iTRAQ-based proteomic analysis reveals potential serum biomarkers of allergic and nonallergic asthma

Identification of plasma protein markers of allergic disease risk: a mendelian randomization approach to proteomic analysis

BACKGROUND

While numerous allergy-related biomarkers and targeted treatment strategies have been developed and employed, there are still signifcant limitations and challenges in the early diagnosis and targeted treatment for allegic diseases. Our study aims to identify circulating proteins causally associated with allergic disease-related traits through Mendelian randomization (MR)-based analytical framework.

METHODS

Large-scale cis-MR was employed to estimate the effects of thousands of plasma proteins on five main allergic diseases. Additional analyses including MR Steiger analyzing and Bayesian colocalisation, were performed to test the robustness of the associations; These findings were further validated utilizing meta-analytical methods in the replication analysis. Both proteome- and transcriptome-wide association studies approach was applied, and then, a protein-protein interaction was conducted to examine the interplay between the identified proteins and the targets of existing medications.

RESULTS

Eleven plasma proteins were identified with links to atopic asthma (AA), atopic dermatitis (AD), and allergic rhinitis (AR). Subsequently, these proteins were classified into four distinct target groups, with a focus on tier 1 and 2 targets due to their higher potential to become drug targets. MR analysis and extra validation revealed STAT6 and TNFRSF6B to be Tier 1 and IL1RL2 and IL6R to be Tier 2 proteins with the potential for AA treatment. Two Tier 1 proteins, CRAT and TNFRSF6B, and five Tier 2 proteins, ERBB3, IL6R, MMP12, ICAM1, and IL1RL2, were linked to AD, and three Tier 2 proteins, MANF, STAT6, and TNFSF8, to AR.

CONCLUSION

Eleven Tier 1 and 2 protein targets that are promising drug target candidates were identified for AA, AD, and AR, which influence the development of allergic diseases and expose new diagnostic and therapeutic targets.

Nasal Lavage Fluid Proteomics Reveals Potential Biomarkers of Asthma Associated with Disease Control

Purpose

Little research has explored the proteomic characteristics of nasal lavage fluid from asthmatic patients. This study aims to investigate whether differentially expressed proteins (DEPs) in nasal lavage fluid can serve as a biomarker to differentiate asthma patients from healthy controls (HCs) and to discern between individuals with well controlled and poorly controlled asthma.

Patients and Methods

We enrolled patients with allergic rhinitis (AR), asthma, or both conditions, and HCs in this study. We recorded patients' demographic and medical history data and administered asthma quality of life questionnaire (AQLQ) and asthma control questionnaire (ACQ). Nasal fluid samples were collected, followed by protein measurements, and proteomic analysis utilizing the data-independent acquisition (DIA) method.

Results

Twenty-four with asthma, 27 with combined asthma+ AR, 25 with AR, and 12 HCs were enrolled. Four proteins, superoxide dismutase 2 (SOD2), serpin B7 (SERPINB7), kallikrein-13 (KLK13), and bleomycin hydrolase (BLMH) were significantly upregulated in nasal lavage fluid samples of asthma without AR, compared to HCs (Fold change ≥2.0, false-discovery rate [FDR] <0.05). Conversely, 56 proteins including secretoglobin family 2A member 1 (SCGB2A1) were significantly downregulated (fold change ≥2.0, FDR <0.05). Furthermore, 96.49% of DEPs including peptidase inhibitor 3 (PI3) and C-X-C motif chemokine 17 (CXCL17) were upregulated in poorly controlled asthma patients without AR relative those with well- or partly controlled asthma (fold change ≥1.5, FDR <0.05). Search tool for the retrieval of interacting genes/proteins (STRING) analysis showed that PI3, with 18 connections, may be pivotal in asthma control.

Conclusion

The study revealed significant alteration in the nasal lavage proteome in asthma without AR patients. Moreover, our results indicated a potential association between the expression of proteome in the upper airway and the level of asthma control. Specifically, PI3 appears to be a key role in the regulation of asthma without AR.

