Omics for the future in asthma

Associations of advanced lung cancer inflammation index with all-cause and respiratory disease mortality in adults with asthma: NHANES, 1999-2018

The Advanced Lung Cancer Inflammation Index (ALI) represents both the inflammatory and nutritional status of the host, but its link with mortality in asthma patients is uncertain. The purpose of this study was to look at the relationship between ALI levels and all-cause and respiratory disease mortality in asthmatic patients. We conducted our research using cohort data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. The National Death Index was used to calculate mortality until December 31, 2019. The study employed multivariate logistic regression to look into the relationship between ALI levels and asthma prevalence. Weighted Kaplan-Meier and multivariate-adjusted Cox analyses were utilized for investigating the relationship between ALI levels and all-cause and respiratory disease mortality in individuals with asthma. A restricted cubic spline (RCS) analysis was used to assess their nonlinear relationship. Subgroup and sensitivity analyses were also performed to evaluate the robustness of the results that were obtained. We enrolled 40,497 people in our study, and 5,469 of them had asthma, representing a 14% prevalence. A median follow-up of 11.19 (9.38, 14.29) years revealed 109 fatalities from respiratory diseases and 724 deaths from all causes. After correcting for several covariates, there was no longer any link (P-trend = 0.2) between ALI levels and the prevalence of asthma. When compared to the lowest quartile, the highest quartile of ALI levels was substantially linked to a lower risk of mortality from respiratory diseases and all causes (all P-trend < 0.001). In the RCS regression model, the relationship between ALI level and both all-cause and respiratory disease mortality in asthmatic participants was nonlinear, with P for nonlinearity of 0.006 and 0.015, respectively. We also discovered that the probability of mortality from respiratory disease decreased progressively to a nadir at an ALI level of 109.13 and then increased as the ALI level increased. Multiple subgroup and sensitivity analyses revealed that ALI was consistently related to lower all-cause and respiratory disease mortality in asthma patients. Our findings suggest that ALI is associated with a reduced risk of all-cause and respiratory disease mortality in asthma patients.

Unraveling heterogeneity and treatment of asthma through integrating multi-omics data

Asthma has become one of the most serious chronic respiratory diseases threatening people's lives worldwide. The pathogenesis of asthma is complex and driven by numerous cells and their interactions, which contribute to its genetic and phenotypic heterogeneity. The clinical characteristic is insufficient for the precision of patient classification and therapies; thus, a combination of the functional or pathophysiological mechanism and clinical phenotype proposes a new concept called "asthma endophenotype" representing various patient subtypes defined by distinct pathophysiological mechanisms. High-throughput omics approaches including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiome enable us to investigate the pathogenetic heterogeneity of diverse endophenotypes and the underlying mechanisms from different angles. In this review, we provide a comprehensive overview of the roles of diverse cell types in the pathophysiology and heterogeneity of asthma and present a current perspective on their contribution into the bidirectional interaction between airway inflammation and airway remodeling. We next discussed how integrated analysis of multi-omics data via machine learning can systematically characterize the molecular and biological profiles of genetic heterogeneity of asthma phenotype. The current application of multi-omics approaches on patient stratification and therapies will be described. Integrating multi-omics and clinical data will provide more insights into the key pathogenic mechanism in asthma heterogeneity and reshape the strategies for asthma management and treatment.

[Highlights 57th SEPAR Congress]

The Spanish Society of Pneumology and Thoracic Surgery (SEPAR) has held its 57th Congress in Valencia from 6 to 8 of June 2024. The SEPAR Congress is the leading meeting for the entire respiratory scientific community, which allows learning about the main scientific advances in this area and provides the ideal situation to create and strengthen ties. This year, under the title "Respiratory Health for everybody", the SEPAR Congress stressed the importance of raising awareness about the importance of caring for and protecting our respiratory system. In this review, we offer a summary of some notable issues addressed in six selected areas of interest: chronic obstructive pulmonary disease (COPD), asthma, interstitial lung diseases (ILDs), pulmonary vascular diseases, sleep and breathing disorders and respiratory physiotherapy.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

