Transcriptome analysis of controlled and therapy-resistant childhood asthma reveals distinct gene expression profiles

Persson, Helena; Kwon, Andrew Tae-Jun; Ramilowski, Jordan A.; Silberberg, Gilad; Söderhäll, Cilla; Orsmark-Pietras, Christina; Nordlund, Björn; Konradsen, Jon R.; Hoon, Michiel de; Melén, Erik; Hayashizaki, Yoshihide; Hedlin, Gunilla; Kere, Juha; Daub, Carsten O.

doi:10.1016/j.jaci.2015.02.026

Cited by 64 publications

(55 citation statements)

References 37 publications

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“…Gene expression profiling captures phenotypes of patients in relation to frequent exacerbations (50) and sensitivity to inhaled steroids (51), thereby discovering novel biological pathways in COPD. Similar findings have recently been published among children with severe asthma when using gene expression profiles of peripheral blood leukocytes (52) and in adults with asthma on the basis of transcriptomics of epithelial cells or laser-dissected airway smooth muscle (53,54). The next step will be to apply similar methodology to broader population samples.…”

Section: The Datasupporting

confidence: 61%

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Thamrin

Frey

Kaminsky

et al. 2016

Am J Respir Crit Care Med

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Respiratory diseases are highly complex, being driven by host-environment interactions and manifested by inflammatory, structural, and functional abnormalities that vary over time. Traditional reductionist approaches have contributed vastly to our knowledge of biological systems in health and disease to date; however, they are insufficient to provide an understanding of the behavior of the system as a whole. In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited to the study of the lung as a complex system. Such integrative approaches take into account the large number of dynamic subunits and their interactions found in biological systems. Borrowing methods from physics and mathematics, it is possible to study the collective behavior of these systems over time and in a multidimensional manner. We first examine the physiological basis for complexity in the respiratory system and its implications for disease. We then expand on the potential applications of systems biology methods to study complex systems, within the context of diagnosis and monitoring of respiratory diseases including asthma, chronic obstructive pulmonary disease (COPD), and critical illness. We summarize the significant advances made in recent years using systems approaches for disease phenotyping, applied to data ranging from the molecular to clinical level, obtained from large-scale asthma and COPD networks. We describe new studies using temporal complexity patterns to characterize asthma and COPD and predict exacerbations as well as predict adverse outcomes in critical care. We highlight new methods that are emerging with this approach and discuss remaining questions that merit greater attention in the field.

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Section: The Datasupporting

confidence: 61%

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Thamrin

Frey

Kaminsky

et al. 2016

Am J Respir Crit Care Med

View full text Add to dashboard Cite

show abstract

“…Given that cellular and transcriptional responses during acute asthma exacerbation are highly complex (6), the gene pathways perturbed may be quite different from those that destabilize asthma control and trigger exacerbation in the days preceding an acute crisis. Although detection of these earlier alterations may be more informative for the development of preventative controller strategies (7), only one small study of differential gene expression and asthma control has been conducted in circulating leukocytes from children in a broadly uncontrolled severe or controlled persistent disease state (8). Although these profiles related to various kinase signaling pathways are biologically relevant, their modest sample sizes inhibit their broad generalization.…”

Section: Discussionmentioning

confidence: 99%

“…Using clinical phenotype data available in Asthma BRIDGE and in CAMP, we developed two composite scores summarizing self-reported asthma control in the preceding 6 months (chronic, [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] and 7 days (acute, 0-28), respectively, each modeled on the ACT questionnaire (11), where higher scores indicate worse asthma control. Based on the medians of the corresponding phenotypic distributions, optimal chronic asthma control was defined as a score less than or equal to six in BRIDGE WB and moderately suboptimal asthma control was defined as a score less than or equal to 11 in BRIDGE CD4 ( Figure 2).…”

Section: Methodsmentioning

confidence: 99%

Gene Expression Profiling in Blood Provides Reproducible Molecular Insights into Asthma Control

