Type I interferon blockade with anifrolumab in patients with systemic lupus erythematosus modulates key immunopathological pathways in a gene expression and proteomic analysis of two phase 3 trials.

INTRODUCTION: Anifrolumab is a type I interferon (IFN) receptor 1 (IFNAR1) blocking antibody approved for treating patients with systemic lupus erythematosus (SLE). Here, we investigated the immunomodulatory mechanisms of anifrolumab using longitudinal transcriptomic and proteomic analyses of the 52-week, randomised, phase 3 TULIP-1 and TULIP-2 trials. METHODS: Patients with moderate to severe SLE were enrolled in TULIP-1 and TULIP-2 and received intravenous anifrolumab or placebo alongside standard therapy. Whole-blood expression of 18 017 genes using genome-wide RNA sequencing (RNA-seq) (pooled TULIP; anifrolumab, n=244; placebo, n=258) and 184 plasma proteins using Olink and Simoa panels (TULIP-1; anifrolumab, n=124; placebo, n=132) were analysed. We compared treatment groups via gene set enrichment analysis using MetaBase pathway analysis, blood transcriptome modules, in silico deconvolution of RNA-seq and longitudinal linear mixed effect models for gene counts and protein levels. RESULTS: Compared with placebo, anifrolumab modulated >2000 genes by week 24, with overlapping results at week 52, and 41 proteins by week 52. IFNAR1 blockade with anifrolumab downregulated multiple type I and II IFN-induced gene modules/pathways and type III IFN-λ protein levels, and impacted apoptosis-associated and neutrophil extracellular traps-(NET)osis-associated transcriptional pathways, innate cell activating chemokines and receptors, proinflammatory cytokines and B-cell activating cytokines. In silico deconvolution of RNA-seq data indicated an increase from baseline of mucosal-associated invariant and γδT cells and a decrease of monocytes following anifrolumab treatment. DISCUSSION: Type I IFN blockade with anifrolumab modulated multiple inflammatory pathways downstream of type I IFN signalling, including apoptotic, innate and adaptive mechanisms that play key roles in SLE immunopathogenesis.

Fluctuation-based clustering reveals phenotypes of patients with different asthma severity.

Serial peak expiratory flow (PEF) measurements can identify phenotypes in severe adult asthma, enabling more targeted treatment. The feasibility of this approach in children has not been investigated. Overall, 105 children (67% male, median age 12.4 years) with a range of asthma severities were recruited and followed up over a median of 92 days. PEF was measured twice daily. Fluctuation-based clustering (FBC) was used to identify clusters based on PEF fluctuations. The patients' clinical characteristics were compared between clusters. Three PEF clusters were identified in 44 children with sufficient measurements. Cluster 1 (27% of patients: n=12) had impaired spirometry (mean forced expiratory volume in 1 s (FEV1) 71% predicted), significantly higher exhaled nitric oxide (≥35 ppb) and uncontrolled asthma (asthma control test (ACT) score <20 of 25). Cluster 2 (45%: n=20) had normal spirometry, the highest proportion of difficult asthma and significantly more patients on a high dose of inhaled corticosteroids (≥800 µg budesonide). Cluster 3 (27%: n=12) had mean FEV1 92% predicted, the highest proportion of patients with no bronchodilator reversibility, a low ICS dose (≤400 µg budesonide), and controlled asthma (ACT scores ≥20 of 25). Three clinically relevant paediatric asthma clusters were identified using FBC analysis on PEF measurements, which could improve telemonitoring diagnostics. The method remains robust even when 80% of measurements were removed. Further research will determine clinical applicability.

Parallel feedback loops control the basal activity of the HOG MAPK signaling cascade.

Tight regulation of the MAP kinase Hog1 is crucial for survival under changing osmotic conditions. Interestingly, we found that Hog1 phosphorylates multiple upstream components, implying feedback regulation within the signaling cascade. Taking advantage of an unexpected link between glucose availability and Hog1 activity, we used quantitative single cell measurements and computational modeling to unravel feedback regulation operating in addition to the well-known adaptation feedback triggered by glycerol accumulation. Indeed, we found that Hog1 phosphorylates its activating kinase Ssk2 on several sites, and cells expressing a non-phosphorylatable Ssk2 mutant are partially defective for feedback regulation and proper control of basal Hog1 activity. Together, our data suggest that Hog1 activity is controlled by intertwined regulatory mechanisms operating with varying kinetics, which together tune the Hog1 response to balance basal Hog1 activity and its steady-state level after adaptation to high osmolarity.

Phosphoproteomic analyses reveal novel cross-modulation mechanisms between two signaling pathways in yeast.

Cells respond to environmental stimuli via specialized signaling pathways. Concurrent stimuli trigger multiple pathways that integrate information, predominantly via protein phosphorylation. Budding yeast responds to NaCl and pheromone via two mitogen-activated protein kinase cascades, the high osmolarity, and the mating pathways, respectively. To investigate signal integration between these pathways, we quantified the time-resolved phosphorylation site dynamics after pathway co-stimulation. Using shotgun mass spectrometry, we quantified 2,536 phosphopeptides across 36 conditions. Our data indicate that NaCl and pheromone affect phosphorylation events within both pathways, which thus affect each other at more levels than anticipated, allowing for information exchange and signal integration. We observed a pheromone-induced down-regulation of Hog1 phosphorylation due to Gpd1, Ste20, Ptp2, Pbs2, and Ptc1. Distinct Ste20 and Pbs2 phosphosites responded differently to the two stimuli, suggesting these proteins as key mediators of the information exchange. A set of logic models was then used to assess the role of measured phosphopeptides in the crosstalk. Our results show that the integration of the response to different stimuli requires complex interconnections between signaling pathways.