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1. Epithelial IL-6 trans-signaling defines a new asthma phenotype with increased airway inflammation.

Author

Jevnikar Z, Östling J, Ax E, Calvén J, Thörn K, Israelsson E, Öberg L, Singhania A, Lau LCK, Wilson SJ, Ward JA, Chauhan A, Sousa AR, De Meulder B, Loza MJ, Baribaud F, Sterk PJ, Chung KF, Sun K, Guo Y, Adcock IM, Payne D, Dahlen B, Chanez P, Shaw DE, Krug N, Hohlfeld JM, Sandström T, Djukanovic R, James A, Hinks TSC, Howarth PH, Vaarala O, van Geest M, and Olsson H

Subjects
Adult, Airway Remodeling, Cells, Cultured, Cohort Studies, Cross-Sectional Studies, Gene Expression Regulation, Humans, Male, Phenotype, Receptors, Interleukin-6 metabolism, Respiratory Hypersensitivity, Signal Transduction, Transcriptome, Asthma immunology, Biomarkers metabolism, Epithelial Cells physiology, Inflammation immunology, Interleukin-6 metabolism, Lung physiology, Sputum metabolism
Abstract

Background: Although several studies link high levels of IL-6 and soluble IL-6 receptor (sIL-6R) to asthma severity and decreased lung function, the role of IL-6 trans-signaling (IL-6TS) in asthmatic patients is unclear., Objective: We sought to explore the association between epithelial IL-6TS pathway activation and molecular and clinical phenotypes in asthmatic patients., Methods: An IL-6TS gene signature obtained from air-liquid interface cultures of human bronchial epithelial cells stimulated with IL-6 and sIL-6R was used to stratify lung epithelial transcriptomic data (Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes [U-BIOPRED] cohorts) by means of hierarchical clustering. IL-6TS-specific protein markers were used to stratify sputum biomarker data (Wessex cohort). Molecular phenotyping was based on transcriptional profiling of epithelial brushings, pathway analysis, and immunohistochemical analysis of bronchial biopsy specimens., Results: Activation of IL-6TS in air-liquid interface cultures reduced epithelial integrity and induced a specific gene signature enriched in genes associated with airway remodeling. The IL-6TS signature identified a subset of patients with IL-6TS-high asthma with increased epithelial expression of IL-6TS-inducible genes in the absence of systemic inflammation. The IL-6TS-high subset had an overrepresentation of frequent exacerbators, blood eosinophilia, and submucosal infiltration of T cells and macrophages. In bronchial brushings Toll-like receptor pathway genes were upregulated, whereas expression of cell junction genes was reduced. Sputum sIL-6R and IL-6 levels correlated with sputum markers of remodeling and innate immune activation, in particular YKL-40, matrix metalloproteinase 3, macrophage inflammatory protein 1β, IL-8, and IL-1β., Conclusions: Local lung epithelial IL-6TS activation in the absence of type 2 airway inflammation defines a novel subset of asthmatic patients and might drive airway inflammation and epithelial dysfunction in these patients., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2019
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2. Plasma proteins elevated in severe asthma despite oral steroid use and unrelated to Type-2 inflammation