CDC167 exhibits potential as a biomarker for airway inflammation in asthma

Current asthma treatments have been discovered to decrease the risk of disease progression. Herein, we aimed to characterize novel potential therapeutic targets for asthma. Differentially expressed genes (DEGs) for GSE64913 and GSE137268 datasets were characterized. Weighted correlation network analysis (WGCNA) was used to identify trait-related module genes within the GSE67472 dataset. The intersection of the module genes of interest, as well as the DEGs, comprised the key module genes that underwent additional candidate gene screening using machine learning. In addition, a bioinformatics-based approach was used to analyze the relative expression levels, diagnostic values, and reverently enriched pathways of the screened candidate genes. Furthermore, the candidate genes were silenced in asthmatic mice, and the inflammation and lung injury in the mice were validated. A total of 1710 DEGs were characterized in GSE64913 and GSE137268 for asthma patients. WGCNA identified 2367 asthma module genes, of which 285 overlapped with 1710 DEGs. Four candidate genes, CDC167, POSTN, SEC14L1, and SERPINB2, were validated using the intersection genes of three machine learning algorithms, including Least Absolute Shrinkage and Selection Operator, Random Forest, and Support Vector Machine. All the candidate genes were significantly upregulated in asthma patients and demonstrated diagnostic utility for asthma. Furthermore, silencing CDC167 reduced the levels of inflammatory cytokines significantly and alleviated lung injury in ovalbumin (OVA)-induced asthmatic mice. Our study demonstrated that CDC167 exhibits potential as diagnostic markers and therapeutic targets for asthma patients.

Omics approaches in asthma research: Challenges and opportunities

Asthma, a chronic respiratory disease with a global prevalence of approximately 300 million individuals, presents a significant societal and economic burden. This multifaceted syndrome exhibits diverse clinical phenotypes and pathogenic endotypes influenced by various factors. The advent of omics technologies has revolutionized asthma research by delving into the molecular foundation of the disease to unravel its underlying mechanisms. Omics technologies are employed to systematically screen for potential biomarkers, encompassing genes, transcripts, methylation sites, proteins, and even the microbiome components. This review provides an insightful overview of omics applications in asthma research, with a special emphasis on genetics, transcriptomics, epigenomics, and the microbiome. We explore the cutting-edge methods, discoveries, challenges, and potential future directions in the realm of asthma omics research. By integrating multi-omics and non-omics data through advanced statistical techniques, we aspire to advance precision medicine in asthma, guiding diagnosis, risk assessment, and personalized treatment strategies for this heterogeneous condition.

Plasma protein signatures of adult asthma

BACKGROUND

Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.

METHODS

Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)).

RESULTS

Meta-analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75-0.79 in training set; AUC = 0.72, 95% CI = 0.69-0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma-atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis.

CONCLUSION

This first large-scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management.

Asthma Phenotypes in the Era of Personalized Medicine

Asthma is a widespread disease affecting approximately 300-million people globally. This condition leads to significant morbidity, mortality, and economic strain worldwide. Recent clinical and laboratory research advancements have illuminated the immunological factors contributing to asthma. As of now, asthma is understood to be a heterogeneous disease. Personalized medicine involves categorizing asthma by its endotypes, linking observable characteristics to specific immunological mechanisms. Identifying these endotypic mechanisms is paramount in accurately profiling patients and tailoring therapeutic approaches using innovative biological agents targeting distinct immune pathways. This article presents a synopsis of the key immunological mechanisms implicated in the pathogenesis and manifestation of the disease's phenotypic traits and individualized treatments for severe asthma subtypes.

Severe Asthma and Biological Therapies: Now and the Future

Recognition of phenotypic variability in pediatric asthma allows for a more personalized therapeutic approach. Knowledge of the underlying pathophysiological and molecular mechanisms (endotypes) of corresponding biomarkers and new treatments enables this strategy to progress. Biologic therapies for children with severe asthma are becoming more relevant in this sense. The T2 phenotype is the most prevalent in childhood and adolescence, and non-T2 phenotypes are usually rare. This document aims to review the mechanism of action, efficacy, and potential predictive and monitoring biomarkers of biological drugs, focusing on the pediatric population. The drugs currently available are omalizumab, mepolizumab, benralizumab, dupilumab, and 1ezepelumab, with some differences in administrative approval prescription criteria between the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Previously, we described the characteristics of severe asthma in children and its diagnostic and therapeutic management.

