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Mass spectrometry-based proteomics analysis of human substantia nigra from Parkinson's disease patients identifies multiple pathways potentially involved in the disease

Open AccessPublished:November 21, 2022DOI:https://doi.org/10.1016/j.mcpro.2022.100452

      Highlights

      Proteome analysis of Parkinson’s brains identified >10,000 proteins. RNA splicing and complement proteins were up-regulated in Parkinson’s brain. Mitoribosome proteins were down-regulated in Parkinson’s brain.

      ABSTRACT

      Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra (SN) of the brain. Despite decades of studies, the precise pathogenic mechanism of PD is still elusive. An unbiased proteomic analysis of PD patient’s brain allows the identification of critical proteins and molecular pathways that lead to dopamine cell death and α-synuclein deposition and the resulting devastating clinical symptoms. In this study, we conducted an in-depth proteome analysis of human SN tissues from 15 PD patients and 15 healthy control (HC) individuals combining Orbitrap mass spectrometry with the isobaric tandem mass tag (TMT)-based multiplexing technology. We identified 10,040 proteins with 1,140 differentially expressed proteins in the SN of PD patients. Pathway analysis showed that the ribosome pathway was the most enriched one, followed by GABAergic synapse, retrograde endocannabinoid signaling, cell adhesion molecules (CAMs), morphine addiction, Prion disease, and Parkinson's disease pathways. Strikingly, the majority of the proteins enriched in the ribosome pathway were mitochondrial ribosomal proteins (mitoribosomes; MRPs). The subsequent protein-protein interaction (PPI) analysis and the weighted gene co-expression network analysis (WGCNA) confirmed that the mitoribosome is the most enriched protein cluster. Furthermore, the mitoribosome was also identified in our analysis of a replication set of 10 PD and 9 HC SN tissues. This study provides potential disease pathways involved in PD and paves the way to study further the pathogenic mechanism of PD.

      Graphical abstract

      Key words

      Abbreviations:

      ACN (acetonitrile), AGC (automatic gain control), AUC (area under the curve), BCA (bicinchoninic acid), bRPLC (basic pH reversed-phase liquid chromatography), CAA (chloroacetamide), DDA (data-dependent acquisition), FA (formic acid), FDR (false-discovery rate), HC (healthy control), HCD (higher-energy collisional dissociation), KEGG (Kyoto encyclopedia of genes and genomes), MP (master pool), MRPs (mitochondrial ribosomal proteins), mtDNA (mitochondrial DNA), PD (Parkinson's disease), PMD (post mortem delay), PPI (protein-protein interaction), ROC (receiver operating characteristic), RPs (ribosomal proteins), RT (room temperature), S/N (signal-to-noise ratios), SAM (significance analysis of microarrays), SN (substantia nigra), TCEP (tris (2-Carboxyethyl) phosphine hydrochloride), TEAB (triethylammonium bicarbonate), TMT (tandem mass tag), WGCNA (weighted gene co-expression network analysis)

      INTRODUCTION

      Parkinson's disease (PD) is the second most common neurodegenerative disorder characterized by the loss of dopaminergic neurons in substantia nigra (SN) of the midbrain (
      • Kalia L.V.
      • Lang A.E.
      Parkinson's disease.
      ,
      • Radhakrishnan D.M.
      • Goyal V.
      Parkinson's disease: a review.
      ,
      • Beitz J.M.
      Parkinson's disease: a review.
      ,
      • Parent M.
      • Parent A.
      Substantia nigra and Parkinson's disease: a brief history of their long and intimate relationship.
      ,
      • Poewe W.
      • Seppi K.
      • Tanner C.M.
      • Halliday G.M.
      • Brundin P.
      • Volkmann J.
      • Schrag A.E.
      • Lang A.E.
      Parkinson disease.
      ,
      • Reich S.G.
      • Savitt J.M.
      Parkinson's disease.
      ). The primary risk factors for PD are age, environmental influences, and genetic predisposition (

      Kouli, A., Torsney, K. M., and Kuan, W. L. (2018) Parkinson’s disease: etiology, neuropathology, and pathogenesis. In: Stoker, T. B., and Greenland, J. C., eds. Parkinson’s Disease: Pathogenesis and Clinical Aspects, Codon Publications, Brisbane (AU)

      ). PD incidence increases with age, with the prevalence of 1% and 4% for people aged over 60 and 80, respectively (
      • Samii A.
      • Nutt J.G.
      • Ransom B.R.
      Parkinson's disease.
      ,
      • Davie C.A.
      A review of Parkinson's disease.
      ). Exposure to pesticides and heavy metals increases the risk of PD (

      Kouli, A., Torsney, K. M., and Kuan, W. L. (2018) Parkinson’s disease: etiology, neuropathology, and pathogenesis. In: Stoker, T. B., and Greenland, J. C., eds. Parkinson’s Disease: Pathogenesis and Clinical Aspects, Codon Publications, Brisbane (AU)

      ). Multiple genes linked to the autosomal dominant form of PD, such as SNCA, LRRK2, and the autosomal recessive form of PD such as Parkin, PINK1, DJ-1, and ATP13A2, have been reported (
      • Kalia L.V.
      • Lang A.E.
      Parkinson's disease.
      ,
      • Poewe W.
      • Seppi K.
      • Tanner C.M.
      • Halliday G.M.
      • Brundin P.
      • Volkmann J.
      • Schrag A.E.
      • Lang A.E.
      Parkinson disease.
      ,

      Kouli, A., Torsney, K. M., and Kuan, W. L. (2018) Parkinson’s disease: etiology, neuropathology, and pathogenesis. In: Stoker, T. B., and Greenland, J. C., eds. Parkinson’s Disease: Pathogenesis and Clinical Aspects, Codon Publications, Brisbane (AU)

      ,
      • Samii A.
      • Nutt J.G.
      • Ransom B.R.
      Parkinson's disease.
      ,
      • Davie C.A.
      A review of Parkinson's disease.
      ). SNCA encodes α-synuclein and one of the typical neuropathologic findings of PD patients is the abnormal deposition of α-synuclein in the cytoplasm of certain neurons (
      • Poewe W.
      • Seppi K.
      • Tanner C.M.
      • Halliday G.M.
      • Brundin P.
      • Volkmann J.
      • Schrag A.E.
      • Lang A.E.
      Parkinson disease.
      ). The G2019S mutation of LRRK2 is associated with an impaired lysosomal autophagy system that is critical in the clearance of oligomeric assemblies of α-synuclein (
      • Xilouri M.
      • Brekk O.R.
      • Stefanis L.
      Alpha-synuclein and protein degradation systems: a reciprocal relationship.
      ). In the limited pathologic studies of patients with mutations in Parkin, a ubiquitin E3 ligase, the pattern of dopamine (DA) neuron loss in the SN without the presence of Lewy bodies is shown (
      • Samii A.
      • Nutt J.G.
      • Ransom B.R.
      Parkinson's disease.
      ,
      • Davie C.A.
      A review of Parkinson's disease.
      ). PINK1 in conjunction with Parkin is highly associated with mitochondria quality control and the relationship between mitochondrial dysfunction and PD pathogenesis is well known (
      • Kalia L.V.
      • Lang A.E.
      Parkinson's disease.
      ). Mutations of DJ1 (PARK7) are involved in increased oxidative stress, which is linked to the pathogenesis of PD (
      • Poewe W.
      • Seppi K.
      • Tanner C.M.
      • Halliday G.M.
      • Brundin P.
      • Volkmann J.
      • Schrag A.E.
      • Lang A.E.
      Parkinson disease.
      ). Mutations of ATP13A2 (PARK9) are associated with the dysregulation of lysosomes and autophagosomes that contribute to PD pathogenesis (
      • Poewe W.
      • Seppi K.
      • Tanner C.M.
      • Halliday G.M.
      • Brundin P.
      • Volkmann J.
      • Schrag A.E.
      • Lang A.E.
      Parkinson disease.
      ). While these mutations have been noted in genetic forms of PD, the dysfunctional pathways that they lead are also implicated in idiopathic PD (
      • Reed X.
      • Bandres-Ciga S.
      • Blauwendraat C.
      • Cookson M.R.
      The role of monogenic genes in idiopathic Parkinson's disease.
      ). Specifically, multiple putative mechanisms are thought to play a role in and include α-synuclein aggregation, mitochondrial dysfunction, abnormal protein clearance, and neuroinflammation among others. (
      • Kalia L.V.
      • Lang A.E.
      Parkinson's disease.
      ,
      • Poewe W.
      • Seppi K.
      • Tanner C.M.
      • Halliday G.M.
      • Brundin P.
      • Volkmann J.
      • Schrag A.E.
      • Lang A.E.
      Parkinson disease.
      ,
      • Pirooznia S.K.
      • Rosenthal L.S.
      • Dawson V.L.
      • Dawson T.M.
      Parkinson disease: translating insights from molecular mechanisms to neuroprotection.
      ,
      • Panicker N.
      • Ge P.
      • Dawson V.L.
      • Dawson T.M.
      The cell biology of Parkinson's disease.
      ). Aggregated pathologic α-synuclein causes neurotoxicity, and it constitutes the major misfolded proteins found in Lewy bodies (

      Kouli, A., Torsney, K. M., and Kuan, W. L. (2018) Parkinson’s disease: etiology, neuropathology, and pathogenesis. In: Stoker, T. B., and Greenland, J. C., eds. Parkinson’s Disease: Pathogenesis and Clinical Aspects, Codon Publications, Brisbane (AU)

      ,
      • Rocha E.M.
      • De Miranda B.
      • Sanders L.H.
      Alpha-synuclein: pathology, mitochondrial dysfunction and neuroinflammation in Parkinson's disease.
      ). Aging, environmental toxins, and genetic predisposition contribute to mitochondrial dysfunction, which is considered a key element in both idiopathic and familial PD (
      • Schapira A.H.
      • Jenner P.
      Etiology and pathogenesis of Parkinson's disease.
      ,
      • Zhu J.
      • Chu C.T.
      Mitochondrial dysfunction in Parkinson's disease.
      ,
      • Moon H.E.
      • Paek S.H.
      Mitochondrial dysfunction in Parkinson's disease.
      ,
      • Bose A.
      • Beal M.F.
      Mitochondrial dysfunction in Parkinson's disease.
      ,
      • Park J.S.
      • Davis R.L.
      • Sue C.M.
      Mitochondrial dysfunction in Parkinson's disease: new mechanistic insights and therapeutic perspectives.
      ,
      • Giannoccaro M.P.
      • La Morgia C.
      • Rizzo G.
      • Carelli V.
      Mitochondrial DNA and primary mitochondrial dysfunction in Parkinson's disease.
      ). Clearance of proteins is an essential cell function to protect cells from proteotoxic stress induced by misfolded and aggregated proteins. Dysfunctional protein clearance is associated with PD pathogenesis. The ubiquitin-proteome system is involved in the clearance of unnecessary proteins in the cell. Thus, the dysregulation of the ubiquitin-proteome system can lead to protein aggregation (
      • Olanow C.W.
      • McNaught K.S.
      Ubiquitin-proteasome system and Parkinson's disease.
      ). The autophagy-lysosomal system, which is involved in the degradation of impaired or misfolded proteins through micro- and macro-autophagy and chaperone-mediated autophagy can also become impaired in PD (
      • Cerri S.
      • Blandini F.
      Role of autophagy in Parkinson's disease.
      ). Neuroinflammation also contributes to pathogenic mechanisms. Levels of inflammatory cytokines that can induce neuronal death are increased in PD (
      • Hirsch E.C.
      • Jenner P.
      • Przedborski S.
      Pathogenesis of Parkinson's disease.
      ,
      • Ma S.X.
      • Seo B.A.
      • Kim D.
      • Xiong Y.
      • Kwon S.H.
      • Brahmachari S.
      • Kim S.
      • Kam T.I.
      • Nirujogi R.S.
      • Kwon S.H.
      • Dawson V.L.
      • Dawson T.M.
      • Pandey A.
      • Na C.H.
      • Ko H.S.
      Complement and coagulation cascades are potentially involved in dopaminergic neurodegeneration in alpha-synuclein-based mouse models of Parkinson's disease.
      ). Despite these insights into PD pathogenesis, disease modifying therapy has not been identified and additional mechanisms remain to be discovered. Along these lines understanding how the proteome changes in PD patients’ brains may provide novel insights into PD pathogenesis.
      Mass spectrometry-based proteomics technology has been considered the gold standard for proteome analyses and has been applied to study PD (
      • Hong Z.
      • Shi M.
      • Chung K.A.
      • Quinn J.F.
      • Peskind E.R.
      • Galasko D.
      • Jankovic J.
      • Zabetian C.P.
      • Leverenz J.B.
      • Baird G.
      • Montine T.J.
      • Hancock A.M.
      • Hwang H.
      • Pan C.
      • Bradner J.
      • et al.
      DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson's disease.
      ,
      • Shi M.
      • Movius J.
      • Dator R.
      • Aro P.
      • Zhao Y.
      • Pan C.
      • Lin X.
      • Bammler T.K.
      • Stewart T.
      • Zabetian C.P.
      • Peskind E.R.
      • Hu S.C.
      • Quinn J.F.
      • Galasko D.R.
      • Zhang J.
      Cerebrospinal fluid peptides as potential Parkinson disease biomarkers: a staged pipeline for discovery and validation.
      ,
      • Shi M.
      • Bradner J.
      • Hancock A.M.
      • Chung K.A.
      • Quinn J.F.
      • Peskind E.R.
      • Galasko D.
      • Jankovic J.
      • Zabetian C.P.
      • Kim H.M.
      • Leverenz J.B.
      • Montine T.J.
      • Ginghina C.
      • Kang U.J.
      • Cain K.C.
      • et al.
      Cerebrospinal fluid biomarkers for Parkinson disease diagnosis and progression.
      ,
      • Dixit A.
      • Mehta R.
      • Singh A.K.
      Proteomics in human Parkinson's disease: present scenario and future directions.
      ,
      • Basso M.
      • Giraudo S.
      • Corpillo D.
      • Bergamasco B.
      • Lopiano L.
      • Fasano M.
      Proteome analysis of human substantia nigra in Parkinson's disease.
      ,
      • Werner C.J.
      • Heyny-von Haussen R.
      • Mall G.
      • Wolf S.
      Proteome analysis of human substantia nigra in Parkinson's disease.
      ,
      • Licker V.
      • Turck N.
      • Kövari E.
      • Burkhardt K.
      • Côte M.
      • Surini-Demiri M.
      • Lobrinus J.A.
      • Sanchez J.C.
      • Burkhard P.R.
      Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson's disease pathogenesis.
      ). Although there have been multiple studies to uncover dysfunctional signaling pathways in SN of PD patients, the number of identified proteins from the studies was still too shallow (< 1,800 proteins) to uncover key pathways due to the limitation of the used methods and instruments, and/or the number of the samples used was too small (10 or less SN samples) (
      • Basso M.
      • Giraudo S.
      • Corpillo D.
      • Bergamasco B.
      • Lopiano L.
      • Fasano M.
      Proteome analysis of human substantia nigra in Parkinson's disease.
      ,
      • Werner C.J.
      • Heyny-von Haussen R.
      • Mall G.
      • Wolf S.
      Proteome analysis of human substantia nigra in Parkinson's disease.
      ,
      • Licker V.
      • Turck N.
      • Kövari E.
      • Burkhardt K.
      • Côte M.
      • Surini-Demiri M.
      • Lobrinus J.A.
      • Sanchez J.C.
      • Burkhard P.R.
      Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson's disease pathogenesis.
      ). To this end, we conducted an in-depth proteome analysis of human SN tissues from 15 PD patients and 15 healthy control (HC) individuals using Orbitrap mass spectrometry. In this study, we employed isobaric tandem mass tag (TMT)-based multiplexing for quantification. The validity of the key pathways was independently verified. This is the first report of a large-scale in-depth proteome analysis of human SN in PD versus controls and provides a foundation for the elucidation of proteomic changes that contribute to PD pathogenesis.

