Systems Biology Findings and Applications in Asthma: History
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Contributor:
Mahmood Yaseen Hachim
,
Fatma Alqutami
,
Ibrahim Yaseen Hachim
,
Saba Al Heialy
,
Hauke Busch
,
Rifat Hamoudi
,
Qutayba Hamid

Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning.

  • asthma
  • systems biology
  • multi-omics

1. Asthma Classification, including Phenotyping and Genotyping

In asthma, several sub-phenotypes of asthma have been implemented, with the most common phenotype being allergic asthma and patients showing exaggerated responses to nonharmful agents [1]. A newer approach to classifying asthma is cellular phenotyping, where the type and quantity of inflammatory cells in the airway are used to guide the selection of ideal biological treatments [2]. Asthma can be broadly classified as eosinophilic or non-eosinophilic (NEA) based on airway or peripheral blood cellular profiles [3]. For example, differentiating between non-eosinophilic and eosinophilic asthma is essential in terms of using corticosteroids, the mainstay therapy in asthma [4] as NEA is generally poorly responsive to corticosteroid treatment, which could potentially worsen the disease [3].
Molecular phenotyping involves quantitating proteins, posttranslational modifications, metabolites, and nucleic acids using high-throughput analytics [4]. Among the various omics, the bronchial epithelium transcriptomics-driven phenotyping of asthmatic patients revealed the potential to discover gene expression profiles characteristic of asthma [5]. Furthermore, bronchial epithelium transcriptomics can identify different molecular mechanisms underlying divergent asthmatic phenotypes and have the power to identify novel clinically efficient biomarkers [6][7]. An example would be using transcriptomic data to determine the differentially expressed gene JMJD2B/KDM4B in asthmatic airway fibroblasts, expressed upon IL-13 exposure [8].
Recently, a combined approach that includes genomic, molecular biology, and a comprehensive phenotyping approach called phenomics was introduced to identify disease subtypes, including asthma [9]. Such an approach might be essential to improve our characterization of severe asthma heterogeneity by implementing evidence-based criteria. Indeed, this might help in the adoption of more individualized therapeutic options for asthmatic patients, including patients with the severe form [9]. Moreover, the extraction of information from the electronic health record (EHR) systems, including clinical history, risk factors, and history of exposure to various materials and allergens and combining them with information obtained from biospecimen collection that involves genetic variants led to the development of new types of studies called phenome-wide association studies (PheWASs) [10]. In addition to its role in exploring novel asthma-related genetic variants and risk loci, it is also essential to identify risk factors and comorbidities [10]. In addition, PheWAS was also found to play a role in identifying new therapeutic candidates and predicting adverse drug effects. For example, one report highlighted asthma as a possible adverse event when using PNPLA3 inhibitors to manage liver diseases [11].

2. Systems Biology Enables the Identification of Biomarkers

Identifying novel markers that can help in a patient’s classification and clinical outcome prediction and response to therapy was and will remain the ultimate goal for many research projects [12][13]. These markers should have precise specifications and criteria such as reliability, ease of collection, and measurement—and not be invasive. For example, using reliable biomarkers to predict asthma patients’ response to steroid therapy early in the disease is particularly important and necessary to achieve optimal response and avoid undesirable side effects [14].
Testing biomarkers is not routinely requested for severe asthma patients, and the most extensively studied biomarkers are those that are related to the T2 phenotype [15][16]. For biomarkers to be ideal, they should be reliably quantifiable, easy to obtain, cost-effective, and can be produced in different clinical settings [17].
PBMCs containing the significant sources of allergic response mediators in asthma can serve as an excellent alternative to the costly and challenging method of obtaining airway samples in severe asthma [18]. Furthermore, the integrative phenotype–genotype approach is a novel, simple, and powerful tool for identifying clinically relevant potential biomarkers. This approach was been recently used to identify clinically essential biomarkers in complex and heterogeneous diseases such as diabetes [19]. Several genes such as SERPINE1, GPRC5A, SFN, ABCA1, MKI67, and RRM2 have been found to be downregulated in severe uncontrolled asthma using PBMCs and, therefore, potential biomarkers [20]. These biomarkers were initially identified from a list of genes using in silico techniques such as genome-wide association studies (GWAS) and methylomes and were further validated using in vitro studies [20].