Oropharyngeal Microbiota Clusters in Children with Asthma or Wheeze Associate with Allergy, Blood Transcriptomic Immune Pathways, and Exacerbation Risk

Rationale: Children with preschool wheezing or school-age asthma are reported to have airway microbial imbalances. Objectives: To identify clusters in children with asthma or wheezing using oropharyngeal microbiota profiles. Methods: Oropharyngeal swabs from the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) pediatric asthma or wheezing cohort were characterized using 16S ribosomal RNA gene sequencing, and unsupervised hierarchical clustering was performed on the Bray-Curtis β-diversity. Enrichment scores of the Molecular Signatures Database hallmark gene sets were computed from the blood transcriptome using gene set variation analysis. Children with severe asthma or severe wheezing were followed up for 12-18 months, with assessment of the frequency of exacerbations. Measurements and Main Results: Oropharyngeal samples from 241 children (age range, 1-17 years; 40% female) revealed four taxa-driven clusters dominated by Streptococcus, Veillonella, Rothia, and Haemophilus. The clusters showed significant differences in atopic dermatitis, grass pollen sensitization, FEV1% predicted after salbutamol, and annual asthma exacerbation frequency during follow-up. The Veillonella cluster was the most allergic and included the highest percentage of children with two or more exacerbations per year during follow-up. The oropharyngeal clusters were different in the enrichment scores of TGF-β (transforming growth factor-β) (highest in the Veillonella cluster) and Wnt/β-catenin signaling (highest in the Haemophilus cluster) transcriptomic pathways in blood (all q values <0.05). Conclusions: Analysis of the oropharyngeal microbiota of children with asthma or wheezing identified four clusters with distinct clinical characteristics (phenotypes) that associate with risk for exacerbation and transcriptomic pathways involved in airway remodeling. This suggests that further exploration of the oropharyngeal microbiota may lead to novel pathophysiologic insights and potentially new treatment approaches.

Current and future developments in the pharmacology of asthma and COPD: ERS seminar, Naples 2022

Pharmacological management of airway obstructive diseases is a fast-evolving field. Several advances in unravelling disease mechanisms as well as intracellular and molecular pathways of drug action have been accomplished. While the clinical translation and implementation of in vitro results to the bedside remains challenging, advances in comprehending the mechanisms of respiratory medication are expected to assist clinicians and scientists in identifying meaningful read-outs and designing clinical studies. This European Respiratory Society Research Seminar, held in Naples, Italy, 5-6 May 2022, focused on current and future developments of the drugs used to treat asthma and COPD; on mechanisms of drug action, steroid resistance, comorbidities and drug interactions; on prognostic and therapeutic biomarkers; on developing novel drug targets based on tissue remodelling and regeneration; and on pharmacogenomics and emerging biosimilars. Related European Medicines Agency regulations are also discussed, as well as the seminar's position on the above aspects.

Proteome analysis reveals novel serum biomarkers for Henoch-Schönlein purpura in Chinese children

PURPOSE

Henoch-Schönlein purpura (HSP) is diagnosed based on characteristic skin changes. This study aimed to identify the serum biomarkers of HSP in children.

EXPERIMENTAL DESIGN

We performed proteomic analysis of serum samples from 38 paired pre- and posttherapy HSP patients and 22 healthy controls using a combination of magnetic bead-based weak cation exchange and MALDI-TOF MS. ClinProTools was used to screen the differential peaks. Then, LC-ESI-MS/MS was performed to identify the proteins. ELISA was used to verify the expression of whole protein in the serum of 92 HSP patients, 14 peptic ulcer disease (PUD) patients and 38 healthy controls, which were prospectively collected. Finally, logistic regression analysis was performed to analyze the diagnostic value of the above predictors and existing clinical indicators.

RESULTS

Seven potential HSP serum biomarker peaks (m/z:1228.95, m/z:1781.22, m/z:1468.43, m/z:1619.53, m/z:1868.41, m/z:1694.05, m/z:1743.25) with higher expression in the pretherapy group and one peak (m/z:1947.41) with lower expression in the pretherapy group were all identified as peptide regions of albumin (ALB), complement C4-A precursor (C4A), tubulin beta chain (TUBB), isoform 1 of fibrinogen alpha chain (FGA), and ezrin (EZR). The expression of identified proteins was validated by ELISA. Multivariate logistic regression analysis showed that serum C4A EZR and ALB were independent risk factors for HSP, serum C4A and lgA were independent risk factors for HSPN, and serum D-dimer was an independent risk factor for abdominal HSP.

