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BMC Veterinary Research

Open Access

The response of porcine monocyte derived macrophages and dendritic cells to SalmonellaTyphimurium and lipopolysaccharide

  • Kamila Kyrova1,
  • Hana Stepanova1,
  • Ivan Rychlik1,
  • Ondrej Polansky1,
  • Lenka Leva1,
  • Zuzana Sekelova1,
  • Martin Faldyna1 and
  • Jiri Volf1Email author
BMC Veterinary Research201410:244

https://doi.org/10.1186/s12917-014-0244-1

Received: 25 April 2014

Accepted: 24 September 2014

Published: 1 October 2014

Abstract

Background

Following infection and initial multiplication in the gut lumen, Salmonella Typhimurium crosses the intestinal epithelial barrier and comes into contact with cells of the host immune system. Mononuclear phagocytes which comprise macrophages and dendritic cells (DC) are of key importance for the outcome of Salmonella infection. Although macrophages and DC may differentiate from a common precursor, their capacities to process and present antigen differ significantly. In this study, we therefore compared the response of porcine macrophages and DC differentiated from peripheral blood monocytes to S. Typhimurium and one of the most potent bacterial pathogen associated molecular patterns, bacterial lipopolysaccharide. To avoid any bias, the expression was determined by protein LC-MS/MS and verified at the level of transcription by quantitative RT-PCR.

Results

Within 4 days of culture, peripheral blood monocytes differentiated into two populations with distinct morphology and expression of MHC II. Mass spectrometry identified 446 proteins in macrophages and 672 in DC. Out of these, 433 proteins were inducible in macrophages either after infection with S. Typhimurium or LPS exposure and 144 proteins were inducible in DC. The expression of the 46 most inducible proteins was verified at the level of transcription and the differential expression was confirmed in 22 of them. Out of these, 16 genes were induced in both cell types, 3 genes (VCAM1, HMOX1 and Serglycin) were significantly induced in macrophages only and OLDLR1 and CDC42 were induced exclusively in DC. Thirteen out of 22 up-regulated genes contained the NF-kappaB binding site in their promoters and could be considered as either part of the NF-kappaB feedback loop (IkappaBalpha and ISG15) or as NF-kappaB targets (IL1beta, IL1alpha, AMCF2, IL8, SOD2, CD14, CD48, OPN, OLDLR1, HMOX1 and VCAM1).

Conclusions

The difference in the response of monocyte derived macrophages and DC was quantitative rather than qualitative. Despite the similarity of the responses, compared to DC, the macrophages responded in a more pro-inflammatory fashion.

Keywords

MacrophageDendritic cellPorcineLPSSalmonellaResponse

Background

Pigs are one of the most important sources of animal proteins for the human population. However, due to intensive rearing, pigs are also highly susceptible to various pathogens including those with zoonotic potential. Salmonella enterica serovar Typhimurium is an example of a zoonotic agent for which pigs represent a reservoir for the human population. In fact, pigs represent the second most common source of Salmonella for humans after poultry and are the most important source if only serovar S. Typhimurium is considered.

Salmonella in pigs is transmitted by the oral-fecal route. Infections of pigs with S. Typhimurium are often asymptomatic [1] although mild diarrhea may be recorded at the early stage of infection [2],[3]. After initial multiplication in the gut lumen, S. Typhimurium invades the intestinal epithelial cells and comes into contact with the host’s immune system [4]. The innate immune responses of different parts of porcine intestinal tract have been repeatedly described [5]-[7]. Of the different leukocyte subpopulations, mononuclear phagocyte cells are of key importance for the outcome of Salmonella infection which comprise of macrophages (MΦ) and dendritic cells (DC). When these cells come into contact with bacterial pathogens, they recognize the pathogens through the presence of pathogen associated molecular patterns (PAMP) present in prokaryotic pathogens but absent from eukaryotic host cells. This leads to the modification of gene expression in MΦ and DC, and secretion of signaling molecules to coordinate responses of other cells of the host immune system. In addition, both MΦ and DC are able to take up, process and present antigens to lymphocytes, thereby inducing the development of an adaptive immune response [8],[9].

