Application of 'omics technologies to biomarker discovery in inflammatory lung diseases

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Proteomic analysis of the serum in dogs with pulmonary hypertension secondary to myxomatous mitral valve disease: the preliminary study

Background

Pulmonary hypertension (PH) is a common complication in dogs with myxomatous mitral valve disease (MMVD), characterized by elevated blood pressure in pulmonary artery. Echocardiography is a reliable technique for PH diagnosis in veterinary medicine. However, it is limited to use as an early detection method. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has found extensive application in the discovery of serum protein biomarkers for various diseases. The objective of this study was to identify serum proteins in healthy control dogs and MMVD dogs both with and without PH using LC-MS/MS.

Materials and methods

In this research, a total of 81 small-breed dogs participated, and they were categorized into three groups: the control (n = 28), MMVD (n = 24) and MMVD+PH (n = 29) groups. Serum samples were collected and analyzed by LC-MS/MS.

Results

Differentially expressed proteins were identified, and the upregulated and downregulated proteins in MMVD+PH group including Myomesin 1 (MYOM1) and Histone deacetylase 7 (HDAC7), Pleckstrin homology domain containing M3 (PLEKHM3), Diacylglycerol lipase alpha (DAGLA) and Tubulin tyrosine ligase like 6 (TTLL6) were selected as proteins of interest in MMVD dogs with PH.

Conclusion

Different types of proteins have been identified in healthy dogs and MMVD dogs with and without PH. Additional studies are needed to investigate the potential of these proteins as biomarkers for PH in dogs with MMVD.

Diminished airway host innate response in people with cystic fibrosis who experience frequent pulmonary exacerbations

RATIONALE

Pulmonary exacerbations are clinically impactful events that accelerate cystic fibrosis (CF) lung disease progression. The pathophysiological mechanisms underlying an increased frequency of pulmonary exacerbations have not been explored.

OBJECTIVES

To compare host immune response during intravenous antibiotic treatment of pulmonary exacerbations in people with CF who have a history of frequent versus infrequent exacerbations.

METHODS

Adults with CF were recruited at onset of antibiotic treatment of a pulmonary exacerbation and were categorised as infrequent or frequent exacerbators based on their pulmonary exacerbation frequency in the previous 12 months. Clinical parameters, sputum bacterial load and sputum inflammatory markers were measured on day 0, day 5 and at the end of treatment. Shotgun proteomic analysis was performed on sputum using liquid chromatography-mass spectrometry.

MEASUREMENTS AND MAIN RESULTS

Many sputum proteins were differentially enriched between infrequent and frequent exacerbators (day 0 n=23 and day 5 n=31). The majority of these proteins had a higher abundance in infrequent exacerbators and were secreted innate host defence proteins with antimicrobial, antiprotease and immunomodulatory functions. Several differentially enriched proteins were validated using ELISA and Western blot including secretory leukocyte protease inhibitor (SLPI), lipocalin-1 and cystatin SA. Sputum from frequent exacerbators demonstrated potent ability to cleave exogenous recombinant SLPI in a neutrophil elastase dependent manner. Frequent exacerbators had increased sputum inflammatory markers (interleukin (IL)-1β and IL-8) and total bacterial load compared to infrequent exacerbators.

CONCLUSIONS

A diminished innate host protein defence may play a role in the pathophysiological mechanisms of frequent CF pulmonary exacerbations. Frequent exacerbators may benefit from therapies targeting this dysregulated host immune response.

BioKA: a curated and integrated biomarker knowledgebase for animals

Biomarkers play an important role in various area such as personalized medicine, drug development, clinical care, and molecule breeding. However, existing animals' biomarker resources predominantly focus on human diseases, leaving a significant gap in non-human animal disease understanding and breeding research. To address this limitation, we present BioKA (Biomarker Knowledgebase for Animals, https://ngdc.cncb.ac.cn/bioka), a curated and integrated knowledgebase encompassing multiple animal species, diseases/traits, and annotated resources. Currently, BioKA houses 16 296 biomarkers associated with 951 mapped diseases/traits across 31 species from 4747 references, including 11 925 gene/protein biomarkers, 1784 miRNA biomarkers, 1043 mutation biomarkers, 773 metabolic biomarkers, 357 circRNA biomarkers and 127 lncRNA biomarkers. Furthermore, BioKA integrates various annotations such as GOs, protein structures, protein-protein interaction networks, miRNA targets and so on, and constructs an interactive knowledge network of biomarkers including circRNA-miRNA-mRNA associations, lncRNA-miRNA associations and protein-protein associations, which is convenient for efficient data exploration. Moreover, BioKA provides detailed information on 308 breeds/strains of 13 species, and homologous annotations for 8784 biomarkers across 16 species, and offers three online application tools. The comprehensive knowledge provided by BioKA not only advances human disease research but also contributes to a deeper understanding of animal diseases and supports livestock breeding.

Hypoxia A Typical Target in Human Lung Cancer Therapy

Lung cancer (LC) is the leading cause of cancer-related death globally. Comprehensive knowledge of the cellular and molecular etiology of LC is perilous for the development of active treatment approaches. Hypoxia in cancer is linked with malignancy, and its phenotype is implicated in the hypoxic reaction, which is being studied as a prospective cancer treatment target. The hypervascularization of the tumor is the main feature of human LC, and hypoxia is a major stimulator of neo-angiogenesis. It was seen that low oxygen levels in human LC are a critical aspect of this lethal illness. However, as there is a considerable body of literature espousing the presumed functional relevance of hypoxia in LC, the direct measurement of oxygen concentration in Human LC is yet to be determined. This narrative review aims to show the importance and as a future target for novel research studies that can lead to the perception of LC therapy in hypoxic malignancies.

Blood-Based Biomarkers for Managing Workload in Athletes: Perspectives for Research on Emerging Biomarkers

At present, various blood-based biomarkers have found their applications in the field of sports medicine. This current opinion addresses biomarkers that warrant consideration in future research for monitoring the athlete training load. In this regard, we identified a variety of emerging load-sensitive biomarkers, e.g., cytokines (such as IL-6), chaperones (such as heat shock proteins) or enzymes (such as myeloperoxidase) that could improve future athlete load monitoring as they have shown meaningful increases in acute and chronic exercise settings. In some cases, they have even been linked to training status or performance characteristics. However, many of these markers have not been extensively studied and the cost and effort of measuring these parameters are still high, making them inconvenient for practitioners so far. We therefore outline strategies to improve knowledge of acute and chronic biomarker responses, including ideas for standardized study settings. In addition, we emphasize the need for methodological advances such as the development of minimally invasive point-of-care devices as well as statistical aspects related to the evaluation of these monitoring tools to make biomarkers suitable for regular load monitoring.

