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Emerging role of monocytes and their intracellular calcium pattern in spondyloarthritis
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Emerging role of monocytes and their intracellular calcium pattern in spondyloarthritis Stefania Moza, Mariagrazia Lorenzinb, Roberta Ramondab, Vittorio Anelonic, Massimo La Rajac, ⁎ Mario Plebania, Daniela Bassoa, a
Laboratory Medicine, Department of Medicine-DIMED, University of Padova, Via Giustiniani 2, 35128 Padova, Italy Rheumatology Unit, Department of Medicine-DIMED, University of Padova, Via Giustiniani 2, 35128 Padova, Italy c UOC Immunotrasfusionale, University-Hospital of Padova, Italy b
ARTICLE INFO
ABSTRACT
Keywords: Spondyloarthritis Inflammation Innate immune response Intracellular calcium flows
Spondyloarthritis (SpA) comprises multifactorial diseases characterized by a complex interplay between an inherited background and environmental factors that lead to immune response dysregulation and inflammation. Unlike for other rheumatic diseases, no specific biomarkers are available in clinical practice for diagnosing SpA. The aim of the present study was to search new potential biomarkers for SpA diagnosis by focusing on the innate immune response. An evaluation was made of the mRNA expression levels of inflammatory cytokines (TNF-α, IL1β, TGF-β1, S100A8, S100A9) and matrix metalloproteinases (MMP3, MMP8, MMP9) in blood mononuclear cells of SpA patients (n = 64) with respect to controls (n = 100). In parallel, the pattern of intracellular calcium flows of blood monocytes was verified in order to ascertain whether any specific fingerprint characterizes innate immune cells in SpA patients. Inflammatory cytokines and MMPs expression levels were not correlated with SpA, while in this disease a reduced expression of the S100A8 and a decreased frequency of monocytes showing intracellular calcium flows were observed. In conclusion, no specific signs of systemic inflammation are detectable in SpA, but the disease affects the “on-off” mechanisms that regulate the concentration of intracellular calcium and calcium-related proteins. This potentially pave the way for the discovery of new biomarkers.
1. Introduction Psoriatic arthritis (PsA) and axial spondyloarthritis (ax-SpA), the latter comprising ankylosing spondylitis (AS) and non-radiographic axial SpA (nr-axSpA), are inflammatory disorders that, in genetically predisposed individuals, typically involve the sacro-ileal joints. The HLA-B27 antigen is the major genetic determinant [1], although genome-wide association studies and copy number variation (CNV) analyses have identified among SpA susceptibility genes those encoding the components of the innate immune system [2,3]. Although SpA pathogenesis remains a matter of debate, it has been suggested that a complex interaction between genetic, immunological and environmental factors plays an important role in triggering this disease through the activation of autoinflammation and autoimmunity. While SpA
shares with the other autoimmune diseases an altered innate immune response, they are neither associated with disease specific autoantibodies nor predominant in females [4]. In SpA patients, not only is there a lack of specific autoantibodies, but no other specific biomarkers are available. Moreover, the unspecific biomarkers of systemic inflammation, namely CRP and/or ESR, are usually within the reference range, increased values being observed only in a minority of patients, mainly those with AS (40–50%). Due to this biochemical gap, SpA is diagnosed almost exclusively on the basis of a combined assessment of symptoms, physical examination and imaging techniques, laboratory examinations playing a minor role, or even no role at all. Consequently, the diagnosis is often delayed thus limiting early therapeutic intervention, of crucial importance in modifying disease progression and avoiding unnecessary diagnostic and therapeutic procedures [5]. There
Abbreviations: AS, Ankylosing spondylitis; ASDAS, Ankylosing Spondylitis Disease Activity Score; Ax-SpA, Axial spondyloarthritis; BASDAI, Bath Ankylosing Spondylitis Disease Activity Index; BASFI, Bath Ankylosing Spondylitis Functional Index; BASMI, Bath Ankylosing Spondylitis Metrology Index; CaSR, Calcium sensing receptor; CASPAR, ClASsification criteria for Psoriatic Arthritis; DAS, Disease Activity Score; DAS-28, Disease Activity Score-28; DMARD, Disease-modifying antirheumatic drugs; MDSC, Myeloid-derived suppressor cell; MMP, Matrix metalloproteinase; nr-axSpA, Non-radiographic axial spondyloarthritis; PsA, Psoriatic arthritis ⁎ Corresponding author. E-mail address: [email protected] (D. Basso). https://doi.org/10.1016/j.cca.2019.10.013 Received 22 August 2019; Received in revised form 15 October 2019; Accepted 15 October 2019 0009-8981/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Stefania Moz, et al., Clinica Chimica Acta, https://doi.org/10.1016/j.cca.2019.10.013
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is therefore an urgent need for sensitive and specific biomarkers for these diseases. In inflamed sacro-ileal joints, monocytes and macrophages modulate cartilage re-modelling by releasing the inflammatory cytokines TNF-α, IL-1 and IL-6, which might also induce the production of MMPs, the main enzymes involved in extra-cellular matrix degradation. Indeed, high expression of MMPs, mainly MMP3, has been found in the synovial tissues of SpA patients [6]. The relevant role in SpA of cytokines, TNF-α in particular, is further supported by the strong therapeutic response to TNF-α inhibitors, which are recommended for patients with symptoms refractory to conventional therapy or for whom conventional therapy has unacceptable side effects [7]. As well as classical cytokines, cartilage re-modelling may also be influenced by other inflammatory molecules, including TGF-β1, but also the calcium binding proteins S100A8 and S100A9, which can induce matrix metalloproteinases expression, as recently demonstrated by us elsewhere [8]. S100A8 and S100A9 are both produced by monocytes, a dynamic cellular population able to infiltrate inflamed tissues differentiating into inflammatory macrophages. Due to their potential role in sustaining chronic inflammation, monocytes have also been identified as potential therapeutic targets [9–11]. Monocytes chemotaxis to the inflamed joints might be regulated by calcium gradients as monocytes can detect variations in extracellular calcium through the activation of the calcium-sensing receptor (CaSR) [11]. Based on the above premises, the aim of the present study was to search for new potential biomarkers enabling SpA diagnosis and monitoring by focusing on the innate immune response. The mRNA expression levels of TNF-α, IL-1β, TGF-β1, S100A8, S100A9, MMP3, MMP8 and MMP9 in blood mononuclear cells were evaluated in SpA patients and compared with values in controls. In parallel, the pattern of intracellular calcium flows of blood monocytes was investigated in order to ascertain whether a specific fingerprint characterizes innate immune cells in SpA patients.
Laboratory Medicine, University-Hospital of Padova, which obtained ISO 9001 certification in 1997, as well as ISO 15189:2012 and CPA-UK accreditations in 2016 and 1995, respectively. 2.3. Genetic analysis Genomic DNA, was extracted from EDTA-K2 peripheral blood using the MagNA Pure System (Roche Diagnostics GmbH, Mannheim, Germany) according to the manufacturer’s instructions. HLA-B27 was determined using Real-Time PCR with ABI Prism 7900 HT (Thermo Fisher Scientific, Waltham, MA USA), using the primer pairs F:5′-CTA CGTGGACGACACGCT-3′ and R:5′-GCAAGGCCAAGGCACAGACT-3′, the fluorogenic probe 5′ FAM-CGTGAGGTTCGACAGC-MGB 3′ and the following amplification cycle: 50 °C for 2 min, and 94 °C for 7 min, followed by 50 cycles at 92 °C for 20 s and at 60 °C for 1 min and 10 s. 2.4. mRNA expression analyses Peripheral blood mononuclear cells (PBMCs) were isolated by gradient centrifugation (Histopaque, 1077, Sigma-Aldrich, Milano, Italy) and total RNA extracted using the High Pure RNA Isolation Kit (Roche) according to the manufacturer's instructions. Total RNA (500 ng) was reverse transcribed into cDNA using Random primers and Superscript TM II RNasiH-Reverse Trascriptase (Thermo Fisher Scientific). Real-Time PCR (ABI Prism 7900 HT, Thermo Fisher Scientific) was used for the relative quantification of mRNA expression in the conditions described elsewhere for S100A8, S100A9, MMP8 and MMP9 [8], and using the primers and probe set supplied by Thermo Fisher Scientific (Taqman Gene Expression Assay catalogue number 4331182) for TNF-α (Hs00174128_m1), IL-1β (Hs01555410_m1), TGF-β1 (Hs00998133_m1) and MMP3 (Hs00968305_m1). Hypoxanthine-guanine phosphoribosyltransferase (HPRT1) was selected as a housekeeping gene control (Hs02800695_m1). All molecular targets were analyzed in duplicate for each sample. PCR was run at the following amplification cycle: 50 °C for 2 min, 95 °C for 10 min, followed by 50 cycles at 95 °C for 15 s and 60 °C for 1 min. To determine the relative mRNA expression levels of all selected genes, we used the comparative Ct method by means of a calibrator sample from a pool obtained from 20 healthy blood donors.
2. Materials and methods 2.1. Studied population In this prospective study, approved by the Local Institutional Ethics Committee of the University-Hospital of Padova (Protocol number: 3024P/13), after obtaining fully informed written consent, 100 control healthy blood donors (58 males and 42 females; mean age ± standard deviation, 46.6 ± 8.5) and 64 SpA patients (39 males and 25 females; mean age ± standard deviation, 39.5 ± 13.2 years) were enrolled from January 2016 to December 2018. Twenty-six (40.6%) of the patients had a confirmed diagnosis of AS, according to the modified New York criteria [12] and 38 (59.3%), a diagnosis of PsA according to the CASPAR criteria [13]. The mean disease duration was 14 years (range 2–38 years). All patients underwent clinical, clinimetric and functional examinations following a standardized protocol. The demographic and clinical characteristics of enrolled patients are shown in Table 1. At enrolment, 55 patients (86%) were on therapy with anti-TNF-α agents (8 with infliximab, 21 with adalimumab, 20 with etanercept, 4 with golimumab, 3 with ustekinumab and 1 with secukinumab); 8 (14%) of the patients had undergone at least one TNF-α inhibitors switch before entering the study. Two patients had combined therapy with DMARDs and anti-TNF-α agents, and 7 were treated only with DMARDs. For a subset of 8 patients who started therapy with anti-TNF-α agent at enrolment, a second sample was obtained after six months from the start of therapy.