Galectin-10 in serum extracellular vesicles reflects asthma pathophysiology

BACKGROUND

Novel biomarkers (BMs) are urgently needed for bronchial asthma (BA) with various phenotypes and endotypes.

OBJECTIVE

We sought to identify novel BMs reflecting tissue pathology from serum extracellular vesicles (EVs).

METHODS

We performed data-independent acquisition of serum EVs from 4 healthy controls, 4 noneosinophilic asthma (NEA) patients, and 4 eosinophilic asthma (EA) patients to identify novel BMs for BA. We confirmed EA-specific BMs via data-independent acquisition validation in 61 BA patients and 23 controls. To further validate these findings, we performed data-independent acquisition for 6 patients with chronic rhinosinusitis without nasal polyps and 7 patients with chronic rhinosinusitis with nasal polyps.

RESULTS

We identified 3032 proteins, 23 of which exhibited differential expression in EA. Ingenuity pathway analysis revealed that protein signatures from each phenotype reflected disease characteristics. Validation revealed 5 EA-specific BMs, including galectin-10 (Gal10), eosinophil peroxidase, major basic protein, eosinophil-derived neurotoxin, and arachidonate 15-lipoxygenase. The potential of Gal10 in EVs was superior to that of eosinophils in terms of diagnostic capability and detection of airway obstruction. In rhinosinusitis patients, 1752 and 8413 proteins were identified from EVs and tissues, respectively. Among 11 BMs identified in EVs and tissues from patients with chronic rhinosinusitis with nasal polyps, 5 (including Gal10 and eosinophil peroxidase) showed significant correlations between EVs and tissues. Gal10 release from EVs was implicated in eosinophil extracellular trapped cell death in vitro and in vivo.

CONCLUSION

Novel BMs such as Gal10 from serum EVs reflect disease pathophysiology in BA and may represent a new target for liquid biopsy approaches.

Serum proteomics identifies S100A11 and MMP9 as novel biomarkers for predicting the early efficacy of sublingual immunotherapy in allergic rhinitis

BACKGROUND

Allergic rhinitis (AR) is a chronic inflammatory disorder, and sublingual immunotherapy (SLIT) is an important therapy. However, SLIT exhibits a wide range of fluctuations and lacks objective monitoring indicators. Therefore, exploring biomarkers for early prediction of the efficacy of SLIT is urgently needed.

METHODS

We recruited two independent cohorts. In the discovery cohort, house dust mite (HDM) -induced AR patients underwent SLIT for at least 1 year, and were categorized into response and no response groups based on early efficacy. Serum proteomics was conducted to detect variations in protein expression levels between the two groups. The candidate proteins were confirmed in the validation cohort with enzyme-linked immunosorbent assay (ELISA), and their predictive values and levels of change before and after treatment were evaluated.

RESULTS

Serum proteomics identified a total of 113 differential proteins between the two groups, including 41 proteins upregulated and 72 downregulated in the no response group than the response group. The top 5 up- and down-regulated proteins were selected for further validation, and ELISA results revealed that serum CCL14, LTA4H, S100A11 and MMP9 levels were significantly elevated, and TGFBI and MASP1 were decreased in the response group than those in the no response group(P < 0.05). Moreover, receiver operating characteristic curves revealed that serum S100A11 and MMP9 exhibited greater ability in predicting the early effectiveness of SLIT (AUC > 0.7, P < 0.05). Furthermore, these two biomarkers exhibited significant reductions 1 year after SLIT, particularly in those patients who responded positively to the treatment (P < 0.05).

CONCLUSION

Serum S100A11 and MMP9 have the potential to serve as biomarkers for early prediction of the effectiveness of SLIT and monitoring the therapeutic effects. The circulating proteomic alterations might contribute to guiding treatment and understanding the mechanism of SLIT in AR patients.

Evaluation of common protein biomarkers involved in the pathogenesis of respiratory diseases with proteomic methods: A systematic review

AIM

Respiratory disease (RD) is one of the most common diseases characterized by lung dysfunction. Many diagnostic mechanisms have been used to identify the pathogenic agents of responsible for RD. Among these, proteomics emerges as a valuable diagnostic method for pinpointing the specific proteins involved in RD pathogenesis. Therefore, in this study, for the first time, we examined the protein markers involved in the pathogenesis of chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), asthma, bronchiolitis obliterans (BO), and chemical warfare victims exposed to mustard gas, using the proteomics method as a systematic study.

