Glycolysis is a ubiquitous pathway thought to be essential for the production of oil in developing seeds of Arabidopsis thaliana and oil crops. Compartmentation of primary metabolism in developing embryos poses a significant challenge for testing this hypothesis and for the engineering of seed biomass production. It also raises the question whether there is a preferred route of carbon from imported photosynthate to seed oil in the embryo. Plastidic pyruvate kinase catalyzes a highly regulated, ATP-producing reaction of glycolysis. The Arabidopsis genome encodes 14 putative isoforms of pyruvate kinases. Three genes encode subunits α, β1, and β2 of plastidic pyruvate kinase. The plastid enzyme prevalent in developing seeds likely has a subunit composition of 4α4β1, is most active at pH 8.0, and is inhibited by Glu. Disruption of the gene encoding the β1 subunit causes a reduction in plastidic pyruvate kinase activity and 60% reduction in seed oil content. The seed oil phenotype is fully restored by expression of the β1 subunit–encoding cDNA and partially by the β2 subunit–encoding cDNA. Therefore, the identified pyruvate kinase catalyzes a crucial step in the conversion of photosynthate into oil, suggesting a preferred plastid route from its substrate phosphoenolpyruvate to fatty acids.

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Background Prognostic tools are required to guide clinical decision-making in COVID-19. Methods We studied the relationship between the ratio of interleukin (IL)-6 to IL-10 and clinical outcome in 80 patients hospitalized for COVID-19, and created a simple 5-point linear score predictor of clinical outcome, the Dublin-Boston score. Clinical outcome was analysed as a three-level ordinal variable (“Improved”, “Unchanged”, or “Declined”). For both IL-6:IL-10 ratio and IL-6 alone, we associated clinical outcome with a) baseline biomarker levels, b) change in biomarker level from day 0 to day 2, c) change in biomarker from day 0 to day 4, and d) slope of biomarker change throughout the study. The associations between ordinal clinical outcome and each of the different predictors were performed with proportional odds logistic regression. Associations were run both “unadjusted” and adjusted for age and sex. Nested cross-validation was used to identify the model for incorporation into the Dublin-Boston score. Findings The 4-day change in IL-6:IL-10 ratio was chosen to derive the Dublin-Boston score. Each 1 point increase in the score was associated with a 5.6 times increased odds for a more severe outcome (OR 5.62, 95% CI -3.22–9.81, P = 1.2 × 10 −9 ). Both the Dublin-Boston score and the 4-day change in IL-6:IL-10 significantly outperformed IL-6 alone in predicting clinical outcome at day 7. Interpretation The Dublin-Boston score is easily calculated and can be applied to a spectrum of hospitalized COVID-19 patients. More informed prognosis could help determine when to escalate care, institute or remove mechanical ventilation, or drive considerations for therapies. Funding Funding was received from the Elaine Galwey Research Fellowship, American Thoracic Society, National Institutes of Health and the Parker B Francis Research Opportunity Award.

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Summary Background Genetic factors influence chronic obstructive pulmonary disease (COPD) risk, but the individual variants that have been identified have small effects. We hypothesised that a polygenic risk score using additional variants would predict COPD and associated phenotypes. Methods We constructed a polygenic risk score using a genome-wide association study of lung function (FEV 1 and FEV 1 /forced vital capacity [FVC]) from the UK Biobank and SpiroMeta. We tested this polygenic risk score in nine cohorts of multiple ethnicities for an association with moderate-to-severe COPD (defined as FEV 1 /FVC <0·7 and FEV 1 <80% of predicted). Associations were tested using logistic regression models, adjusting for age, sex, height, smoking pack-years, and principal components of genetic ancestry. We assessed predictive performance of models by area under the curve. In a subset of studies, we also studied quantitative and qualitative CT imaging phenotypes that reflect parenchymal and airway pathology, and patterns of reduced lung growth. Findings The polygenic risk score was associated with COPD in European (odds ratio [OR] per SD 1·81 [95% CI 1·74–1·88] and non-European (1·42 [1·34–1·51]) populations. Compared with the first decile, the tenth decile of the polygenic risk score was associated with COPD, with an OR of 7·99 (6·56–9·72) in European ancestry and 4·83 (3·45–6·77) in non-European ancestry cohorts. The polygenic risk score was superior to previously described genetic risk scores and, when combined with clinical risk factors (ie, age, sex, and smoking pack-years), showed improved prediction for COPD compared with a model comprising clinical risk factors alone (AUC 0·80 [0·79–0·81] vs 0·76 [0·75–0·76]). The polygenic risk score was associated with CT imaging phenotypes, including wall area percent, quantitative and qualitative measures of emphysema, local histogram emphysema patterns, and destructive emphysema subtypes. The polygenic risk score was associated with a reduced lung growth pattern. Interpretation A risk score comprised of genetic variants can identify a small subset of individuals at markedly increased risk for moderate-to-severe COPD, emphysema subtypes associated with cigarette smoking, and patterns of reduced lung growth. Funding US National Institutes of Health, Wellcome Trust.

