Proteomics of cerebrospinal fluid for biomarker discovery in multiple sclerosis

The discovery of reliable biomarkers, which are eligible for the prediction of both disease progression and response to treatment, means a great challenge in the management of multiple sclerosis (MS), a devastating disease of the central nervous system. The results of recent proteomic findings from the cerebrospinal fluid of MS patients hold promise of finding ideal biomarkers in the near future.

by Dr J. Füvesi, Dr C. Rajda, Dr D. Zádori, Dr K. Bencsik, Prof. Dr L. Vécsei and Prof. Dr J. Bergquist

Multiple Sclerosis
Multiple sclerosis is a demyelinative disorder of the central nervous system that affects mainly young adults. It has a great impact on quality of life, social and family life, and the careers of the patients.

In the majority of cases the disease starts with a relapsing–remitting (RR) phase. After a variable period of time it turns into a secondary progressive (SP) phase characterized by the gradual accumulation of residual symptoms. In 10–15% of cases a continuous progression is observed from the very beginning, this is the primary progressive (PP) form. In very rare fulminant cases frequent relapses with incomplete remissions cause severe disability or even death in a short duration of time.

The diagnosis of multiple sclerosis is still mainly clinical, supported by MRI and cerebrospinal fluid (CSF) analysis findings. The revised McDonald Criteria [1] allow earlier diagnosis, especially in PP MS. The routine diagnostic CSF analysis in MS includes the detection of oligoclonal bands and quantitative IgG analysis. Isoelectric focusing (IEF) on agarose gels followed by immunoblotting is considered the ‘gold standard’ for detecting the presence of oligoclonal bands [2]. The sensitivity of the method is above 95% and the specificity is more than 86%. An increased IgG index and the presence of oligoclonal bands in the CSF support an MS diagnosis.

Although the diagnosis is quite straightforward in most cases, taking into account clinical findings and paraclinical tests, there are still no specific biomarkers to confirm the diagnosis nor do we have any validated prognostic markers to follow the progression of the disorder.

At the time of diagnosis, major problems include the identification of the different clinical forms of the disease and the identification of patients with a potential rapid progression before disability evolves; the differential diagnosis of clinically isolated syndrome (CIS) with optic neuritis as the presenting symptom, where neuromyelitis optica (NMO) spectrum disorder may be an alternative diagnosis. Markers of disease progression are needed to distinguish CIS patients with a high probability to develop clinically definite MS.

There is also a need for biomarkers of response to treatment and biomarkers for better understanding the underlying pathological processes of the disease. This is especially important with the growing variety of treatment options: now it is possible to change therapy in the case of an inadequate treatment response and to escalate MS treatment to more aggressive alternatives. In the near future individualized treatment choices need better classification of patient characteristics.

In order to discover new biomarkers in MS, one should analyse the whole protein content of body fluids, preferentially CSF. Because of its proximity to the central nervous system (CNS), CSF may reflect changes in the CNS that may help differentiate normal and pathological conditions.

Proteomics
Proteomics is the study of protein expression in an organism. There are excellent reviews on proteomic approaches [3–5], so we will discuss here only certain aspects of these methods relevant to multiple sclerosis biomarker research. Mass-spectrometry (MS in Italic to distinguish from multiple sclerosis in this paper) based protein identification strategies include whole-protein analysis (‘top-down’ proteomics) and analysis of enzymatically produced peptides (‘bottom-up’ proteomics) [4]. The latter is the standard for large-scale or high-throughput analysis of highly complex samples, and digestion with trypsin is the most common method. The separation of peptides and proteins is an important element of both approaches.

Mass spectrometry measures the mass-to-charge ratio (m/z) of ionized molecules, and, as multiple distinct peptides can have very similar or identical molecular masses, it can be difficult to identify the overlapping peptides [3]. The use of separation techniques, therefore, reduces the cases of coincident peptide masses simultaneously introduced into the mass spectrometer. One of the most commonly used separation techniques is high-performance liquid chromatography (HPLC) with a capillary column. Peptides of similar molecular mass but different hydrophobicity elute from the LC column and enter the mass spectrometer at different time points, no longer overlapping in the initial MS analysis. Liquid chromatography coupled to mass spectrometry reduces the complexity of the sample and allows more precise protein identification.