      METHODS

      Acquisition of SN samples

      Human SN tissues from 15 PD patients and 15 HC individuals that were used for the acquisition of the discovery data and the human SN tissues from 10 PD patients and 9 HC individuals that were used for the acquisition of the replication data were acquired from the Brain Resource Center at Johns Hopkins University School of Medicine. The clinical information for the samples is provided in Table 1 and Supplemental Table S1. Diagnosis of PD was based on UK Brain Bank clinical criteria and then autopsy confirmation (
      • Hughes A.J.
      • Daniel S.E.
      • Kilford L.
      • Lees A.J.
      Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases.
      ,
      • McKeith I.G.
      • Boeve B.F.
      • Dickson D.W.
      • Halliday G.
      • Taylor J.P.
      • Weintraub D.
      • Aarsland D.
      • Galvin J.
      • Attems J.
      • Ballard C.G.
      • Bayston A.
      • Beach T.G.
      • Blanc F.
      • Bohnen N.
      • Bonanni L.
      • et al.
      Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB consortium.
      ). Healthy controls were individuals without clinical or neuropathological evidence of Parkinsonism. All participants agreed to autopsy prior to their death and their next of kin consented to the autopsy procedure at the time of death. All research was approved by the Johns Hopkins Institutional Review Board. The inclusion criteria for PD are patients with 1) a clinical history of PD with or without dementia; 2) neuropathology changes of Lewy body disease brainstem-predominant, limbic, or neocortical (
      • McKeith I.G.
      • Dickson D.W.
      • Lowe J.
      • Emre M.
      • O'Brien J.T.
      • Feldman H.
      • Cummings J.
      • Duda J.E.
      • Lippa C.
      • Perry E.K.
      • Aarsland D.
      • Arai H.
      • Ballard C.G.
      • Boeve B.
      • Burn D.J.
      • et al.
      Diagnosis and management of dementia with Lewy bodies: third report of the DLB consortium.
      ); and 3) age > 50 years, males and females and any race. The exclusion criteria for PD are patients with any significant neurodegenerative or vascular comorbidity. This study abided by the Declaration of Helsinki principles.
      Table 1Information on the SN samples used in the discovery study
      No.DiagnosisAge at deathSexRacePMD (h)CERAD
      1PD with DEMENTIA, AD DEFINITE64MW21C
      2PD with DEMENTIA, AD PROBABLE.82FW5B
      3PD with DEMENTIA, AD PROBABLE80MW13B
      4PD, no dementia73FW6A
      5PD with DEMENTIA, LBD NEOCORTICAL84MW50
      6PD, LBD NEOCORTICAL, AD74MW19C
      7PD, LBD limbic95FW120
      8PD with DEMENTIA,76MW190
      9PD with DEMENTIA76MW170
      10PD with DEMENTIA, AD PROBABLE85FW11B
      11PD, no dementia86MW22.50
      12PD with DEMENTIA, AD PROBABLE83FW4B
      13PD with DEMENTIA60MW15.50
      14PD with DEMENTIA, AD DEFINITE80FW16C
      15PD with DEMENTIA, AD PROBABLE85MW14B
      16HC, NFT & frequent tau neurites in HP (age-associated tau pathology)76MW250
      17HC, moderate tau+ neurites in HP and ERC67MW370
      18HC, GVD in HP, Tau NFT and neurites in ERC (Braak I)71FB370
      19HC, rare NFT in ERC, no amyloid plaques, old contusions (Braak I)81MW260
      20HC, rare NFTs in ERC and HP, no amyloid plaques (Braak II)80FW370
      21HC, no tau or amyloid lesions67MW250
      22HC, rare tau+ neurites in HP, no amyloid plaques67MW80
      23HC, NFT in ERC but not in HP, no amyloid plaques71FW570
      24HC, rare NFT in HP and ERC (Braak II)66MB250
      25HC, NFT in HP and ERC, no amyloid plaques (Braak II)77FW330
      26HC, NFT in ERC but none in HP.80MB210
      27HC, mild NFT in HP and ERC, no amyloid plaques (Braak II)87FW70
      28HC, NFT in HP, ERC and ITC, plaques in temporal lobe (Braak III)90FB22A
      29HC, no NFT, no amyloid plaques60MW160
      30HC, NFT in ERC and HP, few amyloid plaques in HP87FW35A
      (M: male, F: female, W: white, B: black, PMD: postmortem delay, LBD: Lewy body dementia, AD: Alzheimer’s disease, HP: Hippocampus, ERC: Entorhinal cortex, ITC: inferior temporal cortex, NFT: neurofibrillary tangle, GVD: Granulovacuolar degeneration)

      Sample preparation

      The SN samples from 15 PD patients and 15 HC individuals were lysed by sonication (Branson sonifier 250, ultrasonics, Danbury, CT, USA) in 8 M urea/50 mM triethylammonium bicarbonate (TEAB). The amount of protein in the samples was quantified using a bicinchoninic acid (BCA) assay kit (Pierce; Rockford, IL, USA). To analyze 30 samples using 11-plex TMT method, three batches (sets) of 11-plex TMT experiments were conducted including a reference master pool (MP) in each set. The MP was used for the normalization of the quantification values from the three sets. The MP was prepared by combining an equal amount of protein from all 30 samples. Proteins were reduced and alkylated with 10 mM tris (2-Carboxyethyl) phosphine hydrochloride (TCEP) and 40 mM chloroacetamide (CAA) at room temperature (RT, 22 to 25°C) for 1 h. The proteins were then digested with Lys-C (Lysyl endopeptidase mass spectrometry grade; Fujifilm Wako Pure Chemical Industries Co., Ltd., Osaka, Japan) in a ratio of 1:100 at 37°C for 3 h. Subsequently, trypsin (sequencing grade modified trypsin; Promega, Fitchburg, WI, USA) digestion was conducted by diluting the urea concentration to 2 M by adding the 3 volumes of 50 mM TEAB followed by adding trypsin in a ratio of 1:50 and incubating at 37°C overnight (for 15 h to 18 h). The resulting peptides were desalted with C18 StageTips (3M EmporeTM; 3M, St. Paul, MN, USA) and labeled with 11-plex TMT reagents according to the manufacturer’s instructions (Thermo Fisher Scientific; Waltham, MA, USA). The labeling reaction was performed at RT for 1 h, followed by quenching with 1/10 volume of 1 M Tris-HCl (pH 8.0). The peptides were pooled and pre-fractionated by basic pH reversed-phase liquid chromatography (bRPLC) into 96 fractions, followed by concatenating into 24 fractions by combining every 24th fraction. The Agilent 1260 offline LC system (Agilent Technologies, Santa Clara, CA, USA) was used for bRPLC fractionation, which includes a binary pump, UV detector, an autosampler, and an automatic fraction collector. In brief, the dried samples were reconstituted in solvent A (10 mM TEAB in water, pH 8.5) and loaded onto a column (Agilent 300 Extend-C18 column, 5 μm, 4.6 mm × 25 cm, Agilent Technologies). Peptides were resolved using an increasing gradient of solvent B (10 mM TEAB in 90% acetonitrile (ACN), pH 8.5) at a flow rate of 0.3 mL/min. The total run time was 150 min. Subsequently, the concatenated 24 samples were vacuum dried using a SpeedVac (Thermo Fisher Scientific) and then stored at -80°C until use (
      • Ma S.X.
      • Seo B.A.
      • Kim D.
      • Xiong Y.
      • Kwon S.H.
      • Brahmachari S.
      • Kim S.
      • Kam T.I.
      • Nirujogi R.S.
      • Kwon S.H.
      • Dawson V.L.
      • Dawson T.M.
      • Pandey A.
      • Na C.H.
      • Ko H.S.
      Complement and coagulation cascades are potentially involved in dopaminergic neurodegeneration in alpha-synuclein-based mouse models of Parkinson's disease.
      ,
      • Ramachandran K.V.
      • Fu J.M.
      • Schaffer T.B.
      • Na C.H.
      • Delannoy M.
      • Margolis S.S.
      Activity-dependent degradation of the nascentome by the neuronal membrane proteasome.
      ,
      • Khan S.Y.
      • Ali M.
      • Kabir F.
      • Renuse S.
      • Na C.H.
      • Talbot Jr., C.C.
      • Hackett S.F.
      • Riazuddin S.A.
      Proteome profiling of developing murine lens through mass spectrometry.
      ).
      The preparation of 10 PD and 9 HC samples used for the replication was conducted in the same way as described above, except for the preparation of the MP and the employment of the 10-plex TMT instead of the 11-plex TMT. One HC sample was added to the two sets of 10-plex TMT experiments and used for the normalization of quantification values from the two sets.

      Mass spectrometry

      The peptides were analyzed on an Orbitrap Fusion Lumos Tribrid Mass Spectrometer (Thermo Fisher Scientific) coupled with an Ultimate 3000 RSLCnano nanoflow liquid chromatography system (Thermo Fisher Scientific). The peptides from each fraction were reconstituted in 50 μl of 0.5% formic acid (FA) and 30% of reconstituted peptides solution was loaded on a trap column (Acclaim™ PepMap™ 100, LC C18, 5 μm, 100 μm × 2 cm, nanoViper, Thermo Fisher Scientific) at a flow rate of 8 μl/min. The peptides were resolved at 0.3 μl/min flow rate using an increasing gradient of solvent B (0.1% FA in 95% ACN) on an analytical column (Easy-Spray™ PepMap™ RSLC C18, 2 μm, 75 μm × 50 cm, Thermo Fisher Scientific), which was fitted with an EASY-Spray ion source that was operated at a voltage of about 2.0 kV. The total run time was 120 min. Mass spectrometry analysis was carried out in data-dependent acquisition (DDA) mode with a full scan in the mass-to-charge ratio (m/z) range of 300 to 1,800 in the “Top Speed” mode with 3 sec per cycle. MS1 and MS2 were acquired for the precursor ions and the peptide fragmentation ions, respectively. MS1 scans were measured at a resolution of 120,000 at an m/z of 200. MS2 scans were acquired by fragmenting precursor ions using the higher-energy collisional dissociation (HCD) method, which was set to 35% of collision energy, and detected at a mass resolution of 50,000 at an m/z of 200. Automatic gain control (AGC) targets were set to one million ions for MS1 and 0.05 million ions for MS2. The maximum ion injection time was set to 50 ms for MS1 and 100 ms for MS2. The precursor isolation window was set to 1.6 m/z with 0.4 m/z of offset. Dynamic exclusion was set to 30 sec, and singly-charged ions were rejected. Internal calibration was carried out using the lock mass option (m/z 445.12002) from ambient air (
      • Ma S.X.
      • Seo B.A.
      • Kim D.
      • Xiong Y.
      • Kwon S.H.
      • Brahmachari S.
      • Kim S.
      • Kam T.I.
      • Nirujogi R.S.
      • Kwon S.H.
      • Dawson V.L.
      • Dawson T.M.
      • Pandey A.
      • Na C.H.
      • Ko H.S.
      Complement and coagulation cascades are potentially involved in dopaminergic neurodegeneration in alpha-synuclein-based mouse models of Parkinson's disease.
      ,
      • Ramachandran K.V.
      • Fu J.M.
      • Schaffer T.B.
      • Na C.H.
      • Delannoy M.
      • Margolis S.S.
      Activity-dependent degradation of the nascentome by the neuronal membrane proteasome.
      ,
      • Khan S.Y.
      • Ali M.
      • Kabir F.
      • Renuse S.
      • Na C.H.
      • Talbot Jr., C.C.
      • Hackett S.F.
      • Riazuddin S.A.
      Proteome profiling of developing murine lens through mass spectrometry.
      ).
      The peptides for the replication experiment were analyzed on an LTQ-Orbitrap Elite mass spectrometer (Thermo Fisher Scientific) coupled with an EASY-nano liquid chromatography II system (Thermo Fisher Scientific). The peptides from each fraction were reconstituted in 30 μl of 0.5% FA and 50% of the reconstituted peptides solution was loaded on the trap column at a flow rate of 10 μl/min. The peptides were resolved at 0.25 μl/min flow rate using an increasing gradient of solvent B (0.1% FA in 95% ACN) on an analytical column (75 μm × 50 cm) that was packed in a house for the LTQ-Orbitrap Elite mass spectrometer. Mass spectrometry analysis was carried out in the DDA with a full scan in the m/z range of 300 to 1,700 in top N mode setting to 8 most intense ions. Full MS scans were measured at a resolution of 120,000 at an m/z of 400. MS2 scans were acquired by fragmenting precursor ions using the HCD method and detected at a mass resolution of 30,000 at an m/z of 400. AGC targets were set to one million ions for MS1 and 0.2 million ions for MS2. The maximum ion injection time was set to 100 ms for MS1 and 300 ms for MS2. Dynamic exclusion was set to 60 sec, and singly-charged ions were rejected. Internal calibration was carried out using the lock mass option (m/z 371.101236 and 445.12002) from ambient air.

