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DOI 10.1002/pmic.201300268

REVIEW

Ten years of proteomics in multiple sclerosis Alessandro S. Farias1,2 , Fernando Pradella1,2 , Andrea Schmitt3,4 , Leonilda M. B. Santos2 and Daniel Martins-de-Souza3,4 1

Neuroimmunomodulation Group, Department of Genetics, Evolution and Bioagents, University of Campinas ˜ Paulo, Brazil (UNICAMP) – Campinas, Sao 2 Neuroimmunology Unit, Department of Genetics, Evolution and Bioagents, University of Campinas (UNICAMP) – ˜ Paulo, Brazil Campinas, Sao 3 Research Group of Proteomics, Department of Psychiatry and Psychotherapy, Ludwig Maximilians University (LMU), Munich, Germany 4 ˜ Paulo, Sao ˜ Laboratory of Neurosciences (LIM-27), Institute of Psychiatry, Faculty of Medicine, University of Sao Paulo, Brazil

Multiple sclerosis, which is the most common cause of chronic neurological disability in young adults, is an inflammatory, demyelinating, and neurodegenerative disease of the CNS, which leads to the formation of multiple foci of demyelinated lesions in the white matter. The diagnosis is based currently on magnetic resonance image and evidence of dissemination in time and space. However, this could be facilitated if biomarkers were available to rule out other disorders with similar symptoms as well as to avoid cerebrospinal fluid analysis, which requires an invasive collection. Additionally, the molecular mechanisms of the disease are not completely elucidated, especially those related to the neurodegenerative aspects of the disease. The identification of biomarker candidates and molecular mechanisms of multiple sclerosis may be approached by proteomics. In the last 10 years, proteomic techniques have been applied in different biological samples (CNS tissue, cerebrospinal fluid, and blood) from multiple sclerosis patients and in its experimental model. In this review, we summarize these data, presenting their value to the current knowledge of the disease mechanisms, as well as their importance in identifying biomarkers or treatment targets.

Received: July 1, 2013 Revised: August 19, 2013 Accepted: August 21, 2013

Keywords: Biomedicine / EAE / Inflammation / Multiple Sclerosis / Neurodegeneration

1

Introduction

Multiple sclerosis is the most common cause of chronic neurological disability in young adults. Multiple sclerosis is an inflammatory, demyelinating neurodegenerative disease of the CNS that leads to the formation of multiple foci of demyelinated lesions in the white matter. The diagnosis of multiple Correspondence: Dr. Alessandro S. Farias, Departamento de ˜ e Bioagentes, Instituto de Biologia, UNICAMP, Genetica, Evoluc¸ao Campinas, SP – CEP13083-970, Brazil. E-mail: [email protected] Abbreviations: APO, apolipoprotein; BBB, blood–brain barrier; CIS, clinically isolated syndrome; CSF, cerebrospinal fluid; EAE, experimental autoimmune encephalomyelitis; GFAP, glial fibrillary acid protein; GM, gray matter; HLA, human leukocyte antigen; OCBs, oligoclonal bands; RRMS, relapsing-remitting multiple sclerosis; SPMS, secondary progressive multiple sclerosis

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sclerosis is currently based on magnetic resonance imaging and evidence of dissemination of the lesions in time and space [1, 2]. Panels of biomarkers could be established to determine diagnosis and prognosis and to stratify patients to facilitate the prevention, unequivocal identification, and targeted treatment of multiple sclerosis, respectively. Diagnostic biomarkers would rule out other disorders with similar symptoms, making the invasive collection of cerebrospinal fluid (CSF) unnecessary. Additionally, the molecular mechanisms of multiple sclerosis have not been completely elucidated, especially those related to the neurodegenerative aspects of the disease [3]. Proteomics has the ability to identify biomarkers and improve the understanding of human diseases and is therefore a useful tool for meeting the needs of multiple sclerosis research. Colour Online: See the article online to view Fig. 1 in colour.