Sputum induction and its diagnostic applications in inflammatory airway disorders: a review

Sputum induction is a technique that covers the induction and the subsequent processing of the expectorate primarily for the analysis of cells and different inflammatory biomarkers present in the airways to further understand the pathophysiology of different inflammatory respiratory disorders such as asthma and chronic obstructive pulmonary disease (COPD) as well as the diagnosis of lung diseases such as lung cancer, tuberculosis, and Pneumocystis jirovecii pneumonia. It is a non-invasive, safe, cost-effective, and reliable technique reported to exhibit a high success rate. However, due to being technically demanding and time-consuming and having the need of employing trained staff, this technique is only used in restricted research centres and in limited centres of clinical use. When the sputum is collected after induction, the primary goal is to obtain a differential cell count and evaluate the molecular biomarkers of airway inflammation such as eosinophil cationic protein, eosinophil-derived neurotoxin, major basic protein, tryptase, cytokine production [e.g., interleukin (IL)-5], albumin, and fibrinogen. In addition, cytospins from the processed sputum are used for immunocytochemical staining of cellular products such as EG-2 reactive protein, granulocyte-macrophage colony-stimulating factor, tumour necrosis factor alpha, and IL-8 that play significant roles in understanding the pathophysiology of inflammatory airway diseases. Nowadays, this technique can be further used by performing an additional analysis such as flow cytometry and in situ hybridisation on the sputum supernatant to investigate more the immune response and pathophysiological process of such various respiratory diseases. In addition, the application of sputum fluid phase to assess the biomarkers could be used more routinely in pathological laboratories for diagnosing lung cancer, COPD, and asthma as well as for monitoring lung cancer progression and asthma and COPD treatment, allowing for early detection and a better treatment provided by the clinicians.

Exhaled volatile organic compounds for diagnosis and monitoring of asthma

The asthmatic inflammatory process results in the generation of volatile organic compounds (VOCs), which are subsequently secreted by the airways. The study of these elements through gas chromatography-mass spectrometry (GC-MS), which can identify individual molecules with a discriminatory capacity of over 85%, and electronic-Nose (e-NOSE), which is able to perform a quick onboard pattern-recognition analysis of VOCs, has allowed new prospects for non-invasive analysis of the disease in an "omics" approach. In this review, we aim to collect and compare the progress made in VOCs analysis using the two methods and their instrumental characteristics. Studies have described the potential of GC-MS and e-NOSE in a multitude of relevant aspects of the disease in both children and adults, as well as differential diagnosis between asthma and other conditions such as wheezing, cystic fibrosis, COPD, allergic rhinitis and last but not least, the accuracy of these methods compared to other diagnostic tools such as lung function, FeNO and eosinophil count. Due to significant limitations of both methods, it is still necessary to improve and standardize techniques. Currently, e-NOSE appears to be the most promising aid in clinical practice, whereas GC-MS, as the gold standard for the structural analysis of molecules, remains an essential tool in terms of research for further studies on the pathophysiologic pathways of the asthmatic inflammatory process. In conclusion, the study of VOCs through GC-MS and e-NOSE appears to hold promise for the non-invasive diagnosis, assessment, and monitoring of asthma, as well as for further research studies on the disease.

Gene signatures in U-BIOPRED severe asthma for molecular phenotyping and precision medicine: time for clinical use

INTRODUCTION

The use and generation of gene signatures have been established as a method to define molecular endotypes in complex diseases such as severe asthma. Bioinformatic approaches have now been applied to large omics datasets to define the various co-existing inflammatory and cellular functional pathways driving or characterizing a particular molecular endotype.

AREAS COVERED

Molecular phenotypes and endotypes of Type 2 inflammatory pathways and also of non-Type 2 inflammatory pathways, such as IL-6 trans-signaling, IL-17 activation, and IL-22 activation, have been defined in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes dataset. There has also been the identification of the role of mast cell activation and of macrophage dysfunction in various phenotypes of severe asthma.

EXPERT OPINION

Phenotyping on the basis of clinical treatable traits is not sufficient for understanding of mechanisms driving the disease in severe asthma. It is time to consider whether certain patients with severe asthma, such as those non-responsive to current therapies, including Type 2 biologics, would be better served using an approach of molecular endotyping using gene signatures for management purposes rather than the current sole reliance on blood eosinophil counts or exhaled nitric oxide measurements.

Stratification of asthma by lipidomic profiling of induced sputum supernatant

BACKGROUND

Asthma is a chronic respiratory disease with significant heterogeneity in its clinical presentation and pathobiology. There is need for improved understanding of respiratory lipid metabolism in asthma patients and its relation to observable clinical features.

OBJECTIVE

We performed a comprehensive, prospective, cross-sectional analysis of the lipid composition of induced sputum supernatant obtained from asthma patients with a range of disease severities, as well as from healthy controls.

METHODS

Induced sputum supernatant was collected from 211 adults with asthma and 41 healthy individuals enrolled onto the U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes) study. Sputum lipidomes were characterized by semiquantitative shotgun mass spectrometry and clustered using topologic data analysis to identify lipid phenotypes.

RESULTS

Shotgun lipidomics of induced sputum supernatant revealed a spectrum of 9 molecular phenotypes, highlighting not just significant differences between the sputum lipidomes of asthma patients and healthy controls, but also within the asthma patient population. Matching clinical, pathobiologic, proteomic, and transcriptomic data helped inform the underlying disease processes. Sputum lipid phenotypes with higher levels of nonendogenous, cell-derived lipids were associated with significantly worse asthma severity, worse lung function, and elevated granulocyte counts.

CONCLUSION

We propose a novel mechanism of increased lipid loading in the epithelial lining fluid of asthma patients resulting from the secretion of extracellular vesicles by granulocytic inflammatory cells, which could reduce the ability of pulmonary surfactant to lower surface tension in asthmatic small airways, as well as compromise its role as an immune regulator.

Type-2-low severe asthma endotypes for new treatments: the new asthma frontier

PURPOSE OF REVIEW

Type-2 (T2)-high asthma represents a well defined group of severe eosinophilic asthma for which there are now effective biologic therapies targetting the interleukins (ILs) 4, 5 and 13, and Immunoglobulin E. T2-low asthma detected in the clinic by a low blood eosinophil count remains ill-defined and is the focus of this review.