2.5. Intracellular calcium flows analysis Intracellular calcium [Ca2+]i flows analysis was carried out using an inverted epifluorescence microscope Nikon Eclipse Ti (Nikon Instruments, Amsterdam, Netherlands) and Fluo-4 AM (Thermo Fisher Scientific) as a high affinity Ca2+ indicator. As an illumination source, we used a mercury arc discharge lamp and selected excitation and emission wavelengths with an FITC filter cube (EX 460-500 DM505 EM 510–560 nm). We used 40X1.30 oil objective and, in order to minimize phototoxicity and photobleaching, back-thinned electron multiplied CCD camera DS-U3 Digital sight. After the first minute, required to achieve optimal culture conditions, intracellular fluorescence was continuously monitored (5 frames/sec) for 12 min. The fluorescence signal was quantified by measuring the mean pixel value of a manually selected cellular area for each frame of the image stack using the NIS-Elements software (Nikon Instruments). Monocytes were isolated from 6 mL EDTAK2 peripheral blood of 50/64 SpA patients and 48/100 controls by negative selection with Human Monocyte Enrichment Cocktail (RosetteSep kit, StemCell Technologies, Vancouver, BC, Canada), which allows the removal of non-monocyte cells and erythrocytes targeted with Tetrameric Antibody Complexes (TAC). After twenty minutes incubation of whole blood with the RosetteSep, monocytes were isolated by gradient centrifugation (Histopaque, 1077, Sigma-Aldrich) counted, seeded on coverslips that had been inserted into six well culture plates at the concentration of 106 monocytes/well, and cultured for 24 h in 2 mL of complete medium (RPMI 1640, 10% FCS; Thermo Fisher Scientific). To evaluate intracellular calcium flows, coverslips were incubated for
2.2. Haematological and biochemical indices Complete blood count and ESR, plasma glucose, uric acid, creatinine, alanine aminotransferase (ALT), serum CRP and prealbumin were analyzed by means of the routine procedures of the Department of 2
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Table 1 Demographic, clinical characteristics and clinical outcome measures. The demographic characteristics of controls and patients were obtained at enrolment, while patients’ clinical data and outcome indices are referred to findings obtained at diagnosis of AS or PsA disease. Significant p values are reported in bold face. Controls (n = 100)
AS (n = 26)
PsA (n = 38)
Statistical analysis
58/42 (42%) 46 ± 8 78.8 ± 15.89 171.75 ± 7.72 26.61 ± 4.63 5/100 (5%)
19/7 (26%) 48 ± 15 68.76 ± 9.35* 171.76 ± 7.40 23.38 ± 2.89* 17/26 (65%)
20/18 (47%) 54 ± 10** 75.38 ± 11.11 169.26 ± 8.34 26.28 ± 3.57 7/38 (18%)
χ2 = 2.80 F = 7.36 F = 5.40 F = 1.48 F = 6.28 Fisher's exact
p = 0.245 p = 0.0009 p = 0.0054 p = 0.2305 p = 0.0024 p < 0.0001
Clinical characteristics at diagnosis Family history of SpA, n (%) Age at diagnosis, mean ± SD (yrs) Inflammatory back pain, n (%) Peripheral arthritis, n (%) Enthesitis, n (%) Buttock pain, n (%) Dactylitis, n (%) Uveitis, n (%) Psoriasis, n (%) IBD, n (%) Urethritis/Cervicitis/Diarrhoea, n (%)
9 (35%) 36.5 ± 13.96 25 (96%) 9 (35%) 13 (50%) 22 (84%) 3 (11%) 1 (4%) 5 (19%) 3 (11%) 2 (7%)
25 (66%) 41.65 ± 12.48 22 (58%) 38 (100%) 29 (76%) 10 (26%) 8 (21%) 0 (0%) 35 (92%) 0 (0%) 7 (18%)
χ2 = 6.02 F = 2.39 χ2 = 11.58 χ2 = 33.83 χ2 = 4.74 χ2 = 20.98 χ2 = 0.98 χ2 = 1.48 χ2 = 34.98 χ2 = 4.60 χ2 = 1.47
p = 0.014 p = 0.1269 p = 0.001 p = 0.000 p = 0.029 p = 0.000 p = 0.322 p = 0.223 p = 0.000 p = 0.032 p = 0.225
Clinical outcome measures at diagnosis DAS, mean ± SD BASMI, mean ± SD BASFI, mean ± SD HAQ, mean ± SD BASDAI, mean ± SD ASDAS-PCR, mean ± SD
3.22 3.27 5.52 0.96 6.18 3.12
3.74 1.31 4.62 0.91 6.39 2.98
F = 4.29 F = 21.07 F = 3.45 F = 0.16 F = 0.33 F = 1.02
p = 0.0424 p = 0.0000 p = 0.0681 p = 0.6918 p = 0.5649 p = 0.3174
Demographic characteristics at enrolment Gender M/F (% F) Age mean ± SD (yrs) Weight mean ± SD (Kg) Height mean ± SD (cm) BMI mean ± SD (Kg/m2) HLA-B27 positive/total (% positive)
± ± ± ± ± ±
twenty minutes at 37 °C with 5 μM Fluo-4 AM in imaging buffer (IB) containing 137 mM NaCl, 5 mMKCl, 1.2 mM MgCl2 ,0.44 mM KH2PO4, 4.2 mM NaHCO3, 5 mM glucose, 20 mM HEPES pH 7.4. The cells were carefully washed two times and then incubated with IB at 37 °C for ten minutes in order to allow Fluo-4 AM to stay inside the cells. The coverslip was placed in an appropriate chamber, added 1 mL of IB supplemented with 1 mM CaCl2 and [Ca2+]i flows were then analyzed as described elsewhere [14]. Since the number of monocytes that can be analyzed within a microscopic field is limited, for each patient and control at least two replicates were done. The median number of studied monocytes was 20 (95% CI: 19–27) for controls and 19 (95% CI 21–31) for SpA patients (Mann Whitney test: p = 0.602).
0.96 1.806 1.90 0.52 1.55 0.64
± ± ± ± ± ±
0.99 1.58 1.90 0.53 1.43 0.48
3. Results 3.1. Haematological and biochemical indices in SpA patients Table 2 reports the median and interquartile range of haematological and biochemical parameters evaluated in controls and in SpA patients at enrolment. White blood cells (WBC) and polymorphonuclear cells (PMN) were more numerous in SpA patients than in controls, the highest values being recorded among AS, that also had significantly higher levels of haemoglobin than controls. Overall, in SpA patients lymphocytes were more numerous and levels of glucose and CRP higher than in controls, although no statistically significant differences were found.
2.6. “In vitro” experiments in monocyte cells
3.2. Cytokines and MMPs expression in PBMCs
Intracellular calcium flows of control monocytes were also evaluated in the presence or absence of SpA patients’ sera. Before calcium flows analyses, monocytes isolated from the buffy coat of healthy blood donors as described above, were kept in continuous culture for 24 h in the following conditions: 1. Standard media alone (RPMI 1640, 0.1% Gentamycin, 1% L-Glutamine); 2. Standard media with 10% FCS; 3. Standard media with 10% AS patients’ serum; 4. Standard media with 10% PsA patients’ serum; 5. Standard media with 10% heathy controls’ serum. Patients’ and controls’ serum used for the experiments, pools of three cases each, were used after treatment at 56 °C for 20 min. All experiments were made at least in triplicate.
Table 3 shows the expression levels of cytokines and of MMP8 and MMP9 in the patients and controls. MMP3 mRNA expression was not detected in any samples. S100A8 expression levels were lower in PsA patients than in controls, while no variations were found in the other studied cytokines and MMPs. S100A8 was correlated with S100A9 (r = 0.7692, Bonferroni’s adjusted p value < 0.0001), and MMP8 with MMP9 (r = 0.6257, p < 0.0001). MMP9 was also correlated with S100A8 (r = 0.3742, p < 0.0001) and S100A9 (r = 0.3674, p < 0.0001). TNF-α was correlated with TGF-β1 (r = 0.5729, p < 0.0001) and with IL-1β (r = 0.3964, p < 0.0001).
2.7. Statistical analysis
3.3. Intracellular calcium flows in SpA patients and controls
Mean, median, standard error (SE), standard deviation (SD), interquartile range (IQR) and 95% confidence interval (CI) were used as descriptive statistics. The Shapiro-Wilk W test for normal data, KruskalWallis rank test, Mann Whitney test, χ2 test, Fisher’s exact test, binary logistic regression analysis, Student’s t test for unpaired data, analysis of variance (ANOVA) and Bonferroni’s adjustment of p value for multiple testing were performed using Stata software, version 13.1 (StataCorp, Lakeway Drive, TX, USA).
The recruitment of monocytes at joint inflammatory sites plays a key role in the inflammatory cascade, and in joint destruction. As recently pointed out, calcium signalling in circulating monocytes regulates chemotaxis and cytokine production [11]. In this study, we evaluated whether the pattern of intracellular calcium oscillations in controls (n = 48) differs from that in SpA patients (n = 50). Blood monocytes were isolated by negative selection and then used to ascertain intracellular calcium flows by epifluorescence microscope analysis. 3
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Table 2 Haematological and biochemical parameters in controls, AS and PsA patients at study enrolment. One-way ANOVA for normally distributed and Kruskal-Wallis rank test for the non-parametric variables (Shapiro-Wilk W test) were used. Bonferroni’s adjusted p-values: *= p < 0.01 vs controls; **= p < 0.0001 vs controls; ^ = p < 0.05 vs PsA. Significant p values are reported in bold face.
