Consistency of T2 Gene Signatures in Severe Asthma. Key to Effective Treatments or Merely the Tip of the Iceberg?

RAGE contributes to allergen driven severe neutrophilic airway inflammation via NLRP3 inflammasome activation in mice

Background

Asthma is a major public healthcare burden, affecting over 300 million people worldwide. While there has been great progress in the treatment of asthma, subsets of patients who present with airway neutrophilia, often have more severe disease, and tend to be resistant to conventional corticosteroid treatments. The receptor for advanced glycation endproducts (RAGE) plays a central role in the pathogenesis of eosinophilic asthma, however, it's role in neutrophilic asthma remains largely uninvestigated.

Methods

A mouse model of severe steroid resistant neutrophilic airway disease (SSRNAD) using the common fungal allergen Alternaria alternata (AA) was employed to evaluate the effects of genetic ablation of RAGE and pharmacological inhibition of the NLRP3 inflammasome on neutrophilic airway inflammation.

Results

AA exposure induced robust neutrophil-dominant airway inflammation and increased BALF levels of Th1/Th17 cytokines in wild-type mice, which was significantly reduced in RAGE^-/- mice. Serum levels of IgE and IgG1 were increased similarly in both wild-type and RAGE^-/- mice. Pharmacological inhibition of NLRP3 blocked the effects of AA exposure and NLRP3 inflammasome activation was RAGE-dependent. Neutrophil extracellular traps were elevated in the BALF of wild-type but not RAGE^-/- mice and an atypical population of SiglecF+ neutrophils were identified in the BALF. Lastly, time-course studies found that RAGE expression promoted sustained neutrophil accumulation in the BALF of mice in response to AA.

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.

The axis of the receptor for advanced glycation endproducts in asthma and allergic airway disease

Asthma is a generalized term that describes a scope of distinct pathologic phenotypes of variable severity, which share a common complication of reversible airflow obstruction. Asthma is estimated to affect almost 400 million people worldwide, and nearly ten percent of asthmatics have what is considered "severe" disease. The majority of moderate to severe asthmatics present with a "type 2-high" (T2-hi) phenotypic signature, which pathologically is driven by the type 2 cytokines Interleukin-(IL)-4, IL-5, and IL-13. However, "type 2-low" (T2-lo) phenotypic signatures are often associated with more severe, steroid-refractory neutrophilic asthma. A wide range of clinical and experimental studies have found that the receptor for advanced glycation endproducts (RAGE) plays a significant role in the pathogenesis of asthma and allergic airway disease (AAD). Current experimental data indicates that RAGE is a critical mediator of the type 2 inflammatory reactions which drive the development of T2-hi AAD. However, clinical studies demonstrate that increased RAGE ligands and signaling strongly correlate with asthma severity, especially in severe neutrophilic asthma. This review presents an overview of the current understandings of RAGE in asthma pathogenesis, its role as a biomarker of disease, and future implications for mechanistic studies, and potential therapeutic intervention strategies.

Are We Meeting the Promise of Endotypes and Precision Medicine in Asthma?

While the term asthma has long been known to describe heterogeneous groupings of patients, only recently have data evolved which enable a molecular understanding of the clinical differences. The evolution of transcriptomics (and other 'omics platforms) and improved statistical analyses in combination with large clinical cohorts opened the door for molecular characterization of pathobiologic processes associated with a range of asthma patients. When linked with data from animal models and clinical trials of targeted biologic therapies, emerging distinctions arose between patients with and without elevations in type 2 immune and inflammatory pathways, leading to the confirmation of a broad categorization of type 2-Hi asthma. Differences in the ratios, sources, and location of type 2 cytokines and their relation to additional immune pathway activation appear to distinguish several different (sub)molecular phenotypes, and perhaps endotypes of type 2-Hi asthma, which respond differently to broad and targeted anti-inflammatory therapies. Asthma in the absence of type 2 inflammation is much less well defined, without clear biomarkers, but is generally linked with poor responses to corticosteroids. Integration of "big data" from large cohorts, over time, using machine learning approaches, combined with validation and iterative learning in animal (and human) model systems is needed to identify the biomarkers and tightly defined molecular phenotypes/endotypes required to fulfill the promise of precision medicine.

Sialylation of MUC4β N-glycans by ST6GAL1 orchestrates human airway epithelial cell differentiation associated with type-2 inflammation

Although type-2-induced (T2-induced) epithelial dysfunction is likely to profoundly alter epithelial differentiation and repair in asthma, the mechanisms for these effects are poorly understood. A role for specific mucins, heavily N-glycosylated epithelial glycoproteins, in orchestrating epithelial cell fate in response to T2 stimuli has not previously been investigated. Levels of a sialylated MUC4β isoform were found to be increased in airway specimens from asthmatic patients in association with T2 inflammation. We hypothesized that IL-13 would increase sialylation of MUC4β, thereby altering its function and that the β-galactoside α-2,6-sialyltransferase 1 (ST6GAL1) would regulate the sialylation. Using human biologic specimens and cultured primary human airway epithelial cells (HAECs),we demonstrated that IL-13 increases ST6GAL1-mediated sialylation of MUC4β and that both were increased in asthma, particularly in sputum supernatant and/or fresh isolated HAECs with elevated T2 biomarkers. ST6GAL1-induced sialylation of MUC4β altered its lectin binding and secretion. Both ST6GAL1 and MUC4β inhibited epithelial cell proliferation while promoting goblet cell differentiation. These in vivo and in vitro data provide strong evidence for a critical role for ST6GAL1-induced sialylation of MUC4β in epithelial dysfunction associated with T2-high asthma, thereby identifying specific sialylation pathways as potential targets in asthma.