Although only particular subsets of MΦ and DC can be differentiated from a monocyte precursor, their capacity to take up, process and present antigen differ significantly. There are several papers to date describing the expression profiles of selected genes in porcine monocytes, dendritic cells or macrophages in response to external stimuli [10]-[15]. However, these studies compared the responses either monocytes and monocyte derived dendritic cells (MoDC) [13], two differently generated macrophages [15] or two different DC populations [11],[12]. The response of MΦ and DC derived from monocytes has never been compared. Moreover, all the studies either determined the expression of preselected genes such as TLRs, MHC-II molecules, chemokines and cytokines by quantitative RT-PCR or used the Affymetrix microarray, so the measurements were limited to the level of transcription. Since the general understanding of antigen presentation and associated processes, especially in a porcine model, is far from being completely understood, in this study, we therefore differentiated porcine monocyte-derived macrophages (MoMΦ) and MoDC, and compared their response to S. Typhimurium and one of the most potent bacterial PAMP, bacterial LPS. To avoid any bias, expression was determined by protein LC-MS/MS and verified at the level of transcription by QRT-PCR. Using such an approach, we concluded that the difference in the response of MoMΦ and MoDC was quantitative rather than qualitative, i.e. MoDC responded less extensively than MoMΦ to LPS or S. Typhimurium stimulation.

Results

Cell differentiation

Depending on culture conditions, peripheral blood monocytes differentiated into two populations with distinct morphology within 4 days of culture. Adhered monocytes differentiated into MoMΦ of spherical shape with characteristic short hairy protrusions on their surface. On the other hand, monocytes treated with IL4 and GM-CSF differentiated into MoDC characteristic by elongated cells with numerous dendrites typical of dendritic cells (Figure 1).
Figure 1

Cell morphology after differentiation of peripheral blood monocytes. Fluorescence microscopy and DIC was used to visualize differences in cell morphology. A and B, MoMΦ, C and D, MoDC. A and C, fluorescence microscopy with nuclei stained in blue with DAPI and cytoskeleton stained in green with phalloidin. B and D, DIC.

Flow cytometric analysis further confirmed the difference between the two cell populations. The most remarkable difference was a more than 40 times higher expression of MHC-II molecules on the surface of MoDC compared to MoMΦ. The expression of CD14 and CD11a, when compared to MoMΦ, was numerically but not significantly higher in MoDC (Figure 2). On the other hand, expression of CD172α, CD16, CD163, CD45, TLR-2 and TLR-4 did not differ between both cell types (data not shown). The expression of surface molecules on MoDC and MoMΦ was also determined after S. Typhimurium infection. In response to infection, the amount of CD14 increased in both cell types whereas the expression of the remaining cell surface molecules did not change (Figure 2).
Figure 2

Surface markers of MoMΦ and MoDC differentiated from PBMC of three donor pigs. The Y axis represents the mean fluorescence intensity measured for a particular surface molecule with the error bar representing SD. The percentages of positive cells for individual marker are mentioned in parenthesis and were calculated as a mean of three experiments.

Protein mass spectrometry

The previous experiment showed that CD14 increased both in MoMΦ and MoDC after S. Typhimurium infection. In the next experiment, we therefore determined what other proteins might have increased in expression in both MoMΦ and MoDC. In addition to S. Typhimurium infection, protein expression was also determined in the LPS exposed cell cultures.

Mass spectrometry detected at least once 2191 proteins in MoMΦ and 2204 proteins in MoDC [see Additional file 1]. However, when we applied the filter criteria described below, the number of proteins decreased to 446 proteins which were repeatedly detected in MoMΦ and 672 in MoDC (Figure 3A). Out of the filtered proteins, 400 were detected in both MoMΦ and MoDC, 46 proteins were identified only in MoMΦ and 272 proteins were identified only in MoDC. The comparison of the MoMΦ and MoDC response to LPS and S. Typhimurium showed that MoMΦ were more responsive as the number of proteins induced more than 2 fold was higher in MoMΦ than in MoDC (Figure 3B). The MoMΦ also showed a higher mean fold induction than the MoDC when comparing the proteins induced in both cell types [see Additional file 1].
Figure 3

Numbers of proteins identified in MoMΦ and MoDC by LC-MS/MS analysis. Panel A Proteins expressed in both or either of the cell types. The number of proteins specific for each cell type might be actually lower as these were calculated after applying all filtering criteria described in Methods. Some of the MoDC proteins considered as specific in this figure can be low level expressed also in macrophages. The higher number of proteins expression of which was found to be induced twofold or more following individual stimulations (panel B) shows higher reactivity of MoMΦ in comparison with MoDC. *STM - S. Typhimurium.