      Data analysis

      Proteome Discoverer (version 2.2.0.388; Thermo Fisher Scientific) suite was used for quantitation and identification. During MS2 preprocessing, the top 10 peaks in each window of 100 Da were selected for database search. The tandem mass spectrometry data were then searched using SEQUEST HT algorithms against a human UniProt database that includes both Swiss-Prot and TrEMBL (released in May 2018 with 73,112 entries) with common contaminant proteins (115 entries). The search parameters used were as follows: a) trypsin as a proteolytic enzyme (with up to 2 missed cleavages); b) peptide precursor mass error tolerance of 10 ppm; c) fragment mass error tolerance of 0.02 Da; and d) carbamidomethylation of cysteine (+57.02146 Da) and TMT tags (+229.16293 Da) on lysine and peptide N-termini as fixed modifications; d) oxidation (+15.99492 Da) of methionine as a variable modification. The minimum peptide length was set to 6 amino acids and the minimum number of peptides per protein was set to 1. Peptides and proteins were filtered at a 1% false-discovery rate (FDR) at the PSM level using a percolator node and at the protein level using the protein FDR validator node, respectively. The protein quantification was performed with the following parameters and methods. The most confident centroid option was used for the integration mode while the reporter ion tolerance was set to 20 ppm. The MS order was set to MS2 and the activation type was set to HCD. Both unique and razor peptides were used for peptide quantification, while protein groups were considered for peptide uniqueness. Co-isolation threshold was set to 50%. Reporter ion abundance was computed based on signal-to-noise ratios (S/N) and the missing intensity values were replaced with the minimum value. The average reporter S/N threshold was set to 50. The quantification value corrections for isobaric tags and data normalization were disabled. Protein grouping was performed with a strict parsimony principle to generate the final protein groups. All proteins sharing the same set or subset of identified peptides were grouped, while protein groups with no unique peptides were filtered out. Proteome Discoverer iterated through all spectra and selected PSM with the highest number of unambiguous and unique peptides, and then final protein groups were generated. The Proteome Discoverer summed all the reporter ion abundances of PSMs for the corresponding proteins in the TMT run (
      • Ma S.X.
      • Seo B.A.
      • Kim D.
      • Xiong Y.
      • Kwon S.H.
      • Brahmachari S.
      • Kim S.
      • Kam T.I.
      • Nirujogi R.S.
      • Kwon S.H.
      • Dawson V.L.
      • Dawson T.M.
      • Pandey A.
      • Na C.H.
      • Ko H.S.
      Complement and coagulation cascades are potentially involved in dopaminergic neurodegeneration in alpha-synuclein-based mouse models of Parkinson's disease.
      ,
      • Ramachandran K.V.
      • Fu J.M.
      • Schaffer T.B.
      • Na C.H.
      • Delannoy M.
      • Margolis S.S.
      Activity-dependent degradation of the nascentome by the neuronal membrane proteasome.
      ,
      • Khan S.Y.
      • Ali M.
      • Kabir F.
      • Renuse S.
      • Na C.H.
      • Talbot Jr., C.C.
      • Hackett S.F.
      • Riazuddin S.A.
      Proteome profiling of developing murine lens through mass spectrometry.
      ).

      Experimental design and statistical rationale

      The number of SN samples used in this study was 15 PD samples and 15 HC samples for the main experiment and 10 PD samples and 9 HC samples for the replication experiment. We conducted sample size analysis using the pwr package in R. When we wanted to detect proteins with 1.5 fold differences between groups, the required minimum sample size was 9.4 when the significance level was 0.0001, power was 0.8, sigma was 0.208, and delta was 0.585 (= log21.5). This sigma value of 0.208 was derived from our in-house TMT proteomics experiments. The significance level of 0.0001 was determined based on our previous studies. When we identified several thousands of proteins, the majority of the proteins with P value < 0.0001 showed q-value < 0.05. Based on this sample size analysis result, we decided to use 15 samples per group. The statistical analysis was performed with the Perseus software (version 1.6.0.7). Since we are conducting multiple comparisons, we calculated an FDR by comparing data with and without permutations between groups. For the normalization, the reporter ion intensity values were divided by the MP included in each set followed by dividing by the median values of each protein. The relative abundance values for each sample were z-score transformed after log2 transformation. We removed proteins with one or more missing values before conducting statistical analysis. To remove batch effects, further normalization was conducted with the ComBat package (
      • Johnson W.E.
      • Li C.
      • Rabinovic A.
      Adjusting batch effects in microarray expression data using empirical Bayes methods.
      ). Proteins with q-values < 0.05 were considered differentially expressed in PD compared to HC groups. The fold changes between the two groups were calculated by dividing the average abundance values of each protein of PD patients by the ones of HC individuals. According to our normality test using Shapiro–Wilk test in the dplyr package in R, the majority of the proteins showed normal distribution. Thus, P values between the two groups were calculated by the Student's two-sample t-test. The q-values for the volcano plot were calculated by significance analysis of microarrays (SAM) and a permutation-based FDR estimation (
      • Tusher V.G.
      • Tibshirani R.
      • Chu G.
      Significance analysis of microarrays applied to the ionizing radiation response.
      ). As an orthogonal method to increase the reliability of the selection for differentially expressed proteins between groups, we also used bootstrap receiver operating characteristic (ROC) curve-based statistical analysis (
      • Xu P.
      • Liu X.
      • Hadley D.
      • Huang S.
      • Krischer J.
      • Beam C.
      Feature selection using bootstrapped ROC curves.
      ,
      • Xia J.
      • Broadhurst D.I.
      • Wilson M.
      • Wishart D.S.
      Translational biomarker discovery in clinical metabolomics: an introductory tutorial.
      ,
      • Song J.
      • Ma S.
      • Sokoll L.J.
      • Eguez R.V.
      • Hoti N.
      • Zhang H.
      • Mohr P.
      • Dua R.
      • Patil D.
      • May K.D.
      • Williams S.
      • Arnold R.
      • Sanda M.G.
      • Chan D.W.
      • Zhang Z.
      A panel of selected serum protein biomarkers for the detection of aggressive prostate cancer.
      ,
      • Jigang X.
      • Zhengding Q.
      Bootstrap technique for ROC analysis: a stable evaluation of fisher classifier performance.
      ). Bootstrap ROC analysis was carried out using the fbroc package in R. The sampling for the Bootstrap ROC was conducted with replacement. The area under the curve (AUC) of a bootstrap ROC of two groups in each sampling was computed. Mean and standard deviation (SD) values of AUCs from 1,000 bootstrap ROC were then calculated (
      • Moharramipour A.
      • Mostame P.
      • Hossein-Zadeh G.A.
      • Wheless J.W.
      • Babajani-Feremi A.
      Comparison of statistical tests in effective connectivity analysis of ECoG data.
      ,
      • Moffet E.W.
      • Subramaniam T.
      • Hirsch L.J.
      • Gilmore E.J.
      • Lee J.W.
      • Rodriguez-Ruiz A.A.
      • Haider H.A.
      • Dhakar M.B.
      • Jadeja N.
      • Osman G.
      • Gaspard N.
      • Struck A.F.
      Validation of the 2HELPS2B seizure risk score in acute brain injury patients.
      ). The q-values of bootstrap AUCs analysis data were calculated as follows; 1) The mean AUC values for non-permutated and permuted data were sorted in descending order for proteins with mean AUCs > 0.5 and in ascending order for proteins with mean AUCs < 0.5, 2) The ratios of the protein numbers for the non-permuted data to the protein numbers for the permuted data were calculated as lowering the cut-off threshold, and the ratios were used as q-values.

      Pathway analysis

      The differentially expressed proteins between PD and HC groups in both SAM and bootstrap AUC analyses were used for Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis embedded in DAVID bioinformatics resources (version 6.8) (
      • Kanehisa M.
      • Goto S.
      KEGG: kyoto encyclopedia of genes and genomes.
      ,
      • Huang da W.
      • Sherman B.T.
      • Lempicki R.A.
      Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.
      ). Interactome analysis was carried out by the STRING protein-protein interaction (PPI) databases (version 11) (
      • von Mering C.
      • Huynen M.
      • Jaeggi D.
      • Schmidt S.
      • Bork P.
      • Snel B.
      STRING: a database of predicted functional associations between proteins.
      ,
      • Szklarczyk D.
      • Gable A.L.
      • Lyon D.
      • Junge A.
      • Wyder S.
      • Huerta-Cepas J.
      • Simonovic M.
      • Doncheva N.T.
      • Morris J.H.
      • Bork P.
      • Jensen L.J.
      • Mering C.V.
      STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.
      ). The weighted gene coexpression network analysis (WGCNA) was conducted using the R software package (
      • Langfelder P.
      • Horvath S.
      WGCNA: an R package for weighted correlation network analysis.
      ,
      • Pei G.
      • Chen L.
      • Zhang W.
      WGCNA application to proteomic and metabolomic data analysis.
      ).

      RESULTS

      Quantitative proteome analysis of SN samples

      To identify differentially expressed proteins in the SN of PD patients, we conducted a quantitative proteome analysis of SN samples from 15 PD patients and 15 HC individuals. For the analysis of 30 SN samples using an 11-plex TMT labeling method, we prepared a master pool (MP) by pooling a small portion of 30 SN samples. We added the MP to one of the 11 TMT channels in each TMT experimental set for the purposes of normalization (Supplemental Figure S1). The proteins were digested with trypsin and LysC followed by labeling 11-plex TMT reagents. The peptide samples labeled with TMT were pre-fractionated in 24 fractions with basic pH RPLC and analyzed by LC-MS/MS. In total, 3,167,187 MS/MS spectra were acquired, and 857,332 spectra were assigned to peptides leading to the identification of 134,786 peptides and 9,748 proteins. The number of identified proteins from each TMT experimental set and the overlapping proteins among the sets are presented in the Venn diagram (Figure 1A). The numbers of identified proteins from batches 1, 2, and 3 were 9,088, 9,148, and 9,031, respectively (Figure 1A and Supplemental Data S1). The number of proteins identified in all batches was 8,352. To conduct a statistical analysis of the data acquired from 3 sets of the TMT experiments, the intensity values of each protein in each set were normalized by the ones of MP. We assessed whether the data from 3 TMT experiments still retains a batch effect by conducting a PCA analysis. The three sets still showed a residual batch effect (Figure 1B left). To minimize the batch effect, a further normalization was conducted once again using the Combat package (
      • Davie C.A.
      A review of Parkinson's disease.
      ). The normalized data by the Combat package showed more even distribution suggesting that the batch effect was reduced (Figure 1B right).
      Figure thumbnail gr1
      Figure 1The number of identified proteins and removal of batch effect by the Combat package.(A) The number of the identified proteins in each batch is shown in the Venn diagram. (B) To minimize batch effects of the 3 different TMT experiments, they were further normalized using the Combat package after normalizing each set using MP. Thirty SN samples were shown on a 2D PCA plot to show potential batch effects before (left panel) and after (right panel) the normalization using the Combat package.

