Integrative systems biology approaches in asthma pharmacogenomics

Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data

MOTIVATION

Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges.

RESULTS

We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits.

AVAILABILITY AND IMPLEMENTATION

The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

Pharmacogenetics of childhood uncontrolled asthma

INTRODUCTION

Asthma is a heterogeneous, multifactorial disease with multiple genetic and environmental risk factors playing a role in pathogenesis and therapeutic response. Understanding of pharmacogenetics can help with matching individualized treatments to specific genotypes of asthma to improve therapeutic outcomes especially in uncontrolled or severe asthma.

AREAS COVERED

In this review, we outline novel information about biology, pathways, and mechanisms related to interindividual variability in drug response (corticosteroids, bronchodilators, leukotriene modifiers, and biologics) for childhood asthma. We discuss candidate gene, genome-wide association studies and newer omics studies including epigenomics, transcriptomics, proteomics, and metabolomics as well as integrative genomics and systems biology methods related to childhood asthma. The articles were obtained after a series of searches, last updated November 2022, using database PubMed/CINAHL DB.

EXPERT OPINION

Implementation of pharmacogenetic algorithms can improve therapeutic targeting in children with asthma, particularly with severe or uncontrolled asthma who typically have challenges in clinical management and carry considerable financial burden. Future studies focusing on potential biomarkers both clinical and pharmacogenetic can help formulate a prognostic test for asthma treatment response that would represent true bench to bedside clinical implementation.

Lung function, airway and peripheral basophils and eosinophils are associated with molecular pharmacogenomic endotypes of steroid response in severe asthma

INTRODUCTION

Asthma is a complex disease with heterogeneous expression/severity. There is growing interest in defining asthma endotypes consistently associated with different responses to therapy, focusing on type 2 inflammation (Th2) as a key pathological mechanism. Current asthma endotypes are defined primarily by clinical/laboratory criteria. Each endotype is likely characterised by distinct molecular mechanisms that identify optimal therapies.

METHODS

We applied unsupervised (without a priori clinical criteria) principal component analysis on sputum airway cells RNA-sequencing transcriptomic data from 19 asthmatics from the Severe Asthma Research Program at baseline and 6-8 weeks follow-up after a 40 mg dose of intramuscular corticosteroids. We investigated principal components PC1, PC3 for association with 55 clinical variables.

RESULTS

PC3 was associated with baseline Th2 clinical features including blood (rank-sum p=0.0082) and airway (rank-sum p=0.0024) eosinophilia, FEV₁ change (Kendall tau-b R=-0.333 (-0.592 to -0.012)) and follow-up FEV₁ albuterol response (Kendall tau-b R=0.392 (0.079 to 0.634)). PC1 with blood basophlia (rank-sum p=0.0191). The top 5% genes contributing to PC1, PC3 were enriched for distinct immune system/inflammation ontologies suggesting distinct subject-specific clusters of transcriptomic response to corticosteroids. PC3 association with FEV₁ change was reproduced in silico in a comparable independent 14-subject (baseline, 8 weeks after daily inhaled corticosteroids (ICS)) airway epithelial cells microRNAome dataset.

CONCLUSIONS

Transcriptomic PCs from this unsupervised methodology define molecular pharmacogenomic endotypes that may yield novel biology underlying different subject-specific responses to corticosteroid therapy in asthma, and optimal personalised asthma care. Top contributing genes to these PCs may suggest new therapeutic targets.

Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.

What did we learn from multiple omics studies in asthma?

More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired-presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies.

Gene network approach reveals co-expression patterns in nasal and bronchial epithelium

Nasal gene expression profiling is a new approach to investigate the airway epithelium as a biomarker to study the activity and treatment responses of obstructive pulmonary diseases. We investigated to what extent gene expression profiling of nasal brushings is similar to that of bronchial brushings. We performed genome wide gene expression profiling on matched nasal and bronchial epithelial brushes from 77 respiratory healthy individuals. To investigate differences and similarities among regulatory modules, network analysis was performed on correlated, differentially expressed and smoking-related genes using Gaussian Graphical Models. Between nasal and bronchial brushes, 619 genes were correlated and 1692 genes were differentially expressed (false discovery rate <0.05, |Fold-change|>2). Network analysis of correlated genes showed pro-inflammatory pathways to be similar between the two locations. Focusing on smoking-related genes, cytochrome-P450 pathway related genes were found to be similar, supporting the concept of a detoxifying response to tobacco exposure throughout the airways. In contrast, cilia-related pathways were decreased in nasal compared to bronchial brushes when focusing on differentially expressed genes. Collectively, while there are substantial differences in gene expression between nasal and bronchial brushes, we also found similarities, especially in the response to the external factors such as smoking.

