Introduction

Mitochondrial dysfunction and oxidative damage are central to the neurodegeneration and pathogenesis of Parkinson’s disease (PD). Research evidences from human postmortem brain samples have revealed that mitochondrial damage in the substantia nigra region of PD patients mainly includes inhibition of mitochondrial complex I (CI) [1,2,3].

CI is the first enzyme of the electron transport chain with 44 subunits and molecular weight of ~1 MD. Defect in CI activity has been reported in human diseases [4]. Studies on patient samples from mitochondrial disorders have revealed that specific genetic mutations in different subunits of the complex could potentially contribute to the lowered enzyme activity [5]. Apart from this, post-translational modifications (PTMs) of the CI subunits could potentially alter the structure–function relationship of CI [6]. PTMs are known to regulate protein structure and the associated biochemical pathways. PTMs could either be non-oxidative PTMs such as phosphorylation, acetylation, methylation or oxidative PTMs such as Tryptophan (Trp) and Cysteine (Cys) oxidation, Tyrosine (Tyr) nitration among others. Many PTMs that affect the function of mitochondrial proteins have been noted [6]. The CI subunits are also known to undergo PTMs during normal physiological condition and diseases. Studies on CI in different paradigms have extensively reported oxidative and non-oxidative PTMs [6, 7].

Epidemiological studies have indicated that exposure to toxic chemicals such as pesticides, herbicides and other neurotoxins could potentially induce acute PD or parkinsonism in humans [8]. Most of these neurotoxins such as rotenone (Rot), paraquat (Pq) and 1-methyl-4-phenylpyridinium (MPP+) induced neurodegeneration via mitochondrial dysfunction and selective inhibition of CI [8]. Although these neurotoxic models have been used to study PD pathogenesis, certain questions remain unanswered. Firstly, whether the downstream pathways following exposure to these three toxins are comparable is not explored. Secondly, whether exposure to these three toxins can cause CI inhibition via oxidative damage of different subunits of the complex and whether these are common across the three models are largely unknown.

To address these lacunae, we have in this study compared the downstream pathways that are elicited following exposure to these three toxins in dopaminergic cell lines by carrying out a comprehensive proteomic analysis. We have also isolated CI from these three toxic models and compared the PTMs that could potentially characterize the inhibition of the complex. Finally, molecular dynamic simulation (MDS) approach was employed to understand the structural changes induced by selected oxidative PTMs in the critical subunits of the complex.

Materials and Methods

All the chemicals and solvents were of analytical grade. Routine and bulk chemicals were obtained from Sisco Research Laboratories (SRL) Pvt. Ltd. (Mumbai, Maharashtra, India). Fine chemicals such as Rot, Pq, MPP+, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), Dichlorodihydrofluorescein diacetate (DCF-DA), 5,5'-dithio-bis-(2-nitrobenzoic acid) (DTNB), Glutathione reductase and Anti-dinitrophenyl (DNP) antibody were obtained from Merck-Sigma (St. Louis, MO, USA). Cell culture consumables such as RPMI 1640, Trypsin EDTA from Merck-Sigma, Fetal Bovine serum from PAN Biotech (Aidenbach, Bavaria, Germany) and Antibiotic and antimycotic solution from HIMEDIA (Einhausen, Germany) were obtained. Primary antibodies (against VDAC1, β-actin, Biotin) and CI isolation/immunocapture kit were procured from Abcam (Cambridge, UK) (Cat No. ab109711). Anti-horseradish peroxidase conjugated secondary antibodies (anti-rabbit and anti-goat) were obtained from Bangalore Genei (Bangalore, Karnataka, India). Hydrazide biotin and TMT labelling kit were purchased from Thermo fisher scientific (Waltham, MA, USA). Sequencing grade modified trypsin was obtained from Promega (Madison, WI, USA). Mass spectrometry consumables such as sodium dodecyl sulphate (SDS), triethyl ammonium bicarbonate (TEABC) buffer, ammonium bicarbonate (ABC) buffer, iodoacetamide, dithiothreitol (DTT), acetone, formic acid (FA), acetonitrile (ACN) were obtained from Merck-Sigma.

Cell Culture

We have extensively used Rat dopaminergic 1RB3AN27 (N27) neuronal cell line throughout this study [9]. The cell line was obtained as a kind gift from Dr. Curt Freed, University of Colorado, USA. The cell line was cultured and maintained as previously described [10]. N27 cells were treated at different concentrations of Rot (0–2000 nM) or Pq (0–2000 μM) for 24 h and Rot (0–2000 nM) or Pq (0–200 μM) or MPP+(0–2000 μM) for 48 h, assessed for cell viability using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay [11] and LD25 and LD50 values were calculated at 48 h. (We have used LD25 and LD50 in all experiments except western blot experiments for mitochondrial samples and CI assay).

Alternately, Lactate dehydrogenase (LDH) assay was used to monitor cell viability [12] by measuring the activity in the culture supernatants of N27 cells treated with Rot (250 nM and 500 nM, 48 h), Pq (50 μM and 100 μM, 48 h) and MPP+ (150 μM and 250 μM, 48 h). We have chosen different doses of each neurotoxins to measure LDH, because N27 showed varied sensitivity to the toxins in the MTT assay.

Measurement of Reactive Oxygen Species (ROS)

ROS generation in different neurotoxic models compared to the respective controls was assayed using dihydrodichloro fluorescein diacetate (H2 DCFH-DA) method as described [13].

Total Glutathione (GSH + GSSG) Estimation

The control and neurotoxin treated cells were subjected to total glutathione estimations by 5,5′-dithio-bis-2-nitrobenzoic acid (DTNB) recycling method [10, 13], based on the maximum reaction rate compared with GSSG standards (0–250 ng). All estimations were conducted in triplicate, normalized per protein and expressed as percentage of untreated control.

Isolation of Mitochondria

Mitochondria from control and neurotoxin treated cells were isolated as described [10]. The crude mitochondrial fraction was suspended in isolation buffer and stored as aliquots at – 80 °C. Total protein in the mitochondrial preparation was estimated by Bradford method [14].

Mitochondrial Complex I Assay

CI enzyme activity was assayed in untreated and neurotoxin treated cells as described [10]. The rotenone-sensitive specific activity was calculated and expressed as percentage of untreated control.

