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BackgroundThe MUC5B promoter variant (rs35705950) and telomere length are linked to pulmonary fibrosis and CT-based qualitative assessments of interstitial abnormalities, but their associations with longitudinal quantitative changes of the lung interstitium among community-dwelling adults are unknown.MethodsWe used data from participants in the Multi-Ethnic Study of Atherosclerosis with high-attenuation areas (HAAs, Examinations 1–6 (2000–2018)) and MUC5B genotype (n=4552) and telomere length (n=4488) assessments. HAA was defined as the per cent of imaged lung with attenuation of −600 to −250 Hounsfield units. We used linear mixed-effects models to examine associations of MUC5B risk allele (T) and telomere length with longitudinal changes in HAAs. Joint models were used to examine associations of longitudinal changes in HAAs with death and interstitial lung disease (ILD).ResultsThe MUC5B risk allele (T) was associated with an absolute change in HAAs of 2.60% (95% CI 0.36% to 4.86%) per 10 years overall. This association was stronger among those with a telomere length below an age-adjusted percentile of 5% (p value for interaction=0.008). A 1% increase in HAAs per year was associated with 7% increase in mortality risk (rate ratio (RR)=1.07, 95% CI 1.02 to 1.12) for overall death and 34% increase in ILD (RR=1.34, 95% CI 1.20 to 1.50). Longer baseline telomere length was cross-sectionally associated with less HAAs from baseline scans, but not with longitudinal changes in HAAs.ConclusionsLongitudinal increases in HAAs were associated with the MUC5B risk allele and a higher risk of death and ILD.

Background Polygenic risk scores (PRS) are valuable to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry leading to poor performance in populations of non-European ancestry. Results We introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data. Conclusions We show that PTRS has a significantly higher portability (Wilcoxon p =0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination.

To investigate whether hyperpolarised xenon-129 MRI (HXeMRI) enables regional and physiological resolution of diffusing capacity limitations in chronic obstructive pulmonary disease (COPD), we evaluated 34 COPD subjects and 11 healthy volunteers. We report significant correlations between airflow abnormality quantified by HXeMRI and per cent predicted forced expiratory volume in 1 s; HXeMRI gas transfer capacity to red blood cells and carbon monoxide diffusion capacity (%DLCO); and HXeMRI gas transfer capacity to interstitium and per cent emphysema quantified by multidetector chest CT. We further demonstrate the capability of HXeMRI to distinguish varying pathology underlying COPD in subjects with low %DLCO and minimal emphysema.