In order to limit the risk of systematic errors and achieve a high sample throughput, labelling by means of isobaric tags for relative and absolute quantification (iTRAQ) may be used [6]. Multiple samples may be processed in parallel with this multiplexed approach. The main advantage is that the samples are analysed under exactly the same conditions. The relative abundance of labelled peptides indicates relative changes in protein expression.

LC-MS experiments generate an enormous amount of data, making data analysis one of the most challenging parts of proteomic analysis. Protein identification and quantification is achieved by database searching. Programs, such as Mascot etc., compare observed spectra to predicted spectra for candidate peptides from a protein database. In a recent study Schutzer et al. established a database of the normal human CSF proteome [7].

Proteomics in multiple sclerosis
In recent years a number of papers appeared describing proteomic analysis of CSF or brain tissue of multiple sclerosis patients [8–12]. The first papers in the field analysed pooled samples from a relatively small group of patients [8, 9]. Hammack et al. [8] reported the analysis of a pooled sample of three relapsing–remitting MS patients and a pooled sample of three patients with non-MS inflammatory CNS disorders using two-dimensional gel electrophoresis (2-DE) and peptide mass fingerprinting. They identified four proteins in the gels containing MS CSF that were not reported previously in normal human CSF: CRTAC-1B (cartilage acidic protein), tetranectin (a plasminogen-binding protein), SPARC-like protein (a calcium binding cell signalling glycoprotein) and autotaxin t (a phosphodiesterase).

In the study of Dumont et al. [9] CSF samples from five MS patients (4 RR, one SP) were analysed by 2-DE followed by liquid chromatography tandem mass spectrometry. With this method 15 proteins have been identified that were not previously observed in non-multiple sclerosis CSF 2-DE gels. These proteins were: psoriasin, calmodulin-related protein NB-1, annexin 1, EWI-2, Niemann–Pick disease type C2 protein (NPC-2), semenogelin 1 (SEM1), semenogelin 2 (SEM2), complement factor H-related protein 1 (FHR-1), procollagen C-proteinase enhancer protein (PCPE), aldolase A, N-acetyllactosaminide β-1,3-N-acetylglucosaminyl-transferase, tetranectin, cystatin A, superoxide dismutase 3 and glutathione peroxidase.

Later, publications started to focus on the differentiation of the clinical forms of the disease. Lehmensiek et al. compared CSF samples from RR MS and clinically isolated syndrome (CIS) patients with controls using two-dimensional difference gel electrophoresis (2-D-DIGE) and matrix-assisted laser desorption/ionization – time of flight (MALDI-TOF) mass spectrometry [10]. In RR MS Ig kappa chain NIG93 protein was increased in concentration, while transferrin isoforms, alpha 1 antitrypsin isoforms, alpha 2-HS glycoprotein, Apo E and transthyretin decreased. In a study of Stoop et al. [11] significant differences were observed comparing the peak lists of spectra from CSF of MS patients and patients with other neurological diseases (OND), and also clinically isolated syndrome (CIS) vs OND. Three differentially expressed proteins were identified in the CSF of MS patients compared to CSF of patients with OND: chromogranin A, clusterin and complement C3.

The same group compared proteome profiles of CSF from RR and PP multiple sclerosis and found that they overlap to a large extent [13]. The main detected difference was that protein jagged-1 was less abundant in PP MS compared to RR MS, whereas vitamin D-binding protein was only detected in the RR MS CSF samples. Ottervald et al. found an increased CSF level of vitamin-D-binding protein in SP MS compared to the control [14]. Recently, impaired vitamin D homeostasis has been linked to multiple sclerosis [15]: high serum levels of 25-hydroxyvitamin D correlated with a reduced risk of MS [16] and vitamin D supplementation was proposed as an add-on therapy [17].

