Identifying key genes and functionally enriched pathways in Th2-high asthma by weighted gene co-expression network analysis

Nasal brushing molecular endotyping distinguishes patients with chronic rhinosinusitis with nasal polyps with better response to dupilumab

BACKGROUND

There is evidence of pathophysiologic diversity in chronic rhinosinusitis with nasal polyps (CRSwNP), but data characterizing the molecular endotypes of CRSwNP and their association with treatment are lacking.

OBJECTIVE

This study aimed to identify gene signatures associated with CRSwNP endotypes, clinical features, and dupilumab treatment response.

METHODS

Nasal brushing samples were collected from 89 patients randomized to dupilumab 300 mg every 2 weeks or placebo in the SINUS-52 trial (NCT02898454). Microarrays were used to identify transcriptional clusters and assess the relationship between gene expression and baseline clinical features and clinical response to dupilumab. Endotype signatures were determined using differential expression analysis.

RESULTS

Two distinct transcriptional clusters (C1 and C2) were identified, both with elevated type 2 biomarkers. At baseline, C2 patients had higher mean Nasal Polyp Score and higher type 2 biomarker levels than C1 patients. At week 24, significant improvements in clinical outcomes (dupilumab vs placebo) were observed in both clusters, although the magnitude of improvements was significantly greater in C2 than in C1, and more C2 patients demonstrated clinically meaningful responses. Gene set enrichment analysis supported the existence of 2 molecular endotypes: C2 was enriched in genes associated with type 2 inflammation (including periostin, cadherin-26, and type 2 cysteine protease inhibitors), while C1 was enriched in genes associated with T cell activation and IL-12 production.

CONCLUSIONS

Two distinct gene signatures associated with CRSwNP clinical features were identified; the endotype signatures were associated with clinical outcome measures and magnitude of dupilumab response.

Multi-omics in nasal epithelium reveals three axes of dysregulation for asthma risk in the African Diaspora populations

Asthma has striking disparities across ancestral groups, but the molecular underpinning of these differences is poorly understood and minimally studied. A goal of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to understand multi-omic signatures of asthma focusing on populations of African ancestry. RNASeq and DNA methylation data are generated from nasal epithelium including cases (current asthma, N = 253) and controls (never-asthma, N = 283) from 7 different geographic sites to identify differentially expressed genes (DEGs) and gene networks. We identify 389 DEGs; the top DEG, FN1, was downregulated in cases (q = 3.26 × 10-9) and encodes fibronectin which plays a role in wound healing. The top three gene expression modules implicate networks related to immune response (CEACAM5; p = 9.62 × 10-16 and CPA3; p = 2.39 × 10-14) and wound healing (FN1; p = 7.63 × 10-9). Multi-omic analysis identifies FKBP5, a co-chaperone of glucocorticoid receptor signaling known to be involved in drug response in asthma, where the association between nasal epithelium gene expression is likely regulated by methylation and is associated with increased use of inhaled corticosteroids. This work reveals molecular dysregulation on three axes - increased Th2 inflammation, decreased capacity for wound healing, and impaired drug response - that may play a critical role in asthma within the African Diaspora.

Bioinformatics analysis revealed underlying molecular mechanisms associated with asthma severity and identified GABAergic related pathway as a potential therapy for Th2-high endotype asthma

Background

Asthma, a principally T helper 2 (Th2) cell mediated immunological disease, is categorized into Th2-high and Th2-low endotypes. The influence of these endotypes on clinical characteristics and treatment responsiveness in asthma is yet to be completely understood. This study delves into the underlying molecular mechanisms of Th2 endotypes on asthma.

Methods

Transcriptomics data of airway epithelial and corresponding clinical information were sourced from the GEO. The co-expression modules were established by WGCNA. Cytoscape was applied to construct PPI networks, and hub genes were determined via the Cytohubba plugin. Additionally, a functional enrichment analysis was conducted on the co-expressed genes from the relevant modules. The relative abundances levels of 22 different types of immune cells in asthma patients were evaluated by CIBERSORT algorithm.

Results

There were 471 genes in the pink module significantly correlated with Th2 endotype. Overall, 151 DEGs were identified in the various Th2 endotypes, and 66 were obtained through intersection with the pink module. In the PPI network, the ten most important genes that regulate Th2 endotypes were selected as hub genes. In Th2-high endotype asthma, the hub genes were significantly related to γ-aminobutyric acid (GABA) pathways, indicating that hub genes can mainly regulate Th2-high endotype asthma through GABAergic system.

Conclusions

The severity of asthma is influenced by different Th2 endotypes. GABAergic related hub genes may provide innovative insights for the treatment of Th2-high asthma.

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.

