Transcriptomics, proteomics and metabolomics driven biomarker discovery in COPD: an update

The Current Molecular and Cellular Landscape of Chronic Obstructive Pulmonary Disease (COPD): A Review of Therapies and Efforts towards Personalized Treatment

Chronic obstructive pulmonary disease (COPD) ranks as the third leading cause of global illness and mortality. It is commonly triggered by exposure to respiratory irritants like cigarette smoke or biofuel pollutants. This multifaceted condition manifests through an array of symptoms and lung irregularities, characterized by chronic inflammation and reduced lung function. Present therapies primarily rely on maintenance medications to alleviate symptoms, but fall short in impeding disease advancement. COPD's diverse nature, influenced by various phenotypes, complicates diagnosis, necessitating precise molecular characterization. Omics-driven methodologies, including biomarker identification and therapeutic target exploration, offer a promising avenue for addressing COPD's complexity. This analysis underscores the critical necessity of improving molecular profiling to deepen our comprehension of COPD and identify potential therapeutic targets. Moreover, it advocates for tailoring treatment strategies to individual phenotypes. Through comprehensive exploration-based molecular characterization and the adoption of personalized methodologies, innovative treatments may emerge that are capable of altering the trajectory of COPD, instilling optimism for efficacious disease-modifying interventions.

Proteomic Blood Profiles Obtained by Totally Blind Biological Clustering in Stable and Exacerbated COPD Patients

Although Chronic Obstructive Pulmonary Disease (COPD) is highly prevalent, it is often underdiagnosed. One of the main characteristics of this heterogeneous disease is the presence of periods of acute clinical impairment (exacerbations). Obtaining blood biomarkers for either COPD as a chronic entity or its exacerbations (AECOPD) will be particularly useful for the clinical management of patients. However, most of the earlier studies have been characterized by potential biases derived from pre-existing hypotheses in one or more of their analysis steps: some studies have only targeted molecules already suggested by pre-existing knowledge, and others had initially carried out a blind search but later compared the detected biomarkers among well-predefined clinical groups. We hypothesized that a clinically blind cluster analysis on the results of a non-hypothesis-driven wide proteomic search would determine an unbiased grouping of patients, potentially reflecting their endotypes and/or clinical characteristics. To check this hypothesis, we included the plasma samples from 24 clinically stable COPD patients, 10 additional patients with AECOPD, and 10 healthy controls. The samples were analyzed through label-free liquid chromatography/tandem mass spectrometry. Subsequently, the Scikit-learn machine learning module and K-means were used for clustering the individuals based solely on their proteomic profiles. The obtained clusters were confronted with clinical groups only at the end of the entire procedure. Although our clusters were unable to differentiate stable COPD patients from healthy individuals, they segregated those patients with AECOPD from the patients in stable conditions (sensitivity 80%, specificity 79%, and global accuracy, 79.4%). Moreover, the proteins involved in the blind grouping process to identify AECOPD were associated with five biological processes: inflammation, humoral immune response, blood coagulation, modulation of lipid metabolism, and complement system pathways. Even though the present results merit an external validation, our results suggest that the present blinded approach may be useful to segregate AECOPD from stability in both the clinical setting and trials, favoring more personalized medicine and clinical research.

Multiomics was used to clarify the mechanism by which air pollutants affect chronic obstructive pulmonary disease: A human cohort study