3
WBC (×10 /μL) Haemoglobin (g/L) Platelets (×103/μL) PMN (×103/μL) Monocytes (×103/μL) Lymphocytes (×103/μL) Glucose (mmol/L) Creatinine (μmol/L) Uric acid (mmol/L) ALT (U/L) Prealbumin (mg/L) CRP (mg/L)
Controls (n = 100) Median (IQR)
AS (n = 26) Median (IQR)
PsA (n = 38) Median (IQR)
Statistical analysis
5.2 (4.2–6.2) 138 (124–148) 237 (174–267) 2.8 (2.0–3.5) 0.4 (0.3–0.6) 1.7 (1.3–2.1) 4.9 (4.0–5.4) 82 (63–90) 0.3 (0.2–0.3) 21.5 (14.0–29.0) 289 (225–316) 2.0 (2.0–2.0)
6.6 (4.6–8.3)** 149 (124–160)* 242 (151–320) 3.9 (2.9–5.1)**^ 0.4 (0.3–0.6) 2.2 (1.2–2.7) 5.3 (3.9–5.7) 71 (54–83) 0.3 (0.2–0.3) 24.0 (15.0–31.0) 285 (235–322) 2.0 (2.0–6.3)
6.3 (4.0–7.7)* 146 (124–150) 260 (181–312) 3.3 (2.0–5.1)* 0.4 (0.3–0.6) 2.1 (1.3–2.5) 5.2 (4.2–5.9) 77 (58–84) 0.3 (0.2–0.3) 27.5 (13.0–35.0) 287 (223–321) 2.0 (2.0–4.5)
p < 0.0001 p = 0.0083 F = 2.07, p = 0.1301 p < 0.0001 p = 0.7872 p = 0.0527 p = 0.0525 F = 3.01, p = 0.0519 F = 0.55, p = 0.5806 p = 0.1498 F = 0.60, p = 0.5509 p = 0.1521
Monocytes obtained from healthy blood donors presented evident and frequent intracellular calcium oscillations, unlike monocytes obtained from SpA patients. Fig. 1 shows individual cellular data from representative experiments on 4 controls and 4 SpA patients. For every individual experiment, the number of monocytes exhibiting intracellular calcium oscillations with respect to the whole number of cells examined was calculated and expressed as a percentage (% of cells). Fig. 2 shows the results obtained in controls and patients with AS or PsA. The percentage of active monocytes for AS patients was significantly lower than in controls (F = 6.15, p = 0.003). The frequency of calcium flows of monocytes showing intracellular calcium oscillations was then evaluated. To calculate this frequency, for each monocyte the total number of calcium flows recorded in the course of the 12 min experiment were manually counted and divided by 12 (flows/min). Mean values of single cells data were then calculated for any individual patient and control. The frequency of calcium flows did not differ between patients (0.24 ± 0.02 flows/min, mean ± SEM) and controls (0.22 ± 0.01 flows/min) (Student’s t test: t = 0.838, p = 0.405). The percentage of cells with intracellular calcium oscillations in SpA patients was correlated neither with the severity clinical indices BASDAI, BASFI, BASMI, ASDAS-CRP, DAS and HAQ, nor with the biochemical indices of inflammation (i.e. circulating PMN, lymphocytes, monocytes and CRP). Since all 50 SpA patients were on therapy, we ascertained whether variations in cells with calcium oscillations were correlated with the drug type used. Values found in patients on DMARDs (n = 6, Mean ± SE: 22.928 ± 6.462) did not differ from those in patients on anti-TNF-α (n = 44, Mean ± SE: 21.709 ± 2.394; Student’s t test: t = 0.17, p = 0.861). This result was further confirmed in the subset of eight patients for whom analysis was replicated before and after 6 months from the beginning of anti-TNF-α therapy (Student’s t test for paired data: t = 0.21, p = 0.833). To further verify the association between the reduced intracellular
calcium flows (% of cells) of blood monocytes and SpA, binary logistic regression analysis was performed considering the presence or absence of SpA as dependent variable and, as predictor variables, those that were associated with SpA at univariate statistical analysis (Table 4). Table 5 reports the results of multiple linear regression analyses conducted to evaluate whether the percentages of cells with calcium oscillations were correlated with the expression levels of inflammatory cytokines, S100 proteins and MMPs in SpA patients and controls. 3.4. Serum of SpA patients softens intracellular calcium oscillations in human monocytes The observed disease-related pattern of cells with calcium oscillations might be consequent to intrinsic alterations in monocytes and/or to soluble blood mediators. To verify the latter hypothesis, we performed in vitro experiments using three healthy donors’ blood monocytes conditioned for 24 h with FCS or with human serum pools obtained from healthy subjects (n = 3), from patients with AS (n = 3) and from patients with PsA (n = 3). At the end of the conditioning period, intracellular calcium oscillations of monocytes were analyzed, and the percentages of cells with intracellular calcium flows and the frequency of calcium spikes were calculated as described above. With respect to monocytes cultured in the absence of growth factors (FCS 0%), the percentage of cells with calcium oscillations increased in the presence FCS 10%, but mainly in the presence of healthy human sera (Fig. 3; Kruskal Wallis test: p = 0.038). Monocytes treated with AS or PsA sera were less frequently found to pulse with respect of control sera. Moreover, the frequency of calcium flows was significantly lower in monocytes treated with PsA sera (0.18 ± 0.003 flows/min, mean ± SEM) than in those treated with control (0.24 ± 0.003) or AS sera (0.240 ± 0.001) (One way anova: F = 11.62, p = 0.0009). Overall these data suggest the hypothesis that in SpA patients with respect to controls, there are less soluble mediators released into the bloodstream able to induce monocytic intracellular calcium oscillations. This finding
Table 3 Cytokines, S100A8, S100A9, MMP8 and MMP9 mRNA fold increase in PBMCs of controls, AS and PsA patients. Median values, ranges of the mRNA fold increase and results of the statistical analyses are reported.
TNF-α fold increase IL-1β fold increase TGF-β1 fold increase S100A8 fold increase S100A9 fold increase MMP8 fold increase MMP9 fold increase
Controls (n = 100)
AS (n = 26)
PsA (n = 38)
Median
Range (min–max)
Median
Range (min–max)
Median
Range(min–max)
0.252 0.138 0.847 1.032 0.707 0.25 0.046
0.103–4.724 0.019–83.286 0.011–49.180 0.376–4.347 0.264–3.811 0.010–8 0.004–0.358
0.227 0.078 0.727 0.922 0.642 0.337 0.048
0.102–1.014 0.024–2.085 0.409–4.993 0.398–3.555 0.289–1.547 0.009–8.112 0.007–0.877
0.248 0.076 0.697 0.946 0.628 0.285 0.056
0.130–1.624 0.010–1.624 0.389–5.856 0.189–1.853 0.252–1.375 0.013–3.630 0.006–0.316
4
One-way ANOVA log2 transformed values
F = 1.01, F = 0.79, F = 0.70, F = 3.29, F = 1.94, F = 0.19, F = 0.86,
p = 0.3661 p = 0.4559 p = 0.4983 p = 0.0398 p = 0.1476 p = 0.8253 p = 0.4255
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Fig. 1. Intracellular calcium flows ([Ca2+]i) of monocytes from controls and SpA patients (SpA).
paves the way for the potential identification of new blood biomarkers of disease. To verify whether the variations in calcium flows induced by patients’ sera are associated with functional monocytes alterations potentially harmful for the joints, monocytes MMP8 and MMP9 expression levels were evaluated. In addition to the experimental conditions described above, monocytes were also treated with LPS 1 μg/mL a known activator of the innate immune response. Fig. 4 shows the results obtained. MMP8 expression was significantly correlated with LPS treatment (Two way repeated measures ANOVA: F = 25.32, p = 0.004 for LPS; F = 3.973, p = 0.0813 for serum; F = 2.68, p = 0.1583 for interaction), while MMP9 expression was affected by both LPS and serum treatments (F = 24.51, p = 0.004 for LPS; F = 22.45, p = 0.0022 for serum; F = 13.91, p = 0.0064 for interaction).
4. Discussion SpA comprises two main diseases, AS and PsA, that primarily involve the spine, but can also affect peripheral joints and tissues/organs outside the skeleton, such as eyes, skin and intestine [15–17]. Since the symptoms that patients refer at disease onset, mainly back pain, are non-specific, the diagnosis is often delayed, and repeated cycles of therapy with anti-inflammatory agents are prescribed. The relatively late diagnosis with a consequent delay in appropriate treatment may be associated with disease progression that causes irreversible joint damage. The diagnosis is supported by imaging and unfortunately not enough by biochemistry. In fact, no specific blood biomarkers are available for these diseases. Moreover, despite the intensive inflammatory process involving the sacro-ileal joints, the common blood
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Fig. 2. Percentage of monocytes (% of cells) with intracellular calcium oscillations in controls, Ankylosing spondylitis (AS) and Psoriatic Arthritis (PsA) patients. Bonferroni’s test for pairwise comparisons: *=p < 0.005 with respect to controls.
Fig. 3. Percentage of monocytes (% of cells) with intracellular calcium oscillations in healthy blood donors cultured in the absence or presence of 10% FCS, 10% of healthy controls (Control), 10% of Ankylosing Spondylitis (AS) and 10% of Psoriatic Arthritis patients (PsA) patients’ serum.
Table 4 Binary logistic regression analysis results. The presence or the absence of SpA considered as outcome. Predictive variables: Age, Gender, BMI, WBC, Haemoglobin, PMN, Lymphocytes, Glucose, CRP, HLA-B27, log2 S100A8, and % of cells with intracellular calcium oscillations. The analysis was made considering the patients for whom analysis of intracellular calcium flows was performed (n = 50). SE: Standard Error; CI = Confidence Interval. Significant p values are reported in bold face.
Age (years) Gender BMI (Kg/m2) WBC (×103/µL) Haemoglobin (g/L) PMN (×103/µL) Lymphocytes (×103/µL) Glucose (mmol/L) CRP (mg/L) HLA-B27 log2 S100A8 % of cells Constant
Coefficent
SE
95% CI
p-value
0.101 −0.558 −0.204 1.099 0.032 −0.023 −0.696 0.955 0.070 1.909 −1.629 −0.040 −13.156
0.039 0.813 0.107 0.490 0.027 0.507 0.787 0.388 0.109 0.987 0.608 0.017 6.166
0.024 to 0.177 −2.153 to 1.035 −0.414 to 0.005 0.137 to 2.061 −0.021 to 0.087 −1.018 to 0.971 −2.239 to 0.847 0.194 to 1.716 −0.143 to 0.285 −0.024 to 3.844 −2.823 to −0.436 −0.075 to −0.005 −25.241 to −1.070
0.009 0.492 0.056 0.025 0.240 0.963 0.377 0.014 0.518 0.053 0.007 0.025 0.033
infiltrating the inflamed joints derive from circulating monocytes and contribute to the process of tissue destruction by expressing not only inflammatory cytokines, such as TNF-α, TGF-β1 or IL-1β, and inflammatory proteins, such as the calcium binding proteins S100A8 and S100A9, but also enzymes causing tissue destruction and remodeling, such as metalloproteinases [18–20]. When compared to controls, AS and PsA patients had similar cytokines expression levels in their circulating mononuclear cells. This finding was in part unexpected, since increased protein levels have been reported in these patients and the role of these cytokines, mainly TNF-α and IL-1β, in disease pathogenesis is well established and supported by the dramatic efficacy of anti-TNF-α therapy [21]. Our unexpected findings may be explained by the observation that slight variations in mRNA expression might result in wide variations in the encoded proteins. Moreover, mononuclear cells might enhance the expression levels of inflammatory cytokines only when homed at the joint tissue sites. Despite the lack of association between cytokines expression levels and disease diagnosis, the TNF-α, TGF-β1 and IL-1β variations were correlated with each other, thus suggesting that these inflammatory pathways share common drivers and probably influence each other. The above results suggest that TNF-α, TGF-β1 and IL-1β are not of clinical utility either in SpA diagnosis, or in the stratification of disease severity, since they vary independently from clinical outcome measures. For our analysis of other potential inflammatory biomarkers, we therefore chose the metalloproteinases MMP8 and MMP9 in order to investigate their potential role in SpA. We also included another metalloproteinase, MMP3, reportedly the most promising investigated tool
inflammatory indexes (e.g. CRP and ESR) remain within the reference ranges or are only slightly altered, and this was confirmed by our present results. These considerations have prompted the search for new potential biomarkers that may aid and support diagnosis and monitoring. In this study, our focus was on circulating mononuclear cells as key players in the inflammatory process of SpA. In fact, macrophages
Table 5 Multiple linear regression analysis results. Table reports the correlation of the mRNA expression levels (log2 transformed) of inflammatory cytokines, S100A8, S100A9, MMP8 and MMP9 with the percentage of monocytes showing intracellular calcium flows in SpA patients (n = 50) and controls (n = 48). SE: Standard Error; CI = Confidence Interval. Significant p values are reported in bold face. SpA patients
