Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Biomarkers of systemic inflammation in farmers with musculoskeletal disorders; a plasma proteomic study

  • 1, 2Email author,
  • 1,
  • 3,
  • 4 and
  • 2
BMC Musculoskeletal DisordersBMC series – open, inclusive and trusted201617:206

https://doi.org/10.1186/s12891-016-1059-y

  • Received: 26 January 2016
  • Accepted: 3 May 2016
  • Published:
Open Peer Review reports

Abstract

Background

Farmers have an increased risk for musculoskeletal disorders (MSD) such as osteoarthritis of the hip, low back pain, and neck and upper limb complaints. The underlying mechanisms are not fully understood. Work-related exposures and inflammatory responses might be involved. Our objective was to identify plasma proteins that differentiated farmers with MSD from rural referents.

Methods

Plasma samples from 13 farmers with MSD and rural referents were included in the investigation. Gel based proteomics was used for protein analysis and proteins that differed significantly between the groups were identified by mass spectrometry.

Results

In total, 15 proteins differed significantly between the groups. The levels of leucine-rich alpha-2-glycoprotein, haptoglobin, complement factor B, serotransferrin, one isoform of kininogen, one isoform of alpha-1-antitrypsin, and two isoforms of hemopexin were higher in farmers with MSD than in referents. On the other hand, the levels of alpha-2-HS-glycoprotein, alpha-1B-glycoprotein, vitamin D- binding protein, apolipoprotein A1, antithrombin, one isoform of kininogen, and one isoform of alpha-1-antitrypsin were lower in farmers than in referents. Many of the identified proteins are known to be involved in inflammation.

Conclusions

Farmers with MSD had altered plasma levels of protein biomarkers compared to the referents, indicating that farmers with MSD may be subject to a more systemic inflammation. It is possible that the identified differences of proteins may give clues to the biochemical changes occurring during the development and progression of MSD in farmers, and that one or several of these protein biomarkers might eventually be used to identify and prevent work-related MSD.

Keywords

  • Musculoskeletal disorder
  • Farmers
  • Proteomic
  • Systemic inflammation
  • Occupational medicine

Background

Farmers in industrialized countries have low morbidity in general and common disease entities as cancer and cardiovascular disorders are less frequent among farmers than in the general population [1]. By contrast, farmers have an increased risk of acquiring musculoskeletal disorders (MSD), such as osteoarthritis of the hip, low back pain, and neck and upper limb complaints [13]. The reason for this is not known. We have previously demonstrated that the increased risk cannot be fully explained by known risk factors such as physical work load, psychosocial factors, or life style, indicating that other etiological factors must be involved. [24]. Interestingly, the prevalence of low back pain in farmers is associated not only with other musculoskeletal symptoms but also with chest discomfort, dyspepsia, symptoms from eyes, nose and throat mucous membranes, skin problems, work-related fever attacks, and primary care appointments due to digestive disorders [4]. This comorbidity points to the possibility that MSD in farmers may be part of a more systemic disorder with pathological changes in many organs and tissues. Since blood is in direct contact with all tissues, such changes are likely to be reflected in plasma [5] and quantitative changes in the plasma could thus serve as biomarkers of systemic effects. Moreover, plasma samples are readily available for investigation, thus making analysis of plasma a favorable alternative for examining laboratory signs of a systemic disorder. In a recent study serum samples from patient with discogenic chronic low back pain (DLBP) were analyzed by mass spectrometry to identify biomarkers for DLBP [6].

In this study, our objective was to identify plasma proteins that might differentiate farmers with MSD from matched rural referents. We focused on relatively abundant plasma proteins which can be quantified to the nanogram range using two-dimensional gel electrophoresis (2DE) with silver staining and subsequent identification by mass spectrometry (MS).

Methods

Subjects

The investigated subjects were selected from a prospective study cohort established with the intention to study health promoting factors related to farming and lifestyle [7]. All occupationally active male farmers born between 1930 and 1949 in nine rural municipalities in Sweden were identified in the Swedish National Farm Register and included in the cohort. The cohort represents a variety of farm types and geographical variation. The areas were chosen with consideration to known east-west and north-south cardiovascular disease gradients in the Swedish population [8] and to represent a variety of farm types and geographical variation over the country. Farmers were defined as men who owned or rented a farm and who spent at least 25 h per week farming. The occupational activity was checked with local representatives from the Federation of Swedish Farmers. To each farmer a rural referent, matched by age, sex and residential area, was sampled from the National Population Register. The rural referents were to be occupationally active in other than farming. Altogether 1220 farmers and 1130 rural non-farmers were eligible and included in the cohort. The farmers and the rural referents (n = 2350) were invited to an extensive health survey including questionnaires, interviews, physical examinations and laboratory test [9].

The cross-sectional population-based survey identified farmers with MSD using a questionnaire with a number of items addressing symptoms from back, hip, knee, neck, forearms and shoulders. Information on symptoms from the musculoskeletal system was assessed by self-administrated questionnaire [3]. The subjects were asked to indicate (yes or no) if they had ever (more than occasionally) had pain, aches or discomfort in the neck-shoulder area, the low back, hips or knees. The questionnaire file shows this in more detail (see Additional file 1). Similar symptoms during the last year were also indicated. The subjects were asked to indicate whether they had sought medical advice or treatment for the various symptoms, and concerning low back and knee symptoms, whether they had been on sick leave due to the illness. A large number of farmers and non-farmers (89 %) reported symptoms from musculoskeletal system. The farmers reported significantly more symptoms from low back and hips [4, 9].

