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BACKGROUND Asthma is a chronic respiratory disease with significant heterogeneity in its clinical presentation and pathobiology. There is need for improved understanding of respiratory lipid metabolism in asthma patients and its relation to observable clinical features. OBJECTIVE To perform a comprehensive, prospective, cross-sectional analysis of the lipid composition of induced sputum supernatant obtained from asthma patients with a range of disease severities, as well as healthy controls. METHODS Induced sputum supernatant was collected from 211 asthmatic adults and 41 healthy individuals enrolled in the U-BIOPRED study. Sputum lipidomes were characterised by semi-quantitative shotgun mass spectrometry, and clustered using topological data analysis to identify lipid phenotypes. RESULTS Shotgun lipidomics of induced sputum supernatant revealed a spectrum of nine molecular phenotypes, highlighting not just significant differences between the sputum lipidomes of asthmatics and healthy controls, but within the asthmatic population as well. Matching clinical, pathobiological, proteomic and transcriptomic data informed on the underlying disease processes. Sputum lipid phenotypes with higher levels of non-endogenous, cell-derived lipids were associated with significantly worse asthma severity, worse lung function, and elevated granulocyte counts. CONCLUSION We propose a novel mechanism of increased lipid loading in the epithelial lining fluid of asthmatics, resulting from the secretion of extracellular vesicles by granulocytic inflammatory cells, which could reduce the ability of pulmonary surfactant to lower surface tension in asthmatic small airways, as well as compromise its role as an immune regulator. CLINICAL IMPLICATION Immunomodulation of extracellular vesicle secretion in the lungs may provide a novel therapeutic target for severe asthma.

Rationale Asthma phenotyping requires novel biomarker discovery. Objectives To identify plasma biomarkers associated with asthma phenotypes by application of a new proteomic panel to samples from two well-characterised cohorts of severe (SA) and mild-to-moderate (MMA) asthmatics, COPD subjects and healthy controls (HCs). Methods An antibody-based array targeting 177 proteins predominantly involved in pathways relevant to inflammation, lipid metabolism, signal transduction and extracellular matrix was applied to plasma from 525 asthmatics and HCs in the U-BIOPRED cohort, and 142 subjects with asthma and COPD from the validation cohort BIOAIR. Effects of oral corticosteroids (OCS) were determined by a 2-week, placebo-controlled OCS trial in BIOAIR, and confirmed by relation to objective OCS measures in U-BIOPRED. Results In U-BIOPRED, 110 proteins were significantly different, mostly elevated, in SA compared to MMA and HCs. 10 proteins were elevated in SA versus MMA in both U-BIOPRED and BIOAIR (alpha-1-antichymotrypsin, apolipoprotein-E, complement component 9, complement factor I, macrophage inflammatory protein-3, interleukin-6, sphingomyelin phosphodiesterase 3, TNF receptor superfamily member 11a, transforming growth factor-β and glutathione S-transferase). OCS treatment decreased most proteins, yet differences between SA and MMA remained following correction for OCS use. Consensus clustering of U-BIOPRED protein data yielded six clusters associated with asthma control, quality of life, blood neutrophils, high-sensitivity C-reactive protein and body mass index, but not Type-2 inflammatory biomarkers. The mast cell specific enzyme carboxypeptidase A3 was one major contributor to cluster differentiation. Conclusions The plasma proteomic panel revealed previously unexplored yet potentially useful Type-2independent biomarkers and validated several proteins with established involvement in the pathophysiology of SA. © 2022 European Respiratory Society. All rights reserved.

RATIONALE: New approaches are needed to guide personalized treatment of asthma. OBJECTIVE: To test if urinary eicosanoid metabolites can direct asthma phenotyping. METHODS: Urinary metabolites of prostaglandins (PGs), cysteinyl-leukotrienes (LTs) and isoprostanes were quantified in the Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes (U-BIOPRED) study including 86 adults with mild-to-moderate asthma (MMA), 411 with severe asthma (SA), and 100 healthy controls (HC). Validation was performed internally in 302 SA subjects followed-up after 12-18 months, and externally in 95 adolescents with asthma. MEASUREMENT AND MAIN RESULTS: Metabolite levels in HC were unrelated to age, BMI and sex, except for the PGE2-pathway. Eicosanoid levels were generally greater in MMA relative to HC, with further elevations in SA. However, PGE2-metabolite levels were either the same or lower in male non-smoking asthmatics as in HC. Metabolite levels were unchanged in asthmatics adherent to oral corticosteroid treatment as documented by urinary prednisolone detection, whereas SA treated with omalizumab had lower levels of LTE4 and the PGD2 metabolite 2,3-dinor-11β-PGF2α. High levels of LTE4 and PGD2-metabolites were associated with lower lung-function, and increased levels of exhaled nitric oxide and eosinophil markers in blood, sputum and urine in U-BIOPRED and in adolescents with asthma. These type-2 (T2) asthma associations were reproduced in the follow-up visit of the U-BIOPRED study, and found to be as sensitive to detect T2 inflammation as the established biomarkers. CONCLUSIONS: Monitoring of urinary eicosanoids can identify T2 asthma and introduces a new non-invasive approach for molecular phenotyping of adult and adolescent asthma. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