Exposure to air pollutants has been associated with various adverse health outcomes, including chronic obstructive pulmonary disease (COPD). However, the precise underlying mechanism by which air pollution impacts COPD through remains insufficiently understood. To elucidated the molecular mechanism by which air pollutant exposure contributes to alterations in the gut microbiome and metabolism in AECOPD patients, we employed metagenomics and untargeted metabolomics to analyse the gut microbial, faecal, and serum metabolites. The correlations among air pollutants, gut microbes, serum metabolites, and blood biochemical markers were assessed using generalised additive mixed models and Spearman correlation analysis. The findings revealed that for every 10 μg/m3 increase in PM2.5 concentration, the α-diversity of the gut flora decreased by 2.16% (95% CI: 1.80%-2.53%). We found seven microorganisms that were significantly associated with air pollutants, of which Enterococcus faecium, Bacteroides fragilis, Ruthenibacterium lactatiformans, and Subdoligranulum sp.4_3_54A2FAA were primarily associated with glycolysis. We identified 13 serum metabolites and 17 faecal metabolites significantly linked to air pollutants. Seven of these metabolites, which were strongly associated with air pollutants and blood biochemical indices, were found in both serum and faecal samples. Some of these metabolites, such as 2,5-furandicarboxylic acid, C-8C1P and melatonin, were closely associated with disturbances in lipid and fatty acid metabolism in AECOPD patients. These findings underscore the impact of air pollutants on overall metabolism based on influencing gut microbes and metabolites in AECOPD patients. Moreover, these altered biomarkers establish the biologic connection between air pollutant exposure and AECOPD outcomes.The identification of pertinent biomarkers provides valuable insights for the development of precision COPD prevention strategies.

L-Arginine, as an essential amino acid, is a potential substitute for treating COPD via regulation of ROS/NLRP3/NF-κB signaling pathway

BACKGROUNDS

Chronic obstructive pulmonary disease (COPD) is a frequent and common disease in clinical respiratory medicine and its mechanism is unclear. The purpose of this study was to find the new biomarkers of COPD and elucidate its role in the pathogenesis of COPD. Analysis of metabolites in plasma of COPD patients were performed by ultra-high performance liquid chromatography (UPLC) and quadrupole time-of-flight mass spectrometry (TOF-MS). The differential metabolites were analyzed and identified by multivariate analysis between COPD patients and healthy people. The role and mechanisms of the differential biomarkers in COPD were verified with COPD rats, arginosuccinate synthetase 1 (ASS-l) KO mice and bronchial epithelial cells (BECs). Meanwhile, whether the differential biomarkers can be the potential treatment targets for COPD was also investigated. 85 differentials metabolites were identified between COPD patients and healthy people by metabonomic.

RESULTS

L-Arginine (LA) was the most obvious differential metabolite among the 85 metabolites. Compare with healthy people, the level of LA was markedly decreased in serum of COPD patients. It was found that LA had protective effects on COPD with in vivo and in vitro experiments. Silencing Ass-1, which regulates LA metabolism, and α-methy-DL-aspartic (NHLA), an Ass-1 inhibitor, canceled the protective effect of LA on COPD. The mechanism of LA in COPD was related to the inhibition of ROS/NLRP3/NF-κB signaling pathway. It was also found that exogenous LA significantly improved COPD via regulation of ROS/NLRP3/NF-κB signaling pathway. L-Arginine (LA) as a key metabolic marker is identified in COPD patients and has a protective effect on COPD via regulation of ROS/NLRP3/NF-κB signaling pathway.

CONCLUSION

LA may be a novel target for the treatment of COPD and also a potential substitute for treating COPD.

A Novel Metabolic Score for Predicting the Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease

Background

Chronic obstructive pulmonary disease (COPD) has higher mortality when developing to acute exacerbation (AECOPD); hence, the early intervention of COPD is critical for preventing AECOPD. Exploring the serum metabolites associated with acute exacerbation in patients with COPD will contribute to the early intervention of COPD.

Methods

In the study, a non-targeted metabolomics strategy combined with multivariate statistical methods was performed to explore the metabolic profiling of COPD developing acute exacerbation, to screen the potential metabolites associated with AECOPD and to analyze the potential value of these metabolites in predicting the development of COPD.

Results

Serum lysine, glutamine, 3-hydroxybutyrate, pyruvate and glutamate levels were significantly higher, while 1-methylhistidine, isoleucine, choline, valine, alanine, histidine and leucine levels were significantly lower in AECOPD patients, compared with stable COPD patients after normalization based on the healthy controls. Moreover, eight metabolic pathways were significantly altered (P<0.05) in the serum of AECOPD patients compared with the stable COPD population, including purine metabolism, glutamine and glutamate metabolism, arginine biosynthesis, butyrate metabolism, ketone body synthesis and degradation, and linoleic acid metabolism. In addition, the correlation analysis between metabolites and AECOPD patients demonstrated that an M-score based on a weighted sum of concentrations of four metabolites including pyruvate, isoleucine, 1-methylhistidine and glutamine were significantly associated with the acute exacerbation of pulmonary ventilation function in COPD patients.

