Plasma proteins elevated in severe asthma despite oral steroid use and unrelated to Type-2 inflammation

Role of Complement Components in Asthma: A Systematic Review

Background: Asthma is a chronic inflammatory airway disease characterized by recurrent symptoms in response to a wide range of external stimuli, including allergens, viral infections, and air pollution together with internal host-derived danger signals. The disease is traditionally associated with adaptive immune responses; recent research emphasizes the critical role of innate immunity in its pathogenesis. The complement system, activated as part of the defense mechanisms, plays a crucial role in bridging innate to adaptive immunity. While experimental models demonstrate complement cascade activation in asthma, human studies remain limited. Methods: This systematic review summarizes existing literature on the complement system in asthma patients, gathering data from PubMed, Web of Science, Scopus, and Google Scholar. The protocol was registered in the OSF. Results: Out of 482 initially identified articles, only 24 met the eligibility criteria, revealing disparities in sample origin, methodologies, and populations. Despite observed heterogeneity, a consistent result was found in the elevation of complement regulatory proteins, such as complement Factor H, in samples from patients with asthma compared to those from healthy subjects. Conclusions: The increased level of regulatory proteins, such as Factor H and I highlight that these may influence asthma pathophysiology. The role of complement factors as potential biomarkers of asthma activity and severity needs further evaluation.

Identification and experimental validation of PYCARD as a crucial PANoptosis-related gene for immune response and inflammation in COPD

Chronic inflammatory and immune responses play key roles in the development and progression of chronic obstructive pulmonary disease (COPD). PANoptosis, as a unique inflammatory cell death modality, is involved in the pathogenesis of many inflammatory diseases. We aim to identify critical PANoptosis-related biomarkers and explore their potential effects on respiratory tract diseases and immune infiltration landscapes in COPD. Total microarray data consisting of peripheral blood and lung tissue datasets associated with COPD were obtained from the GEO database. PANoptosis-associated genes in COPD were identified by intersecting differentially expressed genes (DEGs) with genes involved in pyroptosis, apoptosis, and necroptosis after normalizing and removing the batch effect. Furthermore, GO, KEGG, PPI network, WGCNA, LASSO-COX, and ROC curves analysis were conducted to screen and verify hub genes, and the correlation between PYCARD and infiltrated immune cells was analyzed. The effect of PYCARD on respiratory tract diseases and the potential small-molecule agents for the treatment of COPD were identified. PYCARD expression was verified in the lung tissue of CS/LPS-induced COPD mice. PYCARD was a critical PANoptosis-related gene in all COPD patients. PYCARD was positively related to NOD-like receptor signaling pathway and promoted immune cell infiltration. Moreover, PYCARD was significantly activated in COPD mice mainly by targeting PANoptosis. PANoptosis-related gene PYCARD is a potential biomarker for COPD diagnosis and treatment.

Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage

Multi-omics data integration is a complex and challenging task in biomedical research. Consensus clustering, also known as meta-clustering or cluster ensembles, has become an increasingly popular downstream tool for phenotyping and endotyping using multiple omics and clinical data. However, current consensus clustering methods typically rely on ensembling clustering outputs with similar sample coverages (mathematical replicates), which may not reflect real-world data with varying sample coverages (biological replicates). To address this issue, we propose a new consensus clustering with missing labels (ccml) strategy termed ccml, an R protocol for two-step consensus clustering that can handle unequal missing labels (i.e. multiple predictive labels with different sample coverages). Initially, the regular consensus weights are adjusted (normalized) by sample coverage, then a regular consensus clustering is performed to predict the optimal final cluster. We applied the ccml method to predict molecularly distinct groups based on 9-omics integration in the Karolinska COSMIC cohort, which investigates chronic obstructive pulmonary disease, and 24-omics handprint integrative subgrouping of adult asthma patients of the U-BIOPRED cohort. We propose ccml as a downstream toolkit for multi-omics integration analysis algorithms such as Similarity Network Fusion and robust clustering of clinical data to overcome the limitations posed by missing data, which is inevitable in human cohorts consisting of multiple data modalities. The ccml tool is available in the R language (https://CRAN.R-project.org/package=ccml, https://github.com/pulmonomics-lab/ccml, or https://github.com/ZhoulabCPH/ccml).

Proteomic and transcriptomic screening demonstrates increased mast cell-derived CCL23 in systemic mastocytosis

BACKGROUND

Systemic mastocytosis (SM) is a heterogeneous group of mast cell-driven diseases diagnosed by bone marrow sampling. However, there are a limited number of available blood disease biomarkers.

OBJECTIVE

Our aim was to identify mast cell-derived proteins that could potentially serve as blood biomarkers for indolent and advanced forms of SM.

METHODS

We performed a plasma proteomics screening coupled with single-cell transcriptomic analysis in SM patients and healthy subjects.

