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. 2019 Feb 27;196(3):336–344. doi: 10.1111/cei.13273

A subgroup of lupus patients with nephritis, innate T cell activation and low vitamin D is identified by the enhancement of circulating MHC class I‐related chain A

M Pérez‐Ferro 1, F I Romero‐Bueno 1, C Serrano del Castillo 1, I Mahillo 1, A Alvear 1, R Largo 1, G Herrero‐Beaumont 1, O Sánchez‐Pernaute 1,✉
PMCID: PMC6514372  PMID: 30737776

Summary

The major histocompatibility complex (MHC) class I‐related chain A (MICA) is induced upon stress, and labels malfunctioning cells for their recognition by cytotoxic lymphocytes. Alterations in this recognition and also abnormal natural killer (NK) functions have been found in systemic lupus erythematosus (SLE). MICA can be shed from cells, subsequently acting as a soluble decoy receptor (sMICA). Our purpose was to study circulating sMICA levels in relationship with the activation of innate pathways in PBMC in a cohort of lupus patients. NK cells were characterized by flow cytometry. Gene expression of Toll‐like receptors (TLR), interferon (IFN)‐I sensitive genes and MICA were separately analyzed in monocytes, T cells and B cells. Serum sMICA was measured with enzyme‐linked immunosorbent assay (ELISA). In our cohort, NK cell counts dropped in relationship with disease activity. sMICA showed an inverse trend with NK cell counts, as well as a significant association with activity indices, but not with complement decrease. Levels of sMICA associated to proteinuria and active nephritis. A multivariate regression model revealed anti‐nuclear antibody (ANA) titres, the up‐regulation of TLR‐4 in T cells and lower vitamin D as predictors of sMICA enhancement. Interestingly, vitamin D showed an inverse association with proteinuria and a strong correlation with T cell MICA mRNA levels. According to our data, circulating sMICA identifies a subgroup of lupus patients with low vitamin D, innate activation of T cells and nephritis. We propose that lymphocyte shedding could account for the enhancement of sMICA and reflect an immune evasion mechanism driving disease activation in lupus.

Keywords: lupus, lupus nephritis, MHC class I‐related chain A, natural killer cells, Toll‐like receptors, 25 hydroxy vitamin D

Introduction

As it is strongly supported by large‐scale gene association and transcriptomic studies, not only alterations in the adaptive but also in the innate immune system are involved in lupus pathogenesis 1, 2. More than half of the patients show a characteristic up‐regulation of type I interferon (IFN‐I) sensitive genes (ISG) in peripheral blood, in relationship with particular clinical features 3. It is generally considered that the exposure of cell alarmins resulting from tissue damage, but also concurring viral or bacterial infections, can activate innate immune responses in the patients 4. Indeed, besides the major role played by Toll‐like receptors (TLR)‐7 and TLR‐9 in the production of lupus distinctive autoantibodies and in the maturation of autoreactive lymphocytes 5, there is also consistent evidence of the participation of membrane‐bound TLR, such as TLR‐2 and TLR‐4, in disease flares and lupus nephritis (LN) 6, 7. Along with up‐regulating co‐stimulatory molecules and pumping out defensive cytokines to the cell milieu, TLR and other innate receptors evoke pro‐apoptotic signals which facilitate the removal of altered cells 8.

A major homeostatic cytolytic mechanism induced by innate immune triggers is the up‐regulation of major histocompatibility complex (MHC) class I‐related chain A (MICA), which is a ligand for the NKG2D receptor of natural killer cells (NK) and CD8+ T lymphocytes. An increased density of MICA molecules at the membrane makes damaged cells visible for cytotoxic lymphocytes, which can then activate death programmes in the target cell 9, 10. In patients with autoimmune diseases, the local enhancement of MICA and other NKG2D ligands can mediate NK cell‐dependent tissue damage. In this regard, the enrichment in NKG2D ligands observed in kidney biopsies and in urine samples from patients with lupus argues for a pathogenic role of NK cells in LN, although further studies may be warranted 11, 12.

