Publications
44 results found
Pavlidis S, Guo Y, Sun K, et al., 2018, Weighted Gene Co-expression Network Analysis of blood paediatric samples from the U-BIOPRED study identifies oxidative stress association with asthma severity, 28th International Congress of the European-Respiratory-Society (ERS), Publisher: EUROPEAN RESPIRATORY SOC JOURNALS LTD, ISSN: 0903-1936
Burg D, Schofield JPR, Brandsma J, et al., 2018, Large-scale label-free quantitative mapping of the sputum proteome, Journal of Proteome Research, Vol: 17, Pages: 2072-2091, ISSN: 1535-3893
Analysis of induced sputum supernatant is a minimally invasive approach to study the epithelial lining fluid and, thereby, provide insight into normal lung biology and the pathobiology of lung diseases. We present here a novel proteomics approach to sputum analysis developed within the U-BIOPRED (unbiased biomarkers predictive of respiratory disease outcomes) international project. We present practical and analytical techniques to optimize the detection of robust biomarkers in proteomic studies. The normal sputum proteome was derived using data-independent HDMSE applied to 40 healthy nonsmoking participants, which provides an essential baseline from which to compare modulation of protein expression in respiratory diseases. The "core" sputum proteome (proteins detected in ≥40% of participants) was composed of 284 proteins, and the extended proteome (proteins detected in ≥3 participants) contained 1666 proteins. Quality control procedures were developed to optimize the accuracy and consistency of measurement of sputum proteins and analyze the distribution of sputum proteins in the healthy population. The analysis showed that quantitation of proteins by HDMSE is influenced by several factors, with some proteins being measured in all participants' samples and with low measurement variance between samples from the same patient. The measurement of some proteins is highly variable between repeat analyses, susceptible to sample processing effects, or difficult to accurately quantify by mass spectrometry. Other proteins show high interindividual variance. We also highlight that the sputum proteome of healthy individuals is related to sputum neutrophil levels, but not gender or allergic sensitization. We illustrate the importance of design and interpretation of disease biomarker studies considering such protein population and technical measurement variance.
De Meulder B, Lefaudeux D, Bansal AT, et al., 2018, A computational framework for complex disease stratification from multiple large-scale datasets, BMC Systems Biology, Vol: 12, ISSN: 1752-0509
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.
Takahashi K, Pavlidis S, Ng Kee Kwong F, et al., 2018, Sputum proteomics and airway cell transcripts of current and ex-smokers with severe asthma in U-BIOPRED: an exploratory analysis, European Respiratory Journal, Vol: 51, ISSN: 0903-1936
Background: Severe asthma patients with a significant smoking history have airflow obstruction with reported neutrophilia. We hypothesise that multi1omic analysis will enable the definition of smoking and ex1smoking severe asthma molecular phenotypes.Methods The U1BIOPRED severe asthma patients containing current1smokers (CSA), ex1smokers (ESA), non1smokers (NSA) and healthy non1smokers (NH) was examined. Blood and sputum cell counts, fractional exhaled nitric oxide and spirometry were obtained. Exploratory proteomic analysis of sputum supernatants and transcriptomic analysis of bronchial brushings, biopsies and sputum cells was performed. Results Colony stimulating factor (CSF)2 protein levels were increased in CSA sputum supernatants with azurocidin 1, neutrophil elastase and CXCL8 upregulated in ESA. Phagocytosis and innate immune pathways were associated with neutrophilic inflammation in ESA. Gene Set Variation Analysis of bronchial epithelial cell transcriptome from CSA showed enrichment of xenobiotic metabolism, oxidative stress and endoplasmic reticulum stress compared to other groups. CXCL5 and matrix metallopeptidase 12 genes were upregulated in ESA and the epithelial protective genes, mucin 2 and cystatin SN, were downregulated. Conclusion Despite little difference in clinical characteristics, CSA were distinguishable from ESA subjects at the sputum proteomic level with CSA having increased CSF2 expression and ESA patients showed sustained loss of epithelial barrier processes.
