How cytokines co-occur across asthma patients: From bipartite network analysis to a molecular-based classification

Bhavnani, Suresh K.; Victor, Sundar; Calhoun, William J.; Busse, William W.; Bleecker, Eugene R.; Castro, Mario; Ju, Huangxian; Pillai, Regina; Oezguen, Numan; Bellala, Gowtham; Brasier, Allan R.

doi:10.1016/j.jbi.2011.09.006

Cited by 32 publications

(31 citation statements)

References 30 publications

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“…This approach is ideal for the representation of bipartite relationships, which is both more powerful and considerably more complex. Bhavnani et al [34] used this representation to conduct a secondary analysis of the SARP cytokine data. This analysis of the SARP data differed from previous attempts to classify asthma patients, as it did not assume an a priori classification of either patients based on phenotypic (severe vs non-severe or hyper-responsive vs normal), or molecular information.…”

Section: Network Analysis Methodsmentioning

confidence: 99%

Strategies for Molecular Classification of Asthma Using Bipartite Network Analysis of Cytokine Expression

Pillai

Divekar

Brasier

et al. 2012

Curr Allergy Asthma Rep

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Asthma is a chronic inflammatory disease of the airways that leads to various degrees of recurrent respiratory symptoms affecting patients globally. Specific subgroups of asthma patients have severe disease leading to increased healthcare costs and socioeconomic burden. Despite the overwhelming prevalence of the asthma, there are limitations in predicting response to therapy and identifying patients who are at increased risk of morbidity. This syndrome presents with common clinical signs and symptoms; however, awareness of subgroups of asthma patients with distinct characteristics has surfaced in recent years. Investigators attempt to describe the phenotypes of asthma to ultimately assist with diagnostic and therapeutic applications. Approaches to asthma phenotyping are multifold; however, it can be partitioned into 2 essential groups, clinical phenotyping and molecular phenotyping. Innovative techniques such as bipartite network analysis and visual analytics introduce a new dimension of data analysis to identify underlying mechanistic pathways.

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Section: Network Analysis Methodsmentioning

confidence: 99%

Strategies for Molecular Classification of Asthma Using Bipartite Network Analysis of Cytokine Expression

Pillai

Divekar

Brasier

et al. 2012

Curr Allergy Asthma Rep

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“…Asthma, and in particular severe asthma, is a heterogeneous disease as highlighted by the different phenotypes identified using cluster analysis of clinical data (7)(8)(9)(10) and cytokine profiles of airway samples (11)(12)(13). The key benefit of dividing a multidimensional disease, such as asthma, into distinct phenotypes is expected to be more effective treatment targeting.…”

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confidence: 99%

Elevated Sputum Interleukin-5 and Submucosal Eosinophilia in Obese Individuals with Severe Asthma

Desai

Newby

Symon

et al. 2013

Am J Respir Crit Care Med

197

138

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Rationale:The relationship between airway inflammation and obesity in severe asthma is poorly understood. Objectives: We sought to determine the relationship between sputum mediator profiles and the distribution of eosinophilic inflammation and obesity in people with severe asthma. Methods: Clinical parameters and eight mediators in sputum were assessed in 131 subjects with severe asthma from a single center categorized into lean, overweight, and obese groups defined by their body mass index. In an independent group of people with severe asthma (n ¼ 45) and healthy control subjects (n ¼ 19) eosinophilic inflammation was enumerated in bronchial submucosa, blood, and sputum and related to their body mass index. Measurements and Main Results: Sputum IL-5 geometric mean (95% confidence interval) (pg/ml) was elevated in the obese (1.8 [1.2-2.6]) compared with overweight (1.1 [0.8-1.3]; P ¼ 0.025) and lean (0.9 [0.6-1.2]; P ¼ 0.018) subjects with asthma and was correlated with body mass index (r ¼ 0.29; P , 0.001). There was no relationship among body mass index, the sputum cell count, or other sputum mediators. In the bronchoscopy group the submucosal eosinophil number in the subjects with asthma was correlated with body mass index (Spearman rank correlation, r s ¼ 0.38; P ¼ 0.013) and the median (interquartile range) number of submucosal eosinophils was increased in obese (19.4 [11.8-31.2]) (cells per square millimeter) versus lean subjects (8.2 [5.4-14.6]) (P ¼ 0.006). There was no significant association between sputum or peripheral blood eosinophil counts and body mass index. Conclusions: Sputum IL-5 and submucosal eosinophils, but not sputum eosinophils, are elevated in obese people with severe asthma. Whether specific antieosinophilic therapy is beneficial, or improved diet and lifestyle in obese asthma has antiinflammatory effects beyond weight reduction, requires further study.Keywords: asthma; obesity; cytokines; phenotypes; eosinophil Asthma is a common, complex inflammatory disorder affecting about 5% of adults in the general population, of which approximately 5-10% suffer from severe disease (1, 2). Severe disease is often associated with comorbidities, such as obesity, and is particularly important because these patients suffer from substantial morbidity and consume a disproportionately high amount of the overall healthcare resources spent on asthma management (3-6). Asthma, and in particular severe asthma, is a heterogeneous disease as highlighted by the different phenotypes identified using cluster analysis of clinical data (7-10) and cytokine profiles of airway samples (11-13). The key benefit of dividing a multidimensional disease, such as asthma, into distinct phenotypes is expected to be more effective treatment targeting. This has been effectively shown with the success of eosinophilic airway inflammationdirected corticosteroid (14-16) and anti-IL-5 treatment (17-19) to prevent asthma exacerbations in eosinophilic subjects with asthma. The association of obesity and asthma has been evident Support...

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“…Bipartite networks use two sets of nodes and the edges can only connect nodes from different sets. In this study [105] data from the measurement of 18 cytokines in bronchoalveolar lavage fluid in 83 asthma patients was visualized using a bipartite network created with the Kamada – Kawai algorithm [106]. This analysis revealed three main clusters of patients that could be distinguished by cytokine pattern and also clinical parameters.…”

Section: Clinical Applications Of Modeling Approachesmentioning

confidence: 99%

Demystifying the cytokine network: Mathematical models point the way

2017

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Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

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How cytokines co-occur across asthma patients: From bipartite network analysis to a molecular-based classification

Cited by 32 publications

References 30 publications

Strategies for Molecular Classification of Asthma Using Bipartite Network Analysis of Cytokine Expression

Strategies for Molecular Classification of Asthma Using Bipartite Network Analysis of Cytokine Expression

Elevated Sputum Interleukin-5 and Submucosal Eosinophilia in Obese Individuals with Severe Asthma

Demystifying the cytokine network: Mathematical models point the way

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