MATERIALS AND METHODS

A systematic search was performed up to September 2023 on several databases, including PubMed, Scopus, ISI Web of Science, and Cochrane. In total, selected 4246 articles were for evaluation according to the criteria. Finally, 119 studies were selected for this systematic review.

RESULTS

A total of 13,806 proteins were identified, 6471 in COPD, 1603 in Asthma, 5638 in IPF, three in BO, and 91 in mustard gas exposed victims. Alterations in the expression of these proteins were observed in the respective diseases. After evaluation, the results showed that 31 proteins were found to be shared among all five diseases.

CONCLUSION

Although these 31 proteins regulate different factors and molecular pathways in all five diseases, they ultimately lead to the regulation of inflammatory pathways. In other words, the expression of some proteins in COPD and mustard-exposed patients increases inflammatory reactions, while in IPF, they cause lung fibrosis. Asthma, causes allergic reactions due to T-cell differentiation toward Th2.

Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning

There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Recent insights in the role of biomarkers in severe asthma management

Contemporary asthma management requires a proactive and individualized approach, combining precision diagnosis and personalized treatment. The introduction of biologic therapies for severe asthma to everyday clinical practice, increases the need for specific patient selection, prediction of outcomes and monitoring of these costly and long-lasting therapies. Several biomarkers have been used in asthma in disease identification, prediction of asthma severity and prognosis, and response to treatment. Novel advances in the area of personalized medicine regarding disease phenotyping and endotyping, encompass the development and application of reliable biomarkers, accurately quantified using robust and reproducible methods. The availability of powerful omics technologies, together with integrated and network-based genome data analysis, and microbiota changes quantified in serum, body fluids and exhaled air, will lead to a better classification of distinct phenotypes or endotypes. Herein, in this review we discuss on currently used and novel biomarkers for the diagnosis and treatment of asthma.

A Deep Ensemble Predictor for Identifying Anti-Hypertensive Peptides Using Pretrained Protein Embedding

Hypertension (HT), or high blood pressure is one of the most common and main causes in cardiovascular diseases, which is also related to a series of detrimental diseases in humans. Deficiencies in effective treatment in HT are often associated with a series of diseases including multi-infarct dementia, amputation, and renal failure. Therefore, identifying anti-hypertension peptides has the vital realistic significance. Although many bioactive peptides have been developed to reduce blood pressure, they are time-consuming and laborious. In views of the obstacles of the intrinsic methods in antihypertensive peptide (AHTP) classification, computational methods are suggested as a supplement to identify AHTPs. In this study, we develop a comprehensive feature representation algorithm based on pretrained model and convolutional neural network and apply the deep ensemble model to construct the prediction model. The new predictor is used to identify AHTPs in benchmark and independent datasets. It has been shown in the independent test set that the performance is better than the recent methods. Comparative results indicate that our model can shed some light on hypertension therapy and gains more insights of classifying AHTPs. The implements and codes can be found in https://github.com/yuanying566/AHPred-DE.

Current Insights on the Impact of Proteomics in Respiratory Allergies

Respiratory allergies affect humans worldwide, causing extensive morbidity and mortality. They include allergic rhinitis (AR), asthma, pollen food allergy syndrome (PFAS), aspirin-exacerbated respiratory disease (AERD), and nasal polyps (NPs). The study of respiratory allergic diseases requires new technologies for early and accurate diagnosis and treatment. Omics technologies provide the tools required to investigate DNA, RNA, proteins, and other molecular determinants. These technologies include genomics, transcriptomics, proteomics, and metabolomics. However, proteomics is one of the main approaches to studying allergic disorders' pathophysiology. Proteins are used to indicate normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. In this field, the principal goal of proteomics has been to discover new proteins and use them in precision medicine. Multiple technologies have been applied to proteomics, but that most used for identifying, quantifying, and profiling proteins is mass spectrometry (MS). Over the last few years, proteomics has enabled the establishment of several proteins for diagnosing and treating respiratory allergic diseases.