mprovements in asthma outcomes have stalled over the past decade, which may be attributed to treating patients on the basis of a generic diagnostic label. The taxonomy “Treatable Traits” was proposed by Agusti et al (2016) as a precision medicine approach for the diagnosis and management of chronic airway diseases that is based on the identification of genetic, phenotypic and psychosocial characteristics for which therapeutic interventions are known to improve respiratory health ...

Gastro-oesophageal reflux disease (GORD) and obesity are associated with frequent exacerbations and poor quality of life in asthmatics. Multiple mechanisms have been proposed for the effect of obesity, including modification of inflammation affecting epithelial cell proliferation and wound repair, while the role of GORD is poorly understood and proton pump inhibitor (PPI) are of variable efficacy. GORD might exert a deleterious effect by inducing vagal reflex, neuroinflammation and directly ( via microaspiration) triggering airway inflammation. Studies of reflux in animal models and human bronchial epithelial cell culture show varying impact on inflammation and airway remodelling. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Dr PEROTIN has nothing to disclose. Conflict of interest: Dr Schofield has nothing to disclose. Conflict of interest: Dr Wilson has nothing to disclose. Conflict of interest: Dr Ward has nothing to disclose. Conflict of interest: Dr Brandsma has nothing to disclose. Conflict of interest: Dr Strazzeri has nothing to disclose. Conflict of interest: Dr Bansal has nothing to disclose. Conflict of interest: Dr Yang has nothing to disclose. Conflict of interest: Dr Rowe reports and a full time employee and shareholder of Janssen Pharmaceutical Companies of Johnson and Johnson. Conflict of interest: Miss Corfield has nothing to disclose. Conflict of interest: Dr Lutter has nothing to disclose. Conflict of interest: Prof. Shaw reports personal fees and non-financial support from AstraZeneca, personal fees from Boehringer Ingelheim, personal fees from Novartis, personal fees from Teva, personal fees from Circassia, and a grant from GSK, outside the submitted work. Conflict of interest: Dr Bakke reports personal fees from GSK, AZ, Novartis andTeva, outside the submitted work. Conflict of interest: MC have no conflict of interest to disclose. Conflict of interest: Dr Dahlen has nothing to disclose. Conflict of interest: Dr Fowler reports personal fees and non-financial support from AstraZeneca, grants and personal fees from Boehringer Ingelheim, personal fees from Novartis, personal fees from Teva, outside the submitted work. Conflict of interest: Dr Horvath reports personal fees from Astra Zeneca, Boehringer Ingelheim, Novartis, CSL, Chiesi, Roche, GSK, Berlin-Chemie and Sandoz, outside the submitted work. Conflict of interest: Dr Howarth reports personal fees from GSK, outside the submitted work. Conflict of interest: Dr Krug has nothing to disclose. Conflict of interest: Dr Montuschi has nothing to disclose. Conflict of interest: Dr Sanak has nothing to disclose. Conflict of interest: Dr Sandstrom reports other monetary support from Boehringer Ingelheim, outside the submitted work. Conflict of interest: Dr Sun has nothing to disclose. Conflict of interest: Dr Pandis has nothing to disclose. Conflict of interest: Dr Auffray reports grants from Innovative Medicine Initiative, during the conduct of the study. Conflict of interest: Dr De Meulder reports grants from Innovative Medicine Initiative, during the conduct of the study. Conflict of interest: Ms. Lefaudeux reports grants from Innovative Medicine Initiative, grants from Innovative Medicine Initiative, during the conduct of the study. Conflict of interest: Dr Riley reports and I have shares in and I am employed by GSK. Conflict of interest: Dr Sousa has nothing to disclose. Conflict of interest: Dr Dahlen has nothing to disclose. Conflict of interest: Dr Adcock reports grants from EU-IMI, during the conduct of the study. Conflict of interest: KFC has received honoraria for participating in Advisory Board meetings of GSK, AZ, BI, Teva, Novartis and Merck regarding treatments for asthma and chronic obstructive pulmonary disease and has also been renumerated for speaking engagements. Conflict of interest: Dr Sterk reports grants from Innovative Medicines Initiative, during the conduct of the study. Conflict of interest: Dr Skipp has nothing to disclose. Conflict of interest: Dr Collins reports a patent application for use of a genetically modified Drosophila line carrying one or more mammalian genes associated with a chronic respiratory disease and uses to screen the impact of such genes. Conflict of interest: Dr Davies has nothing to disclose. Conflict of interest: Dr Djukanovic reports receiving fees for lectures at symposia organised by Novartis, AstraZeneca and TEVA, consultation for TEVA and Novartis as member of advisory boards, and participation in a scientific discussion about asthma organised by GlaxoSmithKline. He is a co-founder and current consultant, and has shares in Synairgen, a University of Southampton spin out company.