QRT-PCR

Since mass spectrometry was performed in cells originating from only two donor pigs (Table 1), proteomic expression data were therefore confirmed at the transcriptional level by quantitative real-time PCR to which additional samples from 3 different pigs were included. Genes selected for QRT-PCR verification included those in which the summed fold inductions determined by LC-MS/MS following LPS stimulation and S. Typhimurium infection exceeded a factor of 20 in the case of the less responsive MoDC, or a factor of 30 in the case of the more responsive MoMΦ. The selection resulted in 46 genes/proteins (see Additional file 2) and the differential expression was confirmed by QRT-PCR in 22 genes (Table 2).
Table 1

List of samples used in this study

Pig

Cell culture

LC MS (NS/LPS/STM+)

QRT PCR

FACS

Light microscopy

1

A

  

Full FACS analysis*

Yes

2

A

  

Full FACS analysis

Yes

3

A

  

Full FACS analysis

Yes

4

A#

Yes

Yes

CD14 check

Yes

 

B#

Yes

Yes

CD14 check

Yes

5

A#

Yes

Yes

CD14 check

Yes

 

B#

Yes

Yes

CD14 check

Yes

6

A

 

Yes

CD14 check

Yes

7

A

 

Yes

CD14 check

Yes

8

A

 

Yes

CD14 check

Yes

NS non stimulated cells, STM S. Typhimurium.

*Full analysis comprises the FACS measurement of the following surface markers: CD11a, CD14, CD16, CD45, CD163, CD172α, MHC-II, TLR2, TLR4. FACS analysis was performed only for untreated and S. Typhimurium infected cell cultures.

#Two independent batches of MoMΦ and MoDC were obtained from these pigs.

Table 2

Fold inductions of genes identified in this study in MoMΦ or MoDC in response to LPS or Salmonella Typhimurium (STM) exposure determined by QRT-PCR

 

MoDC

MoDC fold induction$

MoMΦ

MoMΦ fold induction

MoMΦ to MoDC

 