Chronic obstructive pulmonary disease (COPD) is associated with age and smoking, but other determinants of the disease are incompletely understood. Clonal hematopoiesis of indeterminate potential (CHIP) is a common, age-related state in which somatic mutations in clonal blood populations induce aberrant inflammatory responses. Patients with CHIP have an elevated risk for cardiovascular disease, but the association with COPD remains unclear. We analyzed whole-genome and exome sequencing data to detect CHIP in 48,835 subjects, of whom 8,444 had moderate-to-very-severe COPD, from four separate cohorts with COPD phenotyping and smoking history. We measured emphysema in murine models in which Tet2 was deleted in hematopoietic cells. In COPDGene, individuals with CHIP had a risk of moderate-to-severe and severe or very severe COPD 1.6 and 2.2 times greater than non-carriers, respectively (adjusted 95% confidence intervals [CI], 1.1 to 2.2 and 1.5 to 3.2). These findings were consistent observed in three additional cohorts and meta-analyses of all subjects. CHIP was also associated with decreased FEV1% predicted in COPDGene (mean between group difference -5.7%; adjusted 95% CI, -8.8 to -2.6), a finding replicated in additional cohorts. Smoke exposure was associated with a small but significant increased risk of having CHIP (OR 1.03 per ten pack-years, 95% CI 1.01-1.05) in the meta-analysis of all subjects. Inactivation of Tet2 in mouse hematopoietic cells exacerbated emphysema development and inflammation in cigarette smoke exposure models. Somatic mutations in blood cells are associated with the development and severity of COPD, independent of age and cumulative smoke exposure.

BACKGROUND: COPD is a leading cause of mortality.RESEARCH QUESTION: We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD.STUDY DESIGN AND METHODS: We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models. We trained the models in subjects with moderate to severe COPD from a subset of subjects in Genetic Epidemiology of COPD (COPDGene) and tested prediction performance in the remainder of individuals with moderate to severe COPD in COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared our model with the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index; BODE modifications; and the age, dyspnea, and airflow obstruction index. RESULTS:We included 2,632 participants from COPDGene and 1,268 participants from ECLIPSE. The top predictors of mortality were 6-min walk distance, FEV 1 % predicted, and age. The top imaging predictor was pulmonary artery-to-aorta ratio. The MLMP-COPD model resulted in a C index $ 0.7 in both COPDGene and ECLIPSE (6.4-and 7.2-year median follow-ups, respectively), significantly better than all tested mortality indexes (P < .05). The MLMP-COPD model had fewer predictors but similar performance to that of other models. The group with the highest BODE scores (7-10) had 56% mortality, whereas the highest mortality group defined by the MLMP-COPD model had 62% mortality (P ¼ .046). INTERPRETATION:An MLMP-COPD model outperformed four existing models for predicting all-cause mortality across two COPD cohorts. Performance of machine learning was similar to that of traditional statistical methods. The model is available online at: https://cdnm. shinyapps.io/cgmortalityapp/.

Background Recent studies have used cluster analysis to identify phenotypic clusters of asthma with differences in clinical traits, as well as differences in response to therapy with anti-inflammatory medications. However, the correspondence between different phenotypic clusters and differences in the underlying molecular mechanisms of asthma pathogenesis remains unclear. Objective To determine whether clinical differences among children with asthma in different phenotypic clusters corresponded to differences in levels of gene expression. Methods We explored differences in gene expression profiles of CD4+ lymphocytes isolated from the peripheral blood of 299 young adult participants in the Childhood Asthma Management Program (CAMP) study. We obtained gene expression profiles from study subjects between 9-14 years after they participated in a randomized, controlled longitudinal study examining the effects of inhaled anti-inflammatory medications over a 48 month study period, and we evaluated the correspondence between our earlier phenotypic cluster analysis, and subsequent follow-up clinical and molecular profiles. Results We found that differences in clinical characteristics observed between subjects assigned to different phenotypic clusters persisted into young adulthood, and that these clinical differences were associated with differences in gene expression patterns between subjects in different clusters. We identified a subset of genes associated with atopic status, and validated the presence of an atopic signature among these genes in an independent cohort of asthmatic subjects, and identified the presence of common transcription factor binding sites (TFBS) corresponding to glucocorticoid receptor (Gr) binding. Conclusion These findings suggest that phenotypic clusters are associated with differences in the underlying pathobiology of asthma. Further experiments are necessary to confirm these findings.