Increased CD69+CCR7+ circulating activated T Cells and STAT3 expression in cutaneous lupus erythematosus patients recalcitrant to antimalarials

Abstract

Background:

Antimalarials are first-line systemic therapy for cutaneous lupus erythematosus (CLE). While some patients unresponsive to hydroxychloroquine (HCQ) alone benefit from the addition of quinacrine (QC), a subset of patients are refractory to both antimalarials.

Methods:

We classified CLE patients as HCQ-responders, HCQ+QC-responders, or HCQ+QC-nonresponders to compare immune profiles. Immunohistochemistry, immunofluorescence, and qRT-PCR were used to characterize inflammatory cells and cytokines in lesional skin.

Results:

Immunohistochemistry showed that CD69+ T cells were higher in HCQ+QC-nonresponders compared to HCQ− and HCQ+QC-responders (p<0.05). Immunofluorescence further identified these cells as CD69+CCR7+ circulating activated T cells. Myeloid dendritic cells were significantly higher in HCQ+QC-responders compared to both HCQ-responders and HCQ+QC-nonresponders. Plasmacytoid dendritic cells were significantly increased in HCQ-responders compared to HCQ− and HCQ+QC-nonresponders. No differences were found in the number of autoreactive T cells, MAC387+ cells, and neutrophils among the groups. CLASI scores of the HCQ+QC-nonresponder group positively correlated with CD69+CCR7+ circulating activated T cells (r=0.6335, p<0.05) and MAC387+ cells (r=0.5726, p<0.05). IL-17 protein expression was higher in HCQ+QC-responders compared to HCQ-responders or HCQ+QC-nonresponders, while IL-22 protein expression did not differ. mRNA expression demonstrated increased STAT3 expression in a subset of HCQ+QC-nonresponders.

Conclusion:

An increased number of CD69+CCR7+ circulating activated T cells and a strong correlation with CLASI scores in the HCQ+QC-nonresponders suggests these cells are involved in antimalarial-refractory skin disease. STAT3 is also increased in HCQ+QC-nonresponders and may also be a potential target for antimalarial-refractory skin disease.

BACKGROUND

Lupus erythematosus (LE) is an autoimmune disease with a wide range of cutaneous and systemic manifestations. Cutaneous LE (CLE) occurs in 70% - 85% of all patients with LE (Fabbri et al., 2003). While antimalarials are considered first line therapy for CLE, only approximately 50% - 60% of patients respond to hydroxychloroquine (HCQ) alone (Chang et al., 2011; Chasset et al., 2018). Approximately two-thirds of HCQ-nonresponders benefit from the addition of quinacrine (QC) (Chang et al., 2011) yet some patients remain recalcitrant to HCQ or combination of HCQ and QC, requiring immunosuppressives (Chang et al., 2013). Given CLE significantly impairs patients’ quality of life (Klein et al., 2011), it is important we further understand the immunologic characteristics of patients unresponsive to antimalarial therapy to more effectively target disease.

Both the pathogenesis of CLE and the mechanism of action of antimalarials remains incompletely understood. CLE pathogenesis is multifactorial involving genetics, UV light, smoking, and infection, leading to keratinocyte apoptosis, T cell and dendritic cell activation, and cytokine release (Robinson et al., 2015). Interferon (IFN)-α, a type I IFN, and tumor necrosis factor (TNF)-α are two important cytokines in CLE pathogenesis. Type 1 IFNs are produced by dendritic cells and keratinocytes in response to UV light, nuclear antigens, and immune complexes (Sarkar et al., 2018; Stannard et al., 2017). IFN-α promotes dendritic cell maturation, T-cell survival, B-cell differentiation, immunoglobulin class switching, and antibody production (Eriksen et al., 2005; Le Bon et al., 2006; Ding et al., 2009; Mikita et al., 2011). It is thought to indirectly recruit cytotoxic T cells and plasmacytoid dendritic cells (pDCs), furthering cutaneous inflammation (Mikita et al., 2011). TNF-α induces keratinocyte apoptosis and may promote autoantigen presentation (Mikita et al., 2011; Nabatian et al., 2012). On the contrary, there have been reports of anti-TNF-α antibody induced CLE (Levine et al, 2010). This may be due to imbalances between type I IFN and TNF-α. TNF-α has been shown to inhibit pDC generation and IFN-α release, ultimately promoting anti-DNA antibodies (Pisetski D., 2000, Palucka et al., 2005). Currently, the role of TNF-α in CLE is not as clear.