log2 TNF-α log2 IL-1β log2 TGF-β1 log2 S100A8 log2 S100A9 log2 MMP8 log2 MMP9 Constant
Controls
Coefficient
SE
95% CI
p-value
Coefficient
SE
95% CI
p-value
3.968 2.770 −2.697 5.346 −3.976 −0.384 1.213 40.578
3.390 2.147 4.339 8.321 9.457 1.859 2.730 14.303
−2.878 to 10.815 −1.567 to 7.107 −11.461 to 6.066 −11.458 to 22.151 −23.075 to 15.122 −4.140 to 3.371 −4.300 to 6.727 11.692 to 69.464
0.249 0.204 0.538 0.524 0.676 0.837 0.659 0.007
−14.557 4.610 −0.659 14.438 −12.894 1.030 3.302 26.436
6.724 3.371 2.155 6.863 5.475 2.858 3.809 19.955
−28.159 to −0.956 −2.208 to 11.429 −5.018 to 3.699 0.554 to 28.321 −23.968 to −1.819 −4.751 to 6.812 −4.402 to 11.007 −13.926 to 66.800
0.037 0.179 0.761 0.042 0.024 0.720 0.391 0.193
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expression did not vary, S100A8 expression was lower in PsA patients than in controls. This finding is hard to explain. One of the mechanisms underlying S100A8 expression might be related to variations in intracellular calcium, which regulates several biological processes of cells. The intracellular calcium balance is strictly regulated by transporters expressed both on the cellular and mitochondrial membranes [24]. Intracellular calcium alterations might be characterized by increased and constant accumulation of the ion, increased frequency in its oscillations, or vice versa (i.e. decreased intracellular levels and/or frequency of oscillations) [25]. These alterations may have different biological effects, depending on cell type and cell context. We verified whether or not circulating monocytes maintain their characteristic pattern of intracellular calcium behavior in the presence of SpA. Our findings clearly demonstrate that monocytes from patients with SpA, but mainly those with AS, lose their typical intracellular calcium pattern. While the majority of monocytes from controls maintained in culture for 24 h show regular calcium oscillations, only a minority of monocytes from AS patients maintain this pattern, while the majority of them do not present regular calcium oscillations, which, when present, had the same frequency of control monocytes. Overall our findings suggest that at the periphery of SpA patients, within the monocyte cellular population, a shift between subsets might occur. The reduced number of monocytes with intracellular calcium oscillations might be the expression of a differentiation shift of monocytes towards the dendritic cell or the mMDSC phenotypes. In fact, as previously demonstrated by us, intracellular calcium flows in these cellular subsets of the monocyte population are reduced or even absent [26]. The role of MDSC as a contributor to arthritis and psoriasis is currently an emerging concept: in cases of psoriasis, Cao et al. described increased monocytic MDSC producing increased amounts of IL-23, IL-1β and the Chemokine (C-C motif) ligand 4 (CCL4), and a de-regulated immunosuppressive loop in this disease has been suggested [27], while Zhang et al. supported the potential pathophysiological effect of these immunosuppressive cells by demonstrating their role in bone erosion in an animal model [28]. Independently from the biological function characterizing monocytes with a reduced intracellular calcium, at multivariate binary logistic analysis, the reduced frequency of these cells was confirmed as one of the four main and independent predictors of SpA together with S100A8 expression, glucose levels and WBC. In the attempt to improve on the understanding of mechanism underlying the accumulation of monocytes with reduced intracellular calcium fluxes in patients with SpA, structural alterations possibly due to genetic variants of the CaRS might be hypothesized. However, the expression of calcium sensing receptors can vary in inflammatory diseases of the joints as demonstrated by Séjourné et al, who found reduced CaSR in monocytes isolated from synovial fluid and peripheral blood from patients with rheumatoid arthritis [11]. In agreement with the latter hypothesis, in our experiments, sera from PsA patients were found to be less efficient than normal sera in inducing intracellular calcium oscillations in the peripheral blood mononuclear cells of healthy blood donors. This effect of PsA sera on monocytes calcium oscillations was correlated, at least in part, with changings in MMPs expression levels: AS sera were less inducers of MMP9 expression than control or PsA sera, while both AS and PsA sera induced a higher MMP8 expression than control sera. Moreover, patients’ sera synergized with LPS in inducing MMP8, while LPS antagonized patients’ sera effects on MMP9. Although at a systemic level MMP8 and MMP9 expression levels did not vary in SpA, primed monocytes at the joint site might express high levels of these, as well as of other, MMPs under the effect of serum derived soluble factors. Although these soluble factors remain unknown, the role of cytokines may be hypothesized, but small metabolites may also be involved. Another potential confounding factor might be therapy, since SpA patients were on treatment at enrolment. However, the percentage of monocytes with intracellular calcium oscillations was not correlated with the type of therapy or of drug used, nor did it vary before and after six months of anti-TNF-α therapy in the subset of eight patients evaluated before and
Fig. 4. MMP8 and MMP9 expression (fold increase with respect to FCS 0%) of monocytes obtained from healthy blood donors and cultured in the absence or presence of LPS 1 μg/mL and in culture media without FCS (FCS 0%), with FCS 10% or with 10% of healthy controls (Control), 10% of Ankylosing Spondylitis (AS) and 10% of Psoriatic Arthritis (PsA) patients’ serum. Bonferroni’s test for pairwise comparisons: * = p < 0.05 with respect to FCS 0% and FCS 10%; **=p < 0.05 with respect to FCS 0%, FCS 10% and AS 10%.
for diagnosing SpA, both at serum protein level and at mRNA expression level [20]. MMP3 mRNA expression was not measurable in either patients or controls. The discrepancy between our results and those previously reported in the literature might be explained by the fact that MMP3 protein has so far been described as increased in serum, while increased transcripts have been described in synovial fluid or tissues [22]. Although MMP3 and mononuclear cells are both potentially involved in disease pathogenesis, this does not necessarily imply that MMP3 is produced directly by blood mononuclear cells. Since other MMPs could be implicated, we focused on MMP8 and MMP9 for which few data are reported in the literature [20]; however, although both were expressed at measurable levels in circulating mononuclear cells, no difference was found between controls and patients for these metalloproteinases which, moreover, not were related to disease clinical activity indices. Further insight is being gained of the role played in inflammatory joint diseases by intracellular calcium as a trigger for the release of inflammatory molecules and of the related calcium binding inflammatory S100 proteins [23]. Two members of this protein family appear of particular relevance: S100A8 and S100A9, an increase in their mRNA and protein expression levels having found in the synovial fluid and in blood of patients with rheumatoid arthritis [19]. S100A8 and S100A9 appear to be involved in joint destruction, probably because they are inducers of metalloproteinases, including MMP8 and MMP9 [8], this association also being supported by our findings that there are significant correlations between MMP9, S100A8 and S100A9. S100A8 and S100A9 proteins are both expressed by mononuclear cells and, based on this premise, we verified whether their expression by circulating mononuclear cells is altered in SpA. While S100A9 7
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Fig. 5. Schematic representation of the hypothetical mechanism linking blood monocytes (Mφ) and sacro-ileal joint inflammation in SpA. In SpA patients’ blood monocytes incur in a subset shift with increasing cells characterized by reduced intracellular calcium flows. Serum-derived factors appear responsible of this effect, but they also induce the expression of the matrix metalloproteinases MMP8 and MMP9. Through the serum induced release of MMPs, primed monocytes migrated at the joint site might shape a micro environment that favors tissue destruction and remodeling. By targeting calcium flows and MMPs new therapies might be identified, while the identification of serum factors might allow the discovery of new biomarkers.
after therapy. Therefore, our findings appear to be mainly disease, rather than drug related. Flow cytometry studies to define blood monocytes subtypes, proteomic and metabolomic studies to identify putative serum factors able to modify monocytes subtypes and function are expected in this field.
[9] W.D. Jackson, K.J. Woollard, Targeting monocyte and macrophage subpopulations for immunotherapy: a patent review (2009–2013), Expert. Opin. Ther. Pat. 24 (2014) 779–790, https://doi.org/10.1517/13543776.2014.914495. [10] J.L. Davignon, M. Hayder, M. Baron, J.F. Boyer, A. Constantin, F. Apparailly, et al., Targeting monocytes/macrophages in the treatment of rheumatoid arthritis, Rheumatology (Oxford) 52 (2013) 590–598, https://doi.org/10.1093/ rheumatology/kes304. [11] C. Séjourné, T. Boudot, P. Objois, M. Fardellone, I. Six Brazier, et al., Expression of the calcium-sensing receptor in monocytes from synovial fluid is increased in osteoarthritis, Joint Bone Spine 84 (2017) 175–181, https://doi.org/10.1016/j. jbspin.2016.03.012. [12] S. van der Linden, H.A. Valkenburg, A. Cats, Evaluation of diagnostic criteria for ankylosing spondylitis. A proposal for modification of the New York criteria, Arthritis Rheum. 27 (1984) 361–368, https://doi.org/10.1002/art.1780270401. [13] W. Taylor, D. Gladman, P. Helliwell, A. Marchesoni, P. Mease, H. Mielants, CASPAR Study Group, Classification criteria for psoriatic arthritis: development of new criteria from a large international study, Arth. Rheum. 54 (2006) 2665–2673, https://doi.org/10.1002/art.21972. [14] D. Basso, E. Greco, A. Padoan, P. Fogar, M. Scorzeto, E. Fadi, et al., Altered intracellular calcium fluxes in pancreatic cancer induced diabetes mellitus: Relevance of the S100A8 N-terminal peptide (NT-S100A8), J. Cell. Physiol. 226 (2011) 456–468, https://doi.org/10.1002/jcp.22355. [15] L. Giovannini, M. Orlandi, C. Lodato, E. Cioffi, S. Tenti, M. Bardelli, et al., One year in review 2015: spondyloarthritis, Clin. Exp. Rheumatol. 33 (2015) (2015) 769–778 PMID: 26690889. [16] N. Garg, F. van den Bosch, A. Deodhar, The concept of spondyloarthritis: where are we now? Best Pract. Res. Clin. Rheumatol. 28 (2014) 663–672, https://doi.org/10. 1016/j.berh.2014.10.007. [17] M. Dougados, D. Baeten, Spondyloarthritis, Lancet 377 (2011) 2127–2137, https:// doi.org/10.1016/S0140-6736(11)60071-8. [18] C. Ambarus, N. Yeremenko, P.P. Tak, D. Baeten, Pathogenesis of spondyloarthritis: autoimmune or autoinflammatory? Curr. Opin. Rheumatol. 24 (2012) 351–358, https://doi.org/10.1097/BOR.0b013e3283534df4. [19] F. Ometto, L. Friso, D. Astorri, C. Botsios, B. Raffeiner, L. Punzi, A. Doria, Calprotectin in rheumatic diseases, Exp. Biol. Med. (Maywood). 242 (2017) 859–873, https://doi.org/10.1177/1535370216681551. [20] S. Moz, A. Aita, D. Basso, R. Ramonda, M. Plebani, L. Punzi, Spondyloarthritis: Matrix Metalloproteinases as Biomarkers of Pathogenesis and Response to Tumor Necrosis Factor (TNF) Inhibitors, Int. J. Mol. Sci. 18 (2017) 830, https://doi.org/10. 3390/ijms18040830. [21] F. Van den Bosch, A. Deodhar, Treatment of spondyloarthritis beyond TNF-alpha blockade, Best Pract. Res. Clin. Rheumatol. 28 (2014) 819–827, https://doi.org/10. 1016/j.berh.2014.10.019. [22] K.G. Liu, Q.H. He, J.W. Tan, G.J. Liao, Expression of TNF-α, VEGF, and MMP-3 mRNAs in synovial tissues and their roles in fibroblast-mediated osteogenesis in ankylosing spondylitis, Genet. Mol. Res. 14 (2015) 6852–6858, https://doi.org/10. 4238/2015.June. 18.28. [23] D. Foell, J. Roth, Proinflammatory S100 proteins in arthritis and autoimmune disease, Arthritis Rheum. 50 (2004) 3762–3771, https://doi.org/10.1002/art. 20631. [24] D.E. Clapham, Calcium signaling, Cell 131 (2007) 1047–1058, https://doi.org/10. 1016/j.cell.2007.11.028.
