). For quantification, signal intensities or spectral counting methods are used (Neilson et al., 2011).

Proteomics has been applied in mitochondrial dysfunction and neurodegenerative disease studies (Zhang et al., 2008; Jiang and Wang, 2012; Hosp et al., 2015). While obtaining specific information on mitochondrial proteome is possible (McDonald and Van Eyk, 2003), the global effect of a certain ETC dysfunction in a cell could elucidate the underlying mechanisms.

Considering that proteome-level alterations are widely seen in many disorders, we wanted to specifically study the effect of complex-specific mitochondrial dysfunction in SH-SY5Y cells. In this study, LC-MSMS–based label-free proteomics analysis was performed on SH-SY5Y neuroblastoma cells treated with 5 different complex-specific ETC inhibitors. We aimed to discover specific alterations and common changes in each group to understand how mitochondrial dysfunction affects the proteome of the cell.

The design of the study has novel aspects that use complex-specific inhibitors to alter mitochondrial respiratory chain function, causing dysregulation in the process of ATP generation. The label-free LC-MSMS analysis used to detect protein expressional changes across all of the 5 ETS complexes provides insight regarding the molecular changes due to ROS generation. It is important to study mitochondrial dysfunction via in vitro cell-based models to understand the genesis of neurodegenerative pathways, despite the fact that different neurological diseases exhibit deficiencies in different ETS complexes and demonstrate different pathological characteristics and symptoms. The study demonstrates that for the onset of neurodegeneration, pathways like protein-folding and metabolic pathways have more important roles.

2. Materials and methods2.1. MaterialsA human neuroblastoma SH-SY5Y cell line was obtained from American Type Culture Collection (Manassas, VA, USA). Dulbecco’s modified Eagle’s medium (DMEM Cat. #32500-035) was purchased from GIBCO (Waltham, MA, USA). Penicillin and streptomycin were purchased from Biol. Industries (Cat. #03-031; Cromwell, CT, USA). Fetal bovine serum (FBS) was obtained from Biochrom (AG Cat. #S0115; Cambridge, UK). MPTP, 3-NP, oligomycin, antimycin-A, sodium azide, acetonitrile (ACN) (LC-MS grade), water (LC-MS grade), dithiothreitol (DTT),

HEPES, trifluoroacetic acid (TFA), formic acid (FA), iodoacetamide (IAA), and sequencing grade modified trypsin (proteomic grade) were purchased from Sigma-Aldrich (St. Louis, MO, USA). SDS and acrylamide-bis (40%) were purchased from Bio-Rad (Hercules, CA, USA). Ammonium bicarbonate (ABC) was purchased from Honeywell (Morris Plains, NJ, USA). RapiGest, an MS-compatible acid-labile surfactant and the internal standard MASSPREP alcohol dehydrogenase digest UniProt Accession #P00330 were purchased from Waters Corporation (Milford, MA, USA).2.2. Cell cultureStable SH-SY5Y cells were grown to 70%–80% confluence in poly-D-lysine–coated 10 cm polystyrene culture dishes, employing DMEM containing 10% FBS, nonessential amino acids (NAA), penicillin (100 units/mL), and streptomycin (100 µg/mL). In order to induce a moderate cell death response, the cells were gradually deprived of serum (1% FBS for 24 h, then 0% serum for 30 min for cells to adjust). Cells were incubated at 37 °C in a humidified atmosphere of 5% CO2 and 95% air. For this study, the SH-SY5Y cell line was chosen, since it has been widely used as a model system because these cells share many biochemical and functional properties of neurons (Xie et al., 2010).2.3. ETC inhibition and MTS cell viability assayA total of 1 × 107 cells were used for each metabolic re-action analysis. An MTS cell proliferation assay kit (colorimetric) (ab197010) was used for the cell vi-ability assays. MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulphophenyl)-2H-tetrazo-lium] was used according to the manufacturer’s instruc-tions. Stable SH-SY5Y cells were treated as described above, then MTS solution (20 μL) was added to each well, and the incubation was prolonged for 1 h at 37 °C in a hu-midified incubator with 5% CO2. The absorbance was set to 490 nm on the microplate reader (PerkinElmer; Waltham, MA, USA). Results were expressed as the percentage of MTS absorbance reduction relative to the control and are presented as the mean ± standard deviation (SD).

Approximately 80% cell viability was the target of each treatment. Once 80%–90% confluence was reached, cells were incubated with fresh DMEM containing 10% FBS, NAA, penicillin (100 units/mL), streptomycin (100 µg/mL) and one of the following ETC inhibitors: 4 µg/mL antimycin-A, 1 mM MPTP, 5 mM 3-NP, 2 mM sodium azide, or 1 µg/mL oligomycin. All treatments with ETC in-hibitors were carried out for 24 h, except 3-NP, which was only 12 h. Cells were counted after each ETC inhibition and overall cell viability was maintained at around 80%. Moreover, a control cell culture was included along with each ETC inhibition reaction.


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2.4. Sample preparation for LC-MS/MSFollowing treatments of the SH-SY5Y neuroblastoma cells with each of the five complex-specific inhibitors, the cells were washed with ice-cold PBS and pelleted for further use. Pelleted cells were then treated with protease inhibitors (Sigma-Aldrich) and then 0.1% RapiGest SF buffer containing 50 mM ABC, pH 7.8, was used for solubilization. Samples were lysed with an ultrasonic homogenizer (5 s on, 5 s off: 3 cycles). The mixture was spun at 15,000 rpm for 10 min to remove cell debris. Samples were then transferred into spin filters (Sartorius Vivaspin 500, 5kDA molecular weight cut-off PES) to remove salts and other contaminants. Samples were concentrated in spin filters until they were approximately 50 µL (Hacariz et al., 2014).