Total Proteomics

Preparation of Cell Extracts

Control and treated N27 cells were sonicated in 1 × PBS with 1 × protease inhibitor cocktail (Sigma-Aldrich) using a probe sonicator for 10 s × 6 cycles (45% amplitude) on ice. The sonicate was centrifuged (10,000 g for 10 min at 4 °C) and the soluble extract corresponding to the supernatant was subjected to protein estimation by Bradford method [14]. During standardization and pilot experiments, we noted that other protocols had limitations including inconsistent protein yield in different replicates and problems with the extent of solubility. Hence this protocol was chosen for preparation of soluble extracts for proteomics experiments. Considering this, the protein profile might not represent the global proteome of the N27 dopaminergic cells.

Sample Preparation and TMT Labelling

Total cellular extracts (with equal protein as determined in the previous section) from untreated control (Group 1), Rot treated (LD25-Group 2 and LD50-Group 3), Pq treated (LD25-Group 4 and LD50-Group 5) MPP+ treated (LD25-Group 6 and LD50-Group 7) were suspended in 2% SDS lysis buffer. The lysate was sonicated on ice and heated at 90 °C for 5 min followed by centrifugation (12,000 rpm for 15 min). Equal amount of protein (250 μg) from each sample was reduced using 5 mM of DTT at 60 °C for 60 min, alkylated with 20 mM iodoacetamide for 20 min at room temperature (RT) in dark, precipitated with chilled acetone at − 20 °C overnight and centrifuged (12,000 rpm at 4 °C for 15 min). The pellets were dissolved in 50 mM Triethyl ammonium bicarbonate (TEABC) buffer (pH 8.5) and then digested with sequencing grade modified trypsin (Promega) at 37 °C for 16 h and dried in a vacuum concentrator. Digested peptides were suspended in 100 μl 50 mM TEABC (pH 8.5) and labelled with TMT reagent as per the manufacturer’s protocol. The samples from different groups were labelled as follows: 126 (control), 127N (Rot-LD25), 128N (Rot-LD50), 128C (Pq-LD25) and 129N (Pq-LD50), 129C (MPP+-LD25) and 130N (MPP+-LD50). The pooled sample was dried and fractionated into twelve fractions using basic pH reversed-phase liquid chromatography (bRPLC) as described [15]. Samples were reconstituted in 1 ml bRPLC solvent A (10 mM TEABC, pH 9.5). Increasing gradient of 7–100% solvent B (10 mM TEABC in 95% acetonitrile, pH 9.5) was employed to fractionate peptides using XBridge C18, 5 μm, 250 × 4.6 mm column (Waters corporation, Milford, MA) with a flow rate of 500 μl/min for 120 min on an Agilent 1200 series HPLC system. The eluting peptides were collected in a 96 well plate and concentrated into 12 fractions. Each fraction was concentrated under vacuum and desalted using C18 stage tip clean up [7] followed by liquid chromatography-tandem mass spectrometry (LC–MS/MS).

LC–MS/MS

The peptides were analysed on an Orbitrap Fusion Tribrid mass spectrometer (Thermo Scientific, Bremen, Germany) interfaced with Easy-nLC 1000 nanoflow LC system (Thermo Scientific, Bremen, Germany). Vacuum dried peptide digests were reconstituted in 0.1% FA and loaded onto a 2 cm long pre-column (75 µm × 2 cm; nano Viper; C18; 3 µm particle and 100 Å pore size) (Thermo scientific Acclaim PepMap 100) using solvent A [0.1% formic acid (FA)] at a flow rate of 3 µl/min. Peptides were then resolved on analytical column (2 µm, 75 µm × 50 cm, 100 Å pore size) (Thermo scientific PepMap™ RSLC C18) using a linear gradient of 5% to 30% of solvent B [0.1% FA in 95% Acetonitrile (ACN)] over 100 min and flow rate of 300 nl/min. The total run time was set to 120 min. The MS was operated in a data-dependent acquisition mode. A precursor survey full scan MS (from m/z 350–1600) was acquired in the Orbitrap at a resolution of 120,000 at 200 m/z. The automatic gain control (AGC) target for MS1 was set as 4 X 105 and ion filling time set as 50 ms. The most intense ions with charge state ≥ 2 was isolated and fragmented using higher-energy collision-trap dissociation (HCD) fragmentation with 34% normalized collision energy and detected at a mass resolution of 50,000 at 200 m/z. The AGC target for MS/MS was set as 1 × 105 and ion filling time set as 100 ms. Isolation width was used as 1.6 m/z.

Data Analysis

Mass spectrometry derived raw data were searched against Rattus norvegicus database from UniProt (UP000002494- May 30th, 2021) along with known MS contaminants using SEQUEST search engine nodes on Proteome Discoverer 2.2 platform [7]. Trypsin was selected as the proteolytic enzyme and two missed cleavages were allowed during the search. Precursor and fragment ion mass tolerance were set to 10 ppm and 0.05 Da respectively. Carbamidomethylation of cysteine, TMT labelling at peptide N-terminus and lysine side chain were selected as static modification and oxidation of methionine was set as a dynamic modification. The data were filtered at 1% protein level false discovery rate (FDR). The reporter ion quantifier node was used to estimate relative quantitation of TMT channels from MS2 scans and normalization option was enabled. For any protein to be considered as significantly dysregulated, fold change above 1.5 as upregulated and below 0.6 as downregulated and p-value < 0.05 were considered. Since the number of differentially expressed proteins were very few, we performed manual analysis, where we identified the functions of each gene using Uniprot followed by literature survey and categorization.

Complex I (CI) Proteomics

Isolation of CI

CI was isolated using immunocapture method from N27 mitochondria using a commercial kit (Abcam) as per the manufacturer’s protocol. Briefly, mitochondria (1 mg) were solubilized in ice-cold 1X PBS containing 1% N -dodecyl β-D-maltoside (DDM) for 30 min on ice and centrifuged (20,000 g for 30 min, 4 °C) [16]. The supernatant was incubated with agarose beads irreversibly cross-linked to CI-specific monoclonal antibody provided as part of the CI immunocapture kit (Abcam) overnight at 4 °C on a rocker. The beads were washed 5 times with 1 × PBS and the bound complex was eluted with 0.1 M glycine–HCl buffer, pH 2.5 supplemented with 0.05% DDM. These eluates were processed for in-solution tryptic digestion.