Although antimalarials have been used for the treatment of LE for over 50 years, we are only beginning to understand their mechanism of action in skin disease. Antimalarials are thought to decrease B cell and dendritic cell activation, cytokine activity, protease activity, and activation of toll-like receptor (TLR)-3/7/8/9 (Wozniacka et al., 2002; Kalia and Dutz., 2007; Zeidi et al., 2019). We recently evaluated the differential inflammatory cell population and cytokine profile of patients who responded to HCQ alone and patients who required additional QC (Zeidi et al., 2019). QC, although now increasingly difficult to acquire, is thought to act synergistically with HCQ by suppressing TNF-α and IFN-α and acting on toll-like receptors (Yan et al., 2020). We found myeloid dendritic cell (mDC) populations and TNF-α were significantly increased in patients requiring both HCQ and QC compared to the patients requiring HCQ alone (Zeidi et al., 2019). However, it remains unclear why some patients do not respond well to antimalarials. In this study, we expand upon our previous report to investigate the cells and cytokines contributing to refractoriness in patients who do not respond to either HCQ alone or HCQ and QC combined. Elucidating the immunologic characteristics of these refractory patients, particularly in direct comparison to patients responsive to either HCQ or HCQ + QC combined, may aid us in more effectively targeting disease.

PATIENTS AND METHODS

Patients

Sixty-five well-characterized patients with a diagnosis of CLE recruited from the Autoimmune Skin Disease Center at the Hospital of the University of Pennsylvania were selected for the study. Patients were recruited in accordance with an approved Institutional Review Board protocol for a long-standing longitudinal clinical and specimen database. All subjects provided written informed consent. They were categorized in terms of response to treatment as (i) HCQ-responders (n=22), (ii) HCQ+QC-responders (n=24), or (iii) HCQ+QC-nonresponders (n=19). HCQ-responders and HCQ+QC responders were the same patient population as previously published, however in this study, findings were extended to antimalarial refractory patients and additional markers in all populations were examined, as outlined below (Zeidi et al, 2019). Lesional skin was biopsied before starting treatment with antimalarials. We defined the treatment failure to HCQ as continued skin activity requiring a second intervention after at least two months of HCQ therapy. We defined treatment failure of HCQ and QC together as failure to respond after two months of combination therapy.

Immunohistochemistry

Fifty-seven skin biopsies of CLE patients (ACLE; n=1, SCLE; n=23, DLE; n=32, LET; n=1) were immunohistochemically investigated for the presence of CD69+ T cells, mDCs, pDCs, MAC387+ cells, autoreactive T cells, neutrophils, IL-17, and IL-22, by employing antibodies against CD69, CD11c, CD123, MAC387, Kv1.3, MPO, IL-17 and IL-22 respectively. Paraffin-embedded skin biopsies were cut into 5 μm sections and mounted on glass slides. Slides were incubated at 60°C overnight, deparaffinized in Citrisolv® (Fischer Scientific Waltham, MA) and rehydrated with serial ethanol dilutions. Antigen retrieval was achieved in EDTA buffer (Thermoscientific, Kalamazoo, MI) in a 95°C pressure cooker for 20 minutes. Endogenous peroxidase activity was deactivated using 0.3% H₂O₂ in deionized H₂O for 5 minutes. Tissues were blocked with 5% BSA protein block (Ab64226, Abcam, Cambridge, MA) for 4 hours and incubated with primary antibody diluted in 0.1% BSA solution (Table S1) overnight at 4°C. Samples were then probed with biotinylated goat anti-mouse and rabbit (Abcam, ab64257) and streptavidin-HRP (PK-6100, Vector laboratories, Burlingame, CA). NOVAred® chromogen (SK-4800, Vector Laboratories) was used to develop stain. Sections were dehydrated and set with Permount® (Fisher Scientific, Fair Lawn, NJ) and glass coverslips. Sections were examined by the Nikon Eclipse 80i microscope (Nikon Instruments Inc., Melville, NY). Positive stained cells were quantified at 40x objective magnification in five non-overlapping adjacent microscopic fields in the dermis. The results were expressed as mean number of cells per high power field (HPF). Protein staining quantification was measured by area fraction of the dermis in one 20x field using NIS-Elements (Basic Research) software. Negative controls were achieved using matched mouse or rabbit isotype antibodies.