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During the last 10 years, proteomic techniques have been used to analyze different biological samples collected from multiple sclerosis patients and experimental models of this disease. In this review, we summarize these data and discuss their value to the current understanding of the disease mechanisms, as well as their importance in identifying potential biomarkers or treatment targets. 1.1 Multiple sclerosis 1.1.1 Clinical features Charcot described multiple sclerosis in the late 19th century, and efforts to understand this disease have not ceased since then. Multiple sclerosis is the most common neurological disability in young adults after traumatic events [4]. The incidence of the disease varies worldwide, with a prevalence that ranges between 2 and 150 per 100 000 depending on the specific population. The clinical symptoms of multiple sclerosis depend on the site of the neurologic lesions. Patients often exhibit an initial clinically isolated syndrome (CIS). In most of patients (∼85–90%), CIS is following by a relapsing-remitting course (RRMS) characterized by recurring attacks or exacerbations of existing deficits (relapses), followed by partial or full recovery (remission). Approximately 10 years later, half of these patients convert to the secondary progressive phase of the disease (secondary progressive multiple sclerosis), in which there is an acceleration of the disability with accumulating and irreversible neurologic deficits. A smaller percentage of individuals (∼10–15%) develop a primary progressive form of multiple sclerosis [1,2,5,6]. Clinically, proteomics may be a helpful tool to stratify multiple sclerosis patients in different phases of the disease based on distinct molecular signatures in biological tissue from the patients. 1.1.2 Causes Although the etiology of multiple sclerosis remains unknown, it is highly unlikely that the disease results from a single causative event. Current evidence suggests the involvement of a complex genetic trait, which most likely requires environmental factors to be triggered. These risk factors most likely act many years before disease onset during a subclinical phase of the disorder. Genetic epidemiological studies have shown that the genetic susceptibility is an important condition for the development of the disease. Moreover, familial aggregation studies have shown that the risk of developing multiple sclerosis is higher for people with family members with multiple sclerosis, especially first-degree relatives (10– 25 times higher). The monozygotic twin of a multiple sclerosis patient has a more than 100-fold higher risk than the general population [7]. Multiple sclerosis is more common in women than in men. However, genetic studies have not found any relevant multiple sclerosis-associated gene on the X chromosome [8]. The increased risk in women may be re C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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lated to the female physiology or to a higher susceptibility to environmental factors. Although the genetic background seems to contribute decisively to the development of multiple sclerosis, many studies have demonstrated a prominent role of the environment in determining the risk of developing multiple sclerosis. The roles of many environmental factors have been studied in recent decades; however, infections, latitude/vitamin D and social behavior (e.g. smoking) are the only factors with strong evidence of being involved in the augmentation of the risk of developing multiple sclerosis [1, 9]. The genetic association with susceptibility to multiple sclerosis has long been known. The first identified genetic risk factor, discovered during 1970s, is associated with the human leukocyte antigen (HLA) region. A strong association was first found with the HLA-DR2 isotype, and DNA-based typing methods later showed that the specific allele involved in this association is DRB1*1501. Each copy of this allele increases the multiple sclerosis risk by approximately threefold in European populations. Recently, studies have identified five alleles in three different loci in the HLA region: the HLADRB1*1501, *0301 and *0801 alleles; the HLA-A*0201 allele; and the HLA-DPB1*0301. These alleles are associated with changes between 26 and 200% in MS risk. Protection from the disease may also be associated with some HLA class II alleles [10–12]. Many years after the discovery of the DR2 allele, genome-wide association studies and other studies have shown that several non-HLA genes have modest effects on the risk of multiple sclerosis. These genes include interleukin-7 receptor ␣ (IL-7RA), interleukin 2 receptor ␣, C-type lectindomain family 16 member A, CD58, tumor-necrosis-factor receptor superfamily member 1A, interferon regulatory factor 8, and CD6. In 2011, the largest genome-wide study completed to date, performed by the International Multiple Sclerosis Genetics Consortium, evaluated 9772 multiple sclerosis cases and 17 376 shared controls. This study identified more the 1 million single-nucleotide polymorphisms. This study confirmed the associations with 23 previously reported loci and identified 29 new candidates [13–16]. Most of these genes were already known to have direct immune functions or to be involved in immunological pathways. Interestingly, many of the identified genes have also been reported to be associated with at least one other autoimmune disease. These results reinforce the hypothesis that some, if not all, autoimmune diseases share similar mechanisms. Sometimes it is possible to identify these phenomena in the clinic, and it is not uncommon for a multiple sclerosis patient to have another autoimmune disorder [17,18]. Although the knowledge about genetic risk factors has increased enormously in the past decade, there are still gaps to be filled. Therefore, further studies are required to answer questions regarding how genetic factors influence the age of onset, evolution, and severity of multiple sclerosis. Given the known influence of genetics on the development of multiple sclerosis, studies to identify predictive biomarkers should employ proteomic technologies and samples from patients’ family members. www.proteomics-journal.com

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1.1.3 Molecular features Currently, it is well accepted that multiple sclerosis is a chronic inflammatory disease of the CNS that is associated with the destruction of the myelin sheaths, leading to demyelination. Along with myelin sheath degradation, there is substantial axonal damage that subsequently jeopardizes nerve conductivity [19]. Although demyelination is the primary characteristic of the multiple sclerosis lesions in the CNS, recent studies have provided evidence that the tissue damage may affect the gray matter (GM) as well. Numerous studies have shown that changes in the GM are closely associated with physical disability and cognitive impairment [20–24]. Cortical lesions occur early during CIS and RRMS, as well as in primary progressive multiple sclerosis, and increase in number and size with progression of the disease [25, 26]. According to the original pathological study, GM lesions comprise 26% of all lesions identified in the CNS [27]. Although certain molecular aspects of multiple sclerosis, such as the demyelination, are well described, the general molecular basis of multiple sclerosis is still unclear. The understanding of these molecular mechanisms may improve with the application of proteomic techniques, given the ability of these techniques to provide systems biology knowledge.