In-solution Tryptic Digestion

The eluates from CI immunocapture experiments were reduced with 10 mM DTT at 60 °C for 60 min, followed by alkylation with 20 mM Iodoacetamide (IAA) for 30 min at RT in dark. Alkylated proteins were precipitated by adding five volumes of chilled acetone and centrifuged at 12,000 rpm, 4 °C for 15 min. The pellets were dissolved in 40 mM ammonium bicarbonate (ABC) and then incubated with sequencing grade trypsin (Promega) (at 1:20, enzyme: protein) at 37 °C for 16 h. The reaction was stopped with 0.1% FA, purified on a C18 column and the peptide mixture was dried in a vacuum concentrator, followed by LC–MS/MS analysis.

LC–MS/MS

The peptides were analysed on an Orbitrap Fusion Tribrid mass spectrometer (Thermo Scientific, Bremen, Germany) interfaced with Easy-nLC 1000 nanoflow liquid chromatography system (Thermo Scientific, Bremen, Germany). Vacuum dried peptide digests were reconstituted in 0.1% FA and loaded onto a 2 cm long pre-column 75 µm × 2 cm, nano Viper, C18, 3 µ particle and 100 Å pore size (Thermo scientific Acclaim PepMap 100) and analytical column 2 µm, 75 µm × 50 cm, 75 µm × 50 cm, 100 Å pore size (Thermo scientific PepMap™ RSLC C18) using a linear gradient of 5% to 30% of solvent B (0.1% FA in 95% ACN) over 100 min and flow rate of 300 nl/min. The total run time was set to 120 min. The MS was operated in a data-dependent acquisition mode. A precursor survey full scan MS (from m/z 350–1600) was acquired in the Orbitrap at a resolution of 120,000 at 200 m/z. The AGC target for MS1 was set as 4 X 105 and ion filling time set 50 ms. The most intense ions with charge state ≥ 2 was isolated and fragmented using HCD fragmentation with 34% normalized collision energy and detected at a mass resolution of 30,000 at 200 m/z. The AGC target for MS/MS was set as 1 × 105 and ion filling time set 100 ms and isolation width was used at 1.6 m/z.

Data Analysis

The acquired MS/MS data were processed through Proteome Discoverer platform (version 2.2 Thermo Scientific) using SEQUEST search algorithm against Rattus norvegicus protein database from UNIPROT (UP000002494- May 30th 2021) containing protein entries along with common MS contaminants. Trypsin allowing a maximum of two missed cleavages were selected as the proteolytic enzyme and oxidation of methionine and carbamidomethyl cysteine were set as dynamic modifications. For MS data, monoisotopic peptide mass tolerance was set to 10 ppm and MS/MS tolerance to 0.5 Da.

Based on the PTMs that were detected in the preliminary analysis without enrichment, the search parameters for the SEQUEST search algorithm focused on oxidation and dioxidation (W, C), trioxidation (C), cysteinylation (C), acetylation (N-terminal of protein and K), nitration (Y), methylation, Dimethylation and trimethylation (K, R), and phosphorylation (S, T, Y) as dynamic modifications and carbamidomethylation (C) as a static modification. While searching for Cysteine PTMs, carbamidomethylation was set as dynamic modification. The data were filtered at 1% level FDR at peptide spectrum matches (PSMs). The confirmation of all PTMs was carried out based on manual analysis of MS data.

Oxyblot

Protein carbonyls in the protein extracts of the neurotoxic models were quantitated either by oxyblot method [17] or Biotin hydrazide method [18]. In oxyblot, the total cell extracts were derivatized by 2,4-dinitrophenylhydrazine (DNPH) followed by dot blot using anti-DNP antibody. In the biotin hydrazide method, total and mitochondrial extracts were derivatized with biotin hydrazide followed by western blot with anti-biotin antibody. The western signal was developed by enhanced chemiluminescence and visualized in a gel documentation system (Biorad). The images were quantified using Image J software [19] and normalized to their respective loading controls [β-Actin (1:3000) for total cellular protein and VDAC1 (1:3000) for mitochondrial protein] and expressed as percentage of untreated control.

Homology Modeling

The protein sequence of CI subunits was obtained from the Uniprot database (www.uniprot.org). Homology modeling was carried out to generate rat CI, using Discovery studio 3.5 with the mouse CI template (98% sequence identity between rat and mouse CI subunits) available at Protein Data Bank (PDB) with ID 6G2J. The protein model thus obtained was subjected to energy minimization and processed by applying CHARMM force field [20]. The potential energy of the structure was calculated using energy protocol available in Accelrys Discovery Studio 3.5.

Molecular Dynamics Simulation (MDS)

Preparation of the System

Five mutually interacting peripheral arm subunits of CI (NDUFV1, NDUFV2, NDUFV3, NDUFS1 and NDUFS4) were considered as a sub-complex for structural analysis. Among these, W433 in NDUFV1 subunit was replaced with oxindolylalanine (oxy-Trp or 2-OH Trp) to generate the oxidized subunit. The unmodified and Trp433 oxidized subcomplexes were subjected to MDS. The MDS solvent system of the subcomplexes was built using Desmond 2019. The Optimized Potentials for Liquid Simulations (OPLS) force field was added to the system and simple extended point charge (SPC) water system, along with a cubic box, was used to model the solvent. 1.5 mM of NaCl ions were added to neutralize the systems in the water-filled box. The MDS was set up for 100 ns under normal NTP [constant number of particles (N), temperature 310 k (T) and pressure-1 bar (P)] conditions. The system was relaxed prior to the simulation.

Analysis of Trajectories

After the simulation, the resultant 100 ns trajectories of the subunits of the unmodified subcomplex were analysed for protein backbone parameters such as Root Mean Square Deviation (RMSD) and Cα for Root Mean Square Fluctuation (RMSF). Further, all the subunits of the subcomplex were assessed for Radius of Gyration (Rg) throughout the duration of the simulation. The RMSD and Rg values were calculated against the simulation time and expressed as the deviation of radius of the selected group of atoms, respectively, in Å. The RMSF values of the Cα were calculated over the range of residues of the subunits and expressed as summation throughout the simulation for each residue fluctuation and denoted in Å. The distance between FMN and iron–sulfur cluster Fe-S301, FMN and iron–sulfur cluster Fe-S502, Fe-S301 and Fe-S502, Fe-S502 and Fe-S803, Fe-S803 and Fe-S802, and Fe-S802 and iron-sulfur cluster Fe-S801 were calculated for the unmodified and oxidized form. The Desmond module was used for the calculation of parameters. Maestro and PyMOL (www.pymol.org) were used for the generation of high-resolution illustrations [7].