5. Conclusion The results reported in the present study indicate that the analysis of the expression levels of inflammatory cytokines, S100A8, S100A9 and MMPs in blood circulating mononuclear cells appear to be of no clinical utility in diagnosing SpA or in stratifying disease groups. SpA patients present a reduced number of monocytes with intracellular calcium oscillations, probably due to shift toward the mMDSC phenotype which might be involved in the SpA inflammatory process, as summarized in Fig. 5. References [1] R.A. Colbert, F. Navid, T. Gill, The role of HLA-B*27 in spondyloarthritis, Best. Pract. Res. Clin. Rheumatol. 31 (2017) 797–815, https://doi.org/10.1016/j.berh. 2018.07.012. [2] International Genetics of Ankylosing Spondylitis Consortium (IGAS), A. Cortes, J. Hadler, J.P. Pointon, P.C. Robinson, T. Karaderi, P. Leo, et al. Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci. Nat. Genet. 45 (2013) 730-738. doi: http://doi.org/10.1038/ ng.2667. [3] D.D. O'Rielly, M. Uddin, P. Rahman, Ankylosing spondylitis: beyond genome-wide association studies, Curr. Opin. Rheumatol. 28 (2016) 337–345, https://doi.org/10. 1097/BOR.0000000000000297. [4] N. Vanaki, S. Aslani, A. Jamshidi, M. Mahmoudi, Role of innate immune system in the pathogenesis of ankylosing spondylitis, Biomed. Pharmacother. 105 (2018) 130–143, https://doi.org/10.1016/j.biopha.2018.05.097. [5] J. Sieper, M. Rudwaleit, M.A. Khan, J. Braun, Concepts and epidemiology of spondyloarthritis, Best. Pract. Res. Clin. Rheumatol. 20 (2006) 401–417, https:// doi.org/10.1016/j.berh.2006.02.001. [6] J. Zhu, D.T. Yu, Matrix metalloproteinase expression in the spondyloarthropathies, Curr. Opin. Rheumatol. 18 (2006) 364–368, https://doi.org/10.1097/01.bor. 0000231904.04548.09. [7] M. Corbett, M. Soares, G. Jhuti, S. Rice, E. Spackman, E. Sideris, et al., Tumour necrosis factor-α inhibitors for ankylosing spondylitis and non-radiographic axial spondyloarthritis: a systematic review and economic evaluation, Health. Technol. Assess. 20 (2016) 1–334, https://doi.org/10.3310/hta20090. [8] S. Moz, D. Basso, A. Padoan, D. Bozzato, P. Fogar, C.F. Zambon, et al., Blood expression of matrix metalloproteinases 8 and 9 and of their inducers S100A8 and S100A9 supports diagnosis and prognosis of PDAC-associated diabetes mellitus, Clin. Chim. Acta 156 (2016) 24–30, https://doi.org/10.1016/j.cca.2016.02.018.
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S. Moz, et al. [25] M.J. Berridge, M.D. Bootman, H.L. Roderick, Calcium signalling: dynamics, homeostasis and remodelling, Nat. Rev. Mol. Cell. Biol. 4 (2003) 517–529, https:// doi.org/10.1038/nrm1155. [26] D. Basso, E. Gnatta, A. Padoan, P. Fogar, S. Furlanello, A. Aita, et al., PDAC-derived exosomes enrich the microenvironment in MDSCs in a SMAD4-dependent manner through a new calcium related axis, Oncotarget 8 (2017) 84928–84944, https:// doi.org/10.18632/oncotarget.20863. [27] L.Y. Cao, J.S. Chung, T. Teshima, L. Feigenbaum, P.D. Cruz, H.T. Jacobe Jret al.,
myeloid-derived suppressor cells in psoriasis are an expanded population exhibiting diverse T-cell-suppressor mechanisms, J. Invest. Dermatol. 136 (2016) 1801–1810, https://doi.org/10.1016/j.jid.2016.02.816. [28] H. Zhang, Y. Huang, S. Wang, R. Fu, C. Guo, H. Wang, et al., Myeloid-derived suppressor cells contribute to bone erosion in collagen-induced arthritis by differentiating to osteoclasts, J. Autoimmun. 65 (2015) 82–89, https://doi.org/10.1016/ j.jaut.2015.08.010.
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Emerging role of monocytes and their intracellular calcium pattern in spondyloarthritis Stefania Moza, Mariagrazia Lorenzinb, Roberta Ramondab, Vittorio Anelonic, Massimo La Rajac, ⁎ Mario Plebania, Daniela Bassoa, a
Laboratory Medicine, Department of Medicine-DIMED, University of Padova, Via Giustiniani 2, 35128 Padova, Italy Rheumatology Unit, Department of Medicine-DIMED, University of Padova, Via Giustiniani 2, 35128 Padova, Italy c UOC Immunotrasfusionale, University-Hospital of Padova, Italy b
ARTICLE INFO
ABSTRACT
Keywords: Spondyloarthritis Inflammation Innate immune response Intracellular calcium flows
Spondyloarthritis (SpA) comprises multifactorial diseases characterized by a complex interplay between an inherited background and environmental factors that lead to immune response dysregulation and inflammation. Unlike for other rheumatic diseases, no specific biomarkers are available in clinical practice for diagnosing SpA. The aim of the present study was to search new potential biomarkers for SpA diagnosis by focusing on the innate immune response. An evaluation was made of the mRNA expression levels of inflammatory cytokines (TNF-α, IL1β, TGF-β1, S100A8, S100A9) and matrix metalloproteinases (MMP3, MMP8, MMP9) in blood mononuclear cells of SpA patients (n = 64) with respect to controls (n = 100). In parallel, the pattern of intracellular calcium flows of blood monocytes was verified in order to ascertain whether any specific fingerprint characterizes innate immune cells in SpA patients. Inflammatory cytokines and MMPs expression levels were not correlated with SpA, while in this disease a reduced expression of the S100A8 and a decreased frequency of monocytes showing intracellular calcium flows were observed. In conclusion, no specific signs of systemic inflammation are detectable in SpA, but the disease affects the “on-off” mechanisms that regulate the concentration of intracellular calcium and calcium-related proteins. This potentially pave the way for the discovery of new biomarkers.
1. Introduction Psoriatic arthritis (PsA) and axial spondyloarthritis (ax-SpA), the latter comprising ankylosing spondylitis (AS) and non-radiographic axial SpA (nr-axSpA), are inflammatory disorders that, in genetically predisposed individuals, typically involve the sacro-ileal joints. The HLA-B27 antigen is the major genetic determinant [1], although genome-wide association studies and copy number variation (CNV) analyses have identified among SpA susceptibility genes those encoding the components of the innate immune system [2,3]. Although SpA pathogenesis remains a matter of debate, it has been suggested that a complex interaction between genetic, immunological and environmental factors plays an important role in triggering this disease through the activation of autoinflammation and autoimmunity. While SpA
shares with the other autoimmune diseases an altered innate immune response, they are neither associated with disease specific autoantibodies nor predominant in females [4]. In SpA patients, not only is there a lack of specific autoantibodies, but no other specific biomarkers are available. Moreover, the unspecific biomarkers of systemic inflammation, namely CRP and/or ESR, are usually within the reference range, increased values being observed only in a minority of patients, mainly those with AS (40–50%). Due to this biochemical gap, SpA is diagnosed almost exclusively on the basis of a combined assessment of symptoms, physical examination and imaging techniques, laboratory examinations playing a minor role, or even no role at all. Consequently, the diagnosis is often delayed thus limiting early therapeutic intervention, of crucial importance in modifying disease progression and avoiding unnecessary diagnostic and therapeutic procedures [5]. There
Abbreviations: AS, Ankylosing spondylitis; ASDAS, Ankylosing Spondylitis Disease Activity Score; Ax-SpA, Axial spondyloarthritis; BASDAI, Bath Ankylosing Spondylitis Disease Activity Index; BASFI, Bath Ankylosing Spondylitis Functional Index; BASMI, Bath Ankylosing Spondylitis Metrology Index; CaSR, Calcium sensing receptor; CASPAR, ClASsification criteria for Psoriatic Arthritis; DAS, Disease Activity Score; DAS-28, Disease Activity Score-28; DMARD, Disease-modifying antirheumatic drugs; MDSC, Myeloid-derived suppressor cell; MMP, Matrix metalloproteinase; nr-axSpA, Non-radiographic axial spondyloarthritis; PsA, Psoriatic arthritis ⁎ Corresponding author. E-mail address: [email protected] (D. Basso). https://doi.org/10.1016/j.cca.2019.10.013 Received 22 August 2019; Received in revised form 15 October 2019; Accepted 15 October 2019 0009-8981/ © 2019 Elsevier B.V. All rights reserved.
Please cite this article as: Stefania Moz, et al., Clinica Chimica Acta, https://doi.org/10.1016/j.cca.2019.10.013
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is therefore an urgent need for sensitive and specific biomarkers for these diseases. In inflamed sacro-ileal joints, monocytes and macrophages modulate cartilage re-modelling by releasing the inflammatory cytokines TNF-α, IL-1 and IL-6, which might also induce the production of MMPs, the main enzymes involved in extra-cellular matrix degradation. Indeed, high expression of MMPs, mainly MMP3, has been found in the synovial tissues of SpA patients [6]. The relevant role in SpA of cytokines, TNF-α in particular, is further supported by the strong therapeutic response to TNF-α inhibitors, which are recommended for patients with symptoms refractory to conventional therapy or for whom conventional therapy has unacceptable side effects [7]. As well as classical cytokines, cartilage re-modelling may also be influenced by other inflammatory molecules, including TGF-β1, but also the calcium binding proteins S100A8 and S100A9, which can induce matrix metalloproteinases expression, as recently demonstrated by us elsewhere [8]. S100A8 and S100A9 are both produced by monocytes, a dynamic cellular population able to infiltrate inflamed tissues differentiating into inflammatory macrophages. Due to their potential role in sustaining chronic inflammation, monocytes have also been identified as potential therapeutic targets [9–11]. Monocytes chemotaxis to the inflamed joints might be regulated by calcium gradients as monocytes can detect variations in extracellular calcium through the activation of the calcium-sensing receptor (CaSR) [11]. Based on the above premises, the aim of the present study was to search for new potential biomarkers enabling SpA diagnosis and monitoring by focusing on the innate immune response. The mRNA expression levels of TNF-α, IL-1β, TGF-β1, S100A8, S100A9, MMP3, MMP8 and MMP9 in blood mononuclear cells were evaluated in SpA patients and compared with values in controls. In parallel, the pattern of intracellular calcium flows of blood monocytes was investigated in order to ascertain whether a specific fingerprint characterizes innate immune cells in SpA patients.