Protein samples were transferred to Eppendorf tubes and protein quantification was performed by the Bradford method (Bradford, 1976). Proteins were reduced with 5 mM DTT at 60 °C for 15 min and alkylated with 10 mM IAA in the dark at room temperature for 1 h. Following alkylation, the samples were trypsinized overnight at 37 °C (1:100 trypsin:protein ratio). After trypsinization, hydrolysis of the acid-labile MS-compatible detergent RapiGest (Waters Corporation) was performed through addition of TFA (0.5% final concentration, pH 2) and ACN to a 1% final volume and incubation at 60 °C in a 600 rpm shaker for 2 h. Before MS analysis, 50 fmol internal standard digest (alcohol dehydrogenase, UniProt #P00330, Waters Corporation) were added to the samples to be used for normalization. The resulting mixture had 500 ng/μL tryptic peptide. After hydrolysis of RapiGest, the mixture was centrifuged for 15 min at 15,000 rpm and an aliquot was taken into LC vial for MS analysis; the rest of the tryptic peptide mixtures were stored at –80 °C (Baykal et al., 2013).2.5. LC–MS/MS analysisReverse phase chromatography was used for peptide sep-aration. A total of 500 ng of tryptic peptides in 2 μL for each experimental group were analyzed by the LC-MS/MS system (nanoACQUITY ultra pressure liquid chromatog-raphy (UPLC) and a SYNAPT high definition mass spec-trometer with NanoLockSpray ion source (Waters Corpo-ration)). Column equilibration was done with 97% mobile phase A (H2O, 0.1% FA) and column temperature was set to 45 °C. Peptides were first separated by the trap column (Symmetry C18 5 μL, 180 µm i.d. × 20 mm) (Waters Cor-poration) by gradient elution. Then they proceed to an an-alytical column (BEH C18, 1.7 µm, 75 µm i.d. × 250 mm) (Waters Corporation). Linear gradient was used from 5% to 40% mobile phase B (ACN, 0.1% FA) at 300 nL/min flow rate over 90 min. Mass drift correction was done with the internal mass calibrant Glu-fibrinopeptide (500 pmol/μL) by infusions through NanoLockSpray ion source at 300 nL/min flow rate every 45 s. Peptide signal data between

50 and 1600 m/z values were collected. For each biological sample, three technical and four biological replicate analy-ses were performed (n = 12).2.6. LC–MS/MS data processingData processing parameters were the default values used with SYNAPT-HDMS system (Haçarız et al., 2012). Peptide mass measurement was performed at low collusion energy (5 eV) and then peptide sequence data collection was performed at high collusion energy (25–40 eV). Data processing (tandem mass spectra extraction, charge state deconvolution and deisotoping) was performed with ProteinLynx Global Server v2.3 software (PLGS) (Waters Corporation) and the IDENTITYE algorithm was used to search against the Homo sapiens reviewed protein database from UniProt. Mass tolerance was set to 20 ppm and a parent ion tolerance was set to 10 ppm. The amino acid sequence of the internal standard (yeast alcohol dehydrogenase, UniProt accession # P00330) was added in the FASTA file of the database. The Apex3D data preparation parameters were set to 0.2 min chromatographic peak width, 10,000 MS TOF resolution, 150 counts for low energy threshold, 50 counts for elevated energy threshold, and 1200 counts for the intensity threshold. The databank search query was set to minimum 3 fragment ion matches per peptide, minimum 7 fragment ion matches per protein, minimum 2 peptide matches per proteins, and 1 missed cleavage. Carboamidomethylation of cysteine-fixed modification and acetyl N-terminal, deamidation of asparagine and glutamine, and oxidation of methionine were set as variable modifications. Absolute quantification of the peptides was calculated with IDENTITYE using the spiked amount of the internal standard (Silva, 2005; Cheng et al., 2009; Bostanci et al., 2010). The false positive rate of the IDENTITYE algorithm is around 3%–4% with a randomized database (D’Aguanno et al., 2011). The quantitative analysis is based on identified proteins that are detected in 3 out of 4 biological repeats. Normalization of the proteins was achieved by using the internal calibrant P00330 (Serhatlı et al., 2013; Yerlikaya et al., 2015). 2.7. Statistical and bioinformatics analysisIn order to test the reliability of the results and to compare all 72 samples (4 biological and 3 technical replicates), sta-tistical methods were used. For this study, Scaffold 3 was used for the determination of statistically significant pro-tein expression changes between groups and PLGS-EZ-info was used for PCA analyses.

Scaffold 3 (Proteome Software) was used for the veri-fication of protein identifications obtained by the PLGS search engine. It enables verification of the peptide iden-tifications by calculating the confidence of each peptide identification. Scaffold uses ANOVA to determine sta-tistically significant protein expression changes between groups (P ≤ 0.05). Scaffold 3 software is also used to clas-sify proteins by their biological functions.


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Principle component analysis (PCA) was used for the reduction of the data, obtaining probabilities, and obser-vation of the data set. The unsupervised method was used to analyze the data. Several different plots can be gener-ated with this method. The score plot shows the differences and similarities among the data sets. The data sets show-ing similarities are grouped together while those that are different are placed further apart. The PCA scores were subject to ANOVA in order to evaluate the statistical dif-ferences among the sample groups. 2.8. Pathway analysisSTRING software (version 10) was used to obtain protein–protein interaction pathways. STRING provides a database of known and predicted protein interactions. These interactions include direct and indirect associations obtained from physical and functional analysis. Only experimentally associated data were included in the result (Szklarczyk et al., 2015). STRING can also obtain functional enrichment in the network. It basically finds the function of the protein given and assigns a false discovery rate for that match.2.9. ImmunoblottingAfter each treatment, the cells were washed with ice-cold PBS and lysed in lysis buffer (50 mM HEPES, 150 mM NaCl, 1 mM EDTA, and 1% NP40) plus protease inhibitors (Sigma-Aldrich). Twenty micrograms of proteins from each sample was resolved over a 10% SDS-PAGE gel and transferred to a nitrocellulose membrane (Millipore). The membrane was first blocked in the blocking buffer (5% nonfat dry milk, 0.1% Tween 20; in PBS) for 2 h at room temperature and then incubated with an antibody against 14-3-3 epsilon (#9635, cell signaling), gamma (#5522, cell signaling), theta (# 9638, cell signaling) and 14-3-3 zeta/delta (#7413, cell signaling) in blocking buffer overnight at 4 °C. After washing, the membrane was incubated with an HRP-conjugated goat antirabbit secondary antibody (#7074, cell signaling) for 1 h at room temperature and detected by chemiluminescence. Equal protein loading was confirmed by stripping the blot and reprobing with a pan-actin antibody (#4968, cell signaling).

3. Results3.1. Mild deficiency in the electron transport complexesInhibition of the ETC complexes through increasing concentrations of the complex-specific inhibitors would cause decreased cellular metabolism and increased reactive oxygen species, and eventually this would lead to cell death via apoptosis or necrosis. Therefore 80% cell viability was the target for a nontoxic dose of inhibitors (Figure 1). This mild effect is preferable to a higher dose and mimics the in vivo effects of specific complex deficiencies.

