Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis

A Machine Learning Approach for Prediction of CDAI Remission with TNF Inhibitors: A Concept of Precision Medicine from the FIRST Registry

INTRODUCTION

This study aimed to develop low-cost models using machine learning approaches predicting the achievement of Clinical Disease Activity Index (CDAI) remission 6 months after initiation of tumor necrosis factor inhibitors (TNFi) as primary biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) for rheumatoid arthritis (RA).

METHODS

Data of patients with RA initiating TNFi as first b/tsDMARD after unsuccessful methotrexate treatment were collected from the FIRST registry (August 2003 to October 2022). Baseline characteristics and 6-month CDAI were collected. The analysis used various machine learning approaches including logistic regression with stepwise variable selection, decision tree, support vector machine, and lasso logistic regression (Lasso), with 48 factors accessible in routine clinical practice for the prediction model. Robustness was ensured by k-fold cross validation.

RESULTS

Among the approaches tested, Lasso showed the advantages in predicting CDAI remission: with a mean area under the curve 0.704, sensitivity 61.7%, and specificity 69.9%. Predicted TNFi responders achieved CDAI remission at an average rate of 53.2%, while only 26.4% of predicted TNFi non-responders achieved remission. Encouragingly, the models generated relied solely on patient-reported outcomes and quantitative parameters, excluding subjective physician input.

CONCLUSIONS

While external cohort validation is warranted for broader applicability, this study highlights the potential for a low-cost predictive model to predict CDAI remission following TNFi treatment. The approach of the study using only baseline data and 6-month CDAI measures, suggests the feasibility of establishing regional cohorts to generate low-cost models tailored to specific regions or institutions. This may facilitate the application of regional/in-house precision medicine strategies in RA management.

Rheumatoid arthritis: advances in treatment strategies

Rheumatoid arthritis (RA) is characterised by severe joint and bone damage due to heightened autoimmune response at the articular sites. Worldwide annual incidence and prevalence rate of RA is 3 cases per 10,000 population and 1%, respectively. Several genetic and environmental (microbiota, smoking, infectious agents) factors contribute to its pathogenesis. Although convention treatment strategies, predominantly Disease Modifying Anti Rheumatic Drugs (DMARDs) and Glucocorticoids (GC), are unchanged as the primary line of treatment; novel strategies consisting of biological DMARDs, are being developed and explored. Personalized approaches using biologicals targetspecific pathways associated with disease progression. However, considering the economic burden and side-effects associated with these, there is an unmet need on strategies for early stratification of the inadequate responders with cDMARDs. As RA is a complex disease with a variable remission rate, it is important not only to evaluate the current status of drugs in clinical practice but also those with the potential of personalised therapeutics. Here, we provide comprehensive data on the treatment strategies in RA, including studies exploring various combination strategies in clinical trials. Our systematic analysis of current literature found that conventional DMARDs along with glucocorticoid may be best suited for early RA cases and a combination of conventional and targeted DMARDs could be effective for treating seronegative patients with moderate to high RA activity. Clinical trials with insufficient responders to Methotrexate suggest that adding biologicals may help in such cases. However, certain adverse events associated with the current therapy advocate exploring novel therapeutic approaches such as gene therapy, mesenchymal stem cell therapy in future.

NETest: serial liquid biopsies in gastroenteropancreatic NET surveillance

Objective

Up to now, serial NETest measurements in individuals assessing the disease course of gastroenteropancreatic neuroendocrine tumors (GEPNETs) at long-term follow-up and treatment response were not studied.

Design

The study was a longitudinal validation study of serial NETest measurements - a blood-based gene expression signature - in 132 patients with GEPNETs on therapy or watch-and-wait strategy.

Methods

Serial samples were collected during 46 (range: 6-71) months of follow-up. NETest scores were compared with Response Evaluation Criteria in Solid Tumors version 1.1-defined treatment response (e.g. no evidence of disease (NED), stable disease (SD) or progressive disease (PD)).