Conclusion

Altogether, the metabolite score based on a weighted sum of concentrations of four serum metabolites was associated with an increased risk of COPD developing acute exacerbation, which will provide a new insight for the understanding of COPD development.

NMR-Based Metabolomics to Evaluate Individual Response to Treatments

The aim of this chapter is to highlight the various aspects of metabolomics in relation to health and diseases, starting from the definition of metabolic space and of how individuals tend to maintain their own position in this space. Physio-pathological stimuli may cause individuals to lose their position and then regain it, or move irreversibly to other positions. By way of examples, mostly selected from our own work using ¹H NMR on biological fluids, we describe the effects on the individual metabolomic fingerprint of mild external interventions, such as diet or probiotic administration. Then we move to pathologies (such as celiac disease, various types of cancer, viral infections, and other diseases), each characterized by a well-defined metabolomic fingerprint. We describe the effects of drugs on the disease fingerprint and on its reversal to a healthy metabolomic status. Drug toxicity can be also monitored by metabolomics. We also show how the individual metabolomic fingerprint at the onset of a disease may discriminate responders from non-responders to a given drug, or how it may be prognostic of e.g., cancer recurrence after many years. In parallel with fingerprinting, profiling (i.e., the identification and quantification of many metabolites and, in the case of selected biofluids, of the lipoprotein components that contribute to the ¹H NMR spectral features) can provide hints on the metabolic pathways that are altered by a disease and assess their restoration after treatment.

Integrative Metabolomics to Identify Molecular Signatures of Responses to Vaccines and Infections

Approaches to the identification of metabolites have progressed from early biochemical pathway evaluation to modern high-dimensional metabolomics, a powerful tool to identify and characterize biomarkers of health and disease. In addition to its relevance to classic metabolic diseases, metabolomics has been key to the emergence of immunometabolism, an important area of study, as leukocytes generate and are impacted by key metabolites important to innate and adaptive immunity. Herein, we discuss the metabolomic signatures and pathways perturbed by the activation of the human immune system during infection and vaccination. For example, infection induces changes in lipid (e.g., free fatty acids, sphingolipids, and lysophosphatidylcholines) and amino acid pathways (e.g., tryptophan, serine, and threonine), while vaccination can trigger changes in carbohydrate and bile acid pathways. Amino acid, carbohydrate, lipid, and nucleotide metabolism is relevant to immunity and is perturbed by both infections and vaccinations. Metabolomics holds substantial promise to provide fresh insight into the molecular mechanisms underlying the host immune response. Its integration with other systems biology platforms will enhance studies of human health and disease.

Global Metabolomics in Allogeneic Hematopoietic Cell Transplantation Recipients Discordant for Chronic Graft-versus-Host Disease

Chronic graft-versus-host disease (cGVHD) remains a significant late effect issue for allogeneic hematopoietic cell transplantation (allo-HCT) survivors, contributing to morbidity and mortality. The etiology of cGVHD is not well elucidated. Owing to a lack of early diagnostic tests and pathophysiology ambiguity, targeted treatments remain limited. Biomarkers for prediction, control response, or prognostication have not yet been identified. Metabolomics, the quantification of metabolites, is a potential biomarker of cGVHD but has not been evaluated in this population. In this study, we examined global metabolites of stored plasma to identify differentially expressed metabolites of individuals discordant for cGVHD following allo-HCT. A descriptive, comparative, cross-sectional study design was used to examine differentially expressed metabolites of plasma samples obtained from 40 adult allo-HCT recipients (20 with cGVHD and 20 without cGVHD) from 2 parent studies. Metabolomics profiling was conducted at the University of Florida's Southeast Center for Integrative Metabolomics. Full experimental methods followed a previously published method. All statistical analyses were performed by a PhD-prepared, trained bioinformatics statistician. There were 10 differentially expressed metabolites between participants with cGVHD and those without cGVHD. Differential metabolites included those related to energy metabolism (n = 3), amino acid metabolism (n = 3), lipid metabolism (n = 2), caffeine metabolism (n = 1), and neurotransmission (n = 1). Serotonin had the greatest fold change (21.01). This study suggests that cGVHD may be associated with expanded cellular energy and potentially mitochondrial dysfunction. The differential metabolic profile between patients with and without cGVHD indicates metabolic perturbations that merit further exploration as potential biomarkers of cGVHD. These findings support the need for further examination using a larger, prospective study design to identify metabolomic risk factors that may signal the need for earlier preventive measures and earlier treatment to reduce cGVHD.