Multiomics Data Triangulation for Asthma Candidate Biomarkers and Precision Medicine

Asthma is a common complex disorder and has been subject to intensive omics research for disease susceptibility and therapeutic innovation. Candidate biomarkers of asthma and its precision treatment demand that they stand the test of multiomics data triangulation before they can be prioritized for clinical applications. We classified the biomarkers of asthma after a search of the literature and based on whether or not a given biomarker candidate is reported in multiple omics platforms and methodologies, using PubMed and Web of Science, we identified omics studies of asthma conducted on diverse platforms using keywords, such as asthma, genomics, metabolomics, and epigenomics. We extracted data about asthma candidate biomarkers from 73 articles and developed a catalog of 190 potential asthma biomarkers (167 human, 23 animal data), comprising DNA loci, transcripts, proteins, metabolites, epimutations, and noncoding RNAs. The data were sorted according to 13 omics types: genomics, epigenomics, transcriptomics, proteomics, interactomics, metabolomics, ncRNAomics, glycomics, lipidomics, environmental omics, pharmacogenomics, phenomics, and integrative omics. Importantly, we found that 10 candidate biomarkers were apparent in at least two or more omics levels, thus promising potential for further biomarker research and development and precision medicine applications. This multiomics catalog reported herein for the first time contributes to future decision-making on prioritization of biomarkers and validation efforts for precision medicine in asthma. The findings may also facilitate meta-analyses and integrative omics studies in the future.

Using a Mixture-of-Bivariate-Regressions Model to Explore Heterogeneity of Effects of the Use of Inhaled Corticosteroids on Gestational Age and Birth Weight Among Pregnant Women With Asthma

Asthma is a heterogeneous disease, and responses to asthma medications vary noticeably among patients. A substantively oriented objective of this study was to explore the potentially heterogeneous effects of exposure to maternal inhaled corticosteroids (ICS) on gestational age (GA) at delivery and birth weight (BW) using a cohort of 6,197 pregnancies among women with asthma (Quebec, Canada, 1998-2008). A methodologically oriented objective was to comprehensively describe the application of a Bayesian 2-component mixture-of-bivariate-regressions model to address this issue and estimate the effects of ICS on GA and BW jointly. Based on the proposed model, no association between ICS and GA/BW was found for a large proportion of asthmatic pregnancies. However, a positive association between ICS exposure and GA/BW was revealed in a small subset of pregnancies comprising mainly preterm and low-birth-weight infants. A novel application of this model was also subsequently performed using BW z score instead of BW as the outcome variable. In conclusion, the studied mixture-of-bivariate-regressions model was useful for detecting heterogeneity in the effect of ICS on GA and BW in our population of women with asthma. These analyses pave the way for analogous uses of this model for general assessment of exposure effect heterogeneity for these perinatal outcomes.

The phosphatidylinositide 3-kinase (PI3K) signaling pathway is a determinant of zileuton response in adults with asthma

Variable responsiveness to zileuton, a leukotriene antagonist used to treat asthma, may be due in part to genetic variation. While individual SNPs were previously associated with zileuton-related lung function changes, specific quantitative trait loci (QTLs) and biological pathways that may contribute have not been identified. In this study, we investigated the hypothesis that genetic variation within biological pathways is associated with zileuton response. We performed an integrative QTL mapping and pathway enrichment study to investigate data from a GWAS of zileuton response, in addition to mRNA expression profiles and leukotriene production data from lymphoblastoid cell lines (LCLs) (derived from asthmatics) that were treated with zileuton or ethanol (control). We identified 1060 QTLs jointly associated with zileuton-related differential LTB₄ production in LCLs and lung function change in patients taking zileuton, of which eight QTLs were also significantly associated with persistent LTB₄ production in LCLs following zileuton treatment (i.e., ‘poor’ responders). Four nominally significant trans-eQTLs were predicted to regulate three candidate genes (SELL, MTF2, and GAL), the expression of which was significantly reduced in LCLs following zileuton treatment. Gene and pathway enrichment analyses of QTL associations identified multiple genes and pathways, predominantly related to phosphatidyl inositol signaling via PI3K. We validated the PI3K pathway activation status in a subset of LCLs demonstrating variable zileuton-related LTB₄ production, and show that in contrast to LCLs that responded to zileuton, the PI3K pathway was activated in poor responder LCLs. Collectively, these findings demonstrate a role for the PIK3 pathway and its targets as important determinants of differential responsiveness to zileuton.

