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  • Open Access

Quantitative proteomic analyses of CD4+ and CD8+ T cells reveal differentially expressed proteins in multiple sclerosis patients and healthy controls

Clinical Proteomics201916:19

https://doi.org/10.1186/s12014-019-9241-5

  • Received: 14 November 2018
  • Accepted: 27 April 2019
  • Published:

Abstract

Background

Multiple sclerosis (MS) is an autoimmune, neuroinflammatory disease, with an unclear etiology. However, T cells play a central role in the pathogenesis by crossing the blood–brain-barrier, leading to inflammation of the central nervous system and demyelination of the protective sheath surrounding the nerve fibers. MS has a complex inheritance pattern, and several studies indicate that gene interactions with environmental factors contribute to disease onset.

Methods

In the current study, we evaluated T cell dysregulation at the protein level using electrospray liquid chromatography–tandem mass spectrometry to get novel insights into immune-cell processes in MS. We have analyzed the proteomic profiles of CD4+ and CD8+ T cells purified from whole blood from 13 newly diagnosed, treatment-naive female patients with relapsing–remitting MS and 14 age- and sex-matched healthy controls.

Results

An overall higher protein abundance was observed in both CD4+ and CD8+ T cells from MS patients when compared to healthy controls. The differentially expressed proteins were enriched for T-cell specific activation pathways, especially CTLA4 and CD28 signaling in CD4+ T cells. When selectively analyzing proteins expressed from the genes most proximal to > 200 non-HLA MS susceptibility polymorphisms, we observed differential expression of eight proteins in T cells between MS patients and healthy controls, and there was a correlation between the genotype at three MS genetic risk loci and protein expressed from proximal genes.

Conclusion

Our study provides evidence for proteomic differences in T cells from relapsing–remitting MS patients compared to healthy controls and also identifies dysregulation of proteins encoded from MS susceptibility genes.

Keywords

  • Multiple sclerosis
  • T cells
  • Mass spectrometry
  • SNPs
  • Autoimmunity
  • Proteomics

Background

Multiple sclerosis (MS) typically affects young adults and is the most common non-traumatic cause of neurological impairment. It affects around 2.5 million individuals worldwide leading to both physical and cognitive deficits [1]. MS is a chronic inflammatory, demyelinating disorder of the central nervous system (CNS) where lymphocyte-mediated inflammation causes demyelination and axonal degeneration. The underlying pathogenesis remains partly unclear, but T lymphocytes, both CD4+ and CD8+ T cells, have long been considered to play pivotal roles in MS pathogenesis [2, 3]. Also, the genetic architecture of MS susceptibility, emerging from genome-wide association studies, indicates an important role for the adaptive immune system, in particular T cells for MS-disease onset [4, 5].

Studies of MS etiology in monozygotic twins and recurrence risk in siblings indicate that MS has a complex inheritance pattern [6]. Furthermore, parent-of-origin effects affect inheritance of MS in rodents, and several studies indicate that gene-environment interactions contribute to MS development. Altogether, this suggests that also epigenetic mechanisms play a role in MS etiology [7]. Both genome-wide studies on epigenetic modifications, such as DNA methylation, as well as transcriptomic analyses in immune cells have been conducted in order to investigate the potential dysregulation of immune cells in MS. Epigenetic profiling in peripheral blood mononuclear cells and in immune cell subtypes, i.e. CD4+ and CD8+ T cells, suggests global differences in DNA methylation between MS patients and healthy controls [812]. Of note, a few single genes displayed significant differential DNA methylation levels between MS patients and healthy controls, but no overlap, except for in the HLA-DRB1 locus [12, 13], was observed between the different studies [7]. Microarray analyses of blood from MS patients and healthy controls indicate dysregulation of T cell pathways during MS pathogenesis [14, 15]. Recent candidate-gene approaches have profiled transcriptional changes in T cells from MS cases and healthy controls, and identified dysregulation of several genes, e.g. MIR-21 and corresponding target genes [16] and THEMIS [17]. However, the correlation between mRNA and protein copy numbers varies widely [18, 19]. Therefore, performing quantitative high-resolution mass spectrometry-based proteomics gives a unique opportunity for system-wide studies at the protein level.

Since the 1970′ies, HLA-DRB1*15:01 has been established as the major genetic risk factor in MS [6]. Recent genome-wide screenings have however identified more than 200 non-HLA single nucleotide polymorphisms (SNPs) associated with MS risk [4, 5, 20]. The majority of the non-HLA MS associated SNPs are non-coding, and an enrichment of these variants is observed in regulatory regions of DNA (DNase hypersensitive sites) in immune cells from the adaptive arm of the immune system, i.e. B and T cells [21]. In addition, given the widespread presence of expression quantitative trait loci (eQTLs) in the genome [22], it is likely that a number of MS-associated SNPs or SNPs inherited together with the MS-associated SNPs might act as eQTLs in immune cells. Indeed, a recent study identified 35 significant eQTLs from 110 non-HLA MS-associated SNPs in peripheral blood mononuclear cells from MS patients [23]. However, whether these expression differences at the transcriptomic levels also persists to the protein level is currently unknown.

The overall objective for this project is to evaluate immune dysregulation at the protein level in MS using liquid chromatography combined with mass spectrometry. We analyzed the proteomic profile of purified immune-cell subsets, i.e. CD4+ and CD8+ T cells, from genotyped relapsing–remitting MS (RRMS) patients and healthy controls, which allows us to disentangle potential cell-subtype specific differences that could not be detected in a heterogeneous cell material, permitting a comprehensive understanding of disease mechanisms of MS. Correlating protein expression with genotypes of MS-associated SNPs allowed for identification of protein expression quantitative trait loci (pQTLs).

Methods

MS patients and healthy controls

Samples from 13 untreated, female Norwegian MS patients with RRMS and 14 age-matched, female Norwegian healthy controls were included (see Table 1 for demographic, clinical and biochemical information). For two of the patients, the EDSS score was assessed by inspection of their medical journals. All patients and healthy controls were self-declared of Nordic ancestry. Patients were recruited from the MS out-patient clinic at the Oslo University Hospital, Oslo, Norway and the healthy controls among hospital employees. All MS patients fulfilled the updated McDonald criteria for MS [24], did not have an ongoing infection and had not experienced a relapse or received steroids in the 3 months prior to enrollment. The diagnosis was set less than 1 year prior to inclusion in the study. The healthy controls did report to have no MS in near family.
Table 1

Characteristics of individual MS patients and summaries of patients and healthy controls

Patient

Age categorya

Years since first MS symtoms

EDSS

MSSS

OCB

MRI lesion categoriesb

Contrast lesions MRI

Symptoms at onset

Family history of MS

MS1

3

6

2.5

7.1

Yes

3

Yes

Visual

No

MS2

1

4

1

2.44

Yes

2

Yes

Brainstem

Yes

MS3

6

7

3

7.93

Yes

1

No

Visual

Yes

MS4

1

0.75

1.5

4.3

Yes

1

Yes

Sensory

No

MS5

1

15

3.5

8.64

Yes

1

No

Sensory

No

MS6

4

0.75

2

5.87

Yes

3

Yes

Brainstem

No

MS7

2

0.5

1

2.44

Yes

3

No

Sensory

No

MS8

4

2

1

2.44

Yes

3

Yes

Visual

Yes

MS9

5

3

2.5

7.08

No

3

Yes

Sensory, bladder/bowel

No

MS10

1

0.75

3

7.93

Yes

1

Yes

Pyramidal

Yes

MS11

6

19

1.5

4.3

Yes

1

No

Sensory

No

MS12

5

14

2.5

7.08

Yes

2

No

Visual

No

MS13

1

1

1.5

4.3

Yes

2

Yes

Sensory

No

Summarized

         

Patients mean or median* (range)

37.2 (25–52)

5.7 (0.75–19)

2 (1–3.5)*

5.5 (2.4–8.6)