Metabolic network reconstruction encompasses existing knowledge about an organism's metabolism and genome annotation, providing a platform for omics data analysis and phenotype prediction. The model alga Chlamydomonas reinhardtii is employed to study diverse biological processes from photosynthesis to phototaxis. Recent heightened interest in this species results from an international movement to develop algal biofuels. Integrating biological and optical data, we reconstructed a genome‐scale metabolic network for this alga and devised a novel light‐modeling approach that enables quantitative growth prediction for a given light source, resolving wavelength and photon flux. We experimentally verified transcripts accounted for in the network and physiologically validated model function through simulation and generation of new experimental growth data, providing high confidence in network contents and predictive applications. The network offers insight into algal metabolism and potential for genetic engineering and efficient light source design, a pioneering resource for studying light‐driven metabolism and quantitative systems biology. Synopsis Algae have garnered significant interest in recent years, especially for their potential application in biofuel production. The hallmark, model eukaryotic microalgae Chlamydomonas reinhardtii has been widely used to study photosynthesis, cell motility and phototaxis, cell wall biogenesis, and other fundamental cellular processes (Harris, 2001 ). Characterizing algal metabolism is key to engineering production strains and understanding photobiological phenomena. Based on extensive literature on C. reinhardtii metabolism, its genome sequence (Merchant et al , 2007 ), and gene functional annotation, we have reconstructed and experimentally validated the genome‐scale metabolic network for this alga, i RC1080, the first network to account for detailed photon absorption permitting growth simulations under different light sources. i RC1080 accounts for 1080 genes, associated with 2190 reactions and 1068 unique metabolites and encompasses 83 subsystems distributed across 10 cellular compartments (Figure 1A ). Its >32% coverage of estimated metabolic genes is a tremendous expansion over previous algal reconstructions (Boyle and Morgan, 2009 ; Manichaikul et al , 2009 ). The lipid metabolic pathways of i RC1080 are considerably expanded relative to existing networks, and chemical properties of all metabolites in these pathways are accounted for explicitly, providing sufficient detail to completely specify all individual molecular species: backbone molecule and stereochemical numbering of acyl‐chain positions; acyl‐chain length; and number, position, and cis – trans stereoisomerism of carbon–carbon double bonds. Such detail in lipid metabolism will be critical for model‐driven metabolic engineering efforts. We experimentally verified transcripts accounted for in the network under permissive growth conditions, detecting >90% of tested transcript models (Figure 1B ) and providing validating evidence for the contents of i RC1080. We also analyzed the extent of transcript verification by specific metabolic subsystems. Some subsystems stood out as more poorly verified, including chloroplast and mitochondrial transport systems and sphingolipid metabolism, all of which exhibited <80% of transcripts detected, reflecting incomplete characterization of compartmental transporters and supporting a hypothesis of latent pathway evolution for ceramide synthesis in C. reinhardtii . Additional lines of evidence from the reconstruction effort similarly support this hypothesis including lack of ceramide synthetase and other annotation gaps downstream in sphingolipid metabolism. A similar hypothesis of latent pathway evolution was established for very long‐chain fatty acids (VLCFAs) and their polyunsaturated analogs (VLCPUFAs) (Figure 1C ), owing to the absence of this class of lipids in previous experimental measurements, lack of a candidate VLCFA elongase in the functional annotation, and additional downstream annotation gaps in arachidonic acid metabolism. The network provides a detailed account of metabolic photon absorption by light‐driven reactions, including photosystems I and II, light‐dependent protochlorophyllide oxidoreductase, provitamin D 3 photoconversion to vitamin D 3 , and rhodopsin photoisomerase; this network accounting permits the precise modeling of light‐dependent metabolism. i RC1080 accounts for effective light spectral ranges through analysis of biochemical activity spectra (Figure 3A ), either reaction activity or absorbance at varying light wavelengths. Defining effective spectral ranges associated with each photon‐utilizing reaction enabled our network to model growth under different light sources via stoichiometric representation of the spectral composition of emitted light, termed prism reactions. Coefficients for different photon wavelengths in a prism reaction correspond to the ratios of photon flux in the defined effective spectral ranges to the total emitted photon flux from a given light source (Figure 3B ). This approach distinguishes the amount of emitted photons that drive different metabolic reactions. We created prism reactions for most light sources that have been used in published studies for algal and plant growth including solar light, various light bulbs, and LEDs. We also included regulatory effects, resulting from lighting conditions insofar as published studies enabled. Light and dark conditions have been shown to affect metabolic enzyme activity in C. reinhardtii on multiple levels: transcriptional regulation, chloroplast RNA degradation, translational regulation, and thioredoxin‐mediated enzyme regulation. Through application of our light model and prism reactions, we were able to closely recapitulate experimental growth measurements under solar, incandescent, and red LED lights. Through unbiased sampling, we were able to establish the tremendous statistical significance of the accuracy of growth predictions achievable through implementation of prism reactions. Finally, application of the photosynthetic model was demonstrated prospectively to evaluate light utilization efficiency under different light sources. The results suggest that, of the existing light sources, red LEDs provide the greatest efficiency, about three times as efficient as sunlight. Extending this analysis, the model was applied to design a maximally efficient LED spectrum for algal growth. The result was a 677‐nm peak LED spectrum with a total incident photon flux of 360 μE/m 2 /s, suggesting that for the simple objective of maximizing growth efficiency, LED technology has already reached an effective theoretical optimum. In summary, the C. reinhardtii metabolic network i RC1080 that we have reconstructed offers insight into the basic biology of this species and may be employed prospectively for genetic engineering design and light source design relevant to algal biotechnology. i RC1080 was used to analyze lipid metabolism and generate novel hypotheses about the evolution of latent pathways. The predictive capacity of metabolic models developed from i RC1080 was demonstrated in simulating mutant phenotypes and in evaluation of light source efficiency. Our network provides a broad knowledgebase of the biochemistry and genomics underlying global metabolism of a photoautotroph, and our modeling approach for light‐driven metabolism exemplifies how integration of largely unvisited data types, such as physicochemical environmental parameters, can expand the diversity of applications of metabolic networks. The genome‐scale metabolic network of Chlamydomonas reinhardtii ( i RC1080) was reconstructed, accounting for >32% of the estimated metabolic genes encoded in the genome, and including extensive details of lipid metabolic pathways. This is the first metabolic network to explicitly account for stoichiometry and wavelengths of metabolic photon usage, providing a new resource for research of C. reinhardtii metabolism and developments in algal biotechnology. Metabolic functional annotation and the largest transcript verification of a metabolic network to date was performed, at least partially verifying >90% of the transcripts accounted for in i RC1080. Analysis of the network supports hypotheses concerning the evolution of latent lipid pathways in C. reinhardtii , including very long‐chain polyunsaturated fatty acid and ceramide synthesis pathways. A novel approach for modeling light‐driven metabolism was developed that accounts for both light source intensity and spectral quality of emitted light. The constructs resulting from this approach, termed prism reactions, were shown to significantly improve the accuracy of model predictions, and their use was demonstrated for evaluation of light source efficiency and design.