Author

Sparreman Mikus, Maria, Kolmert, Johan, Andersson, Lars I, ��stling, J��rgen, Knowles, Richard G, G��mez, Cristina, Ericsson, Magnus, Th��rngren, John-Olof, Emami Khoonsari, Payam, Dahl��n, Barbro, Kupczyk, Maciej, De Meulder, Bertrand, Auffray, Charles, Bakke, Per S, Beghe, Bianca, Bel, Elisabeth H, Caruso, Massimo, Chanez, Pascal, Chawes, Bo, Fowler, Stephen J, Gaga, Mina, Geiser, Thomas, Gjomarkaj, Mark, Horv��th, Ildik��, Howarth, Peter H, Johnston, Sebastian L, Joos, Guy, Krug, Norbert, Montuschi, Paolo, Musial, Jacek, Ni��ankowska-Mogilnicka, Ewa, Olsson, Henric K, Papi, Alberto, Rabe, Klaus F, Sandstr��m, Thomas, Shaw, Dominick E, Siafakas, Nikolaos M, Uhl��n, Mathias, Riley, John H, Bates, Stewart, Middelveld, Roelinde J M, Wheelock, Craig E, Chung, Kian Fan, Adcock, Ian M, Sterk, Peter J, Djukanovic, Ratko, Nilsson, Peter, Dahl��n, Sven-Erik, James, Anna, Publica, AII - Inflammatory diseases, Pulmonology, Centre recherche en CardioVasculaire et Nutrition = Center for CardioVascular and Nutrition research (C2VN), and Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
severe asthma, Inflammation, Proteomics, [SDV]Life Sciences [q-bio], Respiratory Medicine and Allergy, Socio-culturale, Plasma proteins, biomarkers, 610 Medicine & health, oral corticosteroids (OCS), Blood Proteins, Severity of Illness Index, Plasma proteins, severe asthma, chronic obstructive pulmonary disease (COPD), oral corticosteroids (OCS), biomarkers, Asthma, Quality of Life, Humans, Steroids, chronic obstructive pulmonary disease (COPD), Lungmedicin och allergi
Abstract

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-2-independent biomarkers and validated several proteins with established involvement in the pathophysiology of SA.

Published
2021
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3. Stratification of asthma phenotypes by airway proteomic signatures

