Alternative exon usage in TRIM21 determines the antigenicity of Ro52/TRIM21 in systemic lupus erythematosus

The origin and mechanisms of autoantigen generation in systemic lupus erythematosus (SLE) are poorly understood. Here, we identified SLE neutrophils activated in vivo by IFN as a prominent source of Ro52, also known as tripartite motif-containing protein 21 (TRIM21), a critical autoantigen historically thought to be primarily generated by keratinocytes in SLE. Different from mononuclear cells and keratinocytes, SLE neutrophils are enriched in several unique Ro52 species containing a core sequence encoded by exon 4 (Ro52Ex4) in TRIM21. Ro52Ex4 is the main target of anti-Ro52 antibodies and is found in 2 Ro52 variants (Ro52α and an isoform termed Ro52γ) upregulated in SLE neutrophils. Further analysis of Ro52γ revealed a subset of autoantibodies against a unique C-terminal domain (Ro52γCT) generated from a frameshift due to the lack of exon 6 in Ro52γ. Antibodies to Ro52Ex4 and Ro52γCT distinguish SLE patient subsets characterized by distinct clinical, laboratory, treatment, and transcriptional profiles that are not discerned by the "classical"anti-Ro52 antibodies. These studies uncover IFN-activated neutrophils as a key source of unique immunogenic forms of Ro52 in SLE. Moreover, the finding of Ro52Ex4 and Ro52γCT as core targets of anti-Ro52 antibodies focus interest on Ro52γ as the potential isoform toward which immunological tolerance is initially lost in SLE

Extranodal marginal zone lymphoma clonotypes are detectable prior to eMZL diagnosis in tissue biopsies and peripheral blood of Sjögren's syndrome patients through immunogenetics

INTRODUCTION: Activated B cells play a key role in the pathogenesis of primary Sjögren's syndrome (pSS) through the production of autoantibodies and the development of ectopic germinal centers in the salivary glands and other affected sites. Around 5-10% of pSS patients develop B-cell lymphoma, usually extranodal marginal zone lymphomas (eMZL) of the mucosa-associated lymphoid tissue (MALT). The aim of the current study is to investigate if the eMZL clonotype is detectable in prediagnostic blood and tissue biopsies of pSS patients. METHODS/RESULTS: We studied prediagnostic tissue biopsies of three pSS patients diagnosed with eMZL and four pSS controls through immunoglobulin (IG) gene repertoire sequencing. In all three cases, we observed the eMZL clonotype in prediagnostic tissue biopsies. Among controls, we observed transient elevation of clonotypes in two pSS patients. To evaluate if eMZL clonotypes may also be detected in the circulation, we sequenced a peripheral blood mononuclear cell (PBMC) sample drawn at eMZL diagnosis and two years prior to eMZL relapse in two pSS patients. The eMZL clonotype was detected in the peripheral blood prior to diagnosis in both cases. Next, we selected three pSS patients who developed eMZL lymphoma and five additional pSS patients who remained lymphoma-free. We sequenced the IG heavy chain (IGH) gene repertoire in PBMC samples taken a median of three years before eMZL diagnosis. In two out of three eMZL patients, the dominant clonotype in the prediagnostic PBMC samples matched the eMZL clonotype in the diagnostic biopsy. The eMZL clonotypes observed consisted of stereotypic IGHV gene combinations (IGHV1-69/IGHJ4 and IGHV4-59/IGHJ5) associated with rheumatoid factor activity, a previously reported feature of eMZL in pSS. DISCUSSION: In conclusion, our results indicate that eMZL clonotypes in pSS patients are detectable prior to overt eMZL diagnosis in both tissue biopsies and peripheral blood through immunogenetic sequencing, paving the way for the development of improved methods of early detection of eMZL

Targeted multiomics in childhood-onset SLE reveal distinct biological phenotypes associated with disease activity: results from an explorative study