Immunofluorescence

Four lesional skin biopsies from each treatment group were stained by immunofluorescence. Slides were prepared in the same fashion as immunohistochemistry. Tissues were incubated with a primary antibody mixture of CD69, CD3, and CCR7 (Table S1) diluted in 0.1% BSA solution overnight at 4°C. Samples were then probed with a mixture of goat anti-mouse Alexa Fluor 488, goat anti-rabbit Alexa Fluor 568, and goat anti-rat Alexa Fluor 647 (ThermoFisher, Waltham, MA). Negative controls were achieved using matched mouse, rabbit, and goat isotype antibodies. Sections were stained with DAPI and imaged using a Nikon Eclipse Ti fluorescent microscope (Nikon Instruments Inc., Melville, NY). Images were prepared using the ImageJ software.

RNA extraction from formalin fixed paraffin-embedded (FFPE) tissue and quantitative real time RT-PCR

Skin samples from 29 CLE patients (9 HCQ-responders, 11 HCQ+QC-responders, and 9 HCQ+QC-nonresponders) were analyzed by qRT-PCR for gene expression of type I IFN signature genes (OAS1, OASL, ISG15, LY6E, MX1), inflammatory cytokine TNF-α, and STAT transcription factors (STAT1, STAT3, STAT4). Total RNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissue with RNeasy FFPE Kit (Qiagen, CA), and cDNA was synthesized using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, CA) in accordance with the manufacturer’s protocol. Real-time PCR was performed using Taqman gene expression assays (OAS1, Hs00242943_m1; OASL, Hs00388714_m1; ISG15, Hs00192713_m1; LY6E, Hs00158942_m1; MX1, Hs00182073_m1; TNF-α, Hs01113624_g1; STAT1, Hs01013996_m1; STAT3, Hs00374280_m1; STAT4, Hs01028017_m1; and GAPDH, Hs99999905_m1). The level of gene transcript was normalized to GAPDH. For each sample, real-time PCR was performed in triplicate and quantified using the comparative cycle threshold (CT) method (ΔΔCt) (Livak and Schmittgen, 2001).

Statistical analysis

Statistical analysis was performed using GraphPad Prism software (GraphPad Software, Inc., La Jolla, CA, USA). One-way analysis of variance with Bonferroni’s multiple comparison test was used to compare the HCQ-responders, HCQ+QC-responders, and HCQ+QC-nonresponder groups. Values of p < 0.05 were considered significant.

RESULTS

Epidemiologic characteristics of the study population

Among 65 patients included in the study, 22 patients (33.9%) responded well to HCQ (HCQ-responders), 24 patients (36.9%) responded to HCQ and QC combined due to the insufficient efficacy of HCQ (HCQ+QC-responders), and 19 patients (29.2%) did not respond to HCQ and QC combined (HCQ+QC-nonresponder group). Age, sex, Cutaneous Lupus Erythematosus Disease Area and Severity Index (CLASI) activity score, CLE subtypes (ACLE, SCLE, DLE and LET), and the number of American College of Rheumatology criteria in SLE fulfilled were similar in the three groups (Table 1). Previously, several clinical factors, including smoking status (Chasset et al., 2015), presence of associated SLE, cicatricial alopecia, disseminated DLE (Wahie et al., 2011), and hypertrophic DLE (Spann et al., 1988) have been associated with the failure of HCQ in DLE patients. However, Fisher’s exact test and chi-square test found the only difference between the three treatment response groups in this study was age, which was younger in the nonresponder group.

Table 1.