Results

Consecutive NETest scores fluctuated substantially (range: 0-100) over time in individuals with SD (n = 28) and NED (n = 30). Follow-up samples were significantly higher in SD (samples 3-5) and NED subgroups (samples 2-5) compared with baseline results, without changes in imaging. In 82% of untreated patients with PD, consecutive NETest scores consistently remained high. In patients undergoing systemic treatment, the median pre-treatment NETest score in treatment-responders was 76.5 (n = 22) vs 33 (n = 12) in non-responders (P = 0.001). Patients with low pre-treatment scores had 21 months reduced progression-free survival (10 vs 31 months; P = 0.01). The accuracy of the NETest for treatment response prediction was 0.73 (P = 0.009).

Conclusion

In patients not undergoing treatment, consecutive low NETest scores are associated with indolent behavior. Patients who develop PD exhibit elevated scores. Elevated results have important predictive value for treatment responsiveness and could be used for individualizing decisions on systemic therapy. The clinical value of follow-up NETest scores for patients who choose to watch and wait requires further study.

Gene Ontology Analysis Highlights Biological Processes Influencing Non-Response to Anti-TNF Therapy in Rheumatoid Arthritis

Anti-TNF therapy has significantly improved disease control in rheumatoid arthritis, but a fraction of rheumatoid arthritis patients do not respond to anti-TNF therapy or lose response over time. Moreover, the mechanisms underlying non-response to anti-TNF therapy remain largely unknown. To date, many single biomarkers of response to anti-TNF therapy have been published but they have not yet been analyzed as a system of interacting nodes. The aim of our study is to systematically elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis using the gene ontologies of previously published predictive biomarkers. Gene networks were constructed based on published biomarkers and then enriched gene ontology terms were elucidated in subgroups using gene ontology software tools. Our results highlight the novel role of proteasome-mediated protein catabolic processes (p = 2.91 × 10⁻¹⁵) and plasma lipoproteins (p = 4.55 × 10⁻¹¹) in anti-TNF therapy response. The results of our gene ontology analysis help elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis and encourage further study of the highlighted processes.

ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients

Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control of anti-TNF treatment response data. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.

Improvement in clinical disease activity index when treatment selection is informed by the tumor necrosis factor-ɑ inhibitor molecular signature response classifier: analysis from the study to accelerate information of molecular signatures in rheumatoid arthritis

BACKGROUND

A blood-based molecular signature response classifier (MSRC) predicts non-response to tumor necrosis factor-ɑ inhibitors (TNFi) in rheumatoid arthritis (RA).

RESEARCH DESIGN AND METHODS

This is an interim analysis of data collected in the Study to Accelerate Information of Molecular Signatures (AIMS) in RA from patients who received the MSRC test between September 2020 and November 2021. Absolute changes in clinical disease activity index (CDAI) scores from baseline were evaluated at 12 weeks (n = 470) and 24 weeks (n = 274).

RESULTS

Predicted TNFi non-responders who received a biologic or targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) with an alternative mechanism of action (altMOA) experienced up to 1.8-fold greater improvements in CDAI scores than those treated with a TNFi (12 weeks: 12.2 vs 8.0; p-value = 0.083; 24 weeks: 14.2 vs 7.8 p-value = 0.009). In patients with a molecular signature of non-response to TNFi in high disease activity at baseline, this corresponded to 43.2% relative improvement in achieving a lower CDAI disease activity level when likely TNFi non-responders were treated with a non-TNFi therapy (38.9% vs 55.7%). Commensurate improvements in efficiency of spend are expected when TNFi are avoided in favor of altMOA.

CONCLUSIONS

RA treatment selection informed by MSRC test results improves clinical outcomes in real-world care and offers improvements in efficiency of healthcare spending.