The inhibitor concentrations were 1 mM for antimycin-A, 1 mM for 3-NP, 2 mM for MPTP, 2 mM for oligomycin, and 5 mM for sodium azide. All treatments were applied for 24 h except for 3-NP (12 h).3.2. Proteomics analysis revealed 75 differentially expressed proteinsDifferential proteome analysis of SH-SY5Y neuroblastoma cells was performed by nanoLC-MS/MS. Peptide separation was performed by a nanoACQUITY UPLC system (Waters Corporation) coupled to a SYNAPT high-definition mass spectrometer (Waters Corporation). A total of 72 samples in 6 groups (4 biological and 3 technical replicates) were analyzed in the study. Technical replicates are used for the evaluation of the mass spectrometer stability. Mass error for peptide m/z values was 12 ppm across the sample set and the average peptide mass error was 4 ppm. The chromatographic retention time coefficient of variation was calculated (% CV RT) to be around 5% and the average was below 0.5%. The data show that there was little deviation between elution times of the identified peptides. This is important for the accurate quantification of the corresponding peptides.

In this proteomics study, a total of 379 proteins were identified with high confidence (Supplementary table). The PLGS analysis showed that 75 of the identified proteins were differentially expressed (P < 0.05). Proteome profiles of the samples treated with the complex-specific inhibitors are compared to the control sample group and the results are listed in Tables 1–5. Percent coefficient of variation of intensities (%CV Int) were calculated to be around 10% and the intensity cutoff for the expression values were

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Figure 1. Viability percentage of SHSY-5Y cells after incubation with ETC complex-specific inhibitors. Concentrations for the inhibitor were 2 mM, 5 mM, 1 mM, 1 mM, and 2 mM in order. Values are the means ± SD. Student’s t-test was measured versus control. *P ≤ 0.05


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therefore set to 3 times the %CV Int value and so only protein expression changes greater than 30% are listed in the tables.

Scaffold 3 was used for the verification of the protein identifications obtained from the PLGS search. Proteins

with a P-value less than 0.05 were selected and used in PCA analysis.3.3. Differentiation of groups following ETC inhibitionPCA transforms and plots the abundance data in a principle component space. The analysis is useful for identifying

Table 1. Comparison of control and 3-NP–treated samples. Table shows the relative expression change of protein in control and 3-NP–treated SH-SY5Y cells by ratio; 2 refers to treated cells and 1 refers to control. Log(e) ratios and Log(e) standard deviations are also given in the table (P < 0.05).

Accession no. Protein name PLGS

score 2:1 ratio 2:1_ log(e) ratio

2:1_ log(e)std dev

O75526 RNA binding motif protein X linked like 2 251 1 1 1

P04406 Glyceraldehyde 3 phosphate dehydrogenase 6947 0.66 –0.42 0.02

P05386 60S acidic ribosomal protein P1 1709 0.53 –0.64 0.09

P06454 Prothymosin alpha 5027 0.41 –0.89 0.10

P06744 Glucose 6 phosphate isomerase 123 0.64 –0.45 0.08

P06748 Nucleophosmin 1542 0.59 –0.53 0.05

P07195 L-lactate dehydrogenase 621 0.70 –0.31 0.03

P07737 Profilin 1 2627 0.64 –0.44 0.03

P07900 Heat shock protein HSP 90 alpha 1277 0.68 –0.38 0.04

P08238 Heat shock protein HSP 90 beta 2127 0.68 –0.38 0.02

P09104 Gamma enolase 419 0.57 –0.56 0.22

P09211 Glutathione S transferase P 579 0.70 –0.36 0.06

P09651 Heterogeneous nuclear ribonucleoprotein A1 1568 0.60 –0.51 0.03

P10809 60 kDa heat shock protein 702 0.70 –0.34 0.04

P11142 Heat shock cognate 71 kDa protein 2300 0.63 –0.47 0.03

P13639 Elongation factor 2 447 0.66 –0.41 0.04

P13929 Beta enolase 417 0.57 –0.57 0.21

P14618 Pyruvate kinase isozymes M1 M2 1760 0.70 –0.32 0.02

P14866 Heterogeneous nuclear ribonucleoprotein L 183 0.67 –0.40 0.10

P16401 Histone H1 5 592 0.56 –0.58 0.06

P17066 Heat shock 70 kDa protein 6 848 0.55 –0.59 0.17

P22626 Heterogeneous nuclear ribonucleoproteins A2 B1 805 0.63 –0.47 0.04

P23528 Cofilin 1 10887 0.70 –0.32 0.03

P26583 High mobility group protein B2 415 0.69 –0.37 0.08

P30613 Pyruvate kinase isozymes R L 161 0.63 –0.46 0.13

P31943 Heterogeneous nuclear ribonucleoprotein H 176 0.70 –0.32 0.06

P32119 Peroxiredoxin 2 808 0.56 –0.58 0.13

P33778 Histone H2B type 1 B 6189 0.57 –0.56 0.06

P40926 Malate dehydrogenase mitochondrial 2298 0.66 –0.41 0.03

P49411 Elongation factor Tu mitochondrial 285 0.65 –0.43 0.09

P52565 Rho GDP dissociation inhibitor 1 448 0.70 –0.32 0.07

P61978 Heterogeneous nuclear ribonucleoprotein K 189 0.64 –0.45 0.05

P61981 14 3 3 protein gamma 733 0.70 –0.36 0.06

P62158 Calmodulin 1809 0.63 –0.46 0.06

P62258 14 3 3 protein epsilon 926 0.70 –0.36 0.05


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Table 1. (Continued).

