Lung proteomic biomarkers associated with chronic obstructive pulmonary disease

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.

EH domain-containing protein 2 (EHD2): Overview, biological function, and therapeutic potential

EH domain-containing protein 2 (EHD2) is a member of the EHD protein family and is mainly located in the plasma membrane, but can also be found in the cytoplasm and endosomes. EHD2 is also a nuclear-cytoplasmic shuttle protein. After entering the cell nuclear, EHD2 acts as a corepressor of transcription to inhibit gene transcription. EHD2 regulates a series of biological processes. As a key regulator of endocytic transport, EHD2 is involved in the formation and maintenance of endosomal tubules and vesicles, which are critical for the intracellular transport of proteins and other substances. The N-terminal of EHD2 is attached to the cell membrane, while its C-terminal binds to the actin-binding protein. After binding, EHD2 connects with the actin cytoskeleton, forming the curvature of the membrane and promoting cell endocytosis. EHD2 is also associated with membrane protein trafficking and receptor signaling, as well as in glucose metabolism and lipid metabolism. In this review, we highlight the recent advances in the function of EHD2 in various cellular processes and its potential implications in human diseases such as cancer and metabolic disease. We also discussed the prospects for the future of EHD2. EHD2 has a broad prospect as a therapeutic target for a variety of diseases. Further research is needed to explore its mechanism, which could pave the way for the development of targeted treatments.

HHIP protein interactions in lung cells provide insight into COPD pathogenesis

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP , a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP , its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2 , and FBLN5 . These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.

Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema

Rationale: Emphysema is a chronic obstructive pulmonary disease phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies. Objectives: To discover blood omics biomarkers for chest computed tomography-quantified emphysema and develop predictive biomarker panels. Methods: Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training sample of 2,370 COPDGene participants with available blood RNA sequencing, plasma proteomics, and clinical data. Internal validation was conducted in a COPDGene testing sample (n = 1,016), and external validation was done in the ECLIPSE study (n = 526). Because low body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Elastic net models with bootstrapping were also developed in the training sample sequentially using clinical, blood cell proportions, RNA-sequencing, and proteomic biomarkers to predict quantitative emphysema. Model accuracy was assessed by the area under the receiver operating characteristic curves for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: Totals of 3,829 genes, 942 isoforms, 260 exons, and 714 proteins were significantly associated with emphysema (false discovery rate, 5%) and yielded 11 biological pathways. Seventy-four percent of these genes and 62% of these proteins showed mediation by BMI. Our prediction models demonstrated reasonable predictive performance in both COPDGene and ECLIPSE. The highest-performing model used clinical, blood cell, and protein data (area under the receiver operating characteristic curve in COPDGene testing, 0.90; 95% confidence interval, 0.85-0.90). Conclusions: Blood transcriptome and proteome-wide analyses revealed key biological pathways of emphysema and enhanced the prediction of emphysema.

Precision Approaches to Chronic Obstructive Pulmonary Disease Management

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD heterogeneity has hampered progress in developing pharmacotherapies that affect disease progression. This issue can be addressed by precision medicine approaches, which focus on understanding an individual's disease risk, and tailoring management based on pathobiology, environmental exposures, and psychosocial issues. There is an urgent need to identify COPD patients at high risk for poor outcomes and to understand at a mechanistic level why certain individuals are at high risk. Genetics, omics, and network analytic techniques have started to dissect COPD heterogeneity and identify patients with specific pathobiology. Drug repurposing approaches based on biomarkers of specific inflammatory processes (i.e., type 2 inflammation) are promising. As larger data sets, additional omics, and new analytical approaches become available, there will be enormous opportunities to identify high-risk individuals and treat COPD patients based on their specific pathophysiological derangements. These approaches show great promise for risk stratification, early intervention, drug repurposing, and developing novel therapeutic approaches for COPD.

