RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine function in airway smooth muscle cells

Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning method and bioinformatics analyses

BACKGROUND

Severe asthma, which differs significantly from typical asthma, involves specific molecular biomarkers that enhance our understanding and diagnostic capabilities. The objective of this study is to assess the biological processes underlying severe asthma and to detect key molecular biomarkers.

METHODS

We used Weighted Gene Co-Expression Network Analysis (WGCNA) to detect hub genes in the GSE143303 dataset and indicated their functions and regulatory mechanisms using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) annotations. In the GSE147878 dataset, we used Gene Set Enrichment Analysis (GSEA) to determine the regulatory directions of gene sets. We detected differentially expressed genes in the GSE143303 and GSE64913 datasets, constructed a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and validated the model using the GSE147878 dataset and real-time quantitative PCR (RT-qPCR) to confirm the molecular biomarkers.

RESULTS

Using WGCNA, we discovered modules that were strongly correlated with clinical features, specifically the purple module (r = 0.53) and the midnight blue module (r = -0.65). The hub genes within these modules were enriched in pathways related to mitochondrial function and oxidative phosphorylation. GSEA in the GSE147878 dataset revealed significant enrichment of upregulated gene sets associated with oxidative phosphorylation and downregulated gene sets related to asthma. We discovered 12 commonly regulated genes in the GSE143303 and GSE64913 datasets and developed a LASSO regression model. The model corresponding to lambda.min selected nine genes, including TFCP2L1, KRT6A, FCER1A, and CCL5, which demonstrated predictive value. These genes were significantly upregulated or under expressed in severe asthma, as validated by RT-qPCR.

CONCLUSION

Mitochondrial abnormalities affecting oxidative phosphorylation play a critical role in severe asthma. Key molecular biomarkers like TFCP2L1, KRT6A, FCER1A, and CCL5, are essential for detecting severe asthma. This research significantly enhances the understanding and diagnosis of severe asthma.

Proteomic networks and related genetic variants associated with smoking and chronic obstructive pulmonary disease

BACKGROUND

Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.

METHODS

Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS.

RESULTS

We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts.

CONCLUSIONS

In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Reducing language barriers, promoting information absorption, and communication using fanyi

ABSTRACT

Interpreting genes of interest is essential for identifying molecular mechanisms, but acquiring such information typically involves tedious manual retrieval. To streamline this process, the fanyi package offers tools to retrieve gene information from sources like National Center for Biotechnology Information (NCBI), significantly enhancing accessibility. Additionally, understanding the latest research advancements and sharing achievements are crucial for junior researchers. However, language barriers often restrict knowledge absorption and career development. To address these challenges, we developed the fanyi package, which leverages artificial intelligence (AI)-driven online translation services to accurately translate among multiple languages. This dual functionality allows researchers to quickly capture and comprehend information, promotes a multilingual environment, and fosters innovation in academic community. Meanwhile, the translation functions are versatile and applicable beyond biomedicine research to other domains as well. The fanyi package is freely available at https://github.com/YuLab-SMU/fanyi .

Widespread changes in gene expression accompany body size evolution in nematodes

Body size is a fundamental trait that drives multiple evolutionary and ecological patterns. Caenorhabditis inopinata is a fig-associated nematode that is exceptionally large relative to other members of the genus, including Caenorhabditis elegans. We previously showed that C. inopinata is large primarily due to postembryonic cell size expansion that occurs during the larval-to-adult transition. Here, we describe gene expression patterns in C. elegans and C. inopinata throughout this developmental period to understand the transcriptional basis of body size change. We performed RNA-seq in both species across the L3, L4, and adult stages. Most genes are differentially expressed across all developmental stages, consistent with C. inopinata's divergent ecology and morphology. We also used a model comparison approach to identify orthologues with divergent dynamics across this developmental period between the 2 species. This included genes connected to neurons, behavior, stress response, developmental timing, and small RNA/chromatin regulation. Multiple hypodermal collagens were also observed to harbor divergent developmental dynamics across this period, and genes important for molting and body morphology were also detected. Genes associated with transforming growth factor β signaling revealed idiosyncratic and unexpected transcriptional patterns given their role in body size regulation in C. elegans. This widespread transcriptional divergence between these species is unexpected and maybe a signature of the ecological and morphological divergence of C. inopinata. Alternatively, transcriptional turnover may be the rule in the Caenorhabditis genus, indicative of widespread developmental system drift among species. This work lays the foundation for future functional genetic studies interrogating the bases of body size evolution in this group.

