Understanding uncontrolled severe allergic asthma by integration of omic and clinical data.
allergy
asthma
machine learning
metabolomics
proteomics
Journal
Allergy
ISSN: 1398-9995
Titre abrégé: Allergy
Pays: Denmark
ID NLM: 7804028
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
revised:
04
10
2021
received:
28
06
2021
accepted:
02
11
2021
pubmed:
29
11
2021
medline:
7
6
2022
entrez:
28
11
2021
Statut:
ppublish
Résumé
Asthma is a complex, multifactorial disease often linked with sensitization to house dust mites (HDM). There is a subset of patients that does not respond to available treatments, who present a higher number of exacerbations and a worse quality of life. To understand the mechanisms of poor asthma control and disease severity, we aim to elucidate the metabolic and immunologic routes underlying this specific phenotype and the associated clinical features. Eighty-seven patients with a clinical history of asthma were recruited and stratified in 4 groups according to their response to treatment: corticosteroid-controlled (ICS), immunotherapy-controlled (IT), biologicals-controlled (BIO) or uncontrolled (UC). Serum samples were analysed by metabolomics and proteomics; and classifiers were built using machine-learning algorithms. Metabolomic analysis showed that ICS and UC groups cluster separately from one another and display the highest number of significantly different metabolites among all comparisons. Metabolite identification and pathway enrichment analysis highlighted increased levels of lysophospholipids related to inflammatory pathways in the UC patients. Likewise, 8 proteins were either upregulated (CCL13, ARG1, IL15 and TNFRSF12A) or downregulated (sCD4, CCL19 and IFNγ) in UC patients compared to ICS, suggesting a significant activation of T cells in these patients. Finally, the machine-learning model built including metabolomic and clinical data was able to classify the patients with an 87.5% accuracy. UC patients display a unique fingerprint characterized by inflammatory-related metabolites and proteins, suggesting a pro-inflammatory environment. Moreover, the integration of clinical and experimental data led to a deeper understanding of the mechanisms underlying UC phenotype.
Sections du résumé
BACKGROUND
Asthma is a complex, multifactorial disease often linked with sensitization to house dust mites (HDM). There is a subset of patients that does not respond to available treatments, who present a higher number of exacerbations and a worse quality of life. To understand the mechanisms of poor asthma control and disease severity, we aim to elucidate the metabolic and immunologic routes underlying this specific phenotype and the associated clinical features.
METHODS
Eighty-seven patients with a clinical history of asthma were recruited and stratified in 4 groups according to their response to treatment: corticosteroid-controlled (ICS), immunotherapy-controlled (IT), biologicals-controlled (BIO) or uncontrolled (UC). Serum samples were analysed by metabolomics and proteomics; and classifiers were built using machine-learning algorithms.
RESULTS
Metabolomic analysis showed that ICS and UC groups cluster separately from one another and display the highest number of significantly different metabolites among all comparisons. Metabolite identification and pathway enrichment analysis highlighted increased levels of lysophospholipids related to inflammatory pathways in the UC patients. Likewise, 8 proteins were either upregulated (CCL13, ARG1, IL15 and TNFRSF12A) or downregulated (sCD4, CCL19 and IFNγ) in UC patients compared to ICS, suggesting a significant activation of T cells in these patients. Finally, the machine-learning model built including metabolomic and clinical data was able to classify the patients with an 87.5% accuracy.
CONCLUSIONS
UC patients display a unique fingerprint characterized by inflammatory-related metabolites and proteins, suggesting a pro-inflammatory environment. Moreover, the integration of clinical and experimental data led to a deeper understanding of the mechanisms underlying UC phenotype.
Substances chimiques
Antigens, Dermatophagoides
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1772-1785Informations de copyright
© 2021 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.
Références
Global Initiative for Asthma. Global strategy for asthma management and prevention. 2020. www.ginasthma.org
Campo P, Rodríguez F, Sánchez-García S, et al. Phenotypes and endotypes of uncontrolled severe asthma: new treatments. J Investig Allergol Clin Immunol. 2013;23:76-88. quiz 1 p. follow 88.
Agache I, Akdis C, Jutel M, Virchow JC. Untangling asthma phenotypes and endotypes. Allergy. 2012;67:835-846.
Kennedy JL, Heymann PW, Platts-Mills TAE. The role of allergy in severe asthma. Clin Exp Allergy. 2012;42:659-669.
Sánchez-Borges M, Fernandez-Caldas E, Thomas WR, et al. International consensus (ICON) on: clinical consequences of mite hypersensitivity, a global problem. World Allergy Organ J. 2017;10:14.
Julià Serdà G, Cabrera Navarro P, Acosta Fernández O, et al. High prevalence of asthma symptoms in the Canary Islands: climatic influence? J Asthma. 2005;42:507-511.
Juliá-Serdá G, Cabrera-Navarro P, Acosta-Fernández O, et al. High prevalence of asthma and atopy in the Canary Islands, Spain. Int J Tuberc Lung Dis. 2011;15:536-541.