Epidemiologic characteristics of the study population according to treatment response to antimalarials

	HCQ Responders (n=22)	HCQ + QC Responders (n = 24)	Nonresponders (n = 19)	p-value
Age, y, mean +/− SEM	52.2 +/− 4.5	45.4 +/− 2.8	40.8 +/− 2.3	0.05
Sex, n (%)				ns
Male	3 (13.6)	3 (12.5)	4 (21.1)
Female	19 (86.4)	21 (87.5)	15 (78.9)
Race, n (%)				ns
Caucasian	14 (63.6)	15 (62.5)	11 (57.9)
African American	5 (22.7)	8 (33.3)	5 (26.3)
Asian	0 (0)	0 (0)	1 (5.3)
Other	1 (4.5)	1 (4.2)	2 (10.5)
Unknown	2 (9.1)	0 (0)	0 (0)
Smoking Status				ns
Current	7 (31.8)	7 (29.2)	6 (31.6)
Former	5 (22.7)	2 (8.3)	3 (15.8)
Never	9 (40.1)	13 (54.2)	10 (52.6)
Unknown	1 (4.5)	2 (8.3)	0 (0)
CLASI-Activity score, mean +/− SEM	11.5 +/− 2.6 (n=16)	12.6 +/− 2.3 (n=19)	18.3 +/− 2.8 (n=18)	ns
CLE subtypes, n (%)				ns
ACLE	0 (0)	0 (0)	2 (10.5)
SCLE	10 (45.5)	9 (37.5)	8 (42.1)
DLE	11 (50.0)	15 (62.5)	9 (47.4)
LET	2 (9.9)	0 (0)	1 (5.3)
Associated SLE, n (%)				ns
Present	7 (31.8)	9 (37.5)	2 (10.5)
Absent	15 (68.2)	15 (62.5)	17 (89.5)?
Cicatricial alopecia, n (%)				ns
Present	5 (22.7)	12 (50)	8 (42.1)
Absent	14 (63.6)	9 (37.5)	10 (52.6)
Unknown	3 (13.6)	3 (12.5)	1 (5.3)
Disseminated DLE, n (%)				ns
Present	3 (13.6)	6 (25)	5 (26.3)
Absent	19 (86.4)	18 (75)	14 (73.7)
Hypertrophic DLE. n (%)				ns
Present	1 (4.5)	2 (8.3)	1 (5.3)
Absent	21 (95.5)	22 (91.7)	18 (94.7)

Increased number of CD69+ T cells in the lesional skin of HCQ+QC-nonresponder patients

Patients not responsive to HCQ+QC were found to have significantly more CD69+ T cells in lesional skin compared to HCQ-responders and HCQ+QC-responders (p<0.05) (Figure 1a, Figure S1a, Table S2). HCQ+QC-responders had significantly more mDCs compared to HCQ-responders and HCQ+QC-nonresponders (p<0.01) (Figure 1c, Figure S1c, Table S2). There were significantly more pDCs in HCQ-responders compared to the nonresponders (p<0.05) (Figure 1d, Figure S1d, Table S2). The percentage of area stained for Kv1.3, a marker of autoreactive T cells, was not significantly different between nonresponders and responders to antimalarial therapy (Figure 1b, Figure S1b, Figure S2). There was no significant difference in the number of MAC387+ cells (Figure 1e, Figure S1e, Table S2) or neutrophils (Figure 1f, Figure S1f, Table S2) among the three groups.

Figure 1. Immune Cell Quantitation in HCQ-responders, HCQ+QC-responders, and Nonresponders.

(a) There was a significantly higher number of CD69+ T cells in nonresponders compared to HCQ-responders and HCQ+QC-responders (p<0.05). (b) The percentage area stained for Kv1.3, a marker of autoreactive T cells, was not significantly different between the nonresponders and responders to antimalarial therapy. (c) There was a significantly higher number of mDCs in HCQ+QC-responders compared to the nonresponders (p<0.001) and HCQ-responders (p<0.01). (d) There was a significantly higher number of pDCs in the HCQ-responders compared to the nonresponders (p<0.05). (e) There was no significant difference in the number of MAC387+ cells and f. number of neutrophils among the three groups. The graphs show mean ± SEM. *p < 0.05; **p < 0.01; ***p < 0.001.