Laboratory Medicine, University-Hospital of Padova, which obtained ISO 9001 certification in 1997, as well as ISO 15189:2012 and CPA-UK accreditations in 2016 and 1995, respectively. 2.3. Genetic analysis Genomic DNA, was extracted from EDTA-K2 peripheral blood using the MagNA Pure System (Roche Diagnostics GmbH, Mannheim, Germany) according to the manufacturer’s instructions. HLA-B27 was determined using Real-Time PCR with ABI Prism 7900 HT (Thermo Fisher Scientific, Waltham, MA USA), using the primer pairs F:5′-CTA CGTGGACGACACGCT-3′ and R:5′-GCAAGGCCAAGGCACAGACT-3′, the fluorogenic probe 5′ FAM-CGTGAGGTTCGACAGC-MGB 3′ and the following amplification cycle: 50 °C for 2 min, and 94 °C for 7 min, followed by 50 cycles at 92 °C for 20 s and at 60 °C for 1 min and 10 s. 2.4. mRNA expression analyses Peripheral blood mononuclear cells (PBMCs) were isolated by gradient centrifugation (Histopaque, 1077, Sigma-Aldrich, Milano, Italy) and total RNA extracted using the High Pure RNA Isolation Kit (Roche) according to the manufacturer's instructions. Total RNA (500 ng) was reverse transcribed into cDNA using Random primers and Superscript TM II RNasiH-Reverse Trascriptase (Thermo Fisher Scientific). Real-Time PCR (ABI Prism 7900 HT, Thermo Fisher Scientific) was used for the relative quantification of mRNA expression in the conditions described elsewhere for S100A8, S100A9, MMP8 and MMP9 [8], and using the primers and probe set supplied by Thermo Fisher Scientific (Taqman Gene Expression Assay catalogue number 4331182) for TNF-α (Hs00174128_m1), IL-1β (Hs01555410_m1), TGF-β1 (Hs00998133_m1) and MMP3 (Hs00968305_m1). Hypoxanthine-guanine phosphoribosyltransferase (HPRT1) was selected as a housekeeping gene control (Hs02800695_m1). All molecular targets were analyzed in duplicate for each sample. PCR was run at the following amplification cycle: 50 °C for 2 min, 95 °C for 10 min, followed by 50 cycles at 95 °C for 15 s and 60 °C for 1 min. To determine the relative mRNA expression levels of all selected genes, we used the comparative Ct method by means of a calibrator sample from a pool obtained from 20 healthy blood donors.
2. Materials and methods 2.1. Studied population In this prospective study, approved by the Local Institutional Ethics Committee of the University-Hospital of Padova (Protocol number: 3024P/13), after obtaining fully informed written consent, 100 control healthy blood donors (58 males and 42 females; mean age ± standard deviation, 46.6 ± 8.5) and 64 SpA patients (39 males and 25 females; mean age ± standard deviation, 39.5 ± 13.2 years) were enrolled from January 2016 to December 2018. Twenty-six (40.6%) of the patients had a confirmed diagnosis of AS, according to the modified New York criteria [12] and 38 (59.3%), a diagnosis of PsA according to the CASPAR criteria [13]. The mean disease duration was 14 years (range 2–38 years). All patients underwent clinical, clinimetric and functional examinations following a standardized protocol. The demographic and clinical characteristics of enrolled patients are shown in Table 1. At enrolment, 55 patients (86%) were on therapy with anti-TNF-α agents (8 with infliximab, 21 with adalimumab, 20 with etanercept, 4 with golimumab, 3 with ustekinumab and 1 with secukinumab); 8 (14%) of the patients had undergone at least one TNF-α inhibitors switch before entering the study. Two patients had combined therapy with DMARDs and anti-TNF-α agents, and 7 were treated only with DMARDs. For a subset of 8 patients who started therapy with anti-TNF-α agent at enrolment, a second sample was obtained after six months from the start of therapy.
2.5. Intracellular calcium flows analysis Intracellular calcium [Ca2+]i flows analysis was carried out using an inverted epifluorescence microscope Nikon Eclipse Ti (Nikon Instruments, Amsterdam, Netherlands) and Fluo-4 AM (Thermo Fisher Scientific) as a high affinity Ca2+ indicator. As an illumination source, we used a mercury arc discharge lamp and selected excitation and emission wavelengths with an FITC filter cube (EX 460-500 DM505 EM 510–560 nm). We used 40X1.30 oil objective and, in order to minimize phototoxicity and photobleaching, back-thinned electron multiplied CCD camera DS-U3 Digital sight. After the first minute, required to achieve optimal culture conditions, intracellular fluorescence was continuously monitored (5 frames/sec) for 12 min. The fluorescence signal was quantified by measuring the mean pixel value of a manually selected cellular area for each frame of the image stack using the NIS-Elements software (Nikon Instruments). Monocytes were isolated from 6 mL EDTAK2 peripheral blood of 50/64 SpA patients and 48/100 controls by negative selection with Human Monocyte Enrichment Cocktail (RosetteSep kit, StemCell Technologies, Vancouver, BC, Canada), which allows the removal of non-monocyte cells and erythrocytes targeted with Tetrameric Antibody Complexes (TAC). After twenty minutes incubation of whole blood with the RosetteSep, monocytes were isolated by gradient centrifugation (Histopaque, 1077, Sigma-Aldrich) counted, seeded on coverslips that had been inserted into six well culture plates at the concentration of 106 monocytes/well, and cultured for 24 h in 2 mL of complete medium (RPMI 1640, 10% FCS; Thermo Fisher Scientific). To evaluate intracellular calcium flows, coverslips were incubated for
2.2. Haematological and biochemical indices Complete blood count and ESR, plasma glucose, uric acid, creatinine, alanine aminotransferase (ALT), serum CRP and prealbumin were analyzed by means of the routine procedures of the Department of 2
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Table 1 Demographic, clinical characteristics and clinical outcome measures. The demographic characteristics of controls and patients were obtained at enrolment, while patients’ clinical data and outcome indices are referred to findings obtained at diagnosis of AS or PsA disease. Significant p values are reported in bold face. Controls (n = 100)
AS (n = 26)
PsA (n = 38)
Statistical analysis
58/42 (42%) 46 ± 8 78.8 ± 15.89 171.75 ± 7.72 26.61 ± 4.63 5/100 (5%)
19/7 (26%) 48 ± 15 68.76 ± 9.35* 171.76 ± 7.40 23.38 ± 2.89* 17/26 (65%)
20/18 (47%) 54 ± 10** 75.38 ± 11.11 169.26 ± 8.34 26.28 ± 3.57 7/38 (18%)
χ2 = 2.80 F = 7.36 F = 5.40 F = 1.48 F = 6.28 Fisher's exact
p = 0.245 p = 0.0009 p = 0.0054 p = 0.2305 p = 0.0024 p < 0.0001
Clinical characteristics at diagnosis Family history of SpA, n (%) Age at diagnosis, mean ± SD (yrs) Inflammatory back pain, n (%) Peripheral arthritis, n (%) Enthesitis, n (%) Buttock pain, n (%) Dactylitis, n (%) Uveitis, n (%) Psoriasis, n (%) IBD, n (%) Urethritis/Cervicitis/Diarrhoea, n (%)
9 (35%) 36.5 ± 13.96 25 (96%) 9 (35%) 13 (50%) 22 (84%) 3 (11%) 1 (4%) 5 (19%) 3 (11%) 2 (7%)
25 (66%) 41.65 ± 12.48 22 (58%) 38 (100%) 29 (76%) 10 (26%) 8 (21%) 0 (0%) 35 (92%) 0 (0%) 7 (18%)
χ2 = 6.02 F = 2.39 χ2 = 11.58 χ2 = 33.83 χ2 = 4.74 χ2 = 20.98 χ2 = 0.98 χ2 = 1.48 χ2 = 34.98 χ2 = 4.60 χ2 = 1.47
p = 0.014 p = 0.1269 p = 0.001 p = 0.000 p = 0.029 p = 0.000 p = 0.322 p = 0.223 p = 0.000 p = 0.032 p = 0.225
Clinical outcome measures at diagnosis DAS, mean ± SD BASMI, mean ± SD BASFI, mean ± SD HAQ, mean ± SD BASDAI, mean ± SD ASDAS-PCR, mean ± SD
3.22 3.27 5.52 0.96 6.18 3.12
3.74 1.31 4.62 0.91 6.39 2.98
F = 4.29 F = 21.07 F = 3.45 F = 0.16 F = 0.33 F = 1.02
p = 0.0424 p = 0.0000 p = 0.0681 p = 0.6918 p = 0.5649 p = 0.3174
Demographic characteristics at enrolment Gender M/F (% F) Age mean ± SD (yrs) Weight mean ± SD (Kg) Height mean ± SD (cm) BMI mean ± SD (Kg/m2) HLA-B27 positive/total (% positive)
± ± ± ± ± ±
twenty minutes at 37 °C with 5 μM Fluo-4 AM in imaging buffer (IB) containing 137 mM NaCl, 5 mMKCl, 1.2 mM MgCl2 ,0.44 mM KH2PO4, 4.2 mM NaHCO3, 5 mM glucose, 20 mM HEPES pH 7.4. The cells were carefully washed two times and then incubated with IB at 37 °C for ten minutes in order to allow Fluo-4 AM to stay inside the cells. The coverslip was placed in an appropriate chamber, added 1 mL of IB supplemented with 1 mM CaCl2 and [Ca2+]i flows were then analyzed as described elsewhere [14]. Since the number of monocytes that can be analyzed within a microscopic field is limited, for each patient and control at least two replicates were done. The median number of studied monocytes was 20 (95% CI: 19–27) for controls and 19 (95% CI 21–31) for SpA patients (Mann Whitney test: p = 0.602).
0.96 1.806 1.90 0.52 1.55 0.64
± ± ± ± ± ±
0.99 1.58 1.90 0.53 1.43 0.48
3. Results 3.1. Haematological and biochemical indices in SpA patients Table 2 reports the median and interquartile range of haematological and biochemical parameters evaluated in controls and in SpA patients at enrolment. White blood cells (WBC) and polymorphonuclear cells (PMN) were more numerous in SpA patients than in controls, the highest values being recorded among AS, that also had significantly higher levels of haemoglobin than controls. Overall, in SpA patients lymphocytes were more numerous and levels of glucose and CRP higher than in controls, although no statistically significant differences were found.
2.6. “In vitro” experiments in monocyte cells
3.2. Cytokines and MMPs expression in PBMCs
Intracellular calcium flows of control monocytes were also evaluated in the presence or absence of SpA patients’ sera. Before calcium flows analyses, monocytes isolated from the buffy coat of healthy blood donors as described above, were kept in continuous culture for 24 h in the following conditions: 1. Standard media alone (RPMI 1640, 0.1% Gentamycin, 1% L-Glutamine); 2. Standard media with 10% FCS; 3. Standard media with 10% AS patients’ serum; 4. Standard media with 10% PsA patients’ serum; 5. Standard media with 10% heathy controls’ serum. Patients’ and controls’ serum used for the experiments, pools of three cases each, were used after treatment at 56 °C for 20 min. All experiments were made at least in triplicate.
Table 3 shows the expression levels of cytokines and of MMP8 and MMP9 in the patients and controls. MMP3 mRNA expression was not detected in any samples. S100A8 expression levels were lower in PsA patients than in controls, while no variations were found in the other studied cytokines and MMPs. S100A8 was correlated with S100A9 (r = 0.7692, Bonferroni’s adjusted p value < 0.0001), and MMP8 with MMP9 (r = 0.6257, p < 0.0001). MMP9 was also correlated with S100A8 (r = 0.3742, p < 0.0001) and S100A9 (r = 0.3674, p < 0.0001). TNF-α was correlated with TGF-β1 (r = 0.5729, p < 0.0001) and with IL-1β (r = 0.3964, p < 0.0001).