N/A

2*

N/A

N/A

N/A

Healthy controls mean (range)

32.6 (23–47)

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

The table includes data for each individual MS patient at inclusion, from the left: patient identity number; aage category; number of years since first MS symptoms; EDSS; MSSS; presence of OCB in the cerebrospinal fluid; bMRI lesion categories; presence of contrast enhancing lesions (MRI); symptoms at onset and family history of MS. Below follows summary statistics with mean (range) for age category, years since first symptoms and MSSS and median (range) labelled with * for EDSS and MRI lesion categories

EDSS expanded disability status scale, MSSS MS severity score, OCB oligoclonal bands, MRI magnetic resonance imaging, N/A not applicable

aAge category: 1 = 25–29 years; 2 = 30–34 years; 3 = 35–39 years; 4 = 40–44 years; 5 = 45–49 years; 6 = 50–54 years

bMRI lesion categories:: 1 = 0–10 lesions; 2 = 10–20 lesions; 3 = more than 20 lesions

DNA isolation and genotyping

DNA was purified from blood (DNeasy Blood & Tissues Kit, Qiagen, Redwood City, CA, USA). Samples were genotyped with the Human Omni Express BeadChip (Illumina, San Diego, CA, USA).

Isolation of human CD4+ and CD8+ T cells, sample preparation and protein digestion

Peripheral blood mononuclear cells were isolated from whole blood by Lymphoprep (Axis Shield, Dundee, Scotland), before positive selection of CD8+ T cells (EasySep™ Human CD8+ Selection Kit, STEMCELL Technologies, Vancouver, Canada) followed by negative selection of CD4+ T cells (EasySep™ Human CD4+ T cell Isolation kit, STEMCELL Technologies). Cells that achieved cell purity of more than 95% as measured by flow cytometry (Attune Acoustic Focusing Flow Cytometer, Life Technologies, Carlsbad, CA, USA) were included in the study. Two CD8+ T cell samples from MS patients did not reach 95% cell purity and were excluded from the analyses. Antibodies used for flow cytometry analyses were fluorescein isothiocyanate-conjugated mouse anti-human CD4 (clone RTF-4 g, Southern Biotech, Birmingham, AL, USA), mouse anti-human CD8 (clone HIT8a, BD biosciences, San Jose, CA, USA) and mouse IgG1 isotype control (15H6, Southern Biotech).

Sample preparation and protein digestion

The pellet of 1 × 106 cells from each sample was kept until use at − 80 °C. The pellets were then solubilized in 100 μl 0.1 M Tris–HCl pH 7.6 containing 4% SDS and homogenized at room temperature by sonication 3–4 times at 30% amplitude for 30 s with an ultrasonic processor with thumb-petuated pulser (Vibra-cell VC130 PB from Sonics and Materials Inc., Newton, CT, USA). After centrifugation for 10 min at 16,200 × g, supernatants were collected. Protein concentration in samples was measured by Pierce BCA protein assay (Thermo Fisher Scientific, Rockford, IL, USA) and the absorbance values at 562 nm were read on Multiskan FC 3.1 ELISA reader (Thermo Fisher Scientific). To 40 μl supernatant corresponding to about 10 μg protein, 4 μl 1 M DTT was added for reduction and incubated at 95 °C for 5 min. After cooling, SDS removal by dilution with urea and cysteine alkylation, digestion of proteins were accomplished using the filter aided sample preparation (FASP) protocol [25]. On the MicroconR-30 centrifugal filters (Merck Millipore Ltd, Ireland), proteins were digested with a protein-to-trypsin ratio of 50:1 (sequencing grade-modified trypsin from Promega, GmbH, Mannheim, Germany) [26]. After incubation overnight at 37 °C, tryptic peptides were collected by washing the filter three times with 50 mM ammonium bicarbonate pH 8.5, and with 0.5 M NaCl, each step followed by centrifugation at 11,000 × g [25]. Sample cleanup was performed using a reverse-phase OasisR HLB μElution Plate 30 μm (2-mg HLB sorbent, Waters, Milford, MA) [27]. After lyophilization, the dried peptides were suspended in 12 μl of 0.1% formic acid containing 2% acetonitrile. 2 μl were used for protein quantification based on absorbance at 280 nm using a NanoDrop spectrophotometer (Thermo Fisher Scientific). The sample volume was adjusted to 1 μg/μl and approximately 1 μg of the mixture was analyzed with mass spectrometry.

Liquid chromatography–mass spectrometry/mass spectrometry analysis

The peptides were analyzed by electrospray liquid chromatography–tandem mass spectrometry (LC–MS/MS) using a linear ion trap–orbitrap instrument (Orbitrap Elite, Thermo Fisher Scientific). The LC run length of 3 h was performed on a 50 cm analytical column (Acclaim PepMap 100, 50 cm × 75 µm ID nanoViper column, packed with 3 µm C18 beads (Thermo Fisher Scientific)). Peptides were loaded and desalted on a pre-column (Acclaim PepMap 100, 2 cm × 75 µm ID nanoViper column, packed with 3 µm C18 beads (Thermo Fisher Scientific)) with 0.1% (v/v) trifluoroacetic acid, and eluted with a gradient composition as follows: 5% B during trapping (5 min) followed by 5–7% B over 1 min, 7–32% B for the next 129 min, 32–40% B over 10 min, and 40–90% B over 5 min. Elution of very hydrophobic peptides and conditioning of the column were performed during 20 min isocratic elution with 90% B and 20 min isocratic elution with 5% B respectively. Mobile phases A and B with 0.1% formic acid (vol/vol) in water and 100% acetonitrile respectively, and the flow rate was of 270 nl per min. A full scan in the mass area of 300–2000 Da was performed in the Orbitrap. For each full scan performed at a resolution of 240,000, the 12 most intense ions were selected for collision induced dissociation (CID). The settings of the CID were as following: threshold for ion selection was 3000 counts, the target of ions used for CID was 1e4, activation time was 10 ms, isolation window was 2 Da, and normalized collision energy was 35 eV.

Mass spectrometry data analysis

MS raw files were analyzed by the MaxQuant software [28] (version 1.5.6.0), and peak lists were searched against the human SwissProt FASTA database (version May 2017), and a common contaminants database by the Andromeda search engine. As variable modification, methionine oxidation was used and as fixed modification cysteine carbamidomethylation was used. False discovery rate was set to 0.01 for proteins and peptides (minimum length of six amino acids) and was determined by searching a reverse database. Trypsin was set as digestion protease, and a maximum of two missed cleavages were allowed in the database search. Peptide identification was performed with an allowed MS mass deviation tolerance of 20 ppm, and MS/MS fragment ions could deviate by up to 0.5 Da. For accurate intensity-based label-free quantification in MaxQuant [MaxLFQ [29]], the type of label was “1″ for LFQ with a minimum ratio count of “2″. For matching between runs, the retention time alignment window was set to 20 min and the match time window was 0.7 min.

Statistical analyses

The statistical significance between comparisons was evaluated using a two-tailed Student t test, p < 0.05 was considered significant. The equality of variances of patient and control distributions was assessed with an F-test. Consequently, a Student t test with unequal variances was used when the F-test was significant (p < 0.05) and with equal variances otherwise. Area under the ROC curve (AUC) analyses of all significantly expressed proteins (p < 0.05) was calculated using GraphPad Prism 6 (La Jolla, CA, USA). Individual scatter plots of selected proteins (Figs. 4, 5) was created using GraphPad Prism 6. For the genotype-wise comparisons, a Students unpaired t-test with equal variances was performed when the data were normally distributed, if not, the non-parametric Mann U Whitney test was performed (GraphPad Prism 6).