The impact of the immune system on lung injury and regeneration in COPD

COPD is a devastating respiratory condition that manifests via persistent inflammation, emphysema development and small airway remodelling. Lung regeneration is defined as the ability of the lung to repair itself after injury by the proliferation and differentiation of progenitor cell populations, and becomes impaired in the COPD lung as a consequence of cell intrinsic epithelial stem cell defects and signals from the micro-environment. Although the loss of structural integrity and lung regenerative capacity are critical for disease progression, our understanding of the cellular players and molecular pathways that hamper regeneration in COPD remains limited. Intriguingly, despite being a key driver of COPD pathogenesis, the role of the immune system in regulating lung regenerative mechanisms is understudied. In this review, we summarise recent evidence on the contribution of immune cells to lung injury and regeneration. We focus on four main axes: 1) the mechanisms via which myeloid cells cause alveolar degradation; 2) the formation of tertiary lymphoid structures and the production of autoreactive antibodies; 3) the consequences of inefficient apoptotic cell removal; and 4) the effects of innate and adaptive immune cell signalling on alveolar epithelial proliferation and differentiation. We finally provide insight on how recent technological advances in omics technologies and human ex vivo lung models can delineate immune cell-epithelium cross-talk and expedite precision pro-regenerative approaches toward reprogramming the alveolar immune niche to treat COPD.

Machine Learning-Based Proteomics Reveals Ferroptosis in COPD Patient-Derived Airway Epithelial Cells Upon Smoking Exposure

BACKGROUND

Proteomics and genomics studies have contributed to understanding the pathogenesis of chronic obstructive pulmonary disease (COPD), but previous studies have limitations. Here, using a machine learning (ML) algorithm, we attempted to identify pathways in cultured bronchial epithelial cells of COPD patients that were significantly affected when the cells were exposed to a cigarette smoke extract (CSE).

METHODS

Small airway epithelial cells were collected from patients with COPD and those without COPD who underwent bronchoscopy. After expansion through primary cell culture, the cells were treated with or without CSEs, and the proteomics of the cells were analyzed by mass spectrometry. ML-based feature selection was used to determine the most distinctive patterns in the proteomes of COPD and non-COPD cells after exposure to smoke extract. Publicly available single-cell RNA sequencing data from patients with COPD (GSE136831) were used to analyze and validate our findings.

RESULTS

Five patients with COPD and five without COPD were enrolled, and 7,953 proteins were detected. Ferroptosis was enriched in both COPD and non-COPD epithelial cells after their exposure to smoke extract. However, the ML-based analysis identified ferroptosis as the most dramatically different response between COPD and non-COPD epithelial cells, adjusted P value = 4.172 × 10^-6, showing that epithelial cells from COPD patients are particularly vulnerable to the effects of smoke. Single-cell RNA sequencing data showed that in cells from COPD patients, ferroptosis is enriched in basal, goblet, and club cells in COPD but not in other cell types.

CONCLUSION

Our ML-based feature selection from proteomic data reveals ferroptosis to be the most distinctive feature of cultured COPD epithelial cells compared to non-COPD epithelial cells upon exposure to smoke extract.

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.

PTRF-IL33-ZBP1 signaling mediating macrophage necroptosis contributes to HDM-induced airway inflammation

Polymerase 1 and transcript release factor (PTRF, encoding by Cavin-1) regulates interleukin 33 (IL-33) release, which is implicated in asthma development. Z-DNA binding protein 1 (ZBP1)-sensing Z-RNAs induces necroptosis which causes inflammatory diseases. House dust mite (HDM) is the major source of allergen in house dust and is strongly associated with the development of asthma. Whether PTRF via IL-33 and ZBP1 mediates HDM-induced macrophage necroptosis and airway inflammation remains unclear. Here, we found that deficiency of PTRF could reduce lung IL-33, ZBP1, phosphor-receptor-interacting protein kinase 3 (p-RIPK3), and phosphor-mixed lineage kinase domain-like (p-MLKL) (necroptosis executioner), and airway inflammation in an HDM-induced asthma mouse model. In HDM-treated macrophages, ZBP1, p-RIPK3, and p-MLKL levels were markedly increased, and these changes were reversed by deletion of Cavin-1. Deletion of Il33 also reduced expression of ZBP1, p-RIPK3, and p-MLKL in HDM-challenged lungs. Moreover, IL-33 synergizing with HDM boosted expression of ZBP1, p-RIPK3, and p-MLKL in macrophages. In bronchial epithelial cells rather than macrophages and vascular endothelial cells, PTRF positively regulates IL-33 expression. Therefore, we conclude that PTRF mediates HDM-induced macrophage ZBP1/necroptosis and airway inflammation, and this effect could be boosted by bronchial epithelial cell-derived IL-33. Our findings suggest that PTRF-IL33-ZBP1 signaling pathway might be a promising target for dampening airway inflammation.