This large material was used to identify farmers with symptoms from the musculoskeletal system only. Thirteen farmers working with swine confinement and cattle raising and their rural referents (Table 1) were selected and included in this explorative hypothesis generated study.
Table 1

Musculoskeletal symptoms among farmers and referents included in this study

Musculoskeletal symptoms

Farmers

Referents

p-value

N = 13

N = 11

Neck and shoulder pain

9

6

ns

Hands and forearms(numbness)

4

4

ns

Hands and forearms(nocturnal pain)

1

2

ns

Low back pain

6

3

ns

Ischialgia

5

1

ns

Hips problems

10

5

ns

Groin-thigh pain

4

3

ns

Knee joint problems

8

2

ns

All symptoms

13

5

0.007

Sample preparation

All samples were blinded before analysis. Plasma from farmers and referents were prepared at the same time and not separately. 50 μl of plasma was applied onto an albumin & IgG Depletion column (GE Healthcare). Albumin and IgG were removed according to the manufacturer’s recommendation. The samples were then desalted and lyophilized. The protein concentration was determined according to Bradford [10].

2-DE analysis

The lyophilized proteins were dissolved in 0.20 ml urea sample solution according to Görg [11]. 2-DE was performed in a horizontal 2-DE setup (IPGphore and Multiphore from GE Healthcare), as described in detail previously [12] and essentially according to Görg [11]. The samples (containing 50 μg protein for analytical gels and 900 μg for preparative gels) were applied by in-gel rehydration (according to the manufacturer’s instructions) for 12 h using low voltage (30 V) in pH 4–7 IPGs. The proteins were then focused for up to 32 000 Vhs at a maximum voltage of 8000 V. IPGs were either used immediately for second dimensional analysis, or stored at −70 °C until analyzed. The second dimension (SDS-PAGE) was carried out by transferring the proteins to gradient gels cast on GelBond PAG film (0.5/180/245 mm, 11–18%T, 1.5%C, 33–0 % glycerol) running at 30 mA and up to 1000 V for about 5 h. In analytical gels, separated proteins were detected by silver staining according to Schevchenko as described previously [12]. The preparative gels, for MS analysis, were fluorescently stained with SYPRO Ruby [12]. All staining and washing steps were performed with continuous gentle agitation. The protein patterns were analyzed as digitized images, using a CCD camera in combination with a computerized imaging 12-bit system designed for evaluation of 2-DE patterns. The amount of protein in a spot was assessed as background corrected optical density, integrated over all pixels in the spot and expressed as integrated optical density (IOD). In order to correct for differences in total silver stain intensity between different 2-DE images, the amounts of the compared protein spots were quantified as optical density for individual spot per total protein intensity of all spots in the same gel. Thereby ppm-values (parts per million) for all proteins were generated that were evaluated for differences between the groups.

Protein identification

Protein spots were excised using a homemade spot picker. The picked protein spots were digested with trypsin (Promega/SDS Biosciences, Falkenberg, Sweden) as described previously [13]. The trypsinated peptides were applied to mass spectrometry for protein identification. The dried tryptic samples from fluorescently stained proteins were dissolved in 6 μl of 0.1 % formic acid. Peptides were analyzed using an on-line nano-flow HPLC system (EASY-nLC; Proxeon, Bruker Daltonics) in conjugation with the mass spectrometer HCTultra PTM Discovery System (Bruker Daltonics) as previously described [13]. LC-MS/MS spectra were processed by Bruker Daltonics DataAnalysis 3.4 (Bruker Daltonics, Bremen, Germany) and resulting MS/MS data were searched in NCBInr and Swiss-Prot database on MASCOT server: www.matrixscience.com Database search parameters were set as follows: the enzyme trypsin was used; up to one missed cleavage was allowed; fixed modification included were carbamidomethylation of cysteine and oxidation of methionine; mass tolerance for MS precursor ion was 0.8 Da and for MS/MS fragment ion was 0.6 Da; and charge states were varied. Criteria for identification of a protein were at least 3 peptides of the protein should be identified with a MASCOT score over 25 and an expectation value <1. The mass list generated from the major peaks of the MALDI spectra was submitted to a database search (NCBI or SWISS-PROT) using MS-FIT search engines. Restrictions were placed on species (Human), mass tolerance (50 ppm), maximum missed cleavages by trypsin (up to 1) and cysteine modification by carbamidomethylation.

Statistical analysis

Non-parametric Mann-Whitney U test was used as statistical method to calculate significant differences between groups. A p < 0.05 was considered statistically significant. All results are given as mean ± standard deviation. IBM SPSS Statistics 20 was used as statistical software.

