Developing a Diagnostic Model to Predict the Risk of Asthma Based on Ten Macrophage-Related Gene Signatures

Single-cell profiling of bronchoalveolar cells reveals a Th17 signature in neutrophilic severe equine asthma

Severe equine asthma (SEA) is a complex respiratory condition characterized by chronic airway inflammation. It shares many clinical and pathological features with human neutrophilic asthma, making it a valuable model for studying this condition. However, the immune mechanisms driving SEA have remained elusive. Although SEA has been primarily associated with a Th2 response, there have also been reports of Th1, Th17, or mixed-mediated responses. To uncover the elusive immune mechanisms driving SEA, we performed single-cell mRNA sequencing (scRNA-seq) on cryopreserved bronchoalveolar cells from 11 Warmblood horses, 5 controls and 6 with SEA. We identified six major cell types, including B cells, T cells, monocytes-macrophages, dendritic cells, neutrophils, and mast cells. All cell types exhibited significant heterogeneity, with previously identified and novel cell subtypes. Notably, we observed monocyte-lymphocyte complexes and detected a robust Th17 signature in SEA, with CXCL13 upregulation in intermediate monocytes. Asthmatic horses exhibited expansion of the B-cell population, Th17 polarization of the T-cell populations, and dysregulation of genes associated with T-cell function. Neutrophils demonstrated enhanced migratory capacity and heightened aptitude for neutrophil extracellular trap formation. These findings provide compelling evidence for a predominant Th17 immune response in neutrophilic SEA, driven by dysregulation of monocyte and T-cell genes. The dysregulated genes identified through scRNA-seq have potential as biomarkers and therapeutic targets for SEA and provide insights into human neutrophilic asthma.

SARS-CoV-2 accessory proteins involvement in inflammatory and profibrotic processes through IL11 signaling

SARS-CoV-2, the cause of the COVID-19 pandemic, possesses eleven accessory proteins encoded in its genome. Their roles during infection are still not completely understood. In this study, transcriptomics analysis revealed that both WNT5A and IL11 were significantly up-regulated in A549 cells expressing individual accessory proteins ORF6, ORF8, ORF9b or ORF9c from SARS-CoV-2 (Wuhan-Hu-1 isolate). IL11 is a member of the IL6 family of cytokines. IL11 signaling-related genes were also differentially expressed. Bioinformatics analysis disclosed that both WNT5A and IL11 were involved in pulmonary fibrosis idiopathic disease and functional assays confirmed their association with profibrotic cell responses. Subsequently, data comparison with lung cell lines infected with SARS-CoV-2 or lung biopsies from patients with COVID-19, evidenced altered profibrotic gene expression that matched those obtained in this study. Our results show ORF6, ORF8, ORF9b and ORF9c involvement in inflammatory and profibrotic responses. Thus, these accessory proteins could be targeted by new therapies against COVID-19 disease.

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods

Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.