Mechanisms underlying DMARD inefficacy in difficult-to-treat rheumatoid arthritis: a narrative review with systematic literature search

Management of RA patients has significantly improved over the past decades. However, a substantial proportion of patients is difficult-to-treat (D2T), remaining symptomatic after failing biological and/or targeted synthetic DMARDs. Multiple factors can contribute to D2T RA, including treatment non-adherence, comorbidities and co-existing mimicking diseases (e.g. fibromyalgia). Additionally, currently available biological and/or targeted synthetic DMARDs may be truly ineffective (‘true’ refractory RA) and/or lead to unacceptable side effects. In this narrative review based on a systematic literature search, an overview of underlying (immune) mechanisms is presented. Potential scenarios are discussed including the influence of different levels of gene expression and clinical characteristics. Although the exact underlying mechanisms remain largely unknown, the heterogeneity between individual patients supports the assumption that D2T RA is a syndrome involving different pathogenic mechanisms.

Activation of hypothalamic AMPK ameliorates metabolic complications of experimental arthritis

OBJECTIVE

To investigate whether thermogenesis and the hypothalamus may be involved in the physiopathology of experimental arthritis (EA).

METHODS

EA was induced in Lewis male rats by intradermal injection of Freund's complete adjuvant (FCA). Food intake, body weight, plasma cytokines, thermographic analysis, gene and protein expression of thermogenic markers in brown (BAT) and white (WAT) adipose tissue and hypothalamic AMP-activated protein kinase (AMPK) were analyzed. Virogenetic activation of hypothalamic AMPK was performed.

RESULTS

We first demonstrate that EA is associated with increased BAT thermogenesis and browning of subcutaneous WAT (sWAT) leading to elevated energy expenditure. Moreover, rats suffering EA show inhibition of hypothalamic AMPK, a canonical energy sensor modulating energy homeostasis at central level. Notably, specific genetic activation of AMPK in the ventromedial nucleus of the hypothalamus (VMH; a key site modulating energy metabolism) reverses the effect of EA on energy balance, brown fat and browning, as well as promoting an amelioration of the inflammatory status.

CONCLUSION

Overall, these data indicate that EA promotes a central catabolic state that can be targeted and reversed by the activation of hypothalamic AMPK. This might open new therapeutic alternatives to treat rheumatoid arthritis (RA)-associated metabolic comorbidities, improving RA-patients overall prognosis.

A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study

Introduction

Timely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility.

Methods

The protein–protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI.

Results

The 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0–8.3, p value 0.0001). Odds ratios (3.4–8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3–26.6 by ACR, DAS28-CRP, and CDAI metrics.

Conclusion

The MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40744-021-00330-y.

A blood-based molecular signature response classifier (MSRC) integrating next-generation RNA sequencing data with clinical features predicts the likelihood that a patient with rheumatoid arthritis will have an inadequate response to TNFi therapy. Treatment selection guided by test results, with likely inadequate responders appropriately redirected to a different therapy, could improve response rates to TNFi therapies, generate healthcare cost savings, and increase rheumatologists’ confidence in prescribing decisions and altered treatment choices. The MSRC described in this study predicts the likelihood of inadequate response to TNFi therapies among targeted therapy-naïve and TNFi-exposed patients in a multicenter, 24-week blinded prospective clinical study: NETWORK-004. Patients with a molecular signature of non-response are less likely to have an adequate response to TNFi therapies than those patients lacking the signature according to ACR50, ACR70, CDAI, and DAS28-CRP with significant odds ratios of 3.4–8.8 for targeted therapy-naïve patients and 3.3–26.6 for TNFi-exposed patients. This MSRC provides a solution to the long-standing need for precision medicine tools to predict drug response in rheumatoid arthritis—a heterogeneous and progressive disease with an abundance of therapeutic options. These data validate the performance of the MSRC in a blinded prospective clinical study of targeted therapy-naïve and TNFi therapy-exposed patients.

