Protein Biomarkers for COPD Outcomes

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.

The RAGE Axis: A Relevant Inflammatory Hub in Human Diseases

In 1992, a transcendental report suggested that the receptor of advanced glycation end-products (RAGE) functions as a cell surface receptor for a wide and diverse group of compounds, commonly referred to as advanced glycation end-products (AGEs), resulting from the non-enzymatic glycation of lipids and proteins in response to hyperglycemia. The interaction of these compounds with RAGE represents an essential element in triggering the cellular response to proteins or lipids that become glycated. Although initially demonstrated for diabetes complications, a growing body of evidence clearly supports RAGE's role in human diseases. Moreover, the recognizing capacities of this receptor have been extended to a plethora of structurally diverse ligands. As a result, it has been acknowledged as a pattern recognition receptor (PRR) and functionally categorized as the RAGE axis. The ligation to RAGE leads the initiation of a complex signaling cascade and thus triggering crucial cellular events in the pathophysiology of many human diseases. In the present review, we intend to summarize basic features of the RAGE axis biology as well as its contribution to some relevant human diseases such as metabolic diseases, neurodegenerative, cardiovascular, autoimmune, and chronic airways diseases, and cancer as a result of exposure to AGEs, as well as many other ligands.

Association Between Serum Anion Gap and Mortality in Critically Ill Patients with COPD in ICU: Data from the MIMIC IV Database

Background

Serum anion gap (AG) has been proven to be associated with prognosis in critically ill patients. However, few studies have investigated the association between AG and all-cause mortality in critically ill patients with chronic obstructive pulmonary disease (COPD).

Objective

We hypothesized that the initial AG level would predict the mortality risk in critically ill patients with COPD.

Methods

This retrospective cohort study was based on the Medical Information Mart for Intensive Care (MIMIC) IV database. We extracted demographics, vital signs, laboratory tests, comorbidity, and scoring systems from the first 24 hours after patient ICU admission. Multivariable logistic regression analysis models were used to explore the association between serum AG levels and mortality. Interaction and stratified analyses were conducted including age, gender and comorbidity.

Results

A total of 5531 critically ill patients with COPD were enrolled, composed of 53.6% male and 46.4% female with a median age of 73 years. The all-cause mortality of these patients during ICU hospitalization was 13.7%. The risk of all-cause mortality increased as the AG level increased in the univariate logistic regression analysis (OR=1.13, 95% CI: 1.11-1.15, p<0.01). After adjusting for all the covariates in multivariate logistic regression analysis, the odds ratio was 1.06 (95% CI: 1.04-1.09, p<0.01). Compared with the lowest AG group Q1 (≤11mmol/L), the adjusted OR value for AG and mortality in Q2 (12-13mmol/L) was 0.89 (95% CI: 0.63-1.25, p=0.502), Q3 (14-15mmol/L) was 0.95 (95% CI: 0.68-1.34, p=0.788), and Q4 (≥16mmol/L) was 1.49 (95% CI: 1.10-2.02, p=0.009) respectively. In addition, the results of the subgroup and stratified analyses were robust.

Conclusion

AG is positively related to all-cause mortality in critically ill patients with COPD.

Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease

Background

Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.

Methods

Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS.

Results

We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts.

Conclusions

In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Proteomic associations with forced expiratory volume: a Mendelian randomisation study

BACKGROUND

A decline in forced expiratory volume (FEV1) is a hallmark of respiratory diseases that are an important cause of morbidity among the elderly. While some data exist on biomarkers that are related to FEV1, we sought to do a systematic analysis of causal relations of biomarkers with FEV1.

METHODS

Data from the population-based AGES-Reykjavik study were used. Serum proteomic measurements were done using 4782 DNA aptamers (SOMAmers). Data from 1479 participants with spirometric data were used to assess the association of SOMAmer measurements with FEV1 using linear regression. Bi-directional two-sample Mendelian randomisation (MR) analyses were done to assess causal relations of observationally associated SOMAmers with FEV1, using genotype and SOMAmer data from 5368 AGES-Reykjavik participants and genetic associations with FEV1 from a publicly available GWAS (n = 400,102).

RESULTS

In observational analyses, 530 SOMAmers were associated with FEV1 after multiple testing adjustment (FDR < 0.05). The most significant were Retinoic Acid Receptor Responder 2 (RARRES2), R-Spondin 4 (RSPO4) and Alkaline Phosphatase, Placental Like 2 (ALPPL2). Of the 257 SOMAmers with genetic instruments available, eight were associated with FEV1 in MR analyses. Three were directionally consistent with the observational estimate, Thrombospondin 2 (THBS2), Endoplasmic Reticulum Oxidoreductase 1 Beta (ERO1B) and Apolipoprotein M (APOM). THBS2 was further supported by a colocalization analysis. Analyses in the reverse direction, testing whether changes in SOMAmer levels were caused by changes in FEV1, were performed but no significant associations were found after multiple testing adjustments.

CONCLUSIONS

In summary, this large scale proteogenomic analyses of FEV1 reveals circulating protein markers of FEV1, as well as several proteins with potential causality to lung function.