1.2 Experimental autoimmune encephalomyelitis Described by Rivers, Sprunt, and Berry in 1933 [28], the experimental autoimmune encephalomyelitis (EAE) model is the best-characterized and most-studied experimental model for neurological disease. The EAE model shares many clinical and histopathological features with multiple sclerosis, and therefore, it has been used as a preclinical model for the human disease [29]. EAE is an inflammatory demyelinating disease of the CNS. This experimental model can be induced in a large number of genetically susceptible mammal species by immunization with myelin compounds or by the adoptive transfer of pre-activated auto-aggressive T cells [30–32]. The disease was first described as a Th1-mediated pathology [33]. Th1 cells express and release proinflammatory cytokines such as IFN-␥ and TNF-␣ in response to IL-12 stimulation [34]. Th1 cells are found in the CNS lesions of ill animals, and more importantly, the adoptive transfer of Th1 cells from the peripheral organs of an EAE donor can induce the disease in a na¨ıve host [35,36]. However, recent studies have noted the role of Th17 cells in the pathogenesis of EAE and other autoimmune diseases [37–39]. Th17 lymphocytes express and release IL-17 and IL-22 in response to IL-23, IL-6, and TGF-␤ stimulation [34]. The effector phase of the disease is characterized by the migration of auto-aggressive CD4+ T cells into the CNS and by the release of proinflammatory cytokines [36, 40]. The myelin destruction seems to be associated directly with the actions of TNF-␣, LT-␤, proteolytic enzymes, free radicals, and nitric oxide released inside the CNS parenchyma [41–44]. In  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

addition to the direct effect of inflammatory mediators on the myelin sheath, the release of proinflammatory cytokines inside the CNS increases the vascular permeability [45], which is considered an indirect effect of the proinflammatory cytokines on CNS homeostasis. The inflammatory microenvironment of the CNS induces the activation of the microglia, which also release proinflammatory cytokines and nitric oxide [42]. Regulatory T cells play a crucial role in the control of inflammation during EAE development [46–48]. Although many cells with a regulatory phenotype have been described in EAE (Tr1, Th3), the expression of the transcription factor Foxp3 seems to have a pivotal role in the activation of immune suppression and, therefore, in the preservation of immune homeostasis. The enhancement or transfer of regulatory cells normally abrogates the clinical signs of EAE and other cell-based autoimmune diseases [49–51]. Since 1933, the EAE model has been useful to elucidate and validate many mechanisms of multiple sclerosis. Moreover, almost all available treatments for multiple sclerosis were developed or tested in the EAE model.

2

The search for biomarkers

2.1 Biological samples Choosing the proper biological sample is a crucial step in identifying biomarker candidates that might be suitable for clinical use. The blood is the first and more obvious choice, given that it is the most used type of sample for diagnosis and follow-up in clinical practice due to its accessibility and minimally invasive collection procedure. However, several drawbacks must be considered. Albumin and Igs constitute approximately 75% of the total protein weight in blood plasma/serum, and 20 additional proteins make up most of the remaining weight. The other hundreds of low-abundance proteins account for only approximately 1% of the protein weight in plasma/serum [52]. The depletion of the most abundant proteins is the standard solution employed in proteomic studies, but we should always consider the possibility that the depletion of these proteins might also deplete other proteins that may be biomarker candidates [53]. Some authors consider the blood a viable sample to detect CNS alterations when a disruption of the blood–brain barrier (BBB) occurs [54], but such disruptions do not always occur in multiple sclerosis patients. Furthermore, there are not many CNS proteins that are detectable in the blood. However, as an inflammatory disease, the basis of multiple sclerosis depends on cells and humoral factors that are produced in peripheral immune organs and released into the blood. Nevertheless, all studies concerning the identification of potential biomarkers based on immunological features have failed to identify specific markers. As a multifactorial disorder, multiple sclerosis likely involves a set of differentially expressed proteins and genes rather than one particular marker. These proteins, if analyzed in a multifactorial manner, might be useful in www.proteomics-journal.com

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combination for prognostic, diagnostic, or patient stratification goals. The CSF is a clear fluid present in the subarachnoid space, which surrounds the CNS. CSF is present in the intracerebral space of the ventricular system and the spinal cord and flows in a unidirectional manner [55]. The CSF is an ideal sample type for identifying modifications in the CNS and is therefore an interesting source of biomarker candidates. Notably, CSF proteomic data must be carefully evaluated because an increased level of a specific protein may not be related directly to increased expression in the brain tissue but could be due to the degeneration of the CNS tissue, which can also be a biomarker. The major drawback of employing CSF as the biological fluid for clinical applications is the invasive nature of its collection. Furthermore, depletion of high-abundance proteins, such as albumin, IgG, transferrin, and transthyretin, should also be performed prior to the proteomic analysis of CSF. Because multiple sclerosis is a brain disorder, the CNS tissue would be the ideal tissue for discovering biomarkers. However, the impossibility of collecting such tissue from living patients prevents using CNS tissue markers. Despite this limitation, CNS tissue is still a rich source of information for better understanding and characterizing the pathobiology of multiple sclerosis. With this aim, CNS tissues from postmortem human samples and EAE models have been investigated by proteomic techniques and cross-compared with the CSF and serum results. Eventually, the CNS results may be extrapolated to the peripheral tissue in the search for biomarker candidates.