2.7. Statistical analysis
3.3. Intracellular calcium flows in SpA patients and controls
Mean, median, standard error (SE), standard deviation (SD), interquartile range (IQR) and 95% confidence interval (CI) were used as descriptive statistics. The Shapiro-Wilk W test for normal data, KruskalWallis rank test, Mann Whitney test, χ2 test, Fisher’s exact test, binary logistic regression analysis, Student’s t test for unpaired data, analysis of variance (ANOVA) and Bonferroni’s adjustment of p value for multiple testing were performed using Stata software, version 13.1 (StataCorp, Lakeway Drive, TX, USA).
The recruitment of monocytes at joint inflammatory sites plays a key role in the inflammatory cascade, and in joint destruction. As recently pointed out, calcium signalling in circulating monocytes regulates chemotaxis and cytokine production [11]. In this study, we evaluated whether the pattern of intracellular calcium oscillations in controls (n = 48) differs from that in SpA patients (n = 50). Blood monocytes were isolated by negative selection and then used to ascertain intracellular calcium flows by epifluorescence microscope analysis. 3
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Table 2 Haematological and biochemical parameters in controls, AS and PsA patients at study enrolment. One-way ANOVA for normally distributed and Kruskal-Wallis rank test for the non-parametric variables (Shapiro-Wilk W test) were used. Bonferroni’s adjusted p-values: *= p < 0.01 vs controls; **= p < 0.0001 vs controls; ^ = p < 0.05 vs PsA. Significant p values are reported in bold face.
3
WBC (×10 /μL) Haemoglobin (g/L) Platelets (×103/μL) PMN (×103/μL) Monocytes (×103/μL) Lymphocytes (×103/μL) Glucose (mmol/L) Creatinine (μmol/L) Uric acid (mmol/L) ALT (U/L) Prealbumin (mg/L) CRP (mg/L)
Controls (n = 100) Median (IQR)
AS (n = 26) Median (IQR)
PsA (n = 38) Median (IQR)
Statistical analysis
5.2 (4.2–6.2) 138 (124–148) 237 (174–267) 2.8 (2.0–3.5) 0.4 (0.3–0.6) 1.7 (1.3–2.1) 4.9 (4.0–5.4) 82 (63–90) 0.3 (0.2–0.3) 21.5 (14.0–29.0) 289 (225–316) 2.0 (2.0–2.0)
6.6 (4.6–8.3)** 149 (124–160)* 242 (151–320) 3.9 (2.9–5.1)**^ 0.4 (0.3–0.6) 2.2 (1.2–2.7) 5.3 (3.9–5.7) 71 (54–83) 0.3 (0.2–0.3) 24.0 (15.0–31.0) 285 (235–322) 2.0 (2.0–6.3)
6.3 (4.0–7.7)* 146 (124–150) 260 (181–312) 3.3 (2.0–5.1)* 0.4 (0.3–0.6) 2.1 (1.3–2.5) 5.2 (4.2–5.9) 77 (58–84) 0.3 (0.2–0.3) 27.5 (13.0–35.0) 287 (223–321) 2.0 (2.0–4.5)
p < 0.0001 p = 0.0083 F = 2.07, p = 0.1301 p < 0.0001 p = 0.7872 p = 0.0527 p = 0.0525 F = 3.01, p = 0.0519 F = 0.55, p = 0.5806 p = 0.1498 F = 0.60, p = 0.5509 p = 0.1521
Monocytes obtained from healthy blood donors presented evident and frequent intracellular calcium oscillations, unlike monocytes obtained from SpA patients. Fig. 1 shows individual cellular data from representative experiments on 4 controls and 4 SpA patients. For every individual experiment, the number of monocytes exhibiting intracellular calcium oscillations with respect to the whole number of cells examined was calculated and expressed as a percentage (% of cells). Fig. 2 shows the results obtained in controls and patients with AS or PsA. The percentage of active monocytes for AS patients was significantly lower than in controls (F = 6.15, p = 0.003). The frequency of calcium flows of monocytes showing intracellular calcium oscillations was then evaluated. To calculate this frequency, for each monocyte the total number of calcium flows recorded in the course of the 12 min experiment were manually counted and divided by 12 (flows/min). Mean values of single cells data were then calculated for any individual patient and control. The frequency of calcium flows did not differ between patients (0.24 ± 0.02 flows/min, mean ± SEM) and controls (0.22 ± 0.01 flows/min) (Student’s t test: t = 0.838, p = 0.405). The percentage of cells with intracellular calcium oscillations in SpA patients was correlated neither with the severity clinical indices BASDAI, BASFI, BASMI, ASDAS-CRP, DAS and HAQ, nor with the biochemical indices of inflammation (i.e. circulating PMN, lymphocytes, monocytes and CRP). Since all 50 SpA patients were on therapy, we ascertained whether variations in cells with calcium oscillations were correlated with the drug type used. Values found in patients on DMARDs (n = 6, Mean ± SE: 22.928 ± 6.462) did not differ from those in patients on anti-TNF-α (n = 44, Mean ± SE: 21.709 ± 2.394; Student’s t test: t = 0.17, p = 0.861). This result was further confirmed in the subset of eight patients for whom analysis was replicated before and after 6 months from the beginning of anti-TNF-α therapy (Student’s t test for paired data: t = 0.21, p = 0.833). To further verify the association between the reduced intracellular
calcium flows (% of cells) of blood monocytes and SpA, binary logistic regression analysis was performed considering the presence or absence of SpA as dependent variable and, as predictor variables, those that were associated with SpA at univariate statistical analysis (Table 4). Table 5 reports the results of multiple linear regression analyses conducted to evaluate whether the percentages of cells with calcium oscillations were correlated with the expression levels of inflammatory cytokines, S100 proteins and MMPs in SpA patients and controls. 3.4. Serum of SpA patients softens intracellular calcium oscillations in human monocytes The observed disease-related pattern of cells with calcium oscillations might be consequent to intrinsic alterations in monocytes and/or to soluble blood mediators. To verify the latter hypothesis, we performed in vitro experiments using three healthy donors’ blood monocytes conditioned for 24 h with FCS or with human serum pools obtained from healthy subjects (n = 3), from patients with AS (n = 3) and from patients with PsA (n = 3). At the end of the conditioning period, intracellular calcium oscillations of monocytes were analyzed, and the percentages of cells with intracellular calcium flows and the frequency of calcium spikes were calculated as described above. With respect to monocytes cultured in the absence of growth factors (FCS 0%), the percentage of cells with calcium oscillations increased in the presence FCS 10%, but mainly in the presence of healthy human sera (Fig. 3; Kruskal Wallis test: p = 0.038). Monocytes treated with AS or PsA sera were less frequently found to pulse with respect of control sera. Moreover, the frequency of calcium flows was significantly lower in monocytes treated with PsA sera (0.18 ± 0.003 flows/min, mean ± SEM) than in those treated with control (0.24 ± 0.003) or AS sera (0.240 ± 0.001) (One way anova: F = 11.62, p = 0.0009). Overall these data suggest the hypothesis that in SpA patients with respect to controls, there are less soluble mediators released into the bloodstream able to induce monocytic intracellular calcium oscillations. This finding
Table 3 Cytokines, S100A8, S100A9, MMP8 and MMP9 mRNA fold increase in PBMCs of controls, AS and PsA patients. Median values, ranges of the mRNA fold increase and results of the statistical analyses are reported.
TNF-α fold increase IL-1β fold increase TGF-β1 fold increase S100A8 fold increase S100A9 fold increase MMP8 fold increase MMP9 fold increase
Controls (n = 100)
AS (n = 26)
PsA (n = 38)
Median
Range (min–max)
Median
Range (min–max)
Median
Range(min–max)
0.252 0.138 0.847 1.032 0.707 0.25 0.046
0.103–4.724 0.019–83.286 0.011–49.180 0.376–4.347 0.264–3.811 0.010–8 0.004–0.358
0.227 0.078 0.727 0.922 0.642 0.337 0.048
0.102–1.014 0.024–2.085 0.409–4.993 0.398–3.555 0.289–1.547 0.009–8.112 0.007–0.877
0.248 0.076 0.697 0.946 0.628 0.285 0.056
0.130–1.624 0.010–1.624 0.389–5.856 0.189–1.853 0.252–1.375 0.013–3.630 0.006–0.316
4
One-way ANOVA log2 transformed values
F = 1.01, F = 0.79, F = 0.70, F = 3.29, F = 1.94, F = 0.19, F = 0.86,
p = 0.3661 p = 0.4559 p = 0.4983 p = 0.0398 p = 0.1476 p = 0.8253 p = 0.4255
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Fig. 1. Intracellular calcium flows ([Ca2+]i) of monocytes from controls and SpA patients (SpA).
paves the way for the potential identification of new blood biomarkers of disease. To verify whether the variations in calcium flows induced by patients’ sera are associated with functional monocytes alterations potentially harmful for the joints, monocytes MMP8 and MMP9 expression levels were evaluated. In addition to the experimental conditions described above, monocytes were also treated with LPS 1 μg/mL a known activator of the innate immune response. Fig. 4 shows the results obtained. MMP8 expression was significantly correlated with LPS treatment (Two way repeated measures ANOVA: F = 25.32, p = 0.004 for LPS; F = 3.973, p = 0.0813 for serum; F = 2.68, p = 0.1583 for interaction), while MMP9 expression was affected by both LPS and serum treatments (F = 24.51, p = 0.004 for LPS; F = 22.45, p = 0.0022 for serum; F = 13.91, p = 0.0064 for interaction).
4. Discussion SpA comprises two main diseases, AS and PsA, that primarily involve the spine, but can also affect peripheral joints and tissues/organs outside the skeleton, such as eyes, skin and intestine [15–17]. Since the symptoms that patients refer at disease onset, mainly back pain, are non-specific, the diagnosis is often delayed, and repeated cycles of therapy with anti-inflammatory agents are prescribed. The relatively late diagnosis with a consequent delay in appropriate treatment may be associated with disease progression that causes irreversible joint damage. The diagnosis is supported by imaging and unfortunately not enough by biochemistry. In fact, no specific blood biomarkers are available for these diseases. Moreover, despite the intensive inflammatory process involving the sacro-ileal joints, the common blood
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Fig. 2. Percentage of monocytes (% of cells) with intracellular calcium oscillations in controls, Ankylosing spondylitis (AS) and Psoriatic Arthritis (PsA) patients. Bonferroni’s test for pairwise comparisons: *=p < 0.005 with respect to controls.
Fig. 3. Percentage of monocytes (% of cells) with intracellular calcium oscillations in healthy blood donors cultured in the absence or presence of 10% FCS, 10% of healthy controls (Control), 10% of Ankylosing Spondylitis (AS) and 10% of Psoriatic Arthritis patients (PsA) patients’ serum.
Table 4 Binary logistic regression analysis results. The presence or the absence of SpA considered as outcome. Predictive variables: Age, Gender, BMI, WBC, Haemoglobin, PMN, Lymphocytes, Glucose, CRP, HLA-B27, log2 S100A8, and % of cells with intracellular calcium oscillations. The analysis was made considering the patients for whom analysis of intracellular calcium flows was performed (n = 50). SE: Standard Error; CI = Confidence Interval. Significant p values are reported in bold face.