RECENT FINDINGS

By analysing transcriptomic and proteomic expression in sputum samples in U-BIOPRED cohort, both T2-high and -low molecular phenotypes have been described. Using clustering approaches, a neutrophilic-predominant cluster associated with activation markers of neutrophilic and inflammasome activation with interferon and tumour necrosis factor expression, together with a cluster of paucigranulocytic inflammation linked to oxidative phosphorylation and senescence pathways have been described. Using gene set variation analysis, specific molecular phenotypes driven by IL-6 trans-signalling pathway, or those by IL-6, IL-17 and IL-22 pathways were identified linked to a mixed granulocytic or neutrophilic inflammation.

SUMMARY

Previous trials of antineutrophilic agents in asthma have failed because enrolled patients were not specifically chosen for these targeted treatments. Although the T2-low molecular pathways should be validated in other cohorts, the availability of targeted therapies indicated for other autoimmune conditions should encourage a trial of these respective biological therapies for these specific molecular phenotypes.

Exhaled Biomarkers for Point-of-Care Diagnosis: Recent Advances and New Challenges in Breathomics

Cancers, chronic diseases and respiratory infections are major causes of mortality and present diagnostic and therapeutic challenges for health care. There is an unmet medical need for non-invasive, easy-to-use biomarkers for the early diagnosis, phenotyping, predicting and monitoring of the therapeutic responses of these disorders. Exhaled breath sampling is an attractive choice that has gained attention in recent years. Exhaled nitric oxide measurement used as a predictive biomarker of the response to anti-eosinophil therapy in severe asthma has paved the way for other exhaled breath biomarkers. Advances in laser and nanosensor technologies and spectrometry together with widespread use of algorithms and artificial intelligence have facilitated research on volatile organic compounds and artificial olfaction systems to develop new exhaled biomarkers. We aim to provide an overview of the recent advances in and challenges of exhaled biomarker measurements with an emphasis on the applicability of their measurement as a non-invasive, point-of-care diagnostic and monitoring tool.

Metabolomics of Respiratory Diseases

Metabolomics is an expanding field of systems biology that is gaining significant attention in respiratory research. As a unique approach to understanding and diagnosing diseases, metabolomics provides a snapshot of all metabolites present in biological samples such as exhaled breath condensate, bronchoalveolar lavage, plasma, serum, urine, and other specimens that may be obtained from patients with respiratory diseases. In this article, we review the rapidly expanding field of metabolomics in its application to respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and acute lung injury, along with its more severe form, adult respiratory disease syndrome. We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized occupational and environmental materials. With the latest advances in our understanding of the microbiome, we discuss microbiome-derived metabolites that arise from the gut and lung in asthma and COPD that have mechanistic implications for these diseases. Recent literature has suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe diseases which may be fatal for many patients each year.

Omics approaches: interactions at the maternal-fetal interface and origins of child health and disease

Immunoperinatology is an emerging field. Transdisciplinary efforts by physicians, physician-scientists, basic science researchers, and computational biologists have made substantial advancements by identifying unique immunologic signatures of specific diseases, discovering innovative preventative or treatment strategies, and establishing foundations for individualized neonatal intensive care of the most vulnerable neonates. In this review, we summarize the immunobiology and immunopathology of pregnancy, highlight omics approaches to study the maternal-fetal interface, and their contributions to pregnancy health. We examined the importance of transdisciplinary, multiomic (such as genomics, transcriptomics, proteomics, metabolomics, and immunomics) and machine-learning strategies in unraveling the mechanisms of adverse pregnancy, neonatal, and childhood outcomes and how they can guide the development of novel therapies to improve maternal and neonatal health. IMPACT: Discuss immunoperinatology research from the lens of omics and machine-learning approaches. Identify opportunities for omics-based approaches to delineate infection/inflammation-associated maternal, neonatal, and later life adverse outcomes (e.g., histologic chorioamnionitis [HCA]).

Severity of Lung Function Impairment Drives Transcriptional Phenotypes of COPD and Relates to Immune and Metabolic Processes

Purpose

This study sought to characterize transcriptional phenotypes of COPD through unsupervised clustering of sputum gene expression profiles, and further investigate mechanisms underlying the characteristics of these clusters.

Patients and methods

Induced sputum samples were collected from patients with stable COPD (n = 72) and healthy controls (n = 15). Induced sputum was collected for inflammatory cell counts, and RNA extracted. Transcriptional profiles were generated (Illumina Humanref-8 V2) and analyzed by GeneSpring GX14.9.1. Unsupervised hierarchical clustering and differential gene expression analysis were performed, and gene alterations validated in the ECLIPSE dataset (GSE22148).

Results

We identified 2 main clusters (Cluster 1 [n = 35] and Cluster 2 [n = 37]), which further divided into 4 sub-clusters (Sub-clusters 1.1 [n = 14], 1.2 [n = 21], 2.1 [n = 20] and 2.2 [n = 17]). Compared with Cluster 1, Cluster 2 was associated with significantly lower lung function (p = 0.014), more severe disease (p = 0.009) and breathlessness (p = 0.035), and increased sputum neutrophils (p = 0.031). Sub-cluster 1.1 had significantly higher proportion of people with comorbid cardiovascular disease compared to the other 3 sub-clusters (92.5% vs 57.1%, 50% and 52.9%, p < 0.013). Through supervised analysis we determined that degree of airflow limitation (GOLD stage) was the predominant factor driving gene expression differences in our transcriptional clusters. There were 452 genes (adjusted p < 0.05 and ≥2 fold) altered in GOLD stage 3 and 4 versus 1 and 2, of which 281 (62%) were also found to be significantly expressed between these GOLD stages in the ECLIPSE data set (GSE22148). Differentially expressed genes were largely downregulated in GOLD stages 3 and 4 and connected in 5 networks relating to lipoprotein and cholesterol metabolism; metabolic processes in oxidation/reduction and mitochondrial function; antigen processing and presentation; regulation of complement activation and innate immune responses; and immune and metabolic processes.

Conclusion

Severity of lung function drives 2 distinct transcriptional phenotypes of COPD and relates to immune and metabolic processes.

Systems Biology in Asthma

The application of mathematical and computational analysis, together with the modelling of biological and physiological processes, is transforming our understanding of the pathophysiology of complex diseases. This systems biology approach incorporates large amounts of genomic, transcriptomic, proteomic, metabolomic, breathomic, metagenomic and imaging data from disease sites together with deep clinical phenotyping, including patient-reported outcomes. Integration of these datasets will provide a greater understanding of the molecular pathways associated with severe asthma in each individual patient and determine their personalised treatment regime. This chapter describes some of the data integration methods used to combine data sets and gives examples of the results obtained using single datasets and merging of multiple datasets (data fusion and data combination) from several consortia including the severe asthma research programme (SARP) and the Unbiased Biomarkers Predictive of Respiratory Disease Outcomes (U-BIOPRED) consortia. These results highlight the involvement of several different immune and inflammatory pathways and factors in distinct subsets of patients with severe asthma. These pathways often overlap in patients with distinct clinical features of asthma, which may explain the incomplete or no response in patients undergoing specific targeted therapy. Collaboration between groups will improve the predictions obtained using a systems medicine approach in severe asthma.