Integrating Genetics, Transcriptomics, and Proteomics in Lung Tissue to Investigate Chronic Obstructive Pulmonary Disease

The integration of transcriptomic and proteomic data from lung tissue with chronic obstructive pulmonary disease (COPD)-associated genetic variants could provide insight into the biological mechanisms of COPD. Here, we assessed associations between lung transcriptomics and proteomics with COPD in 98 subjects from the Lung Tissue Research Consortium. Low correlations between transcriptomics and proteomics were generally observed, but higher correlations were found for COPD-associated proteins. We integrated COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins to identify regulatory cis-quantitative trait loci (QTLs). Significant expression QTLs (eQTLs) and protein QTLs (pQTLs) were found regulating multiple COPD-associated biomarkers. We investigated mediated associations from significant pQTLs through transcripts to protein levels of COPD-associated proteins. We also attempted to identify colocalized effects between COPD genome-wide association studies and eQTL and pQTL signals. Evidence was found for colocalization between COPD genome-wide association study signals and a pQTL for RHOB and an eQTL for DSP. We applied weighted gene co-expression network analysis to find consensus COPD-associated network modules. Two network modules generated by consensus weighted gene co-expression network analysis were associated with COPD with a false discovery rate lower than 0.05. One network module is related to the catenin complex, and the other module is related to plasma membrane components. In summary, multiple cis-acting determinants of transcripts and proteins associated with COPD were identified. Colocalization analysis, mediation analysis, and correlation-based network analysis of multiple omics data may identify key genes and proteins that work together to influence COPD pathogenesis.

Biomarker-based clustering of patients with chronic obstructive pulmonary disease

Rationale

COPD has been associated repeatedly with single biomarkers of systemic inflammation, ignoring the complexity of inflammatory pathways. This study aimed to cluster patients with COPD based on systemic markers of inflammatory processes and to evaluate differences in their clinical characterisation and examine how these differences may relate to altered biological pathways.

Methods

213 patients with moderate-to-severe COPD in a clinically stable state were recruited and clinically characterised, which included a venous blood sample for analysis of serum biomarkers. Patients were clustered based on the overall similarity in systemic levels of 57 different biomarkers. To determine interactions among the regulated biomarkers, protein networks and biological pathways were examined for each patient cluster.

Results

Four clusters were identified: two clusters with lower biomarker levels (I and II) and two clusters with higher biomarker levels (III and IV), with only a small number of biomarkers with similar trends in expression. Pathway analysis indicated that three of the four clusters were enriched in Rage (receptor for advanced glycation end-products) and Oncostatin M pathway components. Although the degree of airflow limitation was similar, the clinical characterisation of clusters ranged from 1) better functional capacity and health status and fewer comorbidities; 2) more underweight, osteoporosis and static hyperinflation; 3) more metabolically deranged; and 4) older subjects with worse functional capacity and higher comorbidity load.

Conclusions

These new insights may help to understand the functionally relevant inflammatory interactions in the pathophysiology of COPD as a heterogeneous disease.

Applying Next-Generation Sequencing and Multi-Omics in Chronic Obstructive Pulmonary Disease

Microbiomics have significantly advanced over the last decade, driven by the widespread availability of next-generation sequencing (NGS) and multi-omic technologies. Integration of NGS and multi-omic datasets allow for a holistic assessment of endophenotypes across a range of chronic respiratory disease states, including chronic obstructive pulmonary disease (COPD). Valuable insight has been attained into the nature, function, and significance of microbial communities in disease onset, progression, prognosis, and response to treatment in COPD. Moving beyond single-biome assessment, there now exists a growing literature on functional assessment and host-microbe interaction and, in particular, their contribution to disease progression, severity, and outcome. Identifying specific microbes and/or metabolic signatures associated with COPD can open novel avenues for therapeutic intervention and prognosis-related biomarkers. Despite the promise and potential of these approaches, the large amount of data generated by such technologies can be challenging to analyze and interpret, and currently, there remains a lack of standardized methods to address this. This review outlines the current use and proposes future avenues for the application of NGS and multi-omic technologies in the endophenotyping, prognostication, and treatment of COPD.