Molecular biology of BPIFB1 and its advances in disease

Bactericidal/permeability-increasing (BPI)-fold-containing family B member 1 (BPIFB1), also known as long-palate lung and nasal epithelium clone 1 (LPLUNC1), belongs to the BPI-fold-containing family, is a newly discovered natural immune protection molecule, which, having the function of bactericidal and osmotic enhancement protein domain, can respond to the external physical and chemical stimuli. The gene of BPIFB1 is located at chromosome 20q11.21-20q11.22, and contains 16 exons and 15 introns, encoding 484 amino acids. The 5' terminal of the BPIFB1 protein has a signal peptide sequence composed of 19 amino acids. BPIFB1 is abnormally expressed in nasopharyngeal carcinoma (NPC), gastric cancer, and other cancer tissues, regulate chronic infections and inflammation, indicating that it may play an important role in the development of tumors. Meanwhile, BPIFB1 has well-recognized roles in sensing and responding to Gram-negative bacteria due to its structural similarity with BPI protein and lipopolysaccharide (LPS)-binding protein, both of which are innate immune molecules with recognized roles in sensing and responding to Gram-negative bacteria, so it can regulate cystic fibrosis (CF), chronic obstructive pulmonary disease (COPD), asthma, and other respiratory diseases. In this article, we will discuss the progress of BPIFB1 in a variety of diseases and fully understand its function.

An Updated Overview of Metabolomic Profile Changes in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD), a common and heterogeneous respiratory disease, is characterized by persistent and incompletely reversible airflow limitation. Metabolomics is applied to analyze the difference of metabolic profile based on the low-molecular-weight metabolites (<1 kDa). Emerging metabolomic analysis may provide insights into the pathogenesis and diagnosis of COPD. This review aims to summarize the alteration of metabolites in blood/serum/plasma, urine, exhaled breath condensate, lung tissue samples, etc. from COPD individuals, thereby uncovering the potential pathogenesis of COPD according to the perturbed metabolic pathways. Metabolomic researches have indicated that the dysfunctions of amino acid metabolism, lipid metabolism, energy production pathways, and the imbalance of oxidations and antioxidations might lead to local and systematic inflammation by activating the Nuclear factor kappa-light-chain-enhancer of activated B cells signaling pathway and releasing inflammatory cytokines, like interleutin-6 (IL-6), tumor necrosis factor-α, and IL-8. In addition, they might cause protein malnutrition and oxidative stress and contribute to the development and exacerbation of COPD.

Biomarkers and asthma management: analysis and potential applications

PURPOSE OF REVIEW

Asthma features a high degree of heterogeneity in both pathophysiology and therapeutic response, resulting in many asthma patients being treated inadequately. Biomarkers indicative of underlying pathological processes could be used to identify disease subtypes, determine prognosis and to predict or monitor treatment response. However, the newly identified as well as more established biomarkers have different applications and limitations.

RECENT FINDINGS

Conventional markers for type 2-high asthma, such as blood eosinophils, fraction of exhaled nitric oxide, serum IgE and periostin, feature limited sensitivity and specificity despite their significant correlations. More distinctive models have been developed by combining biomarkers and/or using omics techniques. Recently, a model with a positive predictive value of 100% for identification of type 2-high asthma based on a combination of minimally invasive biomarkers was developed.

SUMMARY

Individualisation of asthma treatment regimens on the basis of biomarkers is necessary to improve asthma control. However, the suboptimal properties of currently available conventional biomarkers limit its clinical utility. Newly identified biomarkers and models based on combinations and/or omics analysis must be validated and standardised before they can be routinely applied in clinical practice. The development of robust biomarkers will allow development of more efficacious precision medicine-based treatment approaches for asthma.