3. Genomics and Their Role and Applications in Asthma

The genetic component is a significant factor contributing to asthma etiology, with estimated heritability varying from 35% to 95% [21]. For that reason, significant efforts were made to identify the genetic map associated with asthma and led to the discovery of potential contributory genes [22]. Recently, genome-wide association analyses (GWAS) analysis led to the identification of genetic loci that were shown to be found across many allergic phenotypes, including hay fever/allergic rhinitis, atopic dermatitis, food allergy and asthma [23]. Some of those genes were related to the human leukocyte antigen (HLA) locus, including HLA-DQ/DRB1, HLA-DQA1/2, and HLA-B/C [24]. Others were linked to immune function, including thymic stromal lymphopoietin (TSLP), IL13, IL4, and IL33, which were shown to be associated with the promotion of Th2-type cytokine synthesis and T2 immune response [25]. While some of the discovered genes were linked to asthma pathophysiology, the role of other genes such as WDR36 and CLEC16A is still uncertain and needs further investigation [23]. Other promising asthma candidate genes include the beta-2 adrenergic receptor gene (ADRB2). The role of this gene in the risk of asthma is further supported by the efficacy of inhaled β2-adrenergic receptor agonists in the management of asthma [26]. Other frequently investigated genes include FCER1B (MS4A2), which encode the beta subunit of the high-affinity IgE receptor, TNFA, a proinflammatory cytokine, and the CD14 surface antigen gene [22].
Some genes were linked to specific asthma phenotypes, including ORMDL3/GSDMB/LRRC3C (17q21.1), which is linked with childhood asthma [27]. Other genes showed ethnicity-specific patterns, including PYHIN1, that were associated with asthma but only in persons of African ancestry [28].

4. Transcriptomics and Its Applications in Asthma

This approach includes various types of RNA transcripts profiling, including mRNA, noncoding RNA (ncRNA), and microRNA. Using DNA microarrays and RNA-sequencing (RNA-Seq) in the last decade, many genes and pathways were discovered, improving the understanding of asthma’s molecular mechanisms [29]. Variable cells were suitable for transcriptomic studies in asthma, including whole blood cells, peripheral blood mononuclear cells, and lymphoblastoid B cells. Other transcriptomic analysis sources are the sputum and other epithelial cells obtained from nasal epithelia and bronchial brushing. Bronchoalveolar lavage (BAL) was another source of cells as it might reflect the internal environment present in the lower respiratory tract [30].
White blood cell differential expression analysis between healthy individuals, controlled asthma, and therapy-resistant severe asthma revealed the increment in TAS2R pathways in severe asthma [31]. Another study also revealed a reduction in glucocorticoid receptor signaling and the upregulation of mitogen-activated protein kinase and JNK cascade activity in children with severe asthma compared with those with controlled ones [5]. PBMCs gene expression profiling in asthmatic children revealed a strong association between the high neutrophil count and inadequate treatment control in Th1/Th17-mediated asthma [32]. One lab also identified ten genes involved in cell cycle and proliferation, including ABCA1, GPRC5A, KRT8, SFN, TOP2A, SERPINE1, ANLN, MKI67, NEK2, and RRM2 to derange in the bronchial epithelium and fibroblasts of severe asthmatic patients compared to healthy individuals. SERPINE1, GPRC5A, SFN, ABCA1, MKI67, and RRM2 were also downregulated in the PBMCs of severely uncontrolled asthmatic patients [33].
Other studies across multiple tissue types revealed the differential expression of genes and pathways involved in inflammatory and repair responses and epithelial integrity, in addition to genes involved in innate and adaptive immunity [23]. Some reports also highlighted a strong association between distinct transcriptomic profiles and inflammatory asthma subtypes. For example, elevated periostin (POSTN), CLC, CLCA1, SERPINB2, DNASE1L3, and CPA3 were found to be associated with eosinophilic or T2-driven airway inflammation; in contrast, the expression of DEFB4B, CXCR2, IL1B, ALPL, and other chemokines were linked to neutrophilic or Th17-linked inflammation [34].

