Sera from patients with active systemic lupus erythematosus patients enhance the toll-like receptor 4 response in monocyte subsets

Twenty two patients with SLE, according to the 1997 American College of Rheumatology classification criteria [23] followed at the Lupus Clinic, Rheumatology Department of the University Hospital Center of Coimbra, were recruited and asked to provide a blood sample for this study. Disease activity was assessed at the time of blood collection, according to the SLE Disease Activity Index 2000 (SLEDAI 2 k) [23, 24]. SLE patients were divided into two groups, one with clinically active disease (ASLE) (SLEDAI 2 k ≥ 5; n = 11, 100 % female, median age 27: 19–52 year) and the other with clinically inactive disease (ISLE) (SLEDAI 2 k < 5; n = 11, 64 % female, median age 28: 19–45 year) [25]. Current medication details and additional routine laboratory parameters were collected from the patients’ files (Table 1). Sera samples were stored at −20 °C until analysis.

The healthy control group (HC) comprised 10 healthy individuals (90.0 % female, median age 28.5: 25 – 35 year) who provided blood samples for determination of serum cytokine levels. Five different healthy donors provided an additional blood samples (90 % female; median age 29: 25 – 33 years) collected in heparin anticoagulant tubes (6 mL of blood). These participants were required to complete a brief questionnaire regarding previous or current medical conditions and therapies. All were free from autoimmune disease, other active inflammatory conditions and medication with immunomodulatory drugs.

The study protocol was approved by the ethics committee of the University Hospital Center of Coimbra. All participants provided a signed informed consent prior to any study procedures and the principles of the Helsinki Declaration were fully respected.

To assess the effect of SLE sera upon TNF-α production by normal peripheral blood monocyte subsets and mDCs, sera from 11 ASLE patients, 11 ISLE patients and 10 healthy individuals were used. The heparinized whole blood samples obtained from healthy donors were washed thrice with NaCl 0.9 % saline solution and resuspended in 0.25 mL; then samples were diluted (1:1) in RPMI-1640 medium (Life Technologies – Thermo Fisher Scientific; Carlsbad, C.A., USA), supplemented with 2 mM L-glutamine and antibiotic–antimycotic agent (Life Technologies – Thermo Fisher Scientific) in a total of 0.5 mL. Four different conditions were created: 1) adding 0.05 mL of sera from patients with SLE or HC; 2) adding 100 ng/mL of LPS from Escherichia coli (serotype 055:B5; Sigma) plus 0.05 mL of sera from patients with SLE or HC; 3) adding 100 ng/mL of LPS from Escherichia coli (as a positive control) and 4) an unstimulated condition (as the negative control). All experiments included the presence of 10 μg/mL of Brefeldin A (ref: B7651; Sigma, St. Louis, MO, USA) to prevent the release of cytokines from the cells. Samples were incubated for 6 h at 37 °C in a sterile environment with a 5 % CO₂ humid atmosphere.

After the 6 h incubation period, samples were aliquoted in two different tubes (0.250 mL/tube) in order to analyse the intracellular production of TNF-α in classical and non-classical monocytes as well as in mDCs. For the identification of these populations, cells were stained with the following monoclonal antibody combination: anti-CD45 krome orange (clone: J.33; Beckman Coulter – Immunotech, Marseille, France), anti-CD33 phycoerythrin cyanine 7 tandem (clone: D3HL60.251; Beckman Coulter – Immunotech) anti-CD14 allophycocyanin (clone: RM052; Beckman Coulter – Immunotech) and anti-HLA-DR peridinin chlorophyll protein cyanine 5 (clone: L243; Becton and Dickinson (BD) Biosciences, San Jose, CA, USA). After gentle mixing, cells were incubated for 15 min at room temperature in the dark followed by an intracytoplasmatic permeabilization protocol with IntraPrep Permeabilization Reagent (Beckman Coulter – Immunotech). Cells were fixed and permeabilized according to the manufacturer’s instructions. Thereafter, anti-TNF-α antibody (clone MAb11; BD Pharmingen, San Diego, CA, USA) was added and incubated for 15 min at room temperature in the dark. The cells were then washed twice with phosphate-buffered saline (Gibco BRL-life Technologies) and resuspended in 0.250 mL of this buffer.

Data acquisition was performed in a FACSCanto II flow cytometer (BD Biosciences) with the FACSDiva software (BD Biosciences) using the EuroFlow instrument setup data acquisition standard operating procedures [26]. For each sample at least 250.000 events were acquired.

Data analysis for each variable was performed using the flow cytometry software Infinicyt 1.6 (Cytognos, Salamanca, Spain). The evaluation of TNF-α production was based on the frequency (%) of positive cells within each cell subset and their corresponding expression as determined by the mean fluorescence intensity (MFI), expressed as a relative logical scale.

Since CD16 expression is lost shortly after LPS stimulation, as also reported by others [27‐30], thus precluding the identification of CD16+ monocyte subsets. On the other hand CD33 remains unchanged during LPS stimulation [30] and therefore CD33 was used as an alternative marker to CD16. Using a combination of anti-CD16 Pacific Blue (clone: 3G8; BD Pharmingen), anti-CD14 allophycocyanin, anti-HLA-DR peridinin chlorophyll protein cyanine 5, anti-CD33 phycoerythrin cyanine 7 tandem and anti-CD45 krome orange in unstimulated cells it is possible distinguish between non-classical and classical monocytes base on CD33 and CD14 combination (Fig. 1). mDCs were identified base on their CD33^high/HLA-DR^high expression with intermediate forward and side scatter between lymphocytes and monocytes (Fig. 1) [15, 30].