Basal exp

STM

LPS

Basal exp

STM

LPS

NS

STM

LPS

IL1B

0.06 ± 0.04

106 # ± 101

2358 # ± 1268

0.11 ± 0.06

383 # ± 451

1999 # ± 1743

n

*

n

AMCF2

0.40 ± 0.20

35.3 # ± 33.0

543 # ± 528

0.75 ± 0.52

115 # ± 107

305 # ± 267

n

n

n

CXCL2

0.71 ± 0.28

17.4 # ± 19.1

200 # ± 161

0.19 ± 0.19

155 # ± 102

668 # ± 432

n

n

n

IL1A

0.28 ± 0.14

22.9 # ± 25.3

229 # ± 164

0.24 ± 0.12

86.5 # ± 48.8

187 # ± 57

n

**

n

IL8

15.3 ± 13.7

5.9 ± 5.74

34.2 # ± 21.6

6.78 ± 5.95

22.9 # ± 13.2

113 # ± 66

n

n

n

ISG15

0.88 ± 0.52

2.94 # ± 1.22

2.00 ± 0.94

0.17 ± 0.10

22.1 # ± 2.0

78.9 # ± 104.4

***

n

n

MARCKSL1

0.70 ± 0.60

1.82 ± 0.63

4.07 # ± 2.30

0.25 ± 0.08

17.9 # ± 9.1

42.9 # ± 15.6

n

*

**

SOD2

4.28 ± 4.58

2.13 ± 0.80

12.8 # ± 12.9

4.61 ± 2.55

11.3 # ± 4.6

27.6 # ± 11.6

n

**

n

IκBα

1.15 ± 0.36

2.74 # ± 1.12

8.12 # ± 3.10

1.13 ± 0.57

9.64 # ± 3.72

15.1 # ± 4.8

n

***

n

CD14

0.52 ± 0.19

3.84 # ± 1.52

14.4 # ± 9.6

0.35 ± 0.27

14.0 # ± 9.7

18.0 # ± 20.0

n

n

n

PPA1

1.34 ± 0.84

1.85 # ± 0.7

4.83 # ± 1.93

1.39 ± 0.17

4.42 # ± 1.00

7.02 # ± 1.73

n

***

n

CD48

7.35 ± 4.25

1.83 ± 0.22

3.2 # ± 1.8

9.93 ± 2.43

3.34 # ± 0.71

5.33 # ± 1.90

n

*

*

NF-κB1

1.57 ± 0.41

1.82 ± 0.61

3.49 # ± 1.4

1.97 ± 0.44

2.25 # ± 0.7

4.63 # ± 2.20

n

n

n

ANXA1

15.3 ± 4.7

1.78 ± 0.9

3.74 # ± 1.91

22.9 ± 13.4

1.40 ± 0.38

2.45 # ± 1.01

n

n

n

OPN

86.1 ± 42.8

2.33 ± 1.5

3.42 # ± 2.27

138 ± 72

2.12 ± 1.20

3.81 # ± 2.00

n

n

n

PFDN2

0.09 ± 0.03

1.54 ± 0.51

2.22 # ± 0.63

0.12 ± 0.04

2.12 # ± 0.60

2.63 # ± 0.72

n

n

n

PSMB4

6.18 ± 1.58

1.42 ± 0.30

1.82 # ± 0.31

7.34 ± 1.39

1.73 ± 0.40

2.11 # ± 0.54

n

n

n

MoDC only

         

OLDLR1

9.73 ± 3.74

1.95 # ± 0.5

3.33 # ± 0.91

26.2 ± 3.0

1.43 ± 0.42

1.62 ± 0.41

***

*

n

CDC42

3.52 ± 0.70

1.62 # ± 0.33

1.63 ± 0.33

4.78 ± 1.43

1.60 ± 0.13

1.53 ± 0.61

n

n

n

MoMΦ only

         

VCAM1

0.33 ± 0.25

2.32 ± 1.90

5.52 ± 4.51

0.18 ± 0.19

9.82 # ± 6.24

18.9 # ± 13.6

n

n

n

HMOX1

0.32 ± 0.20

2.32 ± 1.53

1.42 ± 0.51

0.45 ± 0.11

2.34 # ± 1.11

2.44 # ± 0.14

n

n

*

Serglycin

45.9 ± 19.9

1.52 ± 0.71

2.40 ± 1.03

35.7 ± 28.9

3.04 # ± 1.32

2.84 # ± 1.33

n

n

n

$The fold inductions are presented as mean ± SD calculated based on ratios of individual treated samples relative to the mean of appropriate non-stimulated control.

#The numbers signed with # represent significantly different results compared to non-stimulated (NS) cells.

*Statistically significant differences between MoMΦ and MoDC with the same treatment. *p < 0.05; **p < 0.01; ***p < 0.001; n = non-significant.

Out of these, 16 genes were induced both in MoMΦ and MoDC, 3 genes (VCAM1, HMOX1 and Serglycin) were significantly induced in MoMΦ only and OLDLR1 and CDC42 were significantly induced exclusively in MoDC. Out of the 16 genes up-regulated in both cell types, 5 the most up-regulated ones encoded cytokines (IL1β, AMCF2 (the porcine homolog of CXCL 5/6), CXCL2, IL8 and IL1β). In agreement with the proteomic data, the fold inductions determined by QRT-PCR were usually higher in MoMΦ than in MoDCs, despite the fact that the differences between MoMΦ and MoDC responsiveness reached statistical significance only in 2 genes after exposure to LPS and 7 genes after infection with S. Typhimurium. This also means that the responsiveness of MoMΦ and MoDCs to LPS was similar but DCs, unlike MΦ, did not extensively respond to S. Typhimurium infection.

Discussion

In this study we were interested in the interactions of porcine MoMΦ and MoDCs with LPS and S. Typhimurium. Both cell morphology and flow cytometry indicated that the culture conditions led to the differentiation of distinct cell types. Expression of surface molecules observed on MoDC was in agreement with the previous observations [10],[16]. The up-regulation of CD14 in porcine alveolar macrophages following stimulation with LPS or Salmonella infection was also reported earlier [10],[17].