Data processing, principal component and hierarchical clustering analyses

Proteins identified as “only identified by site”, “reverse” or “potential contaminant” by Max Quant were removed from further analyses. In Perseus (Perseus Software, version 1.6.0.7), the normalized LFQ intensities from Max Quant were log2 transformed and the normal distributions were controlled using histogram function for each individual. Proteins with at least 70 percentage valid values in each group (healthy control and MS) were analyzed. Further, hierarchical clustering was performed using Z-scores created by default settings in Perseus. A principal component analysis (PCA) plot was generated using protein intensities as variables, with the missing protein intensity values imputed from the normal distribution using default settings in Perseus.

Ingenuity pathway analyses

QIAGEN’s Ingenuity® pathway Analysis (IPA®, QIAGEN, version 44691306 date; 2018-06-15, build version: 481437M date; 2018-08-25) was used for functional interpretation of significantly regulated proteins. The default settings were used, except only the following confidence, species and tissues and cells were permitted: “only experimentally observed” (confidence), “only mammals” (species) and “only T cells” (primary and cell-lines (tissues and cells)). A Benjamin-Hochberg (B-H) multiple testing correction was used, where a −log(B-H p-value) of 1.3 was considered as significant.

Results

Differential protein expression is observed in T cells between MS patients and healthy control

In this study, we monitored the difference in the proteomic profiles in T cells, i.e. CD4+ and CD8+ T cells, between RRMS patients (n = 13) and healthy controls (n = 14) in a label-free manner. We were able to identify and quantify 2031 and 2259 proteins in CD4+ and CD8+ T cells, respectively. In CD4+ T cells, 228 proteins were differentially expressed (p < 0.05) between MS cases and healthy controls (listed in Additional file 1: Table S1), whereas 195 proteins were differentially expressed between the two groups in CD8+ T cells (listed in Additional file 2: Table S2). Of the differentially expressed proteins, 74% in CD4+ T cells and 64% in CD8+ T cells were more abundant in samples from MS patients compared to healthy controls. The separation of MS versus healthy controls based on these proteins is shown in the principal component analyses (PCA) plot in Fig. 1, where the first component captures 55% (CD4+) and 62% (CD8+) of the variance, whereas the second component captures 11% (CD4+) and 9% (CD8+). Of the differentially expressed proteins, 26 overlapped between CD4+ and CD8+ T cells.
Fig. 1
Fig. 1

Principal component analyses (PCA) of differentially expressed proteins. PCA of proteins significantly different (p < 0.05) in a CD4+ and b CD8+ T cells from MS cases (red) compared to healthy controls (blue)

Ingenuity pathway analyses of differentially expressed proteins

To increase the chance of extracting the true candidate proteins differentially expressed between MS cases and healthy controls with a potential impact on cell function, a more stringent filter for selection was applied. By selecting proteins that fulfilled two of the three following criteria within the group of significantly differential expressed proteins (p < 0.05): (1) p-value cut-off of p < 0.01; (2) area under the curve (AUC) > 0.8 and (3) log2 fold change > [0.2], we created a top-hit list of differentially expressed proteins. Out of the 228 and 195 proteins listed in Additional file 1: Table S1 and Additional file 2: Table S2 from CD4+ and CD8+ T cells, respectively, we ended up with a shorter list of 90 and 61 proteins (Tables 2, 3), where five proteins expressed from the TOMM70A, ACP1, AGL, ATP2A2 and TPM4 genes appeared in both top-hit lists.
Table 2

Top-hit list of differentially expressed proteins in CD4+ T cells

Accession

Protein identity

Gene names

p-value

FC MS versus HC (log2)

Median intensity MS (log2)

MS SD

Median intensity HC (log2)

HC SD

% seq cov

# pep

AUC

Q5JSL3

Dedicator of cytokinesis protein 11

DOCK11

4.69E−05

0.27405

22.73205

0.14968

22.458

0.11384

13

21

0.98

Q03252

Lamin-B2

LMNB2

0.000203

0.2023

26.23395

0.10367

26.03165

0.1219

58.1

42

0.94

Q14978

Nucleolar and coiled-body phosphoprotein 1

NOLC1

0.000306

0.67815

21.4053

0.26237

20.72715

0.36787

16.2

9

0.92

Q2M2I8; Q9NSY1

AP2-associated protein kinase 1

AAK1

0.000457

0.22605

23.1178

0.11404

22.89175

0.12897

33

20

0.92

Q13148

TAR DNA-binding protein 43

TARDBP

0.000642

0.29

23.3943

0.12816

23.1043

0.14754

39.4

11

0.89

P20963

T-cell surface glycoprotein CD3 zeta chain

CD247

0.000907

0.19535

23.48275

0.0965

23.2874

0.18125

60.4

11

0.88

P49959

Double-strand break repair protein MRE11A

MRE11A

0.001405

0.1957

21.44665

0.17074

21.25095

0.15881

21.6

11

0.88

P06239

Tyrosine-protein kinase Lck

LCK

0.001598

0.2009

24.642

0.12459

24.4411

0.13158

49.7

18

0.85

Q9NR56; Q5VZF2; Q9NUK0

Muscleblind-like protein 1

MBNL1

0.001651

0.3464

22.0867

0.19817

21.7403

0.24361

21.6

8

0.87

P35573

Glycogen debranching enzyme; 4-alpha-glucanotransferase; amylo-alpha-1,6-glucosidase