Conversely, NK cells are frequently defective and lower in numbers in patients with active lupus. Moreover, these cells are thought to exert regulatory functions which may help to preserve self‐tolerance, given that failure in the recognition and clearance of damaged cells can prolong the exposure of autoantigens and also the abnormal survival of activated immune cells 13, 14, 15. Between NK‐dependent homeostatic functions, a low expression of MICA in PBMC allows cross‐talk with T helper cells and is probably involved in peripheral tolerance 13, 16, 17. Some studies have suggested that this constitutive expression might be reduced in lupus patients 12, 18, while an enhancement in soluble MICA (sMICA) and other NKG2D ligands has been found in patients’ sera 12, 13. Interestingly, disruption of the MICA expression pathway is an immune evasion strategy evoked by intracellular pathogens and also by cancer cells, allowing their escape from cytotoxicity 10. One of the mechanisms involved is proteolytic shedding of the receptor, which accounts for the frequent increase in circulating sMICA observed in these diseases 19.

In recent years, we have undertaken the study of innate immune processes in a cohort of systemic lupus erythematosus (SLE) patients aiming to characterize patterns of disease activation more effectively. In this study, we hypothesized that disruption of the MICA‐dependent death pathway in immune cells could account for a pathogenic mechanism in lupus.

Methods

Study population

A cross‐sectional study was performed in 36 adults with SLE attending our autoimmunity clinic and in 13 healthy volunteers with similar demographics. Cohort characteristics at enrolment, disease features, activity and concurrent medications are listed in Table 1. All patients fulfilled the American College of Rheumatology revised classification criteria for systemic lupus. At recruitment, patients had to have been on stable treatment for the last 3 months. Activity was assessed with the SLE Disease Activity Index 2000 (SLEDAI‐2 K) and British Isles Lupus Assessment Group (BILAG) 2004. Active disease was defined by any of the following criteria: (1) a ≥ 4 SLEDAI scoring plus a ≥ 2‐point increase in the last 3 months, (2) BILAG categories A or B and (3) BILAG C plus a ≥ 4 SLEDAI scoring. Active lupus nephritis (LN) was defined by any of the following: (1) a > 0·5 g increase in 24‐h urinary protein or a > 50 mg/g increase in the microalbumin–creatinine ratio in the last 3 months, (2) an active sediment (presence of casts, leucocytes and red cells) in the absence of infection and (3) all patients with past history of LN who had not achieved remission. The study was conducted in compliance with the Declaration of Helsinki and was approved by the Institutional Review Board. All subjects provided written informed consent.

Table 1.

Patients’ characteristics

Demographics
Females, n/N (%) 34/36 (94)
Ethnicity, n 20 Caucasian, 13 Hispanic, 3 other
Years of age, median (IQR) 41·5 (17·75)
Years of disease duration, median (IQR) 8·5 (14·5)
Disease characteristics
Positive anti‐dsDNA, n (%) 30 (83)
C′ decrease, n (%) 33 (92)
Use of immunosuppressants, n (%) 26 (72)
Use of anti‐malarials, n (%) 29 (80)
Past history of major organ disease, n (%) 24 (67)
Past history of LN, n (%) 23 (64)
SLICC damage index, mean (s.e.m.) 2·2 (0·4)
Features at the time of the study
SLEDAI, mean (s.e.m.) 7·4 (1·4)
BILAG categories (higher), n 6D, 11C, 13B, 6A
BILAG sum, mean (s.e.m.) 8·8 (1·9)
Active LN, n (%) 9 (25)
Positive anti‐dsDNA (>2‐fold values from threshold), n (%) 9 (25)
C′ decrease, n (%) 17 (47)
On anti‐malarials, n (%) 20 (56)
On corticosteroids, n (%) 27 (75)
On immunosuppressants, n (%) 14 (39)
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IQR = interquartile range; LN = lupus nephritis; SLICC = Systemic Lupus International Collaborative Clinics; SLEDAI = Systemic Lupus Erythematosus Disease Activity Index; BILAG = British Isles Lupus Activity Group; s.e.m. = standard error of the mean.

Natural killer cell counts

Circulating mononuclear cells were characterized using the standard diagnostic method from our institution with a FACSCanto II flow cytometer (BD Biosciences, San Diego, CA, USA) in ≥ 105 leucocytes per reaction, and monoclonal antibodies from BD Biosciences. NK cells were identified within the lymphocyte cell fraction for a CD3–CD19–CD45++CD56+ signal. Acquisition of data was performed using FACSDiva version 6.1 software and the EuroFlow operating procedures; infinicyt software (Cytognos SL, Salamanca, Spain) was used for the analysis.