Total Saponins from Nigella glandulifera Seeds Ameliorate Adjuvant-Induced Rheumatoid Arthritis in Rats by Inhibition of an Inflammatory Response and Bone Erosion

Rheumatoid arthritis (RA) is a widespread inflammatory disease whose clinical manifestations are joint swelling, pain, and disability, affecting approximately 1% of individuals worldwide. Conventional anti-RA drugs currently used in clinic have severe side effects. The present study is aimed at investigating the antiarthritic effects of total saponins from Nigella glandulifera seeds (TSNGS) in rats with adjuvant-induced rheumatoid arthritis (AIA). Arthritis score, paw swelling, and body weight were monitored throughout the period of TSNGS treatment. The histopathological features and levels of cytokines, including IFN-γ, TNF-α, IL-1β, IL-4, IL-6, IL-10, and IL-17A, and OPG/RANKL signaling, were measured to determine the amelioration by TSNGS and its potential mechanisms on the inflammatory response and bone erosion. The differentiation of regulatory T cells (Tregs) in serum was assessed by flow cytometry. The results demonstrate that TSNGS at 10 mg/kg, 50 mg/kg, and 250 mg/kg inhibited AIA-induced clinical score, paw swelling, and histological changes. TSNGS reduced the immune-inflammatory reaction by restoring the secretion and expression of inflammatory cytokines and elevating the proportion of CD4+ CD25+ Tregs, accompanied by an increase in transcription factor Foxp3 levels. TSNGS also displayed bone protection by upregulation of the OPG/RANKL pathway. Collectively, TSNGS inhibited arthritis in AIA rats and so represents a potential novel treatment for RA.

Clinical Utility and Cost Savings in Predicting Inadequate Response to Anti-TNF Therapies in Rheumatoid Arthritis

INTRODUCTION

The PrismRA® test identifies rheumatoid arthritis (RA) patients who are unlikely to respond to anti-tumor necrosis factor (anti-TNF) therapies. This study evaluated the clinical and financial outcomes of incorporating PrismRA into routine clinical care of RA patients.

METHODS

A decision-analytic model was created to evaluate clinical and economic outcomes in the 12-month period following first biologic treatment. Two treatment strategies were compared: (1) observed clinical decision-making based on a 175-patient cohort receiving an anti-TNF therapy as their first biologic after failure of conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) and (2) modeled clinical decision-making of the same population using PrismRA results to inform first-line biologic treatment choice. Modeled costs include biologic drug pharmacy, non-biologic pharmacy, and total medical costs. The odds of inadequate response to anti-TNF therapies and various components of patient care were calculated based on PrismRA results.

RESULTS

Identifying predicted inadequate responders to anti-TNF therapies resulted in a modeled 38% increase in ACR50 response to first-line biologic therapies. The fraction of patients who achieved an ACR50 response to any therapy (TNFi and others) within the 12-month period was 33% higher in the PrismRA-stratified population than in the unstratified population (59 vs. 44%, respectively). When therapy prescriptions were modeled according to PrismRA results, cost savings were modeled for all financial variables: overall costs (4% decreased total, 19% decreased on ineffective treatments), total biologic drug pharmacy (4% total, 23% ineffective), non-biologic pharmacy (2% total, 19% ineffective), and medical costs (6% total, 19% ineffective). Female sex was the clinical metric that showed the greatest association with inadequate response to anti-TNF therapies (odds ratio 2.42, 95% confidence interval 1.20, 4.88).

CONCLUSIONS

If PrismRA is implemented into routine clinical care as modeled, predicting which RA patients will have an inadequate response to anti-TNF therapies could save > $7 million in overall ineffective healthcare costs per 1000 patients tested and increase targeted DMARD response rates in RA.

Targeting Extracellular Vesicles to the Arthritic Joint Using a Damaged Cartilage-Specific Antibody