Results

2-DE analysis and mass spectrometry

To determine proper 2DE conditions for the analysis to be made, plasma proteins were separated in the pI ranges 3–10 and 4–7 (first dimension) followed by SDS-PAGE (second dimension). As illustrated in Fig. 1, most of the spots detected in pI range 3–10 were also detected in the pI range 4–7 (about 90 %). The marked areas in Fig. 1 illustrate the good resolving power when using IPGs in pI range 4–7 compared to 3–10. A few proteins (Fig. 1a, dashed squares9 with pI outside the 4–7 range were lost. Due to the good separation in the pI range 4–7 we decided to analyze all samples using IPGs 4–7. Mapping of the plasma proteome in the pI range 4–7 was performed by mass spectrometry. Seventy five spots could be identified (Table 2, Fig. 2). According to the accession number these identified protein spots represent 20 different proteins. Several of the identified proteins were expressed as different isoforms due to post- translational modifications such as glycosylation and phosphorylation.
Fig. 1
Fig. 1

Comparison between plasma protein separation in pI range of 3–10 (a) and 4–7 (b). The marked areas are some example of the improved resolution using IPGs 4–7

Table 2

Identified proteins in plasma by nLC-MS/MS

Spot Nr

Protein

SwissProt

Mw/pI

Score

Sequence coverage [%]

Nr of unique peptides

1

Alpha-1-antitrypsin

P01009

46878 / 5.37

194

21

7

2

Alpha-1-antitrypsin

  

474

33

12

3

Alpha-1-antitrypsin

  

382

32

15

4

Complement factor B

P00751

86847 / 6.67

225

12

10

5

Complement factor B

  

266

16

12

6

Complement factor B

  

339

15

15

7

Complement factor B

  

252

15

14

8

Alpha-1B-Glycoprotein

P04217

54790 / 5.56

51

1

6

9

Alpha-1B-Glycoprotein

  

108

7

3

10

Alpha-1B-Glycoprotein

  

142

12

5

11

Serotransferrin

P02787

79294 / 6.81

414

19

13

12

Serotransferrin

  

988

52

31

13

Serotransferrin

  

889

42

28

14

Serotransferrin

  

1107

49

31

15

Serotransferrin

  

1078

49

31

16

N-acetylmuramoyl-L-alanine amidase

Q96PD5

62748 / 7.25

31

1

1

17

N-acetylmuramoyl-L-alanine amidase

  

67

3

2

18

N-acetylmuramoyl-L-alanine amidase

  

98

3

1

19

Hemopexin

P02790

52385 / 6.55

212

14

6

20

Hemopexin

  

306

23

11

21

Hemopexin

  

314

26

12

22

Hemopexin

  

329

25

11

23

Hemopexin

  

298

15

10

24

Hemopexin

  

277

20

9

25

Kininogen

P01042

72996 / 6.34

321

11

6

26

Kininogen

  

308

9

5

27

Kininogen

  

203

10

6

28

Hemopexin

P02790

52385 / 6.55

266

20

8

29

Hemopexin

  

49

2

1

30

Beta-2-Glycoprotein 1

P02749

39584 / 8.34

172

16

5

31

Kininogen

P01042

72996 / 6.34

127

8

4

32

Kininogen

  

275

17

8

33

Kininogen

  

236

13

8

34

Kininogen

  

254

15

8

35

Alpha-1-antitrypsin

P01009

46878 / 5.37

962

45

17

36

Alpha-1-antitrypsin

  

730

43

18

37

Alpha-1-antitrypsin

  

1009

47

17

38

Alpha-1-antitrypsin

  

509

42

16

39

Vitamin D-binding protein

P02774

54526 / 5.40

476

36

16

40

Alpha-1-antitrypsin

P01009

46878 / 5.37

620

51

19

41

Vitamin D-binding protein

P02774

54526 / 5.40

381

25

11

42

Antithrombin III

P01008

53025 / 6.32

333

25

10

43

Leucine-rich alpha-2-glycoprotein

P02750

38382 / 6.45

224

17

6

44

Leucine-rich alpha-2-glycoprotein

  

131

14

5

45

Leucine-rich alpha-2-glycoprotein

  

134

17

4

46

Haptoglobin

P00738

45861 / 6.13

302

21

9

47

Haptoglobin

  

273

26

10

48

Haptoglobin

  

296

27

10

49

Zinc-Alpha-2-glycoprotein

P25311

34465 / 5.71

223

17

7

50

Zinc-Alpha-2-glycoprotein

  

226

24

8

51

Clusterin

P10909

53031 / 5.89

219

17

7

52

Clusterin

  

203

14

7

53

Clusterin

  

331

22

9

54

ApoA1

P02647

30759 / 5.56

419

44

13

55

ApoA1

  

775

62

16

56

ApoA1

  

4287

53

14

57

ApoA1

  

850

48

8

58

Haptoglobin

P00738

45861/6.13

  

2

59

Haptoglobin

    

2

60

Transthyretin

P02766

15887/ 5.5

4357

69.4

11

61

Transthyretin

  

8483

64.6

7

62

ApoCIII

P02656

8800/4.7

 

65

4

63

ApoCIII

   

65

4

64

ApoAII

P02652

9300/6.5

 

56

5

65

ApoCII

P02655

8900/4.7

 

96

6

66

Alpha-2-HS-Glycoprotein

P02765

39325/5,4

60245

22.9

9

67

Alpha-2-HS-Glycoprotein

  

60245

20.4

8

68

Alpha-2-HS-Glycoprotein

  

60245

20.4

8

69

Alpha-2-HS-Glycoprotein

  

60245

20.4

8

70

Haptoglobin

P00738

45861/6.13

14986

24.1

11

71

Haptoglobin

  

12798

23.2

8

72

Haptoglobin

  

12728

21.9

8

73

Haptoglobin

  

60393

28.3

11

74

Haptoglobin

  

12798

23.2

8

75

Haptoglobin

  

12728

21.9

8

The spot numbers refer to marked protein spots in Fig. 1

Fig. 2
Fig. 2

A typical 2DE protein pattern of human plasma in pI range of 4–7. The marked spots refer to identified proteins in Table 1