      Statistical analysis for the identification of differentially expressed proteins

      To identify proteins that are potentially involved in the process of PD pathogenesis, statistical analysis was conducted using two different methods; the SAM-based analysis that uses P value and fold change, and the Bootstrap ROC-based analysis that uses the AUC and standard deviation of ROCs calculated by random sampling with replacement (Supplemental Data S2). The differentially expressed proteins were defined by a q-value < 0.05. The number of differential proteins selected by the SAM-based analysis was 1,383 (Figure 2A and Supplemental Table S2). NXT1, SAA1, TPD52L2, LUC7L2, CD63, CAAP1, SERF2, MT1F, PCNP, SDC4, etc. were the most up-regulated proteins, while MRPL28, MRPL13, RTL8C, MRPL37, MRPS24, ELAVL2, MRPS21, SLC6A3, CPNE9, etc. were the most down-regulated proteins. As expected, ALDH1A1 and TH that are uniquely expressed in dopaminergic neurons also showed ∼8 fold down-regulation suggesting dopaminergic neuronal death in the PD patients’ brains. The number of differentially expressed proteins selected by the AUC of the bootstrap ROC was 1,361 (Figure 2B and Supplemental Table S3). When the list of proteins is sorted by SD value in ascending order, TPD52L2, EIF4B, CD63, MCEE, VAPA, LUC7L2, PCNP, MT1F, NIPBL, SERF2, SDC4, etc. were the most up-regulated proteins in PD, while MRPL28, hCG_1984214, MRPL37, MRPS9, RTL8C, MRPS24, TIMM23B, MRPL3, MRPL38, LNPEP, etc. were the most down-regulated proteins in PD. When the differential proteins from the volcano plot with q-value of < 0.05 were compared with the ones from the bootstrap AUC analysis with q-value of < 0.05, 1,140 proteins were common (Figure 2C). We used 1,140 proteins that were common between the two analyses for further analysis.
      Figure thumbnail gr2
      Figure 2Volcano plot and bootstrap AUC analysis of the SN proteins identified from PD patients and HC individuals. (A) The quantified SN proteins from 15 PD patients and 15 HC individuals were plotted on a volcano plot. The curved line is the boundary for a q-value of 0.05. The proteins with q-value < 0.05 are colored in red font. The proteins on the left and right sides of the q-value line were down- and up-regulated in PD, respectively. (B) The quantified SN proteins from 15 PD patients and 15 HC individuals were plotted on a bootstrap AUC plot. The differentially expressed proteins with q-value < 0.05 are shown outside the horizontal lines. (C) The differentially expressed proteins common in the volcano plot and Bootstrap AUC analysis are shown in the Vann diagram.

      Gene set enrichment analysis

      To identify the enriched pathways of the differentially expressed proteins, we conducted gene set enrichment analysis using the KEGG pathway maps. Strikingly, the ribosome pathway was selected as the most enriched pathway, followed by GABAergic synapse, retrograde endocannabinoid signaling, cell adhesion molecules (CAMs), morphine addiction, prion disease, and Parkinson's disease pathways (Table 2 and Supplemental Table S4). The ribosome pathway was enriched with 42 proteins with P value of 1.4 × 10-16 (Figure 3). Out of 42 proteins, 17 and 25 proteins were ribosomal proteins (RPs) and mitochondrial ribosomal proteins (MRPs), respectively. Among the 17 RPs, 2 proteins were up-regulated and 15 proteins were down-regulated in PD (Supplemental Table S5). Among 25 MRPs, all of them were down-regulated in PD (Supplemental Table S6). The GABAergic synapse pathway was enriched with 18 proteins with a P value of 6.2 × 10-5. Eight out of 18 proteins were gamma-aminobutyric acid (GABA) receptor proteins, and 3 out of 18 were guanine nucleotide-binding proteins. The retrograde endocannabinoid signaling pathway was enriched with 18 proteins with a P value of 5.5 × 10-4. Five out of 18 proteins were GABA receptor proteins, and 4 out of 18 were guanine nucleotide-binding proteins. The cell adhesion molecules pathway was enriched with 22 proteins with P value of 7.8 × 10-4, and 3 of them were integrin proteins. The morphine addiction pathway was enriched with 16 proteins with P value of 1.4 × 10-3. Seven out of 16 proteins were GABA receptor proteins, and 3 out of 18 were guanine nucleotide-binding proteins. The prion diseases pathway was enriched with 9 proteins with P value of 1.9 × 10-3, and 3 of them were complement proteins. The Parkinson’s disease pathway was enriched with 20 proteins with a P value of 4.4 × 10-3, and 7 of them were NADH dehydrogenase subcomplex proteins. Interestingly, 3 pathways; GABAergic synapse, retrograde endocannabinoid signaling, and morphine addiction; share GABA receptors and guanine nucleotide-binding proteins, and these shared proteins contribute to the enriched pathways. These results suggest that the main protein clusters formed by differentially expressed proteins in SN of the PD brain are MRPs, RPs, GABA receptors, and NADH dehydrogenase subcomplex proteins.
      Table 2Enriched pathways of the differentially expressed proteins
      TermCount/PH%P value
      Ribosome42/13630.91.40E-16
      GABAergic synapse18/8521.26.20E-05
      Retrograde endocannabinoid signaling18/10117.85.50E-04
      Cell adhesion molecules (CAMs)22/14215.57.80E-04
      Morphine addiction16/9117.61.40E-03
      Prion diseases9/3426.51.90E-03
      Parkinson's disease20/14214.14.40E-03
      (PH: the total number of proteins in the pathway)
      Figure thumbnail gr3
      Figure 3Ribosome pathway map identified by the gene set enrichment analysis. The ribosome pathway that was selected as the most enriched one of the differentially expressed proteins in PD using KEGG pathway analysis is displayed here. The ribosomal proteins (RPs) were colored in orange, and the mitochondrial ribosomal proteins (MRPs) are colored in magenta.

      Interactome analysis

      Although we have identified a few enriched pathways for the differentially expressed proteins in the SN ofthe PD patients, we reasoned that an orthogonal analysis would enable us to narrow down key pathways. For this, we conducted an interactome analysis with the up- and down-regulated proteins to unravel key functional modules using the STRING functional protein association network (
      • Szklarczyk D.
      • Gable A.L.
      • Lyon D.
      • Junge A.
      • Wyder S.
      • Huerta-Cepas J.
      • Simonovic M.
      • Doncheva N.T.
      • Morris J.H.
      • Bork P.
      • Jensen L.J.
      • Mering C.V.
      STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.
      ,
      • Vykoukal J.
      • Sun N.
      • Aguilar-Bonavides C.
      • Katayama H.
      • Tanaka I.
      • Fahrmann J.F.
      • Capello M.
      • Fujimoto J.
      • Aguilar M.
      • Wistuba II,
      • Taguchi A.
      • Ostrin E.J.
      • Hanash S.M.
      Plasma-derived extracellular vesicle proteins as a source of biomarkers for lung adenocarcinoma.
      ). For the up-regulated proteins, RNA splicing-related proteins formed the most connected cluster followed by vesicle-mediated transport and complement cascade pathways (Figure 4A). While activated immune response is well known factor in PD pathogenesis, only three proteins formed a small cluster. Thus, we investigated how many differentially expressed immune response-related proteins were identified. We could identify 9 immune response-related proteins differentially expressed. Interestingly, all the differentially expressed inflammation-related proteins, complement proteins (C1Q, C9, C1B, C1C, C4B, C4A, CFHR1, and C1S), and interferon-gamma receptor 1 (IFNGR1) were increased, clearly showing that the activated complement proteins are one of the potential main causative factors of PD (Supplemental Data S2). For the down-regulated proteins, the MRPs formed the most connected cluster, followed by RPs (Figure 4B). Since MRP and RP proteins formed large clusters only for the down-regulated proteins, we investigated how many of the MRPs and RPs were down-regulated among all the identified proteins. Interestingly, all of the MRPs and the majority of RPs were down-regulated in the SN from PD patients (Table 3 and Table 4). The human genome has 85 RPs and 78 MRPs in the UniProt knowledgebase (
      • UniProt C.
      UniProt: the universal protein knowledgebase in 2021.
      ). In this study, we identified 81 RPs and 70 MRPs. While 19 (23%) out of 81 RPs were dysregulated, 51 (73%) out of 70 MRPs were dysregulated in the SN of the PD patients. Other than ribosomal proteins, respiratory electron transport proteins and tRNA aminoacylation-related proteins formed clusters too. These results suggest that mitochondrial ribosomal functions were more severely compromised in the SN of the PD patients’ brains, followed by the functions of ribosomal proteins, spliceosome proteins, respiratory complex proteins of mitochondria, vesicle-mediated transport proteins, and complement cascade proteins.
      Figure thumbnail gr4
      Figure 4STRING PPI analysis of the differentially expressed proteins in PD. (A) STRING PPI analysis was conducted to estimate the connectivity of the up-regulated proteins. The network contains 634 nodes with 70 edges. The experiment alone was used as an active interaction source with the highest confidence threshold of 0.9 (average node degree: 0.221, average local clustering coefficient: 0.0946, and PPI enrichment P value: 0.00013). The blue, red, and green nodes denote RNA splicing (GO:0008380), vesicle-mediated transport (GO:0016192), and complement cascade pathways (HSA-166658), respectively. The gray nodes belong to other pathways. (B) STRING PPI analysis was conducted to estimate the connectivity of the down-regulated proteins. The network contains 502 nodes with 821 edges. The experiment alone was used as an active interaction source with the highest confidence threshold of 0.9 (average node degree: 3.27, average local clustering coefficient: 0.189, and PPI enrichment P value: < 1.0E-16). The blue, pink, green, and red nodes denote mitochondrial gene expression (GO:0140053), eukaryotic translation elongation (HSA-156842), respiratory electron transport (HSA-611105), and tRNA aminoacylation for protein translation (GO:0006418), respectively. The gray nodes belong to other pathways.
      Table 3List of differentially expressed ribosomal proteins
      Protein nameProtein symbolP valueq-valuez-score (PD/HC)
      60S ribosomal protein L36a-likeRPL36AL5.02E-0602.42
      60S ribosomal protein L37aRPL37A0.0063720.0092781.75
      Ribosomal protein S6 kinase alpha-5RPS6KA50.0049940.0319810.65
      Ribosomal protein S6 kinase alpha-3RPS6KA31.09E-050.017386-0.41
      60S ribosomal protein L30RPL300.0029380.046668-0.46
      60S ribosomal protein L11RPL110.0001920.009427-0.61
      60S ribosomal protein L38RPL380.0001620.007457-0.65
      60S acidic ribosomal protein P0RPLP00.0001240.004742-0.72
      40S ribosomal protein S27-likeRPS27L0.0020750.01566-0.76
      40S ribosomal protein S20RPS200.0008770.0098-0.76
      60S acidic ribosomal protein P2RPLP22.59E-050.001787-0.78
      40S ribosomal protein S28RPS282.22E-050.001374-0.82
      40S ribosomal protein S14RPS140.0003890.005355-0.83
      60S ribosomal protein L9RPL92.32E-060.000464-0.84
      40S ribosomal protein SARPSA1.60E-060.000325-0.86
      40S ribosomal protein S12RPS124.69E-060.00046-0.91
      60S ribosomal protein L10aRPL10A1.67E-060.000233-1.00
      60S acidic ribosomal protein P1RPLP11.12E-060.000222-1.00
      60S ribosomal protein L35RPL350.0019190.00586-1.24
      Table 4List of differentially expressed mitochondrial ribosomal proteins.
      Protein nameProtein symbolP valueq-valuez-score (PD/HC)
      28S ribosomal protein S21, mitochondrialMRPS212.51E-070-2.81
      28S ribosomal protein S24, mitochondrialMRPS243.57E-080-2.24
      39S ribosomal protein L28, mitochondrialMRPL281.51E-090-2.24
      39S ribosomal protein L23, mitochondrialMRPL235.13E-050.000202-1.81
      39S ribosomal protein L13, mitochondrialMRPL131.91E-090-1.78
      39S ribosomal protein L21, mitochondrialMRPL212.46E-060-1.73
      28S ribosomal protein S34, mitochondrialMRPS342.61E-060-1.63
      28S ribosomal protein S9, mitochondrialMRPS97.78E-080-1.58
      39S ribosomal protein L41, mitochondrialMRPL412.26E-060-1.57
      28S ribosomal protein S16, mitochondrialMRPS161.49E-050.000211-1.55
      28S ribosomal protein S7, mitochondrialMRPS73.14E-060.000114-1.55
      39S ribosomal protein L3, mitochondrialMRPL34.22E-070-1.53
      28S ribosomal protein S25, mitochondrialMRPS251.09E-060-1.52
      39S ribosomal protein L24, mitochondrialMRPL249.94E-060.000225-1.47
      28S ribosomal protein S10, mitochondrialMRPS106.81E-070-1.43
      28S ribosomal protein S31, mitochondrialMRPS310.0004640.001912-1.30
      28S ribosomal protein S35, mitochondrialMRPS350.0003330.001547-1.28
      28S ribosomal protein S22, mitochondrialMRPS220.0011020.003783-1.28
      39S ribosomal protein L42, mitochondrialMRPL424.10E-060.000215-1.25
      39S ribosomal protein L47, mitochondrialMRPL476.97E-050.000741-1.20
      39S ribosomal protein L49, mitochondrialMRPL492.82E-050.000442-1.17
      39S ribosomal protein L19, mitochondrialMRPL195.71E-060.00022-1.15
      39S ribosomal protein L37, mitochondrialMRPL372.60E-080-1.14
      39S ribosomal protein L53, mitochondrialMRPL539.83E-060.000291-1.12
      39S ribosomal protein L44, mitochondrialMRPL441.60E-050.000383-1.09
      39S ribosomal protein L1, mitochondrialMRPL10.0001310.001516-1.04
      28S ribosomal protein S27, mitochondrialMRPS270.0006290.004009-1.03
      28S ribosomal protein S18b, mitochondrialMRPS18B2.53E-060.000215-1.03
      28S ribosomal protein S15, mitochondrialMRPS150.0003210.002953-1.00
      28S ribosomal protein S23, mitochondrialMRPS230.0009290.00569-0.99
      39S ribosomal protein L16, mitochondrialMRPL162.06E-070.0002-0.98
      28S ribosomal protein S6, mitochondrialMRPS60.0001790.00238-0.97
      39S ribosomal protein L50, mitochondrialMRPL500.0001870.002615-0.95
      39S ribosomal protein L30, mitochondrialMRPL300.00060.0048-0.95
      39S ribosomal protein L45, mitochondrialMRPL450.0002010.002754-0.94
      28S ribosomal protein S33, mitochondrialMRPS330.001180.007917-0.91
      39S ribosomal protein L20, mitochondrialMRPL202.30E-050.001-0.90
      39S ribosomal protein L10, mitochondrialMRPL100.0001180.002372-0.90
      39S ribosomal protein L38, mitochondrialMRPL383.08E-060.000337-0.90
      28S ribosomal protein S28, mitochondrialMRPS283.69E-050.001225-0.89
      39S ribosomal protein L46, mitochondrialMRPL460.0012040.008648-0.87
      39S ribosomal protein L40, mitochondrialMRPL404.80E-050.001552-0.87
      39S ribosomal protein S30, mitochondrialMRPS302.48E-050.001151-0.87
      39S ribosomal protein L17, mitochondrialMRPL171.01E-050.000979-0.84
      39S ribosomal protein L52, mitochondrialMRPL520.0007310.007355-0.83
      39S ribosomal protein L27, mitochondrialMRPL270.0003050.004996-0.81
      39S ribosomal protein L11, mitochondrialMRPL110.0008530.009026-0.79
      39S ribosomal protein L22, mitochondrialMRPL226.40E-060.00107-0.77
      39S ribosomal protein L14, mitochondrialMRPL140.002370.016919-0.75
      39S ribosomal protein L39, mitochondrialMRPL393.86E-050.002897-0.73
      39S ribosomal protein L4, mitochondrialMRPL40.0039480.037657-0.55