CONCLUSIONS AND CLINICAL RELEVANCE

These findings revealed the specific etiology of HSP from the perspective of serum proteomics. The identified proteins might serve as potential biomarkers for HSP and HSPN diagnoses.

SIGNIFICANCE

Henoch-Schönlein purpura (HSP) is the most common systemic vasculitis in children, and its diagnosis depends primarily on characteristic skin changes. Early diagnosis of non-rash patients is difficult, especially for abdominal and renal types (Henoch-Schönlein purpura nephritis, HSPN). HSPN has poor outcomes, is diagnosed based on urinary protein and/or haematuria, and cannot be detected early in HSP. Patients with an earlier diagnosis of HSPN appear to have better renal outcomes. Our plasma proteomic analysis of HSP in children revealed that HSP patients could be distinguished from healthy controls and peptic ulcer disease patients using complement C4-A precursor (C4A), ezrin, and albumin. C4A and IgA could distinguish HSPN from HSP in the early stages, and D-dimer was a sensitive index used to distinguish abdominal HSP; identifying these biomarkers could promote the early diagnosis of HSP, especially pediatric HSPN and abdominal HSP, thereby improving precision therapy.

Asthma and Wheeze Severity and the Oropharyngeal Microbiota in Children and Adolescents

Rationale: There is a major unmet need for improving the care of children and adolescents with severe asthma and wheeze. Identifying factors contributing to disease severity may lead to improved diagnostics, biomarkers, or therapies. The airway microbiota may be such a key factor. Objectives: To compare the oropharyngeal airway microbiota of children and adolescents with severe and mild/moderate asthma/wheeze. Methods: Oropharyngeal swab samples from school-age and preschool children in the European U-BIOPRED (Unbiased BIOmarkers in the PREDiction of respiratory disease outcomes) multicenter study of severe asthma, all receiving severity-appropriate treatment, were examined using 16S ribosomal RNA gene sequencing. Bacterial taxa were defined as amplicon sequence variants. Results: We analyzed 241 samples from four cohorts: A) 86 school-age children with severe asthma; B) 39 school-age children with mild/moderate asthma; C) 65 preschool children with severe wheeze; and D) 51 preschool children with mild/moderate wheeze. The most common bacteria were Streptococcus (mean relative abundance, 33.5%), Veillonella (10.3%), Haemophilus (7.0%), Prevotella (5.9%), and Rothia (5.5%). Age group (school-age vs. preschool) was associated with the microbiota in β-diversity analysis (F = 3.32, P = 0.011) and in a differential abundance analysis (28 significant amplicon sequence variants). Among all children, we found no significant difference in the microbiota between children with severe and mild/moderate asthma/wheeze in univariable β-diversity analysis (F = 1.99, P = 0.08, N = 241), but a significant difference in a multivariable model (F = 2.66, P = 0.035), including the number of exacerbations in the previous year. Age was also significant when expressed as a microbial maturity score (Spearman Rho, 0.39; P = 4.6 × 10^-10); however, this score was not associated with asthma/wheeze severity. Conclusions: There was a modest difference in the oropharyngeal airway microbiota between children with severe and mild/moderate asthma/wheeze across all children but not in individual age groups, and a strong association between the microbiota and age. This suggests the oropharyngeal airway microbiota as an interesting entity in studying asthma severity, but probably without the strength to serve as a biomarker for targeted intervention.

Developing a Diagnostic Model to Predict the Risk of Asthma Based on Ten Macrophage-Related Gene Signatures

OBJECTIVE

Asthma (AS) is a chronic inflammatory disease of the airway, and macrophages contribute to AS remodeling. Our study aims at screening macrophage-related gene signatures to build a risk prediction model and explore its predictive abilities in AS diagnosis.

METHODS

Three microarray datasets were downloaded from the GEO database. The Limma package was used to screen differentially expressed genes (DEGs) between AS and controls. The ssGSEA algorithm was used to determine immune cell proportions. The Pearson correlation coefficient was computed to select the macrophage-related DEGs. The LASSO and RFE algorithms were implemented to filter the macrophage-related DEG signatures to establish a risk prediction model. Receiver operating characteristic (ROC) curves were used to assess the diagnostic ability of the prediction model. Finally, the qPCR was used to detect the expression of selected differential genes in sputum from healthy people and asthmatic patients.