2.2 Biomarkers for multiple sclerosis Currently, there are no established molecular biomarkers for multiple sclerosis. The diagnosis of this disease is based on many criteria that are not unique to multiple sclerosis and are also associated with other neurological and/or inflammatory pathologies. The current diagnostic criteria for multiple sclerosis include clinical and laboratorial features as well as magnetic resonance image findings. Although the latest diagnostic criteria review excludes the analysis of CSF parameters [56, 57], oligoclonal bands (OCBs), which are present in the CSF of a high percentage (∼90%) of multiple sclerosis patients, are the best parameter to confirm the diagnosis [58]. OCBs are not specific to multiple sclerosis, being present in patients with other neurological and inflammatory diseases or even healthy individuals without neurological conditions [59–61]. OCBs are a result of the intrathecal synthesis of IgG. OCBs remain constant during the evolution of the disease. Treatments such as immunomodulators, rituximab (anti-CD20 monoclonal antibodies), and immunosuppression and even complete bone marrow ablation do not modify the pattern of OCBs. The only evidence of the disappearance of OCBs in the CSF was obtained when the patients were treated with natalizumab [62]. Yet, there  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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Figure 1. (A) Methodologies used in multiple sclerosis and EAE proteomic studies. (B) General biological processes of the proteins found differentially expressed in proteomic studies of multiple sclerosis and EAE.

are a number of autoantibodies, primarily against myelin compounds, that can be found either in the serum or the CSF (or both) of multiple sclerosis patients. These autoantibodies include anti-MBP, anti-MOG, anti-PLP, anti-MAG, and antiHSP60 [63]. However, these autoantibodies are also present in patients with other neurological and non-neurological diseases [64, 65]. Therefore, proteomic studies would also help identify protein biomarkers that can be used to monitor treatment efficacy and disease evolution.

3

Proteomic studies using multiple sclerosis and EAE tissues

3.1 Methodologies Among the articles considered in this review, half of them used 2DE-MS as the central methodology of the study (Fig. 1A). 2DE has been the basis of proteomics since its beginning, but for large-scale proteomic comparisons, 2DE has been progressively replaced by shotgun proteomics techniques. These methodologies tend to be more sensitive and reproducible and are automated, although the bias toward the most highly expressed proteins remains for several of the shotgun approaches [66]. In addition, it is important to highlight the fact that, rather than being an outdated method, www.proteomics-journal.com

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2DE is actually better suited to answer more specific questions [67]. Multiple sclerosis research is also following the trends in proteomics, with 22% of the studies using shotgun-MS setups. An underrepresented technique that tends to be common is SRM/MRM; this technique was used in only one out of the 18 studies reviewed herein. SRM/MRM is a pivotal technique, especially for the identification of biomarkers. Additionally, large-scale analyses of PTMs—for example, phosphoproteomics—have not yet been performed.

3.2 Differentially expressed proteins in multiple sclerosis and EAE In Table 1, we summarize the proteins that were found to be differentially expressed in all proteomic studies performed using multiple sclerosis and EAE tissues. These proteins were classified according to their biological processes using the Human Proteome Reference Database (http://www.hprd.org). Proteins related to cell communication and signaling, immune responses and protein metabolism are the most represented (Fig. 1B). These data reinforce the inflammatory pattern of the disease. Moreover, the majority of differentially regulated proteins are involved in protein metabolism, cell growth and maintenance and cell communication and signaling, which indicates that there is massive disruption of CNS homeostasis. The disruption of CNS homeostasis is most likely a result of the inflammatory process, which leads to the demyelination and neurodegeneration in the CNS.

3.3 Inflammation-related proteins Inflammation in the CNS and demyelination are the main characteristics of multiple sclerosis. Therefore, the regulation of inflammation-related proteins could reveal specific features of the disease, especially in the CNS. The classical humoral inflammatory agents, such as cytokines, Igs and complement, are the primary candidates for differential expression during the inflammatory process associated with multiple sclerosis. However, most of these proteins are responsible for the classical inflammatory processes in any inflammatory condition. Thus, there is constant fluctuation of these markers that could be related to any inflammatory process and not necessarily multiple sclerosis. Moreover, during the course of the disease in patients with RRMS, there may be moments of clinical and inflammatory silence. However, theoretically, the CNS is an immuneprivileged site. Thus, none of the classical immunological players may be present. Therefore, the presence of classical inflammatory mediators in the CNS could represent important evidence of the inflammatory process taking place inside the CNS. Indeed, studies based on gene expression and single-nucleotide polymorphism have shown major participa C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

471 tion of genes involved in inflammatory processes [5]. It must be noted that several inflammatory proteins, such as interleukins, are present in low abundances and are often poorly identified by proteomic techniques [68] due to the bias toward the most abundant proteins, especially when using 2DE and DDA-shotgun-MS. Moreover, because there is a disruption of the BBB, the identification of differentially expressed proteins could be a result of the entrance of peripheral blood cells into the CNS. Indeed, almost all published proteomics-based analyses of CNS tissue or CSF, either from multiple sclerosis patients or EAE model animals, have shown an upregulation of serum albumin, suggestive of BBB disruption. In addition to the increase in serum albumin, there have been consistent reports of complement (C1q, C3, C4, and C9) proteins in the CNS tissue and CSF, both in multiple sclerosis patients and EAE model animals (Table 1). The complement system is made up of a number of proteins that participate in the opsonization of target cells. This opsonization enhances phagocytosis or produces a transmembrane channel, which causes an osmotic disturbance in the target cell and results in cell death [69]. Many studies have found the increased presence of prostaglandins (PGE2 , PGD2 ) in the CSF and CNS tissue (Table 1). Prostaglandins are one of the cardinal signs of acute inflammation and play a crucial role in the inflammatory response [70]. Corroborating the inflammatory conditions in the CNS, some authors have shown an upregulation of ␤2-microglobulin, which is a subunit of MHC-I. The expression of MHC-I (HLA class I molecules in humans) is stimulated by inflammatory conditions, primarily in the presence of proinflammatory cytokines such as IFN-␥ [71]. There are consistent reports of Ig chains. The presence of Ig may also be related to BBB disruption. However, it is possible that at least some of the Ig present is produced intrathecally, as has already been reported for multiple sclerosis patients [72, 73]. Although the presence of autoantibodies is consistent in the serum and CSF of multiple sclerosis patients, it is not yet clear if the autoantibody pattern characterizes the unique pathology of multiple sclerosis or its forms [74]. Interestingly, many authors have observed the upregulation of apolipoprotein (APO) isoforms (Table 1). APOs have also been shown to be associated with an increased risk of Alzheimer’s disease [75]. Although APOs have an important role in the maintenance and repair of neurons [76], recent data suggest that these proteins may have fundamental immunomodulatory roles, primarily through microglia and/or macrophage release [77]. ApoE-knockout mice exhibit an important increase in the levels of pro-inflammatory cytokines [78]. Therefore, the increase in the expression of APO isoforms may be related to the feedback response to the inflammatory process. Interestingly, proteins involved in phagocytosis and exocytosis, such as annexin 1–5 and coronin-1A, are upregulated [79, 80]. These proteins might be directly related to the presence and the activity of proinflammatory cells inside the CNS. There are a considerable number of proteins directly related to the inflammatory response. The presence of these www.proteomics-journal.com