Biomarkers of chronic airflow limitation and COPD identified by mass spectrometry

Rationale

COPD affects 300 million people worldwide and is the third leading cause of death according to World Health Organization global health estimates. Early symptoms are subtle, and so COPD is often diagnosed at an advanced stage. Thus, there is an unmet need for biomarkers that can identify individuals at early stages of the disease before clinical symptoms have manifested. To date, few biomarkers are available for clinical diagnostic use in COPD.

Methods

We evaluated a panel of serum biomarkers related to inflammation and infection for their ability to discriminate between 77 subjects with chronic airflow limitation (CAL) and 142 subjects with COPD, versus 150 healthy subjects (divided into two control groups that were matched with regards to age, gender and smoking to CAL and COPD). Healthy subjects and CAL were from Burden of Obstructive Lung Disease (BOLD), a population-based study. CAL was defined by post-bronchodilatory forced expiratory volume in 1 s/forced vital capacity ratio <0.7 in the BOLD population. COPD subjects were from Tools for Identifying Exacerbations (TIE), a COPD patient cohort. Quantification of 100 biomarker candidates was done by liquid chromatography-tandem mass spectrometry.

Results

Several protein-derived peptides were upregulated in CAL, compared to controls; most notably peptides representing histidine-rich glycoprotein (HRG), α1-acid glycoprotein (AGP1), α1-antitrypsin (α1AT) and fibronectin. Out of these, HRG-, AGP1- and α1AT-specific peptides were also elevated in the COPD cohort.

Conclusion

HRG, AGP1 and α1AT biomarkers distinguish subjects with CAL and COPD from healthy controls. HRG and AGP1 represent novel findings.

An investigation of the risk factors of chronic obstructive pulmonary disease in natural population-based cohorts in China - a nested case-control study

Background

Chronic obstructive pulmonary disease (COPD) has become one of the most significant chronic diseases in China. According to conventional wisdom, smoking is the pathogenic factor. However, current research indicates that the pathophysiology of COPD may be associated with prior respiratory system events (e.g., childhood hospitalization for pneumonia, chronic bronchitis) and environmental exposure (e.g., dust from workplace, indoor combustion particles). Dyspnea, persistent wheezing, and other respiratory symptoms further point to the need for pulmonary function tests in this population. Reducing the burden of chronic diseases in China requires a thorough understanding of the various factors that influence the occurrence of COPD.

Methods

Using a cohort from the natural population, this study used nested case-control analysis. We carried out a number of researches, including questionnaire surveys and pulmonary function testing, in the Northwest and Southeast cohorts of China between 2014 and 2021. After removing any variations in the baseline data between patients and control subjects using propensity score matching analysis, the risk factors were examined using univariate or multivariate regression.

Result

It was discovered that prior history of chronic bronchitis, long-term wheezing symptoms, and environmental exposure-including smoking and biofuel combustion-were risk factors for COPD. Dyspnea, symptoms of mobility limitation, organic matter, and a history of hospitalization for pneumonia at an early age were not significant in the clinical model but their incidence in COPD group is higher than that in healthy population.

Discussion

COPD screening effectiveness can be increased by looking for individuals with chronic respiratory symptoms. Smokers should give up as soon as they can, and families that have been exposed to biofuels for a long time should convert to clean energy or upgrade their ventilation. Individuals who have previously been diagnosed with emphysema and chronic bronchitis ought to be extra mindful of the prevention or advancement of COPD.

Advancing Biomarkers for Treatment of Smoking and Nicotine Dependence: An Overview

The special issue on Biomarkers of Nicotine and Tobacco Dependence reviews the science for precision treatment of nicotine dependence and future opportunities for research on biomarkers for inclusion in tobacco product cessation and switching clinical trials to advance translation. This overview summarizes the articles contributed to the special issue by leading researcher in field of addiction.

Comprehensive Analysis of a Competing Endogenous RNA Co-Expression Network in Chronic Obstructive Pulmonary Disease

Purpose

Chronic obstructive pulmonary disease (COPD) is the main cause of mortality world widely. Non-coding RNAs (lncRNAs) and associated competitive endogenous RNAs (ceRNAs) networks were recently proved to lead to mRNA gene expression downregulation but were still unclear in COPD. This study aims to investigate and elucidate the mechanisms underlying the involvement of ceRNA co-expression networks in COPD pathogenesis.

Methods

Obtained expression signature of data from the Gene Expression Omnibus database and compared the differentially expression of mRNAs and miRNAs between COPD patients and healthy smokers. Predicted the miRNA-lncRNA and miRNA-mRNA interaction using online library and employed CIBERSORT to measure the proportions of the 22 immune cells in the COPD and control groups.

Results

Established a ceRNA-network comprising 11 lncRNAs, 5 miRNAs, and 16 mRNAs. Using the weighted correlation network analysis method, we identified hub genes and hub miRNAs and obtained one core sub-network, XIST, FGD5-AS1, KCNQ1OT1, HOXA11-AS, LINC00667, H19, PRKCQ-AS1, NUTM2A-AS1/has-mir-454-3p/ZNF678, PRRG4. COPD patients had different proportions of immune cells than controls, and these variations were associated with the magnitude of pulmonary function parameters.

Conclusion

The ceRNA-network, particularly the core sub-network, may be a putative goal for COPD, in which specific immune cells were involved.