AGL

0.00177

0.32245

21.79335

0.29915

21.4709

0.18837

18.1

18

0.87

P18085

ADP-ribosylation factor 4

ARF4

0.00199

− 0.29765

21.6712

0.17375

21.96885

0.14457

64.4

10

0.86

O75131; Q96FN4; Q8IYJ1; Q9HCH3; Q9UBL6

Copine-3

CPNE3

0.002255

0.1118

23.9288

0.09682

23.817

0.07363

46.7

19

0.88

P27824

Calnexin

CANX

0.002331

− 0.2029

24.6288

0.09381

24.8317

0.13864

37.7

22

0.85

Q49A26

Putative oxidoreductase GLYR1

GLYR1

0.002442

0.2299

22.8002

0.15088

22.5703

0.13549

40

14

0.88

P12694

2-oxoisovalerate dehydrogenase subunit alpha, mitochondrial

BCKDHA

0.002513

0.2997

20.58005

0.14155

20.28035

0.16289

21.1

6

0.89

P16615

Sarcoplasmic/endoplasmic reticulum calcium ATPase 2

ATP2A2

0.002577

− 0.34015

20.91155

0.24663

21.2517

0.39528

22.5

15

0.85

P31146; REV__Q02818

Coronin-1A

CORO1A

0.002667

0.196

28.77805

0.04311

28.58205

0.14531

63.8

33

0.91

P29401

Transketolase

TKT

0.002709

0.18195

27.0961

0.16375

26.91415

0.08497

68.9

38

0.86

Q00610; P53675

Clathrin heavy chain 1

CLTC

0.00312

− 0.10695

26.3723

0.05858

26.47925

0.08019

58.7

80

0.83

P19971

Thymidine phosphorylase

TYMP

0.003318

− 0.6095

21.51775

0.63532

22.12725

0.52772

51

16

0.85

Q16401

26S proteasome non-ATPase regulatory subunit 5

PSMD5

0.003478

0.12765

23.7053

0.09891

23.57765

0.13094

58.9

21

0.86

Q15084

Protein disulfide-isomerase A6

PDIA6

0.003546

− 0.3043

23.5948

0.25739

23.8991

0.17192

45.9

13

0.86

P07237

Protein disulfide-isomerase

P4HB

0.003888

− 0.1857

25.1359

0.14266

25.3216

0.09151

56.1

27

0.85

O43665

Regulator of G-protein signaling 10

RGS10

0.003925

0.2594

23.5918

0.213

23.3324

0.14464

60.1

12

0.85

P27986; O00459

Phosphatidylinositol 3-kinase regulatory subunit alpha

PIK3R1

0.004008

0.2604

22.56095

0.17873

22.30055

0.21783

38.3

19

0.83

Q9Y4L1

Hypoxia up-regulated protein 1

HYOU1

0.004021

− 0.1815

23.00205

0.13058

23.18355

0.13156

31.8

20

0.83

O75306

NADH dehydrogenase [ubiquinone] iron-sulfur protein 2, mitochondrial

NDUFS2

0.004057

0.13545

22.6738

0.08259

22.53835

0.13156

34.8

12

0.83

Q8WUX9

Charged multivesicular body protein 7

CHMP7

0.004115

0.23275

21.9775

0.21092

21.74475

0.18291

37.1

13

0.81

P07602

Prosaposin; Saposin-A; Saposin-B-Val; Saposin-B; Saposin-C; Saposin-D

PSAP

0.004366

− 0.19325

22.296

0.18336

22.48925

0.42157

12.6

6

0.94

O00422

Histone deacetylase complex subunit SAP18

SAP18

0.004452

0.37715

20.6193

0.18761

20.24215

0.34985

41.8

5

0.87

Q9ULA0

Aspartyl aminopeptidase

DNPEP

0.004664

0.3613

23.6397

0.17228

23.2784

0.18788

53.3

18

0.82

O43681

ATPase ASNA1

ASNA1

0.004954

− 0.11665

22.25215

0.13672

22.3688

0.11129

50.6

10

0.83

O75832

26S proteasome non-ATPase regulatory subunit 10

PSMD10

0.004963

0.21305

21.312

0.24569

21.09895

0.12837

40.3

6

0.89

P30536

Translocator protein

TSPO

0.004964

0.5376

22.44845

0.37985

21.91085

0.337

23.1

3

0.82

P24666

Low molecular weight phosphotyrosine protein phosphatase

ACP1

0.005013

0.2241

22.8028

0.19373

22.5787

0.20543

72.2

8

0.88

Q4G176

Acyl-CoA synthetase family member 3, mitochondrial

ACSF3

0.005127

0.3234

20.8339

0.32659

20.5105

0.20115

19.3

7

0.83

P35611

Alpha-adducin

ADD1

0.005201

0.17245

23.941

0.12213

23.76855

0.20616

44.9

24

0.81

P19525

Interferon-induced, double-stranded RNA-activated protein kinase

EIF2AK2

0.005211

− 0.54585

20.65625

0.47474

21.2021

0.40633

20.1

9

0.87

O75791

GRB2-related adapter protein 2

GRAP2

0.00589

0.1927

23.58335

0.07421

23.39065

0.15601

43

13

0.84

Q16666; Q6N021

Gamma-interferon-inducible protein 16

IFI16

0.006051

− 0.27745

24.51685

0.24674

24.7943

0.12775

43.4

31

0.84

Q9HAV4

Exportin-5

XPO5

0.006457

− 0.402

18.4781

0.23884

18.8801

0.22546

5.1

4

0.87

Q9NRY5

Protein FAM114A2

FAM114A2

0.006779

0.4935

19.3331

0.23485

18.8396

0.34369

15.8

4

0.86

P11177

Pyruvate dehydrogenase E1 component subunit beta, mitochondrial

PDHB

0.006838

0.2322

24.05355

0.11379

23.82135

0.12468

52.9

13

0.83

Q9NZZ3

Charged multivesicular body protein 5

CHMP5

0.006962

− 0.28845

20.37145

0.31795

20.6599

0.20311

40.6

6

0.83

P53634

Dipeptidyl peptidase 1; dipeptidyl peptidase 1 exclusion domain chain; dipeptidyl peptidase 1 heavy chain; dipeptidyl peptidase 1 light chain