Statistical Analysis

Quantitative data represented by bar graphs were accumulated from at least three independent experiments and expressed as mean ± SD. All analyses were performed using Microsoft Excel. For data related to validation experiments, analysis of variance (ANOVA) was performed and p-value < 0.05 was considered as significant. For all the MS based data, statistical analysis was carried out on Proteome Discoverer 2.2, which has integrated database search and statistical algorithms. Statistically corrected MS data was then uploaded on to Perseus (1.6.15.0) (http://www.perseus-framework.org) to generate a cluster based heatmap using Euclidean distance method.

Results

Proteomic Analysis of the Neurotoxic Models of PD

Neurotoxic models using Rot, Pq and MPP+ mimic PD pathology via selective inhibition of CI and mitochondrial dysfunction [21]. However, whether these three neurotoxins induce similar degenerative pathways in dopaminergic neurons and PTMs of CI are not compared.

To address the first objective, we carried out comparative proteomic analysis of Rot, Pq, and MPP+ neuronal cell models of PD at LD25 (representing early events of neurodegeneration) and LD50 (representing the neurotoxic phase) of each toxin compared to untreated control. Characterization of the three models revealed dose-dependent neurotoxicity as shown by cell viability assay (Rot- 0–2000 nM with LD25 = 250 nM and LD50 = 500 nM; Pq- 0–200 μM with LD25 = 50 μM and LD50 = 100 μM; MPP+- 0–2000 μM with LD25 = 150 μM and LD50 = 250 μM) (Supplementary figure S1A–C) consistent with previous reports [22]. Subsequent experiments at LD25 and LD50 (vs. controls) revealed increased LDH activity (Supplementary figure S1D–E), elevated ROS (Supplementary figure S2A–C), lowered total glutathione (Supplementary figure S2D–F), increased protein carbonyls (Supplementary figure S3, S4 and S5) and inhibition of CI activity (Supplementary figure S6) in all the three models, consistent with previous results [11] and corroborating PD-specific neurotoxic mechanisms. Although these experiments were carried out both at 24 h and 48 h timepoints, we noted that the 48 h data was more consistent compared to the 24 h data in terms of cytotoxicity and other parameters across the three toxins. Hence, 48 h treatment regimen was followed throughout the study and used to calculate LD25 and LD50.

Total proteomic analyses of the untreated control (Group 1) and three neurotoxic models at LD25 and LD50 (Groups 2 to 7) were carried out and compared across different groups (Fig. 1A and B). We identified 6400 proteins across all the groups, of which 1046 were mitochondrial and 5354 were non-mitochondrial proteins. Identified proteins were further categorized as dysregulated if they had a fold change ratio ≥ 1.5 and ≤ 0.6 and a p-value of ≤ 0.05. Using the aforementioned criteria, 89 differentially expressed proteins (DEPs) including 32 up-regulated and 57 down-regulated proteins across all the three toxic models, were noted at LD25 and LD50. We analyzed the proteomic data obtained at LD25 and LD50 to obtain a comprehensive view of the molecular mechanisms underlying the neurotoxicity in PD.

Fig. 1
figure 1figure 1

Total Proteomic analysis in neurotoxic PD models. A Schematic representation of workflow of the Proteomics experiment. Total proteins (soluble extract) from control, Rot LD25 (R25), Rot LD50 (R50), Pq LD25 (P25), Pq LD50 (P50), MPP+ LD25 (M25) and MPP+ LD50 (M50) were subjected to tryptic digestion, followed by TMT labeling, fractionation, MS and data analysis. B SDS-PAGE profile of all the groups (C) Heat-map of differentially expressed proteins in different groups. The scale bar indicating the fold change in expression of individual proteins is also shown. Venn diagram shows the number of common and unique (D) up-regulated and (E) down-regulated proteins in Rot-, Pq- and MPP+-treated cells. Volcano plots of the differentially expressed proteins in all the experimental groups are shown in F–K. Individual proteins (p < 0.05) corresponding to the down regulated (> 1.5 fold) and upregulated proteins (< 0.6 fold) (compared to the respective controls) are indicated in green and red respectively, while the proteins with unchanged expression are in black

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Toxin-wise analysis revealed 55 DEPs in the Rot model (including 29 down-regulated and 26 up-regulated proteins), 52 DEPs in the Pq model (35 down-regulated and 17 up-regulated proteins) and 45 DEPs in the MPP+ model (31 down-regulated and 14 up-regulated proteins) (Table 1 and Fig. 1C). Comparison of the proteomics data revealed that many up-regulated (n = 10) and down-regulated (n = 14) proteins were common across the three toxic models (Fig. 1D and E; volcano plots are shown in Fig. 1F–K). Interestingly, MPP+ model revealed relatively fewer number of unique DEPs compared to the other two models.

Table 1 List of Differentially expressed genes across the Rot/Pq/MPP+ models of PD. The accession ID, gene symbol and protein name along with the fold change in expression (vs. control) observed in the total proteomics experiment are provided. UC = unchanged
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Functional classification of DEPs revealed the involvement of biological processes mainly including cell death pathways, nuclear processes, protein and lipid metabolism, structural changes, immune responses, mitochondrial process and others (Fig. 2 and Table 1), most of which were common across the three models. Prominent pathways that could potentially contribute to neurotoxicity included cell death pathways, structural changes and metabolic alterations. Cell death pathways were represented by autophagy and cell cycle proteins. Among these, the protein Sequestosome (Sqstm1), involved in autophagy was significantly upregulated across all the toxins. On the other hand, the cell cycle controlling protein asparagine synthetase [glutamine-hydrolyzing] (Asns) and Prothymosin alpha (Ptma) that negatively regulates apoptosis was significantly downregulated (Table 1). Structural changes included upregulation of cytoskeletal proteins (Vimentin and Caldesmon) and disruption of extracellular matrix. The toxic insult also elicited stress response as indicated by the overexpression of chaperones such as Heat shock protein Hspb1 and Alpha Crystalline (B chain) (Cryab). Metabolic processes contributing to neurotoxicity mainly included lowered antioxidant response (as indicated by downregulation of SOD2) and downregulation of mitochondrial proteins. Altered antioxidant response could correspond to upregulated glutathione peroxidase (that could probably contribute to elevated GSSG, an indicator of oxidative stress) and thioredoxin domain containing protein 1 (Tmx1), that regulates redox dynamics.