Imidazolium-based mass tags for protein biomarker detection using laser desorption ionization mass spectrometry

Novel imidazolium-based mass tags (IMTs) were designed, synthesized and applied to simultaneous in situ analysis of multiple biomarkers on less than 10 cells. The high sensitivity, flexible extensibility and excellent distinguishability of IMTs open new avenues for designing common mass tag templates suitable for mass spectrometric immunoassay and provide an ideal option for multiplex-sensitive detection at the cellular scale.

Blood Gene Expression and Immune Cell Subtypes Associated with Chronic Obstructive Pulmonary Disease Exacerbations

Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AE-COPDs) are associated with a significant disease burden. Blood immune phenotyping may improve our understanding of a COPD endotype at increased risk of exacerbations. Objective: To determine the relationship between the transcriptome of circulating leukocytes and COPD exacerbations. Methods: Blood RNA sequencing data (n = 3,618) from the COPDGene (Genetic Epidemiology of COPD) study were analyzed. Blood microarray data (n = 646) from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study were used for validation. We tested the association between blood gene expression and AE-COPDs. We imputed the abundance of leukocyte subtypes and tested their association with prospective AE-COPDs. Flow cytometry was performed on blood in SPIROMICS (Subpopulations and Intermediate Outcomes in COPD Study) (n = 127), and activation markers for T cells were tested for association with prospective AE-COPDs. Measurements and Main Results: Exacerbations were reported 4,030 and 2,368 times during follow-up in COPDGene (5.3 ± 1.7 yr) and ECLIPSE (3 yr), respectively. We identified 890, 675, and 3,217 genes associated with a history of AE-COPDs, persistent exacerbations (at least one exacerbation per year), and prospective exacerbation rate, respectively. In COPDGene, the number of prospective exacerbations in patients with COPD (Global Initiative for Chronic Obstructive Lung Disease stage ⩾2) was negatively associated with circulating CD8+ T cells, CD4+ T cells, and resting natural killer cells. The negative association with naive CD4+ T cells was replicated in ECLIPSE. In the flow-cytometry study, an increase in CTLA4 on CD4+ T cells was positively associated with AE-COPDs. Conclusions: Individuals with COPD with lower circulating lymphocyte counts, particularly decreased CD4+ T cells, are more susceptible to AE-COPDs, including persistent exacerbations.

Predicting Lung Function Using Biomarkers in Alpha-1 Antitrypsin Deficiency

Lung disease progression in alpha-1 antitrypsin deficiency (AATD) is heterogenous and manifests in different ways. Blood biomarkers are an attractive method of monitoring diseases as they are easy to obtain and repeatable. In non-AATD COPD, blood biomarker panels have predicted disease severity, progression, and mortality. We measured a panel of seven serum biomarkers in 200 AATD patients and compared levels between those with COPD and those without. We assessed whether biomarkers were associated with baseline lung function parameters (FEV1 and TLco) or absolute change in these parameters. In total, 111 patients with a severely deficient genotype of AATD (PiZZ) and COPD were included in the analyses. Pearson's correlation coefficient was measured for biomarker correlations and models were compared using ANOVA. CRP and CCL18 were significantly higher in the serum of AATD COPD versus AATD with no COPD. Biomarkers were not predictive of cross-sectional lung function measurements, however, CC16 was significantly associated with an absolute change in TLco (p = 0.018). An addition of biomarkers to the predictive model for TLco added significant value over covariates alone (R2 0.13 vs. 0.02, p = 0.028). Our findings suggest that CC16 is predictive of emphysema progression in AATD COPD. Proteomics data may reveal alternative candidate biomarkers and further work should include the use of longitudinal biomarker measurements.

Differentiation in TCM patterns of chronic obstructive pulmonary disease by comprehensive metabolomic and lipidomic characterization

Introduction

Chronic obstructive pulmonary disease (COPD) is a complex disease involving inflammation, cell senescence, and autoimmunity. Dialectical treatment for COPD with traditional Chinese medicine (TCM) has the advantage of fewer side effects, more effective suppression of inflammation, and improved immune function. However, the biological base of TCM pattern differentiation in COPD remains unclear.

Methods

Liquid Chromatography-Quadrupole-Orbitrap mass spectrometry (LC-Q-Orbitrap MS/MS) based metabolomics and lipidomics were used to analyze the serum samples from COPD patients of three TCM patterns in Lung Qi Deficiency (n=65), Lung-Kidney Qi Deficiency (n=54), Lung-Spleen Qi Deficiency (n=52), and healthy subjects (n=41). Three cross-comparisons were performed to characterize metabolic markers for different TCM patterns of COPD vs healthy subjects.

Results

We identified 28, 8, and 16 metabolites with differential abundance between three TCM patterns of COPD vs healthy subjects, respectively, the metabolic markers included cortisol, hypoxanthine, fatty acids, alkyl-/alkenyl-substituted phosphatidylethanolamine, and phosphatidylcholine, etc. Three panels of metabolic biomarkers specific to the above three TCM patterns yielded areas under the receiver operating characteristic curve of 0.992, 0.881, and 0.928, respectively, with sensitivity of 97.1%, 88.6%, and 91.4%, respectively, and specificity of 96.4%, 81.8%, and 83.9%, respectively.