Similar responses to free LPS and S. Typhimurium of both cell types indicate that LPS is one of the dominant antigens of S. Typhimurium. The infection of MoMΦ with S. Typhimurium did cause induction of only 3 additional proteins compared to LPS stimulated cells (Figure 3B). In case of MoDC there were 28 proteins solely induced by S. Typhimurium which represents less than one fifth of all proteins induced in MoDC. The higher stimulating potential of LPS compared to infection with S. Typhimurium was likely caused by the soluble character of LPS which was present in media and homogenously stimulated all the cells. On the other hand, only about 10% of cells were invaded by S. Typhimurium (data not shown), so the remaining uninfected cells could respond only to LPS released from bacterial cells into the medium during the experiments. The effect of indirect stimulation of infected cells toward their uninfected by-standers remains to be determined. Both LC-MS/MS and real-time PCR showed that the differences in the expression of genes inducible both in MoMΦ and in MoDC were more pronounced in MoMΦ. Together with the high expression of MHCII, this shows that the primary role of DC is antigen presentation whereas macrophages are involved in modulation of the environment by cytokine signaling.

Eleven genes/proteins, IL1β, AMCF2, CXCL2, IL8, IL1β, ISG15, MARCKSL1, SOD2, IκBα, CD14 and VCAM were induced by more than 10 fold. Out of these, IL1β, CXCL2, AMCF2, IL8, IL1β and the secreted form of ISG15 have a cytokine and/or chemokine function and their expression is related to the NF-κB dependent proinflammatory pathway. The central role of the NF-κB pathway is further supported by the fact that more than half of the identified genes (13 of 22) contain the NF-κB binding site in the promoter. These genes could be considered as either involved in the NF-κB feedback loop (IκBα and ISG15) [14],[18] or act as NF-κB targets: IL1β, IL1β, AMCF2, IL8, SOD2, CD14, CD48, OPN, OLDLR1, HMOX1 and VCAM1 [17]-[28]. IL1β, AMCF2, IL8, CXCL2, IL1β, MARCKSL1, SOD2, PPA1 and ISG15 were also found among the top 10% of the most inducible genes of bone marrow derived porcine macrophages treated with LPS [15].

The differences between MoMΦ and MoDC responses were quite low. This could be caused by the fact that we selected the most inducible and highly abundant proteins, which had similar expression profiles following exposure to LPS or S. Typhimurium, and minority proteins responsible for the specificity of MoMΦ and MoDC remained unrecognized. The exceptions were represented by MARCKS-related gene/protein (MARCKSL1). This gene was expressed and induced in MoMΦ after exposure to both LPS and S. Typhimurium at a significantly higher rate than in MoDC. Finally, its induction in LPS stimulated MoMΦ was more than 40 fold indicating that this was a highly inducible gene.

MARCKSL1 (synonyms MLP, MacMARCKS, F52) is expressed mainly in the brain, reproductive tissues, and macrophages [29],[30] and belongs to a family of the unstructured proteins that mediate cross-talk in cell-to-cell signaling. MARCKSL1 is involved in the regulation of cell migration, adhesion and phagocytosis, as well as in neurosecretion and brain development [31]-[33]. Transcription of MARCKSL1 is also strongly increased upon stimulation with bacterial LPS [34]-[37]. Mancek-Keber et al. [38] showed that the MARCKSL1 protein binds LPS with an affinity sufficient enough to sequester a large fraction of the internalized LPS or intracellular Gram-negative bacteria within the endosome/phagosome.

Conclusions

In this study we have shown that S. Typhimurium and LPS in particular represent one of the most potent activators of signaling pathways of MoMΦ and MoDC leading to a similar response in both cell types. Despite the similarity of the responses, compared to MoDC, the MoMΦ were more pro-inflammatory. This difference was less obvious in response to LPS and more obvious in response to S. Typhimurium. Our results showed that increased pro-inflammatory signaling by MoMΦ compared to MoDC might be, at least in some subsets of mononuclear phagocytes, associated with differential MARCKSL1 protein expression though the exact role of these proteins in differential responsiveness of MoMΦ and MoDC to S. Typhimurium infection remains to be determined.