P62805 Histone H4 26679 0.65 –0.43 0.02

P62826 GTP binding nuclear protein Ran 218 0.64 –0.44 0.06

P62937 Peptidyl prolyl cis trans isomerase A 3493 0.53 –0.63 0.03

P68032 Actin alpha cardiac muscle 1 5539 0.60 –0.51 0.05

P68104 Elongation factor 1 alpha 1 3513 0.59 –0.52 0.03

P78371 T complex protein 1 subunit beta 232 0.64 –0.44 0.10

Q00839 Heterogeneous nuclear ribonucleoprotein U 816 0.63 –0.46 0.03

Q05639 Elongation factor 1 alpha 2 1543 0.59 –0.53 0.03

Q06830 Peroxiredoxin 1 441 0.73 –0.31 0.05

Q08211 ATP-dependent RNA helicase A 84 0.60 –0.51 0.08

Q13509 Tubulin beta 3 chain 9937 0.64 –0.44 0.06

Q13838 Spliceosome RNA helicase BAT1 190 1 1 1

Q14103 Heterogeneous nuclear ribonucleoprotein D0 213 0.64 –0.44 0.06

Q562R1 Beta actin like protein 2 1396 0.21 –1.55 0.09

Q58FF8 Putative heat shock protein HSP 90 beta 2 418 0.49 –0.71 0.19

Q58FG0 Putative heat shock protein HSP 90 alpha A5 291 0.47 –0.75 0.42

Q71U36 Tubulin alpha 1A chain 5172 0.64 –0.44 0.03

Q71UI9 Histone H2A V 6608 0.57 –0.57 0.03

Q8N7X1 RNA binding motif protein X linked like 3 254 1 1 1

Q92841 Probable ATP-dependent RNA helicase DDX17 112 0.39 –0.95 0.11

Q9BYX7 Putative beta-actin–like protein 3 2700 0.35 –1.06 0.27

Q9H4B7 Tubulin beta 1 chain 1185 0.52 –0.66 0.19

Q9Y6H1 Coiled coil helix coiled coil helix domain containingprotein 2 mitochondrial 963 2 2 2

P62805 Histone H4 26679 0.65 –0.43 0.02



P68032 Actin alpha cardiac muscle 1 5539 0.60 –0.51 0.05


P78371 T-complex protein 1 subunit beta 232 0.64 –0.44 0.10



Q06830 Peroxiredoxin 1 441 0.71 –0.35 0.05

Q08211 ATP dependent RNA helicase A 84 0.60 –0.51 0.08




Q562R1 Beta-actin–like protein 2 1396 0.21 –1.55 0.09


Q58FG0 Putative heat shock protein HSP 90 alpha A5 291 0.47 –0.75 0.42


Q71UI9 Histone H2A V 6608 0.57 –0.57 0.03


Q92841 Probable ATP dependent RNA helicase DDX17 112 0.39 –0.95 0.11

Q9BYX7 Putative beta actin like protein 3 2700 0.35 –1.06 0.27


Q9Y6H1 Coiled coil helix coiled coil helix domain containingprotein 2 mitochondrial 963 2 2 2


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Table 2. Comparison of control and sodium azide samples. Table shows the relative expression change of protein in control and sodium azide SH-SY5Y cells by ratio; 2 refers to treated cells and 1 refers to control. Log(e) ratios and Log(e) standard deviations are also given in the table (P < 0.05).

Accessionno. Protein name PLGS score 2:1_ ratio 2:1_

log(e) ratio2:1_ log(e) std dev

P07195 L-lactate dehydrogenase B chain 621 1.38 0.32 0.03

Q58FG0 Putative heat shock protein HSP 90 291 0.32 –1.15 0.24

P60174 Triosephosphate isomerase 2749 1.36 0.31 0.02

P16401 Histone H1 5 592 0.44 –0.82 0.07



Q71UI9 Histone H2A V 6608 0.59 –0.52 0.02

P07437 Tubulin beta chain 11346 0.45 –0.79 0.03

Q92621 Nuclear pore complex protein Nup205 98 0.70 –0.36 0.04

P40926 Malate dehydrogenase mitochondrial 2298 1.68 0.52 0.03


P0CG38 POTE ankyrin domain family member I 2302 0.28 –1.27 0.12


P27348 14 3 3 protein theta 329 0.35 –1.25 0.06

P11021 78 kDa glucose regulated protein 188 2.56 0.94 0.03

P14625 Endoplasmin 131 0.32 0.84 0.04

P61981 14-3-3 protein gamma 733 1.25 0.22 0.06




P62258 14-3-3 protein epsilon 926 0.55 0.35 0.04

P61604 10 kDa heat shock protein mitochondrial 467 2.77 0.20 0.05


P09211 Glutathione S transferase P 579 1.36 0.31 0.05

P06748 Nucleophosmin 1542 0.57 –0.57 0.04


Q15233 Non POU domain containing octamer binding protein 238 2 2 2


P62081 40S ribosomal protein S7 369 0.68 –0.38 0.10

P16949 Stathmin 2116 1.55 0.44 0.03

Q05639 HUMAN Elongation factor 1 alpha 2 1543 0.40 –0.92 0.04


P63104 14-3-3 protein zeta delta 298 1.49 0.40 0.04


P62805 Histone H4 26679 0.52 –0.65 0.02



773

Table 3. Comparison of control and MPTP-treated samples. Table shows the relative expression change of protein in control and MPTP-treated SH-SY5Y cells by ratio; 2 refers to treated cells and 1 refers to control. Log(e) ratios and Log(e) standard deviations are also given in the table. (P < 0.05)

Accessio no. Protein name PLGS score 2:1_ ratio 2:1_ log(e)

ratio2:1_ log(e)std dev

O75526 RNA binding motif protein X linked like 2 251 1 1 1



P15531 Nucleoside diphosphate kinase A 2425 1.95 0.67 0.19

P16401 Histone H1 5 592 0.59 –0.53 0.07

P27348 Protein theta 329 0.32 –1.09 0.06


P49458 Signal recognition particle 9 kDa protein 612 2 2 2

P62158 Calmodulin 1809 0.66 –0.42 0.09

P62805 Histone H4 26679 0.66 –0.41 0.02

Q71UI9 Histone H2A V 6608 0.68 –0.38 0.04

Q99867 Putative tubulin beta 4q chain 1191 0.50 –0.70 0.16


Accessionno. Protein name PLGS score 2:1_ ratio 2:1_ log(e)

ratio2:1_ log(e) std dev

P10809 60 kDa heat shock protein mitochondrial 702 0.71 –0.34 0.04

P60174 Triosephosphate isomerase 2749 0.71 –0.34 0.03

P17066 Heat shock 70 kDa protein 6 848 0.70 –0.35 0.17

P04350 Tubulin beta 4 chain 4695 0.70 –0.36 0.12

P16401 Histone H1 5 592 0.70 –0.36 0.05


Q6S8J3 POTE ankyrin domain family member E 3025 0.58 –0.54 0.14

P13929 Beta enolase 417 0.66 –0.41 0.29



Q71UI9 Histone H2A V 6608 0.71 –0.35 0.03


P63261 Actin cytoplasmic 2 10879 0.58 –0.55 0.02

Q08211 ATP dependent RNA helicase A 84 0.62 –0.48 0.08

Q06830 Peroxiredoxin 1 441 0.70 –0.35 0.05


P14866 Heterogeneous nuclear ribonucleoprotein L 183 0.55 –0.60 0.21

P62158 Calmodulin 1809 0.67 –0.40 0.05

Table 4. Comparison of control and antimycin-A treated samples. Table shows the relative expression change of protein in control and antimycin-A treated SH-SY5Y cells by ratio. 2 refers to treated cells and 1 refers to control. Log(e) ratios and Log(e) standard deviations are also given in the table (P < 0.05).