Discussion

Combining metabolomics and lipidomics can more comprehensively and accurately trace metabolic markers. As a result, the differences in metabolism were proven to underlie different TCM patterns of COPD, which provided evidence to aid our understanding of the biological basis of dialectical treatment, and can also serve as biomarkers for more accurate diagnosis.

Proteomic associations with forced expiratory volume - a Mendelian randomisation study

A decline in forced expiratory volume (FEV1) is a hallmark of obstructive respiratory diseases, an important cause of morbidity among the elderly. While some data exist on biomarkers that are related to FEV1, we sought to do a systematic analysis of causal relations of biomarkers with FEV1. Data from the general population-based AGES-Reykjavik study were used. Proteomic measurements were done using 4,782 DNA aptamers (SOMAmers). Data from 1,648 participants with spirometric data were used to assess the association of SOMAmer measurements with FEV1 using linear regression. Bi-directional Mendelian randomisation (MR) analyses were done to assess causal relations of observationally associated SOMAmers with FEV1, using genotype and SOMAmer data from 5,368 AGES-Reykjavik participants and genetic associations with FEV1 from a publicly available GWAS (n = 400,102). In observational analyses, 473 SOMAmers were associated with FEV1 after multiple testing adjustment. The most significant were R-Spondin 4, Alkaline Phosphatase, Placental Like 2 and Retinoic Acid Receptor Responder 2. Of the 235 SOMAmers with genetic data, eight were associated with FEV1 in MR analyses. Three were directionally consistent with the observational estimate, Thrombospondin 2 (THBS2), Endoplasmic Reticulum Oxidoreductase 1 Beta and Apolipoprotein M. THBS2 was further supported by a colocalization analysis. Analyses in the reverse direction, testing whether changes in SOMAmer levels were caused by changes in FEV1, were performed but no significant associations were found after multiple testing adjustments. In summary, this large scale proteogenomic analyses of FEV1 reveals protein markers of FEV1, as well as several proteins with potential causality to lung function.

Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV1 decline: results from COPDGene and SPIROMICS

Purpose: The purpose of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 s (FEV1) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung Disease (GOLD 0), or with mild-to-moderate (GOLD 1-2) COPD. We trained multiple models to predict rapid FEV1 decline using demographic, clinical and radiologic biomarker data. Training and internal validation data were obtained from the COPDGene study and prediction models were validated against the SPIROMICS cohort. Methods: We used GOLD 0-2 participants (n = 3,821) from COPDGene (60.0 ± 8.8 years, 49.9% male) for variable selection and model training. Accelerated lung function decline was defined as a mean drop in FEV1% predicted of > 1.5%/year at 5-year follow-up. We built logistic regression models predicting accelerated decline based on 22 chest CT imaging biomarker, pulmonary function, symptom, and demographic features. Models were validated using n = 885 SPIROMICS subjects (63.6 ± 8.6 years, 47.8% male). Results: The most important variables for predicting FEV1 decline in GOLD 0 participants were bronchodilator responsiveness (BDR), post bronchodilator FEV1% predicted (FEV1.pp.post), and CT-derived expiratory lung volume; among GOLD 1 and 2 subjects, they were BDR, age, and PRMlower lobes fSAD. In the validation cohort, GOLD 0 and GOLD 1-2 full variable models had significant predictive performance with AUCs of 0.620 ± 0.081 (p = 0.041) and 0.640 ± 0.059 (p < 0.001). Subjects with higher model-derived risk scores had significantly greater odds of FEV1 decline than those with lower scores. Conclusion: Predicting FEV1 decline in at-risk patients remains challenging but a combination of clinical, physiologic and imaging variables provided the best performance across two COPD cohorts.

Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease with high morbidity and mortality, especially in advanced patients. We aimed to develop multi-omics panels of biomarkers for the diagnosis and explore its molecular subtypes.

METHODS

A total of 40 stable patients with advanced COPD and 40 controls were enrolled in the study. Proteomics and metabolomics techniques were applied to identify potential biomarkers. An additional 29 COPD and 31 controls were enrolled for validation of the obtained proteomic signatures. Information on demographic, clinical manifestation, and blood test were collected. The ROC analyses were carried out to evaluate the diagnostic performance, and experimentally validated the final biomarkers on mild-to-moderate COPD. Next, molecular subtyping was performed using proteomics data.

RESULTS

Theophylline, palmitoylethanolamide, hypoxanthine, and cadherin 5 (CDH5) could effectively diagnose advanced COPD with high accuracy (auROC = 0.98, sensitivity of 0.94, and specificity of 0.95). The performance of the diagnostic panel was superior to that of other single/combined results and blood tests. Proteome based stratification of COPD revealed three subtypes (I-III) related to different clinical outcomes and molecular feature: simplex COPD, COPD co-existing with bronchiectasis, and COPD largely co-existing with metabolic syndrome, respectively. Two discriminant models were established using the auROC of 0.96 (Principal Component Analysis, PCA) and 0.95 (the combination of RRM1 + SUPV3L1 + KRT78) in differentiating COPD and COPD with co-morbidities. Theophylline and CDH5 were exclusively elevated in advanced COPD but not in its mild form.

CONCLUSIONS

This integrative multi-omics analysis provides a more comprehensive understanding of the molecular landscape of advanced COPD, which may suggest molecular targets for specialized therapy.