Author

Schofield, James P. R., Burg, Dominic, Nicholas, Ben, Strazzeri, Fabio, Brandsma, Joost, Staykova, Doroteya, Folisi, Caterina, Bansal, Aruna T., Xian, Yang, Guo, Yike, Rowe, Anthony, Corfield, Julie, Wilson, Susan, Ward, Jonathan, Lutter, Rene, Shaw, Dominick E., Bakke, Per S., Caruso, Massimo, Dahlen, Sven-Erik, Fowler, Stephen J., Horvath, Ildiko, Howarth, Peter, Krug, Norbert, Montuschi, Paolo, Sanak, Marek, Sandstrom, Thomas, Sun, Kai, Pandis, Ioannis, Riley, John, Auffray, Charles, De Meulder, Bertrand, Lefaudeux, Diane, Sousa, Ana R., Adcock, Ian M., Chung, Kian Fan, Sterk, Peter J., Skipp, Paul J., Djukanovic, Ratko, Ahmed, H., Allen, D., Badorrek, P., Ballereau, S., Baribaud, F., Batuwitage, M. K., Bedding, A., Behndig, A. F., Berglind, A., Berton, A., Bigler, J., Boedigheimer, M. J., Bonnelykke, K., Brinkman, P., Bush, A., Campagna, D., Casaulta, C., Chaiboonchoe, A., Davison, T., De Meulder, B., Delin, I., Dennison, P., Dodson, P., El Hadjam, L., Erzen, D., Faulenbach, C., Fichtner, K., Fitch, N., Formaggio, E., Gahlemann, M., Galffy, G., Garissi, D., Garret, T., Gent, J., Guillmant-Farry, E., Henriksson, E., Hoda, U., Hohlfeld, J. M., Hu, X., James, A., Johnson, K., Jullian, N., Kerry, G., Klueglich, M., Knowles, R., Konradsen, J. R., Kretsos, K., Krueger, L., Lantz, A. -S, Larminie, C., Latzin, P., Lefaudeux, D., Lemonnier, N., Lowe, L. A., Lutter, R., Manta, A., Mazein, A., McEvoy, L., Menzies-Gow, A., Mores, N., Murray, C. S., Nething, K., Nihlen, U., Niven, R., Nordlund, B., Nsubuga, S., Pellet, J., Pison, C., Pratico, G., Puig Valls, M., Riemann, K., Rocha, J. P., Rossios, C., Santini, G., Saqi, M., Scott, S., Sehgal, N., Selby, A., Soderman, P., Sogbesan, A., Spycher, F., Stephan, S., Stokholm, J., Sunther, M., Szentkereszty, M., Tamasi, L., Tariq, K., Valente, S., van Aalderen, W. M., van Drunen, C. M., Van Eyll, J., Vyas, A., Yu, W., Zetterquist, W., Zolkipli, Z., Zwinderman, A. H., Adriaens, Nora, Aliprantis, Antonios, Alving, Kjell, Bakke, Per, Balgoma, David, Barber, Clair, Baribaud, Frederic, Bates, Stewart, Bautmans, An, Beleta, Jorge, Bochenek, Grazyna, Braun, Armin, Carayannopoulos, Leon, Rocha, Joao Pedro Carvalho da Purificacao, Chaleckis, Romanas, D'Amico, Arnaldo, De Alba, Jorge, De Lepeleire, Inge, Dekker, Tamara, Dijkhuis, Annemiek, Draper, Aleksandra, Edwards, Jessica, Emma, Rosalia, Ericsson, Magnus, Flood, Breda, Gallart, Hector, Gomez, Cristina, Gove, Kerry, Gozzard, Neil, Haughney, John, Hewitt, Lorraine, Hohlfeld, Jens, Holweg, Cecile, Hu, Richard, Hu, Sile, Kamphuis, Juliette, Kennington, Erika J., Kerry, Dyson, Knobel, Hugo, Kolmert, Johan, Kots, Maxim, Kuo, Scott, Kupczyk, Maciej, Lambrecht, Bart, Lone-Latif, Saeeda, Loza, Matthew J., Marouzet, Lisa, Martin, Jane, Masefield, Sarah, Mathon, Caroline, Meah, Sally, Meiser, Andrea, Metcalf, Leanne, Mikus, Maria, Miralpeix, Montse, Monk, Philip, Naz, Shama, Nilsson, Peter, Ostling, Jorgen, Pacino, Antonio, Palkonen, Susanna, Pavlidis, Stelios, Pennazza, Giorgio, Petren, Anne, Pink, Sandy, Postle, Anthony, Powell, Pippa, Rahman-Amin, Malayka, Rao, Navin, Ravanetti, Lara, Ray, Emma, Reinke, Stacey, Reynolds, Leanne, Robberechts, Martine, Roberts, Amanda, Russell, Kirsty, Rutgers, Michael, Santoninco, Marco, Schoelch, Corinna, Sjodin, Marcus, Smids, Barbara, Smith, Caroline, Smith, Jessica, Smith, Katherine M., Thorngren, John-Olof, Thornton, Bob, Thorsen, Jonathan, van de Pol, Marianne, van Geest, Marleen, Versnel, Jenny, Vink, Anton, Wald, Frans, Walker, Samantha, Weiszhart, Zsoka, Wetzel, Kristiane, Wheelock, Craig E., Wiegman, Coen, Williams, Sian, Wilson, Susan J., Woodcock, Ashley, Yang, Xian, Yeyasingham, Elizabeth, Prins, Jan-Bas, Gahlemann, Martina, Visintin, Luigi, Evans, Hazel, Puhl, Martine, Buzermaniene, Lina, Hudson, Val, Bond, Laura, de Boer, Pim, Widdershoven, Guy, Sigmund, Ralf, Supple, David, Hamerlijnck, Dominique, Negus, Jenny, Kamphuis, Julitte, Sergison, Lehanne, Onstein, Susanne, MacNee, William, Bernardini, Renato, Bont, Louis, Wecksell, Per-Ake, Graduate School, AII - Inflammatory diseases, Pulmonology, Ear, Nose and Throat, Epidemiology and Data Science, APH - Methodology, Publica, and Commission of the European Communities