Isolation of peripheral blood mononuclear cell subpopulations

Peripheral blood mononuclear cells (PBMC) were isolated by density centrifugation over Ficoll‐Hypaque. B lymphocytes, monocytes and T lymphocytes were sequentially purified with magnetic sorting using CD19, CD14 and CD3 microbeads, respectively (Miltenyi Biotec SL, Madrid, Spain), as described. Purity of the cell subpopulations was assessed with flow cytometry and found to be higher than 90%.

Gene expression studies

Isolated cell subpopulations were stored at –80ºC in RNALater solution (Invitrogen, Dublin, Ireland) until their use in mRNA studies. Total RNA was isolated from cell lysates with phenol chloroform and column adhered filters (mirVana Paris; Invitrogen), including DNase I digestion, and reversed‐transcribed. Real‐time polymerase chain reaction (PCR) was performed using SYBR green techniques (Applied Biosystems, Foster City, CA, USA). Relative mRNA expression levels were calculated with the comparative threshold cycle method (∆∆Ct) using 18s as housekeeping gene. Primers are listed in Table 2.

Table 2.

List of primers employed for the amplification of specific gene cDNA sequences

Gene Primer sequences Size of amplicon
TLR‐2 Forward: 5′‐GGG TTG AAG CAC TGG ACA AT‐3′
Reverse: 5′‐CTT CCT TGG AGA GGC TGA TG‐3′ 		79 bp
TLR‐4 Forward: 5′‐GAG CTG TAC CGC CTT CTC AG‐3′
Reverse: 5′‐CTG TCC TCC CAC TCC AGG TA‐3′ 		50 bp
IFIT1 Forward: 5′‐TGC TTG AAG TGG ACC CTG AA‐3′
Reverse: 5′‐ATA GGC AGA GAT CGC ATA CCC‐3′ 		59 bp
HERC5 Forward: 5′‐GTT TGG TGG CTG AGC TTG TT‐3′
Reverse: 5′‐TGC CAC CTT CCA CAT GCT AT‐3′ 		58 bp
MICA Forward: 5′‐TGC TGG TGC TTC AGA GTC AT‐3′
Reverse: 5′‐GCA ACA GCA GAA ACA TGG AA‐3′ 		49 bp
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TLR = Toll‐like receptor; IFIT1 = interferon‐induced protein with tetratricopeptide repeats 1; HERC5 = HECT and RLD domain containing E3 ubiquitin protein ligase 5; MICA = MHC class I‐related chain A; bp = base pairs.

Detection of soluble MICA

Circulating soluble MICA (sMICA) levels were determined in 28 patients and 12 healthy controls using a commercial enzyme‐linked immunosorbent assay (ELISA) (Diaclone, Besançon, France) with a sensitivity of 123 pg/ml. Serum samples and standard dilutions were supplemented with 10% fetal calf serum albumin and the assay was run according to the manufacturer’s specifications.

Data analysis

Quantitative variables are described by mean and standard error (s.e.m.) or median and interquartile range (IQR), and qualitative variables are described by absolute and relative frequencies. Correlations were assessed with the Spearman’s rank correlation coefficient, and comparisons between two or three independent groups were performed with Wilcoxon or Kruskal–Wallis tests, respectively. Multivariate linear regression was performed including all variables with a P‐value ≤ 0·2 in the univariate analysis, using a forward stepwise method. A significance level of 5% was considered for all comparisons.

Results

Levels of NK cells and sMICA as markers of disease activity

As shown in Fig. 1, a fall in peripheral blood NK absolute counts was associated with active lupus in our cohort, compared with those patients with inactive disease and with healthy controls. Lower counts of NK cells further showed a tight correlation with activity indices (ρ = –0·58, P < 0·001 with SLEDAI‐2 K and ρ = –0·464, P = 0·005 with BILAG‐2004) and with complement decrease (ρ = 0·588, P < 0·001 and ρ = 0·412, P = 0·014 with C3 and C4, respectively).

Figure 1.