Objective To combine targeted transcriptomic and proteomic data in an unsupervised hierarchical clustering method to stratify patients with childhood-onset SLE (cSLE) into similar biological phenotypes, and study the immunological cellular landscape that characterises the clusters.Methods Targeted whole blood gene expression and serum cytokines were determined in patients with cSLE, preselected on disease activity state (at diagnosis, Low Lupus Disease Activity State (LLDAS), flare). Unsupervised hierarchical clustering, agnostic to disease characteristics, was used to identify clusters with distinct biological phenotypes. Disease activity was scored by clinical SELENA-SLEDAI (Safety of Estrogens in Systemic Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index). High-dimensional 40-colour flow cytometry was used to identify immune cell subsets.Results Three unique clusters were identified, each characterised by a set of differentially expressed genes and cytokines, and by disease activity state: cluster 1 contained primarily patients in LLDAS, cluster 2 contained mainly treatment-naïve patients at diagnosis and cluster 3 contained a mixed group of patients, namely in LLDAS, at diagnosis and disease flare. The biological phenotypes did not reflect previous organ system involvement and over time, patients could move from one cluster to another. Healthy controls clustered together in cluster 1. Specific immune cell subsets, including CD11c+ B cells, conventional dendritic cells, plasmablasts and early effector CD4+ T cells, differed between the clusters.Conclusion Using a targeted multiomic approach, we clustered patients into distinct biological phenotypes that are related to disease activity state but not to organ system involvement. This supports a new concept where choice of treatment and tapering strategies are not solely based on clinical phenotype but includes measuring novel biological parameters

Gene signature fingerprints stratify SLE patients in groups with similar biological disease profiles: a multicentre longitudinal study

OBJECTIVES: Clinical phenotyping and predicting treatment responses in SLE patients is challenging. Extensive blood transcriptional profiling has identified various gene modules that are promising for stratification of SLE patients. We aimed to translate existing transcriptomic data into simpler gene signatures suitable for daily clinical practice. METHODS: Real-time PCR of multiple genes from the IFN M1.2, IFN M5.12, neutrophil (NPh) and plasma cell (PLC) modules, followed by a principle component analysis, was used to identify indicator genes per gene signature. Gene signatures were measured in longitudinal samples from two childhood-onset SLE cohorts (n = 101 and n = 34, respectively), and associations with clinical features were assessed. Disease activity was measured using Safety of Estrogen in Lupus National Assessment (SELENA)-SLEDAI. Cluster analysis subdivided patients into three mutually exclusive fingerprint-groups termed (1) all-signatures-low, (2) only IFN high (M1.2 and/or M5.12) and (3) high NPh and/or PLC. RESULTS: All gene signatures were significantly associated with disease activity in cross-sectionally collected samples. The PLC-signature showed the highest association with disease activity. Interestingly, in longitudinally collected samples, the PLC-signature was associated with disease activity and showed a decrease over time. When patients were divided into fingerprints, the highest disease activity was observed in the high NPh and/or PLC group. The lowest disease activity was observed in the all-signatures-low group. The same distribution was reproduced in samples from an independent SLE cohort. CONCLUSIONS: The identified gene signatures were associated with disease activity and were indicated to be suitable tools for stratifying SLE patients into groups with similar activated immune pathways that may guide future treatment choices

Gene signature fingerprints stratify SLE patients in groups with similar biological disease profiles: a multicentre longitudinal study

OBJECTIVES: Clinical phenotyping and predicting treatment responses in SLE patients is challenging. Extensive blood transcriptional profiling has identified various gene modules that are promising for stratification of SLE patients. We aimed to translate existing transcriptomic data into simpler gene signatures suitable for daily clinical practice. METHODS: Real-time PCR of multiple genes from the IFN M1.2, IFN M5.12, neutrophil (NPh) and plasma cell (PLC) modules, followed by a principle component analysis, was used to identify indicator genes per gene signature. Gene signatures were measured in longitudinal samples from two childhood-onset SLE cohorts (n = 101 and n = 34, respectively), and associations with clinical features were assessed. Disease activity was measured using Safety of Estrogen in Lupus National Assessment (SELENA)-SLEDAI. Cluster analysis subdivided patients into three mutually exclusive fingerprint-groups termed (1) all-signatures-low, (2) only IFN high (M1.2 and/or M5.12) and (3) high NPh and/or PLC. RESULTS: All gene signatures were significantly associated with disease activity in cross-sectionally collected samples. The PLC-signature showed the highest association with disease activity. Interestingly, in longitudinally collected samples, the PLC-signature was associated with disease activity and showed a decrease over time. When patients were divided into fingerprints, the highest disease activity was observed in the high NPh and/or PLC group. The lowest disease activity was observed in the all-signatures-low group. The same distribution was reproduced in samples from an independent SLE cohort. CONCLUSIONS: The identified gene signatures were associated with disease activity and were indicated to be suitable tools for stratifying SLE patients into groups with similar activated immune pathways that may guide future treatment choices