The targeted delivery of therapies to diseased tissues offers a safe opportunity to achieve optimal efficacy while limiting systemic exposure. These considerations apply to many disease indications but are especially relevant for rheumatoid arthritis (RA), as RA is a systemic autoimmune disease which affects multiple joints. We have identified an antibody that is specific to damaged arthritic cartilage (anti-ROS-CII) that can be used to deliver treatments specifically to arthritic joints, yielding augmented efficacy in experimental arthritis. In the current study, we demonstrate that scaffolds enriched with bioactive payloads can be delivered precisely to an inflamed joint and achieve superior efficacy outcomes consistent with the pharmacological properties of these payloads. As a scaffold, we have used extracellular vesicles (EVs) prepared from human neutrophils (PMNs), which possess intrinsic anti-inflammatory properties and the ability to penetrate inflamed arthritic cartilage. EV fortified with anti-ROS-CII (EV/anti-ROS-CII) retained anti-ROS-CII specificity and bound exclusively to the damaged cartilage. Following systemic administration, EV/anti-ROS-CII (a) exhibited the ability to localize specifically in the arthritic joint in vivo and (b) was able to specifically target single (viral IL-10 or anti-TNF) or combined (viral IL-10 and anti-TNF) anti-inflammatory treatments to the arthritic joint, which accelerated attenuation of clinical and synovial inflammation. Overall, this study demonstrates the attainability of targeting a pro-resolving biological scaffold to the arthritic joint. The potential of targeting scaffolds such as EV, nanoparticles, or a combination thereof alongside combined therapeutics is paramount for designing systemically administered broad-spectrum of anti-inflammatory treatments.

Machine Learning to Predict Anti-Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers

OBJECTIVE

Accurate prediction of treatment responses in rheumatoid arthritis (RA) patients can provide valuable information on effective drug selection. Anti-tumor necrosis factor (anti-TNF) drugs are an important second-line treatment after methotrexate, the classic first-line treatment for RA. However, patient heterogeneity hinders identification of predictive biomarkers and accurate modeling of anti-TNF drug responses. This study was undertaken to investigate the usefulness of machine learning to assist in developing predictive models for treatment response.

METHODS

Using data on patient demographics, baseline disease assessment, treatment, and single-nucleotide polymorphism (SNP) array from the Dialogue on Reverse Engineering Assessment and Methods (DREAM): Rheumatoid Arthritis Responder Challenge, we created a Gaussian process regression model to predict changes in the Disease Activity Score in 28 joints (DAS28) for the patients and to classify them into either the responder or the nonresponder group. This model was developed and cross-validated using data from 1,892 RA patients. It was evaluated using an independent data set from 680 patients. We examined the effectiveness of the similarity modeling and the contribution of individual features.

RESULTS

In the cross-validation tests, our method predicted changes in DAS28 (ΔDAS28), with a correlation coefficient of 0.405. It correctly classified responses from 78% of patients. In the independent test, this method achieved a Pearson's correlation coefficient of 0.393 in predicting ΔDAS28. Gaussian process regression effectively remapped the feature space and identified subpopulations that do not respond well to anti-TNF treatments. Genetic SNP biomarkers showed small contributions in the prediction when added to the clinical models. This was the best-performing model in the DREAM Challenge.

CONCLUSION

The model described here shows promise in guiding treatment decisions in clinical practice, based primarily on clinical profiles with additional genetic information.

RNA sequencing to predict response to TNF-α inhibitors reveals possible mechanism for nonresponse in smokers

BACKGROUND

Several studies have employed microarray-based profiling to predict response to tumor necrosis factor-alpha inhibitors (TNFi) in rheumatoid arthritis (RA); yet efforts to validate these targets have failed to show predictive abilities acceptable for clinical practice.

METHODS

The eighty most extreme responders and nonresponders to TNFi therapy were selected from the observational BiOCURA cohort. RNA sequencing was performed on mRNA from peripheral blood mononuclear cells (PBMCs) collected before initiation of treatment. The expression of pathways as well as individual gene transcripts between responders and nonresponders was investigated. Promising targets were technically replicated and validated in n = 40 new patients using qPCR assays.

RESULTS

Before therapy initiation, nonresponders had lower expression of pathways related to interferon and cytokine signaling, while also showing higher levels of two genes, GPR15 and SEMA6B (p = 0.02). The two targets could be validated, however, additional analyses revealed that GPR15 and SEMA6B did not independently predict response, but were rather dose-dependent markers of smoking (p < 0.0001).