      Coexpression analysis using WGCNA

      The gene set enrichment and interactome analyses of the differential proteins in the SN of the PD patients’ brains suggested that mitochondrial ribosome could be the most affected pathway in the PD brains. However, we still could not rule out the possibility that this pathway could be identified by other traits of the samples than the PD pathology. To address this, we conducted an unbiased coexpression analysis using WGCNA, which clusters proteins with similar patterns and calculates correlations of the 26 protein cluster modules with various traits of the samples such as diagnosis, age, sex, and postmortem delay (PMD) (Table 1 and Supplemental Data S3) (
      • Pei G.
      • Chen L.
      • Zhang W.
      WGCNA application to proteomic and metabolomic data analysis.
      ). The WGCNA results showed that the M5 (Cyan), M11 (Green), M12 (Brown), and M13 (Pink) modules showed a positive correlation (P value < 0.05) with PD implying that the proteins in the clusters have a pattern of increased expression level in the PD samples. On the other hand, M21 (Blue), M22 (Magenta), and M23 (Salmon) modules showed a negative correlation (P value < 0.05) with PD implying that the proteins in the cluster have a pattern of decreased expression level in the PD samples (Figure 5 and Supplemental Figure S2). Because the gene set enrichment and interactome analyses showed that MRPs were decreased in PD, we postulated that the MRPs would be clustered in the modules that had the pattern of decreased expression level in the PD samples. For this reason, we conducted a KEGG pathway analysis with the proteins in the M21, M22, and M23 modules to identify the module that has enriched MRPs. The M21 module showed the most significant enrichment with MRPs (Supplemental Figure S3A and Supplemental Table S7 top). In addition, the M23 module also showed the most significant enrichment with RPs (Supplemental Figure S3B and Supplemental Table S7 bottom). Although the M21 module showed almost no correlation with age and sex, it showed a mild positive correlation with PMD. These results suggest that there is the possibility that PMD could affect the identification of mitochondrial ribosomes as an enriched pathway in the PD samples. To rule out this possibility, we conducted statistical analysis by classifying samples into two groups based on PMD. The PMD was divided into low and high based on its median value, resulting in the regrouping of 3 participants. The statistical analysis results showed no differential proteins, suggesting that the down-regulated MRPs are not correlated to PMD but to PD (Supplemental Figure S4).
      Figure thumbnail gr5
      Figure 5The module-trait relationships of the WGCNA of SN proteome data. The module-trait relationships of WGCNA of the SN proteome data were presented in the form of a heatmap. The Pearson correlations between 26 protein cluster modules and 4 traits composed of diagnosis, age, sex, and PMD were calculated and colored on a scale of 1 (positive correlation) to -1 (negative correlation). A protein cluster module was generated by collecting proteins with similar expression patterns across the samples. The correlation values are shown at the top of each box, and the P values are shown on the bottom of each box inside the parenthesis.

      An independent replication experiment of the pathways discovered in the main experiment

      Gene set enrichment and interactome analyses showed that the ribosome pathway, especially MRPs, is a key protein cluster linked to PD pathology. However, we still cannot exclude the possibility that the dysregulated ribosome pathway was a feature unique to the SN samples that we used in the main experiment. Thus, we reasoned that if we could observe similar results from an independent experiment using a different cohort of SN samples, we could have higher confidence in the identified pathways. For this, we analyzed the proteome data of SN from 10 PD patients and 9 HC individuals that was acquired before the main experiment was conducted by an independent researcher using a different mass spectrometer. Statistical and data analysis were performed in the same way as the main experiment (Supplemental Figures S5 and S6 and Supplemental Data S4). The gene set enrichment analysis showed that the ribosome pathway was the most enriched pathway, as was observed in the main experiment (Supplemental Table S8, Supplemental Figure S7, and Supplemental Data S5). The interactome analysis also showed that the MRPs and RPs were the most connected clusters, as observed in the main experiment (Supplemental Figure S8). We identified 76 RPs and 51 MRPs, and 44 (58%) out of 76 RPs and 36 (71%) out of 51 MRPs were dysregulated in SN of PD patients. This replication experiment suggests that the ribosome pathway discovered in the main experiment is linked to PD pathology with high confidence.

