Clinical Efficacy Evaluation of 1-Year Subcutaneous Immunotherapy for Artemisia sieversiana Pollen Allergic Rhinitis by Serum Metabolomics

Recombinant Art v4.01 protein produces immunological tolerance by subcutaneous immunotherapy in a wormwood pollen-driven allergic asthma female mouse model

Art v4.01 is a well-known profilin protein belonging to the pan-allergens group and is commonly involved in triggering allergic asthma, polyallergy, and cross-sensitization. It is also referred to as Wormwood due to its origin. Crude wormwood extracts are applied for allergen-specific immunotherapy (AIT). Whether the recombinant Art v4.01 (rArt v4.01) can produce in vivo immunological tolerance by subcutaneous immunotherapy (SCIT) remains elusive. In this study, to investigate the in vivo immunological response of rArt v4.01, Th2, Th1, Treg, Th17 type-related cytokines and phenotypes of immune cells were tested, facilitating the exploration of the underlying mechanisms. The expression and purification of Art v4.01 were carried out using recombinant techniques. Allergic asthma female BALB/c mice were induced by subcutaneous sensitization of wormwood pollen extract and intranasal challenges. SCIT without adjuvant was performed using the rArt v4.01 and wormwood pollen extract for 2 weeks. Following exposure to challenges, the levels of immunoglobulin E (IgE), cytokines, and inflammatory cells were assessed through enzyme-linked immunosorbent assay (ELISA) and histological examination of sera, bronchoalveolar lavage fluid (BALF), and lung tissue. These parameters were subsequently compared between treatment groups receiving rArt v4.01 and wormwood pollen extract. The rArt v4.01 protein was expressed, which had a high purity (>90%) and an allergenic potency. Compared with the pollen extract, rArt v4.01 was superior in terms of reducing the number of white blood cells (WBCs), total nucleated cells (TNCs), and monocytes (MNs) in BALF and the degree of lung inflammation (1.77±0.99 vs. 2.31±0.80, P > 0.05). Compared with the model group, only rArt v4.01 reduced serum IgE level (1.19±0.25 vs. 1.61±0.17 μg/ml, P = 0.062), as well as the levels of Th2 type-related cytokines (interleukin-4 (IL-4) (107.18±16.17 vs. 132.47±20.85 pg/ml, P < 0.05) and IL-2 (19.52±1.19 vs. 24.02±2.14 pg/ml, P < 0.05)). The study suggested that rArt v4.01 was superior to pollen extract in reducing the number of inflammatory cells in BALF, pneumonitis, levels of pro-inflammatory cytokines, and serum IgE level. These findings confirmed that Art v4.01 could be a potential candidate protein for allergen-specific immunotherapy.

Mechanisms and Predictive Biomarkers of Allergen Immunotherapy in the Clinic

Allergen immunotherapy (AIT) remains to be the only disease-modifying treatment for IgE-mediated allergic diseases such as allergic rhinitis. It can provide long-term clinical benefits when given for 3 years or longer. Mechanisms of immune tolerance induction by AIT are underscored by the modulation of several compartments within the immune system. These include repair of disruption in epithelial barrier integrity, modulation of the innate immune compartment that includes regulatory dendritic cells and innate lymphoid cells, and adaptive immune compartments such as induction of regulatory T and B cells. Altogether, these are also associated with the dampening of allergen-specific TH2 and T follicular helper cell responses and subsequent generation of blocking antibodies. Although AIT is effective in modifying the immune response, there is a lack of validated and clinically relevant biomarkers that can be used to monitor desensitization, efficacy, and the likelihood of response, all of which can contribute to accelerating personalized medication and increasing patient care. Candidate biomarkers comprise humoral, cellular, metabolic, and in vivo biomarkers; however, these are primarily studied in small trials and require further validation. In this review, we evaluate the current candidates of biomarkers of AIT and how we can implement changes in future studies to help us identify clinically relevant biomarkers of safety, compliance, and efficacy.

Mechanisms of Allergen Immunotherapy and Potential Biomarkers for Clinical Evaluation

Allergen-immunotherapy (AIT) is an efficacious and disease-modifying treatment option for IgE-mediated diseases. Among these allergic rhinitis, insect venom allergy, food allergy, and allergic asthma are the most common candidates for AIT. AIT gives rise to clinical immunotolerance which may last for years after the treatment cessation. Mechanisms of AIT include suppression of allergic inflammation in target tissues and stimulation of the production of blocking antibodies, especially IgG4 and IgA. These mechanisms are followed by a reduction of underlying allergen-specific Th2 cell-driven responses to the allergens. Tolerance induction takes place through the desensitization of effector cells and stimulation of regulatory T cells that show their effects by mechanisms involving cell-cell cross-talk, but also other mechanisms, e.g., by the production of immunomodulatory cytokines such as, e.g., IL-10 and TGF-beta. From a personalized medical perspective, there is a need for clinical biomarkers of value in selecting responders and optimizing patient care during AIT. Also, a deeper understanding of underlying mechanistic processes will improve AIT's future outcomes. In this paper, the current knowledge of mechanisms in AIT is reviewed with a special focus on biomarkers of this therapy.