CTSC

0.006992

− 0.36305

20.5409

0.54754

20.90395

0.10359

19.9

7

0.81

Q06546

GA-binding protein alpha chain

GABPA

0.006996

0.2074

21.3763

0.1983

21.1689

0.20734

28

8

0.8

P21399

Cytoplasmic aconitate hydratase

ACO1

0.008051

0.1699

21.4757

0.14153

21.3058

0.20875

20.4

11

0.82

Q9H400

Lck-interacting transmembrane adapter 1

LIME1

0.008125

0.25515

21.11

0.19997

20.85485

0.21307

46.1

7

0.81

Q02750

Dual specificity mitogen-activated protein kinase kinase 1

MAP2K1

0.00822

0.1771

23.2231

0.13291

23.046

0.1348

42.2

14

0.8

O94826

Mitochondrial import receptor subunit TOM70

TOMM70A

0.008231

0.21725

22.34995

0.15186

22.1327

0.20502

34.5

13

0.81

O75475

PC4 and SFRS1-interacting protein

PSIP1

0.008443

0.1899

22.08185

0.1504

21.9335

0.15516

45.5

21

0.8

P02776

Platelet factor 4; platelet factor 4, short form

PF4

0.008535

− 1.5035

24.86845

1.22842

24.67855

1.48716

36.6

5

0.83

Q5XKP0

Protein QIL1

QIL1

0.008552

0.31595

22.7718

0.27181

24.2753

0.34286

62.7

3

0.84

Q9UGI8

Testin

TES

0.008688

0.14215

19.94335

0.09764

19.6274

0.12853

72

31

0.8

Q86VP6; O75155

Cullin-associated NEDD8-dissociated protein 1

CAND1

0.008724

0.11355

25.3342

0.10321

25.19205

0.08176

48.9

46

0.84

Q9C0K0

B-cell lymphoma/leukemia 11B

BCL11B

0.008892

0.2434

25.65085

0.17505

25.5373

0.22495

12.8

8

0.79

P13861; P31323

cAMP-dependent protein kinase type II-alpha regulatory subunit

PRKAR2A

0.008993

0.13145

21.90015

0.12538

21.65675

0.09173

62.1

20

0.81

P07741

Adenine phosphoribosyltransferase

APRT

0.008995

0.19165

23.14455

0.1699

23.0131

0.15824

91.1

17

0.83

P23246

Splicing factor, proline- and glutamine-rich

SFPQ

0.009648

0.12175

25.8719

0.14919

25.68025

0.09657

47.9

31

0.83

P49903

Selenide, water dikinase 1

SEPHS1

0.009747

0.2257

26.39505

0.15139

26.2733

0.15099

41.6

10

0.83

P62995

Transformer-2 protein homolog beta

TRA2B

0.009757

0.17515

22.6718

0.18232

22.4461

0.12504

30.9

8

0.8

Q86XP3

ATP-dependent RNA helicase DDX42

DDX42

0.009985

0.1467

23.91205

0.20211

23.7369

0.12402

22.7

13

0.85

P13010

X-ray repair cross-complementing protein 5

XRCC5

0.01116

0.2196

22.23445

0.1468

22.08775

0.12306

71.2

48

0.82

Q15428

Splicing factor 3A subunit 2

SF3A2

0.011498

0.30175

25.0703

0.2546

25.26055

0.28193

28.7

9

0.85

P37837

Transaldolase

TALDO1

0.011683

0.26525

24.0953

0.16309

23.9199

0.1934

47.2

19

0.8

O94973

AAK1

AP2A2

0.01208

0.40715

22.87215

0.18724

22.7054

0.31295

25

16

0.82

P16150

Leukosialin

SPN

0.012636

0.41995

27.0869

0.31488

26.8673

0.23838

19.5

5

0.8

Q9Y6K5

2-5-oligoadenylate synthase 3

OAS3

0.013062

− 0.58225

24.1071

0.61142

23.9796

0.40043

26.2

21

0.8

P13598

Intercellular adhesion molecule 2

ICAM2

0.013215

− 0.33575

22.4839

0.36073

22.18215

0.13532

14.9

3

0.81

O96000

NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 10

NDUFB10

0.013266

0.2682

27.0077

0.2602

26.74245

0.17232

43

7

0.82

P48059; Q7Z4I7

LIM and senescent cell antigen-like-containing domain protein 1

LIMS1

0.013613

− 1.11835

22.2423

1.06748

22.4974

1.04857

45.8

13

0.83

P0DOX5; P01857

Ig gamma-1 chain C region

IGHG1

0.014981

− 0.8553

21.9197

0.96324

21.51255

0.41275

28.3

9

0.8

P67936

Tropomyosin alpha-4 chain

TPM4

0.015875

− 0.39585

22.849

0.50209

22.42905

0.33951

66.1

27

0.81

Q53QZ3

Rho GTPase-activating protein 15

ARHGAP15

0.016084

0.2283

22.7616

0.11442

22.6226

0.26067

28.8

10

0.8

Q93077; Q7L7L0; P04908

Histone H2A type 1-C; histone H2A type 3; histone H2A type 1-B/E

HIST1H2AC; HIST3H2A; HIST1H2AB

0.016472

0.5318

24.6077

0.68101

24.4913

0.51829

35.4

7

0.83

Q00341

Vigilin

HDLBP

0.017653

− 0.3557

22.1316

0.25975

22.71385

0.26847

5.3

5

0.8

Q9Y3C4

EKC/KEOPS complex subunit TPRKB

TPRKB

0.01784

0.33545

25.2378

0.31196

25.04925

0.2556

56.6

7

0.83

Q96I24

Far upstream element-binding protein 3

FUBP3

0.018912

0.2288

19.18425

0.18212

19.52

0.15408

24.7

9

0.81

P18206

Vinculin

VCL

0.019685

− 0.57615

22.07985

0.84436

21.81165

0.52692

64.2

60

0.83

Q96BW5

Phosphotriesterase-related protein

PTER

0.020487

0.35515

23.0556

0.21492

22.8575

0.29673

24.4

6

0.82

P02775

Platelet basic protein; connective tissue-activating peptide III; TC-2; connective tissue-activating peptide III(1-81); beta-thromboglobulin; neutrophil-activating peptide 2(74); neutrophil-activating peptide 2(73); neutrophil-activating peptide 2; TC-1; Neutrophil-activating peptide 2(1–66); neutrophil-activating peptide 2(1–63)

PPBP

0.022319

− 1.4945

21.6995

1.19479

21.55895

1.23096

38.3

5

0.81

P21333

Filamin-A

FLNA

0.023825

− 0.23365

21.21755

0.43258

22.3359

0.24022

71.6

137

0.81

Q01469; A8MUU1

Fatty acid-binding protein, epidermal

FABP5

0.024356

− 0.5329

26.41625

0.73245

26.33375

0.55502

76.3

11

0.83

O94903

Proline synthase co-transcribed bacterial homolog protein

PROSC

0.024792

0.27275

24.465

0.13221

24.3072

0.20226

37.8

8

0.8

P21291

Cysteine and glycine-rich protein 1

CSRP1

0.026425

− 0.2011

25.4829

0.36999

25.34655

0.16603

64.2

8

0.8

P53041

Serine/threonine-protein phosphatase 5

PPP5C

0.028586

0.2748

23.5436

0.14823

23.3623

0.29589

22.8

8

0.84

Q8WUM0

Nuclear pore complex protein Nup133

NUP133

0.030136

0.272

21.15755

0.26096

22.01285

0.19541

18.3

12

0.81

P09525

Annexin A4

ANXA4

0.032901

− 0.25805

26.25495

0.30546

26.6508

0.2431

47.6

13

0.82

Q04826

HLA class I histocompatibility antigen, B-40 alpha chain

HLA-B

0.033546

− 1.0305

21.98915

0.72834

21.76085

0.84434

44.5

13

0.81

O43704

Sulfotransferase family cytosolic 1B member 1

SULT1B1

0.035541

0.4495

26.00495

0.2866

26.19745

0.42792

39.2

9

0.82

The table displays proteins (n = 90) that are differentially expressed in CD4+ T cells from MS patients compared to healthy controls (HC). The proteins are extracted from Additional file 1: Table S1 and selected by fulfilling at least two of the three criteria: p-value (p < 0.01), area under the curve (AUC) (AUC > 0.8) and log fold-change (FC) > [0.2] between samples from MS patients and healthy controls. The log2-fold changes in MS versus HC are based on normalized values. Accession number, protein identity and gene names are indicated for each protein, in addition to median log2-transformed protein abundances with standard variation (SD) for each group, the percentage of sequence coverage (% seq cov) and number of peptides (# pep) identified for each protein

Table 3

Top-hit list of differentially expressed proteins in CD8+ T cells from MS patients compared healthy controls

Accession

Protein identity

Gene names

p-value

FC MS versus HC (log2)

Median intesity MS (log2)

MS SD

Median intensity HC (log2)

HC SD

% seq cov

# pep

AUC

P36915

Guanine nucleotide-binding protein-like 1

GNL1

0.000363

0.3823

22.9373

0.13239

22.555

0.19548

22.6

9

0.9

P57764

Gasdermin-D

GSDMD

0.0004

− 0.247

23.0081

0.09966

23.2551

0.13969

27.9

8

0.91

Q15027

Arf-GAP with coiled-coil, ANK repeat and PH domain-containing protein 1

ACAP1

0.000818

0.3588

25.3317

0.13259

24.9729

0.21057

44.7

22

0.89

Q14240

Eukaryotic initiation factor 4A-II; eukaryotic initiation factor 4A-II, N-terminally processed