Gene signature fingerprints stratify SLE patients in groups with similar biological disease profiles: a multicentre longitudinal study

OBJECTIVES: Clinical phenotyping and predicting treatment responses in SLE patients is challenging. Extensive blood transcriptional profiling has identified various gene modules that are promising for stratification of SLE patients. We aimed to translate existing transcriptomic data into simpler gene signatures suitable for daily clinical practice. METHODS: Real-time PCR of multiple genes from the IFN M1.2, IFN M5.12, neutrophil (NPh) and plasma cell (PLC) modules, followed by a principle component analysis, was used to identify indicator genes per gene signature. Gene signatures were measured in longitudinal samples from two childhood-onset SLE cohorts (n = 101 and n = 34, respectively), and associations with clinical features were assessed. Disease activity was measured using Safety of Estrogen in Lupus National Assessment (SELENA)-SLEDAI. Cluster analysis subdivided patients into three mutually exclusive fingerprint-groups termed (1) all-signatures-low, (2) only IFN high (M1.2 and/or M5.12) and (3) high NPh and/or PLC. RESULTS: All gene signatures were significantly associated with disease activity in cross-sectionally collected samples. The PLC-signature showed the highest association with disease activity. Interestingly, in longitudinally collected samples, the PLC-signature was associated with disease activity and showed a decrease over time. When patients were divided into fingerprints, the highest disease activity was observed in the high NPh and/or PLC group. The lowest disease activity was observed in the all-signatures-low group. The same distribution was reproduced in samples from an independent SLE cohort. CONCLUSIONS: The identified gene signatures were associated with disease activity and were indicated to be suitable tools for stratifying SLE patients into groups with similar activated immune pathways that may guide future treatment choices

Trained Immunity in Primary Sjögren’s Syndrome: Linking Type I Interferons to a Pro-Atherogenic Phenotype

Background: Trained immunity – or innate immune memory – can be described as the long-term reprogramming of innate immune cells towards a hyperresponsive state which involves intracellular metabolic changes. Trained immunity has been linked to atherosclerosis. A subgroup of patients with primary Sjögren’s syndrome (pSS) exhibits systemic type I interferon (IFN) pathway activation, indicating innate immune hyperactivation. Here, we studied the link between type I IFNs and trained immunity in an in vitro monocytic cell model and peripheral blood mononuclear cells (PBMCs) from pSS patients. Methods: The training stimuli heat killed Candida albicans, muramyl dipeptide, IFNβ, and patient serum were added to THP-1 cells for 24 hours, after which the cells were washed, rested for 48 hours and subsequently re-stimulated with LPS, Pam3Cys, poly I:C, IFNβ or oxLDL for 4-24 hours. PBMCs from pSS patients and healthy controls were stimulated with LPS, Pam3Cys, poly I:C or IFNβ for 0.5-24 hours. Results: Training with IFNβ induced elevated production of pro-atherogenic cytokines IL-6, TNFα and CCL2, differential cholesterol- and glycolysis-related gene expression, and increased glucose consumption and oxLDL uptake upon re-stimulation. Type I IFN production was increased in Candida albicans- and IFNβ-trained cells after LPS re-stimulation, but was reduced after poly I:C re-stimulation. Training with muramyl dipeptide and IFNβ, but not Candida albicans, affected the IFN-stimulated gene expression response to IFNβ re-stimulation. PBMCs from pSS patients consumed more glucose compared with healthy control PBMCs and tended to produce more TNFα and type I IFNs upon LPS stimulation, but less type I IFNs upon poly I:C stimulation. Conclusions: Type I IFN is a trainer inducing a trained immunity phenotype with pro-atherogenic properties in monocytes. Conversely, trained immunity also affects the production of type I IFNs and transcriptional response to type I IFN receptor re-stimulation. The phenotype of pSS PBMCs is consistent with trained immunity. This connection between type I IFN, trained immunity and cholesterol metabolism may have important implications for pSS and the pathogenesis of (subclinical) atherosclerosis in these patients