RESULTS

We obtained 1,189 DEGs between AS and controls from the combined datasets. By evaluating immune cell proportions, macrophages showed a significant difference between the two groups, and 439 DEGs were found to be associated with macrophages. These genes were mainly enriched in the gene ontology-biological process of immune and inflammatory responses, as well as in the KEGG pathways of cytokine-cytokine receptor interaction and biosynthesis of antibiotics. Finally, 10 macrophage-related DEG signatures (EARS2, ATP2A2, COLGALT1, GART, WNT5A, AK5, ZBTB16, CCL17, ADORA3, and CXCR4) were screened as an optimized gene set to predict AS diagnosis, and they showed diagnostic abilities with AUCs of 0.968 and 0.875 in ROC curves of combined and validation datasets, respectively. The mRNA expressions of EARS2, ATP2A2, COLGALT1, and GART in the control group were higher than in AS group, while the expressions of WNT5A, AK5, ZBTB16, CCL17, ADORA3, and CXCR4 in the control group were lower than that in the AS group.

CONCLUSION

We proposed a diagnostic model based on 10 macrophage-related genes to predict AS risk.\.

Recent advances in the treatment of childhood asthma: a clinical pharmacology perspective

INTRODUCTION

Childhood asthma is a complex heterogenous inflammatory disease that can pose a large burden on patients and their caregivers. There is a strong need to adapt asthma treatment to the individual patient taking into account underlying inflammatory profiles, moving from a 'one size fits all' approach toward a much-needed personalized approach.

AREAS COVERED

This review article aims to provide an overview of recent advances in the management and treatment of pediatric asthma, including novel insights on the molecular heterogeneity of childhood asthma, the emergence of biologicals to treat severe asthma, and innovative e-health and home monitoring techniques to make asthma management more convenient and accessible.

EXPERT OPINION

Molecular technologies have provided new treatment leads. E-health and home monitoring technologies have helped to gain more insights into disease dynamics and improve adherence to treatment while bringing health care to the patient. However, uncontrolled childhood asthma is still a major unmet clinical need and precision-medicine approaches are still scarce in clinical practice. Advanced omics methods may help researchers or clinicians to more accurately phenotype and treat subtypes of childhood asthma and gain more insight into the complexity of the disease.

Addressing Exposome: An Innovative Approach to Environmental Determinants in Pediatric Respiratory Health

Developmental age is particularly vulnerable to impacts of environmental exposures. Until recent years, the field of environment and child health has predominantly relied on the study of single exposure-health effect relationships. The exposome is an emerging concept in epidemiology, encompassing the totality of the exposures experienced by an individual throughout life and their changes over time. This innovative approach provides a risk profile instead of individual predictors. Exposome research may contribute to better understand the complex relationships between environmental exposures and childhood respiratory health, in order to implement prevention strategies and mitigate adverse health outcomes across the life span. Indeed, an accurate assessment of the exposome needs several measurements as well as different technologies. High-throughput "omics" technologies may be promising tools to integrate a wide range of exposures. However, analyzing large and complex datasets requires the development of advanced statistical tools. This narrative review summarizes the current knowledge on exposome-based approaches in pediatric respiratory health. Further, it explores practical implementation, associated evidence gaps, research limitations and future research perspectives.

[Genetic aspects involved in asthma]

Asthma is an etiologically heterogeneous disease resulting from a complex interaction between genetic. The genetic aspects involved in asthma, which were analyzed from the perspective of the traditional model of multifactorial inheritance, were susceptibility, host factors, and environmental exposures. In the present paper, studies on their family aggregation, concordance in twins, and heritability were analized; as well as the current knowledge about candidate genes, genome wide association studies, and epigenomics contributions and other omic studies that have increased our knowledge about their pathophysiology and environmental interactions.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

New advances in quantitative proteomics research and current applications in asthma

INTRODUCTION

Asthma is the most common chronic respiratory disease and has been declared a global public health problem by the World Health Organization. Due to the high heterogeneity and complexity, asthma can be classified into different 'phenotypes' and it is still difficult to assess the phenotypes and stages of asthma by traditional methods. In recent years, mass spectrometry-based proteomics studies have made significant progress in sensitivity and accuracy of protein identification and quantitation, and are able to obtain differences in protein expression across samples, which provides new insights into the mechanisms and classification of asthma.