774


P32119 Peroxiredoxin 2 808 0.59 –0.53 0.08


Q92621 Nuclear pore complex protein Nup205 98 0.30 –1.20 0.07

P07737 Profilin 1 2627 0.66 –0.41 0.04

P52565 Rho GDP dissociation inhibitor 1 448 0.70 –0.35 0.10

P34931 Heat shock 70 kDa protein 1 like 841 0.66 –0.41 0.21


P57053 Histone H2B type F 10888 2 2 2

P68363 Tubulin alpha 1B chain 5337 0.51 –0.67 0.03

P27348 14-3-3 protein theta 329 0.70 –0.36 0.08


P61981 14-3-3 protein gamma 733 0.58 –0.55 0.06





P62258 14-3-3 protein epsilon 926 0.54 –0.62 0.09



P06748 Nucleophosmin 1542 0.43 –0.85 0.05



P08107 Heat shock 70 kDa protein 1A 1B 843 0.70 –0.35 0.11

P06733 Alpha enolase 1243 0.68 –0.38 0.03

Q16658 Fascin 352 0.60 –0.32 0.05


Q9Y281 Cofilin 2 856 0.70 –0.25 0.27


Q16629 Serine arginine rich splicing factor 7 322 2 2 2

P04075 Fructose bisphosphate aldolase A 543 0.71 –0.34 0.07


P49411 Elongation factor Tu mitochondrial 285 0.41 –0.88 0.16




P63104 14 3 3 protein zeta delta 298 0.69 –0.37 0.06


P23528 Cofilin 1 10887 0.63 –0.46 0.03

P62805 Histone H4 26679 0.57 –0.56 0.02


775

Table 5. Comparison of control and oligomycin-treated samples. Table shows the relative expression change of protein in control and oligomycin-treated SH-SY5Y cells by ratio; 2 refers to treated cells and 1 refers to control. Log(e) ratios and Log(e) standard deviations are also given in the table (P < 0.05).

Accessionno. Protein name PLGS score 2:1_ ratio 2:1_ log(e)

ratio2:1_ log(e) std dev

P00338 L-lactate dehydrogenase A chain 816 0.70 –0.35 0.04

P00558 Phosphoglycerate kinase 1 424 0.69 –0.37 0.04




P06733 Alpha enolase 1243 0.61 –0.50 0.02

P06748 Nucleophosmin 1542 0.54 –0.62 0.04

P07195 L lactate dehydrogenase B chain 621 0.64 –0.44 0.03


P07737 Profilin 1 2627 0.58 –0.55 0.03

P07900 Heat shock protein HSP 90 alpha 1277 0.69 –0.37 0.03

P08107 Heat shock 70 kDa protein 1A 1B 843 0.70 –0.35 0.10






P11021 78 kDa glucose regulated protein 188 0.71 –0.35 0.04


P12277 Creatine kinase B type 317 0.64 –0.44 0.06



P14625 Endoplasmin 131 0.66 –0.41 0.06

P16401 Histone H1 5 592 0.63 –0.46 0.05

P16949 Stathmin 2116 0.69 –0.37 0.03


P23528 Cofilin 1 10887 0.58 –0.55 0.03

P26583 High mobility group protein B2 OS 415 0.66 –0.41 0.11

P27348 14-3-3 protein theta 329 0.60 –0.33 0.06



P32119 Peroxiredoxin 2 808 0.66 –0.42 0.06

P40926 Malate dehydrogenase mitochondrial 2298 0.63 –0.46 0.03

P60174 Triosephosphate isomerase 2749 0.67 –0.40 0.02



P61981 14-3-3 protein gamma 733 0.58 –0.55 0.04

P62158 Calmodulin 1809 0.59 –0.52 0.03

P62258 14-3-3 protein epsilon 926 0.55 –0.62 0.05

P62805 Histone H4 26679 0.70 –0.35 0.02


776

which component is the strongest one so that one group can be differentiated from another one. PCA also allows the determination of outliers in a data set, which enables a better statistical analysis.

After the MS/MS analysis, 75 statistically significant proteins were identified by the PLGS search engine. The statistically significant protein alterations detected were subjected to PCA. The scores plot shows the similarities and the differences between groups (Figure 2). The scores plot shows how the proteome profiles of the inhibitor-treated samples differ from that of the control group. Samples treated with 3-NP, antimycin-A, and oligomycin are coordinated closer to each other, showing that their proteome profiles have similar traits. On the other hand, cells treated with MPTP and sodium azide exhibited the greatest differentiation in terms of their proteome profiles. 3.4. Protein folding might be affected upon mitochondrial dysfunction STRING software (version 10) was used to understand the interaction between proteins and to obtain function-related pathways. Protein–protein interaction analysis was performed for the statistically significant protein expression changes in each inhibitor-treated sample set. STRING provided two major results regarding the analysis: a protein–protein interaction map regarding the

differentially expressed proteins, and gene ontology results demonstrating the enriched pathways.

Initially, STRING analysis was performed for the differentially expressed proteins in all groups and the results revealed that, although each group was treated with different inhibition agents, the differentially expressed proteins were involved in similar pathways, such as canonical glycolysis and the NAD metabolic processes.

Then the pathway analysis was repeated, taking all the significantly altered proteins into account regardless of the inhibitor type. The analysis incorporating all of the 75 proteins showed that differentially expressed proteins were in close connection with each other (Figure 3).