Gene

ALDH5A1 1433Z A1AG A1AT A1BG A2GL A2MG AACT AATM ABAT ACTB ACTG AINX ALBU ALDOA ALDOC TUBA1 TUBA1B ANGT ANXA1 ANXA2 ANXA3 ANXA5 APA2 APOA1 APOA4 APOC3 APOD APOE APOJ APP1 ATP6V1B2 AZGP1 B2MG B3GNT1 BCAN C1QA C1QC C3 C4 C9 CALB1 CALB2 CALL5 CALM CALML3 CAM2KA CAMK2G CAP1 CAPN3 CASP1

Unit Prot ID

Q8BWF0 P29312 P02763 P01860 P04217 P02750 P01023/Q61838 P01011 P00505 P61922 P60711/P02570 P02571/P63261 P23565 P02770/P02768 P04075 P09117 P68370 Q6P9V9 P01019 P04083 P07355 P12429/O35639 P08758/P48036 P02652 P02647/Q00623 P06727 P02656 P05090 P02649/P08226/ P01909 P51693 P62815 P25311/Q64726 P61769/P01887 O43505 Q96GW7/Q61361 P31720 P02747 P01024/P01027 P08649 Q62930 P12658 P47728/P22676 Q9NZT1 P02593 P27482 Q9uQm7/P11798 Q13555 Q01518 P16259 P31944

D U U U U U U U U D U U U U U U U U U U U U U U U U U U U U U U U U U D U U U U U D U U U U D D U U U

Regulation

Succinate-semialdehyde dehydrogenase 14-3-3-Protein zeta/delta (KCIP-1) Alpha-1-acid glycoprotein 1 precursor Alpha-1-antitrypsin precursor Alpha-1B-glycoprotein precursor Leucinbe-rich alpha-2-glycoprotein Alpha-2-macroglobulin Alpha-1-antichymotropsyn precursor Aspartate aminotransferase mitochondrial (precursor) GABA transaminase Actin, cytoplasmic 1 Actin, cytoplasmic 2 Alpha-internexin Serum albumin precursor Fructose-bisphosphate aldolase A Fructose-bisphosphate aldolase C Tubulin alpha-1 chain Tubulin alpha-2 chain Angiotensingen precursor, EGF-containing Annexin A1 (annexin I) (lipocortin I) Annexin A2 (annexin II) (lipocortin II) Annexin A3 Annexin A5 Apoliprotein A-II precursor Apoliprotein A-I Apoliprotein A-IV Apoliprotein C-III precursor Apolipoprotein D Apoliprotein E Apoliprotein J Amyloid-like proptein precursor Vacuolar ATP sybthase subunit B, brain isoform Zinc-alpha-2-glycoprotein Beta 2 microglubulin N-Acetyllactosaminide beta-1, 3 Brevican core protein Complement C1q subcomp s.u. A Complement C1q subcomponent Complement C3 Complement C4 Complement C9 Calbindin 1 Calbindin 2 Calmodulin-like protein Calmodulin Calmodulin-related protein NB-1 Calcium/calmodulin-dependentprotein kinase type II subunit alpha Calcium/calmodulin-dependent protein kinase type II gamma chain Adenylyl cyclase-associated protein 1 Calpain-3 Caspase-14

Protein description

Table 1. Regulated proteins in EAE and/or multiple sclerosis

 C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[81]

[102]

[102]

[109]

[81]

[102]

[81, 106]

[81, 102] [97]

[81] [81] [81]

[81] [81]

[81]

[102]

[110]

[90]

[90]

[90]

[94] [100] [94] [94]

[94, 95] [94] [94] [105]

[95]

[94] [105] [90]

[92] [92] [95]

[95, 96, 100] [100] [100] [91, 92, 95, 105, 111] [94, 95] [92] [95, 105] [92] [92]

[103] [95] [95]

[92, 94, 95, 105] [94, 95, 105]

[91, 92] [93–95] [92, 95, 95] [95, 96] [100] [94] [93–95, 100, 104, 105] [92]