Precision Medicine in Asthma Therapy

Asthma is a complex, heterogeneous disease that necessitates a proper patient evaluation to decide the correct treatment and optimize disease control. The recent introduction of new target therapies for the most severe form of the disease has heralded a new era of treatment options, intending to treat and control specific molecular pathways in asthma pathophysiology. Precision medicine, using omics sciences, investigates biological and molecular mechanisms to find novel biomarkers that can be used to guide treatment selection and predict response. The identification of reliable biomarkers indicative of the pathological mechanisms in asthma is essential to unravel new potential treatment targets. In this chapter, we provide a general description of the currently available -omics techniques, focusing on their implications in asthma 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.

Spatial multiomic profiling reveals the novel polarization of foamy macrophages within necrotic granulomatous lesions developed in lungs of C3HeB/FeJ mice infected with Mycobacterium tuberculosis

Infection with Mycobacterium tuberculosis leads to the development of tuberculosis (TB) with the formation of granulomatous lesions. Foamy macrophages (FM) are a hallmark of TB granulomas, because they provide the primary platform of M. tuberculosis proliferation and the main source of caseous necrosis. In this study, we applied spatial multiomic profiling to identify the signatures of FM within the necrotic granulomas developed in a mouse model resembling human TB histopathology. C3HeB/FeJ mice were infected with M. tuberculosis to induce the formation of necrotic granulomas in the lungs. Using laser microdissection, necrotic granulomas were fractionated into three distinct regions, including the central caseous necrosis, the rim containing FM, and the peripheral layer of macrophages and lymphocytes, and subjected to proteomic and transcriptomic analyses. Comparison of proteomic and transcriptomic analyses of three distinct granulomatous regions revealed that four proteins/genes are commonly enriched in the rim region. Immunohistochemistry confirmed the localization of identified signatures to the rim of necrotic granulomas. We also investigated the localization of the representative markers for M1 macrophages in granulomas because the signatures of the rim included M2 macrophage markers. The localization of both macrophage markers suggests that FM in necrotic granulomas possessed the features of M1 or M2 macrophages. Gene set enrichment analysis of transcriptomic profiling revealed the upregulation of genes related to M2 macrophage activation and mTORC1 signaling in the rim. These results will provide new insights into the process of FM biogenesis, leading to further understanding of the pathophysiology of TB granulomas.

Neisseria species as pathobionts in bronchiectasis

Neisseria species are frequently identified in the bronchiectasis microbiome, but they are regarded as respiratory commensals. Using a combination of human cohorts, next-generation sequencing, systems biology, and animal models, we show that bronchiectasis bacteriomes defined by the presence of Neisseria spp. associate with poor clinical outcomes, including exacerbations. Neisseria subflava cultivated from bronchiectasis patients promotes the loss of epithelial integrity and inflammation in primary epithelial cells. In vivo animal models of Neisseria subflava infection and metabolipidome analysis highlight immunoinflammatory functional gene clusters and provide evidence for pulmonary inflammation. The murine metabolipidomic data were validated with human Neisseria-dominant bronchiectasis samples and compared with disease in which Pseudomonas-, an established bronchiectasis pathogen, is dominant. Metagenomic surveillance of Neisseria across various respiratory disorders reveals broader importance, and the assessment of the home environment in bronchiectasis implies potential environmental sources of exposure. Thus, we identify Neisseria species as pathobionts in bronchiectasis, allowing for improved risk stratification in this high-risk group.

Short-Term Test for Toxicogenomic Analysis of Ecotoxic Modes of Action in Lemna minor

In the environmental risk assessment of substances, toxicity to aquatic plants is evaluated using, among other methods, the 7 dayLemna sp. growth inhibition test following the OECD TG 221. So far, the test is not applicable for short-term screening of toxicity, nor does it allow evaluation of toxic modes of action (MoA). The latter is also complicated by the lack of knowledge of gene functions in the test species. Using ecotoxicogenomics, we developed a time-shortened 3 day assay inLemna minor which allows discrimination of ecotoxic MoA. By examining the changes in gene expression induced by low effect concentrations of the pharmaceutical atorvastatin and the herbicide bentazon at the transcriptome and proteome levels, we were able to identify candidate biomarkers for the respective MoA. We developed a homology-based functional annotation pipeline for the reference genome ofL. minor, which allowed overrepresentation analysis of the gene ontologies affected by both test compounds. Genes affected by atorvastatin mainly influenced lipid synthesis and metabolism, whereas the bentazon-responsive genes were mainly involved in light response. Our approach is therefore less time-consuming but sensitive and allows assessment of MoA in L. minor. Using this shortened assay, investigation of expression changes of the identified candidate biomarkers may allow the development of MoA-specific screening approaches in the future.

Association between lipid profiles and viral respiratory infections in human sputum samples

Background

Respiratory infections such as influenza account for significant global mortality each year. Generating lipid profiles is a novel and emerging research approach that may provide new insights regarding the development and progression of priority respiratory infections. We hypothesized that select clusters of lipids in human sputum would be associated with specific viral infections (Influenza (H1N1, H3N2) or Rhinovirus).

Methods

Lipid identification and semi-quantitation was determined with liquid chromatography and high-resolution mass spectrometry in induced sputum from individuals with confirmed respiratory infections (influenza (H1N1, H3N2) or rhinovirus). Clusters of lipid species and associations between lipid profiles and the type of respiratory viral agent was determined using Bayesian profile regression and multinomial logistic regression.

Results

More than 600 lipid compounds were identified across the sputum samples with the most abundant lipid classes identified as triglycerides (TG), phosphatidylethanolamines (PE), phosphatidylcholines (PC), Sphingomyelins (SM), ether-PC, and ether-PE. A total of 12 lipid species were significantly different when stratified by infection type and included acylcarnitine (AcCar) (10:1, 16:1, 18:2), diacylglycerols (DG) (16:0_18:0, 18:0_18:0), Lysophosphatidylcholine (LPC) (12:0, 20:5), PE (18:0_18:0), and TG (14:1_16:0_18:2, 15:0_17:0_19:0, 16:0_17:0_18:0, 19:0_19:0_19:0). Cluster analysis yielded three clusters of lipid profiles that were driven by just 10 lipid species (TGs and DGs). Cluster 1 had the highest levels of each lipid species and the highest prevalence of influenza A H3 infection (56%, n = 5) whereas cluster 3 had lower levels of each lipid species and the highest prevalence of rhinovirus (60%; n = 6). Using cluster 3 as the reference group, the crude odds of influenza A H3 infection compared to rhinovirus in cluster 1 was significantly (p = 0.047) higher (OR = 15.00 [95% CI: 1.03, 218.29]). After adjustment for confounders (smoking status and pulmonary comorbidities), the odds ratio (OR) became only marginally significant (p = 0.099), but the magnitude of the effect estimate was similar (OR = 16.00 [0.59, 433.03]).