RESULTS

Plasma proteomics screening identified 19 proteins upregulated in indolent disease compared to healthy, and 16 proteins in advanced disease compared to indolent. Among these, 5 proteins, CCL19, CCL23, CXCL13, IL-10, and IL-12Rβ1, were higher in indolent relative to healthy and in advanced disease compared to indolent. Single-cell RNA sequencing demonstrated that CCL23, IL-10, and IL-6 were selectively produced by mast cells. Notably, plasma CCL23 levels correlated positively with known markers of SM disease severity, namely tryptase levels, percentage bone marrow mast cell infiltration, and IL-6.

CONCLUSION

CCL23 is produced predominantly by mast cells in SM, and CCL23 plasma levels are associated with disease severity, correlating positively with established markers of disease burden, thus suggesting that CCL23 is a specific SM biomarker. In addition, the combination of CCL19, CCL23, CXCL13, IL-10, and IL-12Rβ1 may be useful for defining disease stage.

Antioxidant Intake and Biomarkers of Asthma in Relation to Smoking Status-A Review

Asthma is considered a chronic inflammatory disorder associated with airway hyperresponsiveness (AHR). Increased oxidative stress (OS) is a clinical feature of asthma, which promotes the inflammatory responses in bronchial/airway epithelial cells. Smokers and nonsmokers with asthma have been shown to have increases in several OS and inflammatory biomarkers. However, studies suggest significant differences in OS and inflammation biomarkers between smokers and nonsmokers. A few studies suggest associations between antioxidant intake from diet/supplements and asthma in patients with different smoking status. Evidence is lacking on the protective role of antioxidant vitamin and/or mineral consumption against asthma by smoking status with respect to inflammation and OS biomarkers. Therefore, the aim of this review is to highlight current knowledge regarding the relations between antioxidant intake, asthma, and its associated biomarkers, according to smoking status. This paper can be used to guide future research directions towards the health consequences of antioxidant intake in smoking and nonsmoking asthmatics.

Precision Medicine in Asthma Therapy

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

Reply: U-BIOPRED/BIOAIR proteins: inflammation or infection?

We thank D.L. Hahn and W. Webley for their interesting interpretation of the data in our original article, in which we reported on plasma proteins associated with asthma severity in two independent cohorts, U-BIOPRED and BIOAIR [1].

Protein profiles in plasma remained associated with asthma severity in the European asthma cohort U-BIOPRED when controlling for previous history of respiratory infections https://bit.ly/3rdEL0O

Serum Proteomic Profile of Asthmatic Patients after Six Months of Benralizumab and Mepolizumab Treatment

Severe eosinophilic asthma is characterized by chronic airway inflammation, oxidative stress, and elevated proinflammatory cytokines, especially IL-5. Mepolizumab and benralizumab are both humanized IgG antibodies directed against IL-5 signaling, directly acting on eosinophils count. Together with the complexity of severe asthma classification and patient selection for the targeted treatment, there is also the urgency to clarify the follow-up of therapy to identify biomarkers, in addition to eosinophils, for the optimal duration of treatment, persistence of effectiveness, and safety. To this purpose, here we performed a follow-up study using differential proteomic analysis on serum samples after 1 and 6 months of both therapies and sera from healthy patients. Statistical analysis by PCA and heatmap analyses were performed, and identified proteins were used for enrichment analysis by MetaCore software. The analysis highlighted 82 differences among all considered conditions. In particular, 30 referred to benralizumab time point (T0, T1B, T6B) and 24 to mepolizumab time point (T0, T1M, T6M) analyses. t-SNE and heatmap analyses evidence that the differential serum protein profile at 6 months of both treatments is more similar to that of the healthy subjects. Among the identified proteins, APOAI, APOC-II, and APOC-III are upregulated principally after 6 months of benralizumab treatment, plasminogen is upregulated after 6 months of both treatments and ceruloplasmin, upregulated already after 1 month of benralizumab, becoming higher after 6 months of mepolizumab. Using enrichment analysis, identified proteins were related to lipid metabolism and transport, blood coagulation, and ECM remodeling.

Carboxypeptidase A3—A Key Component of the Protease Phenotype of Mast Cells

Carboxypeptidase A3 (CPA3) is a specific mast cell (MC) protease with variable expression. This protease is one of the preformed components of the secretome. During maturation of granules, CPA3 becomes an active enzyme with a characteristic localization determining the features of the cytological and ultrastructural phenotype of MC. CPA3 takes part in the regulation of a specific tissue microenvironment, affecting the implementation of innate immunity, the mechanisms of angiogenesis, the processes of remodeling of the extracellular matrix, etc. Characterization of CPA3 expression in MC can be used to refine the MC classification, help in a prognosis, and increase the effectiveness of targeted therapy.