Methods

Differentiation of monocyte-derived macrophages and dendritic cells

Blood was collected from healthy 8-10 month old conventional pigs kept at the Veterinary Research Institute for the educational purposes. The animal care protocol for this experiment followed the Czech guidelines for animal experimentation and was approved by the Branch Commission for Animal Welfare of the Ministry of Agriculture of the Czech Republic (permission No MZe 921). Peripheral Blood Mononuclear cells (PBMC) were isolated by gradient centrifugation using Histopaque-1077 (Sigma-Aldrich). Monocytes were sorted from PBMC using indirect magnetic labelling based on the expression of CD14. In the first step, anti-CD14 monoclonal antibody (MIL-2, IgG2b, AbD Serotec) was used and consequently, the CD14-positive cells were captured by goat anti-mouse IgG MicroBeads (Miltenyi Biotec) and sorted using a QuadroMACS separator (Miltenyi Biotec) according to manufacturer’s recommendations. The purity of the sorted cells was checked using a flow cytometer (LSR Fortessa, Becton Dickinson) with more than 90% of the cells being CD14 positive. Approx. 5 × 105 CD-14 positive monocytes resuspended in 1 ml of media were seeded per well on a 24-well microplate.

Half of the isolated monocytes were differentiated using protocol described previously [39] and the resulting cells were considered as MoDC throughout the study. RPMI-1640 medium (PAA) supplemented with antibiotics penicillin/streptomycin (Sigma-Aldrich), 10% heat-inactivated fetal calf serum (PAA), recombinant porcine IL4 (50 ng/ml, R&D Systems) and GM-CSF (20 ng/ml, R&D Systems) was used. The second half of the cells were left to adhere to plastic microplates in DMEM (Gibco Invitrogen) supplemented with penicillin/streptomycin (Sigma-Aldrich) and 10% heat-inactivated and filtered porcine serum (PAA) as described previously [40]. These cells were considered as MoMΦ for the purposes of this study. Both MoMΦ and MoDC were differentiated at 37°C in a 5% CO2 incubator for 4 days. MoMΦ and MoDC were differentiated from peripheral blood monocytes from 8 different pigs, although not all of the cultures were used for all of the analyses (Table 1).

Fluorescence microscopy and flow cytometry

Differentiation of MoMΦ and MoDC was confirmed by flow cytometry and light microscopy. For light microscopy, the cells were grown on 13 mm glass slides as described above. After a 4-day culture, the cells were fixed with 4% paraformaldehyde and labelled with DAPI as a nuclear stain and Alexa Fluor (AF) 488 conjugated phalloidin (Invitrogen) to visualize the actin cytoskeleton. Microscopy was performed using epifluorescence inverted microscope Olympus IX81 equipped with PlanSAPO 40× (NA 0.95) objective using differential interference contrast (DIC), and fluorescence mode to detect DAPI and AF-488 fluorescence.

Cells for flow cytometry were harvested by 0.2% EDTA, washed in PBS and labelled with the following antibodies against surface proteins: anti-CD11a (BL1H8, IgG2b, AbD Serotec), anti-CD14 (MIL-2, IgG2b, AbD Serotec), anti-CD16 (G7, IgG1, AbD Serotec), anti-CD45 (K252.1E4, IgG1, gift from Dr. K. Haverson, University of Bristol, UK), anti-CD172α (DH59B, IgG1, VMRD), anti-CD163 (2A100/11, IgG1, AbD Serotec), anti-MHCII (MSA3, IgG2a, VMRD), anti-TLR2 (1H11, IgG1, provided by Dr. J. Domiguez, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Madrid, Spain), and anti-TLR4 (HTA125, IgG2a, AbD Serotec). As the secondary antibody, AF 647-conjugated mouse isotype-specific goat antisera (Invitrogen) was used. Control samples were stained with secondary antibody only. The threshold for positive cells was set so that control cells stained by secondary antibody only remain ≥ 99% negative (not shown). Flow cytometry was performed using a LSR Fortessa flow cytometer operated by Diva software (Becton Dickinson).

Bacteria and culture conditions

Salmonella enterica serovar Typhimurium 16E5 of porcine origin belonging to phage-type DT104 [41] was used in this study. Bacteria were grown statically in LB broth at 37°C for 18 hours. This culture was diluted 800 × in LB broth and incubated for an additional 6 hours at 37°C to obtain the bacteria in the late logarithmic growth-phase of highly invasive phenotype. Prior to infection of MoMΦ and MoDC, the bacteria were collected by centrifugation and re-suspended in PBS to OD600 = 0.3.