AREAS COVERED

In this article, we summarize research strategies in quantitative proteomics, including labeled, label-free and targeted quantification, and highlight the advantages and disadvantages of each. In addition, new applications of quantitative proteomics and the current status of research in asthma have also been discussed. In this study, online resources such as PubMed and Google Scholar were used for literature retrieval.

EXPERT OPINION

The application of quantitative proteomics in asthma has an important role in identifying asthma subphenotypes, revealing potential pathogenesis and therapeutic targets. But the proteomic studies on asthma are not sufficient, as most of them are in the phase of biomarker discovery.

Microbiome Research and Multi-Omics Integration for Personalized Medicine in Asthma

Asthma is a multifactorial inflammatory disorder of the respiratory system characterized by high diversity in clinical manifestations, underlying pathological mechanisms and response to treatment. It is generally established that human microbiota plays an essential role in shaping a healthy immune response, while its perturbation can cause chronic inflammation related to a wide range of diseases, including asthma. Systems biology approaches encompassing microbiome analysis can offer valuable platforms towards a global understanding of asthma complexity and improving patients' classification, status monitoring and therapeutic choices. In the present review, we summarize recent studies exploring the contribution of microbiota dysbiosis to asthma pathogenesis and heterogeneity in the context of asthma phenotypes-endotypes and administered medication. We subsequently focus on emerging efforts to gain deeper insights into microbiota-host interactions driving asthma complexity by integrating microbiome and host multi-omics data. One of the most prominent achievements of these research efforts is the association of refractory neutrophilic asthma with certain microbial signatures, including predominant pathogenic bacterial taxa (such as Proteobacteria phyla, Gammaproteobacteria class, especially species from Haemophilus and Moraxella genera). Overall, despite existing challenges, large-scale multi-omics endeavors may provide promising biomarkers and therapeutic targets for future development of novel microbe-based personalized strategies for diagnosis, prevention and/or treatment of uncontrollable asthma.

Metabolomics, Microbiota, and In Vivo and In Vitro Biomarkers in Type 2 Severe Asthma: A Perspective Review

Precision medicine refers to the tailoring of therapeutic strategies to the individual characteristics of each patient; thus, it could be a new approach for the management of severe asthma that considers individual variability in genes, environmental exposure, and lifestyle. Precision medicine would also assist physicians in choosing the right treatment, the best timing of administration, consequently trying to maximize drug efficacy, and, possibly, reducing adverse events. Metabolomics is the systematic study of low molecular weight (bio)chemicals in a given biological system and offers a powerful approach to biomarker discovery and elucidating disease mechanisms. In this point of view, metabolomics could play a key role in targeting precision medicine.

Metabolomics, Microbiota, and In Vivo and In Vitro Biomarkers in Type 2 Severe Asthma: A Perspective Review

Precision medicine refers to the tailoring of therapeutic strategies to the individual characteristics of each patient; thus, it could be a new approach for the management of severe asthma that considers individual variability in genes, environmental exposure, and lifestyle. Precision medicine would also assist physicians in choosing the right treatment, the best timing of administration, consequently trying to maximize drug efficacy, and, possibly, reducing adverse events. Metabolomics is the systematic study of low molecular weight (bio)chemicals in a given biological system and offers a powerful approach to biomarker discovery and elucidating disease mechanisms. In this point of view, metabolomics could play a key role in targeting precision medicine.

Potential Metabolic Biomarkers in Adult Asthmatics

Asthma is the most common chronic airway inflammation, with multiple phenotypes caused by complicated interactions of genetic, epigenetic, and environmental factors. To date, various determinants have been suggested for asthma pathogenesis by a new technology termed omics, including genomics, transcriptomics, proteomics, and metabolomics. In particular, the systematic analysis of all metabolites in a biological system, such as carbohydrates, amino acids, and lipids, has helped identify a novel pathway related to complex diseases. These metabolites are involved in the regulation of hypermethylation, response to hypoxia, and immune reactions in the pathogenesis of asthma. Among them, lipid metabolism has been suggested to be related to lung dysfunction in mild-to-moderate asthma. Sphingolipid metabolites are an important mediator contributing to airway inflammation in obese asthma and aspirin-exacerbated respiratory disease. Although how these molecular variants impact the disease has not been completely determined, identification of new causative factors may possibly lead to more-personalized and precise pathway-specific approaches for better diagnosis and treatment of asthma. In this review, perspectives of metabolites related to asthma and clinical implications have been highlighted according to various phenotypes.

