Derangement of cell cycle markers in peripheral blood mononuclear cells of asthmatic patients as a reliable biomarker for asthma control

Lipid metabolism-related genes are involved in the formation of macrophage extracellular traps in allergic airway inflammation

Recent studies have highlighted the critical role of lipid metabolism in macrophages concerning lung inflammation. However, it remains unclear whether lipid metabolism is involved in macrophage extracellular traps (METs). We analyzed the GSE40885 dataset from the GEO database using weighted correlation network analysis (WGCNA) and further selection using the least absolute shrinkage and selection operator (LASSO) regression. We identified ABCA1, SLC44A2, and C3 as key genes jointly involved in lipid metabolism and METs. Additionally, immune infiltration analysis was performed using the Xcell and CIBERSORT algorithms, while single-cell transcriptome analysis was utilized using data from the Tabula Muris database. The expression of key genes was validated in external datasets (GSE42606, GSE27066, GSE137268, and GSE256534). Notably, our results indicated that ABCA1 expression was elevated in patients experiencing acute asthma exacerbations, which aligned with its expression trend in lipopolysaccharide (LPS)-induced macrophages. However, ABCA1 expression was reduced in cases of chronic and severe asthma. Results from immunofluorescence (IF), SYTOX Green staining, and Western blot analyses suggested that ABCA1 may play a role in the formation of METs both in vivo and in vitro. In conclusion, this study indicates that ABCA1 may be involved in METs. ABCA1 may represent a promising therapeutic target for asthma.

Development of apical out trophoblast stem cell derived organoids to model early human pregnancy

The development of trophoblast organoids has enabled investigation of placental physiology, disease, and early maternal-fetal interactions during a previously restricted stage of pregnancy. A key shortcoming in existing trophoblast organoid methodologies is the non-physiologic position of the syncytiotrophoblast (STB) within the inner portion of the organoid, which neither recapitulates in vivo placental villous morphology nor allows for facile modeling of STB exposure to the endometrium or the contents of the intervillous space. Here, we have successfully established apical-out human trophoblast stem cells (hTSC)-sourced organoids with STB forming on the surface of the organoid. These organoids can also be induced to give rise to the extravillous trophoblast (EVT) lineage, which invades into an extracellular matrix-based hydrogel. Compared to previous methods, our organoids more closely mimic developing human placental architecture, offering a novel platform to study normal and abnormal placental development and to model exposures to pharmaceuticals, pathogens, and environmental factors.

Identification of hub genes between moderate to severe asthma and early lung adenocarcinoma through bioinformatics analysis

The objective of this study was to explore the genetic link between moderate to severe asthma and early-stage lung adenocarcinoma (LUAD) using bioinformatic methods. The Cancer Genome Atlas gene-expression profiles for early-stage LUAD and GSE76225 data set for moderate to severe asthma were selected for weighted gene co-expression network analysis, and intersected with the relevant module genes and selected hub genes; the relevant network of hub genes was then determined through a protein-protein interaction network. In addition, gene-set enrichment analysis and gene-set variation analysis (GSVA) were conducted on differentially expressed genes between normal and tumor groups. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enrichment analyses were applied to detect hub gene-related biological functions. Receiver operating characteristic (ROC) curves were employed to confirm the diagnostic value of hub genes. We identified four key genes, of which SFTPC exhibited relatively high value for areas under the ROC curves, indicating high diagnostic value for moderate to severe asthma. The clinical efficacy of SFTPC was thus consistent with GSVA results, indicating that moderate to severe asthma can inhibit the occurrence of early LUAD.

Genomic insights into pediatric intestinal inflammatory and eosinophilic disorders using single-cell RNA-sequencing

Introduction

Chronic inflammation of the gastrointestinal tissues underlies gastrointestinal inflammatory disorders, leading to tissue damage and a constellation of painful and debilitating symptoms. These disorders include inflammatory bowel diseases (Crohn's disease and ulcerative colitis), and eosinophilic disorders (eosinophilic esophagitis and eosinophilic duodenitis). Gastrointestinal inflammatory disorders can often present with overlapping symptoms necessitating the use of invasive procedures to give an accurate diagnosis.

Methods

This study used peripheral blood mononuclear cells from individuals with Crohn's disease, ulcerative colitis, eosinophilic esophagitis, and eosinophilic duodenitis to better understand the alterations to the transcriptome of individuals with these diseases and identify potential markers of active inflammation within the peripheral blood of patients that may be useful in diagnosis. Single-cell RNA-sequencing was performed on peripheral blood mononuclear cells isolated from the blood samples of pediatric patients diagnosed with gastrointestinal disorders, including Crohn's disease, ulcerative colitis, eosinophilic esophagitis, eosinophilic duodenitis, and controls with histologically healthy gastrointestinal tracts.