Fecal and serum metabolomic signatures and gut microbiota characteristics of allergic rhinitis mice model

Background

The etiology of allergic rhinitis (AR) is complicated. Traditional therapy of AR still has challenges, such as low long-term treatment compliance, unsatisfactory therapeutic outcomes, and a high financial burden. It is urgent to investigate the pathophysiology of allergic rhinitis from different perspectives and explore brand-new possible preventative or treatment initiatives.

Objective

The aim is to apply a multi-group technique and correlation analysis to explore more about the pathogenesis of AR from the perspectives of gut microbiota, fecal metabolites, and serum metabolism.

Methods

Thirty BALB/c mice were randomly divided into the AR and Con(control) groups. A standardized Ovalbumin (OVA)-induced AR mouse model was established by intraperitoneal OVA injection followed by nasal excitation. We detected the serum IL-4, IL-5, and IgE by enzyme-linked immunosorbent assay (ELISA), evaluated the histological characteristics of the nasal tissues by the hematoxylin and eosin (H&E) staining, and observed the nasal symptoms (rubs and sneezes) to evaluate the reliability of the AR mouse model. The colonic NF-κB protein was detected by Western Blot, and the colonic histological characteristics were observed by the H&E staining to evaluate inflammation of colon tissue. We analyzed the V3 and V4 regions of the 16S ribosomal DNA (rDNA) gene from the feces (colon contents) through 16S rDNA sequencing technology. Untargeted metabolomics was used to examine fecal and serum samples to find differential metabolites. Finally, through comparison and correlation analysis of differential gut microbiota, fecal metabolites, and serum metabolites, we further explore the overall impact of AR on gut microbiota, fecal metabolites, and host serum metabolism and its correlation.

Results

In the AR group, the IL-4, IL-5, IgE, eosinophil infiltration, and the times of rubs and sneezes were significantly higher than those in the Con group, indicating the successful establishment of the AR model. No differences in diversity were detected between the AR and Con groups. However, there were modifications in the microbiota's structure. At the phylum level, the proportion of Firmicutes and Proteobacteria in the AR group increased significantly, while the proportion of Bacteroides decreased significantly, and the ratio of Firmicutes/Bacteroides was higher. The key differential genera, such as Ruminococcus, were increased significantly in the AR group, while the other key differential genera, such as Lactobacillus, Bacteroides, and Prevotella, were significantly decreased in the Con group. Untargeted metabolomics analysis identified 28 upregulated and 4 downregulated differential metabolites in feces and 11 upregulated and 16 downregulated differential metabolites in serum under AR conditions. Interestingly, one of the significant difference metabolites, α-Linoleic acid (ALA), decreased consistently in feces and serum of AR. KEGG functional enrichment analysis and correlation analysis showed a close relationship between differential serum metabolites and fecal metabolites, and changes in fecal and serum metabolic patterns are associated with altered gut microbiota in AR. The NF-κB protein and inflammatory infiltration of the colon increased considerably in the AR group.

Conclusion

Our study reveals that AR alters fecal and serum metabolomic signatures and gut microbiota characteristics, and there is a striking correlation between the three. The correlation analysis of the microbiome and metabolome provides a deeper understanding of AR's pathogenesis, which may provide a theoretical basis for AR's potential prevention and treatment strategies.