Conclusions

In this study, human sputum lipid profiles were shown to be associated with distinct types of viral infection. Better understanding the relationship between respiratory infections of global importance and lipids contributes to advancing knowledge of pathogenesis of infections including identifying populations with increased susceptibility and developing effective therapeutics and biomarkers of health status.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02091-w.

Novel Potential Diagnostic Serum Biomarkers of Metabolomics in Osteoarticular Tuberculosis Patients: A Preliminary Study

Osteoarticular tuberculosis is one of the extrapulmonary tuberculosis, which is mainly caused by direct infection of Mycobacterium tuberculosis or secondary infection of tuberculosis in other parts. Due to the low specificity of the current detection method, it is leading to a high misdiagnosis rate and subsequently affecting the follow-up treatment and prognosis. Metabolomics is mainly used to study the changes of the body’s metabolites in different states, so it can serve as an important means in the discovery of disease-related metabolic biomarkers and the corresponding mechanism research. Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to detect and analyze metabolites in the serum with osteoarticular tuberculosis patients, disease controls, and healthy controls to find novel metabolic biomarkers that could be used in the diagnosis of osteoarticular tuberculosis. Our results showed that 68 differential metabolites (p<0.05, fold change>1.0) were obtained in osteoarticular tuberculosis serum after statistical analysis. Then, through the evaluation of diagnostic efficacy, PC[o-16:1(9Z)/18:0], PC[20:4(8Z,11Z,14Z,17Z)/18:0], PC[18:0/22:5(4Z,7Z,10Z,13Z,16Z)], SM(d18:1/20:0), and SM[d18:1/18:1(11Z)] were found as potential biomarkers with high diagnostic efficacy. Using bioinformatics analysis, we further found that these metabolites share many lipid metabolic signaling pathways, such as choline metabolism, sphingolipid signaling, retrograde endocannabinoid signaling, and sphingolipid and glycerophospholipid metabolism; these results suggest that lipid metabolism plays an important role in the pathological process of tuberculosis. This study can provide certain reference value for the study of metabolic biomarkers of osteoarticular tuberculosis and the mechanism of lipid metabolism in osteoarticular tuberculosis and even other tuberculosis diseases.

New drugs under development for COPD

The characteristic features of chronic obstructive pulmonary disease (COPD) include inflammation and remodelling of the lower airways and lung parenchyma together with activation of inflammatory and immune processes. Due to the increasing habit of cigarette smoking worldwide COPD prevalence is increasing globally. Current therapies are unable to prevent COPD progression in many patients or target many of its hallmark characteristics which may reflect the lack of adequate biomarkers to detect the heterogeneous clinical and molecular nature of COPD. In this chapter we review recent molecular data that may indicate novel pathways that underpin COPD subphenotypes and indicate potential improvements in the classes of drugs currently used to treat COPD. We also highlight the evidence for new drugs or approaches to treat COPD identified using molecular and other approaches including kinase inhibitors, cytokine- and chemokine-directed biologicals and small molecules, antioxidants and redox signalling pathway inhibitors, inhaled anti-infectious agents and senolytics. It is important to consider the phenotypes/molecular endotypes of COPD patients together with specific outcome measures to target new therapies to particular COPD subtypes. This will require greater understanding of COPD molecular pathologies and a focus on biomarkers of predicting disease subsets and responder/non-responder populations.

Automated Recommendation of Research Keywords from PubMed That Suggest the Molecular Mechanism Associated with Biomarker Metabolites

Metabolomics can help identify candidate biomarker metabolites whose levels are altered in response to disease development or drug administration. However, assessment of the underlying molecular mechanism is challenging considering it depends on the researcher’s knowledge. This study reports a novel method for the automated recommendation of keywords known in the literature that may be overlooked by researchers. The proposed method aided in the identification of Medical Subject Headings (MeSH) terms in PubMed using MeSH co-occurrence data. The intended users are biocurators who have identified specific biomarker metabolites from a metabolomics study and would like to identify literature-reported molecular mechanisms that are associated with both the metabolite and their research area of interest. The proposed method finds MeSH terms that co-occur with a MeSH term of the candidate biomarker metabolite as well as a MeSH term of a researcher’s known keyword, such as the name of a disease. The connectivity score S was determined using association analysis. Pilot analyses demonstrated that, while the biological significance of the obtained MeSH terms could not be guaranteed, the developed method can be useful for finding keywords to further investigate molecular mechanisms in association with candidate biomarker molecules.

Allergen Provocation Tests in Respiratory Research: Building on 50 Years of Experience

Allergen provocation test is an established model of allergic airway diseases, including asthma and allergic rhinitis, allowing the study of allergen-induced changes in respiratory physiology and inflammatory mechanisms in sensitised individuals as well as their associations. In the upper airways, allergen challenge is focused on the clinical and pathophysiological sequelae of the early allergic response and applied both as a diagnostic tool and in research settings. In contrast, the bronchial allergen challenge has almost exclusively served as a research tool in specialised research settings with a focus on the late asthmatic response and the underlying type 2 inflammation. The allergen-induced late asthmatic response is also characterised by prolonged airway narrowing, increased non-specific airway hyperresponsiveness and features of airway remodelling including the small airways, and hence, allows the study of several key mechanisms and features of asthma. In line with these characteristics, the allergen challenge has served as a valued tool to study the crosstalk of the upper and lower airways and in proof of mechanism studies of drug development. In recent years, several new insights into respiratory phenotypes and endotypes including the involvement of the upper and small airways, innovative biomarker sampling methods and detection techniques, refined lung function testing as well as targeted treatment options, further shaped the applicability of the allergen provocation test in precision medicine. These topics, along with descriptions of subject populations and safety, in line with the updated GINA2021, will be addressed in this paper.