Pharmacogenetics and the treatment of asthma

Heterogeneity defines both the natural history of asthma as well as patient's response to treatment. Pharmacogenomics contribute to understand the genetic basis of drug response and thus to define new therapeutic targets or molecular biomarkers to evaluate treatment effectiveness. This review is initially focused on different genes so far involved in the pharmacological response to asthma treatment. Specific considerations regarding allergic asthma, the pharmacogenetics aspects of polypharmacy and the application of pharmacogenomics in new drugs in asthma will also be addressed. Finally, future perspectives related to epigenetic regulatory elements and the potential impact of systems biology in pharmacogenetics of asthma will be considered.

The Effect of Chinese Herbal Medicine Formula mKG on Allergic Asthma by Regulating Lung and Plasma Metabolic Alternations

Asthma is a chronic inflammatory disorder of the airway and is characterized by airway remodeling, hyperresponsiveness, and shortness of breath. Modified Kushen Gancao Formula (mKG), derived from traditional Chinese herbal medicines (TCM), has been demonstrated to have good therapeutic effects on experimental allergic asthma. However, its anti-asthma mechanism remains currently unknown. In the present work, metabolomics studies of biochemical changes in the lung tissue and plasma of ovalbumin (OVA)-induced allergic asthma mice with mKG treatment were performed using ultra high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Partial least squares–discriminate analysis (PLS−DA) indicated that the metabolic perturbation induced by OVA was reduced after mKG treatment. A total of twenty-four metabolites involved in seven metabolic pathways were identified as potential biomarkers in the development of allergic asthma. Among them, myristic acid (L3 or P2), sphinganine (L6 or P4), and lysoPC(15:0) (L12 or P16) were detected both in lung tissue and plasma. Additionally, l-acetylcarnitine (L1), thromboxane B2 (L2), 10-HDoHE (L10), and 5-HETE (L11) were first reported to be potential biomarkers associated with allergic asthma. The treatment of mKG mediated all of those potential biomarkers except lysoPC(15:0) (P16). The anti-asthma mechanism of mKG can be achieved through the comprehensive regulation of multiple perturbed biomarkers and metabolic pathways.

Genetic risk factors for decreased bone mineral accretion in children with asthma receiving multiple oral corticosteroid bursts

BACKGROUND

Long-term intermittent oral corticosteroid (OCS) use in children with asthma leads to significant decreases in bone mineral accretion (BMA).

OBJECTIVE

We aimed to identify genetic factors influencing OCS dose effects on BMA in children with asthma.

METHODS

We first performed a gene-by-OCS interaction genome-wide association study (GWAS) of BMA in 489 white participants in the Childhood Asthma Management Program trial who took short-term oral prednisone bursts when they experienced acute asthma exacerbations. We selected the top-ranked 2000 single nucleotide polymorphisms (SNPs) in the GWAS and determined whether these SNPs also had cis-regulatory effects on dexamethasone-induced gene expression in osteoblasts.

RESULTS

We identified 2 SNPs (rs9896933 and rs2074439) associated with decreased BMA and related to the tubulin γ pathway. The rs9896933 variant met the criteria for genome-wide significance (P = 3.15 × 10(-8) in the GWAS) and is located on the intron of tubulin folding cofactor D (TBCD) gene. The rs2074439 variant (P = 2.74 × 10(-4) in the GWAS) showed strong cis-regulatory effects on dexamethasone-induced tubulin γ gene expression in osteoblasts (P = 8.64 × 10(-4)). Interestingly, we found that BMA worsened with increasing prednisone dose as the number of mutant alleles of the 2 SNPs increased.