Fig. 2
figure 2

Schematic representation of cellular events in neurotoxic PD models. Summary of the trend of different biological processes that are altered at the total proteome level in different neurotoxic models (Rot, Pq and MPP+). Each circle represents each neurotoxin and the common process for all 3 neurotoxins are listed in the middle

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Lowered expression of mitochondrial proteins included downregulation of electron transport chain (ETC) and pyruvate dehydrogenase complex. Among the ETC proteins, subunits of CI (including NDUFS6, NDUFA10, NDUFS1, NDUFS5) were downregulated. Other downregulated ETC proteins included cytochrome C oxidase subunits. Other metabolic proteins altered included succinate ligase [GDP-forming] subunit beta, aldehyde dehydrogenase and dihydrolipoyl dehydrogenase, delta (3,5)-delta (2,4)-dienoyl-CoA isomerase, medium-chain acyl-CoA ligase ACSF2 acyl-coenzyme A dehydrogenase and high affinity cationic amino acid transporter 1 (Table 1). Overall, we propose that neurotoxin-mediated CI inhibition triggers structural and functional pathways leading to neurodegeneration in dopaminergic neurons.

Proteomic Analysis of PTMs in CI of Neurotoxic Models

Proteomics data revealed downregulation of CI subunits in all three models. Since these neurotoxins targeted CI and caused inhibition of enzyme activity (Supplementary figure S6), we investigated whether the complex displayed PTMs following neurotoxic insult. Towards this, CI was isolated from untreated control and the three neurotoxic models by immunoprecipitation method followed by proteomic analysis (Fig. 3A). The subunit composition of the isolated complex was characterized by MS. Accordingly, 43 out of the possible 44 subunits were identified in Rot (both LD25 and LD50), Pq (LD25 and LD50) and MPP+ (LD50) and 42 subunits in control and MPP+ at LD25 (Table 2).

Fig. 3
figure 3

Immunocapture of mitochondrial Complex I (CI) and PTM characterization. A Experimental workflow. Total mitochondria were isolated from Control, Rot, Pq and MPP+. CI was immunocaptured individually from each group and subjected to in-solution tryptic digestion and LC–MS/MS followed by subunit characterization. The MS data was mined to identify PTMs in different subunits of CI. B The table shows the types and number of PTMs at Trp (W), Cys (C), Lys (K) and Arg (R) residues across different control and toxic models (C) Graph shows the number of core and supernumerary subunits with PTMs. D The type and number of PTMs across control and toxic models. E The number of modified peptides in CI in control and toxic models. The subunits underlined in red are the core subunits of CI

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Table 2 Summary of the MS characterization of CI subunits
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The LC–MS/MS data from the control and toxic models were mined for identification of 18 different PTMs (oxidative and non-oxidative) across the subunits of CI (Table 3). Data analysis revealed 66 PTMs in CI subunits, mainly including oxidative modifications (Trp oxidation and Cys oxidation) and limited non-oxidative modifications (Lys acetylation and Arg methylation). The toxic models revealed relatively higher number of PTMs compared to the untreated control. Supernumerary subunits of CI were targeted for PTMs to a greater extent compared to the core subunits, both in control and toxic models. Trp oxidation accounted for most of the oxidative PTMs (n = 62) including oxyindolylalanine (n = 29) and N-formylkynurenine (n = 33). On the other hand, limited Cys oxidation (n = 7) including trioxidation (Cys to Cys-sulfonic acid; n = 6) and cysteinylation (n = 1) was noted (Fig. 3B–D and Table 3). Representative m/z spectra showing Trp oxidation and Cys trioxidation in NDUFV1 are shown in Fig. 4.

Table 3 List of Post Translational Modifications (PTMs) searched in the CI MS data. The type of PTM, targeted residues, mass difference (Δm) and the reaction are shown
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Fig. 4
figure 4

Representative m/z spectra of Trp (W), Cys (C) PTMs in Complex I subunits. m/z spectra of unmodified and modified peptides in NDUFV1 subunit are shown. A–B, W oxidation spectra. A, m/z spectra of a peptide (sequence shown with the susceptible Trp (W) shown in red, lower case) showing unmodified Trp. B, m/z Spectra of the same peptide with oxidized Trp (Trp to Oxyindolylalanine) with mass increase of 16 Da (indicated as + 16 Da). C–D, C oxidation spectra. C, m/z spectra of the peptide (sequence shown with the susceptible C shown in red, lower case) with unmodified C. D, m/z Spectra of the peptide with C Trioxidation (Cys to Cys-sulfonic acid) with mass increase of 48 Da (indicated as + 48 Da)

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Among the three toxins, Rot model showed higher number of PTMs followed by MPP+ and Pq (Fig. 3E and Table 4) with Trp oxidation being the most predominant PTM. Analysis of the distribution of the PTMs across different subunits of CI revealed that the peripheral arm had relatively higher number of PTMs compared to the membrane arm. Among the subunits, NDUFV1 showed the maximum number of PTMs followed by NDUFA9 and NDUFS1 (Fig. 3F and Fig. 5 and Table 4). Table 5 lists the 15 PTMs from our study that were previously identified by other groups.

Table 4 List of PTMs identified in isolated CI. The accession number and symbol of the subunit, annotated peptide sequence [modified residues in lower case (bold)], amino acid (AA) position in the protein, specific PTM and the sample are shown (Rot LD25 = R25, Rot LD50 = R50, Pq LD25 = P25, Pq LD50 = P50, MPP+ LD25 = M25 and MPP+ LD50 = M50)
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Fig. 5
figure 5

Distribution PTMs in different subunits of CI. Schematic representation of mammalian CI structure, showing the distribution of PTMs [mono-, di-, tri-oxidation (Trp, Cys), Cysteinyl (C) and dimethyl (R)] across different subunits in control, Rot, Pq and MPP+ are shown. The names of different subunits of CI (Core subunits in light blue and supernumerary subunits in dark blue) and their arbitrary location in the complex are indicated. The number of filled circles in each subunit correspond to the number of PTMs detected and the colour of the filled circle corresponds to the particular experimental group (control, Rot, Pq and MPP+)

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Table 5 List of CI PTMs identified in this study that were common to the previous studies. The subunit symbol, annotated peptide sequence [modified residues in lower case], the amino acid (AA) position in the protein, specific PTM and reference are shown
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Apart from the identification of PTMs, it is pertinent to understand the effect of these on the structure–function relationship of CI. Towards this, we carried out structural analysis of CI subunits and compared the local structural changes between the unmodified and modified sub-complexes.