Exhaled Metabolite Patterns to Identify Recent Asthma Exacerbations

Asthma is a chronic respiratory disease that can lead to exacerbations, defined as acute episodes of worsening respiratory symptoms and lung function. Predicting the occurrence of these exacerbations is an important goal in asthma management. The measurement of exhaled breath by electronic nose (eNose) may allow for the monitoring of clinically unstable asthma and exacerbations. However, data on its ability to perform this is lacking. We aimed to evaluate whether eNose could identify patients that recently had asthma exacerbations. We performed a cross-sectional study, measuring exhaled breath using the SpiroNose in adults with a physician-reported diagnosis of asthma. Patients were randomly divided into a training (n = 252) and validation (n = 109) set. For the analysis of eNose signals, principal component (PC) and linear discriminant analysis (LDA) were performed. LDA, based on PC1-4, reliably discriminated between patients who had a recent exacerbation from those who had not (training receiver operating characteristic (ROC)–area under the curve (AUC) = 0.76,95% CI 0.69–0.82), (validation AUC = 0.76, 95% CI 0.64–0.87). Our study showed that, exhaled breath analysis using eNose could accurately identify asthma patients who recently had an exacerbation, and could indicate that asthma exacerbations have a specific exhaled breath pattern detectable by eNose.

A reversed phase ultra-high-performance liquid chromatography-data independent mass spectrometry method for the rapid identification of mycobacterial lipids

A rapid reversed-phase ultra-high-performance liquid chromatography-high resolution mass spectrometry based mycobacterial lipidomics approach is described. This method enables the separation of various lipid classes including lipids specific to mycobacterial, such as methoxy mycolic acid and α-mycolic acid. Lipid separation occurs during a relatively short runtime of 14 min on a charged surface hybrid C₁₈ column. A high-resolution quadrupole-time of flight mass spectrometer and a data independent acquisition mode allowed for the simultaneous acquisition of the full scan and collision induced dissociation fragmentation. The proposed method provides lipid detection results equivalent to or better than existing methods, but with a faster throughput and an overall higher sensitivity. The reversed-phase ultra-high-performance liquid chromatography-high resolution mass spectrometry method was shown to obtain structural information for lipids extracted from Mycobacterium smegmatis, but the method is applicable to the analysis of lipids from various bacterial and mammalian cell lines.

The variability of volatile organic compounds in the indoor air of clinical environments

The development of clinical breath-analysis is confounded by the variability of background volatile organic compounds (VOCs). Reliable interpretation of clinical breath-analysis at individual, and cohort levels requires characterisation of clinical-VOC levels and exposures. Active-sampling with thermal-desorption/gas chromatography-mass spectrometry recorded and evaluated VOC concentrations in 245 samples of indoor air from three sites in a large National Health Service (NHS) provider trust in the UK over 27 months. Data deconvolution, alignment and clustering isolated 7344 features attributable to VOC and described the variability (composition and concentration) of respirable clinical VOC. 328 VOC were observed in more than 5% of the samples and 68 VOC appeared in more than 30% of samples. Common VOC were associated with exogenous and endogenous sources and 17 VOC were identified as seasonal differentiators. The presence of metabolites from the anaesthetic sevoflurane, and putative-disease biomarkers in room air, indicated that exhaled VOC were a source of background-pollution in clinical breath-testing activity. With the exception of solvents, and waxes associated with personal protective equipment (PPE), exhaled VOC concentrations above 3µg m^-3are unlikely to arise from room air contamination, and in the absence of extensive survey-data, this level could be applied as a threshold for inclusion in studies, removing a potential environmental confounding-factor in developing breath-based diagnostics.

Therapeutic challenges at the preclinical level for targeted drug development for Opisthorchis viverrini-associated cholangiocarcinoma

INTRODUCTION

Cholangiocarcinoma (CCA) is a malignant tumor of bile duct epithelium with the highest incidence found in Thailand. Some patients are considered suitable for adjuvant therapy and surgical resection is currently the curative treatment for CCA patients. Tumor recurrence is still a hurdle after treatment; hence, finding novel therapeutic strategies to combat CCA is necessary for improving outcome for patients.

AREAS COVERED

We discuss targeted therapies and other novel treatment approaches which include protein kinase inhibitors, natural products, amino acid transporter-based inhibitors, immunotherapy, and drug repurposing. We also examine the challenges of tumor heterogeneity, cancer stem cells (CSCs), the tumor microenvironment, exosomes, multiomics studies, and the potential of precision medicine.

EXPERT OPINION

Because CCA is difficult to diagnose at the early stage, the traditional treatment approaches are not effective for many patients and most tumors recur. Consequently, researchers are exploring multi-aspect molecular carcinogenesis to uncover molecular targets for further development of novel targeted drugs.

Arachidonic acid metabolism is elevated in Mycoplasma gallisepticum and Escherichia coli co-infection and induces LTC4 in serum as the biomarker for detecting poultry respiratory disease

Outbreaks of multiple respiratory diseases with high morbidity and mortality have been frequently reported in poultry industry. Metabolic profiling has showed widespread usage in metabolic and infectious disease for identifying biomarkers and understanding of complex mechanisms. In this study, the non-targeted metabolomics were used on Mycoplasma gallisepticum (MG) and Escherichia coli (E.coli) co-infection model in serum, which showed that Leukotriene C4 (LTC4), Leukotriene D4 (LTD4), Chenodeoxycholate, Linoleate and numerous energy metabolites were varied significantly. KEGG enrichment analysis revealed that the metabolic pathways of linoleic acid, taurine and arachidonic acid (AA) were upregulated. To further characterize the consequences of co-infection, we performed an AA metabolic network pathway with metabolic products and enzyme genes. The results showed that the expression of LTC4 increased extremely significant and accompanied with different degree of infection. Meanwhile, the AA network performed the changes and differences of various metabolites in the pathway when multiple respiratory diseases occurred. Taken together, co-infection induces distinct alterations in the serum metabolome owing to the activation of AA metabolism. Furthermore, LTC4 in serum could be used as the biomarker for detecting poultry respiratory disease.

Abbreviations

MG: Mycoplasma gallisepticum; E.coli: Escherichia coli; AA: Arachidonic acid; LTC4: Leukotriene C4; CRD: chronic respiratory diseases; KEGG: Kyoto Encyclopedia of Genes and Genomes; LTs: leukotrienes; PGs: prostaglandins; NO: nitric oxide; HIS: histamine; PCA: Principal Component Analysis; PLS-DA: Partial Least Squares Discriminant Analysis; CCU: color change unit; UPLC: ultra-performance liquid chromatography; MS: mass spectrometry; DEMs: differentially expressed metabolites; ELISA: enzyme-linked immunosorbent assay; SD: standard deviation; VIP: Variable importance in the projection

Temporal metabolic response yields a dynamic biosignature of inflammation

Chronic low-grade inflammation is a subclinical condition directly and indirectly linked to the development of a wide range of diseases responsible for the vast majority of morbidity. To examine mechanisms coupled to chronic disease, a group of overweight and obese human subjects without known inflammatory diseases participated in a high-fat meal challenge as an acute inflammation stimulus. Analysis of serum metabolites grouped by baseline cytokine levels revealed that single samples had little power in differentiating groups. However, an analysis that incorporated temporal response separated inflammatory response phenotypes and allowed us to create a metabolic signature of inflammation which revealed metabolic components that are crucial to a cytokine-mediated inflammation response. The use of temporal response, rather than a single time point, improved metabolomic prediction of high postprandial inflammation responses and led to the development of a dynamic biosignature as a potential tool for stratifying risk to a wide range of diseases.