A System Pharmacology Multi-Omics Approach toward Uncontrolled Pediatric Asthma

There is a clinical need to identify children with poor asthma control as early as possible, to optimize treatment and/or to find therapeutic alternatives. Here, we present the “Systems Pharmacology Approach to Uncontrolled Pediatric Asthma” (SysPharmPediA) study, which aims to establish a pediatric cohort of moderate-to-severe uncontrolled and controlled patients with asthma, to investigate pathophysiological mechanisms underlying uncontrolled moderate-to-severe asthma in children on maintenance treatment, using a multi-omics systems medicine approach. In this multicenter observational case–control study, moderate-to-severe asthmatic children (age; 6–17 years) were included from four European countries (Netherlands, Germany, Spain, and Slovenia). Subjects were classified based on asthma control and number of exacerbations. Demographics, current and past patient/family history, and clinical characteristics were collected. In addition, systems-wide omics layers, including epi(genomics), transcriptomics, microbiome, proteomics, and metabolomics were evaluated from multiple samples. In all, 145 children were included in this cohort, 91 with uncontrolled (median age = 12 years, 43% females) and 54 with controlled asthma (median age = 11.7 years, 37% females). The two groups did not show statistically significant differences in age, sex, and body mass index z-score distribution. Comprehensive information and diverse noninvasive biosampling procedures for various omics analyses will provide the opportunity to delineate underlying pathophysiological mechanisms of moderate-to-severe uncontrolled pediatric asthma. This eventually might reveal novel biomarkers, which could potentially be used for noninvasive personalized diagnostics and/or treatment.

Topic Evolution Analysis for Omics Data Integration in Cancers

One of the vital challenges for cancer diseases is efficient biomarkers monitoring formation and development are limited. Omics data integration plays a crucial role in the mining of biomarkers in the human condition. As the link between omics study on biomarkers discovery and cancer diseases is deepened, defining the principal technologies applied in the field is a must not only for the current period but also for the future. We utilize topic modeling to extract topics (or themes) as a probabilistic distribution of latent topics from the dataset. To predict the future trend of related cases, we utilize the Prophet neural network to perform a prediction correction model for existing topics. A total of 2,318 pieces of literature (from 2006 to 2020) were retrieved from MEDLINE with the query on “omics” and “cancer.” Our study found 20 topics covering current research types. The topic extraction results indicate that, with the rapid development of omics data integration research, multi-omics analysis (Topic 11) and genomics of colorectal cancer (Topic 10) have more studies reported last 15 years. From the topic prediction view, research findings in multi-omics data processing and novel biomarker discovery for cancer prediction (Topic 2, 3, 10, 11) will be heavily focused in the future. From the topic visuallization and evolution trends, metabolomics of breast cancer (Topic 9), pharmacogenomics (Topic 15), genome-guided therapy regimens (Topic 16), and microRNAs target genes (Topic 17) could have more rapidly developed in the study of cancer treatment effect and recurrence prediction.

Personalized medicine for allergy treatment: Allergen immunotherapy still a unique and unmatched model

The introduction of personalized medicine (PM) has been a milestone in the history of medical therapy, because it has revolutionized the previous approach of treating the disease with that of treating the patient. It is known today that diseases can occur in different genetic variants, making specific treatments of proven efficacy necessary for a given endotype. Allergic diseases are particularly suitable for PM, because they meet the therapeutic success requirements, including a known molecular mechanism of the disease, a diagnostic tool for such disease, and a treatment blocking the mechanism. The stakes of PM in allergic patients are molecular diagnostics, to detect specific IgE to single-allergen molecules and to distinguish the causative molecules from those merely cross-reactive, pursuit of patient's treatable traits addressing genetic, phenotypic, and psychosocial features, and omics, such as proteomics, epi-genomics, metabolomics, and breathomics, to forecast patient's responsiveness to therapies, to detect biomarker and mediators, and to verify the disease control. This new approach has already improved the precision of allergy diagnosis and is likely to significantly increase, through the higher performance achieved with the personalized treatment, the effectiveness of allergen immunotherapy by enhancing its already known and unique characteristics of treatment that acts on the causes.