Figure 1

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The left panel shows distribution of natural killer (NK) cells (cells/mm3) in the subgroups of healthy controls, patients with inactive lupus (iSLE) and patients with active lupus (aSLE). Boxes show 25th to 75th percentiles and median values in each subgroup. P‐values were calculated with Wilcoxon’s method. Right panels show correlations between NK concentration and the activity measures. Respective Spearman’s rank correlation coefficients are shown in each plot. SLEDAI‐2K = Systemic Erythematous Lupus Activity Index 2000’s version; BILAG 2004 = British Isles Lupus Activity Group 2004’s score.

Interestingly, circulating sMICA concentration increased in parallel to the reduction of NK cell numbers, albeit not reaching significance (ρ = –0·353, P = 0·071). In agreement, a consistent association was found between the enhancement of sMICA and the activity scores (ρ = 0·378, P = 0·047 with SLEDAI‐2K and ρ = 0·428, P = 0·023 with BILAG‐2004) (Fig. 2). However, there was no correlation between sMICA and complement levels or anti‐dsDNA antibody titres (not shown) and no cut‐off values of sMICA could discriminate clearly between patients and controls or between active and inactive disease (Fig. 2).

Figure 2.

Figure 2

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The relationship between serum levels of soluble major histocompatibility complex (MHC) class I‐related chain A (MICA) and natural killer (NK) cell concentration, as well as their association with activity indices, are shown with the respective Spearman’s correlation’s coefficients. The upper right panel shows the distribution of soluble MICA in the subgroups of healthy controls, patients with inactive lupus (iSLE) and patients with active lupus (aSLE). Boxes show 25th to 75th percentiles and median values in each subgroup. No significant differences were found using Wilcoxon’s method.

Associations between circulating sMICA and disease features

Regarding clinical features at the time of the study, sMICA was particularly high in patients with active LN compared to the rest of the cohort (P = 0·029) and to healthy subjects (P = 0·012), and a strong correlation between sMICA levels and proteinuria was observed (ρ = 0·525, P = 0·004) (Fig. 3a). Also of interest, sMICA increased in relationship with anti‐nuclear antibody (ANA) titres (ρ = 0·517, P = 0·005) and with the daily prednisone dose (ρ = 0·555, P = 0·002), while it showed a negative correlation with levels of vitamin D (ρ = –0·466, P = 0·029) (Fig. 3b).

Figure 3.

Figure 3

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(a) The left panel shows distribution of soluble major histocompatibility complex (MHC) class I‐related chain A (MICA) in the subgroups of healthy controls, patients without active nephritis (iLN) and those with active nephritis (aLN). Boxes show 25th to 75th percentiles and median values in each subgroup. P‐values were calculated with Wilcoxon’s method. In the right panel, MICA levels are plotted in relationship with the amount of urinary protein (mg/24 h). (b) Scatterplots show association between soluble MICA and anti‐nuclear antibody (ANA) titres, dose of prednisone (mg/day) and levels of 25 hydroxy vitamin D3, respectively. The Spearman’s correlation coefficients are shown in the plots.

Relationship between innate activation of PBMC subsets and MICA levels

We examined the gene expression of TLR and ISG in each of the major PBMC subpopulations. Of note, we could observe a strong relationship between sMICA and the up‐regulation of TLR‐2 (ρ = 0·591, P = 0·002) and TLR‐4 (ρ = 0·563, P = 0·003) in T lymphocytes, as well as with the transcripts of the ISG IFN‐induced protein with tetratricopeptide repeats 1 (IFIT1) (ρ = 0·452, P = 0·035) and HECT and RLD domain containing E3 ubiquitin protein ligase 5 (HERC5) (ρ = 0·470, P = 0·018) in these cells (Fig. 4a). There was an inverse trend between MICA mRNA levels in B lymphocytes and circulating sMICA (ρ = –0·413, P = 0·08), and also a significant inverse association between B cell MICA expression and the amount of proteinuria (ρ = –0·468, P = 0·024) (Fig. 4b).

Figure 4.