Gene ontology search results obtained from STRING analysis showed that many of the altered proteins are involved in metabolic pathways, such as NAD and NADH metabolic process, and glycolysis (Table 6a). Table 6 shows how many molecules are in a certain group (the number of genes) and also the false discovery rate for each specific pathway. Since STRING is a database of known and predicted protein–protein interactions, it builds its score based on probability calculations. The combined score is computed by combining the probabilities from the different evidence channels (for example, experiments) and corrected for the probability of randomly observing an




P63104 14-3-3 protein zeta delta 298 0.59 –0.53 0.05


P68363 Tubulin alpha 1B chain 5337 0.51 –0.67 0.02


Q02878 60S ribosomal protein L6 193 0.62 –0.48 0.07


Q06830 Peroxiredoxin 1 441 0.60 –0.51 0.04




Q16658 Fascin 352 0.58 –0.54 0.04

Q562R1 Beta actin like protein 2 1396 0.70 –0.35 0.05


Q6S8J3 POTE ankyrin domain family member E 3025 3.32 1.20 0.10

Q71UI9 Histone H2A V 6608 0.70 –0.35 0.03


Q99867 Putative tubulin beta 4q chain 1191 0.63 –0.47 0.45



777

Figure 2. Scores plot graph shows the differentiation of the groups. Each different colored dot represents a different group. Each group has 12 dots (4 biological and 3 technical repeats). The circle represents the significant data points.

interaction. Hence a false discovery rate is given for each gene ontology result. The false discovery rate is quite low (below 1E-8), which shows that the data are statistically significant.

The pathway analysis provides a broad view of protein–protein interactions, but the complexity of the molecular maps makes it harder to differentiate specific pathways associated with each inhibitor treatment. Hence, the STRING test was repeated with only the proteins that showed over 60% upregulation or downregulation. In this way, the proteins that caused the greatest changes and their influence were taken into account. The simplified protein map included 29 proteins (Figure 4). Besides showing the interactions between differentially expressed proteins, STRING also demonstrates the source of interaction. While pink lines represent experimentally determined data, yellow lines represent data curated from the database. The other lines represent predicted interactions.

Figure 4 shows a closely related interaction pathway. Some of these proteins are as follows: 14-3-3 zeta/delta (YWHAZ), 14-3-3 theta (YWHAQ), eukaryotic elongation factor 1 (EEF1), heat shock proteins (HSP), peptidyl prolyl isomerase A (PPIA), and prothymosin alpha (PTMA).

Interestingly, the proteins with the highest expression change were involved in pathways related to protein folding and response to unfolded proteins (Table 6b).

Similar to Table 6a, Table 6b shows that the false discovery rate is quite low and the data are statistically significant.3.5. Alteration in the 14-3-3 protein levels upon mitochondrial dysfunctionVerification of the protein expression change was done with Western blot analysis. Highly conserved phosphoserine/phosphothreonine-binding proteins, 14-3-3 proteins regulate a wide range of enzymes (Kleppe, 2011). Several isoforms of the 14-3-3 protein (epsilon, zeta/delta, theta, and gamma) were identified in the mass spectrometry results. In all of the sample groups, most of the isoforms were differentially expressed. In order to verify the mass spectrometry results, the expression levels of the 14-3-3 protein isoforms (epsilon, zeta/delta, gamma, and theta) were analyzed by Western blot analysis (Figures 5a–5f). Image J software was used to calculate band intensities. Western blot analysis validated that the mass spectrometry results were correct for 14-3-3 protein level changes.

4. DiscussionIn this study, protein expression profiles of mitochondrial dysfunction cell models, acquired by mild ETC complex-specific inhibition of the SH-SY5Y neuroblastoma cell line, were obtained in order to understand the relationship between mitochondrial dysfunction and neurodegeneration.


778

Inhibition of ETC complexes by specific inhibitors is known to induce oxidative stress, apoptosis, and distortion of many different signaling pathways (Schapira et al., 1990; Lin and Beal, 2006). Upon inhibition, there are alterations in the molecular machinery to adapt to the new cellular state. In this study, proteome changes were identified via label-free LC-MS/MS analysis of the cell lysates. Our aim was to see if alternate pathways are affected due to mitochondrial dysfunction triggered by blocking individual complexes of the ETC system. Although there is quite a lot of evidence that mitochondrial dysfunction is involved in neurodegenerative mechanisms (Schon et al., 2013), the debate about mitochondrial dysfunction being the primary reason for neurodegenerative disorders is still continuing.

With label-free differential proteome analysis of whole-cell extracts, a total 379 of proteins were identified and 75 of the proteins were found to be differentially expressed with a minimum 30% change. As indicated in the results, the mass error for m/z measurement was around 12 ppm, while the average mass error was 4 ppm. The results show the accuracy of the mass spectrometer as well as the validity of the protein identifications. The minimal variation in chromatographic retention times enabled the quantification of the proteins across sample sets accurately. Protein expression results were verified by Western blot analysis for the 14-3-3 protein isoforms (Figure 5a–5f).

After identifications and quantifications were completed, the group differences were visualized using PCA analysis. The analysis showed that each sample group

Figure 3. Protein interaction pathway analysis involving all differentially expressed proteins generated by STRING software. All differentially expressed proteins (75, which are statistically significant, >30%) in treated samples were analyzed by STRING. As seen from the figure, most of the proteins were closely connected to each other. Evidence view was used in STRING to create the protein interaction pathway.


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Table 6. Enrichment of gene ontology biological processes in differentially expressed proteins. Top pathways that have high significance (1.06E-8 and lower) are listed. A. Gene ontology annotations that belong to all differentially expressed proteins. B. Gene ontology annotations are given for the 29 proteins that showed the greatest expression difference. False discovery rates are given in the tables.

A.

GO_id Term Number of genes False discovery rate

GO:0061621 canonical glycolysis 10 5.5E-18

GO:0061620 glycolytic process through glucose-6-phosphate 10 5.5E-18

GO:0061615 glycolytic process through fructose-6-phosphate 10 5.5E-18

GO:0006007 glucose catabolic process 10 1.3E-17

GO:0019674 NAD metabolic process 11 4.93E-17

GO:0006734 NADH metabolic process 10 1.17E-16

GO:0019320 hexose catabolic process 10 3.16E-15

GO:0046365 monosaccharide catabolic process 10 1.17E-14

B.

GO_id Term Number of genes False discovery rate

GO:0006457 protein folding 6 1.17E-16

GO:0006986 response to unfolded protein 6 3.16E-15

GO:0006165 nucleoside diphosphate phosphorylation 5 1.17E-13

GO:0042981 regulation of apoptotic process 10 1.17E-13

has a differentiating proteome. All the groups showed clear differentiation from the control samples (Figure 2). While the proteomes of the cells treated with MPTP and sodium azide exhibited great differences compared to the control sample, cells treated with 3-NP, oligomycin, and antimycin-A show less differentiation (Figure 2).