Protein changes in farmers with MSD compared to rural referents

More than 200 protein spots were detected in all samples. To investigate possible differences in the plasma proteomes of farmers with MSD and rural referents, protein spots present in at least 50 % of the gels in either group were matched, quantified for intensity, and compared between the groups. Proteins whose concentrations were statistically different in the two groups were then identified using mass spectrometry. As shown in Table 3, the levels of leucine-rich alpha-2-glycoprotein, haptoglobin, complement factor B, serotransferrin, one isoform of kininogen (spot1507), one isoform of alpha-1-antitrypsin (spot 2404), and two isoforms of hemopexin (spots 6603 and 7602) were higher in farmers with MSD than in rural referents. On the other hand, the levels of alpha-2-HS-glycoprotein, alpha-1B-glycoprotein, vitamin D- binding protein, apolipoprotein A1, antithrombin, one isoform of kininogen (spot 2607), and one isoform of alpha-1-antitrypsin (spot 3802) were lower (Table 2). Figure 3 pinpoints the statistically differing proteins on a 2DE gel, while Fig. 4 illustrates how the different isoforms of kininogen, alpha-1-antitrypsin, and hemopexin were quantitatively expressed in farmers with MSD and referents, respectively.
Table 3

Proteins that were present differently in the plasma of farmers with MSD and their matched rural referents (RR)

Spot

Protein

MSD vs RR

Accession number

OD (MSD) Mean ± (SD) × 103

OD (RR) Mean ± (SD) × 103

p-value

311

Alpha-2-HS-Glycoprotein

P02765

4.768 (3.556)

7.720 (2.386)

0.046

1318

Leucine-rich alpha glycoprotein

P02750

4.587 (3.355)

2.081 (1.285)

0.04

1507

Kininogen

P01042

5.288 (2.631)

3.425 (1.684)

0.049

2404

Alpha1-anti trypsin

P01009

12.938 (7.270)

7.799 (6.398)

0.04

2607

Kininogen

P01042

6.885 (3.786)

10.114 (2.964)

0.03

3802

Alpha1-anti trypsin

P01009

0.884 (0.840)

3.117 (3.168)

0.019

4806

Alpha-1B-glycoprotein

P04217

4.677 (2.359)

6.487 (1.138)

0.01

5507

Vitamin D binding protein

P02774

5.506 (2.400)

8.585 (2.693)

0.03

6007

Apolipoprotein A1

P02647

3.765 (3.513)

6.838 (2.857)

0.010

6502

Antithrombin III

P01008

1.940 (1.750)

5.128 (2.950)

0.005

6603

Hemopexin

P02790

7.613 (5.647)

2.377 (2.225)

0.019

7101

Haptoglobin

P00738

5.455 (1.552)

3.573 (1.382)

0.008

7602

Hemopexin

P02790

10.729 (3.657)

4.121 (2.744)

0.000

8707

Complement factor B

P02790

2.468 (2.920)

1.226 (2.029)

0.022

8811

Serotransferrin

P02787

8.633 (3.460)

5.355 (2.047)

0.010

The spot numbers refer to the marked spots in Fig. 3

Arrow up (↑) indicates up-regulated and arrow down (↓) down-regulated proteins in farmers with MSD compared to RR

Fig. 3
Fig. 3

Significantly changed proteins in farmers with musculoskeletal disorders compared to rural referents

Fig. 4
Fig. 4

Quantitative data for protein isoforms that significantly (p < 0.05) changed in farmers with musculoskeletal disorders compared to rural referents. The blue marked spots are the different isoforms and the red circle is the significantly changed protein

Discussion

Using 2-DE and mass spectrometry proteomic techniques, we were able to identify 15 proteins that were present differently in the plasma of farmers with MSD and their matched rural referents. Several of the proteins with plasma levels higher among farmers than among referents are well known as mediators or indicators of inflammation. For example, complement factor B is essential for complement activation via the alternate pathway and so plays an important role in inflammatory conditions such as arthritis [1416] and muscle inflammation [17]. Haptoglobin is established as an inflammation-inducible plasma protein [18] that exerts a regulatory activity on immune cell responses and host immunity [19]. Accordingly, haptoglobin was found to be a monocyte chemoattractant whose chemotactic potential was mediated, at least in part, by its interaction with chemokine (C-C motif) receptor 2 [20]. Recently, Park and co-workers demonstrated that the C-terminal haptoglobin fragment is generated by plasmin in local inflammatory environments, suggesting that this fragment might be applied as a novel biomarker for the diagnosis and prognosis of inflammatory joint diseases such as rheumatoid arthritis [21]. Hemopexin belongs to the acute phase reactants, the synthesis of which is induced after inflammation. Aside from a systemic role in preventing heme-mediated oxidative stress, hemopexin might act as a local antioxidant at sites of injury and so play a role in inflammation [22].

