Integrator complex regulates NELF-mediated RNA polymerase II pause/release and processivity at coding genes

International audienc

Differences between serum polar lipid profiles of male and female rheumatoid arthritis patients in response to glucocorticoid treatment

Objective: As there are pharmacological differences between males and females, and glucocorticoid (GC) treatment is associated with increased cardiovascular mortality rate in rheumatoid arthritis (RA) patients, it is important to study serum polar lipid profiles of male and female patients in response to GC therapy. Gender differences may require an adjustment to the treatment strategy for a selection of patients. Methods: Serum samples from 281 RA patients were analysed using a targeted lipidomics platform. The differences in GC use and gender on polar lipid profiles were cross sectionally examined by multiple linear regressions, while correcting for confounding factors. Results: Differences in polar lipids between GC users and non-GC users in females and males were merely restricted to lysophospholipids (lysophosphatidylcholines and lysophosphatidylethanolamines). Lysophospholipids in female patients treated with GCs were significantly higher than female patients not treated with GCs (p = 6.0 E−6), whereas no significant difference was observed in male GC users versus non-users (p = 0.397). Conclusion: The lysophospholipid profiles in response to GCs were significantly different between male and female RA patients, which may have implications for the cardiovascular risk of GC treatment

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis

<div><p>In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients’ response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were <i>sn1</i>-LPC(18:3-ω3/ω6), <i>sn1</i>-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.</p></div

Remaining metabolites and their estimated contribution in the prediction of response to TNFi in the final prediction model.

<p>Remaining metabolites and their estimated contribution in the prediction of response to TNFi in the final prediction model.</p

Baseline characteristics of all selected TNFi initiating subjects (n = 173), and split for all EULAR non-responders (n = 64) and good responders (n = 60).

<p>Baseline characteristics of all selected TNFi initiating subjects (n = 173), and split for all EULAR non-responders (n = 64) and good responders (n = 60).</p

Flowchart of statistical analyses.

<p>(A) Prediction of response to TNFi: All steps to build a prediction model on TNFi response were performed on the TNFi subset with EULAR good-response or non-response (n = 124). (B) Sensitivity analysis on the complete cohort of TNFi initiating patients. (C) Metabolites associated with disease activity. Analyses to investigate metabolites association with CRP, ESR or DAS28 were performed on the total cohort of patients using bDMARDs (n = 231; including TNFi and non-TNFi treated patients). Blue boxes/circles indicate (selection of) respectively metabolites or clinical parameters, whereas orange boxes indicate the performed analyses. bDMARDs: biological disease-modifying anti-rheumatic drugs; CRP: C-reactive protein; DAS28: disease activity score based on a 28-joint count; ESR: erythrocyte sedimentation rate; GEE: generalized estimating equation, LC-MS: liquid chromatography coupled to mass spectrometry; ROC: receiver operating characteristic; TNFi: TNF-α inhibitor.</p

Visualization of the associations between metabolites and disease activity − general inflammation (log-transformed CRP and ESR) and RA-specific inflammation (DAS28)–based on the complete cohort of bDMARD users (n = 231).

<p>The metabolites that associated with either CRP, ESR or DAS based on linear generalized estimating equations (GEE), were grouped according to metabolic classes (LPCs, FAs, amines and oxylipins), which are represented as color-coded symbols adjacent to the metabolites. The metabolites in these metabolic classes showed comparable associations with CRP, ESR and/or DAS28. FAs positively- and the lysophospholipids negatively associated with CRP, ESR and/or DAS28; the association between other the oxylipins and amines with CRP, ESR and/or DAS28 were mixed, based on their metabolic functions. Positive associations are indicated with red lines, negative associations with blue lines; thicker lines indicate a more significant association. CRP, C-reactive protein; DAS28, disease activity score based on 28 joint counts; ESR: erythrocyte sedimentation rate; FA, fatty acid; LPE, lysophosphatidylethanolamine; LPC, lysophosphatidylcholine</p

Net reclassification index of prediction models for good- and non-responders to TNFi.

<p>Net reclassification index of prediction models for good- and non-responders to TNFi.</p