Subjects
0301 basic medicine, Male, Proteomics, Allergy, Proteome, Neutrophils, Respiratory Medicine and Allergy, Transcriptome, 0302 clinical medicine, neutrophils, Forced Expiratory Volume, Immunology and Allergy, CD44, 610 Medicine & health, Lungmedicin och allergi, phenotypes, Middle Aged, medicine.anatomical_structure, Phenotype, 1107 Immunology, Female, eosinophils, medicine.symptom, Life Sciences & Biomedicine, Adult, Settore BIO/14 - FARMACOLOGIA, Immunology, Computational biology, 03 medical and health sciences, Young Adult, proteomics, Eosinophilia, medicine, Humans, U-BIOPRED Study Group, Asthma, Aged, Science & Technology, Microarray analysis techniques, business.industry, Sputum, biomarkers, DEGRADATION, Eosinophil, medicine.disease, Eosinophils, EXACERBATIONS, 030104 developmental biology, 030228 respiratory system, business
Abstract

Background: Stratification by eosinophil and neutrophil counts increases our understanding of asthma and helps target therapy, but there is room for improvement in our accuracy in prediction of treatment responses and a need for better understanding of the underlying mechanisms. Objective: We sought to identify molecular subphenotypes of asthma defined by proteomic signatures for improved stratification. Methods: Unbiased label-free quantitative mass spectrometry and topological data analysis were used to analyze the proteomes of sputum supernatants from 246 participants (206 asthmatic patients) as a novel means of asthma stratification. Microarray analysis of sputum cells provided transcriptomics data additionally to inform on underlying mechanisms. Results: Analysis of the sputum proteome resulted in 10 clusters (ie, proteotypes) based on similarity in proteomic features, representing discrete molecular subphenotypes of asthma. Overlaying granulocyte counts onto the 10 clusters as metadata further defined 3 of these as highly eosinophilic, 3 as highly neutrophilic, and 2 as highly atopic with relatively low granulocytic inflammation. For each of these 3 phenotypes, logistic regression analysis identified candidate protein biomarkers, and matched transcriptomic data pointed to differentially activated underlying mechanisms. Conclusion: This study provides further stratification of asthma currently classified based on quantification of granulocytic inflammation and provided additional insight into their underlying mechanisms, which could become targets for novel therapies. asthma proteomics biomarkers eosinophils neutrophils

Published
2019
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4. Adaptation of a Bioinformatics Microarray Analysis Workflow for a Toxicogenomic Study in Rainbow Trout.

Author

Depiereux, Sophie, De Meulder, Bertrand, Bareke, Eric, Berger, Fabrice, Le Gac, Florence, Depiereux, Eric, and Kestemont, Patrick

Subjects
*SEX hormones, *GONADS, *BIOINFORMATICS, *MICROARRAY technology, *BIOMARKERS, *BIOCHEMICAL mechanism of action
Abstract

Sex steroids play a key role in triggering sex differentiation in fish, the use of exogenous hormone treatment leading to partial or complete sex reversal. This phenomenon has attracted attention since the discovery that even low environmental doses of exogenous steroids can adversely affect gonad morphology (ovotestis development) and induce reproductive failure. Modern genomic-based technologies have enhanced opportunities to find out mechanisms of actions (MOA) and identify biomarkers related to the toxic action of a compound. However, high throughput data interpretation relies on statistical analysis, species genomic resources, and bioinformatics tools. The goals of this study are to improve the knowledge of feminisation in fish, by the analysis of molecular responses in the gonads of rainbow trout fry after chronic exposure to several doses (0.01, 0.1, 1 and 10 μg/L) of ethynylestradiol (EE2) and to offer target genes as potential biomarkers of ovotestis development. We successfully adapted a bioinformatics microarray analysis workflow elaborated on human data to a toxicogenomic study using rainbow trout, a fish species lacking accurate functional annotation and genomic resources. The workflow allowed to obtain lists of genes supposed to be enriched in true positive differentially expressed genes (DEGs), which were subjected to over-representation analysis methods (ORA). Several pathways and ontologies, mostly related to cell division and metabolism, sexual reproduction and steroid production, were found significantly enriched in our analyses. Moreover, two sets of potential ovotestis biomarkers were selected using several criteria. The first group displayed specific potential biomarkers belonging to pathways/ontologies highlighted in the experiment. Among them, the early ovarian differentiation gene foxl2a was overexpressed. The second group, which was highly sensitive but not specific, included the DEGs presenting the highest fold change and lowest p-value of the statistical workflow output. The methodology can be generalized to other (non-model) species and various types of microarray platforms. [ABSTRACT FROM AUTHOR]