Analogously, some of the proteins with plasma levels lower among the farmers than among the referents (Table 3) are known to have anti-inflammatory properties and actions. Thus, antitrombin III has been shown to inhibit cytokine and tissue factor production in endothelial cells [23], attenuate CD11b/CD18 expression in activated neutrophils and monocytes [24], suppress granule secretion in activated platelets [25], prevent apoptosis of hepatocytes [26, 27], and improve endotoxin-induced acute lung injury in rats [28]. Alpha-2-HS-glycoprotein a multifunctional molecule that may serve as an inhibitor of systemic inflammation in mice [29] and humans [30, 31]; notably, its serum levels were significantly decreased in patients with RA [32, 33]. Vitamin D-binding protein (VDBP) is primarily known as a transport protein but was found to be down-regulated in serum of experimental animals with systemic inflammation [34] and patients with sepsis [35, 36]. Moreover, Oh and coworkers recently found this protein to be down-regulated in serum of patients with carpal tunnel syndrome [37], a condition causing pain, impairment, and disability. The molecular mechanisms connecting inflammation with decreased VDBP are not clear, but because VDBP is a precursor for macrophage activating factor (MAF) it may be speculated that the decreased levels reflect increased formation of MAF and ensuing activation of macrophages [38] and neutrophils [39] at sites of inflammation. Alternatively, MAF may function positively by causing death of the macrophages when activated macrophages are no longer needed at the site of inflammation [40] Clearly, much further investigation is needed to elucidate why farmers with MSD appear to have decreased serum levels of VDBP.

One of the most important aspects of using 2DE is the capability of resolving many post-translationally modified proteins that may appear as isoforms. We identified two isoforms of hemopexin that both were present in higher amounts in the plasma of farmers (Table 3, Fig. 4). Moreover, we identified two proteins (kininogen and alpha-1-antitrypsin) that were present differently in the plasma of farmers and referents depending on isoform, i.e., one isoform was present in a higher amount in the farmer group vs the referent group, and another isoform was present in a higher amount in the referent group vs the farmer group (Table 3, Fig. 4). While we are unable to conclusively explain these findings, it can be noted that these proteins, too, are being associated with inflammatory processes and diseases. Thus, kininogen is the precursor of bradykinin, a well-known mediator of inflammation and pain [4143] that plays a crucial role in rheumatoid arthritis [44]. Alpha-1-antitrypsin can exert anti-inflammatory activity in many tissues and organs including the musculoskeletal system; notably, low levels of alpha-1-antitrypsin have been suggested to contribute to the development of rheumatoid arthritis [45] and MSD [46]. Yet, changes in isoforms of kininogen, alpha-1-antitrypsin, and hemopexin have not previously been reported. We do not know the exact chemical nature of the isoforms found in our study and are currently attempting to ascertain post-translational modifications. Characterization of the chemical nature of these protein isoforms, determination of the tissue(s) that produce them, and the relationship between these isoforms and other interacting proteins—all of these, as they relate to the causal events of MSD, may lead to a further understanding of the progression to MSD in farmers.

While the levels of leucine-rich alpha-2- glycoprotein were higher in the farmer group, the levels of alpha-1B-glycoprotein were higher in the referent group (Table 3). Whether these proteins, too, are involved in inflammation or not remains unclear. The physiological role of leucine-rich alpha-2- glycoprotein is not known; however, its 33 % sequence homology with a beta-type phospholipase A2 inhibitor [47] suggests the possibility that it might modulate inflammation and pain through inhibition of phospholipase A2 activity. As to alpha-1B-glycoprotein, decreased plasma levels of this protein were found in patients with rheumatoid arthritis [32] but little is known about the function of this protein and the biological relevance of this finding is therefore uncertain. The reason why serotransferrin (the major iron-transporting plasma protein) was higher in the farmer group is also uncertain; interestingly, its plasma levels were affected in an experimental model of neuropathic pain [48]. Apolipoprotein A1 (ApoA1) has been shown to have anti-inflammatory properties and to reduce atherosclerosis [49]; one would expect, therefore, that farmers (who are at lower risk for cardiovascular disease than the general population) had higher (rather than lower) apoA1 than referents. On the other hand, the level of apoA1 in lipoproteins has been found to correlate positively with PCYOX1 [50], an enzyme which generates H2O2 and so probably plays a role in atherogenesis [51]. Based on this finding, Sun and coworkers suggested that high apoA1 content might be associated with increased risk of cardiovascular disease [50]. Further investigation is required to clarify the reason why farmers with MSD appear to have decreased serum levels of apo A1.

Although we were able to identify a protein profile that differentiates farmers with MSD from referents using proteomic techniques, the methods used in this study may pose some limitations. We used isoelectric points and molecular weight of the proteins to separate the proteins onto a two-dimensional gel. We were limited therefore to investigating proteins within a certain range of isoelectric points and molecular weight, thereby excluding several proteins that may be potential candidates for biomarkers of MSD in farmers. Furthermore, we used a depletion column to remove the high abundant proteins (albumin and IgG) before analysis these proteins give substantial background to the 2DE profiles and make analysis difficult [5]; it is not impossible that this procedure might have eliminated some other candidates. As to the protein resolution, we have little reason to believe that the methods used have posed further limitations; rather, an advantage of 2DE compared to other separation methods is the quality of protein resolution [52] as shown by the capability of resolving many post-translationally modified proteins that may appear as isoforms. Identity of each protein spot was determined by mass spectrometry, which is the method of choice for accurate probability based protein identification. Moreover, it is unlikely that the results are flawed by unacceptable analytical errors; densitometric analyses combined with silver staining of proteins are highly quantitative and a median coefficient of variation of approximately 8 % has been reported using similar parameters as used in our procedure (i.e., fluorescent staining, PDQuest software) [53].