It was expected that the individual complex inhibition in the ETC system would result in alterations in alternate pathways, considering the PCA results. Although PCA shows a differentiation, when the data were analyzed with STRING software and biological process results were obtained where the enriched pathways were listed, there was no actual differentiation between groups. Therefore, in order to see the bigger picture, all groups were included for the STRING protein–protein interaction analysis and for the gene ontology analysis. STRING analysis revealed that the most highlighted pathways were involved in metabolic processes, such as glycolysis and NAD metabolism, for all differentially expressed groups (Table 6).

The same analysis was performed for all differentially expressed proteins. Likewise, the majority of the proteins were involved in cellular and metabolic processes. It has been previously shown that neuronal energy metabolism rate is decreased in neurodegenerative diseases (Yin et al., 2012) and these findings also support this fact. Some of the metabolism-related proteins that were identified in this study were L-lactate dehydrogenase, phosphoglycerate

kinase 1, fructose bisphosphate aldolase-A, glyceraldehyde 3 phosphate dehydrogenase, malate dehydrogenase, alpha enolase, and gamma enolase. A reduction in the expression of these proteins proves that the energy metabolism of the cells was altered because of the mitochondrial dysfunction triggered by neurotoxins. The calculated protein expression changes were around 30%–70%, suggesting that even mild deficiencies were sufficient to alter mitochondrial functions.

Importantly, STRING uncovered several pathways that were strongly enriched in either the up- or downregulated protein lists (Figure 4). The protein–protein interactions involved in the regulation of protein synthesis were clustered in this pathway map (Figure 4). Significantly, a downregulation in the protein regulatory proteins (EEF1 and EEF2) and ribosomal proteins (RPLP 1, HNRP) may indicate a change in protein synthesis. Another cluster of interaction was centered on the heat shock proteins (HSPA, HSP90) (Figure 4). The results suggest that there may be a change in the protein regulatory mechanisms, such as protein folding and response to unfolded proteins (Table 6b). It is possible that mild inhibition of the ETC complexes affects heat shock proteins and the protein-folding mechanism fails, and afterwards proteins such as EEFs are downregulated to avoid further failure of the HSPs. It is also possible that mitochondrial dysfunction might have triggered protein unfolding or misfoldings and


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even apoptosis (Fribley et al., 2009). However, how these proteins affect each other should further be studied.

Prothymosin alpha (PTMS) was downregulated in four of the inhibition groups (3-NP, sodium azide, antimycin-A, and oligomycin). This protein has been previously suggested to have a function in learning and memory (Fernandez-Medarde et al., 2007) and also in neuroprotection (Maeda, 2016). In the network map (Figure 4), PTMS seems to interact with protein phosphatase 1, suggesting that its function may be regulated by phosphorylation. It is possible to assume that mitochondrial dysfunction through ETC inhibition affects the regulation of PTMS, but its role should be further studied.

Another interesting result is the downregulation of nucleophosmin 1 (NPM1) in four inhibition groups (3-

NP, sodium azide, antimycin-A, and oligomycin) by 40%–60%. Nucleophosmin is a nucleolar phosphoprotein that regulates various processes, including genomic integrity and proliferation. It has been suggested that nucleolar stress caused by changes in the distribution of nucleolar proteins such as nucleophosmin can have an important role in neurodegeneration (Parlato and Bierhoff, 2015). Recent studies suggest that NPM1 has a role in neuronal viability and protection against mHTT toxicity (Pfister and Mello, 2016). Further investigation of NPM1 is important to have a better understanding of its role.

The data produced from this study are important for understanding the ETC complex inhibitors’ effects on the proteome. Proteomics and bioinformatics analyses showed that these inhibitors result in alterations in metabolic processes like glycolysis. The results show

Figure 4. Protein interaction pathway analysis involving 29 highly differentially expressed proteins generated by STRING software. In this analysis, only 29 proteins that showed the highest expression differences (statistically significant, >60%) were involved in the analysis. Evidence view was used in STRING to create the protein interaction pathway.


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that complex-specific inhibition of SH-SY5Y cells with ETC inhibitors causes mild mitochondrial dysfunction and aberrant regulation in the proteome related to metabolism and ATP production; however, significant complex-specific changes between the inhibitor-treated groups were not observed. Since the highly differentially regulated proteins were involved in protein folding and response to misfolded proteins, mitochondrial

dysfunction and protein misfoldings in neurodegenerative diseases could be triggers for each other. Moreover, the data obtained show that proteomics could offer different views in understanding the mechanisms of mitochondrial dysfunction.

The study design has novel aspects incorporating complex-specific inhibitors to alter mitochondrial complex chain functions causing dysregulation in the

Figure 5. Western blot analysis of 14-3-3 proteins for inhibition groups was performed. Western blot analysis was carried out for all groups (a, b, c, d, e) using 20 µg of whole-cell lysate and 14-3-3 antibodies (epsilon, gamma, theta, and zeta/delta isoforms). Pan-actin was used as loading control. f. Image J software was used to obtain relative expression changes using the protein bands in Western analysis. na: not applicable, refers to the fact that those proteins were not identified in specified sample groups.


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process of ATP generation. Moreover, label-free LC-MS/MS analysis was performed for the detection of protein expressional changes across all 5 ETS complexes. Results provide insight regarding the molecular changes due to mitochondrial dysfunction. It is important to study mitochondrial dysfunction via in vitro cell-based models to understand the genesis of neurodegenerative pathways,

despite the fact that different neurological diseases exhibit deficiencies in different ETS complexes and demonstrate different pathological characteristics and symptoms. Our study aims to show that for the on-set of neurodegeneration pathways like protein folding and metabolic pathways have more important roles for disease pathology.

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1

Supplementary Table. Detailed list of all identified proteins.

1Ac

cess

ion

Entr

yDe

scrip

tion

mW

(Da)

pI (p

H)PL

GS S

core

Pept

ides

Theo

retic

al

Pept

ides

Cove

rage

(%

)

Prec

urso

r RM

S M

ass

Erro

r (p

pm)

Prod

ucts

Dige

st

Pept

ides

Mod

ified

Pe

ptid

es

Prod

ucts

RM

S M

ass

Erro

r (p

pm)

Prod

ucts

RM

S RT

Err

or (m

in)

Amou

nt

(fmol

)Am

ount

(n

gram

s)Pr

otei

n ID

2P6

2318

P623

18

SMD3

HU

MAN

Sm

all n

ucle

ar

ribon

ucle

opro

tein

Sm

D3

OS

Hom

o sa

pien

s GN

SN

RPD3

PE

1 SV

113

907

10.7

325

313.