BackgroundPrior work suggests different interstitial lung diseases (ILDs) that share the radiological usual interstitial pneumonia (UIP) pattern have an overall worse prognosis. However, epidemiological data with longitudinal sampling and replication remains lacking.MethodsData was used from the Pulmonary Fibrosis Foundation Patient Registry (PFF-PR) (n=932) and a meta-cohort of ILD research studies (n=1579). Linear mixed-effects models and Cox proportional hazard models were used to determine forced vital capacity (FVC) slopes and 5-year transplant-free survival, respectively, by ILD diagnosis and UIP radiological pattern. Secondarily, we examined FVC and survival by diagnosis and radiological fibrosis quantified by data-driven texture analysis (DTA) in the PFF-PR. Models were adjusted for age, sex, smoking and antifibrotic and immunosuppression medication use.ResultsThe proportions of idiopathic pulmonary fibrosis (IPF), fibrotic hypersensitivity pneumonitis (FHP) and connective tissue disease (CTD)-ILD were the following for PFF-PR (70%, 11%, 19%) and meta-cohort (21%, 32%, 47%). In the PFF-PR, CTD-ILD with UIP CT pattern was associated with slower FVC decline (−34.4 mL/year) compared with IPF (−158.4 mL/year) and longer transplant-free survival (HR 0.50, 95% CI 0.29 to 0.85). This was replicated in the meta cohort for FVC (−53.1 vs −185.9 mL/year, p<0.0001) and survival (HR 0.38, 95% CI 0.27 to 0.53). A similar pattern was seen using DTA to objectively categorise patients into higher and lower radiological fibrosis. Between IPF and FHP-UIP, FVC decline was not significantly different in the PFF-PR (−203.4 vs −158.4 mL/year, p=0.58) and meta-cohort (−124.0 vs −185.9 mL/year, p=0.25).ConclusionsEven in the presence of a UIP CT pattern, there may still be differences in lung function over time and survival, particularly for CTD-ILD.

Large airway dimensions on computed tomography (CT) have been associated with lung function, symptoms, and exacerbations in chronic obstructive pulmonary disease (COPD), as well as with symptoms in smokers with preserved spirometry. Their prognostic significance in persons without lung disease remains undefined. To examine associations between large airway dimensions on CT and respiratory outcomes in a population-based cohort of adults without prevalent lung disease. The Multi-Ethnic Study of Atherosclerosis recruited participants ages 45-84 years without cardiovascular disease in 2000-2002; we excluded participants with prevalent chronic lower respiratory disease (CLRD). Spirometry was measured in 2004-2006 and 2010-2012. CLRD hospitalizations and deaths were classified by validated criteria through 2014. The average wall thickness for a hypothetical airway of 10-mm lumen perimeter on CT (Pi10) was calculated using measures of airway wall thickness and lumen diameter. Models were adjusted for age, sex, principal components of ancestry, body mass index, smoking, pack-years, scanner, percent emphysema, genetic risk score, and initial forced expiratory volume in 1 second (FEV ) percent predicted. Greater Pi10 was associated with 9% faster FEV decline (95% confidence interval [CI], 2 to 15%; P = 0.012) and increased incident COPD (odds ratio, 2.22; 95% CI, 1.43-3.45; P = 0.0004) per standard deviation among 1,830 participants. Over 78,147 person-years, higher Pi10 was associated with a 57% higher risk of first CLRD hospitalization or mortality (P = 0.0496) per standard deviation. Of Pi10's component measures, both greater airway wall thickness and narrower lumen predicted incident COPD and CLRD clinical events. In adults without CLRD, large airway dimensions on CT were prospectively associated with accelerated lung function decline and increased risks of COPD and CLRD hospitalization and mortality.