Metabolomics Identifies Novel Blood Biomarkers of Pulmonary Function and COPD in the General Population

Determination of metabolomic signatures of pulmonary function and chronic obstructive pulmonary disease (COPD) in the general population could aid in identification and understanding of early disease processes. Metabolome measurements were performed on serum from 4742 individuals (2354 African-Americans and 1529 European-Americans from the Atherosclerosis Risk in Communities study and 859 Europeans from the Cooperative Health Research in the Region of Augsburg study). We examined 368 metabolites in relation to cross-sectional measures of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), their ratio (FEV1/FVC) and COPD using multivariable regression followed by meta-analysis. At a false discovery rate of 0.05, 95 metabolites were associated with FEV1 and 100 with FVC (73 overlapping), including inverse associations with branched-chain amino acids and positive associations with glutamine. Ten metabolites were associated with FEV1/FVC and seventeen with COPD (393 cases). Enriched pathways of amino acid metabolism were identified. Associations with FEV1 and FVC were not driven by individuals with COPD. We identified novel metabolic signatures of pulmonary function and COPD in African and European ancestry populations. These may allow development of biomarkers in the general population of early disease pathogenesis, before pulmonary function has decreased to levels diagnostic for COPD.

The modification of indoor PM2.5 exposure to chronic obstructive pulmonary disease in Chinese elderly people: A meet-in-metabolite analysis

BACKGROUND

Exposure to airborne fine particulate matter (PM_2.5) has been associated with a variety of adverse health outcomes including chronic obstructive pulmonary disease (COPD). However, the linkages between PM_2.5 exposure, PM_2.5-related biomarkers, COPD-related biomarkers and COPD remain poorly elucidated.

OBJECTIVES

To investigate the linkages between PM_2.5 exposure and COPD outcome by using the meet-in-middle strategy based on urinary metabolic biomarkers.

METHODS

A cross-sectional study was designed to illustrate the mentioned quadripartite linkages. Indoor PM_2.5 and its element components were assessed in 41 Chinese elderly participants including COPD patients and their healthy spouses. Metabolic biomarkers involved in PM_2.5 exposure and COPD were identified by using urinary metabolomics. The associations between PM_2.5- and COPD-related biomarkers were investigated by statistics and metabolic pathway analysis.

RESULTS

Seven metabolites were screened and identified with significant correlations to PM_2.5 exposure, which were majorly involved in purine and amino acid metabolism as well as glycolysis. Ten COPD-related metabolic biomarkers were identified, which suggested that amino acid metabolism, lipid and fatty acid metabolism, and glucose metabolism were disturbed in the patients. Also, PM_2.5 and its many elemental components were significantly associated with COPD-related biomarkers. We observed that the two kinds of biomarkers (PM_2.5- and COPD-related) integrated in a locally connected network and the alterations of these metabolic biomarkers can biologically link PM_2.5 exposure to COPD outcome.

CONCLUSIONS

Our study indicated the modification of PM_2.5 to COPD via both modes of action of lowering participants' antioxidation capacity and decreasing their lung energy generation; this information would be valuable for the prevention strategy of COPD.

Searching for biomarkers of chronic obstructive pulmonary disease using proteomics: The current state

Detection of proteins which may be potential biomarkers of disorders represents a big step forward in understanding the molecular mechanisms that underlie pathological processes. In this context proteomics plays the important role of opening a path for the identification of molecular signatures that can potentially assist in early diagnosis of several clinical disturbances. Aim of this report is to provide an overview of the wide variety of proteomic strategies that have been applied to the investigation of chronic obstructive pulmonary disease (COPD), a severe disorder that causes an irreversible damage to the lungs and for which there is no cure yet. The results in this area published over the past decade show that proteomics indeed has the ability of monitoring alterations in expression profiles of proteins from fluids/tissues of patients affected by COPD and healthy controls. However, these data also suggest that proteomics, while being an attractive tool for the identification of novel pathological mediators of COPD, remains a technique mainly generated and developed in research laboratories. Great efforts dedicated to the validation of these biological signatures will result in the proof of their clinical utility.

