Toward clinically applicable biomarkers for asthma: An <scp>EAACI</scp> position paper

Diamant, Zuzana; Vijverberg, Susanne J. H.; Alving, Kjell; Bakırtaş, Arzu; Bjermer, Leif; Čustović, Adnan; Dahlén, Sven Erik; Gaga, Mina; Wijk, Roy Gerth van; Giacco, Stefano Del; Hamelmann, Eckard; Heaney, Liam G.; Heffler, Enrico; Kalaycı, Ömer; Κostikas, Κonstantinos; Lutter, René; Olin, Anna Carin; Sergejeva, Svetlana; Simpson, Angela; Sterk, Peter J.; Tufvesson, Ellen; Agache, Ioana; Seys, Sven

doi:10.1111/all.13806

Cited by 156 publications

(176 citation statements)

References 204 publications

(258 reference statements)

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“…For example, CHD4 is cited as a key transcription factor that controls Th2 inflammation in asthma 37 .Because it seems that the CHD4 complex is a regulator of Th2 inflammation, a known biomarker of asthma, the sparse topology in Figure 11 as compared to Figure 10 seems to confirm that a dysregulation of this nature as also a biomarker of asthma. In this way, the CHD4 complex may serve as a key drug target for the control of inflammation, which is known to be applicable to medicine as asthma control 38,39 .…”

Section: Discussionmentioning

confidence: 99%

Novel meta-analysis pipeline of heterogeneous high-throughput gene expression datasets reveals dysregulated interactions and pathways in asthma

Guo

Kaushik

Nadeau

2019

Preprint

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Introduction: Asthma is a complex and chronic inflammatory disorder with varying degrees of airway inflammation. It affects ~235 million people worldwide, and about 8% of the United States population. Unlike single-gene disorders, asthma phenotypes are guided by a highly variable combination of genotypes, making it a complex disease to study computationally. Recently, several independent high-throughput gene expression studies in bioinformatics have identified and proposed numerous molecular drivers involved in asthma initiation and progression. However, there is a poor consensus in our understanding of the molecular factors involved in the mechanism of this disease due to inherent genetic heterogeneity. Such an uncertainty in bioinformatics studies have led to a "reproducibility crisis" in the field, where similar analyses can often yield greatly varying results. In this study, we seek to harness heterogeneity in asthma by applying a meta-analysis that explores varying tissue environments. Methods: We use three publicly-available microarray gene expression datasets, belonging to different tissues in asthma patients, from NCBI's Gene Expression Omnibus (GEO). As a meta-analysis, we apply a mixed-model effect size test to determine differentially expressed (DE) genes across all three studies. Then, The datasets are pre-processed and subjected to Weighted Gene Co-expression Network Analysis (WGCNA) for identification of functional modules. Using module preservation, we determine modules in asthma that were not preserved in the healthy condition, then combine the three with a Fisher's test for a set of asthma-unique modules. These modules are explored using functional analysis (i.e. GO term analysis). Using the DE genes as well as known transcription factors, we re-construct Gene Regulatory Networks (GRNs) for each of our shortlisted modules. We then studied the topology of these GRNs using hive plots to reveal underlying dysregulations, paving the way for future analyses. Results: Our analysis reveals a novel perspective to a key interaction in asthma inflammatory regulation, the CHD4-CCL26 transcription relation. Our hive plot analysis is able to explore this gene interaction beyond the typical "over-expression, under-expression" results from typical bioinformatics studies. We reveal that CCL26, an important regulator of asthma, appears to increase in expression and topological degree in asthma, but loses connection to CHD4, which seems to be characteristic to the asthma disease. Such analysis suggests that the topology of gene networks, above simply expression values, may be key to understanding the nuanced interactions between fundamental biomarkers and drug targets in complex diseases like asthma.

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Section: Discussionmentioning

confidence: 99%

Novel meta-analysis pipeline of heterogeneous high-throughput gene expression datasets reveals dysregulated interactions and pathways in asthma

Guo

Kaushik

Nadeau

2019

Preprint

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“…In daily practice, physicians decide on currently available (type 2) targeted treatment options based on (non) invasive clinical and laboratory parameters, indicative of distinct asthma phenotypes or endotypes (Figure 1). Therapeutic response can thus be predicted by various biomarkers, either alone or in combination . Presently, the most frequently studied and commonly used biomarkers include eosinophils (from peripheral blood or sometimes from induced sputum), serum total IgE and fractional exhaled nitric oxide (FENO) levels.…”

Section: Figurementioning

confidence: 99%

Blood eosinophils: In quest of a Holy Grail for personalized asthma treatment with biologicals

Jeseňák

Diamant

2020

Allergy

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“…37 The seventh domain concerns in vivo clinical biomarkers including exhaled breath analysis and allergen provocation studies that are beyond the scope of this review but reviewed elsewhere. [38][39][40][41][42] Assays in all six ex vivo biomarker domains show promise on the basis of association with clinical improvement, although there are limitations in their predictive nature, specificity and sensitivity.…”

Section: And Direct B Cells To Inducementioning

confidence: 99%

Recent developments and highlights in immune monitoring of allergen immunotherapy

Zelm

McKenzie

Varese

et al. 2019

Allergy

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Allergic diseases are the most common chronic immune-mediated disorders and can manifest with an enormous diversity in clinical severity and symptoms. Underlying mechanisms for the adverse immune response to allergens and its downregulation by treatment are still being revealed. As a result, there have been, and still are, major challenges in diagnosis, prediction of disease progression/evolution and treatment.Currently, the only corrective treatment available is allergen immunotherapy (AIT).AIT modifies the immune response through long-term repeated exposure to defined doses of allergen. However, as the treatment usually needs to be continued for several years to be effective, and can be accompanied by adverse reactions, many patients face difficulties completing their schedule. Long-term therapy also potentially incurs high costs. Therefore, there is a great need for objective markers to predict or to monitor individual patient's beneficial changes in immune response during therapy so that efficacy can be identified as early as possible. In this review, we specifically address recent technical developments that have generated new insights into allergic disease pathogenesis, and how these could potentially be translated into routine laboratory assays for disease monitoring during AIT that are relatively inexpensive, robust and scalable. K E Y W O R D Sallergen immunotherapy, allergy, biomarker, immune monitoring | 2343 ZELM Et aL.

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Toward clinically applicable biomarkers for asthma: An EAACI position paper

Cited by 156 publications

References 204 publications

Novel meta-analysis pipeline of heterogeneous high-throughput gene expression datasets reveals dysregulated interactions and pathways in asthma

Novel meta-analysis pipeline of heterogeneous high-throughput gene expression datasets reveals dysregulated interactions and pathways in asthma

Blood eosinophils: In quest of a Holy Grail for personalized asthma treatment with biologicals

Recent developments and highlights in immune monitoring of allergen immunotherapy

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