9305

29

24.6

032

1.91

9911

20

20.8

434

0.01

4880

756

5.25

280.

0731

1958

5

3Q

1523

3Q

1523

3

NO

NO

HU

MAN

Non

PO

U d

omai

n co

ntai

ning

oct

amer

bin

ding

pro

tein

OS

Hom

o sa

pien

s GN

NO

NO

PE

1 SV

454

197

9.36

9117

0.46

757

3517

.622

117

.233

733

72

22.2

138

0.02

0208

072

3.06

870.

1664

1372

3

4Q

1662

9Q

1662

9

SRSF

7 HU

MAN

Ser

ine

argi

nine

rich

sp

licin

g fa

ctor

7 O

S Ho

mo

sapi

ens G

N

SRSF

7 PE

1 S

V 1

2735

012

.223

922

5.16

644

1513

.025

210

.567

317

31

13.3

584

0.01

9660

236

9.92

040.

2715

2005

1

5Q

9NW

38Q

9NW

38

FAN

CL H

UM

AN E

3 ub

iqui

tin p

rote

in

ligas

e FA

NCL

OS

Hom

o sa

pien

s GN

FA

NCL

PE

1 SV

242

876

5.99

9519

0.80

568

2522

.43.

6637

355

219

.168

40.

0219

8807

27.

4328

0.31

8968

40

6O

1524

0O

1524

0VG

F HU

MAN

Neu

rose

cret

ory

prot

ein

VGF

OS

Hom

o sa

pien

s GN

VGF

PE

1 SV

267

217

4.56

4130

6.29

4415

5722

.764

211

.762

776

111

23.9

563

0.02

0317

067.

4097

0.49

8423

293

7Q

5T1J

5Q

5T1J

5

CHCH

9 HU

MAN

Put

ativ

e co

iled

coil

helix

co

iled

coil

helix

dom

ain

cont

aini

ng

prot

ein

9 m

itoch

ondr

ial

1547

910

.276

169

1.75

273

427

.152

314

.867

418

31

21.5

682

0.02

3870

206

00

3242

8Q

5JXB

2Q

5JXB

2

UE2

NL

HUM

AN P

utat

ive

ubiq

uitin

co

njug

atin

g en

zym

e E2

N li

ke O

S Ho

mo

sapi

ens G

N U

BE2N

L PE

5 S

V 1

1736

65.

5637

557.

6575

517

32.6

797

9.61

5630

50

21.7

637

0.02

2582

028

5.49

440.

0955

2227

5

9Q

9259

8Q

9259

8

HS10

5 HU

MAN

Hea

t sho

ck p

rote

in 1

05

kDa

OS

Hom

o sa

pien

s GN

HSP

H1 P

E 1

SV

196

803

5.09

9110

3.42

0211

7016

.200

524

.018

755

112

29.8

560.

0253

4561

68.

1664

0.79

191

88

10Q

9HC3

5Q

9HC3

5

EMAL

4 HU

MAN

Ech

inod

erm

m

icro

tubu

le a

ssoc

iate

d pr

otei

n lik

e 4

OS

Hom

o sa

pien

s GN

EM

L4 P

E 1

SV 3

1088

485.

9357

75.2

951

976

9.27

6228

.778

742

61

26.9

262

0.01

9738

566

30.4

192

3.31

3260

13

11Q

9971

4Q

9971

4

HCD2

HU

MAN

3 h

ydro

xyac

yl C

oA

dehy

drog

enas

e ty

pe 2

OS

Hom

o sa

pien

s GN

HSD

17B1

0 PE

1 S

V 3

2690

67.

8378

436.

3802

517

27.5

862

21.3

036

315

023

.164

90.

0178

9426

3.14

030.

0845

8668

12P3

9019

P390

19RS

19 H

UM

AN 4

0S ri

boso

mal

pro

tein

S19

O

S Ho

mo

sapi

ens G

N R

PS19

PE

1 SV

216

050

10.7

3226

9.89

763

1728

.275

910

.692

622

30

23.3

662

0.01

9800

758

5.07

750.

0815

1852

4

13Q

71U

36Q

71U

36TB

A1A

HUM

AN T

ubul

in a

lpha

1A

chai

n O

S Ho

mo

sapi

ens G

N T

UBA

1A P

E 1

SV 1

5010

34.

7622

7383

.646

2934

34.5

898

4.88

3929

913

215

.372

80.

0245

1297

314

6.56

517.

3482

2064

8

14Q

8N4N

3Q

8N4N

3KL

H36

HUM

AN K

elch

like

pro

tein

36

OS

Hom

o sa

pien

s GN

KLH

L36

PE 1

SV

169

851

5.79

913

3.63

675

4812

.013

16.2

761

275

020

.831

50.

0225

281

24.1

411

1.68

7410

541

15P3

1948

P319

48

STIP

1 HU

MAN

Str

ess i

nduc

ed

phos

phop

rote

in 1

OS

Hom

o sa

pien

s GN

ST

IP1

PE 1

SV

162

599

6.38

5811

7.27

424

649.

7606

37.8

031

154

022

.575

40.

0222

6218

211

.672

60.

7312

2017

4

16A0

AVI2

A0AV

I2FR

1L5

HUM

AN F

er 1

like

pro

tein

5 O

S Ho

mo

sapi

ens G

N F

ER1L

5 PE

2 S

V 2

2418

067.

8257

79.5

569

817

56.

1634

25.1

749

458

323

.853

70.

0244

9194

57.

3878

1.78

7672

18

17P0

4792

P047

92HS

PB1

HUM

AN H

eat s

hock

pro

tein

bet

a 1

OS

Hom

o sa

pien

s GN

HSP

B1 P

E 1

SV 2

2276

85.

9588

374.

0919

315

20.9

756

7.44

6817

30

20.9

752

0.02

5573

878

5.96

220.

1358

9229

18P1

1586

P115

86

C1TC

HU

MAN

C 1

tetr

ahyd

rofo

late

sy

ntha

se c

ytop

lasm

ic O

S Ho

mo

sapi

ens

GN M

THFD

1 PE

1 S

V 3

1014

956.

9108

109.

6188

673

8.12

838.

7208

316

226

.475

40.

0230

8138

33.8

406

3.43

6819

23

Documents

Proteomics analysis of mitochondrial dysfunction triggered