RNA polymerase II (RNAPII) pausing/termination shortly after initiation is a hallmark of gene regulation. Here, we show that negative elongation factor (NELF) interacts with Integrator complex subunits (INTScom), RNAPII and Spt5. The interaction between NELF and INTScom subunits is RNA and DNA independent. Using both human immunodeficiency virus type 1 promoter and genome-wide analyses, we demonstrate that Integrator subunits specifically control NELF-mediated RNAPII pause/release at coding genes. The strength of RNAPII pausing is determined by the nature of the NELF-associated INTScom subunits. Interestingly, in addition to controlling RNAPII pause-release INTS11 catalytic subunit of the INTScom is required for RNAPII processivity. Finally, INTScom target genes are enriched in human immunodeficiency virus type 1 transactivation response element/NELF binding element and in a 3' box sequence required for small nuclear RNA biogenesis. Revealing these unexpected functions of INTScom in regulating RNAPII pause-release and completion of mRNA synthesis of NELF-target genes will contribute to our understanding of the gene expression cycle.
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 sn1-LPC(18:3-ω3/ω6), sn1-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.
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