Background
Juvenile dermatomyositis (JDM) is a complex autoimmune disease characterized by weakness and rashes [
1]. Myositis-specific autoantibodies (MSA) define phenotypic features and prognosis within JDM, with anti-TIF1, anti-NXP2, and anti-MDA5 autoantibodies being the most common MSA groups in JDM [
1‐
4]. Although the etiology of JDM is presently unknown, multiple genetic and environmental factors contribute [
1,
5]. Interferon (IFN)-regulated genes (IRG) are upregulated in the blood, muscle, and skin of patients with JDM and adult dermatomyositis (DM) and correlate with disease activity [
6‐
13], although the exact source, mechanism, and role of IFN remain unclear and detailed assessments by MSA group are lacking.
Mendelian autoinflammatory interferonopathies, which are associated with a strong IRG signature, include Chronic Atypical Neutrophilic Dermatosis with Lipodystrophy and Elevated temperature (CANDLE) caused by additive loss-of-function mutations in proteasome components [
14‐
16] and STING-Associated Vasculopathy with onset during Infancy (SAVI), resulting from gain-of-function mutations in the Stimulator of IFN genes (STING) protein [
16,
17]. These conditions have a very strong IRG signature and blocking IFN signaling with a Janus kinase (JAK) inhibitor correlates with clinical improvement and IRG-S decrease in the majority of 18 CANDLE and SAVI patients, with 50% of CANDLE patients achieving persistent clinical remission [
16,
18‐
20]. A direct comparison of JDM to the monogenic interferonopathies may provide insight into the role of IFN in JDM, particularly as CANDLE and SAVI share some clinical features with JDM. For example, CANDLE is also associated with lipodystrophy, joint contractures, and myositis, while SAVI has frequent vasculopathy including distal ulcerations and interstitial lung disease [
1,
16,
21].
Utilizing a 28 IRG score (IRG-S), which was developed and validated as a biomarker in CANDLE and SAVI [
22], we evaluated peripheral blood IRG expression in JDM. Given prominent IRG signatures and overlapping clinical features of JDM with conditions with IFN-driven pathogenesis based on genetic mutations (CANDLE and SAVI), we aimed to better characterize and understand the role of IFN in JDM and its MSA subgroups through direct comparison to patients with CANDLE and SAVI.
Methods
Patient selection
Subjects were enrolled in National Institutes of Health institutional review board-approved natural history studies (Table
1). Active JDM patients (
n = 57) met probable or definite Bohan and Peter criteria [
23]. CANDLE (
n = 11) and SAVI (
n = 7) patients were genetically defined and used as positive interferonopathy controls [
18]. Neonatal-onset multisystem inflammatory disease (NOMID) patients (
n = 18) served as autoinflammatory controls (IL-1 mediated), in addition to 26 healthy controls (HC), both of which were IFN-negative [
22]. HC were not age- or gender-matched to JDM patients, as we previously did not find significant differences in IRG-S based on these variables [
22]. All JDM patients consented to a NIH/NIEHS IRB-approved protocol. All CANDLE, SAVI, NOMID, and HC patients consented to a NIH/NIAID IRB-approved protocol (NCT02974595). MSAs were identified by validated immunoprecipitation and immunoblotting methods [
24]. MSAs with adequate numbers of JDM patients for analysis included anti-p155/140 (TIF1) (
n = 21), anti-MJ (NXP2) (
n = 11), anti-MDA5 (
n = 11) autoantibodies, and MSA-negative (
n = 9). Clinical data are available to characterize features for 56 JDM patients.