CONCLUSIONS

The study did not identify new transcripts ready to use in clinical practice, yet GPR15 and SEMA6B were recognized as candidate explanatory markers for the reduced treatment success in RA smokers.

Traditional Chinese medicine formula Bi-Qi capsule alleviates rheumatoid arthritis-induced inflammation, synovial hyperplasia, and cartilage destruction in rats

BACKGROUND

Traditional Chinese medicine (TCM) formula Bi-Qi capsule (Bi-Qi) is a commonly prescribed drug to treat rheumatoid arthritis (RA). However, the mechanism of Bi-Qi-mediated amelioration of RA pathogenesis is still a mystery. Collagen induced arthritis (CIA) in rats is an established model that shares many similarities with RA in humans. In this study we investigated the effect of Bi-Qi on the pathogenesis of CIA in rats.

METHODS

CIA was developed in Sprague-Dawley (S.D) rats (n = 60, female) and used as a model resembling RA in humans. Rats were treated with a high or moderate dose of Bi-Qi, or methotrexate (MTX). Effects of the treatment on local joint and systemic inflammation, synovial hyperplasia, cartilage destruction, and other main features in the pathogenesis of CIA were analyzed.

RESULTS

Inflamed and swollen ankles and joints were observed in arthritic rats, while Bi-Qi or MTX treatment alleviated these symptoms. Only the Bi-Qi moderate dose decreased RA-induced serum levels of tumor necrosis factor-alpha (TNF-α). Both Bi-Qi and MTX reduced the interleukin (IL)-18 serum level. Protein levels of cartilage oligomeric matrix protein and osteopontin in serum, synovium, and cartilage were elevated in arthritic rats, while Bi-Qi alleviated these effects. Synovial hyperplasia, inflammatory cell infiltration in synovium and a high degree of cartilage degradation was observed in RA, and Bi-Qi or MTX alleviated this effect. Bi-Qi at the moderate dose was the most effective in mitigating CIA-related clinical complications.

CONCLUSIONS

Our findings showed that Bi-Qi alleviates CIA-induced inflammation, synovial hyperplasia, cartilage destruction, and the other main features in the pathogenesis of CIA. This provides fundamental evidence for the anti-arthritic properties of Bi-Qi and corroborates the use of Bi-Qi TCM formula for the treatment of RA.

Metformin ameliorates experimental-obesity-associated autoimmune arthritis by inducing FGF21 expression and brown adipocyte differentiation

Rheumatoid arthritis (RA) is a systemic autoimmune disease involving excessive inflammation. Recently, RA associated with a metabolic disorder was revealed to be non-responsive to RA medications. Metformin has been reported to have a therapeutic effect on RA and obesity. The aim of this investigation was to study the therapeutic effect and the underlying mechanism of metformin's action in an experimental model of collagen-induced arthritis (CIA) associated with obesity. Metformin was administered daily for 13 weeks to mice with CIA that had been fed a high-fat diet. Metformin ameliorated the development of CIA in obese mice by reducing autoantibody expression and joint inflammation. Furthermore, metformin decreased the expression levels of pSTAT3 and pmTOR and had a small normalizing effect on the metabolic profile of obese CIA mice. In addition, metformin increased the production of pAMPK and FGF21. Metformin also induced the differentiation of brown adipose tissue (BAT), which led to a reciprocal balance between T helper (Th) 17 and regulatory T (Treg) cells in vitro and in vivo. These results suggest that metformin can dampen the development of CIA in obese mice and reduce metabolic dysfunction by inducing BAT differentiation. Thus, metformin could be a therapeutic candidate for non-responsive RA.

Lymphatic delivery of etanercept via nanotopography improves response to collagen-induced arthritis

Background

Evidence suggests lymphatic function mediates local rheumatoid arthritis (RA) flares. Yet biologics that target the immune system are dosed systemically via the subcutaneous (SC) administration route, thereby inefficiently reaching local lymphatic compartments. Nanotopography has previously been shown to disrupt tight cellular junctions, potentially enhancing local lymphatic delivery and potentially improving overall therapeutic efficacy.