Results

We identified 730 (FDR < 0.05) differentially expressed genes between individuals with gastrointestinal disorders and controls across eight immune cell types.

Discussion

There were common patterns among GI disorders, such as the widespread upregulation of MTRNR2L8 across cell types, and many differentially expressed genes showed distinct patterns of dysregulation among the different gastrointestinal diseases compared to controls, including upregulation of XIST across cell types among individuals with ulcerative colitis and upregulation of Th2-associated genes in eosinophilic disorders. These findings indicate both overlapping and distinct alterations to the transcriptome of individuals with gastrointestinal disorders compared to controls, which provide insight as to which genes may be useful as markers for disease in the peripheral blood of patients.

Knockdown of KIF23 alleviates the progression of asthma by inhibiting pyroptosis

BACKGROUND

Asthma is a chronic disease affecting the lower respiratory tract, which can lead to death in severe cases. The cause of asthma is not fully known, so exploring its potential mechanism is necessary for the targeted therapy of asthma.

METHOD

Asthma mouse model was established with ovalbumin (OVA). H&E staining, immunohistochemistry and ELISA were used to detect the inflammatory response in asthma. Transcriptome sequencing was performed to screen differentially expressed genes (DEGs). The role of KIF23 silencing in cell viability, proliferation and apoptosis was explored by cell counting kit-8, EdU assay and flow cytometry. Effects of KIF23 knockdown on inflammation, oxidative stress and pyroptosis were detected by ELISA and western blot. After screening KIF23-related signalling pathways, the effect of KIF23 on p53 signalling pathway was explored by western blot.

RESULTS

In the asthma model, the levels of caspase-3, IgG in serum and inflammatory factors (interleukin (IL)-1β, KC and tumour necrosis factor (TNF)-α) in serum and bronchoalveolar lavage fluid were increased. Transcriptome sequencing showed that there were 352 DEGs in the asthma model, and 7 hub genes including KIF23 were identified. Knockdown of KIF23 increased cell proliferation and inhibited apoptosis, inflammation and pyroptosis of BEAS-2B cells induced by IL-13 in vitro. In vivo experiments verified that knockdown of KIF23 inhibited oxidative stress, inflammation and pyroptosis to alleviate OVA-induced asthma mice. In addition, p53 signalling pathway was suppressed by KIF23 knockdown.

CONCLUSION

Knockdown of KIF23 alleviated the progression of asthma by suppressing pyroptosis and inhibited p53 signalling pathway.

The G protein-coupled receptor GPRC5A-a phorbol ester and retinoic acid-induced orphan receptor with roles in cancer, inflammation, and immunity

GPRC5A is the first member of a new class of orphan receptors coupled to G proteins, which also includes GPRC5B, GPRC5C, and GPRC5D. Since its cloning and identification in the 1990s, substantial progress has been made in understanding the possible functions of this receptor. GPRC5A has been implicated in a variety of cellular events, such as cytoskeleton reorganization, cell proliferation, cell cycle regulation, migration, and survival. It appears to be a central player in different pathological processes, including tumorigenesis, inflammation, immune response, and tissue damage. The levels of GPRC5A expression differ depending on the type of cancer, with increased expression in colon, pancreas, and prostate cancers; decreased expression in lung cancer; and varied results in breast cancer. In this review, we discuss the early discovery of GPRC5A as a phorbol ester-induced gene and later as a retinoic acid-induced gene, its regulation, and its participation in important canonical pathways related to numerous types of tumors and inflammatory processes. GPRC5A represents a potential new target for cancer, inflammation, and immunity therapies.

An Inflammation-Associated Prognosis Model for Hepatocellular Carcinoma Based on Adenylate Uridylate- (AU-) Rich Element Genes