CONCLUSIONS

We have identified 2 novel tubulin γ pathway SNPs, rs9896933 and rs2074439, showing independent interactive effects with cumulative corticosteroid dose on BMA in children with asthma receiving multiple OCS bursts.

Pharmacogenetic Markers of Drug Efficacy and Toxicity

The action of a drug is dictated by its pharmacokinetic and pharmacodynamics properties, both of which can vary in different individuals because of environmental and genetic factors. Pharmacogenetics, the study of genetic factors determining drug response, has the potential to improve clinical outcomes through targeting therapies, individualizing dosing, preventing adverse drug reactions, and potentially rescuing previously failed therapies. Although there have been significant advances in pharmacogenetics over the last decade, only a few have been translated into clinical practice. However, with new rapid genotyping technologies, regulatory modernization, novel clinical trial designs, systems approaches, and integration of pharmacogenetic data into decision support systems, there is hope that pharmacogenetics, as an important component of the overall drive towards personalized medicine, will advance more quickly in the future. There will continue to be a need for collaboration between centers all over the world, and multisector working, capitalizing on the current data revolution.

Systems biology of asthma and allergic diseases: a multiscale approach

Systems biology is an approach to understanding living systems that focuses on modeling diverse types of high-dimensional interactions to develop a more comprehensive understanding of complex phenotypes manifested by the system. High-throughput molecular, cellular, and physiologic profiling of populations is coupled with bioinformatic and computational techniques to identify new functional roles for genes, regulatory elements, and metabolites in the context of the molecular networks that define biological processes associated with system physiology. Given the complexity and heterogeneity of asthma and allergic diseases, a systems biology approach is attractive, as it has the potential to model the myriad connections and interdependencies between genetic predisposition, environmental perturbations, regulatory intermediaries, and molecular sequelae that ultimately lead to diverse disease phenotypes and treatment responses across individuals. The increasing availability of high-throughput technologies has enabled system-wide profiling of the genome, transcriptome, epigenome, microbiome, and metabolome, providing fodder for systems biology approaches to examine asthma and allergy at a more holistic level. In this article we review the technologies and approaches for system-wide profiling, as well as their more recent applications to asthma and allergy. We discuss approaches for integrating multiscale data through network analyses and provide perspective on how individually captured health profiles will contribute to more accurate systems biology views of asthma and allergy.

Systems biology approach for subtyping asthma; where do we stand now?

PURPOSE OF REVIEW

The purpose of this review is to discuss the present systems biology approach to asthma and how it is helping to define asthma subtypes. Although the general concept of systems biology will be discussed, the article will focus on recent developments in the field related to asthma.

RECENT FINDINGS

The most recent work in systems biology and asthma has occurred in the area of genomics (e.g., pharmacogenomics and gene-environment interactions), protein interaction networks [e.g., interleukin (IL)-33/IL-1 receptor-like 1 signaling], cluster analysis of asthma patients (e.g., application of severe asthma research program clusters to a general urban asthma population), and multiscale approaches to asthma encompassing data from the molecule to whole organ (e.g., modeling of airways hyperresponsiveness).

SUMMARY

The results of recent work in this area have led to new insight into gene-cytokine and protein-protein networks involved in asthma, a better determination of key clinical factors associated with asthma subtypes, and the beginning of sophisticated multiscale approaches to modeling, understanding and predicting the behavior of the asthmatic lung.

Trials and tribulations of 'omics data analysis: assessing quality of SIMCA-based multivariate models using examples from pulmonary medicine