EIF4A2

0.001679

0.2287

25.7838

0.1338

25.5551

0.31408

75.4

23

0.92

Q9GZP4

PITH domain-containing protein 1

PITHD1

0.001791

0.1974

22.8647

0.13746

22.3619

0.14509

47.9

8

0.91

P10155

60 kDa SS-A/Ro ribonucleoprotein

TROVE2

0.001865

0.1765

25.7638

0.08234

25.9917

0.13787

37.4

16

0.87

P14174

Macrophage migration inhibitory factor

MIF

0.002217

0.3238

23.462

0.21611

23.2646

0.28198

36.5

4

0.88

Q96ST3

Paired amphipathic helix protein Sin3a

SIN3A

0.002395

0.2302

25.1124

0.11111

24.9359

0.14227

14.2

13

0.85

P06703

Protein S100-A6

S100A6

0.002446

− 0.7296

26.8304

0.62637

26.5066

0.61209

52.2

4

0.9

P51452

Dual specificity protein phosphatase 3

DUSP3

0.002706

− 0.5138

23.1048

0.19565

22.8746

0.4382

29.2

4

0.88

O75431

Metaxin-2

MTX2

0.002927

0.293

25.2256

0.21249

25.9552

0.11457

33.8

5

0.82

Q8TBC4

NEDD8-activating enzyme E1 catalytic subunit

UBA3

0.002937

0.1439

24.5971

0.10266

24.3764

0.1286

51.6

13

0.85

P30405; Q6BAA4

Peptidyl-prolyl cis–trans isomerase F, mitochondrial

PPIF

0.003314

− 1.0344

21.1705

0.33533

21.6843

0.63539

40.1

8

0.84

P21953

2-oxoisovalerate dehydrogenase subunit beta, mitochondrial

BCKDHB

0.003651

0.2586

22.4515

0.21161

22.1585

0.24451

15.3

4

0.83

Q8TCD5

5(3)-deoxyribonucleotidase, cytosolic type

NT5C

0.003791

0.3211

24.2434

0.14217

24.0995

0.45523

54.2

7

0.86

P57737

Coronin-7

CORO7

0.004431

0.147

23.7522

0.148

23.3999

0.0888

49.4

28

0.86

O94925

Glutaminase kidney isoform, mitochondrial

GLS

0.0047

0.1506

22.33535

0.10161

22.9562

0.13926

45

22

0.85

Q3ZCW2

Galectin-related protein

LGALSL

0.005089

− 2.2604

21.7926

0.93109

22.827

0.91857

61

8

0.86

P63151; Q00005; Q9Y2T4

Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform

PPP2R2A

0.005319

0.243

21.8042

0.15983

21.5456

0.24976

48.1

12

0.84

O94826

Mitochondrial import receptor subunit TOM70

TOMM70A

0.005477

0.235

23.0302

0.0878

22.7091

0.2231

32.1

13

0.89

Q13586; Q9P246

Stromal interaction molecule 1

STIM1

0.005533

− 0.2404

26.1487

0.20417

26.0017

0.23545

32.7

16

0.82

P13224

Platelet glycoprotein Ib beta chain

GP1BB

0.005768

− 1.9102

24.998

0.8499

24.8474

1.00102

23.8

5

0.82

O00186

Syntaxin-binding protein 3

STXBP3

0.005812

0.193

22.9285

0.11405

23.296

0.15529

10.5

5

0.87

P20645

Cation-dependent mannose-6-phosphate receptor

M6PR

0.006115

− 0.1934

22.3302

0.22866

23.6924

0.17682

22

4

0.84

Q96RQ3

Methylcrotonoyl-CoA carboxylase subunit alpha, mitochondrial

MCCC1

0.007633

0.5028

21.2898

0.19278

23.5502

0.34081

16.4

7

0.83

P78417

Glutathione S-transferase omega-1

GSTO1

0.007795

− 0.2279

23.6102

0.09562

23.1469

0.19987

59.3

14

0.79

P24666

Low molecular weight phosphotyrosine protein phosphatase

ACP1

0.008395

0.2207

23.6258

0.18286

23.3828

0.19924

72.2

8

0.82

Q9H0R4

Haloacid dehalogenase-like hydrolase domain-containing protein 2

HDHD2

0.008617

0.3523

24.1578

0.21888

23.9228

0.23008

67.2

8

0.84

Q12913

Receptor-type tyrosine-protein phosphatase eta

PTPRJ

0.008808

− 0.62085

24.2463

0.30864

24.4867

0.44153

10.9

11

0.83

P49327

Fatty acid synthase; [acyl-carrier-protein] S-acetyltransferase;[acyl-carrier-protein] S-malonyltransferase; 3-oxoacyl-[acyl-carrier-protein] synthase; 3-oxoacyl-[acyl-carrier-protein] reductase; 3-hydroxyacyl-[acyl-carrier-protein] dehydratase; enoyl-[acyl-carrier-protein] reductase; oleoyl-[acyl-carrier-protein] hydrolase

FASN

0.009384

− 0.3675

23.3745

0.18085

25.2847

0.4045

10.9

18

0.8

P04275

von Willebrand factor; von Willebrand antigen 2

VWF

0.009824

− 1.3622

22.1391

1.21897

21.9461

1.05341

13

25

0.84

P35573

Glycogen debranching enzyme; 4-alpha-glucanotransferase; amylo-alpha-1,6-glucosidase

AGL

0.010066

0.4633

21.956

0.26486

21.7305

0.29541

20.9

22

0.8

Q8TDQ7

Glucosamine-6-phosphate isomerase 2

GNPDA2

0.010164

0.2255

23.3542

0.28146

23.8593

0.21281

59.8

10

0.84

P16615

Sarcoplasmic/endoplasmic reticulum calcium ATPase 2

ATP2A2

0.010248

− 0.5051

24.5083

0.30171

24.3585

0.32708

28.7

21

0.81

Q13555; Q13554

Calcium/calmodulin-dependent protein kinase type II subunit gamma; calcium/calmodulin-dependent protein kinase type II subunit beta

CAMK2G; CAMK2B

0.010445

0.2908

22.9757

0.12761

22.6849

0.25143

24.6

10

0.82

P12931; Q9H3Y6; P42685; P08581; Q04912

Proto-oncogene tyrosine-protein kinase Src

SRC

0.01081

− 1.20015

22.8527

0.73685

24.05285

0.66689

37.5

15

0.81

Q15120

[Pyruvate dehydrogenase (acetyl-transferring)] kinase isozyme 3, mitochondrial

PDK3

0.010829

0.24475

20.7149

0.28722

20.47015

0.26592

12.1

3

0.81

P05556

Integrin beta-1

ITGB1

0.010894

− 0.4327

24.5472

0.41637

24.9799

0.37748

32.8

19

0.81

Q9P0J1

[Pyruvate dehydrogenase [acetyl-transferring]]-phosphatase 1, mitochondrial

PDP1

0.011178

0.3474

22.5526

0.14746

22.2052

0.18283

16.9

7

0.82

P01137

Transforming growth factor beta-1; latency-associated peptide

TGFB1

0.012983

− 0.87595

24.8165

0.44917

24.6229

0.65195

29.5

7

0.81

P14770

Platelet glycoprotein IX

GP9

0.013113

− 1.25275

29.9764

0.73326

29.8298

0.84609

30.5

5

0.82

P05386

60S acidic ribosomal protein P1

RPLP1

0.014256

0.2666

22.157

0.16745

22.4676

0.17334

94.7

5

0.81

Q02083

N-acylethanolamine-hydrolyzing acid amidase; N-acylethanolamine-hydrolyzing acid amidase subunit alpha; N-acylethanolamine-hydrolyzing acid amidase subunit beta

NAAA

0.014286

0.43565

23.0686

0.40847

23.6457

0.49594

27.9

8

0.87

P50148; P29992; O95837

Guanine nucleotide-binding protein G(q) subunit alpha

GNAQ

0.014465

− 0.487

24.8592

0.48364

24.5987

0.51884

30.6

8

0.82

O14828

Secretory carrier-associated membrane protein 3

SCAMP3

0.014503

− 0.2404

22.5342

0.16111

22.3259

0.19252

22.8

5

0.8

P67936; Q2TAC2

Tropomyosin alpha-4 chain

TPM4

0.015754

− 0.598

23.0621

0.52458

22.8134

0.46362

70.6

27

0.81

O14561

Acyl carrier protein, mitochondrial

NDUFAB1

0.015958

0.2347

23.5152

0.23866

23.7086

0.12417

21.2

4

0.82

Q00653

Nuclear factor NF-kappa-B p100 subunit; nuclear factor NF-kappa-B p52 subunit

NFKB2

0.016322

0.322

22.11305

0.2449

22.989

0.28366

15.8

9

0.81

P35244

Replication protein A 14 kDa subunit

RPA3

0.016781

0.2976

23.4742

0.16891

24.72695

0.38956

86.8

7

0.8

O95379

Tumor necrosis factor alpha-induced protein 8

TNFAIP8

0.016869

0.2251

22.0409

0.1305

23.8998

0.38972

40.4

5

0.8

Q9NY12

H/ACA ribonucleoprotein complex subunit 1

GAR1

0.01728

0.2208

27.6053

0.18001

28.7912

0.21825

29

5

0.8

P16109

P-selectin

SELP

0.017721

− 2.0401

24.1549

1.03363

23.9684

1.06785

29

14

0.81

Q96RP9

Elongation factor G, mitochondrial

GFM1

0.020163

0.288

24.2375

0.19021

24.3248

0.22518

13.2

7

0.8

Q96F86

Enhancer of mRNA-decapping protein 3

EDC3

0.02024

0.44525

26.7743

0.34422

26.5077

0.41686

12.4

3

0.82

P08134

Rho-related GTP-binding protein RhoC

RHOC

0.022534

0.3601

23.692

0.17871

23.25635

0.48992

65.8

10

0.81

Q15283

Ras GTPase-activating protein 2

RASA2

0.025799

0.2581

25.2002

0.25399

25.0644

0.3012

14.5

9

0.8

O95866

Protein G6b

G6B

0.025824

− 1.4023

21.8137

0.77427

22.3007

0.97969

23.7

5

0.81

O75874

Isocitrate dehydrogenase [NADP] cytoplasmic

IDH1

0.026221

− 0.3508

21.6884

0.25198

21.9288

0.44714

52.4

17

0.81

P09564

T-cell antigen CD7

CD7

0.027969

0.3583

22.542

0.19792

22.6533

0.35246

16.7

4

0.82

O75439

Mitochondrial-processing peptidase subunit beta

PMPCB

0.031524

0.2241

22.4262

0.21703

24.1114

0.27829

22.1

7

0.83

P24158

Myeloblastin

PRTN3

0.049099

− 0.55295

28.221

0.57502

28.819

0.84918

20.7

4

0.81

The table displays proteins (n = 61) that are differentially expressed in CD8+ T cells from MS patients compared to healthy controls (HC). The proteins are extracted from Additional file 2: Table S2 and selected by fulfilling at least two of the three criteria: p-value (p < 0.01), area under the curve (AUC) (AUC > 0.8) and log fold-change (FC) > [0.2] between samples from MS patients and healthy controls. The log2-fold changes in MS versus HC is based on normalized values. Accession number, protein identity and gene names are indicated for each protein, in addition to median log2-transformed protein abundances with standard variation (SD) for each group, the percentage of sequence coverage (% sequence coverage) and number of peptides (# peptides) identified for each protein