Molecular Modelling and MDS of CI Subunits

Our MS data revealed that Trp oxidation is the most predominant PTM in the neurotoxic models, potentially contributing to the altered structure–function relationship of the complex. Since the data were generated using a rat neuronal cell line (N27), we chose to generate a 3D model of rat CI using the available mouse complex structure (PDB ID: 6G2J) as template, using standard methods [23]. The obtained protein model was energy minimized to determine the proper molecular arrangement in space and considered an unmodified CI (Fig. 6A).

Fig. 6
figure 6

Homology modeling of rat CI. A Homology modeling of Rat CI built using mouse model (PDB: 6G2J) as a template. The structure shows 44 subunits of the complex (each labelled with the constituent helices shown in different colours), FMN co-factor (green) and Fe-S clusters (yellow-orange). B Enlarged view of CI sub-complex considered for the molecular dynamics study. The five subunits of the sub-complex consisting of NDUFV1(blue), NDUFV2(green), NDUFV3 (yellow), NDUFS1 (pink) and NDUFS4 (red) are shown. The co-factor (FMN) and Fe-S clusters (yellow-orange) along with the Trp433 of NDUFV1 targeted for oxidation are shown

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Next, we selected five peripheral arm subunits (NDUFV1, NDUFV2, NDUFV3, NDUFS1 and NDUFS4) that interact with each other and harbor the critical sites including the FMN binding site and Fe-S clusters. This sub-complex was chosen for structural analysis by MDS experiment to assess the effects of Trp oxidation (Fig. 6B i and ii). After careful analysis of Trp oxidation events in these subunits, Trp433 in NDUFV1 was chosen for further study. Since this residue was proximal to the NADH, FMN and Fe-S site and was oxidized (Trp to oxyindolylalanine) in the neurotoxic model, we chose to assess the structural perturbations caused by this PTM. Accordingly, the sub-complex (with five subunits) with Trp433 in NDUFV1 was replaced with oxyindolylalanine (and all the other Trp residues across the five subunits present in the unmodified state) was considered as “modified” structure and compared with the unmodified sub-complex.

MDS analysis (0–100 ns or 0–1000 frames) of the unmodified and modified sub-complexes were carried out, followed by the calculation of RMSD, RMSF and Rg for all the subunits (Fig. 7). The RMSD values of the modified subunits showed significant variation in subunits NDUFV1 and the interacting subunit NDUFS4. The backbone of the modified NDUFV1and NDUFS4 subunit displayed altered RMSD from 160 (16 ns) and 60 (6 ns) frames respectively. However, the other interacting subunits of NDUFV1 i.e., NDUFV2, NDUFV3 and NDUFS1 did not show significant structural changes following the oxidation of Trp433 (Fig. 7A–E). RMSF data showed significantly increased fluctuation in modified NDUFV1 and NDUFV3 whereas modified NDUFV2 and NDUFS1 showed decreased fluctuation compared to unmodified conditions (Fig. 7G). The changes noted in the RMSF data indicate local structural changes in individual subunits. However, the overall structure of the sub-complex was relatively unaltered as indicated by Rg analysis (Fig. 7F).

Fig. 7
figure 7

Molecular dynamics simulation (MDS) of CI subcomplex. RMSD of all 5 chains A NDUFV1, B NDUFV2, C NDUFV3, D NDUFS1 and E NDUFS4 is shown. The RMSD curves the unmodified and modified (Trp 433) chains are shown in different colours. The Rg of all the five chains of the CI subcomplex is shown in (F). G RMSF of all 5 Chains with modified Trp433 (dark yellow) and unmodified (light yellow), are shown. The RMSF of each subunit is demarcated from the next by a solid line. The RMSF corresponding to Trp433 is indicated. The significant increase and decrease in RMSF (in Ao) of the modified subcomplex is indicated by red and blue arrows respectively

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Since most of the subunits showed local conformational changes in selected regions, we investigated whether these could impinge on the distances between consecutive Fe-S clusters, thereby potentially altering the enzyme activity. We noted that the distance between Fe-S 301 (N1a) to Fe-S 502 (N3) (in NDUFV1) and Fe-S 802 (N4) to Fe-S 801 (N5) (both in NDUFS1) were decreased by ~2 Å (Fig. 8) in the entire simulation, indicating that oxidation of W433 alters the local confirmation in NDUFV1 and the neighbouring subunits indicating the long-distance conformational changes induced by a single W oxidation.

Fig. 8
figure 8figure 8

Distance analysis between FMN and the first 5 consecutive Fe-S clusters based on MDS data. Distance between A FMN to FeS 301 residues, B FMN to FeS 502 (N3), C FeS 301 (N1a) to FeS 502 (N3), D Fes 502 (N3) to FeS 803 (N1b), E FeS 803 (N1b) to FeS 802 (N4), F FeS 802 (N4) to FeS 801 (N5) in unmodified (light green), and Trp433 modified (dark green) subcomplexes and are shown. The distance (shown in Ao) between FMN and first five consecutive Fe-S clusters within the sub-complex in both unmodified and Trp433 modified conditions at G 0 ns, H 50 ns and I 100 ns are shown

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The local conformational changes in these subunits were further analysed by hydrogen bond analysis at the subunit level and the residue level. The number of hydrogen bonds between NDUFV1 and NDUFS1 and between NDUFV1 and NDUFS4 were significantly reduced in the modified structure, compared to unmodified conditions (Fig. 9C and D). However, the hydrogen bonding between NDUFV1 and NDUFV2 and between NDUFV1 and NDUFV3 were relatively unaltered throughout the simulation (Figs. 9A to D). Structural analysis at the residue level revealed significant increase in the distance between the sidechains of W433 and the neighbouring residues G437 in the modified structure (3.68 Å at 100 ns) compared to the unmodified structure (2.23 Å at 100 ns) thereby leading to loss of hydrogen bond between the two residues (Fig. 9I). Interestingly, the loss of this hydrogen bond converted the helical structure into a loop, thereby indicating alterations in the local secondary structure.