Lung proteome and metabolome endotype in HIV-associated obstructive lung disease

Purpose

Obstructive lung disease is increasingly common among persons with HIV, both smokers and nonsmokers. We used aptamer proteomics to identify proteins and associated pathways in HIV-associated obstructive lung disease.

Methods

Bronchoalveolar lavage fluid (BALF) samples from 26 persons living with HIV with obstructive lung disease were matched to persons living with HIV without obstructive lung disease based on age, smoking status and antiretroviral treatment. 6414 proteins were measured using SomaScan® aptamer-based assay. We used sparse distance-weighted discrimination (sDWD) to test for a difference in protein expression and permutation tests to identify univariate associations between proteins and forced expiratory volume in 1 s % predicted (FEV₁ % pred). Significant proteins were entered into a pathway over-representation analysis. We also constructed protein-driven endotypes using K-means clustering and performed over-representation analysis on the proteins that were significantly different between clusters. We compared protein-associated clusters to those obtained from BALF and plasma metabolomics data on the same patient cohort.

Results

After filtering, we retained 3872 proteins for further analysis. Based on sDWD, protein expression was able to separate cases and controls. We found 575 proteins that were significantly correlated with FEV₁ % pred after multiple comparisons adjustment. We identified two protein-driven endotypes, one of which was associated with poor lung function, and found that insulin and apoptosis pathways were differentially represented. We found similar clusters driven by metabolomics in BALF but not plasma.

Conclusion

Protein expression differs in persons living with HIV with and without obstructive lung disease. We were not able to identify specific pathways differentially expressed among patients based on FEV₁ % pred; however, we identified a unique protein endotype associated with insulin and apoptotic pathways.

Relationship between Antithrombin III Activity and Mortality in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease

We aimed to explore the role of antithrombin III (AT-III) activity in diagnosing patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and chronic bronchitis, and its relationship with all-cause mortality of AECOPD patients. We performed univariate and multivariate Cox regression analyses of the factors determining all-cause mortality. We recruited 279 patients with AECOPD and 91 with chronic bronchitis. On admission, patients with AECOPD had lower AT-III activity (80.7 vs. 86.35%, p = 0.002) and higher neutrophil percentages (70.12 vs. 66.40%, p = 0.02) than those with chronic bronchitis. The patients who died were older (78 vs. 73 years, p < 0.001); had higher CRP (39.05 vs. 5.65 mg/L, p < 0.001), D-dimer (1.72 vs. 0.46 mg/L, p < 0.001), FIB (3.56 vs. 3.05 g/L, p = 0.01) levels; and exhibited lower AT-III activity (71.29 vs. 82.94%, p < 0.001) than the survivors. The AT-III area under the receiver operating characteristic curve for predicting COPD all-cause mortality was 0.75 (p < 0.001), optimal cutoff point 79.75%, sensitivity 86.8%, and specificity 57.1%. Multivariate Cox regression analyses showed that increased levels of CRP (HR = 1.005, p = 0.02), D-dimer (HR = 1.17, p = 0.01), WBC count (HR = 1.11, p = 0.002), and reduced AT-III activity (HR = 0.97, p = 0.02) were independent prognostic factors for all-cause mortality. Patients with AT-III ≤ 79.75% were 4.52 times (p = 0.001) more likely to die than those with AT-III > 79.75%. AT-III activity was lower in patients with AECOPD than in those with chronic bronchitis and is potentially useful as an independent predictor of all-cause mortality in patients with AECOPD: reduced AT-III activity and increased CRP and D-dimer levels indicate a higher risk of all-cause mortality.

Comparative proteomic analysis of mustard lung as a complicated disease using systems biology approach

During Iraq-Iran conflict, chemical weapons, particularly SM gas, were used numerous times, whose aftereffects are still present. This study aimed to compare serum proteome in the chronic ML (n = 10) and HC (n = 10). TMT label-based quantitative proteomics was used to examine serums from two groups. Among total significant proteins, 14 proteins were upregulated (log₂ ≥ FC 0.5, p 0.05), and 6 proteins were downregulated (log₂ ≤ FC - 0.5, p 0.05). By helping PPI network, and EA, 11 main pathways connected to significantly different protein expression levels were discovered, including inflammatory and cell adhesion signaling pathways. It may be deduced that the wounded organs of exposed individuals experience poor repair cycles of cell degeneration and regeneration because certain repair signals were elevated while other structural and adhesion molecules were downregulated. The systems biology approach can help enhance our basic knowledge of biological processes, and contribute to a deeper understanding of pathophysiological mechanisms, as well as the identification of potential biomarkers of disease.

Impact of Spirometrically Confirmed Chronic Obstructive Pulmonary Disease on Arterial Stiffness and Surfactant Protein D After Percutaneous Coronary Intervention. The CATEPOC Study

Background

Several mechanisms have been proposed to explain why chronic obstructive pulmonary disease (COPD) impairs the prognosis of coronary events. We aimed to explore COPD variables related to a worse prognosis in patients undergoing percutaneous coronary intervention (PCI).