Experimental infection

Prior to infection, the medium was replaced by serum free DMEM without antibiotics. MoMΦ and MoDC were infected with S. Typhimurium at a multiplicity of infection equal to 1 for 1 h. Free bacteria were then washed away and gentamicin was added to fresh medium (100 μg/ml) to kill any remaining extracellular bacteria. One hour later, the medium was replaced with fresh medium containing 15 μg/ml gentamicin to prevent multiplication of extracellular bacteria that were eventually released during culture from dead cells. LPS from S. Typhimurium (Sigma-Aldrich) at a concentration of 1 μg/ml was used as another stimulus. Negative controls included an assay performed without any contact with S. Typhimurium or LPS. Eighteen hours after infection or LPS stimulation, the extracellular transport of proteins was blocked by the addition of 10 μg/ml of Brefeldin A (Sigma-Aldrich). Six hours later, the cells were washed with PBS and lysed by TRI reagent (Sigma-Aldrich) for RNA and protein purification.

Mass spectrometry and proteome analysis

Proteins from cell cultures were lysed with TRI Reagent and precipitated from the phenolic phase with acetone according to the manufacturer’s recommendations. Dried protein pellets were dissolved in 300 μl of 8 M urea and processed by modified FASP method [42] using a Vivacon 500 device with MWCO of 10 kDa (Sartorius Stedim Biotech). Dissolved proteins were washed twice with 8 M urea and reduced by 10 m M DTT. After reduction proteins were incubated with 50 mM iodoacetamide and washed twice with 25 mM triethylammonium bicarbonate buffer. Trypsin added to sample in 1:50 ratio (w/w) was used as digestive enzyme.

Tryptic peptides were labeled using the stable isotope dimethyl labeling method as described elsewhere [43]. Three combinations of formaldehyde and cyanoborohydride isotopomers were used for labelling; proteins from non-stimulated cells were labeled with a combination of CH2O and NaBH3CN (light tag), Salmonella-infected cells with CD2O and NaBH3CN (medium tag) and LPS-stimulated cells with 13CD2O and NaBD3CN (heavy tag). Prior to LC-MS/MS analysis, the labeled samples were mixed at a 1:1:1 ratio and analyzed in 3 independent LC-MS/MS runs using the Dionex UltiMate 3000 RSLC nano system connected to a Obitrap Velos Pro mass spectrometer (Thermo Scientific). Such setup enabled semi quantitative analysis of protein abundance. The injected sample was desalted and pre-concentrated during the first 3 min of LC-MS/MS run on the Acclaim PepMap C18 trapping column (2 cm × 75 μm, 3 μm particles) at a flow rate of 5 μl/min using the loading mobile phase consisting of 0.1% formic acid in 98/2 H2O/ACN (vol/vol). Chromatographic separation was performed on an EASY-Spray C18 separation column (15 cm × 75 μm, 3 μm particles) at a flow rate of 400 nl/min using the acetonitrile gradient. High resolution (30,000 FWHM at 400 m/z) MS spectra were acquired for the 390-1700 m/z interval. Low resolution MS/MS spectra were acquired in Linear Ion Trap in a data-dependent manner - the top 10 precursors (in terms of abundance) were fragmented using CID fragmentation at a normalized collision energy of 35.

Data were analyzed using the Proteome Discoverer (v.1.4). MS/MS spectra identification was performed by searching SEQUEST against the Sus scrofa database (Uniprot, on 4th of September, 2013) and precursor and fragment mass tolerance for searches were 10 ppm and 0.6 Da respectively. Only peptides with a false discovery rate of <1% were included in quantification. Only proteins which quantification was based on 12 or more particular peptide quantifications were considered as reliable. The peptide quantifications were based on ratios of peptide peak areas in stimulated and non-stimulated cells.

RNA purification, reverse transcription and quantitative real-time PCR

Total RNA was isolated from the water phase of TRI-reagent using RNeasy kit (Qiagen) and immediately reverse transcribed using M-MLV reverse transcriptase (Invitrogen) and oligo-T primer. The resulting cDNA was diluted tenfold is sterile water and used as a template in QRT-PCR immediately or was stored at -20°C until use.

