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Objectives: Autoantibody responses increase years before the onset of inflammatory arthritis (IA) and are stable during transitioning from clinically suspect arthralgia (CSA) to IA. Cytokine and chemokine levels also increase years before IA onset. However, the course in the at-risk stage of CSA during progression to disease or non-progression is unknown. To increase the understanding of processes mediating disease development, we studied the course of cytokine, chemokine and related receptors gene expression in CSA patients during progression to IA and in CSA patients who ultimately did not develop IA. Methods: Whole-blood RNA expression of 37 inflammatory cytokines, chemokines and related receptors was determined by dual-colour reverse transcription multiplex ligation-dependent probe amplification in paired samples of CSA patients at CSA onset and either at IA development or after 24 months without IA development. ACPA-positive and ACPA-negative CSA patients developing IA were compared at CSA onset and during progression to IA. Generalised estimating equations tested changes over time. A false discovery rate approach was applied. Results: None of the cytokine/chemokine genes significantly changed in expression between CSA onset and IA development. In CSA patients without IA development, G-CSF expression decreased (P = 0.001), whereas CCR6 and TNIP1 expression increased (P < 0.001 and P = 0.002, respectively) over a 2 year period. Expression levels in ACPA-positive and ACPA-negative CSA patients who developed IA were similar. Conclusion: Whole-blood gene expression of assessed cytokines, chemokines and related receptors did not change significantly from CSA to IA development. This suggests that changes in expression of these molecules may not be related to the final process of developing chronicity and may have occurred preceding CSA onset. Changes in gene expression in CSA patients without IA development may provide clues for processes related to resolution.

Leprosy is an infectious disease caused by Mycobacterium leprae leading to irreversible disabilities along with social exclusion. Leprosy is a spectral disease for which the clinical outcome after M. leprae infection is determined by host factors. The spectrum spans from anti-inflammatory T helper-2 (Th2) immunity concomitant with large numbers of bacteria as well as antibodies against M. leprae antigens in multibacillary (MB) leprosy, to paucibacillary (PB) leprosy characterised by strong pro-inflammatory, Th1 as well as Th17 immunity. Despite decades of availability of adequate antibiotic treatment, transmission of M. leprae is unabated. Since individuals with close and frequent contact with untreated leprosy patients are particularly at risk to develop the disease themselves, prophylactic strategies currently focus on household contacts of newly diagnosed patients. It has been shown that BCG (re)vaccination can reduce the risk of leprosy. However, BCG immunoprophylaxis in contacts of leprosy patients has also been reported to induce PB leprosy, indicating that BCG (re)vaccination may tip the balance between protective immunity and overactivation immunity causing skin/nerve tissue damage. In order to identify who is at risk of developing PB leprosy after BCG vaccination, amongst individuals who are chronically exposed to M. leprae, we analyzed innate and adaptive immune markers in whole blood of household contacts of newly diagnosed leprosy patients in Bangladesh, some of which received BCG vaccination. As controls, individuals from the same area without known contact with leprosy patients were similarly assessed. Our data show the added effect of BCG vaccination on immune markers on top of the effect already induced by M. leprae exposure. Moreover, we identified BCG-induced markers that differentiate between protective and disease prone immunity in those contacts.

Background: Leprosy, a chronic infectious disease caused by Mycobacterium leprae, is often late- or misdiagnosed leading to irreversible disabilities. Blood transcriptomic biomarkers that prospectively predict those who progress to leprosy (progressors) would allow early diagnosis, better treatment outcomes and facilitate interventions aimed at stopping bacterial transmission. To identify potential risk signatures of leprosy, we collected whole blood of household contacts (HC, n=5,352) of leprosy patients, including individuals who were diagnosed with leprosy 4-61 months after sample collection. Methods: We investigated differential gene expression (DGE) by RNA-Seq between progressors before presence of symptoms (n=40) and HC (n=40), as well as longitudinal DGE within each progressor. A prospective leprosy signature was identified using a machine learning approach (Random Forest) and validated using reverse transcription quantitative PCR (RT-qPCR). Findings: Although no significant intra-individual longitudinal variation within leprosy progressors was identified, 1,613 genes were differentially expressed in progressors before diagnosis compared to HC. We identified a 13-gene prospective risk signature with an Area Under the Curve (AUC) of 95.2%. Validation of this RNA-Seq signature in an additional set of progressors (n=43) and HC (n=43) by RT-qPCR, resulted in a final 4-gene signature, designated RISK4LEP (MT-ND2, REX1BD, TPGS1, UBC) (AUC=86.4%). Interpretation: This study identifies for the first time a prospective transcriptional risk signature in blood predicting development of leprosy 4 to 61 months before clinical diagnosis. Assessment of this signature in contacts of leprosy patients can function as an adjunct diagnostic tool to target implementation of interventions to restrain leprosy development. Funding: This study was supported by R2STOP Research grant, the Order of Malta-Grants-for-Leprosy-Research, the Q.M. Gastmann-Wichers Foundation and the Lepros

Background: Established rheumatoid arthritis (RA) patients display differentially expressed genes coding for cytokine/chemokine-mediated immunity compared to healthy controls. It is unclear, however, if in the pre-arthritis phase of clinically suspect arthralgia (CSA) expression of immune genes differ between patients who do or do not develop clinically evident inflammatory arthritis (IA). Methods: Two hundred thirty-six consecutive patients presenting with arthralgia clinically suspected for progression to RA were followed until IA development or else for median 24 months (IQR 12–26). Baseline whole blood RNA expression was determined for a previously identified set of 133 genes associated with the innate and adaptive immune system by dual-color reverse-transcription multiplex ligation-dependent probe amplification (dcRT-MLPA) profiling. Cox proportional hazard models were used. Results: Twenty percent of CSA patients developed IA. After correction for multiple testing, expression levels of six genes (IFNG, PHEX, IGF-1, IL-7R, CD19, CCR7) at the time of presentation were associated with progression to IA. PHEX and IGF-1 were highly correlated with each other (ρ = 0.97). In multivariable analysis correcting for the different genes, expressions of IL-7R and IGF-1 were independently associated with IA development (p = 0.025, p = 0.046, respectively). Moreover, IL-7R and IGF-1 remained significantly associated even after correction for known predictors (ACPA, CRP, imaging-detected subclinical joint inflammation; p = 0.039, p = 0.005, respectively). These genes are also associated with RA development. Conclusions: IL-7R and IGF-1 were differentially expressed between CSA patients who did or did not progress to IA, independent from regularly used predictors. These biomarkers may become helpful in prognostication of CSA patients. Furthermore, because both genes are associated with T cell functioning, T cell dysregulation may mediate progression from arthralgia to arthr, Pattern Recognition and Bioinformatics

Following publication of the original article [1], a typesetting error was reported. The equal contribution note for this article was incorrect. The corrected statement is given below. † Ellis Niemantsverdriet and Erik B. van den Akker contributed equally and are joint first authors. † Annemieke Geluk and Annette H. M. van der Helm-van Mil contributed equally and are joint last authors. The original article [1] has been corrected., Pattern Recognition and Bioinformatics