Figure 4

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(a) Levels of soluble major histocompatibility complex (MHC) class I‐related chain A (MICA) in sera are plotted in relationship with the gene expression levels of Toll‐like receptors (TLR)‐2 and ‐4, and the interferon (IFN)‐I sensitive genes, HECT and RLD domain containing E3 ubiquitin protein ligase 5 (HERC5) and IFN‐induced protein with tetratricopeptide repeats 1 (IFIT1), in T lymphocytes. (b) In the upper panel, the correlation between serum soluble MICA levels and the gene expression of MICA in B cells is shown. The lower panel shows correlation between gene expression levels of MICA in B cells and the amount or urinary protein. Spearman’s correlation coefficients are shown in each of the graphs.

Multivariate regression model of sMICA enhancement

Linear regression was performed in order to determine which traits could define more clearly the mechanisms associated with the up‐regulation of sMICA. Table 3 shows variables yielding a ≤ 0·2 P‐value in the univariate analysis, all of which were tested in the multivariate model. As shown in Table 3, a regression model with ANA titres, T cell TLR‐4 expression and lower vitamin D was the best predictor for sMICA enhancement in our cohort. Intriguingly, the inclusion of vitamin D in the regression equation increased the adjusted R 2 to 0·79, thus pointing to this parameter as the most powerful factor in the association. In view of this effect, we further analysed the distribution of vitamin D levels in our cohort and could observe an inverse relationship with proteinuria (ρ = –0·398, P = 0·036), as well as a tight correlation with T cell MICA gene expression (ρ = 0·526, P = 0·008) (Fig. 5).

Table 3.

Linear regression model

Variable Coefficient IC 95% P
Univariate analysis 24‐h Urinary protein 1·99 (0·59, 3·39) 0·007
Active LN 2533·93 (400, 4668) 0·022
ANA titres 4·04 (1·26, 6·82) 0·006
BILAG sum 106·75 (32·5, 181) 0·007
SLEDAI 132·8 (32·4, 233) 0·012
Activity 1494·06 (–563, 3551) 0·147
NK counts –6·15 (–13·6, 1·29) 0·101
Prednisone (mg) 113·07 (38·3, 188) 0·005
Vitamin D –49·68 (–119, 20) 0·152
CD3 TLR‐2 RNA 155·27 (46·8, 264) 0·007
CD3 TLR‐4 RNA 142·79 (43·1, 242) 0·007
CD3 HERC5 RNA 13·59 (–1·85, 29·0) 0·082
Multivariate model Intercept 3631 (2326, 4937)
ANA titres 3·21 (1·47, 4·95) 0·001
CD3 TLR‐4 RNA 165 (104, 224) <0·001
Vitamin D –73·7 (–113, –34) 0·001
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CI = confidence interval; LN = lupus nephritis; ANA = anti‐nuclear antibody; BILAG = British Isles Lupus Activity Group; SLEDAI = Systemic Lupus Erythematosus Disease Activity Index; NK = natural killer; TLR = Toll‐like receptor; HERC5 = HECT and RLD domain containing E3 ubiquitin protein ligase 5.

Figure 5.

Figure 5

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The graphs show the association of hydroxyl vitamin D3 levels with urinary protein and major histocompatibility complex (MHC) class I‐related chain A (MICA) gene expression levels in T lymphocytes, respectively. Spearman’s correlation coefficients are shown in each of the graphs.

Discussion

Whether NK cells act as friends or foes in lupus remains controversial. Notwithstanding their participation in tissue damage, NK regulatory functions involved in the recognition of stressed cells are considered to be a protection against autoimmunity 20. It has been therefore suggested that NK cells exert opposite actions in different clinical contexts. In our patients, a fall in NK cells was a surrogate marker of disease activity, a finding which is consistent with previous observations 13, 14. Moreover, a recent population‐based analysis of transcriptomic patterns could confirm a generalized down‐regulation of NK cell/cytotoxicity‐associated pathways in children with lupus 21.

Interestingly, in our cohort the drop of NK cell counts was paralleled by an enhancement of sMICA, which most probably acts as a decoy receptor for cytotoxic cells 22. However, in agreement with previous studies, the increase in circulating sMICA did not correlate with complement decrease or with anti‐dsDNA antibody titres 12, 13. This lack of association possibly reflects disease heterogeneity, and highlights the need to characterize disease subtypes associated with specific activation signatures. In agreement with our data, a specific disease cluster independent of complement activation might be defined by a subgroup of patients with LN, showing innate activation of T lymphocytes and increased circulating sMICA. Conversely, it has been argued that patients with SLE could have anti‐MICA antibodies impairing the accuracy of sMICA measures 13, a fact which could hinder the use of this molecule as a biomarker in clinical practice.