The mRNA sequences related to the LC-MS/MS identified proteins obtained from the GeneBank database were aligned to the genomic sequences using Spidey tool [44] and primers for QRT-PCR were designed over an introns using Primer3 software [45]. The complete list of primer used is available in Additional file 3. QRT-PCR was performed in 3 μl volumes in 384-well-plate format using the QuantiTec SYBR Green PCR Kit (Qiagen) and Innovadyne Nanodrop pipetting station (IDEX Health & Science LLC, Oak Harbor) for PCR mix dispensing. Amplification of PCR products and signal detection were performed using a LightCycler 480 (Roche) with an initial denaturation step at 95°C for 15 min followed by 40 cycles of PCR (95°C for 20 s, 60°C for 30 s and 72°C for 30 s). Each sample was subjected to quantitative real-time PCR in triplicate and only replicates with correct curves and melting temperatures were included in the analysis. The mean values were calculated and used for subsequent analysis.

The expression levels of target genes were determined as follows. The threshold cycle values (Ct) of genes of interest were normalized against the geometric mean of three the most stable genes identified according to Genorm algorithm [46]. These included hypoxanthine phosphoribosyl transferase I (HPRT), TATA box binding protein 1 (TBP1), and succinate dehydrogenase complex subunit A (SDHA). Tested, but excluded house-keeping genes, included HMBS, ACTB and GAPDH. Finally, the relative expression of each gene of interest was calculated as 2-ΔCt.

Statistical analysis

Data are presented as the mean ± SD or as a fold induction relative to the average expression in non-treated cells. However, for statistical analyses, Ct values obtained by QRT-PCR were used and the statistical significance was determined by one-way ANOVA with Tukey’s post hoc test. (Prism, Graph Pad Software, La Jolla). Differences were considered significant if p < 0.05.

Authors’ contributions

KK participated in the design of the study, carried out the cell cultures, light microscopy, QRT-PCR and helped to draft the manuscript. HS and LL carried out the cell isolation and the flow cytometry. OP and ZS carried out the proteomics. IR and MF helped to draft the manuscript. JV conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

Additional files

Abbreviations

ACN: 

Acetonitrile

ACTB: 

Actin Beta

AMCF2: 

Alveolar Macrophage Chemotactic Factor-2

CDC42: 

Cell Division Control Protein 42

CID: 

Collision-Induced Dissociation

CXCL2: 

Chemokine (C-X-C motif) Ligand 2

DTT: 

Dithiotreitol

FWHM: 

Full Width at Half Maximum

GAPDH: 

Glyceraldehyde 3-phosphate Dehydrogenase

GM-CSF: 

Granulocyte-Macrophage Colony Stimulating Factor

HMBS: 

hydroxymethylbilane Synthase

HMOX1: 

Heme Oxygenase 1

HPRT: 

Hypoxanthine Phosphoribosyltransferase 1

IL1β: 

Interleukin-1 Alpha

IL1β: 

Interleukin-1 Beta

IL4: 

Interleukin-4

IL8: 

Interleukin-8

IκBα: 

Nuclear Factor of Kappa Light Polypeptide Gene Enhancer in B-cells Inhibitor Alpha

ISG15: 

Interferon Stimulated Gene-15

MARCKSL1: 

MARCKS-Related Protein

MWCO: 

Molecular Weight Cut-off

NF-κB: 

Nuclear Factor of Kappa Light Polypeptide Gene Enhancer in B-cells

OLDLR1: 

Oxidized Low Density Lipoprotein Receptor 1

OPN: 

Osteopontin

PAMP: 

Pathogen Associated Molecular Pattern

PBMC: 

Peripheral Blood Mononuclear Cells

PPA1: 

Pyrophosphatase (inorganic) 1

SDHA: 

Succinate Dehydrogenase Complex Subunit A

SOD2: 

Superoxid Dismutase

TBP1: 

TATA Box-Binding Protein

VCAM: 

Vascular Cell Adhesion Molecule 1

Declarations

Acknowledgements

This study was supported by project MZE 0002716202, AdmireVet (CZ.105/2.1.00/01.0006) and MikroDok (CZ.1.07/2.3.00/30.0064). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors wish to thank Peter Eggenhuizen and Lee Clayton Elsheimer for their English language corrections. The authors got the permission to acknowledge them.

Authors’ Affiliations

(1)
Veterinary Research Institute

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© Kyrova et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

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