Published
2015
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5. Intersex Occurrence in Rainbow Trout (Oncorhynchus mykiss) Male Fry Chronically Exposed to Ethynylestradiol.

Author

Depiereux, Sophie, Liagre, Mélanie, Danis, Lorraine, De Meulder, Bertrand, Depiereux, Eric, Segner, Helmut, and Kestemont, Patrick

Subjects
INTERSEXUALITY, RAINBOW trout, ETHINYL estradiol, FISH morphology, GENE expression in fishes, SEXUAL dimorphism, BIOMARKERS, FISHES
Abstract

This study aimed to investigate the male-to-female morphological and physiological transdifferentiation process in rainbow trout (Oncorhynchus mykiss) exposed to exogenous estrogens. The first objective was to elucidate whether trout develop intersex gonads under exposure to low levels of estrogen. To this end, the gonads of an all-male population of fry exposed chronically (from 60 to 136 days post fertilization – dpf) to several doses (from environmentally relevant 0.01 µg/L to supra-environmental levels: 0.1, 1 and 10 µg/L) of the potent synthetic estrogen ethynylestradiol (EE2) were examined histologically. The morphological evaluations were underpinned by the analysis of gonad steroid (testosterone, estradiol and 11-ketotestosterone) levels and of brain and gonad gene expression, including estrogen-responsive genes and genes involved in sex differentiation in (gonads: cyp19a1a, ER isoforms, vtg, dmrt1, sox9a2; sdY; cyp11b; brain: cyp19a1b, ER isoforms). Intersex gonads were observed from the first concentration used (0.01 µg EE2/L) and sexual inversion could be detected from 0.1 µg EE2/L. This was accompanied by a linear decrease in 11-KT levels, whereas no effect on E2 and T levels was observed. Q-PCR results from the gonads showed downregulation of testicular markers (dmrt1, sox9a2; sdY; cyp11b) with increasing EE2 exposure concentrations, and upregulation of the female vtg gene. No evidence was found for a direct involvement of aromatase in the sex conversion process. The results from this study provide evidence that gonads of male trout respond to estrogen exposure by intersex formation and, with increasing concentration, by morphological and physiological conversion to phenotypic ovaries. However, supra-environmental estrogen concentrations are needed to induce these changes. [ABSTRACT FROM AUTHOR]

Published
2014
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6. A computational framework for complex disease stratification from multiple large-scale datasets

Author

De Meulder, Bertrand, Lefaudeux, Diane, Bansal, Aruna T, Mazein, Alexander, Chaiboonchoe, Amphun, Ahmed, Hassan, Balaur, Irina, Saqi, Mansoor, Pellet, Johann, Ballereau, Stéphane, Lemonnier, Nathanaël, Sun, Kai, Pandis, Ioannis, Yang, Xian, Batuwitage, Manohara, Kretsos, Kosmas, Van Eyll, Jonathan, Bedding, Alun, Davison, Timothy, Dodson, Paul, Larminie, Christopher, Postle, Anthony, Corfield, Julie, Djukanovic, Ratko, Chung, Kian Fan, Adcock, Ian M, Guo, Yi-Ke, Sterk, Peter J, Manta, Alexander, Rowe, Anthony, Baribaud, Frédéric, Auffray, Charles, and U-BIOPRED Study Group And The ETRIKS Consortium

Subjects
Machine Learning, Quality Control, ‘Omics data, Systems Biology, Systems medicine, Cluster Analysis, Disease, False Positive Reactions, Stratification, Molecular signatures, Biomarkers, 3. Good health
Abstract

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.

7. Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms.

Author

Mazein, Alexander, Ostaszewski, Marek, Kuperstein, Inna, Watterson, Steven, Le Novère, Nicolas, Lefaudeux, Diane, De Meulder, Bertrand, Pellet, Johann, Balaur, Irina, Saqi, Mansoor, Nogueira, Maria Manuela, He, Feng, Parton, Andrew, Lemonnier, Nathanaël, Gawron, Piotr, Gebel, Stephan, Hainaut, Pierre, Ollert, Markus, Dogrusoz, Ugur, and Barillot, Emmanuel

Subjects
SYSTEMS biology, MEDICAL research, PHENOTYPES, TRANSLATIONAL research, BIOMARKERS
Abstract

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes. [ABSTRACT FROM AUTHOR]

Published
2018
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8. A computational framework for complex disease stratification from multiple large-scale datasets

Author

De Meulder, B., Lefaudeux, D., Bansal, A. T., Mazein, A., Chaiboonchoe, A., Ahmed, H., Balaur, I., Saqi, M., Pellet, J., Ballereau, S., Lemonnier, N., Sun, K., Pandis, I., Yang, X., Batuwitage, M., Kretsos, K., van Eyll, J., Bedding, A., Davison, T., Dodson, P., Larminie, C., Postle, A., Corfield, J., Djukanovic, R., Chung, K. F., Adcock, I. M., Guo, Y. -K., Sterk, P. J., Manta, A., Rowe, A., Baribaud, F., Auffray, C., Gibeon, D., Hoda, U., Kuo, S., Meah, S., Meiser, A., Fleming, L. J., Hu, S., Pavlidis, S., Rossios, C., Russel, K., Wiegman, C., Nezhad, A. T., Oehmichen, A., O'Malley, D., Guitton, F., Emam, I., Agapow, P., Rice, P., Miles, S., Elyasigomari, V., Bel, E., Brinkman, P., Dekker, T., Dijkhuis, A., Hashimoto, S., Hekking, P. -P., Lone-Latif, S., Lutter, R., Ravanetti, L., Smids, B., van Aalderen, W., van de Pol, M., van Drunen, K., van Drunen, M., Wagener, A., Zwinderman, K., Adriaens, N., Carusi, A. M., Richard, F., Nogueira, M. M., Taibi, N., Brasier, O., Aliprantis, A., Alving, K., Faulenbach, C., Braun, A., Hohlfeld, J., Krug, N., Badorrek, P., Bakke, P., Berglind, A., Chaleckis, R., Dahlen, B., Delin, I., Gallart, H., Gomez, C., Hedlin, G., Henriksson, E., James, A. J., Kolmert, J., Konradsen, J., Kupczyk, M., Lantz, A. -S., Lazarinis, L., Mathon, C., Middelveld, R., Naz, S., Nordlund, B., Petren, A., Reinke, S., Sjodin, M., Soderman, P., Strandberg, K., Wheelock, C. E., Zetterquist, W., Balgoma, D., Brandsma, J., Burg, D., Dennison, P., Nicholas, B., Schofield, J. P. R., Skipp, P. J., Staykova, D., Tariq, K., Ward, J., Wilson, S. J., Barber, C., Loza, M. J., Bautmans, A., Sandstrom, T., Behndig, A. F., De Alba, J., Beleta, J., Berton, A., de Verdier, M. G., Nihlen, U., Ostling, J., Dalentoft, T., Lindgren, E., Boedigheimer, M. J., Hu, R., Hu, X., Yu, W., Bigler, J., Bonnelykke, K., Thorsen, J., Vising, N., Bisgaard, H., Bochenek, G., Caruso, M., Emma, R., Campagna, D., Thornton, B., Carayannopoulos, L., Gent, J., Manzies-Gow, A., Sogbesan, A., da Purificacao Rocha, P. C., Pedro, J., Chanez, P., Edwards, J., Flood, B., Hudson, V., Kennington, E. J., Metcalf, L., Rahman-Amin, M., Reynolds, L., Roberts, A., Smith, J., Supple, D., Versnel, J., Walker, S., Coleman, C., Hasan, S., Compton, C., Myles, D., Riley, J., Sousa, A. R., Yeyasingham, E., Pennazza, G., Santoninco, M., D'Amico, A., Dahlen, S. -E., de