Oehmichen A, Guitton F, Sun K, et al., 2017, eTRIKS analytical environment: A modular high performance framework for medical data analysis, 2017 IEEE International Conference on Big Data (BIGDATA), Pages: 353-360
Hekking PP, Loza MJ, Pavlidis S, et al., 2017, Transcriptomic gene signatures associated with persistent airflow limitation in patients with severe asthma, European Respiratory Journal, Vol: 50, ISSN: 1399-3003
Rationale:A proportion of severe asthma patients suffers from persistent airflow limitation, often associated with more symptoms and exacerbations. Little is known about the underlying mechanisms. Aiming for discovery of unexplored potential mechanisms, we used Gene Set Variation Analysis (GSVA), a sensitive technique that can detect underlying pathways in heterogeneous samples. Methods: Severe asthma patients from the U-BIOPRED cohort with persistent airflow limitation (post-bronchodilator FEV1/FVC ratio < lower limit of normal) were compared to those without persistent airflow limitation. Gene expression was assessed on the total RNA of sputum cells, nasal brushings and endobronchial brushings and biopsies. GSVA was applied to identify differentially-enriched pre-defined gene signatures based on all available gene expression publications and data on airways disease.Results: Differentially-enriched gene signatures were identified in nasal brushings (1), sputum (9), bronchial brushings (1) and bronchial biopsies (4), that were associated with response to inhaled steroids, eosinophils, IL-13, IFN-alpha, specific CD4+ T-cells and airway remodeling.Conclusion: Persistent airflow limitation in severe asthma has distinguishable underlying gene networks that are associated with treatment, inflammatory pathways and airway remodeling. These results point towards targets for the therapy of persistent airflow limitation in severe asthma.
Hekking PP, Loza MJ, Pavlidis S, et al., 2017, Pathway discovery using transcriptomic profiles in adult-onset severe asthma, Journal of Allergy and Clinical Immunology, Vol: 141, Pages: 1280-1290, ISSN: 1097-6825
RationaleAdult-onset severe asthma is characterized by highly symptomatic disease despite high intensity asthma treatments. Understanding of the underlying pathways of this heterogeneous disease needed for the development of targeted treatments. Gene Set Variation Analysis (GSVA) is a statistical technique to identify gene profiles in heterogeneous samples.ObjectiveTo identify gene profiles associated with adult-onset severe asthma.MethodsThis was a cross-sectional, observational study in which adult patients with adult-onset of asthma (defined as starting at ≥18yrs old) as compared to childhood-onset severe asthma (<18 yrs) were selected from the U-BIOPRED cohort. Gene expression was assessed on the total RNA of induced sputum (n=83), nasal brushings (n=41), and endobronchial brushings (n=65) and biopsies (n=47) (Affymetrix HT HG-U133+ PM). GSVA was used to identify differentially enriched pre-defined gene signatures of leukocyte lineage, inflammatory and induced lung injury pathways.ResultsSignificant differentially enriched gene signatures in patients with adult-onset as compared to childhood-onset severe asthma were identified in nasal brushings (5 signatures), sputum (3 signatures) and endobronchial brushings (6 signatures). Signatures associated with eosinophilic airway inflammation, mast cells and group 3 innate lymphoid cells (ILC3) were more enriched in adult-onset severe asthma, whereas signatures associated with induced lung injury were less enriched in adult-onset severe asthma.ConclusionsAdult-onset severe asthma is characterized by inflammatory pathways involving eosinophils, mast cells and ILC3s. These pathways could represent useful targets for the treatment of adult-onset severe asthma.
Rossios C, Pavlidis S, Hoda U, et al., 2017, Sputum transcriptomics reveal upregulation of IL-1 receptor family members in patients with severe asthma, Journal of Allergy and Clinical Immunology, Vol: 141, Pages: 560-570, ISSN: 1097-6825
BACKGROUND: Sputum analysis in asthmatic patients is used to define airway inflammatory processes and might guide therapy. OBJECTIVE: We sought to determine differential gene and protein expression in sputum samples from patients with severe asthma (SA) compared with nonsmoking patients with mild/moderate asthma. METHODS: Induced sputum was obtained from nonsmoking patients with SA, smokers/ex-smokers with severe asthma, nonsmoking patients with mild/moderate asthma (MMAs), and healthy nonsmoking control subjects. Differential cell counts, microarray analysis of cell pellets, and SOMAscan analysis of sputum analytes were performed. CRID3 was used to inhibit the inflammasome in a mouse model of SA. RESULTS: Eosinophilic and mixed neutrophilic/eosinophilic inflammation were more prevalent in patients with SA compared with MMAs. Forty-two genes probes were upregulated (>2-fold) in nonsmoking patients with severe asthma compared with MMAs, including IL-1 receptor (IL-1R) family and nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain containing 3 (NRLP3) inflammasome members (false discovery rate < 0.05). The