Barber D, Arias J, Boquete M, et al. Analysis of mite allergic patients in a diverse territory by improved diagnostic tools. Clin Exp Allergy. 2012;42:1129-1138.
Plaza MV. GEMA4.0. Guía española para el manejo del asma. Arch Bronconeumol. 2015;51:2-54.
Peters SP, Ferguson G, Deniz Y, Reisner C. Uncontrolled asthma: a review of the prevalence, disease burden and options for treatment. Respir Med. 2006;100:1139-1151.
Suárez-Lorenzo I, Cruz-Niesvaara D, Rodríguez-Gallego C, Rodríguez de Castro F, Carrillo-Diaz T. Epidemiological study of the allergic population in the north of Gran Canaria. J Investig Allergol Clin Immunol. 2018;28:212-215.
European Respiratory Society. European Lung White Book. 2003;16-25.
Rodríguez-Coira J, Delgado-Dolset M, Obeso D, et al. Troubleshooting in large-scale LC-ToF-MS metabolomics analysis: solving complex issues in big cohorts. Metabolites. 2019;9:247.
Gil-de-la-Fuente A, Godzien J, Saugar S, et al. CEU mass mediator 3.0: a metabolite annotation tool. J Proteome Res. 2019;18:797-802.
Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: a review. Biotechnol Adv. 2021;49:107739.
Triba MN, Le Moyec L, Amathieu R, et al. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol Biosyst. 2015;11:13-19.
Chen M, Zhou K, Chen X, et al. Metabolomic analysis reveals metabolic changes caused by bisphenol a in rats. Toxicol Sci. 2014;138:256-267.
Fiehn O, Garvey WT, Newman JW, Lok KH, Hoppel CL, Adams SH. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One. 2010;5:e15234.
Putluri N, Shojaie A, Vasu VT, et al. Metabolomic profiling reveals potential markers and bioprocesses altered in bladder cancer progression. Cancer Res. 2011;71:7376-7386.
Hajiloo M, Damavandi B, HooshSadat M, et al. Breast cancer prediction using genome wide single nucleotide polymorphism data. BMC Bioinformatics. 2013;14:S3.
Juliá-Serdá G, Cabrera-Navarro P, Acosta-Fernández O, Martín-Pérez P, García-Bello MA, Antó-Boqué J. Prevalence of sensitization to Blomia tropicalis among young adults in a temperate climate. J Asthma. 2012;49:349-354.
Villaseñor A, Rosace D, Obeso D, et al. Allergic asthma: an overview of metabolomic strategies leading to the identification of biomarkers in the field. Clin Exp Allergy. 2017;47:442-456.
Crestani E, Harb H, Charbonnier L-M, et al. Untargeted metabolomic profiling identifies disease-specific signatures in food allergy and asthma. J Allergy Clin Immunol. 2020;145:897-906.
Delgado-Dolset MI, Obeso D, Sánchez-Solares J, et al. Understanding systemic and local inflammation induced by nasal polyposis: role of the allergic phenotype. Front Mol Biosci. 2021;8:409.
Vinaixa M, Samino S, Saez I, Duran J, Guinovart JJ, Yanes O. A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Metabolites. 2012;2:775-795.
Yoder M, Zhuge Y, Yuan Y, et al. Bioactive lysophosphatidylcholine 16:0 and 18:0 are elevated in lungs of asthmatic subjects. Allergy Asthma Immunol Res. 2014;6:61.
Pang Z, Wang G, Wang C, Zhang W, Liu J, Wang F. Serum metabolomics analysis of asthma in different inflammatory phenotypes: a cross-sectional study in Northeast China. Biomed Res Int. 2018;2018:1-14.
Knuplez E, Curcic S, Theiler A, et al. Lysophosphatidylcholines inhibit human eosinophil activation and suppress eosinophil migration in vivo. Biochim Biophys Acta - Mol Cell Biol Lipids. 2020;1865:158686.
Zhu X, Learoyd J, Butt S, et al. Regulation of eosinophil adhesion by lysophosphatidylcholine via a non-store-operated Ca 2+ channel. Am J Respir Cell Mol Biol. 2007;36:585-593.
Kariyawasam HH, Robinson DS. The role of eosinophils in airway tissue remodelling in asthma. Curr Opin Immunol. 2007;19:681-686.
Oestvang J, Anthonsen MW, Johansen B. LysoPC and PAF trigger arachidonic acid release by divergent signaling mechanisms in monocytes. J Lipids. 2011;2011:1-11.
McGeachie MJ, Dahlin A, Qiu W, et al. The metabolomics of asthma control: a promising link between genetics and disease. Immunity, Inflamm Dis. 2015;3:224-238.
Nie X, Wei J, Hao Y, et al. Consistent biomarkers and related pathogenesis underlying asthma revealed by systems biology approach. Int J Mol Sci. 2019;20:4037.