Conclusions

In conclusion, we have identified a number of proteins that are present differently in the plasma of farmers with MSD and their rural referents. Many of these proteins are known to be mediators or indicators of inflammation, supporting the hypothesis that farmers with MSD may be subject to a more systemic inflammation. The implication of these findings is worth considering. First, it is possible that the identified proteins may give clues to the biochemical changes occurring during the development and progression of MSD in farmers and so contribute to a deeper understanding of the pathogenetic mechanisms behind MSD and related symptoms/findings in other organ systems. Second, one or several or a combination of the identified proteins might possibly be used as a biomarker(s) of MSD in farmers. Much further investigation is therefore warranted, including comparisons of farmers with and without MSD and referents with and without MSD to elucidate how MSD protein picture relates to exposure. Third, if it turns out that a plasma biomarker(s) appears early during the development of MSD in farmers, this biomarker(s) might eventually be used to identify farmers who are at greater risk and to prevent progression of the disease process.

This study provides novel information about the biological mechanisms behind MSD in farmers and makes it possible to explain the link between health outcomes and the work environment. We identify biomarkers of systemic inflammation that contribute to better understanding of the pathophysiological processes involved in MSD. Although farmers are a relatively small occupational group in Sweden today, the identification of the causes of work-related MSD in this group may be a valuable tool for understanding of similar phenomenon in other groups. The results of this study thus may contribute to a better understanding of how problems in the musculoskeletal system generally arise.

Ethics approval and consent to participate

The cohort study has been approved by the Research Ethics Committee at the Karolinska Institute in Stockholm, Sweden, and by the Regional Ethics Board in Uppsala, Sweden. All men who participated in the health surveys gave their informed consent.

Consent for publication

Not applicable.

Availability of data and materials

The data set on which the conclusions of the paper rely are partly included in the manuscript. The study is not finished yet therefore additional data are available on request to corresponding author.

Abbreviations

2DE: 

two-dimensional gel electrophoresis

MS: 

mass spectrometry

MSD: 

musculoskeletal disorders

VDBP: 

vitamin D-binding protein

Declarations

Acknowledgement

We would like to thank Louise Eskilsson, from Occupational and Environmental Medicine (Linköping, Sweden), for the valuable help with aliquoting of the plasma samples.

Funding

This study was supported by The Swedish Farmers’ Foundation for Agricultural Research (H0935072) and The Swedish Rheumatism Association (R-420491).

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Division of Community Medicine, Department of Medical and Health Sciences, Faculty of Health Sciences, Linköping University and Pain and Rehabilitation Center, Anaesthetics, Operations and Specialty Surgery Center, Region Östergötland, Linköping, SE-581 85, Sweden
(2)
Division of Neuro and Inflammation Science, Department of Clinical and Experimental Medicine, Linköping University, Occupational and Environmental Medicine Center, Heart and Medicine Center, Region Östergötland, Linköping, Sweden
(3)
Department of Research and Development, Region Kronoberg, Växjö, Sweden
(4)
Department of Public Health and Caring Sciences, Family Medicine and Clinical Epidemiology Sections, Uppsala University, Uppsala, Sweden