5. Microbiome and Asthma

While many multi-omic studies focus on genomic and transcriptomic approaches, there has been an increase in using omics technologies in assessing the effect of the environment on the disease [35]. Current research data suggest that environmental factors such as microbiome and diet interact with genetic and epigenetic factors, leading to disturbance in immune development and the balance of vital inflammatory pathways.
Several studies link changes in the gut microbiome and early life risk factors for diseases such as asthma, though they have focused on one risk factor impacting individuals rather than populations [36][37]. Therefore, a systems biology approach via microbiome analysis can provide an understanding of asthma and its complexity and improve patient classification methods, status monitoring, and therapeutic choices [38].
Microbial products are one of the candidates for primary asthma prevention as exposure to them or their products results in the reprogramming of innate immunity as well as protection against allergies and asthma development in children [39]. An example would be neutrophilic asthma, which involves airway microbiota [40]. Refractory neutrophilic asthma has been associated with specific microbial signatures using microbiome data alongside host multi-omic data [38]. Such signatures include predominant pathogenetic bacteria, including Gammaproteobacteria, especially species from Haemophilus and Moraxella [38].
Chronic cough caused by persistent bacterial bronchitis (PBB) has been previously misdiagnosed as asthma, and patients have incorrectly prescribed asthma therapies, resulting in an escalation of their symptoms. Previously PBB consisted of a single diagnosis, but it is now divided into several subtypes (Table 1), indicating the utmost importance of the need for extended endotyping. Furthermore, such extensive subtyping signals a move towards omics and systems biology due to the similar responses in phenotypes across different diseases and conditions—such is the case of neutrophilic airway disease [41].
Table 1. Different subtypes of persistent bacterial bronchitis.
Subtypes Definition
PBB—micro History of chronic cough; positive BAL cultures; 2-week amoxicillin–clavulanic acid course
PBB—clinical History of chronic cough; 2-week amoxicillin–clavulanic acid course
PBB—extended PBB-micro or PBB extended; 4-week antibiotic course
PBB—recuring More than 3 attacks of PBB-micro or PBB-clinical annually
PBB = persistent bacterial bronchitis; BAL = bronchoalveolar.