Table 1Demographics of each condition
Age at evaluation (year) | 9.5 [5.9–13.2] | 6.1 [5.2–15.7] | 16.9 [6.1–18.1] | 8.5 [5.3–17.1] | 25.9 [9.7–39.4] |
Gender | Female | 34 (60) | 5 (45) | 3 (43) | 7 (39) | 17 (65) |
Race | White | 36 (63) | 4 (36) | 7 (100) | 12 (67) | 15 (58) |
Hispanic | 4 (7) | 4 (36) | 0 | 3 (17) | 8 (31) |
Black | 3 (5) | 2 (18) | 0 | 1 (6) | 1 (4) |
Multiple race | 10 (18) | 0 | 0 | 1 (6) | 1 (4) |
Other | 4 (7) | 1 (9) | 0 | 1 (6) | 1 (4) |
Disease duration (months) | 10.7 [4.9–36.7] | NA | NA | NA | NA |
Materials
From a single sample per patient of whole blood collected in PAXgene tubes (Qiagen, Germantown, MD), total RNA was extracted. NanoString Technologies™ (Seattle, WA) was used for gene expression analysis, with scores calculated as a sum of
Z-scores for each of 28 IRGs [
22]. Exploratory analysis (data not shown) of complete blood count parameters (white blood cell count as well as absolute neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts) between JDM (
n = 55) versus CANDLE (
n = 10) and SAVI (
n = 5) FDR corrected for multiple comparisons did not detect any significant differences, so no further normalization based on these counts was performed.
Analysis methods
Analysis was performed using R version 3.5.0 (The R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-9000-51-07-0,
http://www.r-project.org), GraphPad Prism7 (GraphPad Software, San Diego, CA), JMP13 (SAS Institute, Cary, NC), or SYSTAT13 (Systat Software, San Jose, CA).
Whole blood score comparisons
With JDM overall, IRG-S were compared to CANDLE, SAVI, NOMID, and HC, and FDR corrected for multiple comparisons using the Benjamini, Krieger, and Yekutieli method [
25], with significant
q values < 0.05. Exploratory analysis compared subgroups of JDM (highest quartile of JDM IRG-S (JDM-HQ), JDM IRG-S above HC, or a specific MSA group) to each other, autoinflammatory conditions and/or HC by Kruskal-Wallis tests, followed by Dunn’s multiple comparisons tests (uncorrected), with
p < 0.05 considered significant.
Principal component analysis
For principal component analysis (PCA) of JDM, JDM-HQ, and MSA subgroups with the autoinflammatory diseases and controls, all PCAs had adequate samples, as indicated by Kaiser-Meyer Olkin test (values ≥ 0.86) and Bartlett’s sphericity test (p values < 0.0001). Five unrotated PCAs were performed using normalized gene counts, each with autoinflammatory diseases and HC data, differentiated by whether they included all JDM patients or a MSA subgroup as follows: PCA-A (all JDM, CANDLE, SAVI, NOMID, HC), PCA-B (anti-MDA5 autoantibody-positive subgroup of JDM, CANDLE, SAVI, NOMID, HC), PCA-C (anti-NXP2 autoantibody-positive subgroup of JDM, CANDLE, SAVI, NOMID, HC), PCA-D (anti-TIF1 autoantibody-positive subgroup of JDM, CANDLE, SAVI, NOMID, HC), and PCA-E (MSA-negative subgroup of JDM, CANDLE, SAVI, NOMID, HC). One anti-TIF1 JDM patient with suspected viral bronchitis had outlying IRG expression (> 99th percentile) and principal component (PC) scores, and was removed from the analysis. The component loadings are the correlations of the individual gene normalized Z-scores with the given principal component. A stronger loading (e.g., greater than 0.40 or less than − 0.40) indicates a stronger relationship or contribution of that gene with that principal component. As with correlations, these can be negative or positive.
Gene proportion analysis
The contribution of each of the 28 individual IRGs was compared to the total IRG Z-score, which was normalized so that no gene component was negative. Each gene’s proportion of the total normalized IRG-S in JDM-HQ was compared to that in CANDLE and SAVI. The analysis was limited to JDM-HQ (n = 14); this subgroup of JDM has an IRG-S in the same range as CANDLE and SAVI for a more equitable comparison of individual gene proportions. As our goal was to assess whether the pattern of dysregulation in JDM IRG-S was the same as in CANDLE and SAVI, we assessed gene proportions from scores in the same IRG-S range in order to limit differences due simply to different levels of overall dysregulation.