Reduced urine volume and changed renal sphingolipid metabolism in P2ry14-deficient mice

The UDP-glucose receptor P2RY14, a rhodopsin-like G protein-coupled receptor (GPCR), was previously described as receptor expressed in A-intercalated cells of the mouse kidney. Additionally, we found P2RY14 is abundantly expressed in mouse renal collecting duct principal cells of the papilla and epithelial cells lining the renal papilla. To better understand its physiological function in kidney, we took advantage of a P2ry14 reporter and gene-deficient (KO) mouse strain. Morphometric studies showed that the receptor function contributes to kidney morphology. KO mice had a broader cortex relative to the total kidney area than wild-type (WT) mice. In contrast, the area of the outer stripe of the outer medulla was larger in WT compared to KO mice. Transcriptome comparison of the papilla region of WT and KO mice revealed differences in the gene expression of extracellular matrix proteins (e.g., decorin, fibulin-1, fibulin-7) and proteins involved in sphingolipid metabolism (e.g., small subunit b of the serine palmitoyltransferase) and other related GPCRs (e.g., GPR171). Using mass spectrometry, changes in the sphingolipid composition (e.g., chain length) were detected in the renal papilla of KO mice. At the functional level, we found that KO mice had a reduced urine volume but an unchanged glomerular filtration rate under normal chow and salt diets. Our study revealed P2ry14 as a functionally important GPCR in collecting duct principal cells and cells lining the renal papilla and the possible involvement of P2ry14 in nephroprotection by regulation of decorin.

Cystatin SN in type 2 inflammatory airway diseases

Cystatin SN, encoded by CST1, belongs to the type 2 (T2) cystatin protein superfamily. In the past decade, several publications have highlighted the association between cystatin SN and inflammatory airway diseases including chronic rhinosinusitis, rhinitis, asthma, chronic obstructive pulmonary disease, and chronic hypersensitivity pneumonitis. It is, therefore, crucial to understand the role of cystatin SN in the wider context of T2 inflammatory diseases. Here, we review the expression of cystatin SN in airway-related diseases with different endotypes. We also emphasize the physiological and pathological roles of cystatin SN. Physiologically, cystatin SN protects host tissues from destructive proteolysis by cysteine proteases present in the external environment or produced via internal dysregulated expression. Pathologically, the secretion of cystatin SN from airway epithelial cells initiates and amplifies T2 immunity and subsequently leads to disease. We further discuss the development of cystatin SN as a T2 immunity marker that can be monitored noninvasively and assist in airway disease management. The discovery, biology, and inhibition capability are also introduced to better understand the role of cystatin SN in airway diseases.

A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification

Background

Asthma significantly impacts human life and health as a chronic disease. Traditional treatments for asthma have several limitations. Artificial intelligence aids in cancer treatment and may also accelerate our understanding of asthma mechanisms. We aimed to develop a new clinical diagnosis model for asthma using artificial neural networks (ANN).

Methods

Datasets (GSE85566, GSE40576, and GSE13716) were downloaded from Gene Expression Omnibus (GEO) and identified differentially expressed CpGs (DECs) enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models. In addition, two external validation datasets (GSE40576 and GSE137716) were used to validate the diagnostic ability of the model.

Results

The methylation analysis tool (ChAMP) considered DECs that were up-regulated (n =121) and down-regulated (n =20). GO results showed enrichment of actin cytoskeleton organization and cell-substrate adhesion, shigellosis, and serotonergic synapses. RF (random forest) analysis identified 10 crucial DECs (cg05075579, cg20434422, cg03907390, cg00712106, cg05696969, cg22862094, cg11733958, cg00328720, and cg13570822). ANN constructed the clinical model according to 10 DECs. In two external validation datasets (GSE40576 and GSE137716), the Area Under Curve (AUC) for GSE137716 was 1.000, and AUC for GSE40576 was 0.950, confirming the reliability of the model.

Conclusion

Our findings provide new methylation markers and clinical diagnostic models for asthma diagnosis and treatment.

Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps and asthma comorbidity using bioinformatics approaches

Patients with chronic rhinosinusitis with nasal polyps (CRSwNP) and asthma comorbidity (ACRSwNP) present severe symptoms and are more likely to relapse. However, the pathogenesis of ACRSwNP is not fully understood. The aim of this study was to explore the underlying pathogenesis of ACRSwNP using bioinformatics approaches. ACRSwNP-related differentially expressed genes (DEGs) were identified by the analysis of the GSE23552 dataset. The clusterProfiler R package was used to carry out functional and pathway enrichment analysis. A protein–protein interaction (PPI) network was built using the STRING database to explore key genes in the pathogenesis of ACRSwNP. The bioinformatics analysis results were verified through qRT-PCR. The Connectivity Map (CMap) database was used to predict potential drugs for the treatment of ACRSwNP. A total of 36 DEGs were identified, which were mainly enriched in terms of regulation of immune response and detection sensory perception of taste. Thirteen hub genes including AZGP1, AQP9, GAPT, PIP, and PRR4 were identified as potential hub genes in ACRSwNP from the PPI network. Analysis of the GSE41861 dataset showed that upregulation of CST1 in nasal mucosa was associated with asthma. qRT-PCR detection confirmed the bioinformatics analysis results. Tacrolimus and spaglumic acid were identified as potential drugs for the treatment of ACRSwNP from the CMap database. The findings of this study provide insights into the pathogenesis of ACRSwNP and may provide a basis for the discovery of effective therapeutic modalities for ACRSwNP.