Method

We first characterized nanotopography (SOFUSA™) delivery of an anti-TNF drug, etanercept, by comparing pharmacokinetic profiles to those obtained by conventional SC, intravenous (IV), and intradermal (ID) routes of administration, and assessed uptake of radiolabeled etanercept in draining lymph nodes (LNs) in single dosing studies. We then compared etanercept efficacy in a progressive rat model of collagen-induced arthritis (CIA), administered systemically via SC route of administration; via the regional lymphatics through ID delivery; or through a nanotopography (SOFUSA™) device at 10, 12, and 14 days post CIA induction. Measurements of hind limb swelling and near-infrared fluorescence (NIRF) imaging of afferent lymph pumping function and reflux were conducted on days 11, 13, and 18 post CIA induction and compared to untreated CIA animals. Univariate and multivariate analysis of variance were used to compare the group differences for percentage swelling and lymphatic contractile activity.

Results

Even though all three modes of administration delivered an equal amount of etanercept, SOFUSA™ delivery resulted in increased lymphatic pumping and significantly reduced swelling as compared to untreated, ID, and SC groups. Pharmacokinetic profiles in serum and LN uptake studies showed that using the nanotopography device resulted in the greatest uptake and retention in draining LNs.

Conclusions

Locoregional lymphatic delivery of biologics that target the immune system may have more favorable pharmacodynamics than SC or IV administration. Nanotopography may provide a more efficient method for delivery of anti-TNF drugs to reverse impairment of lymphatic function and reduce swelling associated with RA flares.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-017-1323-z) contains supplementary material, which is available to authorized users.

IL-17 axis accelerates the inflammatory progression of obese in mice via TBK1 and IKBKE pathway

Obesity mediates immune inflammatory response and induces IL-17 expression. Adipgenesis can be regulated by IL-17 and it causes TBK1 activation. The inhibition of TBK1 and the inhibition of I IKBKE reduces inflammatory response and improves obesity. It is hypothesized that IL-17 deficiency inhibits obesity progression and inflammation. 3T3-L1 preadipocytes were differentiated in vitro and treated with IL-17. RAW264.7 cells and differentiated 3T3-L1 were pretreated with TBK1 inhibitor and then stimulated with IL-17. Wild-type and IL-17 knock out mice were fed with high-fat diet. IL-17 inhibits adipocyte differentiation from mouse-derived 3T3-L1 preadipocytes and reduces mRNA expression of proadipogenic transcription factors and adipokines in adipocyte cells. IL-17 also showed up-regulation of mRNA levels of inflammatory cytokines in RAW cells. The inhibitor of TBK1 and IKBKE attenuates the effect of IL-17. Loss of IL-17 deficiency improves diet-induced obesity, fatty liver, glucose and lipid metabolism in mice. The expression of TBK1 and IKBKE decreased in the spleen and liver of IL-17 deficiency mice. Moreover, the inflammatory response within the visceral adipose tissue and Th1 cells were inhibited, however, M2 macrophage and Th2 cells increased in IL-17 deficiency mice. IL-17 inhibits adipogenesis where a lack of IL-17 ameliorates glucose metabolism. As well, the inhibition of TBK1 reduces inflammation induced by IL-17. Therefore, IL-17 may be involved in the development of obesity and metabolic dysfunction in a TBK1-dependent manner.