Biomarkers of disease progression are emerging as new targets of proteomics. In our recently published paper we analysed the CSF of a rare fulminant case of MS and compared it with RR MS and control samples [18]. The aim of this study was to identify proteins related to rapid progression. The presented bottom-up strategy, based on isobaric tag labelling in conjunction with enzymatic digestion followed by nanoLC coupled off-line to MALDI TOF/TOF MS resulted in the identification of 78 proteins. Seven proteins were found to be upregulated in both fulminant MS samples but not in the relapsing–remitting case compared to the control. These proteins included Ig kappa and gamma-1 chain C region, complement C4-A, fibrinogen beta chain, serum amyloid A protein, neural cell adhesion molecule 1 and beta-2-glycoprotein 1. These proteins are involved in the immune response, blood coagulation, cell proliferation and cell adhesion.

Disease progression may be examined by analysing CSF samples from CIS patients who remain CIS and CIS patients who convert to clinically definite multiple sclerosis. Comabella et al. [19, 20] analysed pooled CSF samples with
isobaric labelling and mass spectrometry. They found that chitinase 3-like 1, ceruloplasmin and vitamin D-binding protein were upregulated in CSF of patients converted to clinically definite MS. In order to validate their results, the authors determined the levels of these selected proteins by enzyme-linked immunosorbent assay (ELISA) in individual CSF samples. Only chitinase 3-like 1 was validated. In a second validation step CSF chitinase 3-like 1 levels were measured in an independent CIS cohort and its level was again significantly increased in CIS patients who later converted to MS, compared to patients who remained as CIS. High CSF levels of this protein significantly correlated with the number of gadolinium enhancing and T2 lesions on baseline brain MRI scans and disability progression during follow-up.

The search for biomarkers that are able to identify patients at high risk of rapid progression becomes increasingly important with the appearance of more aggressive treatment possibilities. In another ongoing study we currently analyse LC-Fourier transform ion cyclotron resonance (FTICR) MS [20–22] data of a larger set of CSF samples from a variety of clinical forms of MS and matched controls.

Despite the increasing number of studies investigating potential biomarkers of MS disease progression and response to therapy, there is still no protein that is repeatedly identified and validated by different groups. This may be due to the relatively small sample sizes and the heterogeneity of the methods applied. Large scale multi-centre projects using standard methods for collecting, storing and analysing the samples are necessary to validate these preliminary results and integrate candidate biomarkers into the pathomechanism of the disease.

A great step in this direction is the BIOMS project, which aims a standardized sample collection, storage and processing during the preanalytical steps to rule out the differences occurred by sample preparation [23–25] and test the different methods and hypotheses on a great sample number in multiple centres to shed light on the sources of errors using different methods. One of these initiatives was the neurofilament validation study, which is a candidate biomarker in multiple sclerosis [26]. Another validation study tested two different methods of detecting the neutralizing antibodies against interferon-beta therapy, which is a biomarker of therapy in MS [27].

In the future multi-centre studies on standardized samples and methods can bring us closer to solve the questions regarding the pathological processes and the classification of patients to the most appropriate therapy.

Acknowledgement
TÁMOP-4.2.2.A-11/1KONV/-2012-0052 and The Swedish Research Council 621-2011-4423 are gratefully acknowledged for financial support.

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The authors
Judit Füvesi1 PhD, MD; Cecilia Rajda1 PhD, MD; Dénes Zádori1 PhD, MD; Krisztina Bencsik1 PhD, MD; László Vécsei1,2 PhD, MD; and Jonas Bergquist3,4* PhD, MD

1 Department of Neurology, Faculty of Medicine, Albert Szent-Györgyi Clinical Center, University of Szeged, Szeged, Hungary
2 Neuroscience Research Group of Hungarian Academy of Sciences and University of Szeged, Szeged, Hungary
3 Analytical Chemistry, Department of Chemistry-Biomedical Center, Uppsala University, Uppsala, Sweden
4 Science for Life Laboratory (SciLife Lab), Uppsala University, Uppsala, Sweden

*Corresponding author
E-mail: jonas.bergquist@kemi.uu.se