      DISCUSSION

      In this study, we conducted mass spectrometry-based proteome analysis of human SN brain tissue samples from 15 PD patients and 15 HC individuals using the TMT labeling method. This is the first in-depth proteome analysis of the human SN region from PD patients and HC individuals in which we identified ∼10,000 proteins. In this study, we conducted two different statistical analyses, the SAM-based one and the bootstrap ROC-based one, to find differentially expressed proteins between the two groups. The SAM-based statistical analysis is the most widely used in the proteomics field. However, while conducting SAM-based statistical analysis, variable q-value cutoff lines can be generated depending on the S0 values that users set. When the S0 value is 0, the q-value cutoff line is solely affected by P values. As the S0 value increases, more weight is given to the fold change than the P value in determining the q-value cutoff line. Therefore, the proteins in the proximity of the q-value cutoff line are subjected to be included or excluded depending on the S0 value that users set. To minimize this ambiguity, we added another layer of statistical analysis by employing the bootstrap ROC. Since bootstrap analysis uses resampling approaches, it outperforms Student t-statistics in finding true-positive and true-negative proteins (
      • Zhao S.
      • Yang Z.
      • Musa S.S.
      • Ran J.
      • Chong M.K.C.
      • Javanbakht M.
      • He D.
      • Wang M.H.
      Attach importance of the bootstrap t test against Student's t test in clinical epidemiology: a demonstrative comparison using COVID-19 as an example.
      ). Thus, the two different statistical analyses employed in this study would be helpful in sifting true-positive differentially expressed proteins with reduced ambiguity. Gene set enrichment analysis using differentially expressed proteins in PD showed that ribosome, GABAergic synapse, retrograde endocannabinoid signaling, cell adhesion molecule, morphine addiction, prion diseases, and Parkinson’s disease pathways were the most enriched ones, suggesting that they could be potentially involved in the PD pathogenesis. Strikingly, the majority of the ribosomal proteins enriched in the gene set enrichment analysis were mitoribosomes. The subsequent STRING PPI analysis and WGCNA also showed that mitoribosomes formed the largest highly connected cluster. In addition, more than 50% of the proteins enriched in the Parkinson’s disease pathway are mitochondria-related proteins. These results indicate that many mitochondria-related proteins are dysregulated in the SN of PD patients, consistent with many previous reports of abnormal mitochondrial function in PD (
      • Bose A.
      • Beal M.F.
      Mitochondrial dysfunction in Parkinson's disease.
      ,
      • Park J.S.
      • Davis R.L.
      • Sue C.M.
      Mitochondrial dysfunction in Parkinson's disease: new mechanistic insights and therapeutic perspectives.
      ,
      • Licker V.
      • Turck N.
      • Kövari E.
      • Burkhardt K.
      • Côte M.
      • Surini-Demiri M.
      • Lobrinus J.A.
      • Sanchez J.C.
      • Burkhard P.R.
      Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson's disease pathogenesis.
      ,
      • Mai N.
      • Chrzanowska-Lightowlers Z.M.
      • Lightowlers R.N.
      The process of mammalian mitochondrial protein synthesis.
      ,
      • Macdonald R.
      • Barnes K.
      • Hastings C.
      • Mortiboys H.
      Mitochondrial abnormalities in Parkinson's disease and Alzheimer's disease: can mitochondria be targeted therapeutically?.
      ).
      Previously, van Dijk et al. performed the proteomic analysis with human locus coeruleus brain tissues from 6 PD patients and 6 HC individuals identifying 2,495 proteins with 87 differential proteins (
      • van Dijk K.D.
      • Berendse H.W.
      • Drukarch B.
      • Fratantoni S.A.
      • Pham T.V.
      • Piersma S.R.
      • Huisman E.
      • Brevé J.J.
      • Groenewegen H.J.
      • Jimenez C.R.
      • van de Berg W.D.
      The proteome of the locus ceruleus in Parkinson's disease: relevance to pathogenesis.
      ). They discovered that the main affected pathways were mitochondrial dysfunction, oxidative stress, protein misfolding, cytoskeleton dysregulation, and inflammation. Lachén-Montes et al. performed proteome analysis with human olfactory bulb tissues from 12 PD patients and 8 HC individuals, quantifying 1,629 proteins with 268 differentially expressed proteins (
      • Lachén-Montes M.
      • González-Morales A.
      • Iloro I.
      • Elortza F.
      • Ferrer I.
      • Gveric D.
      • Fernández-Irigoyen J.
      • Santamaría E.
      Unveiling the olfactory proteostatic disarrangement in Parkinson's disease by proteome-wide profiling.
      ). They discovered modulation in ERK1/2, MKK3/6, and PDK1/PKC signaling axis. Basso et al. performed proteome analysis with human SN brain tissues from 4 PD patients and 4 HC individuals identifying 44 proteins with 9 proteins with abundance change (
      • Basso M.
      • Giraudo S.
      • Corpillo D.
      • Bergamasco B.
      • Lopiano L.
      • Fasano M.
      Proteome analysis of human substantia nigra in Parkinson's disease.
      ). Werner et al. performed proteome analysis with human SN tissues from 5 PD patients and 5 HC individuals identifying 38 proteins with 16 differentially expressed proteins (
      • Werner C.J.
      • Heyny-von Haussen R.
      • Mall G.
      • Wolf S.
      Proteome analysis of human substantia nigra in Parkinson's disease.
      ). They discovered alterations of GSH-related proteins as well as alterations of proteins involved in retinoid metabolism. Licker et al. performed proteome analysis with human SN from 3 PD patients and 3 HC individuals employing a TMT-based LC-MS/MS analysis identifying 1,795 proteins with 204 differentially expressed proteins (
      • Licker V.
      • Turck N.
      • Kövari E.
      • Burkhardt K.
      • Côte M.
      • Surini-Demiri M.
      • Lobrinus J.A.
      • Sanchez J.C.
      • Burkhard P.R.
      Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson's disease pathogenesis.
      ). They discovered that the most altered pathways were mitochondrial dysfunction, oxidative stress, or cytoskeleton impairment. Choi et al. and Gómez and Ferrer also performed proteome analysis with human cortex brain tissues from PD patients (
      • Choi J.
      • Levey A.I.
      • Weintraub S.T.
      • Rees H.D.
      • Gearing M.
      • Chin L.S.
      • Li L.
      Oxidative modifications and down-regulation of ubiquitin carboxyl-terminal hydrolase L1 associated with idiopathic Parkinson's and Alzheimer's diseases.
      ,
      • Choi J.
      • Sullards M.C.
      • Olzmann J.A.
      • Rees H.D.
      • Weintraub S.T.
      • Bostwick D.E.
      • Gearing M.
      • Levey A.I.
      • Chin L.S.
      • Li L.
      Oxidative damage of DJ-1 is linked to sporadic Parkinson and Alzheimer diseases.
      ,
      • Gómez A.
      • Ferrer I.
      Increased oxidation of certain glycolysis and energy metabolism enzymes in the frontal cortex in Lewy body diseases.
      ). Choi et al. reported altered expression of ubiquitin carboxyl-terminal hydrolase L1 and oxidative damage of DJ1 in the PD brain (
      • Choi J.
      • Levey A.I.
      • Weintraub S.T.
      • Rees H.D.
      • Gearing M.
      • Chin L.S.
      • Li L.
      Oxidative modifications and down-regulation of ubiquitin carboxyl-terminal hydrolase L1 associated with idiopathic Parkinson's and Alzheimer's diseases.
      ,
      • Choi J.
      • Sullards M.C.
      • Olzmann J.A.
      • Rees H.D.
      • Weintraub S.T.
      • Bostwick D.E.
      • Gearing M.
      • Levey A.I.
      • Chin L.S.
      • Li L.
      Oxidative damage of DJ-1 is linked to sporadic Parkinson and Alzheimer diseases.
      ). Gómez and Ferrer reported oxidative damage of aldolase A, enolase 1, and glyceraldehyde dehydrogenase (
      • Gómez A.
      • Ferrer I.
      Increased oxidation of certain glycolysis and energy metabolism enzymes in the frontal cortex in Lewy body diseases.
      ). Consistent with our study, these proteomics studies of PD using human brain tissues suggested that the affected pathways in the PD brains were mitochondrial dysfunction. In addition, these other proteomic studies indicated that oxidative stress, protein misfolding, cytoskeleton impairment, and inflammation play a role in the pathogenesis of PD. Although mitochondrial dysfunction is well known in PD, little is known about the involvement of mitoribosomes. Billingsley et al. reported that MRPS34, a mitoribosome, could be a PD risk gene (
      • Billingsley K.J.
      • Barbosa I.A.
      • Bandrés-Ciga S.
      • Quinn J.P.
      • Bubb V.J.
      • Deshpande C.
      • Botia J.A.
      • Reynolds R.H.
      • Zhang D.
      • Simpson M.A.
      • Blauwendraat C.
      • Gan-Or Z.
      • Gibbs J.R.
      • Nalls M.A.
      • Singleton A.
      • et al.
      Mitochondria function associated genes contribute to Parkinson's disease risk and later age at onset.
      ). Since mitoribosomes are involved in the translation of mitochondrial proteins encoded by mtDNA, the down-regulated mitoribosome would affect the translation of mitochondrial proteins encoded by mtDNA (mtDNA-encoded proteins). Our study showed that 4 out of 5 mtDNA-encoded proteins show a trend of down-regulation in SN of PD, but those mtDNA-encoded proteins did not show statistically significant differences. There are multiple explanations for why mtDNA-encoded protein levels did not change while mitoribosome proteins were down-regulated. The first possible explanation is that mtDNA-encoded proteins were down-regulated in neuronal cells but up-regulated in other cell types. When we analyze the mixture of proteins from multiple cell types, the outcome of the summed protein abundance often misleads the interpretation of the results. The second possible explanation is that the mtDNA-encoded proteins have longer protein turnover and they were less affected by the down-regulation of mitoribosomes. Thus, cell-type-specific proteome analysis and protein turnover study on mitochondrial proteins would provide a clue on why mtDNA-encoded proteins were not down-regulated.
      GABAergic synapse, retrograde endocannabinoid signaling, and morphine addiction pathways that were enriched with GABA receptor proteins suggested that GABA-related pathways were also potentially compromised in the SN of PD patients. The direct relevance between PD pathogenesis and the GABAergic system is unknown, but their potential indirect relevance has already been reported by several research groups (
      • Muñoz M.D.
      • de la Fuente N.
      • Sánchez-Capelo A.
      TGF-β/Smad3 signalling modulates GABA neurotransmission: Implications in Parkinson's disease.
      ,
      • Murueta-Goyena A.
      • Andikoetxea A.
      • Gómez-Esteban J.C.
      • Gabilondo I.
      Contribution of the GABAergic system to non-motor manifestations in premotor and early stages of Parkinson's disease.
      ). For example, although SN does not have GABAergic neurons, the SN pars reticulata (SNr) region has receptors for GABAergic projection exons (
      • Tepper J.M.
      • Lee C.R.
      GABAergic control of substantia nigra dopaminergic neurons.
      ). It is known that dopamine depletion induced by dopaminergic neuronal death in SN pars compacta (SNc) of PD patients affects GABAergic transmission in basal ganglia and this, in turn, possibly affected the expression of GABAergic receptors in SNr (
      • Faynveitz A.
      • Lavian H.
      • Jacob A.
      • Korngreen A.
      Proliferation of inhibitory input to the substantia nigra in experimental parkinsonism.
      ). Therefore, dysregulation of GABA receptor proteins in the SN of PD could be considered a consequence of dopaminergic neuronal death. At a glance, it would be considered that the down-regulation of GABAergic receptors will lead to the up-regulation of glutamatergic neurons. We identified 20 glutamate receptor proteins in this study. Interestingly, 3 proteins with q-values < 0.05 (SAM analysis) were down-regulated and most of the remaining proteins also showed a trend of down-regulation although their q-values > 0.05 (Supplemental Table S9). According to the basal ganglia neural circuit, SNr receives GABAergic transmission from the caudate/putamen and SNr sends GABAergic transmission to the thalamus. On the other hand, both SNc and SNr receive glutamatergic transmissions from the subthalamic nucleus. Subsequently, SNc sends a dopaminergic transmission to caudate/putamen and SNr sends GABAergic transmission to the thalamus (
      • Mallet N.
      • Delgado L.
      • Chazalon M.
      • Miguelez C.
      • Baufreton J.
      Cellular and synaptic dysfunctions in Parkinson's disease: stepping out of the striatum.
      ,
      • Nickols H.H.
      • Conn P.J.
      Development of allosteric modulators of GPCRs for treatment of CNS disorders.
      ). Therefore, the down-regulated GABAergic receptors in SNr will result in reduced GABAergic transmission from SNr to the thalamus, not affecting glutamatergic transmission in SN. So, the down-regulated glutamatergic receptors in SN discovered in this study can be explained by dopaminergic neuronal death in SNc, because the dopaminergic neuronal death will result in the loss of glutamatergic receptors on dopaminergic neurons. Another possible explanation of the down-regulated glutamatergic receptors is that it was the consequence of downscaling of the glutamatergic receptors caused by constitutive glutamatergic stimulation. It is already known that the consistent stimulation of DA neurons by glutamatergic stimulation from the subthalamic nucleus is involved in PD pathogenesis (
      • Chatha B.T.
      • Bernard V.
      • Streit P.
      • Bolam J.P.
      Synaptic localization of ionotropic glutamate receptors in the rat substantia nigra.
      ).
      Further study to understand their correlation is required. The Parkinson’s disease pathway was enriched with 20 differentially expressed proteins. TH and SLC6A3, which are dopaminergic neuron-specific proteins, showed down-regulation (
      • Poulin J.F.
      • Zou J.
      • Drouin-Ouellet J.
      • Kim K.Y.
      • Cicchetti F.
      • Awatramani R.B.
      Defining midbrain dopaminergic neuron diversity by single-cell gene expression profiling.
      ). SLC18A2, a transmembrane protein that transports monoamines, also showed down-regulation. When SLC18A2 function is inhibited, dopamine cannot be released into the synapse via a typical release mechanism (
      • Lohr K.M.
      • Masoud S.T.
      • Salahpour A.
      • Miller G.W.
      Membrane transporters as mediators of synaptic dopamine dynamics: implications for disease.
      ). The down-regulation of TH, SLC6A3, and SLC18A2 can be explained by dopaminergic neuronal death in SN. On the other hand, GPR37, which is a putative substrate of Parkin, was increased. This protein is known to be linked to juvenile PD, and misfolded GPR37 has been found in Lewy bodies. Elderly GPR37 knockout mice displayed deficits in motor performance and properly folded GPR37 can have a neuroprotective effect (
      • Zhang X.
      • Mantas I.
      • Fridjonsdottir E.
      • Andrén P.E.
      • Chergui K.
      • Svenningsson P.
      Deficits in motor performance, neurotransmitters and synaptic plasticity in elderly and experimental parkinsonian mice lacking GPR37.
      ). UBE2L3 is an E2 ubiquitin-conjugating enzyme that plays a role in Parkin-mediated mitochondrial elimination (
      • Geisler S.
      • Vollmer S.
      • Golombek S.
      • Kahle P.J.
      The ubiquitin-conjugating enzymes UBE2N, UBE2L3 and UBE2D2/3 are essential for parkin-dependent mitophagy.
      ). COX6B1, COX7B, NDUFA1, NDUFA4L2, NDUFAB1, NDUFB2, NDUFB3, NDUFB9, NDUFC1, UQCRH, and UQCRQ are mitochondrial proteins (
      • Duggan A.T.
      • Kocha K.M.
      • Monk C.T.
      • Bremer K.
      • Moyes C.D.
      Coordination of cytochrome c oxidase gene expression in the remodelling of skeletal muscle.
      ,
      • Dang Q.L.
      • Phan D.H.
      • Johnson A.N.
      • Pasapuleti M.
      • Alkhaldi H.A.
      • Zhang F.
      • Vik S.B.
      Analysis of human mutations in the supernumerary subunits of complex I.
      ,
      • Wen J.J.
      • Garg N.
      Oxidative modification of mitochondrial respiratory complexes in response to the stress of trypanosoma cruzi infection.
      ). Thus, the dysregulation of UBE2L3, COX6B1, COX7B, NDUFA1, NDUFA4L2, NDUFAB1, NDUFB2, NDUFB3, NDUFB9, NDUFC1, UQCRH, and UQCRQ are potentially linked to mitochondrial dysfunction too. In addition to the proteins that were manifested in the gene set enrichment analysis, NXT1, SAA1, TPD52L2, LUC7L2, CD63, CAAP1, SERF2, MT1F, PCNP, etc. were significantly up-regulated, and RTL8C, ELAVL2, CPNE9, ALDH1A1, KCNJ6, etc., were significantly down-regulated in PD. A strong increase of a metallothionein protein, MT1F, in the astrocytes in PD SN was previously reported consistent with our findings (
      • Michael G.J.
      • Esmailzadeh S.
      • Moran L.B.
      • Christian L.
      • Pearce R.K.
      • Graeber M.B.
      Up-regulation of metallothionein gene expression in parkinsonian astrocytes.
      ). ALDH1A1 is involved in the catabolism of reactive dopamine metabolites in dopaminergic neurons (
      • Carmichael K.
      • Evans R.C.
      • Lopez E.
      • Sun L.
      • Kumar M.
      • Ding J.
      • Khaliq Z.M.
      • Cai H.
      Function and regulation of ALDH1A1-positive nigrostriatal dopaminergic neurons in motor control and Parkinson's disease.
      ), and the reduction of ALDH1A1 in PD SN reflects the loss of dopaminergic neuronal functions. However, little is known about the relevance of the rest of the proteins to PD.
      In addition to the pathways revealed by the gene set enrichment analysis, the STRING PPI analysis exhibited highly clustered nodes that were not revealed by the gene set enrichment analysis such as RNA splicing-related proteins, vesicle-mediated transport proteins, complement cascade-related proteins, and tRNA aminoacylation-related proteins. The implication of aberrant alternative splicing of Parkinson’s disease-related proteins in the PD pathogenesis has been reported; alternative splicing of SNCA can accelerate or decelerate the aggregation of α-synuclein, several pathogenic mutations affect LRRK2 alternative splicing, and alternative spliced PARK2 (Parkin) variants are implicated in juvenile Parkinsonism (
      • Li D.
      • McIntosh C.S.
      • Mastaglia F.L.
      • Wilton S.D.
      • Aung-Htut M.T.
      Neurodegenerative diseases: a hotbed for splicing defects and the potential therapies.
      ,
      • La Cognata V.
      • D'Agata V.
      • Cavalcanti F.
      • Cavallaro S.
      Splicing: is there an alternative contribution to Parkinson's disease?.
      ,
      • Fu R.H.
      • Liu S.P.
      • Huang S.J.
      • Chen H.J.
      • Chen P.R.
      • Lin Y.H.
      • Ho Y.C.
      • Chang W.L.
      • Tsai C.H.
      • Shyu W.C.
      • Lin S.Z.
      Aberrant alternative splicing events in Parkinson's disease.
      ). The dysregulation of the vesicle-mediated transport pathway is also well known to be involved in PD pathogenesis (
      • Ebanks K.
      • Lewis P.A.
      • Bandopadhyay R.
      Vesicular dysfunction and the pathogenesis of Parkinson's disease: clues from genetic studies.
      ,
      • Singh P.K.
      • Muqit M.M.K.
      Parkinson's: a disease of aberrant vesicle trafficking.
      ). For example, VPS35, which is one of the known PD-related genes, encodes the protein that transports endosomal cargoes to vesicles and tubes, and the mutation on VPS35 results in the dysregulation of the vesicle transports (
      • Williams E.T.
      • Chen X.
      • Moore D.J.
      VPS35, the retromer complex and Parkinson's disease.
      ). The complement cascade proteins also have been reported to be involved in PD pathogenesis (
      • Ma S.X.
      • Seo B.A.
      • Kim D.
      • Xiong Y.
      • Kwon S.H.
      • Brahmachari S.
      • Kim S.
      • Kam T.I.
      • Nirujogi R.S.
      • Kwon S.H.
      • Dawson V.L.
      • Dawson T.M.
      • Pandey A.
      • Na C.H.
      • Ko H.S.
      Complement and coagulation cascades are potentially involved in dopaminergic neurodegeneration in alpha-synuclein-based mouse models of Parkinson's disease.
      ,
      • Carpanini S.M.
      • Torvell M.
      • Morgan B.P.
      Therapeutic inhibition of the complement system in diseases of the central nervous system.
      ,
      • Gregersen E.
      • Betzer C.
      • Kim W.S.
      • Kovacs G.
      • Reimer L.
      • Halliday G.M.
      • Thiel S.
      • Jensen P.H.
      Alpha-synuclein activates the classical complement pathway and mediates complement-dependent cell toxicity.
      ,
      • Loeffler D.A.
      • Camp D.M.
      • Conant S.B.
      Complement activation in the Parkinson's disease substantia nigra: an immunocytochemical study.
      ). Ma et al. reported that the complement and coagulation cascade has been dysregulated in two representative PD mouse models (
      • Ma S.X.
      • Seo B.A.
      • Kim D.
      • Xiong Y.
      • Kwon S.H.
      • Brahmachari S.
      • Kim S.
      • Kam T.I.
      • Nirujogi R.S.
      • Kwon S.H.
      • Dawson V.L.
      • Dawson T.M.
      • Pandey A.
      • Na C.H.
      • Ko H.S.
      Complement and coagulation cascades are potentially involved in dopaminergic neurodegeneration in alpha-synuclein-based mouse models of Parkinson's disease.
      ). Loeffler et al. reported activation of the complement pathway in the SN of PD patients (
      • Loeffler D.A.
      • Camp D.M.
      • Conant S.B.
      Complement activation in the Parkinson's disease substantia nigra: an immunocytochemical study.
      ). Gregersen et al. reported that α-synuclein-mediated activation of the classical complement pathway in α-synuclein expressing cellular model (
      • Gregersen E.
      • Betzer C.
      • Kim W.S.
      • Kovacs G.
      • Reimer L.
      • Halliday G.M.
      • Thiel S.
      • Jensen P.H.
      Alpha-synuclein activates the classical complement pathway and mediates complement-dependent cell toxicity.
      ). However, little is known about the involvement of tRNA aminoacylation-related proteins. It seems that the cluster formation of tRNA aminoacylation-related proteins could be caused by the down-regulation of ribosomal proteins.
      To analyze 30 samples, we conducted 3 batches of TMT experiments in this study. Although the 3 batches of 11-plex TMT-based data were normalized by the reference sample, an obvious batch effect was observed, and further normalization by the ComBat package minimized it. This result suggests that simple normalization by a common reference sample is not enough to remove the batch effect when multiple batches of TMT experiments are conducted. In this study, we discovered multiple dysregulated pathways occurred in PD patients’ brains, and especially the mitochondrial pathway was the most dysregulated one. We cannot exclude the possibility that these pathways are only observable during the terminal stage since the tissue samples used in this study were from postmortem brains at the terminal stage of PD. Recently, we reported the α-synuclein gut-to-brain propagation mouse model that best recapitulates the Braak hypothesis (
      • Kim S.
      • Kwon S.H.
      • Kam T.I.
      • Panicker N.
      • Karuppagounder S.S.
      • Lee S.
      • Lee J.H.
      • Kim W.R.
      • Kook M.
      • Foss C.A.
      • Shen C.
      • Lee H.
      • Kulkarni S.
      • Pasricha P.J.
      • Lee G.
      • et al.
      Transneuronal propagation of pathologic alpha-synuclein from the gut to the brain models Parkinson's disease.
      ). The SN proteome change of the mouse model over the disease progression would potentially provide a clue when the mitoribosome dysfunction appears. Furthermore, we should deconvolute which cell types manifest this mitoribosome dysfunction through cell-type-specific proteome analysis. Despite these limitations, this study has discovered that mitoribosome dysfunction is potentially involved in the PD pathogenesis process for the first time, and this study paves the way to future studies investigating mechanisms of PD pathogenesis.