CD69+ cells are CD3+ T cells and express CCR7

After observing increased CD69+ cells in CLE skin biopsies, we performed immunofluorescence staining to more accurately phenotype them. In a sample nonresponder biopsy, CCR7 was found on almost all immune cells and colocalized with CD3 (Figure 2d), with healthy control staining depicted in Figure S3. CD69 colocalized with CD3 (Figure 2e), identifying them as T cells. Further overlay of all three markers revealed colocalization of CCR7, CD3, and CD69 (Figure 2f) identifying CCR7+CD69+ T cells in skin lesions of CLE patients. This implicates them as circulating activated T cells.

Figure 2: CD69+ cells are CD3+ T cells and express CCR7.

Triple labeling using (a) CCR7 (red); (b) CD3 (green); and (c) CD69 (magenta) of 4 lesional biopsies per treatment group was performed to identify CD69+ T cells. (d) CCR7 was found on almost all immune cells and overlapped with CD3 as see in yellow. (e) CD69 was found to overlap completely CD3 (white) identifying them as T cells. (f) Further overlay of all three markers revealed overlap of CCR7, CD3, and CD69 (white).

CLASI score correlated with the number of CD69+CCR7+ circulating activated T cells and MAC387+ cells

In the HCQ+QC-nonresponder group, there is a significant positive correlation between the CLASI score and the number of CD69+CCR7+ T cells (r=0.6254, p=0.017) (Figure 3c) and MAC387+ cells (r=0.5726, p=0.041) (Figure 3f). There was no significant correlation between CLASI score with the number of CD69+ T cells and MAC387+ cell counts in the HCQ-responders (Figure 3a, 3d) and HCQ+QC-responders (Figures 3b, 3e).

Figure 3. Cytokine Staining in HCQ-responders, HCQ+QC-responders, and Nonresponders.

(a) There was a significantly higher percentage of area stained for IL-17 in the HCQ+QC-responders compared to HCQ-responders and nonresponders (p<0.05). (b) There was no significant difference in percentage of area stained for IL-22 between the nonresponders and responders to antimalarials. The graphs show mean ± SEM. *p < 0.05.

High IL-17 protein expression in the HCQ+QC-responders compared to HCQ-responders and nonresponders

IL-17 protein expression was significantly higher in HCQ+QC responders compared to HCQ-responders and antimalarial-nonresponders (Figure 4a, Figure S2a). IL-22 protein expression was not significantly different between groups (p<0.05) (Figure 4b, Figure S2b).

Figure 4. CLASI Score versus CD69+ T cells and MAC387+ cells in HCQ-responders, HCQ+QC-responders, and Nonresponders.

For the nonresponders, there was a significant positive correlation between the CLASI score with the number of CD69+ T cells and MAC387+ cells (p<0.05, Figures 3c&3f). There was no significant correlation between the CLASI score with the number of CD69+ T cells and MAC387+ cell counts in the HCQ-responders and HCQ+QC-responders.

Gene expression

Gene expression of type I IFN signatures, OAS1, OASL, ISG15, LY6E, and MX1 in lesional CLE skin were significantly upregulated in the HCQ-responders (n=9) compared to the HCQ+QC-responders (n=11) and -nonresponders (n=9) (Figure 5a–e). TNF-α was significantly downregulated in the HCQ-responders compared to HCQ+QC-responders and nonresponders (Figure 5f). However, TNF-α was not significantly different between HCQ+QC-responders and nonresponders, suggesting a higher level of TNF-α reflects refractoriness to HCQ. Gene expression of STAT1 and STAT4 corresponded with type I IFN signatures and were increased in HCQ-responders compared to both HCQ+QC-responders and nonresponders (Figure 5g, i). In contrast, mRNA expression of STAT3 in nonresponders was significantly increased compared to HCQ responders and marginally increased compared to HCQ+QC-responders (p = 0.0658) (Figure 5h).

Figure 5. Type 1 Interferon, TNF, and STAT Gene Expression in HCQ-responders, HCQ+QC-responders, and Nonresponders.

The nonresponders had a significantly decreased expression of type 1 IFN signatures, STAT1, and STAT 4 than HCQ responders, whereas the gene expression of TNF-α and STAT3 was significantly elevated in nonresponders. The nonresponders and HCQ+QC-responders had a similar expression profile for all analyzed genes, except for STAT3. Gene expression of STAT3 showed a tendency of increased expression in nonresponders compared to HCQ-responders and HCQ+QC responders, although the statistical significance was confirmed only against HCQ-responders. The graphs show mean ± SEM. *p < 0.05; **p < 0.01; ***p < 0.001.