The ingenuity pathway analyses (IPA) software was used for network analyses of the top-hit proteins (Tables 2, 3) from the CD4+ and CD8+ T cell data sets separately. After correcting for multiple testing, we identified 14 biological processes in CD4+ T cells that were affected by the presence of MS disease (Fig. 2), however, no pathways were significant for CD8+ T cells. When performing network analyses of the entire list of 195 differentially expressed proteins (p < 0.05) from CD8+ T cells, two pathways were significant after multiple testing, i.e. the sirtuin signaling pathway and the protein kinase A pathway (data not shown). In the CD4+ T cell data set, mainly T cell activation pathways, such as CTLA4, CD28, T cell receptor, PKCθ and iCOS-iCOSL signaling and calcium-induced T lymphocyte apoptosis were identified. In addition, general pathways as for instance the pentose phosphate pathway in addition to immune related pathways were represented.
Fig. 2
Fig. 2

Enriched pathways in CD4+ T cells from MS patients. The graph displays the cellular pathways enriched in the proteomic profiles of the top-hit regulated proteins from MS patients as compared with healthy controls in CD4+ T cells after correcting for multiple testing (p-value, left axis). The orange line represents the ratio of the number of proteins in the data set of differentially expressed proteins divided by the number of proteins in the reference data set for that specific pathway (right axis)

Hierarchical clustering

The normalized intensities of the 90 and 61 proteins in the top-hit list (Tables 2, 3) in CD4+ and CD8+ T cells from MS patients and healthy controls were used as input to hierarchical clustering in Perseus (Fig. 3). The proteomic profiles for each cell type were divided into two groups consisting mainly of (1) MS and (2) healthy control samples. The differentially expressed proteins are divided into two major groups that are oppositely regulated between MS patients and healthy controls. Using IPA, we did not detect any enrichment for specific biological pathways if we separately analyzed proteins that are either up- or down-regulated in CD8+ T cells from MS patients. However, in the proteins that are upregulated in MS CD4+ T cells, there is an enrichment for T cell specific activation pathways, in addition to general pathways such as the pentose phosphate and sirtuin pathways. For the proteins that are down-regulated in MS CD4+ T cell samples, network analyses in IPA showed enrichment of proteins in integrin signaling and endocytic pathways (data not shown). Of note, we observed three exceptions where two MS patients clustered together with the healthy controls (one for each data set) and one healthy control clustered with MS patients in the CD8+ T cell data set.
Fig. 3
Fig. 3

Hierarchical clustering of differentially expressed proteins. The heatmaps show the hierarchical clustering of differentially expressed proteins from the top-hit list fulfilling two out of the three criteria: p < 0.01, AUC > 0.8 and log fold change > [0.2] in a CD4+ T cells and b CD8+ T cells from MS patients and healthy control using Perseus. Red: upregulated in MS samples, green: down-regulated in MS samples, grey: missing values. MS (black): samples from MS patients; HC (blue): samples from healthy controls

Analyses of proteins expressed by MS susceptibility genes

To date more than 200 non-HLA associated MS risk SNPs have been identified by genome-wide approaches [4, 5, 20]. We next selectively analyzed the abundance of proteins expressed from the gene(s) most proximal to these MS-associated SNPs in order to identify proteins with a potential impact on MS disease. For intergenic MS-associated SNPs, we analyzed the abundance of the proteins expressed from the most proximal gene both upstream and downstream of the SNPs. Not all MS susceptibility genes are expressed in T cells, and in our samples, we detected 31 proteins encoded from MS susceptibility genes in CD4+ T cells and 37 proteins in CD8+ T cells. Of these, eight proteins (seven in CD4+ T and one in CD8+ T cells) were differentially expressed in samples from MS cases versus healthy controls (Fig. 4).
Fig. 4
Fig. 4

Differential expression of proteins encoded by MS susceptibility genes. The scatter plots represent the log2-transformed protein abundances of proteins expressed from indicated MS susceptibility genes in CD4+ T cells and CD8+ T cells from MS patients (MS) and healthy controls (HC). Student t tests were used to compare the groups as specified in Materials and Methods. The horizontal lines represents the median within the groups

To assess the functional link between GWAS-identified risk variants and disease, we evaluated whether there was any correlation between MS risk genotypes and expression of proteins encoded from the most proximal gene(s). For proteins that did not display any difference in abundance in samples from MS cases and healthy controls, i.e. 24 and 36 proteins from CD4+ and CD8+ T cells, respectively, samples (from both MS patients and healthy controls) were pooled by carriers of the risk allele at each SNP as compared to samples from individuals homozygous for the protective allele for each SNP. We observed a genotype-dependent expression of proteins expressed from the STAT3 and LEF1 genes in CD4+ T cells and the RUNX3 gene in CD8+ T cells (Fig. 5). However, after multiple testing these correlations did not reach statistical significance.
Fig. 5
Fig. 5

Genotype-dependent expression of proteins encoded by MS susceptibility genes. The scatter plots display the log2-transformed protein abundances of proteins expressed from indicated MS susceptibility genes as function of the MS risk SNP genotype in samples from CD4+ T cells (left and middle plot) and CD8+ T cells (right plot) from both MS patients and healthy controls sorted for the genotype of indicated MS-susceptibility SNPs. For normalized distributions (LEF1 and RUNX3), Student t-test were performed, otherwise (STAT3), the non-parametric Mann U Whitney test was performed to compare the groups. The horizontal lines represents the median within the groups

Discussion

MS is considered as an autoimmune disorder of the CNS and the pathological immune dysregulation involves an interaction between the innate and adaptive immune system. T cells are thought to be one of the main cellular drivers for disease development, and from genome-wide association screens, a significant enrichment of genetic loci encoding proteins in T-cell specific pathways is observed [5]. Nevertheless functional and epigenomic annotation studies of genetic risk loci suggests that also other cells of the immune system are involved [5, 21, 30]. Proteomic profiling of whole blood or peripheral blood mononuclear cells could contribute to achieve mechanistic insights behind the development of MS pathology. However, such samples are heterogeneous in their cellular composition, so any cell-specific variation may be overshadowed by variation in the proportions of the various cell types. In the current study, we therefore purified CD4+ and CD8+ T cells and compared their respective proteomic profiles between RRMS patients and healthy controls using liquid chromatography–tandem mass spectrometry. Our study provides evidence for proteomic differences in T cells from RRMS patients compared to healthy controls and identifies three putative pQTLs for proteins encoded by three MS susceptibility genes.

MS is an inflammatory disease that affects the CNS. The cerebrospinal fluid is an obvious fluid to perform proteomic profiling into search for biomarkers of MS, as it reflects ongoing pathological and inflammatory processes in the CNS. However, in the current study, we are examining immune cell subsets, i.e. CD4+ and CD8+ T cells that enables us to identify proteins and pathways involved in MS development. We are aware of that also other cells of the immune system, including B cells and innate cells such as NK cells and dendritic cells in addition to brain-resident immune cells, i.e. astrocytes [20], have potential impact on MS pathogenesis. However, this study enables us to achieve mechanistic insights into T-cell mediated pathology of MS. Identification of novel proteins and pathways involved in MS pathology could enable progress in the development of new drug targets in order to improve the clinical outcome of MS.