Croteau‐Chonka¹,

Qiu²,

Martínez

et al. 2017

Am J Respir Crit Care Med

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Rationale: Maintaining optimal symptom control remains the primary objective of asthma treatment. Better understanding of the biologic underpinnings of asthma control may lead to the development of improved clinical and pharmaceutical approaches.Objectives: To identify molecular pathways and interrelated genes whose differential expression was associated with asthma control.Methods: We performed gene set enrichment analyses of asthma control in 1,170 adults with asthma, each with gene expression data derived from either whole blood (WB) or unstimulated CD4 1 T lymphocytes (CD4), and a self-reported asthma control score representing either the preceding 6 months (chronic) or 7 days (acute). Our study comprised a discovery WB cohort (n = 245, chronic) and three independent, nonoverlapping replication cohorts: a second WB set (n = 448, acute) and two CD4 sets (n = 300, chronic; n = 77, acute). Measurements and Main Results:In the WB discovery cohort, we found significant overrepresentation of genes associated with asthma control in 1,106 gene sets from the Molecular Signatures Database (false discovery rate, ,5%). Of these, 583 (53%) replicated in at least one replication cohort (false discovery rate, ,25%). Suboptimal control was associated with signatures of eosinophilic and granulocytic inflammatory signals, whereas optimal control signatures were enriched for immature lymphocytic patterns. These signatures included two related biologic processes related to activation by TREM-1 (triggering receptor expressed on myeloid cells 1) and lipopolysaccharide. Conclusions:Together, these results demonstrate the existence of specific, reproducible transcriptomic components in blood that vary with degree of asthma control and implicate a novel biologic target (TREM-1).

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“…Children with severe asthma have poor disease control despite high doses of inhaled corticosteroids and additional therapy. This leads to poor quality of life, early deterioration of lung function, and significant consumption of health care resources (Persson et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

A network approach predicts NFKBIA and BIRC3 as pathogenic genes in childhood asthma

Gao

2016

Genet. Mol. Res.

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ABSTRACT. This study aimed to predict pathogenic genes of childhood asthma based on molecular interaction networks and gene expression data. Known pathogenic genes identified from the human protein-protein interaction network were denoted as seed genes, and were included in network A. We extracted sub-network B (pathogenic network), which consisted of genes that interacted with at least two seed genes. We assigned a weight to select the pathogenic genes from this network according to its interactions and co-expressions with the seed genes. We also conducted ClusterONE analysis for the pathogenic network, and determined the statistical significance of the predicted clusters through a significance score (SS). Lastly, we investigated the biological pathways of the seed and candidate genes based on information obtained from the KEGG database. In network A, we identified 172 interactions and 125 genes that interacted with seed genes. In the pathogenic network, we found 51 genes and 102 interactions. The top 10 candidate genes with high weight scores were recorded. The SS of the predicted clusters demonstrated that 3 gene clusters were statistically relevant for childhood asthma. Pathway analysis showed that the seed genes and the top 10 candidate genes were significantly enriched in the same three biological processes. NFKBIA and BIRC3, which were involved in all three biological processes, may therefore be pathogenic genes. Using the network approach, we predicted that the pathogenic genes NFKBIA and BIRC3 are associated with childhood asthma. This information can provide guidelines for future experimental verification of childhood asthma.

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Transcriptome analysis of controlled and therapy-resistant childhood asthma reveals distinct gene expression profiles

Abstract: Circulating leukocytes from children with controlled asthma and those with SA have distinct gene expression profiles, demonstrating the possible development of specific molecular biomarkers and supporting the need for novel therapeutic approaches.

Cited by 64 publications

References 37 publications

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet

Gene Expression Profiling in Blood Provides Reproducible Molecular Insights into Asthma Control

A network approach predicts NFKBIA and BIRC3 as pathogenic genes in childhood asthma

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