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Dynamic responses often provide insight into disease pathology

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Temporal metabolic responses to acute inflammation were explored in obese people

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Temporal metabolite levels differentiated low, mid, and high inflammation groups

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Inflammation-linked metabolites were shown to be predictors of cytokine responses

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.

Proteome Characterization of BALF Extracellular Vesicles in Idiopathic Pulmonary Fibrosis: Unveiling Undercover Molecular Pathways

In the longtime challenge of identifying specific, easily detectable and reliable biomarkers of IPF, BALF proteomics is providing interesting new insights into its pathogenesis. To the best of our knowledge, the present study is the first shotgun proteomic investigation of EVs isolated from BALF of IPF patients. Our main aim was to characterize the proteome of the vesicular component of BALF and to explore its individual impact on the pathogenesis of IPF. To this purpose, ultracentrifugation was chosen as the EVs isolation technique, and their purification was assessed by TEM, 2DE and LC-MS/MS. Our 2DE data and scatter plots showed considerable differences between the proteome of EVs and that of whole BALF and of its fluid component. Analysis of protein content and protein functions evidenced that EV proteins are predominantly involved in cytoskeleton remodeling, adenosine signaling, adrenergic signaling, C-peptide signaling and lipid metabolism. Our findings may suggest a wider system involvement in the disease pathogenesis and support the importance of pre-fractioning of complex samples, such as BALF, in order to let low-abundant proteins-mediated pathways emerge.

Sputum proteomic analysis for distinguishing between pulmonary tuberculosis and non-tuberculosis using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS): preliminary results

OBJECTIVES

The aim was to evaluate the feasibility and diagnostic contribution of protein profiling using matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) applied to sputum to diagnose pulmonary tuberculosis.

METHODS

Sputum samples collected from patients suspected of having pulmonary tuberculosis were analysed using MALDI-TOF MS. Using the differentially expressed protein peaks, we compared three groups of patients, including those with confirmed pulmonary tuberculosis (PTB), those without tuberculosis but with a lower respiratory tract infection (non-TB LRTI) and those without tuberculosis and without an LRTI (non-TB controls).

RESULTS

A total of 102 patients included 35 PTB, 36 non-TB LRTI and 31 non-TB controls. The model differentiated between the PTB patients and the non-TB controls using the 25 most differentially expressed protein peaks, with a sensitivity of 97%, 95% CI 85-100%, and a specificity of 77%, 95% CI 59-90%. The model distinguished the PTB patients from the non-TB LRTI patients using the ten most differentially expressed protein peaks, with a sensitivity of 80%, 95% CI 63-92%, and a specificity of 89%, 95% CI 74-97%. We observed that the negative predictive value of MALDI-TOF MS sputum analysis was higher (96%, 95% CI 80-100%) than that of direct sputum microscopic examination and sputum culture (78%, 95% CI 62-89%) for non-TB controls. When MALDI-TOF MS sputum analysis and direct microscopic examination were combined, the negative predictive value reached 94%, 95% CI 80-99%, for non-TB LRTI patients.

DISCUSSION

These results suggest that MALDI-TOF MS sputum analysis coupled with microscopic examination could be used as a screening tool for diagnosing pulmonary TB.

Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications

Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.

The effects of exercise training on lipid profile in patients with sarcoidosis

This study aimed to determine the use of lipid profiling to assess the effects of moderate intensity exercise training (ET) on patients with sarcoidosis. Fourteen patients with sarcoidosis (mean age, 46.0 ± 9.6 years) were examined before and after 3-week of ET programme in hospital settings. Symptoms (fatigue: FAS, dyspnoea: MRC), lung function tests and physical function tests (6 MWT, muscle force) were measured before and after ET. Proton nuclear magnetic resonance (NMR) spectroscopy combined with orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to determine lipid profile before and after ET. Twenty-five NMR signals from lipid compounds were selected for further analysis as well as serum lipid and inflammatory markers. Three weeks of ET results in improvement of symptoms (FAS: 27.5 vs. 21.0; p < 0.001, MRC: 0.86 vs. 0.14; p = 0.002) and physical function (6MWT: 508.43 vs. 547.29; p = 0.039). OPLS-DA analysis of the lipid profiles of patients with sarcoidosis revealed differences among the samples before and after ET, including decreases in fatty acids (p < 0.017), triglycerides (p < 0.022) and total cholesterol (p < 0.020). Other changes included shifts in fatty acids oxidation products and triacylglycerol esters. A short-time, in-hospital exercise training benefits patients with sarcoidosis by enhancing their physical function. Additionally, positive effect on lipid profile was observed also in this study. It is suggested that lipid profiling could become a new prognostic method to assess effects of pulmonary rehabilitation in patients with sarcoidosis.

Road to effective data curation for translational research

Translational research today is data-intensive and requires multi-stakeholder collaborations to generate and pool data together for integrated analysis. This leads to the challenge of harmonization of data from different sources with different formats and standards, which is often overlooked during project planning and thus becomes a bottleneck of the research progress. We report on our experience and lessons learnt about data curation for translational research garnered over the course of the European Translational Research Infrastructure & Knowledge management Services (eTRIKS) program (https://www.etriks.org), a unique, 5-year, cross-organizational, cross-cultural collaboration project funded by the Innovative Medicines Initiative of the EU. Here, we discuss the obstacles and suggest what steps are needed for effective data curation in translational research, especially for projects involving multiple organizations from academia and industry.

Advances in asthma genetics

While asthma has a strong genetic component, our current ability to systematically understand and predict asthma risk remains low, despite over a hundred genetic associations. The reasons for this unfilled gap range from technical limitations of current approaches to fundamental deficiencies in the way we understand asthma. These are discussed in the context of genomic advances.

Metabolic Profiles of Whole Serum and Serum-Derived Exosomes Are Different in Head and Neck Cancer Patients Treated by Radiotherapy

BACKGROUND

In general, the serum metabolome reflects the patient's body response to both disease state and implemented treatment. Though serum-derived exosomes are an emerging type of liquid biopsy, the metabolite content of these vesicles remains under researched. The aim of this pilot study was to compare the metabolite profiles of the whole serum and serum-derived exosomes in the context of differences between cancer patients and healthy controls as well as patients' response to radiotherapy (RT).