Fig. 9
figure 9figure 9

Hydrogen bond analysis at subunit and residues level of the subcomplex: The number of hydrogen bonds measured in unmodified (blue) and Trp433 modified (Green) CI subcomplex between A NDUFV1 and NDUFV2, B NDUFV1 And NDUFV3, C NDUFV1 and NDUFS1 and D NDUFV1 and NDUFS4 are shown. The hydrogen bonding measured at the residue level in the unmodified vs. modified subcomplex between Trp433 and its interacting neighboring residue G430 of NDUFV1 is shown in (E). The distance between Trp433 and Gly430 (F) and between Trp433 and Gly437 G in both unmodified (yellow) and Trp433 modified sub-complex (green) are shown (The hydrogen bond data between Trp433 and Gly437 are not shown). Pymol structures of the distances (between W433 and its neighboring residues G30 and G437 at H 0 ns I 50 ns and J 100 ns of the MDS experiment are shown. K–L, Altered local structure in NDUFV1 showing Trp433 and its neighbouring residues Gly430 and Gly437

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Discussion

Many neurotoxic PD models have been characterized but none of them recapitulate all the pathological features of the disease [21]. Models employing neurotoxins have focused on CI inhibition-mediated mitochondrial damage as the primary event of PD pathogenesis. Considering the varied response of neurotoxins that targets CI, we tried to identify common characteristics of neurodegeneration in PD using three neurotoxins that inhibit CI. The characterization of these models will not only provide insights into CI damage and cell death but also assist in developing novel therapeutics.

Proteomic Changes in the Neurotoxic Models of PD

Previous “omics” studies in PD patients and models have identified degenerative pathways including mitochondrial dysfunction, impaired energy production, oxidative stress, proteasomal dysfunction, impaired cytoskeleton organization, or elevated inflammation [24,25,26,27]. Many genetic models of PD have revealed mitochondrial dysfunction and oxidative damage. Parkin-/- mice display altered expression of glycolytic, mitochondrial (subunits of TCA, OXPHOS and pyruvate dehydrogenase) and antioxidant proteins (peroxiredoxins) [28, 29]. Previous studies including ours [11, 30] linked mutant α-synuclein with impaired energy metabolism, mitochondrial dysfunction and oxidative stress [30]. Similarly, PINK1 deficiency induces proteomic changes linked to impaired glycolysis, mitochondrial respiration [31, 32] and oxidative stress [33].

Proteomics in the Rot model revealed altered expression of proteins implicated in mitochondrial, endoplasmic reticulum, autophagy, cytokinesis, cell cycle and cytoskeleton functions [34, 35]. Our previous microarray study in the MPP+ model noted differential expression of mitochondrial, synaptic and autophagy genes linked with apoptosis, neuroinflammation, neurotransmission and cytoskeleton organization [36, 37]. MPTP models have revealed alterations in proteins of redox, mitochondrial [38] and protein deglycase (DJ-1) function [39].

Although gene and protein expression data are available for MPP+ and Rot model, such studies in Pq model are limited. Further, comparative proteomic analysis across three neurotoxic models (Rot, Pq and MPP+) is not reported so far. The current study noted 10 up-regulated and 14 down-regulated proteins common across three neurotoxic models (Fig. 1D and E), that were associated with cell death pathways, mitochondrial proteins, structural changes, calcium and antioxidant function among others (Fig. 2). Mitochondrial proteins with altered expression included CI subunits (NDUFA10-l1, NDUFS1, NDUFS5-6), cytochrome C oxidase subunits, other metabolic proteins (Table 1) and altered cytoskeletal proteins (vimentin, caldesmon) (Table 1). Our study also showed altered expression of aminoacyl tRNA biosynthesis proteins (Table 1), highlighted in a previous study [26].

Among the antioxidant proteins, Superoxide dismutase-2 (SOD2) ubiquitously expressed in the brain [39] showed down-regulation (Table 1), which could contribute to oxidative damage [39] and neurodegeneration as noted in PD patients [40].

Structural changes induced by the toxins included altered expression of cytoskeletal proteins altered such as vimentin and the actin-binding Caldesmon (Fig. 2 and Table 1), necessary for mitochondrial trafficking in neurons [41] as noted in neurodegenerative diseases including PD [42]. Similarly, Caveolin-1, involved in endocytosis regulates synaptic remodeling and transmission and neurotrophic signaling [43,44,45], with a role in ageing was downregulated in the three models in our study (Table 1). Loss of caveolin-1 enhanced oxidative stress and neurodegeneration as previously noted [46, 47].

Calcium binding proteins are necessary to maintain physiological calcium levels and regulate excitotoxicity [48, 49]. The Ca2+-binding protein S100A4, involved in modulating various cellular functions [50], was overexpressed in all three models (Fig. 2 and Table1). The neuroprotective role of S100A4 in PD has been demonstrated [51], although its neurotoxic role is currently unknown.

The role of Dual specificity phosphatase (DUSPs) in protein phosphorylation dynamics is noted in CNS disease [52, 53] and their altered expression might play an essential role in PD pathogenesis [54]. Our study showed overexpression of DUSPs in all three models (fold change 1.69–2.81) with implications for PD.

The overview of the proteomic data highlighted the similarity among the downstream pathways in the three neurotoxic models. This could be due to the fact that the three toxins target CI and potentially induce oxidative stress and mitochondrial damage. We believe that since mitochondrial dysfunction is connected to metabolic changes including ETC and oxidative phosphorylation, altered calcium and redox dynamics and could be linked to cell death pathways, they could form a cascade leading to neurodegeneration. However, the chronology of these events is not clearly understood since some pathways could work synergistically to exacerbate the neurotoxic effect.

Structural Implications of Protein Oxidation in CI

The optimal protein conformation of cellular proteins could be influenced by PTMs. Oxidative PTMs alter the structure–function relationship of proteins and contribute to ageing and neurodegeneration [55]. Oxidative and nitrative modifications have been reported in human samples and experimental models of neurodegenerative diseases such as PD and Alzheimer’s disease (AD), which includes reversible modifications such as cysteine oxidation [56, 57]. Danielson et al. [58] reported quantification of reversible oxidation of 34 distinct cysteine residues out of a total 130 present in murine CI in a glutathione depletion model of PD with structural implications for iron-sulfur clusters highlighting the importance of their redox status in electron transport function. Similarly, the link between redox proteome and protein aggregation in AD pathogenesis has been established [59]. Newman et al. [60] demonstrated a significant increase in S-glutathionylated proteins in the AD human brain samples via redox proteomic approach highlighting the importance of reversible cysteine proteomic changes in neurodegeneration.