[98]

[107]

[107]

[98]

Brain

[103]

[108] [103]

[108]

[99, 103]

[103]

[103]

[103]

[99]

Blood

A. S. Farias et al.

[101] [101] [101]

[101]

[101] [101]

[101]

[101]

[97]

[90]

CSF

Brain

CSF

Spinal Cord

Multiple sclerosis reference

EAE reference

472 Proteomics 2014, 14, 467–480

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Gene

CERU CFAB CFAH CFHR1 CFI CH3L1 CKB CLAB2 CLU CNTN1 CNTN1 CO6A3 COX5A COX5B COX5b CPLX1 CPLX1 CRKL CSTA CVD1 CYTC DCD DCTN2 DDAH2 DHE3 DKK3 DLAT DLG4 DRP-2 EIF4G1 ENO1 FAM3C FBL3 FBLN1 FETUA FGA FGB FGG FJX1 GAPDH GC GELS GFAP GLEX5 GLUD1 GLUL GPX3 HAEMO HBA HBA HBB

Unit Prot ID

P00450 P00751 P08603 Q03591 P05156 P36222/Q61362 P07335 Q08331 P10909/Q06890 Q12860 Q12860/P12960 P12111 P12787 P19536 P10606 P63040 P63040 P46109 P01040 Q8WYAD P01034 P81605 Q13561 Q6MG60 P00367 Q9UBP4 P08461 Q62108 P47942 Q04637 P04764/P06733 Q92520 Q12805 P23142/Q08879 P02765/P29699 P02671 P02675 P02679 Q86VR8 P04406/P04797 P02774/P21614 P06396 P47819 Q80Y14 P26443 P09606 P22352 P02790 P69905 P01922 P68871

Table 1. Continued

U U U U U U U D U U D U U U D U D U U U U U U U U U U U U U U U U U U U U U D U U U U U D U U U U U U

Regulation

Ceruloplasmin precursor Complement factor B precursor Complement factor H precursor Complement factor H-related protein 1 Complement factor I Chitinase-3-like protein 1 Cratine kinase B-type Calbindin 2 Clusterin Contactin 1 Contactin-1 Collagen alpha 3 (VI) chain Cytochrome c oxidase Va Cytochrome c oxidase Vb Cytochrome c oxidase, subunit 5b Complexin 1 Complexin I CRKL protein Cystatin A Carnitine deficiency-associated protein Cystatin-C precursor Dermcidin Dynactin subunit 2 Dimethylarginine dimethylaminohydrolase 2 Glutamate dehydrogenase 1 mitochondrial (precursor) Dikkopf-related protein-3 precursor Dihydropoamide acetyltransferase Postsynaptic density protein 95 Dihydropyrimidase-related protein Translation-initiation factor elF-4-gamma Alpha-enolase FAM3C EGF-containing fibulin-like extracellular Fibulin 1 Alpha-2-HS-glycoprotein (Fetuin-A) Fibrinogen, alpha chain Fibrinogen beta-chain precursor Fibrinogen gamma chain precursor Four-jointed box protein 1 Glyceraldehyde-3-P-dehydrogenase Vitamin D binding protein Gelsolin precursor Glial fibrillary acidic protein, astrocyte Glutaredoxin 5 Mitochondrial glutamate dehydrogenase 1 Glutamine synthetase Plasma glutathione peroxidase precursor Hemopexin precursor Hemoglobin Hemoglobin alpha chain Hemoglobin ␤-chain

Protein description

 C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[101]

[101]

[81]

[81, 97]

[81]

[102]

[81]

[81]

[81]

[81] [81, 97]

[81]

[90]

[110]

[90]

[110] [110] [110] [110] [110]

[110]

[95] [105] [95] [95, 105] [95] [95]

[95] [95] [95, 105]

[111] [95] [95, 112] [111] [111] [95, 105] [92, 94] [95, 105] [94, 105, 111] [95] [93] [95]

[95] [94] [95] [111] [111] [111] [95] [111] [111]

[95] [95] [93] [95]

[94] [111] [95] [105] [105] [94]

[98]

Brain

CSF

Brain

CSF

Spinal Cord

Multiple sclerosis reference

EAE reference

[108]

[103]

[103]

[103]

[103]

Blood

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Gene

HIST1H4 HP HPT HPX HRNR HSBP1 HSPA8 HV3D / HV3E IC1 IFI35 IGG2A IGH3 IGHA1 IGHG1 IGHG1C IGHG2 IGHG3 IGHM IGKC IGLC1 IGSF8 K1C1 K2C1 KCNMA1 KLK6 KNG KPYM KRT8 KV106 KV108 KV201 KV302 LAC2 LASP1 LCN1 LDHB LGALS7 LMNA LONRF2 LYC MAP1 MBP MDH1 MMP2 MX1 MYH1 MYL12B NDUFS8 NEFL NEFM NPC2

Unit Prot ID

P62805 Q61646 P00738 P20059/Q91X72 Q86YZ3 P04792 P63018 P01765 / P01766 P05155 P80217 P20760 P01867 P01876 P01869 P20759 P20062/P01859 P01860 P01871 P01834/P01835 P01842 Q969P0 P35527 P04264 Q08460 Q92876 P01042 P14618 P11679 P01598 P01600 P01614 P01620 P20767 P61792 P31025 P07195 P47929 P48679 Q1L5Z9 P00695 P01048 P02686 O88989 P08253/P33434 P20591 P12882 Q3THE2 Q08E91 P07196 P12839 Q15668