Treatable Mechanisms in Asthma

Asthma is a heterogeneous condition, but firm identification of heterogeneity-focused treatments is still lacking. Dividing patients into subgroups of asthma pheno-/endotypes based on combined clinical and cellular biological characteristics and linking them to targeted treatments could be a potentially useful approach to personalize therapy for better outcomes. Nonetheless, there are still many problems related to the identification and validation of asthma phenotypes and endotypes. Alternatively, a precision-medicine strategy for the management of patients with airways disease that is free from the traditional diagnostic labels and based on identifying "treatable traits" in each patient might be preferable. However, it would represent a quite unsophisticated approach because the definition of a treatable trait is too imprecise. In fact, there is still no understanding of the mechanisms underlying treatable traits that allow directing any targeted therapies against any particular treatable trait. Fortunately, in-depth identification of underlying molecular pathways to guide targeted treatment in individual patients is in progress thanks to the improvement in big data management obtained from '-omic' sciences that is greatly increasing knowledge concerning asthma.

Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0

BACKGROUND

To date, most studies involving high-throughput analyses of sputum in asthma and COPD have focused on identifying transcriptomic signatures of disease. No whole-genome methylation analysis of sputum cells has been performed yet. In this context, the highly variable cellular composition of sputum has potential to confound the molecular analyses.

METHODS

Whole-genome transcription (Agilent Human 4 × 44 k array) and methylation (Illumina 450 k BeadChip) analyses were performed on sputum samples of 9 asthmatics, 10 healthy and 10 COPD subjects. RNA integrity was checked by capillary electrophoresis and used to correct in silico for bias conferred by RNA degradation during biobank sample storage. Estimates of cell type-specific molecular profiles were derived via regression by quadratic programming based on sputum differential cell counts. All analyses were conducted using the open-source R/Bioconductor software framework.

RESULTS

A linear regression step was found to perform well in removing RNA degradation-related bias among the main principal components of the gene expression data, increasing the number of genes detectable as differentially expressed in asthma and COPD sputa (compared to controls). We observed a strong influence of the cellular composition on the results of mixed-cell sputum analyses. Exemplarily, upregulated genes derived from mixed-cell data in asthma were dominated by genes predominantly expressed in eosinophils after deconvolution. The deconvolution, however, allowed to perform differential expression and methylation analyses on the level of individual cell types and, though we only analyzed a limited number of biological replicates, was found to provide good estimates compared to previously published data about gene expression in lung eosinophils in asthma. Analysis of the sputum methylome indicated presence of differential methylation in genomic regions of interest, e.g. mapping to a number of human leukocyte antigen (HLA) genes related to both major histocompatibility complex (MHC) class I and II molecules in asthma and COPD macrophages. Furthermore, we found the SMAD3 (SMAD family member 3) gene, among others, to lie within differentially methylated regions which has been previously reported in the context of asthma.

CONCLUSIONS

In this methodology-oriented study, we show that methylation profiling can be easily integrated into sputum analysis workflows and exhibits a strong potential to contribute to the profiling and understanding of pulmonary inflammation. Wherever RNA degradation is of concern, in silico correction can be effective in improving both sensitivity and specificity of downstream analyses. We suggest that deconvolution methods should be integrated in sputum omics analysis workflows whenever possible in order to facilitate the unbiased discovery and interpretation of molecular patterns of inflammation.

Pharmacogenomic Response of Inhaled Corticosteroids for the Treatment of Asthma: Considerations for Therapy

There is a large interindividual variability in response to ICSs in asthma. About 70% of the variance in ICS response is likely due at least partially to genetically determined characteristics of target genes. In this article, we examine the effects on the ICS response of gene variations in the corticosteroid pathway, and in the pharmacokinetics of corticosteroids, and also those outside the corticosteroid pathway, which have the potential to influence corticosteroid activity. Although the available evidence indicates that responses to ICSs in asthma are influenced by different genetic variants, there are still deep uncertainties as to whether a real association between these genetic variants and corticosteroid response could also possibly exist because there are difficulties in reproducing pharmacogenetic findings. This explains at least partly the insufficient use of pharmacogenomic data when treating asthmatic patients, which creates a real limitation to the proper use of ICSs in an era of precision medicine that links the right patient to the right treatment. Knowing and dealing with the genetic factors that influence the therapeutic ICS response is a fundamental condition for prescribing the right dose of ICS to the right patient at the right time.