Background: Eosinophils play an important role in the pathophysiology of asthma being implicated in airway epithelial damage and airway wall remodeling. We determined the genes associated with airway remodeling and eosinophilic inflammation in patients with asthma. Methods: We analyzed the transcriptomic data from bronchial biopsies of 81 patients with moderate-to-severe asthma of the U-BIOPRED cohort. Expression profiling was performed using Affymetrix arrays on total RNA. Transcription binding site analysis used the PRIMA algorithm. Localization of proteins was by immunohistochemistry. Results: Using stringent false discovery rate analysis, MMP-10 and MET were significantly overexpressed in biopsies with high mucosal eosinophils (HE) compared to low mucosal eosinophil (LE) numbers. Immunohistochemical analysis confirmed increased expression of MMP-10 and MET in bronchial epithelial cells and in subepithelial inflammatory and resident cells in asthmatic biopsies. Using less-stringent conditions (raw P-value  0.5), we defined a 73-gene set characteristic of the HE compared to the LE group. Thirty-three of 73 genes drove the pathway annotation that included extracellular matrix (ECM) organization, mast cell activation, CC-chemokine receptor binding, circulating immunoglobulin complex, serine protease inhibitors, and microtubule bundle formation pathways. Genes including MET and MMP10 involved in ECM organization correlated positively with submucosal thickness. Transcription factor binding site analysis identified two transcription factors, ETS-1 and SOX family proteins, that showed positive correlation with MMP10 and MET expression. Conclusion: Pathways of airway remodeling and cellular inflammation are associated with submucosal eosinophilia. MET and MMP-10 likely play an important role in these processes.

Background Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-‘omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-‘omics signatures of disease states. Methods The framework is divided into four major steps: dataset subsetting, feature filtering, ‘omics-based clustering and biomarker identification. Results We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-‘omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. Conclusions This framework will help health researchers plan and perform multi-‘omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine. Electronic supplementary material The online version of this article (10.1186/s12918-018-0556-z) contains supplementary material, which is available to authorized users.

Background Lung epithelial lining fluid (ELF)—sampled through sputum induction—is a medium rich in cells, proteins and lipids. However, despite its key role in maintaining lung function, homeostasis and defences, the composition and biology of ELF, especially in respect of lipids, remain incompletely understood. Objectives To characterise the induced sputum lipidome of healthy adult individuals, and to examine associations between different ELF lipid phenotypes and the demographic characteristics within the study cohort. Methods Induced sputum samples were obtained from 41 healthy non-smoking adults, and their lipid compositions analysed using a combination of untargeted shotgun and liquid chromatography mass spectrometry methods. Topological data analysis (TDA) was used to group subjects with comparable sputum lipidomes in order to identify distinct ELF phenotypes. Results The induced sputum lipidome was diverse, comprising a range of different molecular classes, including at least 75 glycerophospholipids, 13 sphingolipids, 5 sterol lipids and 12 neutral glycerolipids. TDA identified two distinct phenotypes differentiated by a higher total lipid content and specific enrichments of diacyl-glycerophosphocholines, -inositols and -glycerols in one group, with enrichments of sterols, glycolipids and sphingolipids in the other. Subjects presenting the lipid-rich ELF phenotype also had significantly higher BMI, but did not differ in respect of other demographic characteristics such as age or gender. Conclusions We provide the first evidence that the ELF lipidome varies significantly between healthy individuals and propose that such differences are related to weight status, highlighting the potential impact of (over)nutrition on lung lipid metabolism. Electronic supplementary material The online version of this article (10.1007/s11306-018-1412-2) contains supplementary material, which is available to authorized users.