DISCUSSION

Antimalarial drugs are first line therapy for CLE but approximately 15% - 20% remain recalcitrant to either HCQ alone or HCQ and QC combined (Chang et al, 2011). It is therefore important to elucidate the immunologic properties of nonresponders to allow more targeted therapy for patients with CLE. This study expands upon our previous report (Zeidi et al, 2019) and evaluates the differential expression of inflammatory cells and cytokines in skin lesions of patients not responsive to either HCQ alone or the combination of HCQ plus QC. Here we found significantly increased numbers of CD69+CCR7+ circulating activated T cells and increased expression of STAT3 in HCQ+QC-nonresponders compared to HCQ− and HCQ+QC-responders. Additionally, CD69+CCR7+ circulating activated T cells and MAC387+ cells correlated with CLASI scores in the HCQ+QC-nonresponders, suggesting a potentially important role in driving disease activity in antimalarial nonresponders.

CD69 expression on T cells has multiple implications for activation, differentiation, and migration (Cibrian et al., 2017). It is thought to interact with ligands on dendritic cells to inhibit Th1 and Th17 responses while promoting regulatory T cells (Toscano et al, 2007, Martin et al., 2010, de la Fuente et al., 2014, Lin et al., 2015). Increased numbers of CD69+ cells in nonresponders and correlation with severity may be a reactive process attempting to curb Th1 or Th17 activity. In contrast, CD69 may also contribute to proinflammatory states via inhibition of T cell egression and many are using it to help define tissue resident memory T cells (Turk and Molodtsov, 2018). Traditionally, tissue resident memory T cells lack expression of CCR7; however, we observed positive CCR7 expression on CD69 cells in CLE giving them a circulating phenotype (Mami-Chouaib and Tartour, 2019). This CCR7 may be relatively lower compared to circulating T cells in the blood, but requires further investigation given the pleomorphic effects of CD69 on T cells.

MAC387 (calprotectin) is frequently found in inflamed tissue and is produced by a number of activated immune cells such as monocytes, macrophages, and neutrophils. A positive correlation in nonresponders suggests a potential means by which patients are refractory to antimalarials. As suggested in the literature, calprotectin may augment autoimmune disease by involving neutrophil extracellular traps, promoting leukocyte extravastation, and costimulating autoreactive T cells (Ometto et al., 2017). These processes may be augmented in CLE nonresponders, leading to increased antigen presentation and autoreactivity.

Expanding upon our prior study (Zeidi et al, 2019), we found the number of mDCs was very similar between HCQ-responders and HCQ+Q nonresponders but significantly fewer than seen in HCQ+QC-responders. pDCs were significantly greater in HCQ-responders compared to the nonresponder group. We saw a similar pattern with the type 1 IFN gene expression, supporting the notion that pDCs are critically related to type 1 IFN pathogenesis. We additionally found increased TNF-α expression in both nonresponders and HCQ+QC-responders compared to HCQ-responders. TNF-α has been shown to inhibit release of IFN-α by pDCs (Palucka et al, 2005), consistent with our findings. Given TNF-α expression in HCQ+QC-nonresponders was significantly greater than HCQ-responders, nonresponders may be driven more by a TNF-α process although the cell source and effect on a protein level is unclear at this time. QC decreases TNF-α (Alves et al, 2017) and in a subset of patients with significantly elevated TNF-α, the addition of QC does suppress disease.

The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway plays a pivotal role in modulating the immune system. STAT proteins are involved in signal transduction of over fifty cytokines, hormones, and growth factors, cellular proliferation, differentiation, activation, maturation and survival of all cell types. We evaluated three different STATs: STAT 1, 3, and 4. STAT1/4 are activated by type I IFNs engaging IFN-α receptors, resulting in further induction of proinflammatory genes (Ivashkiv and Donlin, 2014, Levy and Darnell, 2002). In contrast, STAT3 plays a negative regulatory role of STAT1-mediated inflammatory responses (Ho and Ivashkiv, 2006). We found that STAT1/4 were significantly increased in HCQ-responders compared to nonresponders. STAT3 was significantly increased in the nonresponders compared to the HCQ-responders and marginally increased compared to HCQ+QC-responders.