Integration of multi-omics datasets enables molecular classification of COPD

Chronic obstructive pulmonary disease (COPD) is an umbrella diagnosis caused by a multitude of underlying mechanisms, and molecular sub-phenotyping is needed to develop molecular diagnostic/prognostic tools and efficacious treatments.The objective of these studies was to investigate whether multi-omics integration improves the accuracy of molecular classification of COPD in small cohorts.Nine omics data blocks (comprising mRNA, micro RNA, proteomes and metabolomes) collected from several anatomical locations from 52 female subjects were integrated by similarity network fusion (SNF). Multi-omics integration significantly improved the accuracy of group classification of COPD patients from healthy never-smokers and from smokers with normal spirometry, reducing required group sizes from n=30 to n=6 at 95% power. Seven different combinations of four to seven omics platforms achieved >95% accuracy.For the first time, a quantitative relationship between multi-omics data integration and accuracy of data-driven classification power has been demonstrated across nine omics data blocks. Integrating five to seven omics data blocks enabled 100% correct classification of COPD diagnosis with groups as small as n=6 individuals, despite strong confounding effects of current smoking. These results can serve as guidelines for the design of future systems-based multi-omics investigations, with indications that integrating five to six data blocks from several molecular levels and anatomical locations suffices to facilitate unsupervised molecular classification in small cohorts.

Blood biomarkers in chronic airways diseases and their role in diagnosis and management

INTRODUCTION

The complexity and heterogeneous nature of asthma and chronic obstructive pulmonary disease (COPD) results in difficulties in diagnosing and treating patients. Biomarkers that can identify underlying mechanisms, identify patient phenotypes and to predict treatment response could be of great value for adequate treatment. Areas covered: Biomarkers play an important role for the development of novel targeted therapies in airways disease. Blood biomarkers are relatively non-invasive, easy to obtain and easy to apply in routine care. Several blood biomarkers are being used to diagnose and monitor chronic airways diseases, as well as to predict response to treatment and long-term prognosis. Blood eosinophils are the best studied biomarker, the most applied in clinical practice, and until now the most promising of all blood biomarkers. Other blood biomarkers, including serum periostin, IgE and ECP and plasma fibrinogen are less studied and less relevant in clinical practice. Recent developments include the use of antibody assays of many different cytokines at the same time, and 'omics' techniques and systems medicine. Expert commentary: With the exception of blood eosinophils, the use of blood biomarkers in asthma and COPD has been rather disappointing. Future research using new technologies like big-data analysis of blood samples from real-life patient cohorts will probably gain better insight into underlying mechanisms of different disease phenotypes. Identification of specific molecular pathways and associated biomarkers will then allow the development of new targets for precision medicine.

Phenotyping of Chronic Obstructive Pulmonary Disease Based on the Integration of Metabolomes and Clinical Characteristics

Apart from the refined management-oriented clinical stratification of chronic obstructive pulmonary disease (COPD), the molecular pathologies behind this highly prevalent disease have remained obscure. The aim of this study was the characterization of patients with COPD, based on the metabolomic profiling of peripheral blood and exhaled breath condensate (EBC) within the context of defined clinical and demographic variables. Mass-spectrometry-based targeted analysis of serum metabolites (mainly amino acids and lipid species), untargeted profiles of serum and EBC of patients with COPD of different clinical characteristics (n = 25) and control individuals (n = 21) were performed. From the combined clinical/demographic and metabolomics data, associations between clinical/demographic and metabolic parameters were searched and a de novo phenotyping for COPD was attempted. Adjoining the clinical parameters, sphingomyelins were the best to differentiate COPD patients from controls. Unsaturated fatty acid-containing lipids, ornithine metabolism and plasma protein composition-associated signals from the untargeted analysis differentiated the Global Initiative for COPD (GOLD) categories. Hierarchical clustering did not reveal a clinical-metabolomic stratification superior to the strata set by the GOLD consensus. We conclude that while metabolomics approaches are good for finding biomarkers and clarifying the mechanism of the disease, there are no distinct co-variate independent clinical-metabolic phenotypes.