Rationale: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. Objectives: Using transcriptomic profiling of airway tissues, we sought to define the molecular phenotypes of severe asthma. Methods: The transcriptome derived from bronchial biopsies and epithelial brushings of 107 subjects with moderate to severe asthma were annotated by gene set variation analysis using 42 gene signatures relevant to asthma, inflammation, and immune function. Topological data analysis of clinical and histologic data was performed to derive clusters, and the nearest shrunken centroid algorithm was used for signature refinement. Measurements and Main Results: Nine gene set variation analysis signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper cell type 2 cytokines and lack of corticosteroid response (group 1 and group 3). Group 1 had the highest submucosal eosinophils, as well as high fractional exhaled nitric oxide levels, exacerbation rates, and oral corticosteroid use, whereas group 3 patients showed the highest levels of sputum eosinophils and had a high body mass index. In contrast, group 2 and group 4 patients had an 86% and 64% probability, respectively, of having noneosinophilic inflammation. Using machine learning tools, we describe an inference scheme using the currently available inflammatory biomarkers sputum eosinophilia and fractional exhaled nitric oxide levels, along with oral corticosteroid use, that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. Conclusions: This analysis demonstrates the usefulness of a transcriptomics-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target T-helper cell type 2-mediated inflammation and/or corticosteroid insensitivity.

With the rise of multi-socket multi-core CPUs a lot of effort is being put into how to best exploit their abundant CPU power. In a shared memory setting the multi-socket CPUs are equipped with their own memory module, and access memory modules across sockets in a non-uniform access pattern (NUMA). Memory access across socket is relatively expensive compared to memory access within a socket. One of the common solutions to minimize across socket memory access is to partition the data, such that the data affinity is maintained per socket.In this paper we explore the role of memory mapped storage to provide transparent data access in a NUMA environment, without the need of explicit data partitioning. We compare the performance of a database engine in a distributed setting in a multi-socket environment, with a database engine in a NUMA oblivious setting. We show that though the operating system tries to keep the data affinity to local sockets, a significant remote memory access still occurs, as the number of threads increase. Hence, setting explicit process and memory affinity results into a robust execution in NUMA oblivious plans. We use micro-experiments and SQL queries from the TPC-H benchmark to provide an in-depth experimental exploration of the landscape, in a four socket Intel machine.

The Blink project’s ambitious goal is to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. Blink is a new DBMS aimed primarily at read-mostly BI query processing that exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Blink always scans (portions of) the data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorpo

Intuitively, aggressive work sharing among concurrent queries in a database system should always improve performance by eliminating redundant computation or data accesses. We show that, contrary to common intuition, this is not always the case in practice, especially in the highly parallel world of chip multiprocessors. As the number of cores in the system increases, a trade-off appears between exploiting work sharing opportunities and the available parallelism. To resolve the trade-off, we develop an analytical approach that predicts the effect of work sharing in multi-core systems. Database systems can use the model to determine, statically or at runtime, whether work sharing is beneficial and apply it only when appropriate. The contributions of this paper are as follows. First, we introduce and analyze the effects of the trade-off between work sharing and parallelism on database systems running complex decision-support queries. Second, we propose an intuitive and simple model that can evaluate the trade-off using real-world measurement approximations of the query execution processes. Furthermore, we integrate the model into a prototype database execution engine, and demonstrate that selective work sharing according to the model outperforms never-share static schemes by 20% on average and always-share ones by 2.5x.

Prior research shows that database system performance is dominated by off-chip data stalls, resulting in a concerted effort to bring data into on-chip caches. At the same time, high levels of integration have enabled the advent of chip multiprocessors and increasingly large (and slow) on-chip caches. These two trends pose the imminent technical and research challenge of adapting high-performance data management software to a shifting hardware landscape. In this paper we characterize the performance of a commercial database server running on emerging chip multiprocessor technologies. We find that the major bottleneck of current software is data cache stalls, with L2 hit stalls rising from oblivion to become the dominant execution time component in some cases. We analyze the source of this shift and derive a list of features for future database designs to attain maximum performance.