Table 1. Continued

U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U

Regulation

Histone H4 Haptoglobin Haptoglobin precursor Hemopoxin Hornerin Heat-shock protein beta-1 Heat shock cognate 71 kDa protein Ig heavy chain V_III region Plasma protease C1 inhibitor precursor Interferon-induced 35 Kda protein Ig gamma-2A chain C region Ig gamma-2B chain C region Ig A1 chain C Ig gamma-1 chain C region, membrane bound form Ig gamma-1 chain C region Ig gamma-2C chain C region Ig gamma-3 chain C region Ig UM chain C region Ig kappa chain C region Ig lambda C region Immunoglobulin superfamily member 8 K1C1-HUMAN keratin K2C1-HUMAN keratin Calcium-activated potassium channel alpha 1 Kallikrein 6 precursor Kininogen precursor Pyruvate kinase isozymes M1/M2 Cytokeratin-8 Ig kappa chain V-III Ig kappa chain VLJ Ig kappa chain V-II region Ig Kappa chain V_III region Ig lambda-2 chain C region LIM ans SH3 protein 1 Lipocalin L-lactate dehydrogenase chain B Galectin-7 Lamin-A LON peptidase N-terminal domain and RING finger protein Lysozyme T-Kinnogen 1 Myelin basic protein Malate dehydrogenase, cytoplasmic Matrix Metalloproteinase 2 (gelatinase A) Interferon-induced GTP-binding protein Mx1 Beta-myosin Myosin regulatory light chain NADH dehydrogenase (ubiquinone) Fe-S protein 8 Neurofilament light polypeptide Neurofilament medium polypeptide Epididymal secretory protein E1 precursor

Protein description

 C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[81]

[81]

[102]

[81]

[102]

[81]

[81, 102]

[110] [110]

[107]

[110]

[110]

[90]

[100] [100] [100, 112] [95] [94, 95, 105] [105] [105] [105] [105] [111]

[95] [95] [105] [95] [95] [95] [95] [95] [95] [95] [95] [95, 105] [95, 105] [95] [111] [95] [95] [94, 95, 105] [95] [95] [105] [111] [95] [95] [95] [95] [95, 105] [100, 105] [100] [95] [95] [95, 105] [100] [105]

Brain

[103]

[108]

[103]

[103]

[103]

Blood

A. S. Farias et al.

[101]

[101]

[101]

[101] [101] [101] [101] [101] [101]

[101]

[110]

[95] [95] [95, 105] [95] [95]

CSF

Brain

CSF

Spinal Cord

Multiple sclerosis reference

EAE reference

474 Proteomics 2014, 14, 467–480

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 C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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NPM1 NUC OSTP P4HB PA28 PCOLCE PDIA3 PDIA3 PEBP1 PEDF PGHD PLMN PPP1CC PRPF18 PSME1 PSME1 PTGIS Q96DK0 Q96PF6 Q9UL85 Q9UL91 Q9UQS6 QKI RCL1 RETBP SERPINA1 SERPINA3 SERPINE2 SET SGSM1 SIPA1L1 SNAP25 SNCA SRC STMN1 STMN1 TF THY1 TRF UBE2K UBIQ UBP53 VIM VSNL1 VSNL1 VTNC WDR70 ZNF255 ZNF268 ZNF394 ZSWIM7

Q61937 P09405 Q96IZ1 P07237/P09103 Q4FK54 Q15113 P11598/O15033 Q27773 P30086/P70296/P31044 P36955 P41222 P00747 P63087 Q9JKB8 Q06323 P97371 Q16647 Q96DK0 Q96PF6 Q9UL85 Q9UL91 Q9UQS6 Q91XU1 Q9Y2P8 P02753 P01009/P17475 P011011 P07093 Q9EQU5 Q2NKQ1 O43166 P60879 P37840 P12931 P54227 P54227 P02787/Q921I1 P04216/P01831 Q63916 P61087 Q9UEF2 Q70EK8 P08670/P20152 P62761 Q4W4C9 P04004 Q9NW82 Q9UID9 P02679 Q53GI3 Q19AV6