Methods

Patients with an acute coronary event treated by PCI were prospectively included. One month after discharge, clinical characteristics, comorbidities measured with the Charlson index, and prognostic coronary scales (logistic EuroSCORE; GRACE 2.0) were collected. Post-bronchodilator spirometry, arterial stiffness, and serum inflammatory and myocardial biomarkers were measured. Lung plasmatic biomarkers (Surfactant protein D, desmosine, and Clara cell secretory protein-16) were determined with ELISA. COPD was defined by the fixed ratio (FEV1/FVC <70%). Spirometric values were also analyzed as continuous variables using adjusted and non-adjusted ANCOVA analysis. Finally, we evaluated the presence of a respiratory pattern defined by non-stratified spirometric values and pulmonary biomarkers.

Results

A total of 164 patients with a mean age of 65 (±10) years (79% males) were included. COPD was diagnosed in 56 (34%) patients (68% previously undiagnosed). COPD patients had a longer smoking history, higher scores on the EuroSCORE (p < 0.0001) and GRACE 2.0 (p < 0.001) scales, and more comorbidities (p = 0.006). Arterial stiffness determined by pulse wave velocity was increased in COPD patients (7.35 m/s vs 6.60 m/s; p = 0.006). Serum values of high sensitive T troponin (p = 0.007) and surfactant protein D (p = 0.003) were also higher in COPD patients. FEV1% remained significantly associated with arterial stiffness and surfactant protein D in the adjusted ANCOVA analysis. In the cluster exploration, 53% of the patients had a respiratory pattern.

Conclusion

COPD affects one-third of patients with an acute coronary event and frequently remains undiagnosed. Several mechanisms, including arterial stiffness and SPD, were increased in COPD patients. Their relationship with the prognosis should be confirmed with longitudinal follow-up of the cohort.

Prediction of COPD acute exacerbation in response to air pollution using exosomal circRNA profile and Machine learning

Ambient fine particulate matter (PM2.5) is linked to an increased risk of chronic obstructive pulmonary disease (COPD) exacerbations, which significantly increase the risk of mortality in COPD patients. Identifying the subtype of COPD patients who are sensitive to environmental aggressions is necessary. Using in vitro and in vivo PM2.5 exposure models, we demonstrate that exosomal hsa_circ_0005045 is upregulated by PM2.5 and binds to the protein cargo peroxiredoxin2, which functionally aggravates hallmarks of COPD by recruiting neutrophil elastase and triggering in situ release of tumor necrosis factor (TNF)-α by inflammatory cells. The biological function of hsa_circ_0005045 associated with aggravation of COPD is validated using exosome-transplantation and conditional circRNA-knockdown murine models. By sorting the major components of PM2.5, we find that PM2.5-bound heavy metals, which are distinguishable from the components of cigarette smoke, trigger the elevation of exosomal hsa_circ_0005045. Finally, using machine learning models in a cohort with 327 COPD patients, the PM2.5 exposure-sensitive COPD patients are characterized by relatively high hsa_circ_0005045 expression, non-smoking, and group C (mMRC 0-1 (or CAT < 10) and ≥ 2 exacerbations (or ≥ 1 exacerbation leading to hospital admission) in the past year). Thus, our results suggest that environmental reduction in PM2.5 emission provides a targeted approach to protecting non-smoking COPD patients against air pollution-related disease exacerbation.

Unique and shared systemic biomarkers for emphysema in Alpha-1 Antitrypsin deficiency and chronic obstructive pulmonary disease

BACKGROUND

Alpha-1 Antitrypsin (AAT) deficiency (AATD), the most common genetic cause of emphysema presents with unexplained phenotypic heterogeneity in affected subjects. Our objectives to identify unique and shared AATD plasma biomarkers with chronic obstructive pulmonary disease (COPD) may explain AATD phenotypic heterogeneity.

METHODS

The plasma or serum of 5,924 subjects from four AATD and COPD cohorts were analyzed on SomaScan V4.0 platform. Using multivariable linear regression, inverse variance random-effects meta-analysis, and Least Absolute Shrinkage and Selection Operator (LASSO) regression we tested the association between 4,720 individual proteins or combined in a protein score with emphysema measured by 15th percentile lung density (PD15) or diffusion capacity (DLCO) in distinct AATD genotypes (Pi*ZZ, Pi*SZ, Pi*MZ) and non-AATD, PiMM COPD subjects. AAT SOMAmer accuracy for identifying AATD was tested using receiver operating characteristic curve analysis.

FINDINGS

In PiZZ AATD subjects, 2 unique proteins were associated with PD15 and 98 proteins with DLCO. Of those, 68 were also associated with DLCO in COPD also and enriched for three cellular component pathways: insulin-like growth factor, lipid droplet, and myosin complex. PiMZ AATD subjects shared similar proteins associated with DLCO as COPD subjects. Our emphysema protein score included 262 SOMAmers and predicted emphysema in AATD and COPD subjects. SOMAmer AAT level <7.99 relative fluorescence unit (RFU) had 100% sensitivity and specificity for identifying Pi*ZZ, but it was lower for other AATD genotypes.

INTERPRETATION

Using SomaScan, we identified unique and shared plasma biomarkers between AATD and COPD subjects and generated a protein score that strongly associates with emphysema in COPD and AATD. Furthermore, we discovered unique biomarkers associated with DLCO and emphysema in PiZZ AATD.