Biomarkers of AIT: Models of prediction of efficacy

Allergic rhinitis is an IgE-mediated inflammation that remains a clinical challenge, affecting 40% of the UK population with a wide range of severity from nasal discomfort to life-threatening anaphylaxis. It can be managed by pharmacotherapeutics and in selected patients by allergen immunotherapy (AIT), which provides long-term clinical efficacy, especially during peak allergy season. However, there are no definitive biomarkers for AIT efficacy. Here, we aim to summarize the key adaptive, innate, humoral, and metabolic advances in biomarker identification in response to AIT. Mechanisms of efficacy consist of an immune deviation towards TH1-secreting IFN-γ, as well as an induction of IL10+ cTFR and TREG have been observed. TH2 cells undergo exhaustion after AIT due to chronic allergen exposure and correlates with the exhaustion markers PD-1, CTLA-4, TIGIT, and LAG3. IL10+ DCREG expressing C1Q and STAB are induced. KLRG1+ IL10+ ILC2 were shown to be induced in AIT in correlation with efficacy. BREG cells secreting IL-10, IL-35, and TGF-β are induced. Blocking antibodies IgG, IgA, and IgG4 are increased during AIT; whereas inflammatory metabolites, such as eicosanoids, are reduced. There are multiple promising biomarkers for AIT currently being evaluated. A panomic approach is essential to better understand cellular, molecular mechanisms and their correlation with clinical outcomes. Identification of predictive biomarkers of AIT efficacy will hugely impact current practice allowing physicians to select eligible patients that are likely to respond to treatment as well as improve patients' compliance to complete the course of treatment.

Chinese Guideline on Allergen Immunotherapy for Allergic Rhinitis: The 2022 Update

In the last few decades, there has been a progressive increase in the prevalence of allergic rhinitis (AR) in China, where it now affects approximately 250 million people. AR prevention and treatment include allergen avoidance, pharmacotherapy, allergen immunotherapy (AIT), and patient education, among which AIT is the only curative intervention. AIT targets the disease etiology and may potentially modify the immune system as well as induce allergen-specific immune tolerance in patients with AR. In 2017, a team of experts from the Chinese Society of Allergy (CSA) and the Chinese Allergic Rhinitis Collaborative Research Group (C2AR2G) produced the first English version of Chinese AIT guidelines for AR. Since then, there has been considerable progress in basic research of and clinical practice for AIT, especially regarding the role of follicular regulatory T (TFR) cells in the pathogenesis of AR and the use of allergen-specific immunoglobulin E (sIgE) in nasal secretions for the diagnosis of AR. Additionally, potential biomarkers, including TFR cells, sIgG4, and sIgE, have been used to monitor the incidence and progression of AR. Moreover, there has been a novel understanding of AIT during the coronavirus disease 2019 pandemic. Hence, there was an urgent need to update the AIT guideline for AR by a team of experts from CSA and C2AR2G. This document aims to serve as professional reference material on AIT for AR treatment in China, thus improving the development of AIT across the world.

Prediction of clinical efficacy of subcutaneous immunotherapy for Artemisia sieversiana pollen allergic rhinitis by serum metabolomics

BACKGROUND/PURPOSE

Specific immunotherapy is the only effective etiological treatment for allergic rhinitis, but subcutaneous immunotherapy has a slow onset and poor compliance. Predicting the clinical efficacy of subcutaneous immunotherapy in advance can reduce unnecessary medical costs and resource waste. This study aimed to identify metabolites that could predict the efficacy of subcutaneous immunotherapy on seasonal allergic rhinitis by serum metabolomics.

METHODS

Patients (n = 43) with Artemisia sieversiana pollen allergic rhinitis were enrolled and treated with subcutaneous immunotherapy for one year. Patients were divided into the ineffective group (n = 10) and effective group (n = 33) according to the therapeutic index. Serum samples were collected before treatment. Metabolomics was determined by liquid chromatography-mass spectrometry combined with gas chromatography-mass spectrometry and analyzed differential compounds and related metabolic pathways.

RESULTS

A total of 129 differential metabolites (P < 0.05) were identified and 4 metabolic pathways, namely taurine and hypotaurine metabolism, pentose and glucuronate interconversions, pentose phosphate pathway, and alanine, aspartate, and glutamate metabolism, were involved.

CONCLUSION

Some metabolites, such as hypotaurine, taurine, and l-alanine, have the potential to become predictive biomarkers for effective subcutaneous immunotherapy.

Circulating MIF Associated With Disease Severity and Clinical Response of Sublingual Immunotherapy in House Dust Mite-Induced Allergic Rhinitis

Background: Macrophage migration inhibitory factor (MIF) is described as a pro-inflammatory cytokine involved in many inflammatory and allergic disorders, but the role of MIF in allergic rhinitis (AR) remains poorly clarified. The aim of this study was to investigate the association between circulating MIF levels and house dust mite (HDM)-induced AR, and evaluate MIF as a potential biomarker in reflecting disease severity and predicting the clinical response of sublingual immunotherapy (SLIT) in HDM-induced AR patients.