inflammasome proteins nucleotide-binding oligomerization domain, leucine rich repeat and pyrin domain containing 1 (NLRP1), NLRP3, and nucleotide-binding oligomerization domain (NOD)-like receptor C4 (NLRC4) were associated with neutrophilic asthma and with sputum IL-1β protein levels, whereas eosinophilic asthma was associated with an IL-13-induced TH2 signature and IL-1 receptor-like 1 (IL1RL1) mRNA expression. These differences were sputum specific because no activation of NLRP3 or enrichment of IL-1R family genes in bronchial brushings or biopsy specimens in patients with SA was observed. Expression of NLRP3 and of the IL-1R family genes was validated in the Airway Disease Endotyping for Personalized Therapeutics cohort. Inflammasome inhibition using CRID3 prevented airway hyperresponsiveness and airway inflammati
Wilson SJ, Ward JA, Sousa AR, et al., 2016, Severe asthma exists despite suppressed tissue inflammation: findings of the U-BIOPRED study., European Respiratory Journal, Vol: 48, Pages: 1307-1319, ISSN: 1399-3003
The U-BIOPRED study is a multicentre European study aimed at a better understanding of severe asthma. It included three steroid-treated adult asthma groups (severe nonsmokers (SAn group), severe current/ex-smokers (SAs/ex group) and those with mild-moderate disease (MMA group)) and healthy controls (HC group). The aim of this cross-sectional, bronchoscopy substudy was to compare bronchial immunopathology between these groups.In 158 participants, bronchial biopsies and bronchial epithelial brushings were collected for immunopathologic and transcriptomic analysis. Immunohistochemical analysis of glycol methacrylate resin-embedded biopsies showed there were more mast cells in submucosa of the HC group (33.6 mm(-2)) compared with both severe asthma groups (SAn: 17.4 mm(-2), p<0.001; SAs/ex: 22.2 mm(-2), p=0.01) and with the MMA group (21.2 mm(-2), p=0.01). The number of CD4(+) lymphocytes was decreased in the SAs/ex group (4.7 mm(-2)) compared with the SAn (11.6 mm(-2), p=0.002), MMA (10.1 mm(-2), p=0.008) and HC (10.6 mm(-2), p<0.001) groups. No other differences were observed.Affymetrix microarray analysis identified seven probe sets in the bronchial brushing samples that had a positive relationship with submucosal eosinophils. These mapped to COX-2 (cyclo-oxygenase-2), ADAM-7 (disintegrin and metalloproteinase domain-containing protein 7), SLCO1A2 (solute carrier organic anion transporter family member 1A2), TMEFF2 (transmembrane protein with epidermal growth factor like and two follistatin like domains 2) and TRPM-1 (transient receptor potential cation channel subfamily M member 1); the remaining two are unnamed.We conclude that in nonsmoking and smoking patients on currently recommended therapy, severe asthma exists despite suppressed tissue inflammation within the proximal airway wall.
Supratak A, Wu C, Dong H, et al., 2016, Survey on Feature Extraction and Applications of Biosignals, MACHINE LEARNING FOR HEALTH INFORMATICS: STATE-OF-THE-ART AND FUTURE CHALLENGES, Editors: Holzinger, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 161-182, ISBN: 978-3-319-50477-3
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- Citations: 23
Sun K, Buchan N, Larminie C, et al., 2014, The integrated disease network, INTEGRATIVE BIOLOGY, Vol: 6, Pages: 1069-1079, ISSN: 1757-9694
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- Citations: 20
Sun K, Goncalves JP, Larminie C, et al., 2014, Predicting disease associations via biological network analysis, BMC BIOINFORMATICS, Vol: 15, ISSN: 1471-2105
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- Citations: 72
Hayes W, Sun K, Przulj N, 2013, Graphlet-based measures are suitable for biological network comparison, BIOINFORMATICS, Vol: 29, Pages: 483-491, ISSN: 1367-4803
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- Citations: 55
Sun K, Larminie C, Przulj N, 2011, Disease re-classi cation via integration of biological networks, Departmental Technical Report: 11/8, Publisher: Department of Computing, Imperial College London, 11/8
Currently, human diseases are classi ed as they were in the late 19th century, by consideringonly symptoms of the a ected organ. With a growing body of transcriptomic,proteomic, metabolomic and genomics data sets describing diseases, we ask whether theold classi cation still holds in the light of modern biological data. These large-scale andcomplex biological data can be viewed as networks of inter-connected elements.We propose to rede ne human disease classi cation by considering diseases as systemsleveldisorders of the entire cellular system. To do this, we will integrate di erenttypes of biological data mentioned above. A network-based mathematical model will bedesigned to represent these integrated data, and computational algorithms and tools willbe developed and implemented for its analysis. In this report, a review of the researchprogress so far will be presented, including 1) a detailed statement of the researchproblem, 2) a literature survey on relative research topics, 3) reports of on-going work,and 4) future research plans.2
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