CI is a major target for protein oxidation-mediated inhibition of enzyme activity [6]. MS of CI subunits have identified PTMs including (1) glutathionylation of the 75 kDa subunit (Cys531 and Cys704) [61], (2) oxidation of NDUFS1 (Cys92, Cys463, and Cys554), NDUFS2 (Cys347), NDUFS7 (Cys59 and Cys80) [58] (3) oxidation of B17.2 (Trp61) [62] (4) dioxidation of tryptophan in NDUFV1, NDUFA5, NDUFA9, NDUFS1, NDUFS2, NDUFS4, NDUFS7, and NDUFS8 [63] (5) nitration of B14 (Tyr122), B15 (Tyr46, Tyr50, and Tyr51) [62] (6) phosphorylation of MWFE (Ser55) [64] (7) dimethylation of 49 kDa subunit (Arg85) [64] and (8) hydroxylation of PSST subunit (Arg77) [64].

Several amino acids are vulnerable to oxidation with Cys and Trp among the frequently oxidized amino acids. Trp oxidation generates three species: oxindolylalanine (with increased mass of + 16 Da over Trp), N-formylkynurenine (+ 32 Da), and kynurenine (+ 4 Da). It should be noted that Trp oxidation in a peptide is a specific event and is dependent on the neighboring residues [65]. However, sample preparation is critical to identify Trp oxidation since methods using gel electrophoresis could generate false-positive Trp oxidative PTMs [65]. This point is not applicable to the current study since we have employed only in-solution methods.

Studies have reported Trp oxidation in mitochondrial proteins under physiological conditions. Taylor et al. [63] identified Trp oxidation in the CI subunits NDUFV1 and NDUFA9. Our previous studies reported Trp oxidation in mitochondrial proteins in mouse models and human samples of muscle pathologies [7]. MS analysis of CI in the current study detected widespread Trp oxidation among CI subunits (Fig. 3F and Table 5), with Rot model displaying relatively higher number of oxidation events compared to the other two models.

None of the studies till date have assessed the structural effects of Trp oxidation on CI. Rat CI has 161 Trp residues across all the subunits, among which 62 residues were oxidized in all three neurotoxic models (Fig. 3C and Table 5). The peripheral arm of CI showed higher number of oxidized Trp compared to the membrane arm (Fig. 3F and Table 5), with NDUFV1 displaying the maximum number of oxidized Trp residues compared to other subunits. We noted that the Trp residues which are susceptible to oxidation were located either at the end of the secondary structure or in an open loop and were either completely or partially exposed to the solvent, and were surrounded by nonpolar amino acids [7].

CI structure facilitates electron transfer and proton translocation [66]. Mutations in CI subunits are linked with mitochondrial diseases [67]. Since, the subunit organization is critical to generate a physiologically functional CI [68], mutations and PTMs could alter the structure–function relationship [6, 55]. Structural alterations in one subunit could induce structural changes in other subunits of the complex. For e.g., our previous MDS study demonstrated that oxidation of Trp395 in UQCRC1 subunit of CIII caused structural changes in the other subunits, thereby altering the flexibility of the complex, potentially impairing the electron transfer [69]. Subtle structural changes could have profound effect on protein function. This is exemplified by the optimal distance between consecutive Fe-S clusters in CI, which is critical for its activity. Our previous MDS analysis in CI subunits NDUFV1, NDUFS1 and NDUFV2 showed that phosphorylation induced local structural alterations, thereby altering the efficiency of electron transfer from FMN, ultimately affecting the CI activity [70]. The optimal distance between successive Fe-S in protein complexes is ~14 Å [71]. In CI, the electron transfer from NADH to ubiquinone requires the presence of at least seven Fe-S clusters (N1b, N2, N3, N4, N5, N6a and N6b), that form a ~95 Å long chain of redox centers [72]. Altered distance between consecutive Fe-S clusters, either delays the electron transfer or causes a short-circuit [73].

Considering the technical limitation in carrying out MDS on the entire CI, we generated an unmodified and modified sub-complex containing five peripheral arm subunits that interact with FMN site and Fe-S clusters. MDS data revealed that PTM at W433 altered the arrangement of Fe-S clusters as indicated by the decreased distance between Fe-S 301 (N1a) to Fe-S 502 (N3) and increased distance between Fe-S 802 (N4) to Fe-S 801 (N5) at 100 ns (Fig. 8C, F and I). Other structural changes included decreased hydrogen bonding between NDUFV1-NDUFS1 and NDUFV1-NDUFS4 (Fig. 9C and D) and altered hydrogen bonding between Trp433 and Gly437 (Fig. 9I). We are tempted to speculate that local conformational changes could probably contribute to altered structure–function relationship of the complex. However, such studies have certain limitations. Firstly, the study was not carried out on the entire complex. Secondly, we have not considered all the PTMs across of CI. Whether these structural changes are noted during the disease progression in the human brain needs to be considered for clinical implications of PD. Further, assessment of other oxidative mechanisms including protein carbonylation, cysteine oxidation could provide additional information about the neurotoxic mechanisms at the protein level, although we have not conducted any quantitative proteomics in these models to ascertain the same. It is possible that regulation of cysteine redox proteome on its own or following Trp oxidation could have structural implications for CI in particular and functional implications for mitochondrial function in general.

Considering that oxidative stress and protein oxidation have potential structural and functions effects on mitochondrial function including CI, antioxidants such as n-acetyl cysteine have antioxidant and neuroprotective potential with therapeutic implications for neurodegeneration and Parkinson’s disease. Our previous studies have demonstrated that natural antioxidants such as curcumin and their derivatives have neuroprotective effects against mitochondrial dysfunction, CI dysfunction and oxidative damage using in vitro and in vivo models [10, 13, 74,75,76]. Similarly, soluble extract from Bacopa monnieri has been tested for their antioxidant and neuroprotective effects in neurotoxic models in vivo [77,78,79,80] with implications for neurodegenerative diseases.

To our knowledge, this is one of the first studies that has combined analysis of downstream pathways induced by the CI specific toxins along with the assessment of the structural changes induced by the PTMs in these models. This could provide insights not only into the function of CI but also highlight the critical residues important for the catalytic activity, that are targeted for oxidative PTMs in the neurotoxic models.