      DATA AND SOFTWARE AVAILABILITY

      All mass spectrometry data and search results have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD037684 and project name ‘Mass spectrometry-based proteomics analysis of human substantia nigra from Parkinson's disease patients identifies multiple pathways potentially involved in the disease’. Reviewers can access the dataset by using ‘[email protected]’ as ID and ‘7SzgZRTo’ as a password.

      ACKNOWLEDGMENTS

      This work was supported by an NIH grant (U01NS097049 to T.M.D. and L.S.R.). We acknowledge an NIH shared instrumentation grant (S10OD021844, to T.M.D.).

      REFERENCES

        • Kalia L.V.
        • Lang A.E.
        Parkinson's disease.
        Lancet. 2015; 386: 896-912
        • Radhakrishnan D.M.
        • Goyal V.
        Parkinson's disease: a review.
        Neurol India. 2018; 66: S26-S35
        • Beitz J.M.
        Parkinson's disease: a review.
        Front Biosci (Schol Ed). 2014; 6: 65-74
        • Parent M.
        • Parent A.
        Substantia nigra and Parkinson's disease: a brief history of their long and intimate relationship.
        Can J Neurol Sci. 2010; 37: 313-319
        • Poewe W.
        • Seppi K.
        • Tanner C.M.
        • Halliday G.M.
        • Brundin P.
        • Volkmann J.
        • Schrag A.E.
        • Lang A.E.
        Parkinson disease.
        Nat Rev Dis Primers. 2017; 3: 1-21
        • Reich S.G.
        • Savitt J.M.
        Parkinson's disease.
        Med Clin North Am. 2019; 103: 337-350
      1. Kouli, A., Torsney, K. M., and Kuan, W. L. (2018) Parkinson’s disease: etiology, neuropathology, and pathogenesis. In: Stoker, T. B., and Greenland, J. C., eds. Parkinson’s Disease: Pathogenesis and Clinical Aspects, Codon Publications, Brisbane (AU)

