Expression of the readthrough transcript CiDRE in alveolar macrophages boosts SARS-CoV-2 susceptibility and promotes COVID-19 severity.

Lung infection during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via the angiotensin-I-converting enzyme 2 (ACE2) receptor induces a cytokine storm. However, the precise mechanisms involved in severe COVID-19 pneumonia are unknown. Here, we showed that interleukin-10 (IL-10) induced the expression of ACE2 in normal alveolar macrophages, causing them to become vectors for SARS-CoV-2. The inhibition of this system in hamster models attenuated SARS-CoV-2 pathogenicity. Genome-wide association and quantitative trait locus analyses identified a IFNAR2-IL10RB readthrough transcript, COVID-19 infectivity-enhancing dual receptor (CiDRE), which was highly expressed in patients harboring COVID-19 risk variants at the IFNAR2 locus. We showed that CiDRE exerted synergistic effects via the IL-10-ACE2 axis in alveolar macrophages and functioned as a decoy receptor for type I interferons. Collectively, our data show that high IL-10 and CiDRE expression are potential risk factors for severe COVID-19. Thus, IL-10R and CiDRE inhibitors might be useful COVID-19 therapies.

Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10-8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.

Splicing QTL analysis focusing on coding sequences reveals mechanisms for disease susceptibility loci.

Splicing quantitative trait loci (sQTLs) are one of the major causal mechanisms in genome-wide association study (GWAS) loci, but their role in disease pathogenesis is poorly understood. One reason is the complexity of alternative splicing events producing many unknown isoforms. Here, we propose two approaches, namely integration and selection, for this complexity by focusing on protein-structure of isoforms. First, we integrate isoforms with the same coding sequence (CDS) and identify 369-601 integrated-isoform ratio QTLs (i2-rQTLs), which altered protein-structure, in six immune subsets. Second, we select CDS incomplete isoforms annotated in GENCODE and identify 175-337 isoform-ratio QTL (i-rQTL). By comprehensive long-read capture RNA-sequencing among these incomplete isoforms, we reveal 29 full-length isoforms with unannotated CDSs associated with GWAS traits. Furthermore, we show that disease-causal sQTL genes can be identified by evaluating their trans-eQTL effects. Our approaches highlight the understudied role of protein-altering sQTLs and are broadly applicable to other tissues and diseases.

How do imports and exports affect green productivity? New evidence from partially linear functional-coefficient models.

Globalization and income disparities have raised an urgent need to re-examine the environmental consequences of international trade. Using a global panel dataset covering 93 economies from 1980 to 2017, this paper explores the heterogeneous impacts of international trade on green productivity. Unlike previous studies that impose strict linear assumptions on functional forms, we adopt a newly developed partially linear functional-coefficient model to estimate the specific response functions of green productivity to imports and exports at different income levels, thus emphasizing the potential role of income heterogeneity. The results demonstrate that (1) imports and exports have different non-linear effects on green productivity; (2) imports do not significantly affect green productivity in lower-income countries (relative income level is less than 0.5), but imports increasingly promote green productivity in high-income countries; (3) exports hinder green productivity in extremely low-income countries (relative income level is less than 0.1), while gradually improving green productivity in high-income countries (relative income level is larger than 0.6); and (4) imports and exports promote green productivity more significantly by technological progress rather than efficiency improvements. The stimulus effect from induced technological progress is only observed in higher-income countries.

Enhancing Military Burn- and Trauma-Related Acute Kidney Injury Prediction Through an Automated Machine Learning Platform and Point-of-Care Testing.

CONTEXT.­: Delayed recognition of acute kidney injury (AKI) results in poor outcomes in military and civilian burn-trauma care. Poor predictive ability of urine output (UOP) and creatinine contribute to the delayed recognition of AKI. OBJECTIVE.­: To determine the impact of point-of-care (POC) AKI biomarker enhanced by machine learning (ML) algorithms in burn-injured and trauma patients. DESIGN.­: We conducted a 2-phased study to develop and validate a novel POC device for measuring neutrophil gelatinase-associated lipocalin (NGAL) and creatinine from blood samples. In phase I, 40 remnant plasma samples were used to evaluate the analytic performance of the POC device. Next, phase II enrolled 125 adults with either burns that were 20% or greater of total body surface area or nonburn trauma with suspicion of AKI for clinical validation. We applied an automated ML approach to develop models predicting AKI, using a combination of NGAL, creatinine, and/or UOP as features. RESULTS.­: Point-of-care NGAL (mean [SD] bias: 9.8 [38.5] ng/mL, P = .10) and creatinine results (mean [SD] bias: 0.28 [0.30] mg/dL, P = .18) were comparable to the reference method. NGAL was an independent predictor of AKI (odds ratio, 1.6; 95% CI, 0.08-5.20; P = .01). The optimal ML model achieved an accuracy, sensitivity, and specificity of 96%, 92.3%, and 97.7%, respectively, with NGAL, creatinine, and UOP as features. Area under the receiver operator curve was 0.96. CONCLUSIONS.­: Point-of-care NGAL testing is feasible and produces results comparable to reference methods. Machine learning enhanced the predictive performance of AKI biomarkers including NGAL and was superior to the current techniques.

Polygenic burdens on cell-specific pathways underlie the risk of rheumatoid arthritis.

Recent evidence suggests that a substantial portion of complex disease risk alleles modify gene expression in a cell-specific manner. To identify candidate causal genes and biological pathways of immune-related complex diseases, we conducted expression quantitative trait loci (eQTL) analysis on five subsets of immune cells (CD4+ T cells, CD8+ T cells, B cells, natural killer (NK) cells and monocytes) and unfractionated peripheral blood from 105 healthy Japanese volunteers. We developed a three-step analytical pipeline comprising (i) prediction of individual gene expression using our eQTL database and public epigenomic data, (ii) gene-level association analysis and (iii) prediction of cell-specific pathway activity by integrating the direction of eQTL effects. By applying this pipeline to rheumatoid arthritis data sets, we identified candidate causal genes and a cytokine pathway (upregulation of tumor necrosis factor (TNF) in CD4+ T cells). Our approach is an efficient way to characterize the polygenic contributions and potential biological mechanisms of complex diseases.

A Case of Polymyxin b-Immobilized Fiber Column Treatment for Rapidly Progressive Interstitial Pneumonia Associated with Clinically Amyopathic Dermatomyositis.

We report a case of rapidly progressive interstitial pneumonia associated with clinically amyopathic dermatomyositis who responded to single course of polymyxin b-immobilized fiber column treatment. Initial treatment with pulsed corticosteroids and cyclophosphamide, intravenous immunoglobulin, and cyclosporine seemed to suppress the activity of interstitial lung disease temporarily, but signs of relapse were detected such as elevation of serum KL-6 level and progressing pulmonary shadows in chest computed tomography scan. After polymyxin b-immobilized fiber column treatment, the areas of pulmonary shadows drastically decreased. Gradually, arterial partial pressure of oxygen/fraction of inspired oxygen (PaO2/FiO2) ratio recovered, and serum ferritin level and KL-6 level decreased. These findings indicate that polymyxin b-immobilized fiber column treatment could be promising in combination with conventional therapy for rapidly progressive interstitial pneumonia associated with clinically amyopathic dermatomyositis, especially at the early phase of relapse.