Misexpression of inactive genes in whole blood is associated with nearby rare structural variants.

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole-blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and a third of inactive protein-coding genes. Using 2,821 paired whole-genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions, and gene inversion. Overall, we develop misexpression as a type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

Deep Learning-based Segmentation of Computed Tomography Scans Predicts Disease Progression and Mortality in Idiopathic Pulmonary Fibrosis.

Rationale: Despite evidence demonstrating a prognostic role for computed tomography (CT) scans in idiopathic pulmonary fibrosis (IPF), image-based biomarkers are not routinely used in clinical practice or trials. Objectives: To develop automated imaging biomarkers using deep learning-based segmentation of CT scans. Methods: We developed segmentation processes for four anatomical biomarkers, which were applied to a unique cohort of treatment-naive patients with IPF enrolled in the PROFILE (Prospective Observation of Fibrosis in the Lung Clinical Endpoints) study and tested against a further United Kingdom cohort. The relationships among CT biomarkers, lung function, disease progression, and mortality were assessed. Measurements and Main Results: Data from 446 PROFILE patients were analyzed. Median follow-up duration was 39.1 months (interquartile range, 18.1-66.4 mo), with a cumulative incidence of death of 277 (62.1%) over 5 years. Segmentation was successful on 97.8% of all scans, across multiple imaging vendors, at slice thicknesses of 0.5-5 mm. Of four segmentations, lung volume showed the strongest correlation with FVC (r = 0.82; P < 0.001). Lung, vascular, and fibrosis volumes were consistently associated across cohorts with differential 5-year survival, which persisted after adjustment for baseline gender, age, and physiology score. Lower lung volume (hazard ratio [HR], 0.98 [95% confidence interval (CI), 0.96-0.99]; P = 0.001), increased vascular volume (HR, 1.30 [95% CI, 1.12-1.51]; P = 0.001), and increased fibrosis volume (HR, 1.17 [95% CI, 1.12-1.22]; P < 0.001) were associated with reduced 2-year progression-free survival in the pooled PROFILE cohort. Longitudinally, decreasing lung volume (HR, 3.41 [95% CI, 1.36-8.54]; P = 0.009) and increasing fibrosis volume (HR, 2.23 [95% CI, 1.22-4.08]; P = 0.009) were associated with differential survival. Conclusions: Automated models can rapidly segment IPF CT scans, providing prognostic near and long-term information, which could be used in routine clinical practice or as key trial endpoints.

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.