We set out to evaluate the direct effect of sera from patients with SLE upon TNF-α mRNA expression by monocytes subsets and dendritic cells from normal individuals in the absence of LPS. For this purpose, a heparinized peripheral blood sample from one healthy subject was washed thrice with NaCl 0.9 % saline solution and diluted 1:1 in RPMI-1640 medium supplemented with 2 mM L-glutamine and antibiotic–antimycotic agent. 0.05 mL of serum was added in a final volume of 0.5 mL: 3 from ASLE, 3 from ISLE and 3 from HC. Each sample was incubated in quadruplicate for 6 h, at 37 °C in a sterile environment under 5 % CO₂.

For the cell sorting of classical monocytes, non-classical monocytes and mDCs, cells from each sample were resuspended in a final volume of 1 mL per sample, and lysed with 10 mL of NH₄Cl solution (Sigma) to remove the red blood cells. After 20 min of incubation, samples were centrifuged (5 min, at 540 × g) and the cell pellet was stained with the following monoclonal antibodies combination: anti-CD45 Krome Orange, anti-HLA-DR fluorescein isothiocyanate (clone: Immu-357; Beckman Coulter – Immunotech), anti-CD14 peridin chlorophyll protein – Cyanin 5.5 (clone:M5E2; BD Pharmingen), anti-CD33 phycoerythrin (clone:P67.6; BD Biosciences) and anti-CD123 allophycocyanin (clone: 9 F5; BD Pharmingen). Next, the cells were incubated for 20 min at room temperature in the dark, washed and resuspended in phosphate-buffered saline (Gibco BRL-life Technologies).

Cell-sorting and purification were performed in FACSAria II cell sorter (BD Biosciences) using the FACSDiva software (BD Biosciences). Classical monocytes were identified and sorted by HLA-DR⁺/CD14^high/CD33^high/CD45^high phenotype, non-classical monocytes were HLA-DR^inter/CD33^inter/CD123^negCD45^high, and mDCs were defined as HLA-DR^high/CD33^high/CD14^neg/CD123^neg.

Sorted cell populations were then centrifuged for 5 min at 300 × g and the pellets were resuspended in 350 μL of RLT Lysis Buffer (Qiagen, Hilden, Germany). Total RNA extraction was performed with the RNeasy Micro kit (Qiagen) according to the supplier’s instructions. Total RNA was eluted in a 14 μl volume of RNase-free water. In order to quantify the amount of total RNA extracted and verify RNA integrity, samples were analyzed using a 6000 Nano Chip kit, in an Agilent 2100 bioanalyzer (Agilent Technologies, Walbronn, Germany) and 2100 expert software, according to the manufacturer’s instructions. RNA was reverse transcribed with iScript^TM Reverse Transcription Supermix for RTqPCR (Bio-Rad, Hercules, Calif., USA), according to the manufacturer’s instructions. Relative quantification of gene expression using real-time PCR was performed in the LightCycler 480 II (Roche Diagnostics, Rotkreuz, Switzerland). Real-time PCR reactions were carried out using 1 × QuantiTect SYBR Green PCR Master Mix (Qiagen), 1 × QuantiTect Primer Assay TRAF1 (QT00095732) (Qiagen) and 20 ng of cDNA sample, in a total volume of 10 μL. The reactions were performed using the following thermal profile: 15 min at 95 °C, and 50 cycles of 15 s at 94 °C, 30 s at 55 °C and 30 s at 72 °C. Melting point analysis was done to ensure amplification of the specific product. Real-time PCR results were analyzed with the LightCycler software (Roche Diagnostics). GeNorm Reference Gene Selection kit (PrimerDesign Ltd., Southampton, England) in conjunction with the geNorm software (PrimerDesign Ltd.) were used to select the reference genes to normalize data. The reference genes used for gene expression analysis of classical, non-classical monocytes and mDC were the beta-2-microglobulin (B2M) (QT00088935) and the ubiquitin-c (UBC) (QT00234430). The normalized TNF-α gene expression were calculated using the delta-Ct method [31].

Measurements of IL-17F, IL-17A, IL1-7E, IL-10, IL-12p70, IL-13, IL-15, IL-22, IL-21, IL-23, IFN-γ and TNF-α were performed in all serum samples by Luminex xMAP using the MILLIPLEX MAP Human TH17 Magnetic Bead Panel (EMD Millipore, Billerica, MA, USA). The serum cytokine levels were determined by comparison with a standard curve obtained using the corresponding recombinant human cytokines

Results were expressed as median/mean and range/interquartile range. Statistical evaluation of data was performed through non-parametric tests: The χ ² and Fisher’s exact tests were used to evaluate the significance of associations between categorical variables. Continuous variables were compared by Kruskal-Wallis test and Mann–Whitney U test. A Spearman’s rank correlation was applied to assess the association between different parameters. The statistical analyses were performed using Statistical Package for Social Sciences IBM SPSS 20 (IBM, Armonk, NY. USA) and Graphpad Prism version 5 (GraphPad Software, San Diego, CA, USA). Differences were considered statistically significant when the p value was less than 0.05.