FUNDING

This work was supported by a grant from the Alpha-1 Foundation to RPB. COPDGene was supported by Award U01 HL089897 and U01 HL089856 from the National Heart, Lung, and Blood Institute. Proteomics for COPDGene was supported by NIH 1R01HL137995. GRADS was supported by Award U01HL112707, U01 HL112695 from the National Heart, Lung, and Blood Institute, and UL1TRR002535 to CCTSI; QUANTUM-1 was supported by the National Heart Lung and Blood Institute, the Office of Rare Diseases through the Rare Lung Disease Clinical Research Network (1 U54 RR019498-01, Trapnell PI), and the Alpha-1 Foundation. COPDGene is also supported by the COPD Foundation through contributions made to an Industry Advisory Board that has included AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer, and Sunovion.

Trident Nano-Indexing the Proteomics Table: Next-Version Clustering of Iron Carbide NPs and Protein Corona

Protein corona composition and precise physiological understanding of differentially expressed proteins are key for identifying disease biomarkers. In this report, we presented a distinctive quantitative proteomics table of molecular cell signaling differentially expressed proteins of corona that formed on iron carbide nanoparticles (NPs). High-performance liquid chromatography/electrospray ionization coupled with ion trap mass analyzer (HPLC/ESI-Orbitrap) and MASCOT helped quantify 142 differentially expressed proteins. Among these proteins, 104 proteins showed upregulated behavior and 38 proteins were downregulated with respect to the control, whereas 48, 32 and 24 proteins were upregulated and 8, 9 and 21 were downregulated CW (control with unmodified NPs), CY (control with modified NPs) and WY (modified and unmodified NPs), respectively. These proteins were further categorized on behalf of their regularity, locality, molecular functionality and molecular masses using gene ontology (GO). A STRING analysis was used to target the specific range of proteins involved in metabolic pathways and molecular processing in different kinds of binding functionalities, such as RNA, DNA, ATP, ADP, GTP, GDP and calcium ion bindings. Thus, this study will help develop efficient protocols for the identification of latent biomarkers in early disease detection using protein fingerprints.

Advances in phosphoproteomics and its application to COPD

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) was the third leading cause of global death in 2019, causing a huge economic burden to society. Therefore, it is urgent to identify specific phenotypes of COPD patients through early detection, and to promptly treat exacerbations. The field of phosphoproteomics has been a massive advancement, compelled by the developments in mass spectrometry, enrichment strategies, algorithms, and tools. Modern mass spectrometry-based phosphoproteomics allows understanding of disease pathobiology, biomarker discovery, and predicting new therapeutic modalities.

AREAS COVERED

In this article, we present an overview of phosphoproteomic research and strategies for enrichment and fractionation of phosphopeptides, identification of phosphorylation sites, chromatographic separation and mass spectrometry detection strategies, and the potential application of phosphorylated proteomic analysis in the diagnosis, treatment, and prognosis of COPD disease.

EXPERT OPINION

The role of phosphoproteomics in COPD is critical for understanding disease pathobiology, identifying potential biomarkers, and predicting new therapeutic approaches. However, the complexity of COPD requires the more comprehensive understanding that can be achieved through integrated multi-omics studies. Phosphoproteomics, as a part of these multi-omics approaches, can provide valuable insights into the underlying mechanisms of COPD.

Proteome analysis of urinary biomarkers in a cigarette smoke-induced COPD rat model

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory airway disease caused by inhalation of cigarette smoke (CS) and other harmful gases and particles.

METHODS

This study aimed to explore potential urinary biomarkers for CS-induced COPD based on LC-MS/MS analysis.

RESULTS

A total of 340 urinary proteins were identified, of which 79 were significantly changed (30, 31, and 37 at week 2, 4 and 8, respectively). GO annotation of the differential urinary proteins revealed that acute-phase response, response to organic cyclic compounds, complement activation classical pathway, and response to lead ion were significantly enriched at week 2 and 4. Another four processes were only enriched at week 8, namely response to oxidative stress, positive regulation of cell proliferation, thyroid hormone generation, and positive regulation of apoptotic process. The PPI network indicated that these differential proteins were biologically connected in CS-exposed rats. Of the 79 differential proteins in CS-exposed rats, 56 had human orthologs. Seven proteins that had changed at week 2 and 4 when there were no changes of pulmonary function and pathological morphology were verified as potential biomarkers for early screening of CS-induced COPD by proteomic analysis. Another six proteins that changed at week 8 when obvious airflow obstruction was detected were verified as potential biomarkers for prognostic assessment of CS-induced COPD.

CONCLUSIONS

These results reveal that the urinary proteome could sensitively reflect pathological changes in CS-exposed rats, and provide valuable clues for exploring COPD biomarkers.

Chronic obstructive pulmonary disease risk assessment tools: is one better than the others?

PURPOSE OF REVIEW

Risk assessment tools are essential in COPD care to help clinicians identify patients at higher risk of accelerated lung function decline, respiratory exacerbations, hospitalizations, and death.

RECENT FINDINGS

Conventional methods of assessing risk have focused on spirometry, patient-reported symptoms, functional status, and a combination of these tools in composite indices. More recently, qualitatively and quantitatively assessed chest imaging findings, such as emphysema, large and small airways disease, and pulmonary vascular abnormalities have been associated with poor long-term outcomes in COPD patients. Although several blood and sputum biomarkers have been investigated for risk assessment in COPD, most still warrant further validation. Finally, novel remote digital monitoring technologies may be valuable to predict exacerbations but their large-scale performance, ease of implementation, and cost effectiveness remain to be determined.