In-Depth Serum Proteomics by DIA-MS with In Silico Spectral Libraries Reveals Dynamics during the Active Phase of Systemic Juvenile Idiopathic Arthritis

In serum proteomics using mass spectrometry, the number of detectable proteins is reduced due to high-abundance proteins, such as albumin. However, recently developed data-independent acquisition mass spectrometry (DIA-MS) proteomics technology has made it possible to remarkably improve the number of proteins in a serum analysis by removing high-abundance proteins. Using this technology, we analyzed sera from patients with systemic juvenile idiopathic arthritis (sJIA), a rare pediatric disease. As a result, we identified 2727 proteins with a wide dynamic range derived from various tissue leakages. We also selected 591 proteins that differed significantly in their active phases. These proteins were involved in many inflammatory processes, and we also identified immunoproteasomes, which were not previously found in serum, suggesting that they may be involved in the pathogenesis of sJIA. A detailed high-depth DIA-MS proteomic analysis of serum may be useful for understanding the pathogenesis of sJIA and may provide clues for the development of new biomarkers.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

New advances in quantitative proteomics research and current applications in asthma

INTRODUCTION

Asthma is the most common chronic respiratory disease and has been declared a global public health problem by the World Health Organization. Due to the high heterogeneity and complexity, asthma can be classified into different 'phenotypes' and it is still difficult to assess the phenotypes and stages of asthma by traditional methods. In recent years, mass spectrometry-based proteomics studies have made significant progress in sensitivity and accuracy of protein identification and quantitation, and are able to obtain differences in protein expression across samples, which provides new insights into the mechanisms and classification of asthma.

AREAS COVERED

In this article, we summarize research strategies in quantitative proteomics, including labeled, label-free and targeted quantification, and highlight the advantages and disadvantages of each. In addition, new applications of quantitative proteomics and the current status of research in asthma have also been discussed. In this study, online resources such as PubMed and Google Scholar were used for literature retrieval.

EXPERT OPINION

The application of quantitative proteomics in asthma has an important role in identifying asthma subphenotypes, revealing potential pathogenesis and therapeutic targets. But the proteomic studies on asthma are not sufficient, as most of them are in the phase of biomarker discovery.

Advances and highlights in biomarkers of allergic diseases

During the past years, there has been a global outbreak of allergic diseases, presenting a considerable medical and socioeconomical burden. A large fraction of allergic diseases is characterized by a type 2 immune response involving Th2 cells, type 2 innate lymphoid cells, eosinophils, mast cells, and M2 macrophages. Biomarkers are valuable parameters for precision medicine as they provide information on the disease endotypes, clusters, precision diagnoses, identification of therapeutic targets, and monitoring of treatment efficacies. The availability of powerful omics technologies, together with integrated data analysis and network‐based approaches can help the identification of clinically useful biomarkers. These biomarkers need to be accurately quantified using robust and reproducible methods, such as reliable and point‐of‐care systems. Ideally, samples should be collected using quick, cost‐efficient and noninvasive methods. In recent years, a plethora of research has been directed toward finding novel biomarkers of allergic diseases. Promising biomarkers of type 2 allergic diseases include sputum eosinophils, serum periostin and exhaled nitric oxide. Several other biomarkers, such as pro‐inflammatory mediators, miRNAs, eicosanoid molecules, epithelial barrier integrity, and microbiota changes are useful for diagnosis and monitoring of allergic diseases and can be quantified in serum, body fluids and exhaled air. Herein, we review recent studies on biomarkers for the diagnosis and treatment of asthma, chronic urticaria, atopic dermatitis, allergic rhinitis, chronic rhinosinusitis, food allergies, anaphylaxis, drug hypersensitivity and allergen immunotherapy. In addition, we discuss COVID‐19 and allergic diseases within the perspective of biomarkers and recommendations on the management of allergic and asthmatic patients during the COVID‐19 pandemic.