Associative analysis of multi-omics data indicates that acetylation modification is widely involved in cigarette smoke-induced chronic obstructive pulmonary disease

We aimed to study the molecular mechanisms of chronic obstructive pulmonary disease (COPD) caused by cigarette smoke more comprehensively and systematically through different perspectives and aspects and to explore the role of protein acetylation modification in COPD. We established the COPD model by exposing C57BL/6J mice to cigarette smoke for 24 weeks, then analyzed the transcriptomics, proteomics, and acetylomics data of mouse lung tissue by RNA sequencing (RNA-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and associated these omics data through unique algorithms. This study demonstrated that the differentially expressed proteins and acetylation modification in the lung tissue of COPD mice were co-enriched in pathways such as oxidative phosphorylation (OXPHOS) and fatty acid degradation. A total of 19 genes, namely, ENO3, PFKM, ALDOA, ACTN2, FGG, MYH1, MYH3, MYH8, MYL1, MYLPF, TTN, ACTA1, ATP2A1, CKM, CORO1A, EEF1A2, AKR1B8, MB, and STAT1, were significantly and differentially expressed at all the three levels of transcription, protein, and acetylation modification simultaneously. Then, we assessed the distribution and expression in different cell subpopulations of these 19 genes in the lung tissues of patients with COPD by analyzing data from single-cell RNA sequencing (scRNA-seq). Finally, we carried out the in vivo experimental verification using mouse lung tissue through quantitative real-time PCR (qRT-PCR), Western blotting (WB), immunofluorescence (IF), and immunoprecipitation (IP). The results showed that the differential acetylation modifications of mouse lung tissue are widely involved in cigarette smoke-induced COPD. ALDOA is significantly downregulated and hyperacetylated in the lung tissues of humans and mice with COPD, which might be a potential biomarker for the diagnosis and/or treatment of COPD.

Peptide location fingerprinting identifies species- and tissue-conserved structural remodelling of proteins as a consequence of ageing and disease

Extracellular matrices (ECMs) in the intervertebral disc (IVD), lung and artery are thought to undergo age-dependant accumulation of damage by chronic exposure to mechanisms such as reactive oxygen species, proteases and glycation. It is unknown whether this damage accumulation is species-dependant (via differing lifespans and hence cumulative exposures) or whether it can influence the progression of age-related diseases such as atherosclerosis. Peptide location fingerprinting (PLF) is a new proteomic analysis method, capable of the non-targeted identification of structure-associated changes within proteins. Here we applied PLF to publicly available ageing human IVD (outer annulus fibrosus), ageing mouse lung and human arterial atherosclerosis datasets and bioinformatically identified novel target proteins alongside common age-associated differences within protein structures which were conserved between three ECM-rich organs, two species, three IVD tissue regions, sexes and in an age-related disease. We identify peptide yield differences across protein structures which coincide with biological regions, potentially reflecting the functional consequences of ageing or atherosclerosis for macromolecular assemblies (collagen VI), enzyme/inhibitor activity (alpha-2 macroglobulin), activation states (complement C3) and interaction states (laminins, perlecan, fibronectin, filamin-A, collagen XIV and apolipoprotein-B). Furthermore, we show that alpha-2 macroglobulin and collagen XIV exhibit possible shared structural consequences in IVD ageing and arterial atherosclerosis, providing novel links between an age-related disease and intrinsic ageing. Crucially, we also demonstrate that fibronectin, laminin beta chains and filamin-A all exhibit conserved age-associated structural differences between mouse lung and human IVD, providing evidence that ECM, and their associating proteins, may be subjected to potentially similar mechanisms or consequences of ageing across both species, irrespective of differences in lifespan and tissue function.

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.