Precision medicine in airway diseases: moving to clinical practice

On February 21, 2017, a European Respiratory Society research seminar held in Barcelona discussed how to best apply precision medicine to chronic airway diseases such as asthma and chronic obstructive pulmonary disease. It is now clear that both are complex and heterogeneous diseases, that often overlap and that both require individualised assessment and treatment. This paper summarises the presentations and discussions that took place during the seminar. Specifically, we discussed the need for a new taxonomy of human diseases, the role of different players in this scenario (exposome, genes, endotypes, phenotypes, biomarkers and treatable traits) and a number of unanswered key questions in the field. We also addressed how to deploy airway precision medicine in clinical practice today, both in primary and specialised care. Finally, we debated the type of research needed to move the field forward.

Using omics approaches to understand pulmonary diseases

Omics approaches are high-throughput unbiased technologies that provide snapshots of various aspects of biological systems and include: 1) genomics, the measure of DNA variation; 2) transcriptomics, the measure of RNA expression; 3) epigenomics, the measure of DNA alterations not involving sequence variation that influence RNA expression; 4) proteomics, the measure of protein expression or its chemical modifications; and 5) metabolomics, the measure of metabolite levels. Our understanding of pulmonary diseases has increased as a result of applying these omics approaches to characterize patients, uncover mechanisms underlying drug responsiveness, and identify effects of environmental exposures and interventions. As more tissue- and cell-specific omics data is analyzed and integrated for diverse patients under various conditions, there will be increased identification of key mechanisms that underlie pulmonary biological processes, disease endotypes, and novel therapeutics that are efficacious in select individuals. We provide a synopsis of how omics approaches have advanced our understanding of asthma, chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS), idiopathic pulmonary fibrosis (IPF), and pulmonary arterial hypertension (PAH), and we highlight ongoing work that will facilitate pulmonary disease precision medicine.

A Review of Pulmonary Toxicity of Electronic Cigarettes in the Context of Smoking: A Focus on Inflammation

The use of electronic cigarettes (e-cigs) is increasing rapidly, but their effects on lung toxicity are largely unknown. Smoking is a well-established cause of lung cancer and respiratory disease, in part through inflammation. It is plausible that e-cig use might affect similar inflammatory pathways. E-cigs are used by some smokers as an aid for quitting or smoking reduction, and by never smokers (e.g., adolescents and young adults). The relative effects for impacting disease risk may differ for these groups. Cell culture and experimental animal data indicate that e-cigs have the potential for inducing inflammation, albeit much less than smoking. Human studies show that e-cig use in smokers is associated with substantial reductions in blood or urinary biomarkers of tobacco toxicants when completely switching and somewhat for dual use. However, the extent to which these biomarkers are surrogates for potential lung toxicity remains unclear. The FDA now has regulatory authority over e-cigs and can regulate product and e-liquid design features, such as nicotine content and delivery, voltage, e-liquid formulations, and flavors. All of these factors may impact pulmonary toxicity. This review summarizes current data on pulmonary inflammation related to both smoking and e-cig use, with a focus on human lung biomarkers. Cancer Epidemiol Biomarkers Prev; 26(8); 1175-91. ©2017 AACR.

Metabolomics and Its Application in the Development of Discovering Biomarkers for Osteoporosis Research

Osteoporosis is a progressive skeletal disorder characterized by low bone mass and increased risk of fracture in later life. The incidence and costs associated with treating osteoporosis cause heavy socio-economic burden. Currently, the diagnosis of osteoporosis mainly depends on bone mineral density and bone turnover markers. However, these indexes are not sensitive and accurate enough to reflect the osteoporosis progression. Metabolomics offers the potential for a holistic approach for clinical diagnoses and treatment, as well as understanding of the pathological mechanism of osteoporosis. In this review, we firstly describe the study subjects of osteoporosis and bio-sample preparation procedures for different analytic purposes, followed by illustrating the biomarkers with potentially predictive, diagnosis and pharmaceutical values when applied in osteoporosis research. Then, we summarize the published metabolic pathways related to osteoporosis. Furthermore, we discuss the importance of chronological data and combination of multi-omics in fully understanding osteoporosis. The application of metabolomics in osteoporosis could provide researchers the opportunity to gain new insight into the metabolic profiling and pathophysiological mechanisms. However, there is still much to be done to validate the potential biomarkers responsible for the progression of osteoporosis and there are still many details needed to be further elucidated.