The expression level of the 3 IFN response genes (
CXCL10,
GPB1,
SOCS1) that are regulated by STAT1 and have nuclear factor kappa B (NF-κB) binding sites are differentially regulated in some conditions with elevated IRG-S, distinguishing a subset of diseases from CANDLE and SAVI [
26]. Also, as the contribution of different interferons can be variable, the expression level of 9 of the IFN response genes (
CXCL10,
EPSTI1,
IFIT1,
IFIT2,
IFIT3,
ISG15,
LY6E,
SOCS1,
USP18) with a greater increase in expression when induced by IFN-γ than IFN-α, over all 28 genes, was assessed as this has also been described to be variable in other interferonopathies versus CANDLE and SAVI [
26]. Both the ratio of 3 NF-κB-related genes compared to the other 25 IRGs (“NF-κB ratio”) and the latter ratio of 9 genes over 28 (“IFN-γ ratio”) [
26] were calculated based on normalized counts with analysis as above in JDM patients with elevated scores or IRG-S above healthy control range [
22] and described by MSA group versus CANDLE and SAVI.
IFN score correlations in JDM
IRG-S were correlated by Spearman’s rank (
rs) with core set disease activity and damage assessments in JDM patients overall and in patients with anti-TIF1 autoantibodies [
27] (Table
2). IRG-S were also correlated with corticosteroid doses. IRG-S was compared between JDM patients receiving specific medications or not by the Mann-Whitney test and analyzed by the number of steroid-sparing medications received via Kruskal-Wallis and Dunn’s (uncorrected) tests.
Table 2Spearman’s rank correlations between selected myositis disease measures and interferon-regulated gene (IRG) scores
28 IRG score (n = 57) | 40.0 [1.8–236.0] | 40.0 [1.8–236.0] | 41.7 [−4.4–174.2] | 0.96** | 0.99** |
Physician global activity (0–10 VAS, n = 56) | 2.2 [1.5–3.7] | 0.39|| | 0.31 | 0.42|| | 0.39|| |
Total MMT (0–260, n = 36) | 242 [228–252] | − 0.36§ | − 0.13 | − 0.38§ | − 0.33 |
CMAS (0–52, n = 38) | 43 [37–48] | − 0.26 | − 0.22 | − 0.21 | − 0.18 |
CHAQ/HAQ (0–3.0, n = 42) | 0.75 [0.25–1.375] | 0.28 | 0.23 | 0.24 | 0.30 |
MDAAT (0–10 VAS, n = 39) |
Muscle | 2.0 [1.0–2.9] | 0.31 | 0.29 | 0.40§ | 0.30 |
Constitutional | 1.3 [0.3–3.3] | 0.09 | 0.21 | 0.09 | 0.11 |
Cutaneous | 2.4 [1.1–3.8] | 0.29 | 0.62§ | 0.33§ | 0.30 |
Pulmonary | 0.6 [0.0–1.4] | 0.23 | 0.44 | 0.29 | 0.21 |
Skeletal (joint) | 0.9 [0.0–2.1] | 0.35§ | − 0.12 | 0.42|| | 0.36§ |
Extra-muscular activity | 1.9 [1.2–3.2] | 0.47|| | 0.76|| | 0.53¶ | 0.48|| |
(0–10 VAS, n = 40) |
Disease Activity Score (0–20, n = 39) | 11 [9–13] | 0.33§ | 0.58§ | 0.35§ | 0.33§ |
DAS skin (0–9, n = 40) | 6 [5–7] | 0.16 | 0.73|| | 0.18 | 0.19 |
DAS muscle (0–11, n = 39) | 5 [3.75–7] | 0.25 | 0.24 | 0.26 | 0.23 |
Muscle enzymes (U/L) |
CK (ULN 252 U/L, n = 56) | 90 [52–170] | −0.09 | − 0.18 | − 0.11 | − 0.07 |
Aldolase (ULN 7 U/L, n = 54) | 7.5 [5.8–10.1] | 0.24 | 0.18 | 0.25 | 0.24 |
AST (ULN 34 U/L, n = 56) | 24 [18–33] | 0.42|| | 0.24 | 0.38|| | 0.43¶ |
LDH (ULN 226 U/L, n = 56) | 186 [161–214] | 0.41|| | 0.27 | 0.39|| | 0.39|| |
Physician global damage (0–10, n = 46) | 1.0 [0.4–2.0] | −0.05 | 0.04 | −0.09 | − 0.05 |
MDI total damage (0–110, n = 34) | 4.0 [1.8–8.0] | 0.16 | 0.37 | 0.15 | 0.17 |
Two published IRG-S from DM patients,
IFIT1,
IRF7, and
ISG15 [
6] and
EPSTI1,
HERC5,
IFI27,
IFI44,
IFI44L,
IFI6,
IFIT1,
IFIT3,
ISG15,
MX1,
OAS1,
OAS3, and
RSAD2 [
7], were calculated by summing the
Z-score of relevant genes for correlation with JDM disease assessments.