METHODS

Serum samples were collected from 10 healthy volunteers and 10 patients with head and neck cancer before and after RT. Metabolites extracted from serum and exosomes were analyzed by the gas chromatography-mass spectrometry (GC-MS).

RESULTS

An untargeted GC-MS-based approach identified 182 and 46 metabolites in serum and exosomes, respectively. Metabolites that differentiated cancer and control samples, either serum or exosomes, were associated with energy metabolism. Serum metabolites affected by RT were associated with the metabolism of amino acids, sugars, lipids, and nucleotides.

CONCLUSIONS

cancer-related features of energy metabolism could be detected in both types of specimens. On the other hand, in contrast to RT-induced changes observed in serum metabolome, this pilot study did not reveal a specific radiation-related pattern of exosome metabolites.

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.

Optimized Combination of Multiple Graphs With Application to the Integration of Brain Imaging and (epi)Genomics Data

With the rapid development of high-throughput technologies, a growing amount of multi-omics data are collected, giving rise to a great demand for combining such data for biomedical discovery. Due to the cost and time to label the data manually, the number of labelled samples is limited. This motivated the need for semi-supervised learning algorithms. In this work, we applied a graph-based semi-supervised learning (GSSL) to classify a severe chronic mental disorder, schizophrenia (SZ). An advantage of GSSL is that it can simultaneously analyse more than two types of data, while many existing models focus on pairwise data analysis. In particular, we applied GSSL to the analysis of single nucleotide polymorphism (SNP), functional magnetic resonance imaging (fMRI) and DNA methylation data, which accounts for genetics, brain imaging (endophenotypes), and environmental factors (epigenomics) respectively. While parameter selection has been an open challenge for most models, another key contribution of this work is that we explored the parameter space to interpret their meaning and established practical guidelines. Based on the practical significance of each hyper-parameter, a relatively small range of candidate values can be determined in a data-driven way to both optimize and speed up the parameter tuning process. We validated the model through both synthetic data and a real SZ dataset of 184 subjects from the Mental Illness and Neuroscience Discovery (MIND) Clinical Imaging Consortium. In comparison to several existing approaches, our algorithm achieved better performance in terms of classification accuracy. We also confirmed the significance of several brain regions associated with SZ.

Volatile organic compounds associated with diagnosis and disease characteristics in asthma - A systematic review

BACKGROUND

Metabolomics refers to study of the metabolome, the entire set of metabolites produced by a biological system. The application of metabolomics to exhaled breath samples - breathomics - is a rapidly growing field with potential application to asthma diagnosis and management.

OBJECTIVES

We aimed to review the adult asthma breathomic literature and present a comprehensive list of volatile organic compounds identified by asthma breathomic models.

METHODS

We undertook a systematic search for literature on exhaled volatile organic compounds in adult asthma. We assessed the quality of studies and performed a qualitative synthesis.

RESULTS

We identified twenty studies; these were methodologically heterogenous with a variable risk of bias. Studies almost universally reported breathomics to be capable of differentiating - with moderate or greater accuracy - between samples from healthy controls and those with asthma; and to be capable of phenotyping disease. However, there was little concordance in the compounds upon which discriminatory models were based.

CONCLUSION

Results to-date are promising but validation in independent prospective cohorts is needed. This may be challenging given the high levels of inter-individual variation. However, large-scale, multi-centre studies are underway and validation efforts have been aided by the publication of technical standards likely to increase inter-study comparability. Successful validation of breathomic models for diagnosis and phenotyping would constitute an important step towards personalised medicine in asthma.

Sputum microbiome profiles identify severe asthma phenotypes of relative stability at 12 to 18 months

BACKGROUND

Asthma is a heterogeneous disease characterized by distinct phenotypes with associated microbial dysbiosis.

OBJECTIVES

Our aim was to identify severe asthma phenotypes based on sputum microbiome profiles and assess their stability after 12 to 18 months. A further aim was to evaluate clusters' robustness after inclusion of an independent cohort of patients with mild-to-moderate asthma.

METHODS

In this longitudinal multicenter cohort study, sputum samples were collected for microbiome profiling from a subset of the Unbiased Biomarkers in Prediction of Respiratory Disease Outcomes adult patient cohort at baseline and after 12 to 18 months of follow-up. Unsupervised hierarchical clustering was performed by using the Bray-Curtis β-diversity measure of microbial profiles. For internal validation, partitioning around medoids, consensus cluster distribution, bootstrapping, and topological data analysis were applied. Follow-up samples were studied to evaluate within-patient clustering stability in patients with severe asthma. Cluster robustness was evaluated by using an independent cohort of patients with mild-to-moderate asthma.

RESULTS

Data were available for 100 subjects with severe asthma (median age 55 years; 42% males). Two microbiome-driven clusters were identified; they were characterized by differences in asthma onset, smoking status, residential locations, percentage of blood and/or sputum neutrophils and macrophages, lung spirometry results, and concurrent asthma medications (all P values < .05). The cluster 2 patients displayed a commensal-deficient bacterial profile that was associated with worse asthma outcomes than those of the cluster 1 patients. Longitudinal clusters revealed high relative stability after 12 to 18 months in those with severe asthma. Further inclusion of an independent cohort of 24 patients with mild-to-moderate asthma was consistent with the clustering assignments.

CONCLUSION

Unbiased microbiome-driven clustering revealed 2 distinct robust phenotypes of severe asthma that exhibited relative overtime stability. This suggests that the sputum microbiome may serve as a biomarker for better characterizing asthma phenotypes.

Needs for Systems Approaches to Better Treat Individuals With Severe Asthma: Predicting Phenotypes and Responses to Treatments

Asthma is a frequent heterogeneous multifactorial chronic disease whose severe forms remain largely uncontrolled despite the availability of many drugs and educational therapy. Several phenotypes and endotypes of severe asthma have been described over the last two decades. Typical type-2-immunity-driven asthma remains the most frequent phenotype, and several targeted therapies have been developed and are now available. On the contrary, non-type-2 immunity-driven severe asthma is less understood and still requires efficient innovative therapies. A personalized approach would allow improving asthma control with the help of robust biomarkers able to predict phenotypes/endotypes, exacerbations, response to targeted treatments and, in the future, possible curative options. Some data from large multicenter cohorts have emerged in recent years, especially in transcriptomics. These data have to be integrated and reproduced longitudinally to provide a systems approach for asthma care. In this focused review, the needs for such an approach and the available data will be reviewed as well as the next steps for achieving personalized medicine in asthma.