IGF1R as a Potential Pharmacological Target in Allergic Asthma

Background: Asthma is a chronic lung disease characterized by reversible airflow obstruction, airway hyperresponsiveness (AHR), mucus overproduction and inflammation. Although Insulin-like growth factor 1 receptor (IGF1R) was found to be involved in asthma, its pharmacological inhibition has not previously been investigated in this pathology. We aimed to determine if therapeutic targeting of IGF1R ameliorates allergic airway inflammation in a murine model of asthma. Methods: C57BL/6J mice were challenged by house dust mite (HDM) extract or PBS for four weeks and therapeutically treated with the IGF1R tyrosine kinase inhibitor (TKI) NVP-ADW742 (NVP) once allergic phenotype was established. Results: Lungs of HDM-challenged mice exhibited a significant increase in phospho-IGF1R levels, incremented AHR, airway remodeling, eosinophilia and allergic inflammation, as well as altered pulmonary surfactant expression, all of being these parameters counteracted by NVP treatment. HDM-challenged lungs also displayed augmented expression of the IGF1R signaling mediator p-ERK1/2, which was greatly reduced upon treatment with NVP. Conclusions: Our results demonstrate that IGF1R could be considered a potential pharmacological target in murine HDM-induced asthma and a candidate biomarker in allergic asthma.

Irbesartan ameliorates chronic mountain sickness in a rat model via the cholesterol metabolism: An iTRAQ -based proteomics analysis

OBJECTIVE

To study the effects of irbesartan on pulmonary artery lesions in a rat model with chronic mountain sickness (CMS) and identify the biomarkers involved.

METHODS

In this study, we used a rat model of CMS to evaluate the therapeutic effect of irbesartan by measuring pulmonary artery pressure and evaluating the histopathology of the pulmonary artery. We also used proteomics technology to identify differentially expressed proteins (DEPs) in the serum and performed bioinformatics analysis. Results were then verified by enzyme linked immunosorbent assay (ELISA) and immunohistochemistry (IHC).

RESULTS

Irbesartan treatment induced a significant decrease (P < 0.05) in the pulmonary artery pressure of CMS rats. Histopathological and electron microscope further confirmed that high altitude hypoxia induced changes in the structure of the pulmonary artery tissue and caused ultrastructural lesions. Proteomics analysis identified 40 DEPs; bioinformatics analysis further revealed that the cholesterol metabolism pathway plays a crucial role in the occurrence of CMS. ELISA and IHC verified that several DEPs (Apo-A1, Apo-C1, Apo-E, IGF-1, Profilin1, and Col1a1) represent critical biological markers in pulmonary artery disease caused by CMS.

CONCLUSIONS

Irbesartan significantly improved pulmonary artery damage in a rat model of CMS possibly by impacting on the cholesterol metabolism pathway and by reducing damage to vascular endothelial cells. Irbesartan also inhibited the expression levels of IGF-1, Profilin1 and Col1a1 to relieve pulmonary artery pressure and improve lung function by inhibiting vascular remodeling. Several proteins were identified as potential biomarkers of CMS, including Apo-A1, Apo-C1, Apo-E, IGF-1, Profilin1, and Col1a1.

Biomarkers in Hereditary Angioedema

A biomarker is a defined characteristic measured as an indicator of normal, biologic, pathogenic processes, or biological responses to an exposure or intervention. Diagnostic biomarkers are used to detect a disease or a subtype of a disease; monitoring biomarkers are measured serially to assess a medical condition; response biomarkers are used to check biologic response following a medical intervention; predictive biomarkers are used to identify patients who are more likely to respond to a medical intervention; and prognostic biomarkers are used to assess the future likelihood of a clinical event. Although biomarkers have been extensively investigated and validated in many diseases and pathologies, very few are currently useful for the diagnosis, evaluation of disease activity, and treatment of hereditary angioedema (HAE). Pathophysiologic pathways involved in HAE reveal a plethora of molecules from the complement, coagulation, and fibrinolysis systems or from the vascular endothelium, which may serve as biomarkers. The most promising candidates, together with their laboratory readout systems, should be evaluated with regard to their analytical and clinical validity and utility. To be highly specific, such biomarkers should be linked to the pathomechanisms of HAE, particularly the bradykinin-generating cascade. Additionally, major advances in high-throughput omics-based technologies may facilitate the discovery of new candidate biomarkers in the future. This review will cover the existing as well as future potential biomarkers that will support the diagnosis, monitor disease activity, and can be used to assess the efficacy of new avenues of therapy of HAE and other forms of angioedema.