6. Metabolomics and Breathomics in Asthma Research

Metabolomics is one of the systems biology studies that usually involve a comprehensive measurement of metabolites, including quantifying and assessing low molecular weight compounds in various biological samples [42]. The wide range of biospecimens that can be used in metabolism, including several other non-invasive techniques such as plasma, serum, and exhaled breath, highlighted the potential benefits of using such techniques in asthma research [43]. Such an approach might be essential for discovering potential pathogenic pathways involved in asthma pathogenesis and novel biomarkers discovery, in addition to therapy response prediction [44]. A recent study using metabolomics identified a distinct metabolic profile in asthma that could precisely differentiate between mild, moderate, and severe forms of asthma [44]. Their report highlighted a modest systemic metabolic shift in a severity-dependent manner in asthmatic patients [44]. While exogenous metabolites, including elevated dietary lipids, represent a primary metabolic shift in mild asthma, the moderate and severe asthmatic patients showed a distinct, abundant metabolite including sphingosine-1-phosphate (S1P), OEA as well as N-palmitoyltaurine [44]. Other reports also showed a distinct metabolic profile of severe asthma compared to mild-to-moderate asthma, including amino acids metabolism, which usually showed elevation in β-alanine or lysine levels [45][46]. Several lipid mediators were also shown to correlate positively with asthma severity, including sphingolipids, free fatty acids, and eicosanoids (LTE4) [44][47]. Another report also highlighted serum glycerophospholipid metabolic profile as a marker that was able to differentiate between eosinophilic and non-eosinophilic asthma [48]. Another important application of metabolomics in asthma is the prediction of resistance to steroid therapy. Higher levels of linoleic acid metabolite were found to be responsible for steroid resistance through the NF-κB pathway [49].
Recently, breathomics, an evolving branch of metabolomics that includes the analysis of exhaled breath condensate, was found to be beneficial in the characterization of asthmatic subjects, including asthma endotyping. A novel study revealed that the use of 15 volatile organic compounds in exhaled breath samples was able to stratify asthmatic patients into eosinophilic (>2% sputum eosinophilia) endotype and neutrophilic (≥40% sputum neutrophilia) endotype [50]. Moreover, the evaluation of volatile organic compounds (VOCs) might predict the body’s chemistry changes and be found to be able to monitor the pharmacodynamics and pharmacokinetics of many drugs used in asthma management [51][52]. For example, one report highlighted that exhaled breath volatile organic compound levels could predict the responsiveness of steroids in mild/moderate asthma with accuracy greater than sputum eosinophils and FeNO [53].

7. Epigenome, Environment, and Asthma

Epigenomics includes heritable biochemical modifications that cause changes in gene expression without changes in the DNA sequence. Those modifications include DNA methylation, histone modifications, and microRNAs (miRNAs) [54]. Epigenetic changes were proposed to play a role in the pathogenesis of asthma. For example, one report showed that exposure to a high methyl donor diet in utero might cause an increment in airway inflammation and an increase in the serum IgE that might facilitate the T2 phenotype in lymphocytes as well as the hypermethylation of Runx [55].
A methylation array-based report performed on bronchial brushings and mucosal biopsy revealed a novel methylation mark that can differentiate between asthmatic and atopic individuals compared to healthy controls [56]. Moreover, other epigenetic mechanisms were shown to be involved in the regulation of cytokines and transcription factors involved in T cell differentiation [57]. One report highlighted the role of the global DNA demethylation agent (5-AZA) in allergic airway disease by preventing Th2 skewing, and Th1/Th2 rebalance [58].
A study focused on genome-wide histone modification profiles in a group of cells, including naive, TH1, and TH2 cells obtained from the blood of asthmatic patients compared to healthy individuals which identified enhancers associated with the TH2 memory cell that showed distinct histone H3 Lys4 dimethyl (H3K4Me2) enrichment, which differs according to the status of asthma [59].
Reports also highlighted the presence of up-and-down-regulated miRNAs in asthmatic patients compared to healthy individuals. Those miRs were found to be associated with epithelium development, homeostasis, and inflammatory pathways [60]. This includes let-7f, miR-181c, and miR-487b, which were found to be elevated in mild asthma patients compared to normal. In contrast, miR-203 was shown to be reduced in those patients compared to the healthy control [60]. In addition, some miRNAs were also found to be associated with allergy risk. For example, both miR-155 and miR-146 were found to play an essential role in T cells skewed towards Th2 versus Th1/Th17 [61].
Epigenomic changes may also appear as a response to interaction with endogenous or exogenous environment factors [62]. Moreover, reports also showed evidence of a possible role of environment-gene interactions in determining asthmatic phenotypes [63]. For example, one report showed an increment in protocadherin-20 (PCDH20) methylation in asthmatic patients who smoke. This change was evident even after environmental factors were adjusted [64]. Evidence also emerged about a possible synergistic interaction between genetic and epigenetic variations in the modulation of gene expression. Examples include DNA methylation interaction with SNPs variations in T-helper 2 pathway, interleukin-4 receptor gene [65].

This entry is adapted from the peer-reviewed paper 10.3390/life12101562

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