JDM disease activity and damage core measures [
27] (Additional file
1: Table S1) were compared among IRG-S above and below 48.9, the 95th percentile of HC [
22], using Mann-Whitney or Fisher’s exact tests. Myositis Disease Activity Assessment Tool (MDAAT) cardiovascular and gastrointestinal visual analog scales (VAS) were excluded, as few patients had activity in these systems. Non-redundant measures (
rs < 0.7) that differentiated high and low IRG-S were included in backward stepwise logistic regression to examine those associated (alpha < 0.15) with high IRG-S (> 48.9).
IP-10 (CXCL10) as part of a Luminex assay (Bio-Rad, Hercules, CA) was assessed in serum or plasma of JDM patients available, to compare the IRG-S with IFN-signaling at the protein level by Spearman’s correlation.
Discussion
This study analyzed an IRG-S in JDM and MSA groups within JDM versus Mendelian interferonopathies (CANDLE and SAVI), an IL-1 mediated, non-IFN autoinflammatory disease control (NOMID), and healthy controls. PCA and gene proportion analysis suggested similarities of the IRG pattern elevation in JDM and anti-MDA5 autoantibody-positive patients to SAVI and a moderate correlation of IRG-S with clinical measures.
Blood IRG-S in JDM patients ranged from the levels in HC to as high as CANDLE, and SAVI, but were primarily between HC/NOMID and CANDLE/SAVI. We confirmed that some JDM patients’ IRG-S overlap with HC (54% in our cohort) [
7,
28].
IFI27 was found to be most dynamically upregulated [
22], contributing a higher proportion when the IRG-S is higher (data not shown). We found that although
IFI27 contributed the most to the total IRG-S in JDM-HQ, SAVI, and CANDLE with similarly high overall scores, the proportion of
IFI27 to the total IRG-S was lower in JDM-HQ. After excluding
IFI27, four genes (
HERC5,
MX1,
OAS3,
OASL) contributed a higher proportion to the total IRG-S in JDM-HQ versus CANDLE, whereas one gene,
IFIT1, had an increased proportion in JDM-HQ compared to both CANDLE and SAVI. IFIT1 is a negative regulator of STING, which is an adaptor protein essential for IFN-β induction. IFIT1 disrupts the STING interaction with mitochondrial anti-viral signaling protein (MAVS) and TANK-binding kinase (TBK1), which decreases IFN-β expression [
29], and may present a counter-regulatory mechanism to “dampen” STING and IFN-β signaling in JDM. Interestingly, SAVI, caused by gain-of-function mutations in STING leading to constitutive activation of IFN-β through the STING-TBK1-IRF3 pathway [
16], has a more complete overlap of IRG-S with JDM than CANDLE suggesting that STING and IFN-β may be preferentially activated in JDM. In DM, the higher correlation of serum IFN-β with disease activity and peripheral blood IRG signature versus IFN-α [
10] could be consistent with a pathogenic role of STING in JDM. JDM patients with elevated IRG-S had lower NF-κB ratios, similar to canonical interferonopathies CANDLE and SAVI, indicating it is unlikely JDM has much concomitant NF-kB signaling seen in other conditions with an elevated IRG-S [
26]. Overall, JDM with elevated IRG-S had higher IFN-γ ratios than CANDLE and SAVI, which is consistent with previous reports of IFN-γ co-localizes with CD3
+ T cells in untreated JDM muscle biopsies and increase in both type I and type I interferon-regulated genes in JDM muscle [
8], which may differentiate JDM from autoinflammatory CANDLE and SAVI. Differences in these ratios by MSA groups merit evaluation in larger groups.