SUMMARY

Given the complex heterogeneity of COPD, any single metric is unlikely to fully capture the risk of poor long-term outcomes. Therefore, clinicians should review all available clinical data, including spirometry, symptom severity, functional status, chest imaging, and bloodwork, to guide personalized preventive care of COPD patients. The potential of machine learning tools and remote monitoring technologies to refine COPD risk assessment is promising but remains largely untapped pending further investigation.

Epigenetic scores for the circulating proteome as tools for disease prediction

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

Dynamic Urinary Proteome Changes in Ovalbumin-Induced Asthma Mouse Model Using Data-Independent Acquisition Proteomics

Background

In this work, we aim to investigate dynamic urinary proteome changes during asthma development and to identify potential urinary protein biomarkers for the diagnosis of asthma.

Methods

An ovalbumin (OVA)-induced mouse model was used to mimic asthma. The urinary proteome from asthma and control mice was determined using data-independent acquisition combined with high-resolution tandem mass spectrometry.

Results

Overall, 331 proteins were identified, among which 53 were differentially expressed (26, 24, 14 and 20 on days 2, 8, 15 and 18, respectively; 1.5-fold change, adjust P<0.05). Gene Ontology annotation of the differential proteins showed that the acute-phase response, innate immune response, B cell receptor signaling pathway, and complement activation were significantly enriched. Protein–protein interaction network revealed that these differential proteins were partially biologically connected in OVA-induced asthma, as a group. On days 2 and 8, after two episodes of OVA sensitization, six differential proteins (CRAMP, ECP, HP, F2, AGP1, and CFB) were also reported to be closely associated with asthma. These proteins may hold the potential for the early screening of asthma. On days 15 and 18, after challenged with 1% OVA by inhalation, seven differential proteins (VDBP, HP, CTSE, PIGR, AAT, TRFE, and HPX) were also reported to be closely associated with asthma. Thus, these proteins hold the potential to be biomarkers for the diagnosis of asthma attack.

Conclusion

Our results indicate that the urinary proteome could reflect dynamic pathophysiological changes in asthma progression.

Fibrinogen: A Feasible Biomarker in Identifying the Severity and Acute Exacerbation of Chronic Obstructive Pulmonary Disease

Background

Chronic obstructive pulmonary disease (COPD) is no longer considered a disease exclusive to the respiratory system. It is a multipronged disease with both lung and systemic involvement. Although the forced expiratory volume (FEV) in one second is one of the most commonly used markers to assess disease severity, in recent years, biomarkers such as interleukin-1 beta, serum C-X-C motif chemokine ligand 10, fibrinogen, soluble receptor for advanced glycation, surfactant protein D, and club cell secretory protein have been proven to be effective markers to assess disease severity.

Objective

The current study aimed to test the association of fibrinogen levels with increased exacerbation of COPD per year and lower lung function and to discuss its potential utility as a biomarker.

Methodology

A total of 105 participants were enrolled in the study. The study participants included 35 stable COPD patients, 35 COPD patients with acute exacerbation, and 35 non-COPD healthy controls (matched for age and gender). All patients above 18 years of age who were diagnosed with COPD as per the Global Initiative for Chronic Obstructive Disease (GOLD) guidelines were considered for inclusion in the study. The patients were divided into stable COPD group and acute exacerbations of COPD (AECOPD) group based on the Anthonisen criteria. Sociodemographic factors, six-minute walk test, Medical Research Council Dyspnea Scale, and COPD Assessment Test scale were computed. Spirometry according to the American Thoracic Society guidelines and hematological investigations including serum fibrinogen were performed. Additionally, GOLD staging and severity indices were used to determine the clinical phenotyping of COPD, namely, ADO (age, dyspnea, airflow obstruction) index, BODE (body mass index, airflow obstruction, dyspnea, and exercise capacity) index, and DOSE (dyspnea, obstruction, smoking, exacerbation) index.

Results

Plasma fibrinogen level was significantly higher in the COPD groups compared to the control group. Plasma fibrinogen level was elevated in AECOPD compared to stable COPD patients. In addition, fibrinogen levels showed a positive correlation with important functional indices and prognostic markers such as BODE, ADO, and DOSE indices and a negative correlation with lung function. The odds of predicting an acute exacerbation of COPD for patients with FEV of <50% and FEV of >50% were 17.2 (area under the curve [AUC] = 0.825; sensitivity = 90.4%; specificity = 62.79%) and 15.1 (AUC = 0.791; sensitivity = 57.7%; specificity = 92.5%), respectively.

Conclusions

Plasma fibrinogen has the potential to be an important biomarker in the management of COPD and its exacerbation due to its ability to be responsive to the COPD disease statuses such as the severity of COPD and AECOPD.

Single-nucleotide polymorphism rs2070600 regulates AGER splicing and the sputum levels of the COPD biomarker soluble receptor for advanced glycation end-products

The COPD susceptibility SNP rs2070600 affects the levels of the COPD biomarker sRAGE in sputum as well as splicing of AGER. Moreover, @PouwelsScience et al. demonstrate large differences in sRAGE levels between serum and sputum. https://bit.ly/3t0pJtK.