Advances and recent developments in asthma in 2020

In this review, we discuss recent publications on asthma and review the studies that have reported on the different aspects of the prevalence, risk factors and prevention, mechanisms, diagnosis, and treatment of asthma. Many risk and protective factors and molecular mechanisms are involved in the development of asthma. Emerging concepts and challenges in implementing the exposome paradigm and its application in allergic diseases and asthma are reviewed, including genetic and epigenetic factors, microbial dysbiosis, and environmental exposure, particularly to indoor and outdoor substances. The most relevant experimental studies further advancing the understanding of molecular and immune mechanisms with potential new targets for the development of therapeutics are discussed. A reliable diagnosis of asthma, disease endotyping, and monitoring its severity are of great importance in the management of asthma. Correct evaluation and management of asthma comorbidity/multimorbidity, including interaction with asthma phenotypes and its value for the precision medicine approach and validation of predictive biomarkers, are further detailed. Novel approaches and strategies in asthma treatment linked to mechanisms and endotypes of asthma, particularly biologicals, are critically appraised. Finally, due to the recent pandemics and its impact on patient management, we discuss the challenges, relationships, and molecular mechanisms between asthma, allergies, SARS-CoV-2, and COVID-19.

Advances and novel developments in molecular allergology

The continuous search for new allergens and the design of allergen derivatives improves the understanding of their allergenicity and aids the design of novel diagnostic and immunotherapy approaches. This article discusses the recent developments in allergen and epitope discovery, allergy diagnostics and immunotherapy. Structural information is crucial for the elucidation of cross-reactivity of marker allergens such as the walnut Jug r 6 or that of nonhomologous allergens, as shown for the peanut allergens Ara h 1 and 2. High-throughput sequencing, liposomal nanoallergen display, bead-based assays, and protein chimeras have been used in epitope discovery. The binding of natural ligands by the birch pollen allergen Bet v 1 or the mold allergen Alt a 1 increased the stability of these allergens, which is directly linked to their allergenicity. We also report recent findings on the use of component-resolved approaches, basophil activation test, and novel technologies for improvement of diagnostics. New strategies in allergen-specific immunotherapy have also emerged, such as the use of virus-like particles, biologics or novel adjuvants. The identification of dectin-1 as a key player in allergy to tropomyosins and the formyl peptide receptor 3 in allergy to lipocalins are outstanding examples of research into the mechanism of allergic sensitization.

Physiological and Proteomic Analysis of Penicillium digitatum in Response to X33 Antifungal Extract Treatment

Penicillium digitatum is a widespread pathogen among Rutaceae species that causes severe fruit decay symptoms on infected citrus fruit (known as citrus green mold). The employment of fungicides can effectively control the citrus green mold, significantly reducing agricultural economic loss. In this study, we found that the X33 antifungal extract produced by Streptomyces lavendulae strain X33 inhibited the hyphae polarization of P. digitatum. Additionally, physiological and proteomic analysis strategies were applied to explore the inhibitory mechanism of the X33 antifungal extract of the S. lavendulae strain X33 on the mycelial growth of P. digitatum. A total of 277 differentially expressed proteins, consisting of 207 upregulated and 70 downregulated, were identified from the comparative proteomics analysis. The results indicated that the X33 antifungal extract induced mitochondrial membrane dysfunction and cellular integrity impairment, which can affect energy metabolism, oxidative stress, and transmembrane transport. The improved alkaline phosphatase activity and extracellular conductivity, increased H₂O₂ and malondialdehyde contents, and inhibition of energy, amino acid, and sugar metabolism indicated that the oxidative stress of P. digitatum is induced by the X33 antifungal extract. These findings provided insight into the antifungal mechanism of the X33 antifungal extract against P. digitatum by suggesting that it may be an effective fungicide for controlling citrus postharvest green mold.