Chronic obstructive pulmonary disease (COPD) has been associated with numerous genetic variants, yet the extent to which its genetic risk is mediated by variation in lung structure remains unknown. To characterize associations between a genetic risk score (GRS) associated with COPD susceptibility and lung structure on computed tomography (CT). We analyzed data from MESA Lung (Multi-Ethnic Study of Atherosclerosis Lung Study), a U.S. general population-based cohort, and SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study). A weighted GRS was calculated from 83 SNPs that were previously associated with lung function. Lung density, spatially matched airway dimensions, and airway counts were assessed on full-lung CT. Generalized linear models were adjusted for age, age squared, sex, height, principal components of genetic ancestry, smoking status, pack-years, CT model, milliamperes, and total lung volume. MESA Lung and SPIROMICS contributed 2,517 and 2,339 participants, respectively. Higher GRS was associated with lower lung function and increased COPD risk, as well as lower lung density, smaller airway lumens, and fewer small airways, without effect modification by smoking. Adjustment for CT lung structure, particularly small airway measures, attenuated associations between the GRS and FEV /FVC by 100% and 60% in MESA and SPIROMICS, respectively. Lung structure (  < 0.0001), but not the GRS (  > 0.10), improved discrimination of moderate-to-severe COPD cases relative to clinical factors alone. A GRS associated with COPD susceptibility was associated with CT lung structure. Lung structure may be an important mediator of heritability and determinant of personalized COPD risk.

Background Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features. Methods Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS. Results We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts. Conclusions In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Aims/hypothesis Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European. Methods Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis - and trans -SNPs. The models were applied to genome-wide association study summary statistics of 250,127 type 2 diabetes cases and 1,222,941 controls from different racial and ethnic populations. Results We identified three, 44 and one protein associated with type 2 diabetes risk in Asian, European and Hispanic/Latino populations, respectively. Meta-analysis identified 40 proteins associated with type 2 diabetes risk across the populations, including well-established as well as novel proteins not yet implicated in type 2 diabetes development. Conclusions/interpretation Our study improves our understanding of the aetiology of type 2 diabetes in diverse populations. Data availability The summary statistics of multi-ethnic type 2 diabetes GWAS of MVP, DIAMANTE, Biobank Japan and other studies are available from The database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v3.p1. MESA genetic, proteome and covariate data can be accessed through dbGaP under phs000209.v13.p3. All code is available on GitHub ( https://github.com/Arthur1021/MESA-1K-PWAS ). Graphical Abstract

Protein biomarkers are associated with mortality in cardiovascular disease, but their effect on predicting respiratory and all-cause mortality is not clear. We tested whether a protein risk score (protRS) can improve prediction of all-cause mortality over clinical risk factors in smokers. We utilized smoking-enriched (COPDGene, LSC, SPIROMICS) and general population-based (MESA) cohorts with SomaScan proteomic and mortality data. We split COPDGene into training and testing sets (50:50) and developed a protRS based on respiratory mortality effect size and parsimony. We tested multivariable associations of the protRS with all-cause, respiratory, and cardiovascular mortality, and performed meta-analysis, area-under-the-curve (AUC), and network analyses. We included 2232 participants. In COPDGene, a penalized regression-based protRS was most highly associated with respiratory mortality (OR 9.2) and parsimonious (15 proteins). This protRS was associated with all-cause mortality (random effects HR 1.79 [95% CI 1.31–2.43]). Adding the protRS to clinical covariates improved all-cause mortality prediction in COPDGene (AUC 0.87 vs 0.82) and SPIROMICS (0.74 vs 0.6), but not in LSC and MESA. Protein–protein interaction network analyses implicate cytokine signaling, innate immune responses, and extracellular matrix turnover. A blood-based protein risk score predicts all-cause and respiratory mortality, identifies potential drivers of mortality, and demonstrates heterogeneity in effects amongst cohorts.