Boer, P., Robberechts, M., De Lepeleire, I., Fitch, N., Garret, T., Wagers, S., Draper, A., Thorngren, J. -O., Ericsson, M., Erpenbeck, V., Kluglich, M., Nething, K., Riemann, K., Schoelch, C., Seibold, W., Sigmund, R., Wald, F., Wetzel, K., Fichtner, K., Erzen, D., Galffy, G., Horvath, I., Szentkereszty, M., Tamasi, L., Fowler, S. J., Krueger, L., Singer, F., Frey, U., Gahlemann, M., Geiser, T., Hewitt, L., Howarth, P., Marouzet, L., Martin, J., Pink, S., Ray, E., Roberts, G., Smith, C., Gove, K., Gozzard, N., Williams, S., Haughney, J., Higgenbottam, T., Matthews, J. G., Holweg, C., Rutgers, M., Kamphuis, J., Kerry, D., Vink, A., Knobel, H., Knowles, R., Shaw, D. E., Smith, K. M., Know, A., Kots, M., Lambrecht, B., Masefield, S., Nilsson, P., Mikus, M., Miralpeix, M., Monk, P., Mores, N., Valente, S., Montuschi, P., Murray, C. S., Musial, J., Pacino, A., Pahus, L., Palkonen, S., Powel, P., Rao, N., Santini, G., Vestbo, J., von Garnier, C., Weiszhart, Z., Woodcock, A., Biryukov, M., Schneider, R., Herzinger, S., Satagopam, V., Gu, W., da Silva, A. B., Tielmann, A., Bergeron, J., Gaudette, A., Silberberg, A., Henderson, D., Hayat, S., Elefsinioti, A., Moltzen, E. K., Harbo, I. S., Birgitte, J., Bratfalean, D., Houston, P., Kisler, B., Capdevila, F. B., Verbeeck, D., Marchetti, G., Rahal, G., Schuermann, H. D., Mazuranok, L., Hendlich, M., Painell'S, L., Marren, D., Martasek, J., Rimell, J., Romacker, M., Braxenthaler, M., Sansone, S. -A., Rocca-Serra, P., Commission of the European Communities, Pulmonology, Graduate School, Experimental Immunology, Paediatric Pulmonology, Ear, Nose and Throat, Epidemiology and Data Science, APH - Methodology, ARD - Amsterdam Reproduction and Development, Consortium, U-Biopred Study Group And The Etriks, Rocca-Serra, P, Sansone, S, De Meulder, Bertrand [0000-0002-2108-7657], and Apollo - University of Cambridge Repository

Subjects
Quality Control, 0301 basic medicine, Computer science, Bioinformatics, Systems biology, Big data, Environmental data, Machine Learning, Set (abstract data type), 03 medical and health sciences, Structural Biology, Modelling and Simulation, Cluster Analysis, U-BIOPRED Study Group and the eTRIKS Consortium, Disease, False Positive Reactions, Cluster analysis, Molecular signatures, Molecular Biology, lcsh:QH301-705.5, ‘Omics data, 'Omics data, business.industry, Systems Biology, Applied Mathematics, 1199 Other Medical And Health Sciences, Data science, 3. Good health, Computer Science Applications, Systems medicine, 030104 developmental biology, lcsh:Biology (General), Feature (computer vision), Modeling and Simulation, Stratification, Scale (map), business, Biomarkers, Research Article
Abstract

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.

Published
2018
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