References

  1. Thelin N, Holmberg S, Nettelbladt P, Thelin A. Mortality and morbidity among farmers, non-farming rural men and urban referents. a prospective population-based study. Int J Occup Environ Health. 2009;15:21–8.View ArticlePubMedGoogle Scholar
  2. Thelin A, Vingård E, Holmberg S. Osteoarthritis of the hip joint and farm work. Am J Ind Med. 2004;45:202–9.View ArticlePubMedGoogle Scholar
  3. Holmberg S, Thelin A, Stiernstrom E, Svardsudd K. The impact of physical work exposure on musculoskeletal symptoms among farmers and rural non-farmers. Ann Agric Environ Med. 2003;10:179–84.PubMedGoogle Scholar
  4. Holmberg S, Thelin A, Stiernström EL, Svärdsudd K. Low back pain comorbidity among male farmers and rural referents: a population-based study. Ann Agric Environ Med. 2005;12:261–8.PubMedGoogle Scholar
  5. Thadikkaran L, Siegenthaler MA, Crettaz D, Queloz PA, Schneider P, Tissot JD. Recent advances in blood-related proteomics. Proteomics. 2005;5:3019–34.View ArticlePubMedGoogle Scholar
  6. Zhang Y, Jiang R, Guo T, Wu S, Ma W. MALDI-TOF-MS serum protein profiling for developing diagnostic models and identifying serum markers for discogenic low back pain. BMC Musculoskelet Disord. 2014;15:3–10.View ArticleGoogle Scholar
  7. Stiernström EL, Holmberg S, Thelin A, Svärdsudd K. A prospective study of morbidity and mortality rates among farmers and rural and urban nonfarmers. J Clin Epidemiol. 2001;54:121–6.View ArticlePubMedGoogle Scholar
  8. Nerbrand C, Svärdsudd K, Hörte LG, Tibblin G. Geographical variation of mortality from cardiovascular diseases. The Project ‘Myocardial Infarction in mid-Sweden’. Eur Heart J. 1991;12:4–9.View ArticlePubMedGoogle Scholar
  9. Holmberg S, Stiernström EL, Thelin A, Svärdsudd K. Musculoskeletal symptoms among farmers and Non-farmers: a population-based study. Int J Occup Environ Health. 2002;8:339–45.View ArticlePubMedGoogle Scholar
  10. Bradford MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem. 1976;72:248–54.View ArticlePubMedGoogle Scholar
  11. Gorg A, Obermaier C, Boguth G, Harder A, Scheibe B, Wildgruber R, et al. The current state of two-dimensional electrophoresis with immobilized pH gradients. Electrophoresis. 2000;21:1037–53.Google Scholar
  12. Ghafouri B, Stahlbom B, Tagesson C, Lindahl M. Newly identified proteins in human nasal lavage fluid from non-smokers and smokers using two-dimensional gel electrophoresis and peptide mass fingerprinting. Proteomics. 2002;2:112–20.View ArticlePubMedGoogle Scholar
  13. Olausson P, Gerdle B, Ghafouri N, Larsson B, Ghafouri B. Identification of proteins from interstitium of trapezius muscle in women with chronic myalgia using microdialysis in combination with proteomics. PLoS One. 2012;7:e52560.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Banda NK, Thurman JM, Kraus D, Wood A, Carroll MC, Arend WP, et al. Alternative complement pathway activation is essential for inflammation and joint destruction in the passive transfer model of collagen-induced arthritis. J Immunol. 2006;177:1904–12.Google Scholar
  15. Katz Y, Stav D, Barrt J, Passwelli JH. IL-13 results in differential regulation of the complement proteins C3 and factor B in tumor necrosis factor [TNF]-stimulated fibroblasts. Clin Exp Immunol. 1995;101:150–6.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Katz Y, Nadiv O, Rapoport MJ, Loos M. IL-17 regulates gene expression and protein synthesis of the complement system, C3 and factor B, in skin fibroblasts. Clin Exp Immunol. 2000;120:22–9.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Frenette J, Cai B, Tidball JG. Complement activation promotes muscle inflammation during modified muscle use. Am J Pathol. 2000;156:2103–10.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Wang Y, Kinzie E, Berger FG, Lim SK, Baumann H. Haptoglobin, an inflammation-inducible plasma protein. Redox Rep. 2001;6:379–85.View ArticlePubMedGoogle Scholar
  19. Huntoon KM, Wang Y, Eppolito CA, Barbour KW, Berger FG, Shrikant PA, et al. The acute phase protein haptoglobin regulates host immunity. J Leukoc Biol. 2008;84:170–81.Google Scholar
  20. Maffei M, Funicello M, Vottari T, Gamucci O, Costa M, Lisi S, et al. The obesity and inflammatory marker haptoglobin attracts monocytes via interaction with chemokine [C-C motif] receptor 2 [CCR2]. BMC Biol. 2009;7:87.Google Scholar
  21. Park HJ, Oh MK, Kim NH, Cho ML, Kim IS. Identification of a specific haptoglobin C-terminal fragment in arthritic synovial fluid and its effect on interleukin-6 expression. Immunology. 2013;140:133–41.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Tolosano E, Altruda F. Hemopexin: structure, function, and regulation. DNA Cell Biol. 2002;21:297–306.View ArticlePubMedGoogle Scholar
  23. Wiedermann CJ, Römisch J. The anti-inflammatory actions of antithrombin--a review. Acta Med Austriaca. 2002;29:89–92.View ArticlePubMedGoogle Scholar
  24. Gritti D, Malinverno A, Gasparetto C, Wiedermann CJ, Ricevuti R. Attenuation of leukocyte beta 2-integrin expression by antithrombin-III. Int J Immunopathol Pharmacol. 2004;17:27–32.View ArticlePubMedGoogle Scholar
  25. Doi T, Adachi S, Takai S, Matsushima-Nishiwaki R, Kato H, Enomoto Y, et al. Antithrombin III suppresses ADP-induced platelet granule secretion: inhibition of HSP27 phosphorylation. Arch Biochem Biophys. 2009;489:62–7.Google Scholar
  26. Huang CY, Sheen-Chen SM, Ho HT, Tang RP, Eng HL. Antithrombin-III attenuates hepatocyte apoptosis in bile duct ligated rat: a striking cellular change. Surg Innov. 2010;17:132–5.View ArticlePubMedGoogle Scholar
  27. Isik S, Tuncyurek P, Zengin NI, Demirbag AE, Atalay F, Yilmaz S, et al. Antithrombin prevents apoptosis by regulating inflammation in the liver in a model of cold ischemia/warm reperfusion injury. Hepatogastroenterol. 2012;59:453–7.Google Scholar