        • Samii A.
        • Nutt J.G.
        • Ransom B.R.
        Parkinson's disease.
        Lancet. 2004; 363: 1783-1793
        • Davie C.A.
        A review of Parkinson's disease.
        Br Med Bull. 2008; 86: 109-127
        • Xilouri M.
        • Brekk O.R.
        • Stefanis L.
        Alpha-synuclein and protein degradation systems: a reciprocal relationship.
        Mol Neurobiol. 2013; 47: 537-551
        • Reed X.
        • Bandres-Ciga S.
        • Blauwendraat C.
        • Cookson M.R.
        The role of monogenic genes in idiopathic Parkinson's disease.
        Neurobiol Dis. 2019; 124: 230-239
        • Pirooznia S.K.
        • Rosenthal L.S.
        • Dawson V.L.
        • Dawson T.M.
        Parkinson disease: translating insights from molecular mechanisms to neuroprotection.
        Pharmacol Rev. 2021; 73: 33-97
        • Panicker N.
        • Ge P.
        • Dawson V.L.
        • Dawson T.M.
        The cell biology of Parkinson's disease.
        J Cell Biol. 2021; 220: 1-31
        • Rocha E.M.
        • De Miranda B.
        • Sanders L.H.
        Alpha-synuclein: pathology, mitochondrial dysfunction and neuroinflammation in Parkinson's disease.
        Neurobiol Dis. 2018; 109: 249-257
        • Schapira A.H.
        • Jenner P.
        Etiology and pathogenesis of Parkinson's disease.
        Mov Disord. 2011; 26: 1049-1055
        • Zhu J.
        • Chu C.T.
        Mitochondrial dysfunction in Parkinson's disease.
        J Alzheimers Dis. 2010; 20: S325-S334
        • Moon H.E.
        • Paek S.H.
        Mitochondrial dysfunction in Parkinson's disease.
        Exp Neurobiol. 2015; 24: 103-116
        • Bose A.
        • Beal M.F.
        Mitochondrial dysfunction in Parkinson's disease.
        J Neurochem. 2016; 139: 216-231
        • Park J.S.
        • Davis R.L.
        • Sue C.M.
        Mitochondrial dysfunction in Parkinson's disease: new mechanistic insights and therapeutic perspectives.
        Curr Neurol Neurosci Rep. 2018; 18: 21
        • Giannoccaro M.P.
        • La Morgia C.
        • Rizzo G.
        • Carelli V.
        Mitochondrial DNA and primary mitochondrial dysfunction in Parkinson's disease.
        Mov Disord. 2017; 32: 346-363
        • Olanow C.W.
        • McNaught K.S.
        Ubiquitin-proteasome system and Parkinson's disease.
        Mov Disord. 2006; 21: 1806-1823
        • Cerri S.
        • Blandini F.
        Role of autophagy in Parkinson's disease.
        Curr Med Chem. 2019; 26: 3702-3718
        • Hirsch E.C.
        • Jenner P.
        • Przedborski S.
        Pathogenesis of Parkinson's disease.
        Mov Disord. 2013; 28: 24-30
        • Ma S.X.
        • Seo B.A.
        • Kim D.
        • Xiong Y.
        • Kwon S.H.
        • Brahmachari S.
        • Kim S.
        • Kam T.I.
        • Nirujogi R.S.
        • Kwon S.H.
        • Dawson V.L.
        • Dawson T.M.
        • Pandey A.
        • Na C.H.
        • Ko H.S.
        Complement and coagulation cascades are potentially involved in dopaminergic neurodegeneration in alpha-synuclein-based mouse models of Parkinson's disease.
        J Proteome Res. 2021; 20: 3428-3443
        • Hong Z.
        • Shi M.
        • Chung K.A.
        • Quinn J.F.
        • Peskind E.R.
        • Galasko D.
        • Jankovic J.
        • Zabetian C.P.
        • Leverenz J.B.
        • Baird G.
        • Montine T.J.
        • Hancock A.M.
        • Hwang H.
        • Pan C.
        • Bradner J.
        • et al.
        DJ-1 and alpha-synuclein in human cerebrospinal fluid as biomarkers of Parkinson's disease.
        Brain. 2010; 133: 713-726
        • Shi M.
        • Movius J.
        • Dator R.
        • Aro P.
        • Zhao Y.
        • Pan C.
        • Lin X.
        • Bammler T.K.
        • Stewart T.
        • Zabetian C.P.
        • Peskind E.R.
        • Hu S.C.
        • Quinn J.F.
        • Galasko D.R.
        • Zhang J.
        Cerebrospinal fluid peptides as potential Parkinson disease biomarkers: a staged pipeline for discovery and validation.
        Mol Cell Proteomics. 2015; 14: 544-555
        • Shi M.
        • Bradner J.
        • Hancock A.M.
        • Chung K.A.
        • Quinn J.F.
        • Peskind E.R.
        • Galasko D.
        • Jankovic J.
        • Zabetian C.P.
        • Kim H.M.
        • Leverenz J.B.
        • Montine T.J.
        • Ginghina C.
        • Kang U.J.
        • Cain K.C.
        • et al.
        Cerebrospinal fluid biomarkers for Parkinson disease diagnosis and progression.
        Ann Neurol. 2011; 69: 570-580
        • Dixit A.
        • Mehta R.
        • Singh A.K.
        Proteomics in human Parkinson's disease: present scenario and future directions.
        Cell Mol Neurobiol. 2019; 39: 901-915
        • Basso M.
        • Giraudo S.
        • Corpillo D.
        • Bergamasco B.
        • Lopiano L.
        • Fasano M.
        Proteome analysis of human substantia nigra in Parkinson's disease.
        Proteomics. 2004; 4: 3943-3952
        • Werner C.J.
        • Heyny-von Haussen R.
        • Mall G.
        • Wolf S.
        Proteome analysis of human substantia nigra in Parkinson's disease.
        Proteome Sci. 2008; 6: 1-14
        • Licker V.
        • Turck N.
        • Kövari E.
        • Burkhardt K.
        • Côte M.
        • Surini-Demiri M.
        • Lobrinus J.A.
        • Sanchez J.C.
        • Burkhard P.R.
        Proteomic analysis of human substantia nigra identifies novel candidates involved in Parkinson's disease pathogenesis.
        Proteomics. 2014; 14: 784-794
        • Hughes A.J.
        • Daniel S.E.
        • Kilford L.
        • Lees A.J.
        Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases.
        J Neurol Neurosurg Psychiatry. 1992; 55: 181-184
        • McKeith I.G.
        • Boeve B.F.
        • Dickson D.W.
        • Halliday G.
        • Taylor J.P.
        • Weintraub D.
        • Aarsland D.
        • Galvin J.
        • Attems J.
        • Ballard C.G.
        • Bayston A.
        • Beach T.G.
        • Blanc F.
        • Bohnen N.
        • Bonanni L.
        • et al.
        Diagnosis and management of dementia with Lewy bodies: fourth consensus report of the DLB consortium.
        Neurology. 2017; 89: 88-100
        • McKeith I.G.
        • Dickson D.W.
        • Lowe J.
        • Emre M.
        • O'Brien J.T.
        • Feldman H.
        • Cummings J.
        • Duda J.E.
        • Lippa C.
        • Perry E.K.
        • Aarsland D.
        • Arai H.
        • Ballard C.G.
        • Boeve B.
        • Burn D.J.
        • et al.
        Diagnosis and management of dementia with Lewy bodies: third report of the DLB consortium.
        Neurology. 2005; 65: 1863-1872
        • Ramachandran K.V.
        • Fu J.M.
        • Schaffer T.B.
        • Na C.H.
        • Delannoy M.
        • Margolis S.S.
        Activity-dependent degradation of the nascentome by the neuronal membrane proteasome.
        Mol Cell. 2018; 71: 169-177
        • Khan S.Y.
        • Ali M.
        • Kabir F.
        • Renuse S.
        • Na C.H.
        • Talbot Jr., C.C.
        • Hackett S.F.
        • Riazuddin S.A.
        Proteome profiling of developing murine lens through mass spectrometry.
        Invest Ophthalmol Vis Sci. 2018; 59: 100-107
        • Johnson W.E.
        • Li C.
        • Rabinovic A.
        Adjusting batch effects in microarray expression data using empirical Bayes methods.
        Biostatistics. 2007; 8: 118-127
        • Tusher V.G.
        • Tibshirani R.
        • Chu G.
        Significance analysis of microarrays applied to the ionizing radiation response.
        Proc Natl Acad Sci U S A. 2001; 98: 5116-5121
        • Xu P.
        • Liu X.
        • Hadley D.
        • Huang S.
        • Krischer J.
        • Beam C.
        Feature selection using bootstrapped ROC curves.
        Journal of Proteomics & Bioinformatics. 2014; S9: 1-10
        • Xia J.
        • Broadhurst D.I.
        • Wilson M.
        • Wishart D.S.
        Translational biomarker discovery in clinical metabolomics: an introductory tutorial.
        Metabolomics. 2013; 9: 280-299
        • Song J.
        • Ma S.
        • Sokoll L.J.
        • Eguez R.V.
        • Hoti N.
        • Zhang H.
        • Mohr P.
        • Dua R.
        • Patil D.
        • May K.D.
        • Williams S.
        • Arnold R.
        • Sanda M.G.
        • Chan D.W.
        • Zhang Z.
        A panel of selected serum protein biomarkers for the detection of aggressive prostate cancer.
        Theranostics. 2021; 11: 6214-6224
        • Jigang X.
        • Zhengding Q.
        Bootstrap technique for ROC analysis: a stable evaluation of fisher classifier performance.
        Journal of Electronics (China). 2007; 24: 523-527
        • Moharramipour A.
        • Mostame P.
        • Hossein-Zadeh G.A.
        • Wheless J.W.
        • Babajani-Feremi A.
        Comparison of statistical tests in effective connectivity analysis of ECoG data.
        J Neurosci Methods. 2018; 308: 317-329
        • Moffet E.W.
        • Subramaniam T.
        • Hirsch L.J.
        • Gilmore E.J.
        • Lee J.W.
        • Rodriguez-Ruiz A.A.
        • Haider H.A.
        • Dhakar M.B.
        • Jadeja N.
        • Osman G.
        • Gaspard N.
        • Struck A.F.
        Validation of the 2HELPS2B seizure risk score in acute brain injury patients.
        Neurocrit Care. 2020; 33: 701-707
        • Kanehisa M.
        • Goto S.
        KEGG: kyoto encyclopedia of genes and genomes.
        Nucleic Acids Res. 2000; 28: 27-30
        • Huang da W.
        • Sherman B.T.
        • Lempicki R.A.
        Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.
        Nat Protoc. 2009; 4: 44-57
        • von Mering C.
        • Huynen M.
        • Jaeggi D.
        • Schmidt S.
        • Bork P.
        • Snel B.
        STRING: a database of predicted functional associations between proteins.
        Nucleic Acids Res. 2003; 31: 258-261
        • Szklarczyk D.
        • Gable A.L.
        • Lyon D.
        • Junge A.
        • Wyder S.
        • Huerta-Cepas J.
        • Simonovic M.
        • Doncheva N.T.
        • Morris J.H.
        • Bork P.
        • Jensen L.J.
        • Mering C.V.
        STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.
        Nucleic Acids Res. 2019; 47: D607-D613
        • Langfelder P.
        • Horvath S.
        WGCNA: an R package for weighted correlation network analysis.
        BMC Bioinformatics. 2008; 9: 1-13
        • Pei G.
        • Chen L.
        • Zhang W.
        WGCNA application to proteomic and metabolomic data analysis.
        Methods Enzymol. 2017; 585: 135-158
        • Vykoukal J.
        • Sun N.
        • Aguilar-Bonavides C.
        • Katayama H.
        • Tanaka I.
        • Fahrmann J.F.
        • Capello M.
        • Fujimoto J.
        • Aguilar M.
        • Wistuba II,
        • Taguchi A.
        • Ostrin E.J.
        • Hanash S.M.
        Plasma-derived extracellular vesicle proteins as a source of biomarkers for lung adenocarcinoma.
        Oncotarget. 2017; 8: 95466-95480
        • UniProt C.
        UniProt: the universal protein knowledgebase in 2021.
        Nucleic Acids Res. 2021; 49: D480-D489
        • Zhao S.
        • Yang Z.
        • Musa S.S.
        • Ran J.
        • Chong M.K.C.
        • Javanbakht M.
        • He D.
        • Wang M.H.
        Attach importance of the bootstrap t test against Student's t test in clinical epidemiology: a demonstrative comparison using COVID-19 as an example.
        Epidemiol Infect. 2021; 149: 1-6
        • Mai N.
        • Chrzanowska-Lightowlers Z.M.
        • Lightowlers R.N.
        The process of mammalian mitochondrial protein synthesis.
        Cell Tissue Res. 2017; 367: 5-20
        • Macdonald R.
        • Barnes K.
        • Hastings C.
        • Mortiboys H.
        Mitochondrial abnormalities in Parkinson's disease and Alzheimer's disease: can mitochondria be targeted therapeutically?.
        Biochem Soc Trans. 2018; 46: 891-909
        • van Dijk K.D.
        • Berendse H.W.
        • Drukarch B.
        • Fratantoni S.A.
        • Pham T.V.
        • Piersma S.R.
        • Huisman E.
        • Brevé J.J.
        • Groenewegen H.J.
        • Jimenez C.R.
        • van de Berg W.D.
        The proteome of the locus ceruleus in Parkinson's disease: relevance to pathogenesis.
        Brain Pathol. 2012; 22: 485-498
        • Lachén-Montes M.
        • González-Morales A.
        • Iloro I.
        • Elortza F.
        • Ferrer I.
        • Gveric D.
        • Fernández-Irigoyen J.
        • Santamaría E.
        Unveiling the olfactory proteostatic disarrangement in Parkinson's disease by proteome-wide profiling.
        Neurobiol Aging. 2019; 73: 123-134
        • Choi J.
        • Levey A.I.
        • Weintraub S.T.
        • Rees H.D.
        • Gearing M.
        • Chin L.S.
        • Li L.
        Oxidative modifications and down-regulation of ubiquitin carboxyl-terminal hydrolase L1 associated with idiopathic Parkinson's and Alzheimer's diseases.
        J Biol Chem. 2004; 279: 13256-13264
        • Choi J.
        • Sullards M.C.
        • Olzmann J.A.
        • Rees H.D.
        • Weintraub S.T.
        • Bostwick D.E.
        • Gearing M.
        • Levey A.I.
        • Chin L.S.
        • Li L.
        Oxidative damage of DJ-1 is linked to sporadic Parkinson and Alzheimer diseases.
        J Biol Chem. 2006; 281: 10816-10824
        • Gómez A.
        • Ferrer I.
        Increased oxidation of certain glycolysis and energy metabolism enzymes in the frontal cortex in Lewy body diseases.
        J Neurosci Res. 2009; 87: 1002-1013
        • Billingsley K.J.
        • Barbosa I.A.
        • Bandrés-Ciga S.
        • Quinn J.P.
        • Bubb V.J.
        • Deshpande C.
        • Botia J.A.
        • Reynolds R.H.
        • Zhang D.
        • Simpson M.A.
        • Blauwendraat C.
        • Gan-Or Z.
        • Gibbs J.R.
        • Nalls M.A.
        • Singleton A.
        • et al.
        Mitochondria function associated genes contribute to Parkinson's disease risk and later age at onset.
        NPJ Parkinsons Dis. 2019; 5: 1-9
        • Muñoz M.D.
        • de la Fuente N.
        • Sánchez-Capelo A.
        TGF-β/Smad3 signalling modulates GABA neurotransmission: Implications in Parkinson's disease.
        Int J Mol Sci. 2020; 21: 1-22
        • Murueta-Goyena A.
        • Andikoetxea A.
        • Gómez-Esteban J.C.
        • Gabilondo I.
        Contribution of the GABAergic system to non-motor manifestations in premotor and early stages of Parkinson's disease.
        Front Pharmacol. 2019; 10: 1294
        • Tepper J.M.
        • Lee C.R.
        GABAergic control of substantia nigra dopaminergic neurons.
        Prog Brain Res. 2007; 160: 189-208
        • Faynveitz A.
        • Lavian H.
        • Jacob A.
        • Korngreen A.
        Proliferation of inhibitory input to the substantia nigra in experimental parkinsonism.
        Front Cell Neurosci. 2019; 13: 1-11
        • Mallet N.
        • Delgado L.
        • Chazalon M.
        • Miguelez C.
        • Baufreton J.
        Cellular and synaptic dysfunctions in Parkinson's disease: stepping out of the striatum.
        Cells. 2019; 8: 1005
        • Nickols H.H.
        • Conn P.J.
        Development of allosteric modulators of GPCRs for treatment of CNS disorders.
        Neurobiol Dis. 2014; 61: 55-71
        • Chatha B.T.
        • Bernard V.
        • Streit P.
        • Bolam J.P.
        Synaptic localization of ionotropic glutamate receptors in the rat substantia nigra.
        Neuroscience. 2000; 101: 1037-1051
        • Poulin J.F.
        • Zou J.
        • Drouin-Ouellet J.
        • Kim K.Y.
        • Cicchetti F.
        • Awatramani R.B.
        Defining midbrain dopaminergic neuron diversity by single-cell gene expression profiling.
        Cell Rep. 2014; 9: 930-943
        • Lohr K.M.
        • Masoud S.T.
        • Salahpour A.
        • Miller G.W.
        Membrane transporters as mediators of synaptic dopamine dynamics: implications for disease.
        Eur J Neurosci. 2017; 45: 20-33
        • Zhang X.
        • Mantas I.
        • Fridjonsdottir E.
        • Andrén P.E.
        • Chergui K.
        • Svenningsson P.
        Deficits in motor performance, neurotransmitters and synaptic plasticity in elderly and experimental parkinsonian mice lacking GPR37.
        Front Aging Neurosci. 2020; 12: 1-11
        • Geisler S.
        • Vollmer S.
        • Golombek S.
        • Kahle P.J.
        The ubiquitin-conjugating enzymes UBE2N, UBE2L3 and UBE2D2/3 are essential for parkin-dependent mitophagy.
        J Cell Sci. 2014; 127: 3280-3293
        • Duggan A.T.
        • Kocha K.M.
        • Monk C.T.
        • Bremer K.
        • Moyes C.D.
        Coordination of cytochrome c oxidase gene expression in the remodelling of skeletal muscle.
        J Exp Biol. 2011; 214: 1880-1887
        • Dang Q.L.
        • Phan D.H.
        • Johnson A.N.
        • Pasapuleti M.
        • Alkhaldi H.A.
        • Zhang F.
        • Vik S.B.
        Analysis of human mutations in the supernumerary subunits of complex I.
        Life (Basel). 2020; 10: 296
        • Wen J.J.
        • Garg N.
        Oxidative modification of mitochondrial respiratory complexes in response to the stress of trypanosoma cruzi infection.
        Free Radic Biol Med. 2004; 37: 2072-2081
        • Michael G.J.
        • Esmailzadeh S.
        • Moran L.B.
        • Christian L.
        • Pearce R.K.
        • Graeber M.B.
        Up-regulation of metallothionein gene expression in parkinsonian astrocytes.
        Neurogenetics. 2011; 12: 295-305
        • Carmichael K.
        • Evans R.C.
        • Lopez E.
        • Sun L.
        • Kumar M.
        • Ding J.
        • Khaliq Z.M.
        • Cai H.
        Function and regulation of ALDH1A1-positive nigrostriatal dopaminergic neurons in motor control and Parkinson's disease.
        Front Neural Circuits. 2021; 15: 1-9
        • Li D.
        • McIntosh C.S.
        • Mastaglia F.L.
        • Wilton S.D.
        • Aung-Htut M.T.
        Neurodegenerative diseases: a hotbed for splicing defects and the potential therapies.
        Transl Neurodegener. 2021; 10: 16
        • La Cognata V.
        • D'Agata V.
        • Cavalcanti F.
        • Cavallaro S.
        Splicing: is there an alternative contribution to Parkinson's disease?.
        Neurogenetics. 2015; 16: 245-263
        • Fu R.H.
        • Liu S.P.
        • Huang S.J.
        • Chen H.J.
        • Chen P.R.
        • Lin Y.H.
        • Ho Y.C.
        • Chang W.L.
        • Tsai C.H.
        • Shyu W.C.
        • Lin S.Z.
        Aberrant alternative splicing events in Parkinson's disease.
        Cell Transplant. 2013; 22: 653-661
        • Ebanks K.
        • Lewis P.A.
        • Bandopadhyay R.
        Vesicular dysfunction and the pathogenesis of Parkinson's disease: clues from genetic studies.
        Front Neurosci. 2019; 13: 1-13
        • Singh P.K.
        • Muqit M.M.K.
        Parkinson's: a disease of aberrant vesicle trafficking.
        Annu Rev Cell Dev Biol. 2020; 36: 237-264
        • Williams E.T.
        • Chen X.
        • Moore D.J.
        VPS35, the retromer complex and Parkinson's disease.
        J Parkinsons Dis. 2017; 7: 219-233
        • Carpanini S.M.
        • Torvell M.
        • Morgan B.P.
        Therapeutic inhibition of the complement system in diseases of the central nervous system.
        Front Immunol. 2019; 10: 1-17
        • Gregersen E.
        • Betzer C.
        • Kim W.S.
        • Kovacs G.
        • Reimer L.
        • Halliday G.M.
        • Thiel S.
        • Jensen P.H.
        Alpha-synuclein activates the classical complement pathway and mediates complement-dependent cell toxicity.
        J Neuroinflammation. 2021; 18: 177
        • Loeffler D.A.
        • Camp D.M.
        • Conant S.B.
        Complement activation in the Parkinson's disease substantia nigra: an immunocytochemical study.
        J Neuroinflammation. 2006; 3: 29
        • Kim S.
        • Kwon S.H.
        • Kam T.I.
        • Panicker N.
        • Karuppagounder S.S.
        • Lee S.
        • Lee J.H.
        • Kim W.R.
        • Kook M.
        • Foss C.A.
        • Shen C.
        • Lee H.
        • Kulkarni S.
        • Pasricha P.J.
        • Lee G.
        • et al.
        Transneuronal propagation of pathologic alpha-synuclein from the gut to the brain models Parkinson's disease.
        Neuron. 2019; 103 (e627): 627-641