Sokolowska M, Chen L-Y, Liu Y, et al. Dysregulation of lipidomic profile and antiviral immunity in response to hyaluronan in patients with severe asthma. J Allergy Clin Immunol. 2017;139:1379-1383.
Miyata J, Arita M. Role of omega-3 fatty acids and their metabolites in asthma and allergic diseases. Allergol Int. 2015;64:27-34.
Sokolowska M, Rovati GE, Diamant Z, et al. Current perspective on eicosanoids in asthma and allergic diseases - EAACI Task Force consensus report, part I. Allergy. 2021;76(1):114-130. 10.1111/all.14295
Petrache I, Berdyshev EV. Ceramide signaling and metabolism in pathophysiological states of the lung. Annu Rev Physiol. 2016;78:463-480.
Reinke SN, Gallart-Ayala H, Gómez C, et al. Metabolomics analysis identifies different metabotypes of asthma severity. Eur Respir J. 2017;49:1601740.
Obeso D, Mera-Berriatua L, Rodríguez-Coira J, et al. Multi-omics analysis points to altered platelet functions in severe food-associated respiratory allergy. Allergy. 2018;73:2137-2149.
Mendelson K, Evans T, Hla T. Sphingosine 1-phosphate signalling. Development. 2014;141:5-9.
Kertys M, Grendar M, Kosutova P, Mokra D, Mokry J. Plasma based targeted metabolomic analysis reveals alterations of phosphatidylcholines and oxidative stress markers in guinea pig model of allergic asthma. Biochim Biophys Acta - Mol Basis Dis. 2020;1866:165572.
Comhair SAA, McDunn J, Bennett C, Fettig J, Erzurum SC, Kalhan SC. Metabolomic endotype of asthma. J Immunol. 2015;195:643-650.
Jung J, Kim S-H, Lee H-S, et al. Serum metabolomics reveals pathways and biomarkers associated with asthma pathogenesis. Clin Exp Allergy. 2013;43:425-433.
Farraia M, Cavaleiro Rufo J, Paciência I, et al. Metabolic interactions in asthma. Eur Ann Allergy Clin Immunol. 2019;51:196.
Ananieva EA, Powell JD, Hutson SM. Leucine metabolism in T cell activation: mTOR signaling and beyond. Adv Nutr Int Rev J. 2016;7:798S-805S.
Garcia-Zepeda EA, Combadiere C, Rothenberg ME, et al. Human monocyte chemoattractant protein (MCP)-4 is a novel CC chemokine with activities on monocytes, eosinophils, and basophils induced in allergic and nonallergic inflammation that signals through the CC chemokine receptors (CCR)-2 and -3. J Immunol. 1996;157:5613-5626.
Li CW, Zhang KK, Li TY, et al. Expression profiles of regulatory and helper T-cell-associated genes in nasal polyposis. Allergy. 2012;67:732-740.
Morris SR, Chen B, Mudd JC, et al. ‘Inflammescent’ CX3CR1+CD57+ CD8 T cells are generated and expanded by IL-15. JCI Insight. 2020;5. 10.1172/jci.insight.132963
Grabstein K, Eisenman J, Shanebeck K, et al. Cloning of a T cell growth factor that interacts with the beta chain of the interleukin-2 receptor. Science. 1994;264:965-968.
He H, Suryawanshi H, Morozov P, et al. Single-cell transcriptome analysis of human skin identifies novel fibroblast subpopulation and enrichment of immune subsets in atopic dermatitis. J Allergy Clin Immunol. 2020;145:1615-1628.
Kaiser A, Donnadieu E, Abastado J-P, Trautmann A, Nardin A. CC chemokine ligand 19 secreted by mature dendritic cells increases naive T cell scanning behavior and their response to rare cognate antigen. J Immunol. 2005;175:2349-2356.
Takamura K, Fukuyama S, Nagatake T, et al. Regulatory role of lymphoid chemokine CCL19 and CCL21 in the control of allergic rhinitis. J Immunol. 2007;179:5897-5906.
Mendez-Barbero N, Yuste-Montalvo A, Nuñez-Borque E, et al. The TNF-like weak inducer of the apoptosis/fibroblast growth factor-inducible molecule 14 axis mediates histamine and platelet-activating factor-induced subcutaneous vascular leakage and anaphylactic shock. J Allergy Clin Immunol. 2020;145:583-596.e6.
Suwanpradid J, Shih M, Pontius L, et al. Arginase1 deficiency in monocytes/macrophages upregulates inducible nitric oxide synthase to promote cutaneous contact hypersensitivity. J Immunol. 2017;199:1827-1834.
Oberst A, Dillon CP, Weinlich R, et al. Catalytic activity of the caspase-8-FLIP L complex inhibits RIPK3-dependent necrosis. Nature. 2011;471:363-368.
Bell BD, Leverrier S, Weist BM, et al. FADD and caspase-8 control the outcome of autophagic signaling in proliferating T cells. Proc Natl Acad Sci USA. 2008;105:16677-16682.
Venables WN, Ripley BD. Modern Applied Statistics with S-Plus. Springer; 2013.