Methods: In this study, we enrolled 160 persistent HDM-induced AR patients (AR group), including 48 mild AR patients (MAR group) and 112 moderate–severe AR patients (MSAR group), and 77 healthy controls (HC group). Circulating levels of MIF were measured by ELISA, and the relationship between MIF concentrations and disease severity was assessed. In the MSAR group, 106 patients were assigned to receive SLIT for 3 years. At the end of the study, patients were categorized into good response group and poor response group, and associations between clinical variables or biomarkers and clinical response were analyzed by the multivariate regression analysis.

Results: The concentrations of serum MIF were significantly higher in AR patients than in HCs, especially in those with MSAR. Moreover, circulating MIF levels were positively correlated with TNSS, VAS, serum HDM–specific IgE, total IgE, blood eosinophil count, and blood eosinophil percentage (all p < 0.05). Eighty MSAR patients finally completed SLIT, 45 patients obtained good response, and 35 patients resulted in poor response. The serum levels of MIF were significantly lower in the good-response group than in the poor-response group (p < 0.001). The receiver operating characteristic analysis for MIF showed good accuracy for predicting clinical response of SLIT (area under the curve = 0.877, p < 0.001). The multivariate regression analysis demonstrated that serum MIF was an independent factor for SLIT responsiveness.

Conclusion: Serum MIF appeared to be an important biological indicator in reflecting disease severity and an independent predictor for clinical responsiveness of SLIT in HDM-induced AR patients.

Metabolomics reveals sex-specific metabolic shifts and predicts the duration from positive to negative in non-severe COVID-19 patients during recovery process

Metabolic profiling in COVID-19 patients has been associated with disease severity, but there is no report on sex-specific metabolic changes in discharged survivors. Herein we used an integrated approach of LC-MS-and GC-MS-based untargeted metabolomics to analyze plasma metabolic characteristics in men and women with non-severe COVID-19 at both acute period and 30 days after discharge. The results demonstrate that metabolic alterations in plasma of COVID-19 patients during the recovery and rehabilitation process were presented in a sex specific manner. Overall, the levels of most metabolites were increased in COVID-19 patients after the cure relative to acute period. The major plasma metabolic changes were identified including fatty acids in men and glycerophosphocholines and carbohydrates in women. In addition, we found that women had shorter length of hospitalization than men and metabolic characteristics may contribute to predict the duration from positive to negative in non-severe COVID-19 patients. Collectively, this study shed light on sex-specific metabolic shifts in non-severe COVID-19 patients during the recovery process, suggesting a sex bias in prognostic and therapeutic evaluations based on metabolic profiling.

Leukotriene A4 Hydrolase Is a Candidate Predictive Biomarker for Successful Allergen Immunotherapy

Background

Allergic rhinitis is a common disorder that affects 10% to 40% of the population worldwide. Allergen immunotherapy (AIT) represents the only therapy that has the potential to resolve clinical symptoms of allergic rhinitis. However, up to 30% of patients do not respond to AIT. Biomarkers predicting the clinical efficacy of AIT as early as possible would significantly improve the patient selection and reduce unnecessary societal costs.

Methods

Artemisia pollen allergic patients who received at least 1-year AIT were enrolled. Clinical responses before and after 1-year AIT were evaluated to determine AIT responders. Artemisia specific IgE and IgG4 levels were measured by using ImmunoCAP and enzyme-linked immunosorbent assay (ELISA) separately. Stepwise regression analysis was performed to identify which rhinitis-relevant parameters explained the most variability in AIT results. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics was applied to identify the potential candidate biomarkers in the sera of responders and non-responders collected before and after 1-year therapy. The diagnostic performance of the potential biomarkers was then assessed using enzyme-linked immunosorbent assay (ELISA) in 30 responders and 15 non-responders.

Results

Artemisia specific IgE and IgG4 levels were elevated only in the responders. Regression analysis of allergic rhinitis-relevant parameters provided a robust model that included two most significant variables (sneeze and nasal congestion). Thirteen candidate biomarkers were identified for predicting AIT outcomes. Based on their association with allergy and protein fold change (more than 1.1 or less than 0.9), four proteins were identified to be potential biomarkers for predicting effective AIT. However, further ELISA revealed that only leukotriene A₄ hydrolase (LTA₄H) was consistent with the proteomics data. The LTA₄H level in responders increased significantly (P < 0.001) after 1-year therapy, while that of non-responders remained unchanged. Assessment of LTA₄H generated area under curve (AUC) value of 0.844 (95% confidence interval: 0.727 to 0.962; P < 0.05) in distinguishing responders from the non-responders, suggesting that serum LTA₄H might be a potential biomarker for predicting the efficiency of AIT.

Conclusion

Serum LTA₄H may be a potential biomarker for early prediction of an effective AIT.