Hepatocellular carcinoma (HCC) is a typical inflammation-driven cancer and ranks sixth in the incidence rate worldwide. The role of adenylate uridylate- (AU-) rich element genes (AREGs) in HCC remains unclear. HCC-related datasets were acquired from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Differentially expressed AREGs (DE-AREGs) between HCC samples and healthy controls were identified. The univariate Cox and LASSO analyses were performed to determine the prognostic genes. Furthermore, a signature and corresponding nomogram were configured for the clinical prediction of HCC. The potential signature-related biological significance was explored using functional and pathway enrichment analysis. Additionally, immune infiltration analysis was also performed. Finally, the expression of prognostic genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR). A total of 189 DE-AREGs between normal and HCC samples were identified, wherein CENPA, TXNRD1, RABIF, UGT2B15, and SERPINE1 were selected to generate an AREG-related signature. Moreover, the prognostic accuracy of the AREG-related signature was also confirmed. Functional analysis indicated that the high-risk score was related to various functions and pathways. Inflammation and immune-related analyses indicated that the difference of T cell and B cell receptor abundance, microvascular endothelial cells (MVE), lymphatic endothelial cells (lye), pericytes, stromal cells, and the six immune checkpoints was statistically significant between the different risk groups. Similarly, RT-qPCR outcomes of these signature genes were also significant. In conclusion, an inflammation-associated signature based on five DE-AREGs was constructed, which could act as a prognostic indicator of patients with HCC.

Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia

Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning chronic lung disease and in stratifying patients at greater risk for BPD. The objective of this study is to develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Secondary analysis of whole blood microarray data from 97 very low birth weight neonates on day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned to a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared with gestational age or birth weight. This study adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. Neonates with BPD (n = 62 subjects) exhibited a lower median gestational age (26.0 wk vs. 30.0 wk, P < 0.01) and birth weight (800 g vs. 1,280 g, P < 0.01) compared with non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birth weight was 87.8% and 87.2%, respectively. The machine learning models, using a combination of five genes, revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were associated with BPD. A derived five-gene whole blood signature can accurately predict BPD in the first week of life.

The Role of Systems Biology in Deciphering Asthma Heterogeneity

Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.

Identification of Hub Genes and Potential Biomarkers for Childhood Asthma by Utilizing an Established Bioinformatic Analysis Approach

Childhood asthma represents a heterogeneous disease resulting from the interaction between genetic factors and environmental exposures. Currently, finding reliable biomarkers is necessary for the clinical management of childhood asthma. However, only a few biomarkers are being used in clinical practice in the pediatric population. In the long run, new biomarkers for asthma in children are required and would help direct therapy approaches. This study aims to identify potential childhood asthma biomarkers using a genetic-driven biomarkers approach. Herein, childhood asthma-associated Single Nucleotide Polymorphisms (SNPs) were utilized from the GWAS database to drive and facilitate the biomarker of childhood asthma. We uncovered 466 childhood asthma-associated loci by extending to proximal SNPs based on r2 > 0.8 in Asian populations and utilizing HaploReg version 4.1 to determine 393 childhood asthma risk genes. Next, the functional roles of these genes were subsequently investigated using Gene Ontology (GO) term enrichment analysis, a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and a protein−protein interaction (PPI) network. MCODE and CytoHubba are two Cytoscape plugins utilized to find biomarker genes from functional networks created using childhood asthma risk genes. Intriguingly, 10 hub genes (IL6, IL4, IL2, IL13, PTPRC, IL5, IL33, TBX21, IL2RA, and STAT6) were successfully identified and may have been identified to play a potential role in the pathogenesis of childhood asthma. Among 10 hub genes, we strongly suggest IL6 and IL4 as prospective childhood asthma biomarkers since both of these biomarkers achieved a high systemic score in Cytohubba’s MCC algorithm. In summary, this study offers a valuable genetic-driven biomarker approach to facilitate the potential biomarkers for asthma in children.

Identification of key genes and pathways between mild-moderate and severe asthmatics via bioinformatics analysis

Severe asthma is the main reason for death and disability caused by asthma. However, effective biomarkers for severe asthma have not been identified. Here, we aimed to identify potential biomarkers in severe asthma. We identified 202 differentially expressed genes (DEGs) between severe asthma and mild-moderate asthma after integrating the results from GSE69683 and GSE27011 datasets. The enrichment analysis indicated that 202 DEGs were associated with metabolism- and immune-related processes. 10 hub genes were identified by Cytoscape and five of these genes’ AUC (area under the curve) values were greater than 0.6 in GSE69683. The AUC value reached to 0.701 when combined SEC61A1 and ALDH18A1 expression. The expression of the five hub genes was verified in an external dataset. The network analysis revealed that transcription factor (TF) WT1, ZEB1, RERE, FOSL1, and miR-20a may be involved in the development of asthma. In addition, we found cyclosporine and acetaminophen could interact with these hub genes and may be negatively associated with most of the five hub genes according to previous reports. Overall, key genes were identified between mild-moderate and severe asthmatics, which contributed to the understanding of the development of asthma.

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.