The relationship between sMICA levels and proteinuria found in our cohort could point to innate activation of kidney cells as the source of circulating sMICA, as has already been suggested 10, 11. However, circulating sMICA might also derive from PBMC shedding. In agreement, decreased MICA expression has been found in PBMC subpopulations from patients with SLE. In our patients, mRNA levels in B lymphocytes changed in the opposite direction to sMICA and to the amount of proteinuria. In spite of the scant number of patients with active LN at enrolment these findings may warrant further research, as they could point to immune escape as a mechanism accounting for the impairment of NK functions in patients with LN 23. As an example, the family of herpesviridae, whose ability to trigger lupus initiation and exacerbation is widely recognized, displays an array of MICA‐targeted immune evasion strategies which not only involve shedding, but also transcriptional suppression and early degradation of the molecule 24, 25.

Different studies have shown that levels of vitamin D are lower in patients with active lupus and LN 26, 27. Although vitamin D insufficiency in the patients has been traditionally attributed to sun protection and low intake the same trend is observed in other autoimmune diseases, thus suggesting that it arises more probably as a consequence of an increased expenditure. Indeed, immune cells significantly up‐regulate vitamin D receptor (VDR) transcription upon activation and proliferation (reviewed in [28]). In turn, through the binding of VDR, vitamin D induces the expression of anti‐proliferative/pro‐apoptotic molecules, thereby evoking immune tolerance 29, 30. Interestingly, recent data showed that MICA stands as a VDR‐sensitive molecule, through which vitamin D renders tumour cells susceptible to NK cytotoxicity 31. According to this view, in our patients the gene expression of MICA in T cells was not associated with the up‐regulation of TLR or ISG, as could have been expected, but paralleled levels of vitamin D instead. All these observations suggest that vitamin D could help to restore homeostasis of the immune system during flares, and that its deprivation may jeopardize MICA‐dependent cell growth control.

In addition, the inverse relationship between circulating sMICA and vitamin D found in our cohort suggests that the vitamin could prevent MICA shedding. Alternatively, sMICA impairment of NK functions could promote the uncontrolled proliferation of immune cells which, in turn, would facilitate the depletion of vitamin D. Further research may be warranted to solve this chicken or egg dilemma, as well as to determine whether this association is shared by other cohorts or different disease populations.

Conversely, it is known that vitamin D facilitates the progression of autophagy, which accounts for a homeostatic mechanism contributing to the resolution of viral infections. In this regard, a relationship has been found recently between the vitamin deficiency and suppression of autophagy‐related genes in PBMC from patients with active lupus 32. Hence, it may be speculated that vitamin D deprivation could have particular relevance in those patients with virally infected immune cells, as lack of vitamin D in these cells would not only impair stress‐induced MICA expression, but also jeopardize phagosome control of the viral load, consequently enabling virus‐driven MICA‐targeting immune evasion strategies.

In summary, we propose a particular disease phenotype characterized by the disruption of MICA‐dependent cytotoxicity in patients with innate activation of T cells and possibly facilitated by low vitamin D levels. Notably, this activation pathway appears not to be captured by conventional activity markers. However, taking into consideration the limited size of our study, our results would need to be replicated in additional cohorts of lupus patients.

Disclosures

The authors have no conflicts of interest to declare.

Author contributions

M. P.‐F., F. I. R.‐B. and A. A. participated in recruitment and data collection, C. S. del C. was responsible for FACS studies, M. P.‐F. and O. S.‐P. performed the molecular studies, R. L. was responsible for soluble MICA quantification, R. L. and G. H.‐B. provided technical support, I. M. supervised the statistical analysis and O. S.‐P. designed the study and wrote the manuscript. All authors provided advice and approved the final manuscript.

Acknowledgements

This research was supported by a grant from the Spanish Ministry for Health, non‐oriented research program, Fondo de Investigación de la Seguridad Social (FIS‐PI10/00337).

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