HtrA2 suppresses autoimmune arthritis and regulates activation of STAT3

Rheumatoid arthritis (RA) is an autoimmune disease that is related to the induction of T helper (Th)17 cells, which secrete interleukin-17, and activation of the signal transducer and activator of transcription (STAT) 3. The expression of high-temperature requirement protein A (HtrA) 2, a serine protease involved in apoptosis, was decreased in RA patients nonresponsive to drug treatment of RA. The aim of this study was to determine whether overexpression of HtrA2 has a therapeutic effect on RA. Th17 differentiation, osteoclastogenesis, and lymphocyte activation are increased in motor neuron degeneration (mnd)2 mice, which lack HtrA2 activity because of a missense mutation (Ser276Cys) in the protease domain of HtrA2. The inhibitor of HtrA2 also increased Th17 differentiation. On the other hand, HtrA2 induced cleavage of STAT3 and overexpression of HtrA2 attenuated CIA in a mouse model. HtrA2 overexpression inhibited plaque development as well as the differentiation of Th17 in ApoE^-/- mice after immunization with proteoglycans to induce a hyperlipidemia-based RA animal model. The therapeutic function of HtrA2 in inflammatory diseases is linked with Th17 development and the STAT3 pathway in splenocytes. These results suggest that HtrA2 participates in immunomodulatory activity where the upregulation of HtrA2 may shed light on therapeutic approaches to RA and hyperlipidemia.

TNFα in the regulation of Treg and Th17 cells in rheumatoid arthritis and other autoimmune inflammatory diseases

TNFα is a principal pro-inflammatory cytokine vital for immunity to infections. However, its excessive production is involved in chronic inflammation and disease pathology in autoimmune diseases. Evidence for its pathogenic role is validated by the fact that its neutralisation by therapeutic agents in vivo is beneficial in ameliorating disease and controlling symptoms. Paradoxically, however, treatment with TNFα inhibitors can either have no clinical effects, or even exacerbate disease in some patients. The explanation for such contradictory outcomes may lay in how and which downstream signalling pathways are activated and drive disease. TNFα causes its effects by binding to either or both of two membrane-bound receptors, TNFR1 and TNFR2. Engagement of the receptors can induce cell death or cell proliferation. T cells both produce and respond to TNFα and depending on whether the cytokine is membrane-bound or soluble and the level of expression of its two receptors, the biological outcome can be distinct. In addition, polymorphisms in genes encoding TNFα and T cell signalling proteins can significantly impact the outcome of TNFα receptor engagement. Early studies revealed that effector T cells in patients with rheumatoid arthritis (RA) are hyporesponsive due to chronic exposure to TNFα. However, recent evidence indicates that the relationship between TNFα and T cell responses is complex and, at times, can be paradoxical. In addition, there is controversy as to the specific effects of TNFα on different T cell subsets. This review will summarise knowledge on how TNFα modulates T cell responses and the effect of engaging either of its two receptors. Furthermore, we discuss how such interactions can dictate the outcome of treatment with TNFα inhibitors.

Limits of Peripheral Blood Mononuclear Cells for Gene Expression-Based Biomarkers in Juvenile Idiopathic Arthritis

Juvenile Idiopathic Arthritis (JIA) is one of the most common chronic disease conditions affecting children in the USA. As with many rheumatic diseases, there is growing interest in using genomic technologies to develop biomarkers for either diagnosis or to guide treatment ("personalized medicine"). Here, we explore the use of gene expression patterns in peripheral blood mononuclear cells (PBMC) as a first step approach to developing such biomarkers. Although PBMC carry many theoretical advantages for translational research, we have found that sample heterogeneity makes RNASeq on PBMC unsuitable as a first-step method for screening biomarker candidates in JIA. RNASeq studies of homogeneous cell populations are more likely to be useful and informative.

Seq2pathway: an R/Bioconductor package for pathway analysis of next-generation sequencing data

Seq2pathway is an R/Python wrapper for pathway (or functional gene-set) analysis of genomic loci, adapted for advances in genome research. Seq2pathway associates the biological significance of genomic loci with their target transcripts and then summarizes the quantified values on the gene-level into pathway scores. It is designed to isolate systematic disturbances and common biological underpinnings from next-generation sequencing (NGS) data. Seq2pathway offers Bioconductor users enhanced capability in discovering collective pathway effects caused by both coding genes and cis-regulation of non-coding elements.

AVAILABILITY AND IMPLEMENTATION

The package is freely available at http://www.bioconductor.org/packages/release/bioc/html/seq2pathway.html.

CONTACT

xyang2@uchicago.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.