U, upregulated; D, downregulated

Gene

Unit Prot ID

Table 1. Continued

U D U U U U U U U U U U U U U U U U U U U U D U U U U U U U U D U U U D U D U U U U U U D U U U U U D

Regulation

Nucleophosmin Nucleolin protein fragment Secreted phosphopratein 1 (osteopontin) Protein disulfide-isomerase Proteasome activator complex subunit 28 Procollagen C-endopeptidase enhancer Protein disulfide-isomerase A3 precursor Protein disulfide-isomerase A3 Phosphatidylethanolamine binding protein Pigment epithelium-derived factor precursor Prostaglandin-H2 D Plasminogen precursor Protein phosphatase 1, catalytic subunit, gamma-1 Potassium channel regulatory factor Protease activator complex subunit 1 Proteasome activator complex subunit 1 Prostacyclin synthase CDNA FLJ25298 Kappa 1 light chain variable region Myosin-reactive immunoglobulin heavy kappa chain Myosin-reactive immunoglobulin heavy chain Fibronectin Quaking Protein RNA 3 -terminal phosphate cyclase-like protein Retinol-binding-protein precursor Alpha 1-antiproteinase Alpha 1-antichymotrypsin Glia-derived nexin Phosphatase 2A inhibitor Small G protein signaling modulator 1 Signal-induced proliferation-associated 1-like protein 1 Synaptosomal-associated protein 25 Synuclein, alpha Proto-oncogene tyrosine-protein kinase Stathmin 1 Stathmin 1 Serotransferrin Thy-1 membrane glycoprotein Transferrin Ubiquitin-conjugating enzyme E2-25K isoform 2 Ubiquitin UBC Inactive ubiquitin carboxyl-terminal hydrolase 53 Vimentin Visinin-like protein Visinin-like protein Vitronectin precursor WD repeat-containing protein 70 Zinc finger protein 255 Zinc finger protein 268 Zinc finger protein 394 Zinc finger SWIM domain-containing protein 7

Protein description

[101] [101]

[102]

[97, 102]

[97, 102]

[102]

[81]

[81] [106] [102]

[81, 97] [81, 97] [81]

[81, 97, 102] [102]

[110] [110]

[110]

[110] [110]

[110] [110]

[110]

[110]

[110]

[94]

[95]

[91]

[105]

[104] [104]

[105] [105] [90, 96] [96] [95, 104, 105]

[105] [94] [95] [105] [105] [95, 105]

[105] [105] [105] [105] [105] [105] [105] [105] [105] [105]

[95] [105]

[105]

Brain

[99] [99] [99] [99] [99]

[108] [108]

[108] [108]

[103]

[103]

[103] [103]

[113]

Blood

CSF

Brain

CSF

Spinal Cord

Multiple sclerosis reference

EAE reference

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475

476

A. S. Farias et al.

proteins is consistent in the literature and is a clear indicator of the inflammatory activity of the disease, especially when they are found in the CSF and/or CNS tissue.

3.4 Neurodegeneration-related proteins The majority of the differentially expressed proteins described in multiple sclerosis tissues are somehow related to the neurodegenerative process of the disease. The most frequently reported change in regulation in multiple sclerosis patients or EAE models is related to structural filaments, especially neurofilaments (Table 1). The downregulation of neurofilaments in the CNS tissue or upregulation in the CSF is indicative of a neurodegenerative process [81]. Many studies have found an upregulation of glial fibrillary acid protein (GFAP). GFAP is an intermediary filament that is mainly expressed by astrocytes. The upregulation of GFAP is most likely a result of glial scarring as a consequence of the neurodegenerative process [82, 83]. In addition to the structural disturbance, there is also a large number of differentially expressed proteins associated with cellular metabolism, indicative of the deregulation of the homeostatic state of CNS cells, which is most likely related to the inflammation in the CNS. In addition, proteins with enzymatic properties, such as calpain-3, metalloproteinases, alpha-enolase, proteinase activator complex, are upregulated. These enzymes might be directly related to the neurodegenerative process. It is not yet clear how these enzymes act during the pathogenesis of multiple sclerosis. However, many of these enzymes have already reported to be associated with other neurodegenerative diseases [84–87]. Interestingly, some authors have shown that a potassium channel is deregulated (Table 1). In a recent work, researchers reported on a group of multiple sclerosis patients in which a potassium channel was an autoantigen [88]. Proteomicsbased studies have provided an important contribution to confirm an early neurodegenerative process during multiple sclerosis. The neurodegenerative process was thought to occur after the initial inflammatory response. However, today it is clear that both processes are linked starting from the early stage of the disease [3].

4

Conclusion

Despite the large number of studies that have been aimed to identify biomarkers for multiple sclerosis during the last 10 years, we have not found any reliable protein biomarkers. However, these studies have found many potential candidates. Differentially expressed proteins may be helpful to better understand the pathobiology of the disease. Indeed, the entire therapeutic armory for multiple sclerosis is based on the immunologic aspects of the disease, and there is not a single drug targeting the neurodegenerative aspect. Therefore, the differentially expressed proteins identified by several studies may be essential to find new targets and/or monitor  C 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Proteomics 2014, 14, 467–480

current and new treatments. Moreover, studies have demonstrated the importance of CSF studies, which were excluded from the latest revision of the diagnosis criteria. The quest for biomarkers of multiple sclerosis may be more complex than those for other neurodegenerative or inflammatory diseases. Due to the combination of inflammatory and neurodegenerative components, multiple sclerosis will not have a single biomarker but a group of differentially expressed proteins that together might be used to distinguish the unique pathology of multiple sclerosis. The proteomics-based analysis of multiple sclerosis patients and experimental models over the past 10 years has provided an ocean of data but no certainty. We suggest that these data must be considered as the fundamental basis for further studies, perhaps in a more targeted manner. In addition, state-of-the-art proteomic technologies must be employed, including the analysis of proteins PTM, protein–protein interaction, and strategies that can drive basic scientific findings closer to the bedside [89]. Although we still have in multiple sclerosis research a long road to travel ahead of us, proteomics has helped to pave it better. The authors have declared no conflict of interest.

5

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Ten years of proteomics in multiple sclerosis.

Multiple sclerosis, which is the most common cause of chronic neurological disability in young adults, is an inflammatory, demyelinating, and neurodeg...
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