Respiratory diseases are multifactorial heterogeneous diseases that have proved recalcitrant to understanding using focused molecular techniques. This trend has led to the rise of 'omics approaches (e.g., transcriptomics, proteomics) and subsequent acquisition of large-scale datasets consisting of multiple variables. In 'omics technology-based investigations, discrepancies between the number of variables analyzed (e.g., mRNA, proteins, metabolites) and the number of study subjects constitutes a major statistical challenge. The application of traditional univariate statistical methods (e.g., t-test) to these "short-and-wide" datasets may result in high numbers of false positives, while the predominant approach of p-value correction to account for these high false positive rates (e.g., FDR, Bonferroni) are associated with significant losses in statistical power. In other words, the benefit in decreased false positives must be counterbalanced with a concomitant loss in true positives. As an alternative, multivariate statistical analysis (MVA) is increasingly being employed to cope with 'omics-based data structures. When properly applied, MVA approaches can be powerful tools for integration and interpretation of complex 'omics-based datasets towards the goal of identifying biomarkers and/or subphenotypes. However, MVA methods are also prone to over-interpretation and misuse. A common software used in biomedical research to perform MVA-based analyses is the SIMCA package, which includes multiple MVA methods. In this opinion piece, we propose guidelines for minimum reporting standards for a SIMCA-based workflow, in terms of data preprocessing (e.g., normalization, scaling) and model statistics (number of components, R2, Q2, and CV-ANOVA p-value). Examples of these applications in recent COPD and asthma studies are provided. It is expected that readers will gain an increased understanding of the power and utility of MVA methods for applications in biomedical research.

Pharmacogenetics: implications of race and ethnicity on defining genetic profiles for personalized medicine

Pharmacogenetics is being used to develop personalized therapies specific to subjects from different ethnic or racial groups. To date, pharmacogenetic studies have been primarily performed in trial cohorts consisting of non-Hispanic white subjects of European descent. A "bottleneck" or collapse of genetic diversity associated with the first human colonization of Europe during the Upper Paleolithic period, followed by the recent mixing of African, European, and Native American ancestries, has resulted in different ethnic groups with varying degrees of genetic diversity. Differences in genetic ancestry might introduce genetic variation, which has the potential to alter the therapeutic efficacy of commonly used asthma therapies, such as β2-adrenergic receptor agonists (β-agonists). Pharmacogenetic studies of admixed ethnic groups have been limited to small candidate gene association studies, of which the best example is the gene coding for the receptor target of β-agonist therapy, the β2-adrenergic receptor (ADRB2). Large consortium-based sequencing studies are using next-generation whole-genome sequencing to provide a diverse genome map of different admixed populations, which can be used for future pharmacogenetic studies. These studies will include candidate gene studies, genome-wide association studies, and whole-genome admixture-based approaches that account for ancestral genetic structure, complex haplotypes, gene-gene interactions, and rare variants to detect and replicate novel pharmacogenetic loci.

From phenotypes to endotypes to asthma treatment

PURPOSE OF REVIEW

The current guidelines for asthma diagnosis and management do not recognize that different phenotypes of asthma exist, with significant variations in the manifestation of airway inflammation, symptoms, severity, and response to treatment. This article will critically review new approaches to classify asthma together with the emerging endotype-driven therapeutic strategies.

RECENT FINDINGS

Several new approaches for classifying asthma are available, from precision and deep phenotyping to identification of novel causal pathways and translation of biomarkers into pathway-specific diagnostic tests. New phenotypes, such as epigenetic phenotypes, asthmatic granulomatosis, or neurophenotypes are described. Large clinical trials testing the endotype-driven approach are increasingly successful, but the dissociated effect and the drug efficacy at the target site remain unsolved issues. Profiling the Th2 low and the resident cell compartment of asthma are major unmet needs in asthma endotyping.

SUMMARY

Each of the hallmark characteristics of asthma (inflammation, remodeling, airway hyperreactivity) is the expression of a complex network of molecules, very diverse both within any given patient in time and between any two patients. Some of these networks are repetitive across individuals with asthma and specific for clinical expression, gene-environment interaction and inflammatory cell profiles represent novel endotype-specific diagnostic and therapeutic strategies.

Using systems approaches to address challenges for clinical implementation of pharmacogenomics

Many genetic variants have been shown to affect drug response through changes in drug efficacy and likelihood of adverse effects. Much of pharmacogenomic science has focused on discovering and clinically implementing single gene variants with large effect sizes. Given the increasing complexities of drug responses and their variability, a systems approach may be enabling for discovery of new biology in this area. Further, systems approaches may be useful in addressing challenges in moving these data to clinical implementation, including creation of predictive models of drug response phenotypes, improved clinical decision-making through complex biological models, improving strategies for integrating genomics into clinical practice, and evaluating the impact of implementation programs on public health.