A recent study suggested mycophenolate mofetil inhibits phosphorylation of STAT3 in SLE patients, significantly suppressing disease (Slight-Webb et al, 2019). In CLE, mycophenolate mofetil has been shown to be highly effective in patients resistant to antimalarials (Gammon et al, 2011). Given this and our finding of significantly elevated STAT3 in nonresponders compared to HCQ-responders, patients with high STAT3 may benefit from earlier mycophenolate mofetil therapy. Of the HCQ+QC-nonresponders without increased STAT3 levels, there are likely alternative factors contributing to their non-responsiveness to antimalarials.

IL-17 is known to stimulate T cells, increase production of autoantibodies, and trigger production of inflammatory cytokines and chemokines (Robinson et al, 2015). We found significantly increased levels of IL-17 in the HCQ+QC-responders compared to both HCQ+QC-nonresponders and HCQ-responders. Despite higher STAT3 in the HCQ+QC-nonresponders, IL-17 was not increased in this group suggesting multiple sources and complexity of the cytokine regulation. Therefore, the presence of IL-17 in CLE is still unclear but several studies, including ours, have reported IL-17 expression in lesional skin. One study reported IL-17 protein expression in more than 80% of CLE specimens and a positive correlation with IFN-α and MxA protein expression (Oh et al, 2011). They hypothesized type 1 IFNs produced by pDCs may indirectly stimulate autoreactive T cells, including Th17 cells (Oh et al, 2011).

This study is limited by the use of traditional cell surface markers to represent cell types. These markers may be fluid thus resulting in the transient expression on other cell types. This initial study to characterize the inflammatory landscape of nonresponsive CLE patients will require additional studies to understand the molecular distribution of the observed differences and their mechanistic properties.

CONCLUSIONS

The pathogenesis of CLE is complex and incompletely understood. Moreover, response to antimalarials is variable, underscoring the likelihood of immunologic differences. This paper addresses patients refractory to antimalarials, a group not addressed in our previous paper (Zeidi et al, 2019). We examined several cell types and gene expressions in lesional skin of patients with CLE who respond to both antimalarial regimens relative to our previous work with those responding to HCQ alone or HCQ + QC. This work identifies CD69+CCR7+ T cells, MAC387+ cells, and STAT3 as potentially important players in causing and perpetuating disease refractory to antimalarials. Targeting these cells and pathways may allow us to effectively treat disease in the roughly 15%- 20% of patients with CLE who fail current treatment. CLE significantly impairs patients’ quality of life and therefore deserves a more complete understanding of disease pathogenesis.

Supplementary Material

1

Figure S1. Representative immunohistochemical protein expression of a) CD69, b) Kv1.3, c) CD11c, d) CD123, e) MAC387, and f) MPO in HC and patients responsive to HCQ alone, responsive to HCQ plus QC, and nonresponsive to HCQ plus QC. All images at 200x total magnification.

Figure S2. Representative immunohistochemical protein expression of a) IL-17 and b) IL-22 in HC and patients responsive to HCQ alone, responsive to HCQ plus QC, and nonresponsive to HCQ plus QC. All images at 200x total magnification.

Figure S3. Representative immunofluorescence protein expression of CCR7 in HC and NR CLE skin. All images at 200x total magnification.

Figure S4. Isotype immunohistochemical controls for mouse, rat and rabbit in CLE skin. All images at 200x total magnification.

Abbreviations used:

ACLE

Acute cutaneous lupus erythematosus

CLE

Cutaneous lupus erythematosus

DLE

Discoid lupus erythematosus

HCQ

Hydroxychloroquine

IFN

Interferon

IL

Interleukin

LE

Lupus erythematosus

LET

Lupus erythematosus tumidus

mDCs

Myeloid dendritic cells

MPO

Myeloperoxidase

pDCs

Plasmacytoid dendritic cells

QC

Quinacrine

SCLE

Subacute cutaneous lupus erythematosus

STATs

Signal transducer and activator of transcription proteins

TLRs

Toll-like receptors

TNF-α

Tumor necrosis factor-alpha

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available at Mendeley Data, doi: 10.17632/9c63f8x3sb.