  28. Sun HM, Hong LZ, Shen XK, Lin XQ, Song Y, Shi Y. Antithrombin-III without concomitant heparin improves endotoxin-induced acute lung injury rats by inhibiting the activation of mitogen-activated protein kinase. Chin Med J. 2009;122:2466–71.PubMedGoogle Scholar
  29. Li W, Zhu S, Li J, Huang Y, Zhou R, Fan X, et al. A hepatic protein, fetuin-A, occupies a protective role in lethal systemic inflammation. PLoS One. 2011;8:e16945.Google Scholar
  30. Minas M, Mystridou P, Georgoulias P, Pournaras S, Kostikas K, Gourgoulianis KI. Fetuin-A is associated with disease severity and exacerbation frequency in patients with COPD. COPD. 2013;10:28–34.View ArticlePubMedGoogle Scholar
  31. Oncu K, Yazgan Y, Tanoglu A, Kaplan M, Ermis F, Ipcioglu OM, et al. Can serum fetuin-A be regarded as an inflammatory marker among patients with familial Mediterranean fever? Dig Dis Sci. 2013;58:3212–7.Google Scholar
  32. Doherty NS, Littman BH, Reilly K, Swindell AC, Buss JM, Anderson NL. Analysis of changes in acute-phase plasma proteins in an acute inflammatory response and in rheumatoid arthritis using two-dimensional gel electrophoresis. Electrophoresis. 1998;19:355–63.View ArticlePubMedGoogle Scholar
  33. Sato H, Kazama JJ, Wada Y, Kuroda T, Narita I, Gejyo F, et al. Decreased levels of circulating alpha2-Heremans-Schmid glycoprotein/Fetuin-A [AHSG] in patients with rheumatoid arthritis. Intern Med. 2007;46:1685–91.Google Scholar
  34. Kim JC, Kim JY, Yeom SR, Jeong BY, Hwang HZ, Park KJ, et al. 2-D DIGE and MS/MS analysis of protein serum expression in rats housed in concrete and clay cages in winter. Proteomics. 2008;8:3632–44.Google Scholar
  35. Hattori N, Oda S, Sadahiro T, Nakamura M, Abe R, Shinozaki K, et al. YKL-40 identified by proteomic analysis as a biomarker of sepsis. Shock. 2009;32:393–400.Google Scholar
  36. Jeng L, Yamshchikov AV, Judd SE, Blumberg HM, Martin GS, Ziegler TR, et al. Alterations in vitamin D status and anti-microbial peptide levels in patients in the intensive care unit with sepsis. J Transl Med. 2009;23:28.Google Scholar
  37. Oh YM, Ma TZ, Kwak YG, Eun JP. Proteomic evaluation to identify biomarkers for carpal tunnel syndrome: a comparative serum analysis. Connect Tissue Res. 2013;54:76–81.View ArticlePubMedGoogle Scholar
  38. Yamamoto N, Naraparaju VR. Structurally well-defined macrophage activating factor derived from vitamin D3-binding protein has a potent adjuvant activity for immunization. Immunol Cell Biol. 1998;76:237–44.View ArticlePubMedGoogle Scholar
  39. Nagasawa H, Uto Y, Sasaki H, Okamura N, Murakami A, Kubo S, et al. Gc protein [vitamin D-binding protein]: Gc genotyping and GcMAF precursor activity. Anticancer Res. 2005;25:3689–95.Google Scholar
  40. Gumireddy K, Reddy CD, Swamy N. Mitogen-activated protein kinase pathway mediates DBP-maf-induced apoptosis in RAW 264.7 macrophages. J Cell Biochem. 2003;90:87–96.View ArticlePubMedGoogle Scholar
  41. Ueno A, Oh-ishi S. Roles for the kallikrein-kinin system in inflammatory exudation and pain: lessons from studies on kininogen-deficient rats. J Pharmacol Sci. 2003;93:1–20.View ArticlePubMedGoogle Scholar
  42. Del Rosso M, Fibbi G, Pucci M, Margheri F, Serrati S. The plasminogen activation system in inflammation. Front Biosci. 2008;1:4667–86.View ArticleGoogle Scholar
  43. Kashuba E, Bailey J, Allsup D, Cawkwell L. The kinin-kallikrein system: physiological roles, pathophysiology and its relationship to cancer biomarkers. Biomarkers. 2013;18:279–96.View ArticlePubMedGoogle Scholar
  44. Cassim B, Shaw OM, Mazur M, Misso NL, Naran A, Langlands DR, et al. Kallikreins, kininogens and kinin receptors on circulating and synovial fluid neutrophils: role in kinin generation in rheumatoid arthritis. Rheumtol [Oxford]. 2009;48:490–6.Google Scholar
  45. Cox DW, Huber O. Rheumatoid arthritis and alpha-1-antitrypsin. Lancet. 1976;5:1216–7.Google Scholar
  46. Duckers JM, Shale DJ, Stockley RA, Gale NS, Evans BA, Cockcroft JR, et al. Cardiovascular and musculskeletal co-morbidities in patients with alpha 1 antitrypsin deficiency. Respir Res. 2010;7:173.Google Scholar
  47. Tan EC, Bahrami S, Kozlov AV, Kurvers HA, Ter Laak HJ, Nohl H, et al. The oxidative response in the chronic constriction injury model of neuropathic pain. J Surg Res. 2009;152:84–8.View ArticlePubMedGoogle Scholar
  48. Okumura K, Ohkura N, Inoue S, Ikeda K, Hayashi K. A novel phospholipase A2 inhibitor with leucine-rich repeats from the blood plasma of Agkistrodon blomhoffii siniticus. Sequence homologies with human leucine-rich alpha2-glycoprotein. J Biol Chem. 1998;31:19469–75.View ArticleGoogle Scholar
  49. Tuteja S, Rader DJ. High-density lipoproteins in the prevention of cardiovascular disease: changing the paradigm. Clin Pharmacol Ther. 2014;96:48–56.View ArticlePubMedGoogle Scholar
  50. Sun HY, Chen SF, Lai MD, Chang TT, Chen TL, Li PY, et al. Comparative proteomic profiling of plasma very-low-density and low-density lipoproteins. Clin Chim Acta. 2010;411:336–44.Google Scholar
  51. Banfi C, Brioschi M, Barcella S, Wait R, Begum S, Galli S, et al. Proteomic analysis of human low-density lipoprotein reveals the presence of prenylcysteine lyase, a hydrogen peroxide-generating enzyme. Proteomics. 2009;9:1344–52.Google Scholar
  52. Smilansky Z. Automatic registration for images of two-dimensional protein gels. Electrophoresis. 2001;22:1616–26.View ArticlePubMedGoogle Scholar
  53. Raman B, Cheung A, Marten MR. Quantitative comparison and evaluation of two commercially available, two-dimensional electrophoresis image analysis software packages, Z3 and Melanie. Electrophoresis. 2002;23:2194–202.View ArticlePubMedGoogle Scholar

Copyright

© Ghafouri et al. 2016

Advertisement