Hierarchical clustering of the differentially expressed proteins from our top-hit list of 90 and 61 proteins from CD4+ and CD8+ T cells, respectively, divided the samples into two main groups with MS patients and healthy controls. Of note, for each of the cell types, there was one MS patient sample (not the same in the two cell types) clustering with the healthy control group. One of these patients (MS12) has a benign form of MS, and in contrast to all other patients, this patient is currently electively untreated (3 years after inclusion to the study). One healthy control also groups with the MS patients for CD8+ T cells; however, whether this control experienced an undetected inflammatory condition or have developed autoimmunity after sample collection giving rise to a proteomic profile similar to MS cases is not known. Even though we have separated immune-cell subsets from the entire pool of immune cells in blood, we acknowledge that these sub-populations can be divided further into different subpopulations such as Th1 and Th2 cells, effector, memory and regulatory T cells. Whether the individuals not clustering with their own group have differences in the proportion of CD4+ and CD8+ T cell subsets is not known and could potentially affect the proteomic profile achieved. The fold change in protein abundance in T cells from MS patients and healthy controls are modest. However, enrichment in specific pathways (see Fig. 2) suggests that they collectively may have an impact on selected T cell responses. Also, the study is limited by the small sample size, and further studies are needed to validate and verify the biological impact of selected proteins in T cells.

Of the top-ten (based on p-value) differentially expressed proteins in each cell type, only three of them have previously been identified to have a potential role for MS, either through a genetic association, i.e. Lck [20], as a biomarker for MS progression and severity, i.e. macrophage migration inhibitory factor (MIF) [31, 32] or in functional studies, where gasdermin-D (GSDMD) is shown to promote inflammatory demyelination both in human cells and in murine models [33]. Of note, a selection of the top hit proteins in T cells [TAR binding protein (TARDBP), calnexin (CANX) and AP2 associated kinase 1 (AAK1)] have been shown to play important roles for other neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease and amyotrophic lateral sclerosis [3437], suggesting common disease mechanisms across neurodegenerative disorders and highlighting the importance for these proteins also in immune cells.

MS is an inflammatory disease, and therefore it is no surprise that the differentially expressed proteins in CD4+ T cells are enriched for pathways related to T cell activation or immune function. Whether these pathways are affected because of the active inflammation that is characteristic for the early phase of RRMS or whether similar changes can be detected prior to disease onset is not known. MS develops in genetic susceptible individuals, and genome-wide screenings have highlighted the importance of genes involved in T cell differentiation, in CD4+ T cells in particular [5]. Interestingly, we have identified eight proteins encoded by MS susceptibility genes (LCK, GRAP2, CD5, ZC3HAV1, SAE1, EPPK1 and CD6 in CD4+ T cells and TNFAIP8 in CD8+ T cells), which are more abundant in T cells from MS patients compared to healthy controls. This underlines the potential role for these MS susceptibility genes in T cells during MS development prior to disease onset.

Furthermore, correlating MS risk genotype with protein expression from genes proximal to MS risk SNPs, we identified three potential pQTLs, i.e. rs1026916, rs9992731 and rs6672420. Samples from individuals homozygous for the protective allele displayed higher expression of the specified proteins compared to samples from individual being a carrier of the risk allele. Even though these correlations did not reach statistical significance after multiple testing, the data indicate that these SNP-protein pairs are of relevance to study further as the corresponding MS associated SNPs could act as pQTLs. Interestingly, the rs1026916 SNP has previously been shown to act as an eQTL for STAT3 (at the mRNA level) in skeletal muscle and tibial artery [38]. Rs1026916 lies within a region with moderately high histone H3 acetylation levels, but outside DNAse clusters and transcription factor binding sites [39]. Whether this SNP affects transcription factor binding and thereby regulates transcription remains to be analyzed. Our study further suggests a functional implication of this SNP or a SNP tagged by rs1026916 in T cells. Neither rs6672420 nor rs9992731 are reported to act as an eQTLs [38]. However, the correlation between mRNA and protein copy numbers can vary widely [18, 19] and this study suggests that these SNPs could act as pQTLs in T cells. In contrast to rs9992731 that is not situated in any typical gene-regulatory region, in silico analyses suggests that rs6672420 might affect gene expression, as it is located in a region shown by chromatin immunoprecipitation to be bound by RNA polymerase 2 (POLR2A) and the STAT5A transcription factor [39]. Confirmatory studies in T cells need to be pursued in order to confirm the relationship between genotype at rs6672420, transcription factor occupancy and gene and protein expression of RUNX3. Altogether, the reported pQTLs suggests further exploration of LEF1, STAT3 and RUNX3 to understand the molecular pathways involved in disease with the ultimate goal to identify new therapeutic targets.

Conclusion

We show that there is a dysregulation at the protein level in T cells from RRMS patients at an early stage of disease. Pathway analyses, pinpoints to the importance of CD4+ T-cell specific activation pathway, which is indicative of an inflammatory condition. By specifically analyzing proteins expressed from MS susceptibility genes, eight proteins were found to be dysregulated in T cells from MS patients. In addition, we identified three novel pQTLs, which might contribute to mechanistically understand the molecular background of MS development and the biology behind three SNPs that have been identified as MS susceptibility gene variants through genome-wide screenings.

Abbreviations

MS: 

multiple sclerosis

RRMS: 

relapsing remitting MS

HC: 

healthy control

SNP: 

single nucleotide polymorphism

CNS: 

central nervous system

EDSS: 

extended disability status scale

eQTL: 

expression quantitative trait locus

pQTL: 

protein quantitative trait locus

PCA: 

principal component analyses

LFQ: 

label-free quantification

Declarations

Authors’ contributions

TB and FB conceived the idea and planned the study. PBH, EAH, HFH, TB, AE and ISB recruited patients and healthy controls. PBH, EAH and HFH performed clinical examination of the MS patients. TB, AE, SDB and ISB collected samples. AD and OM carried out mass spectrometry. AE, TB, SDB, AD, OM and FB analyzed and interpreted the data. TB wrote the manuscript. TB and AE prepared figures and tables. All authors read and approved the final manuscript.

Acknowledgements

We thank all patients and healthy controls for participation and research nurses involved in the collection of samples included in the study.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

MS raw files have been uploaded into the Proteomics IDEntifications (PRIDE) database [40].

Consent for publication

Not applicable.

Ethics approval and consent to participate

The Regional Committee for Medical and Health Research Ethics South East, Norway approved the study. All study participants received oral and written information and written informed consent was obtained from all study participants.

Funding

The study was funded by the South Eastern Norway Regional Health Authority (Grant No. 2017114), the Norwegian Research Council (Grant No. 240102), OsloMet – Oslo Metropolitan University, Biogen, Sanofi Genzyme and the Odd Fellow Society. The founders had no role in the design of the study and collection, analysis, decision to publish, interpretation of data or preparation of the manuscript.

Publisher’s Note

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Authors’ Affiliations

(1)
Department of Mechanical, Electronics and Chemical Engineering, Faculty of Technology, Art and Design, Oslo Met – Oslo Metropolitan University, Postboks 4, St. Olavs Plass, 0130 Oslo, Norway
(2)
Neuroscience Research Unit, Oslo University Hospital, Rikshospitalet, Domus Medica 4, Nydalen, Postboks 4950, 0424 Oslo, Norway
(3)
Department of Research, Innovation and Education, Oslo University Hospital, Oslo, Norway
(4)
Institute of Clinical Medicine, University of Oslo, Oslo, Norway
(5)
Department of Neurology, Oslo University Hospital, Ullevål, Postboks 4950, 0424 Nydalen, Oslo, Norway
(6)
Proteomics Unit at University of Bergen (PROBE), Department of Biomedicine, University of Bergen, Postboks 7804, 5020 Bergen, Norway

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Copyright

© The Author(s) 2019

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