SAVI is a vasculopathy characterized by violaceous plaques and/or nodules, often with features of vascular damage with gangrene and infarcts in acral areas such as at the tips of the fingers and/or toes [
16,
17]. JDM is also a small-vessel vasculopathy with dilated and tortuous periungual capillaries, endarteropathy, and capillary loss noted on muscle biopsy, and patients may develop vasomotor instability in acral areas [
3,
30,
31]. Both JDM and SAVI share features of vasculopathy leading to thrombosis, tissue ischemia, and infarction, consistent with both phenotypic overlap and potentially shared role of IFN in vasculopathy pathogenesis [
16,
17,
30,
31]. Although Janus kinase (JAK) inhibitor therapy does not specifically or fully block IFN-β or constitutively activated STING, it does inhibit type I and II IFN signaling [
32] and several reports have noted clinical improvement in SAVI with decreased skin vasculopathy and stabilization of lung disease on JAK inhibitor therapy [
20,
33]. Similarly, a limited number of cases in refractory JDM patients have noted improvement of skin and muscle symptoms with JAK inhibitor therapy [
34,
35,
38].
MSA groups continue to evolve as an important predictor of subgroups within JDM both regarding phenotype and prognosis. Among the IRG-S of MSA group patients, the anti-MDA5 autoantibody-positive IRG-S overlapped most closely with SAVI by PCA. The anti-MDA5 autoantibody subgroup of JDM is characterized by increased cutaneous ulceration and interstitial lung disease (ILD) with less muscle disease [
1‐
3], also observed in our cohort, with more overlap with these vasculopathic features seen in SAVI than CANDLE in general [
15‐
17], also specifically observed in our cohort. JAK inhibitor therapy has been noted to show improved outcomes in refractory anti-MDA5 autoantibody adult and juvenile dermatomyositis, including with rapidly progressive ILD [
36‐
38].
Furthermore, the peripheral blood IRG-S correlates moderately with disease activity measures, including muscle and extra-muscular components [
6,
7,
10]. Multivariable regression analysis identified weakness (MMT-26) and increased skeletal features (skeletal VAS) as the most important factors associated with high IRG-S in JDM overall. MSA-negative patients having elevated IRG-S differs from previous reports of lower IFN chemokine scores in MSA-negative patients, although this was a different type of score and the methodology to determine MSAs also differed [
39]. In anti-TIF1 autoantibody-positive patients, who characteristically tend to have more photosensitive rashes, the IRG-S correlated more strongly with skin disease-activity measures although anti-TIF1 patients did not have significantly higher skin activity in our cohort. Unfortunately, small numbers of patients precluded multivariable regression analysis within the anti-TIF1 MSA group.
Though type I IFN itself is in extremely low concentrations in the blood or serum, IRGs are readily quantifiable and have been associated with elevation of serum IFN-α and IFN-β in DM [
7,
10]. Notably, our gene expression assessment on NanoString™, which does not require amplification, has low inter-assay and inter-observer variability and excellent reproducibility [
22] and may hold value as a potential biomarker in the clinical setting compared to a traditional polymerase chain reaction or PCR-based methods used in other studies [
6,
7,
28] that are more difficult to standardize particularly across centers. Additionally, the IRG-S correlated significantly with peripheral protein IP-10. The “gold standard” to validate the peripheral IFN score would be a comparison to the affected skin and/or muscle, in which elevated IRG expression has been previously reported [
8,
9]. Although correlations between skin/muscle and blood IRG expression have not been performed, the correlation of the blood IRG expression with muscle and skin disease activity in our Nanostring™ assay and other reports using PCR analyses [
6,
7,
10] holds promise as a valuable disease activity marker, though further validation is needed.
Limitations of this study include the evaluation of referred JDM patients at a single time point who have received multiple immunosuppressive therapies. We generally did not find that the IRG-S correlated with any specific medications. However, muscle IRG expression in DM and adult polymyositis and peripheral interferon-regulated chemokine score in refractory adult and pediatric myositis was reported to be reduced with rituximab therapy [
40,
41]. The number of patients with Mendelian autoinflammatory diseases and within MSA subgroups was small, but the uniform elevation of the IRG signature and disease-specific differences still allowed for meaningful analysis. Lastly, we evaluated whole blood; thus, the differential contributions of various cell types to the IRG elevation could not be assessed. Though we could not normalize for specific different cell populations within whole blood, it is reassuring that exploratory analysis of white blood cells and differential cell counts between JDM versus CANDLE and SAVI did not find any significant differences (data not shown).
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.