Age (years) Gender BMI (Kg/m2) WBC (×103/µL) Haemoglobin (g/L) PMN (×103/µL) Lymphocytes (×103/µL) Glucose (mmol/L) CRP (mg/L) HLA-B27 log2 S100A8 % of cells Constant
Coefficent
SE
95% CI
p-value
0.101 −0.558 −0.204 1.099 0.032 −0.023 −0.696 0.955 0.070 1.909 −1.629 −0.040 −13.156
0.039 0.813 0.107 0.490 0.027 0.507 0.787 0.388 0.109 0.987 0.608 0.017 6.166
0.024 to 0.177 −2.153 to 1.035 −0.414 to 0.005 0.137 to 2.061 −0.021 to 0.087 −1.018 to 0.971 −2.239 to 0.847 0.194 to 1.716 −0.143 to 0.285 −0.024 to 3.844 −2.823 to −0.436 −0.075 to −0.005 −25.241 to −1.070
0.009 0.492 0.056 0.025 0.240 0.963 0.377 0.014 0.518 0.053 0.007 0.025 0.033
infiltrating the inflamed joints derive from circulating monocytes and contribute to the process of tissue destruction by expressing not only inflammatory cytokines, such as TNF-α, TGF-β1 or IL-1β, and inflammatory proteins, such as the calcium binding proteins S100A8 and S100A9, but also enzymes causing tissue destruction and remodeling, such as metalloproteinases [18–20]. When compared to controls, AS and PsA patients had similar cytokines expression levels in their circulating mononuclear cells. This finding was in part unexpected, since increased protein levels have been reported in these patients and the role of these cytokines, mainly TNF-α and IL-1β, in disease pathogenesis is well established and supported by the dramatic efficacy of anti-TNF-α therapy [21]. Our unexpected findings may be explained by the observation that slight variations in mRNA expression might result in wide variations in the encoded proteins. Moreover, mononuclear cells might enhance the expression levels of inflammatory cytokines only when homed at the joint tissue sites. Despite the lack of association between cytokines expression levels and disease diagnosis, the TNF-α, TGF-β1 and IL-1β variations were correlated with each other, thus suggesting that these inflammatory pathways share common drivers and probably influence each other. The above results suggest that TNF-α, TGF-β1 and IL-1β are not of clinical utility either in SpA diagnosis, or in the stratification of disease severity, since they vary independently from clinical outcome measures. For our analysis of other potential inflammatory biomarkers, we therefore chose the metalloproteinases MMP8 and MMP9 in order to investigate their potential role in SpA. We also included another metalloproteinase, MMP3, reportedly the most promising investigated tool
inflammatory indexes (e.g. CRP and ESR) remain within the reference ranges or are only slightly altered, and this was confirmed by our present results. These considerations have prompted the search for new potential biomarkers that may aid and support diagnosis and monitoring. In this study, our focus was on circulating mononuclear cells as key players in the inflammatory process of SpA. In fact, macrophages
Table 5 Multiple linear regression analysis results. Table reports the correlation of the mRNA expression levels (log2 transformed) of inflammatory cytokines, S100A8, S100A9, MMP8 and MMP9 with the percentage of monocytes showing intracellular calcium flows in SpA patients (n = 50) and controls (n = 48). SE: Standard Error; CI = Confidence Interval. Significant p values are reported in bold face. SpA patients
log2 TNF-α log2 IL-1β log2 TGF-β1 log2 S100A8 log2 S100A9 log2 MMP8 log2 MMP9 Constant
Controls
Coefficient
SE
95% CI
p-value
Coefficient
SE
95% CI
p-value
3.968 2.770 −2.697 5.346 −3.976 −0.384 1.213 40.578
3.390 2.147 4.339 8.321 9.457 1.859 2.730 14.303
−2.878 to 10.815 −1.567 to 7.107 −11.461 to 6.066 −11.458 to 22.151 −23.075 to 15.122 −4.140 to 3.371 −4.300 to 6.727 11.692 to 69.464
0.249 0.204 0.538 0.524 0.676 0.837 0.659 0.007
−14.557 4.610 −0.659 14.438 −12.894 1.030 3.302 26.436
6.724 3.371 2.155 6.863 5.475 2.858 3.809 19.955
−28.159 to −0.956 −2.208 to 11.429 −5.018 to 3.699 0.554 to 28.321 −23.968 to −1.819 −4.751 to 6.812 −4.402 to 11.007 −13.926 to 66.800
0.037 0.179 0.761 0.042 0.024 0.720 0.391 0.193
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expression did not vary, S100A8 expression was lower in PsA patients than in controls. This finding is hard to explain. One of the mechanisms underlying S100A8 expression might be related to variations in intracellular calcium, which regulates several biological processes of cells. The intracellular calcium balance is strictly regulated by transporters expressed both on the cellular and mitochondrial membranes [24]. Intracellular calcium alterations might be characterized by increased and constant accumulation of the ion, increased frequency in its oscillations, or vice versa (i.e. decreased intracellular levels and/or frequency of oscillations) [25]. These alterations may have different biological effects, depending on cell type and cell context. We verified whether or not circulating monocytes maintain their characteristic pattern of intracellular calcium behavior in the presence of SpA. Our findings clearly demonstrate that monocytes from patients with SpA, but mainly those with AS, lose their typical intracellular calcium pattern. While the majority of monocytes from controls maintained in culture for 24 h show regular calcium oscillations, only a minority of monocytes from AS patients maintain this pattern, while the majority of them do not present regular calcium oscillations, which, when present, had the same frequency of control monocytes. Overall our findings suggest that at the periphery of SpA patients, within the monocyte cellular population, a shift between subsets might occur. The reduced number of monocytes with intracellular calcium oscillations might be the expression of a differentiation shift of monocytes towards the dendritic cell or the mMDSC phenotypes. In fact, as previously demonstrated by us, intracellular calcium flows in these cellular subsets of the monocyte population are reduced or even absent [26]. The role of MDSC as a contributor to arthritis and psoriasis is currently an emerging concept: in cases of psoriasis, Cao et al. described increased monocytic MDSC producing increased amounts of IL-23, IL-1β and the Chemokine (C-C motif) ligand 4 (CCL4), and a de-regulated immunosuppressive loop in this disease has been suggested [27], while Zhang et al. supported the potential pathophysiological effect of these immunosuppressive cells by demonstrating their role in bone erosion in an animal model [28]. Independently from the biological function characterizing monocytes with a reduced intracellular calcium, at multivariate binary logistic analysis, the reduced frequency of these cells was confirmed as one of the four main and independent predictors of SpA together with S100A8 expression, glucose levels and WBC. In the attempt to improve on the understanding of mechanism underlying the accumulation of monocytes with reduced intracellular calcium fluxes in patients with SpA, structural alterations possibly due to genetic variants of the CaRS might be hypothesized. However, the expression of calcium sensing receptors can vary in inflammatory diseases of the joints as demonstrated by Séjourné et al, who found reduced CaSR in monocytes isolated from synovial fluid and peripheral blood from patients with rheumatoid arthritis [11]. In agreement with the latter hypothesis, in our experiments, sera from PsA patients were found to be less efficient than normal sera in inducing intracellular calcium oscillations in the peripheral blood mononuclear cells of healthy blood donors. This effect of PsA sera on monocytes calcium oscillations was correlated, at least in part, with changings in MMPs expression levels: AS sera were less inducers of MMP9 expression than control or PsA sera, while both AS and PsA sera induced a higher MMP8 expression than control sera. Moreover, patients’ sera synergized with LPS in inducing MMP8, while LPS antagonized patients’ sera effects on MMP9. Although at a systemic level MMP8 and MMP9 expression levels did not vary in SpA, primed monocytes at the joint site might express high levels of these, as well as of other, MMPs under the effect of serum derived soluble factors. Although these soluble factors remain unknown, the role of cytokines may be hypothesized, but small metabolites may also be involved. Another potential confounding factor might be therapy, since SpA patients were on treatment at enrolment. However, the percentage of monocytes with intracellular calcium oscillations was not correlated with the type of therapy or of drug used, nor did it vary before and after six months of anti-TNF-α therapy in the subset of eight patients evaluated before and
Fig. 4. MMP8 and MMP9 expression (fold increase with respect to FCS 0%) of monocytes obtained from healthy blood donors and cultured in the absence or presence of LPS 1 μg/mL and in culture media without FCS (FCS 0%), with FCS 10% or with 10% of healthy controls (Control), 10% of Ankylosing Spondylitis (AS) and 10% of Psoriatic Arthritis (PsA) patients’ serum. Bonferroni’s test for pairwise comparisons: * = p < 0.05 with respect to FCS 0% and FCS 10%; **=p < 0.05 with respect to FCS 0%, FCS 10% and AS 10%.
for diagnosing SpA, both at serum protein level and at mRNA expression level [20]. MMP3 mRNA expression was not measurable in either patients or controls. The discrepancy between our results and those previously reported in the literature might be explained by the fact that MMP3 protein has so far been described as increased in serum, while increased transcripts have been described in synovial fluid or tissues [22]. Although MMP3 and mononuclear cells are both potentially involved in disease pathogenesis, this does not necessarily imply that MMP3 is produced directly by blood mononuclear cells. Since other MMPs could be implicated, we focused on MMP8 and MMP9 for which few data are reported in the literature [20]; however, although both were expressed at measurable levels in circulating mononuclear cells, no difference was found between controls and patients for these metalloproteinases which, moreover, not were related to disease clinical activity indices. Further insight is being gained of the role played in inflammatory joint diseases by intracellular calcium as a trigger for the release of inflammatory molecules and of the related calcium binding inflammatory S100 proteins [23]. Two members of this protein family appear of particular relevance: S100A8 and S100A9, an increase in their mRNA and protein expression levels having found in the synovial fluid and in blood of patients with rheumatoid arthritis [19]. S100A8 and S100A9 appear to be involved in joint destruction, probably because they are inducers of metalloproteinases, including MMP8 and MMP9 [8], this association also being supported by our findings that there are significant correlations between MMP9, S100A8 and S100A9. S100A8 and S100A9 proteins are both expressed by mononuclear cells and, based on this premise, we verified whether their expression by circulating mononuclear cells is altered in SpA. While S100A9 7
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Fig. 5. Schematic representation of the hypothetical mechanism linking blood monocytes (Mφ) and sacro-ileal joint inflammation in SpA. In SpA patients’ blood monocytes incur in a subset shift with increasing cells characterized by reduced intracellular calcium flows. Serum-derived factors appear responsible of this effect, but they also induce the expression of the matrix metalloproteinases MMP8 and MMP9. Through the serum induced release of MMPs, primed monocytes migrated at the joint site might shape a micro environment that favors tissue destruction and remodeling. By targeting calcium flows and MMPs new therapies might be identified, while the identification of serum factors might allow the discovery of new biomarkers.
after therapy. Therefore, our findings appear to be mainly disease, rather than drug related. Flow cytometry studies to define blood monocytes subtypes, proteomic and metabolomic studies to identify putative serum factors able to modify monocytes subtypes and function are expected in this field.
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