Cloud-based introduction to BASH programming for biologists

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning', https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement. This module delivers learning materials introducing the utility of the BASH (Bourne Again Shell) programming language for genomic data analysis in an interactive format that uses appropriate cloud resources for data access and analyses. The next-generation sequencing revolution has generated massive amounts of novel biological data from a multitude of platforms that survey an ever-growing list of genomic modalities. These data require significant downstream computational and statistical analyses to glean meaningful biological insights. However, the skill sets required to generate these data are vastly different from the skills required to analyze these data. Bench scientists that generate next-generation data often lack the training required to perform analysis of these datasets and require support from bioinformatics specialists. Dedicated computational training is required to empower biologists in the area of genomic data analysis, however, learning to efficiently leverage a command line interface is a significant barrier in learning how to leverage common analytical tools. Cloud platforms have the potential to democratize access to the technical tools and computational resources necessary to work with modern sequencing data, providing an effective framework for bioinformatics education. This module aims to provide an interactive platform that slowly builds technical skills and knowledge needed to interact with genomics data on the command line in the Cloud. The sandbox format of this module enables users to move through the material at their own pace and test their grasp of the material with knowledge self-checks before building on that material in the next sub-module. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Differentially expressed heterogeneous overdispersion genes testing for count data

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size, where overdispersion refers to the empirical phenomenon that the variance of read counts is larger than the mean of read counts. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes when the number of replicates is limited as long as the number of conditions is large. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq2 and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

Tsc22d3 promotes morphine tolerance in mice through the GPX4 ferroptosis pathway

BACKGROUND

Morphine tolerance refers to gradual reduction in response to drug with continuous or repeated use of morphine, requiring higher doses to achieve same effect.

METHODS

The morphine tolerance dataset GSE7762 profiles, obtained from gene expression omnibus (GEO) database, were used to identify differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) was applied to explore core modules of DEGs related to morphine tolerance. Core genes were input into Comparative Toxicogenomics Database (CTD). Animal experiments were performed to validate role of Tsc22d3 in morphine tolerance and its relationship with ferroptosis-related pathway.

RESULTS

500 DEGs were identified. DEGs were primarily enriched in negative regulation of brain development, neuronal apoptosis processes, and neurosystem development. Core gene was identified as Tsc22d3. Tsc22d3 gene-associated miRNAs were mmu-miR-196b-5p and mmu-miR-196a-5p. Compared to Non-morphine tolerant group, Tsc22d3 expression was significantly upregulated in Morphine tolerant group. Tsc22d3 expression was upregulated in Morphine tolerant+Tsc22d3_OE, expression of HIF-1alpha, GSH, GPX4 in GPX4 ferroptosis-related pathway showed a more pronounced decrease. As Tsc22d3 expression was downregulated in Morphine tolerant+Tsc22d3_KO, expression of HIF-1alpha, GSH, GPX4 in GPX4 ferroptosis-related pathway exhibited a more pronounced increase. Upregulation of Tsc22d3 in Morphine tolerant+Tsc22d3_OE led to a more pronounced increase in expression of apoptosis proteins (P53, Caspase-3, Bax, SMAC, FAS). The expression of inflammatory factors (IL6, TNF-alpha, CXCL1, CXCL2) showed a more pronounced increase with upregulated Tsc22d3 expression in Morphine tolerant+Tsc22d3_OE.

CONCLUSIONS

Tsc22d3 is highly expressed in brain tissue of morphine-tolerant mice, activating ferroptosis pathway, enhancing apoptosis, promoting inflammatory responses in brain cells.

MAD-FC: A fold change visualization with readability, proportionality, and symmetry

We propose a fold change transform that demonstrates a combination of visualization properties exhibited by log and linear plots of fold change. A fold change visualization should ideally exhibit: (1) readability, where fold change values are recoverable from datapoint position; (2) proportionality, where fold change values of the same direction are proportionally distant from the point of no change; (3) symmetry, where positive and negative fold changes of the same magnitude are equidistant to the point of no change; and (4) high dynamic range, where datapoint values are distinguishable across orders of magnitude within a fixed plot area and pixel resolution. A linear visualization has readability and partial proportionality but lacks high dynamic range and symmetry (because negative direction fold changes are bound between [0, 1] while positive are between (1, ∞)). Log plots of fold change have partial readability, high dynamic range, and symmetry, but lack proportionality because of the log transform. We outline a new transform, named mirrored axis distortion of fold change (MAD-FC), that extends a linear visualization of fold change data to exhibit readability, proportionality, and symmetry (but still has the limited dynamic range of linear plots). We illustrate the use of MAD-FC with biomedical data using various fold change plots. We argue that MAD plots may be a more useful visualization than log or linear plots for applications that do not require a high dynamic range (less than 8 units in log2 space).

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Plasma protein signatures of adult asthma

BACKGROUND

Adult asthma is complex and incompletely understood. Plasma proteomics is an evolving technique that can both generate biomarkers and provide insights into disease mechanisms. We aimed to identify plasma proteomic signatures of adult asthma.

METHODS

Protein abundance in plasma was measured in individuals from the Agricultural Lung Health Study (ALHS) (761 asthma, 1095 non-case) and the Atherosclerosis Risk in Communities study (470 asthma, 10,669 non-case) using the SOMAScan 5K array. Associations with asthma were estimated using covariate adjusted logistic regression and meta-analyzed using inverse-variance weighting. Additionally, in ALHS, we examined phenotypes based on both asthma and seroatopy (asthma with atopy (n = 207), asthma without atopy (n = 554), atopy without asthma (n = 147), compared to neither (n = 948)).

RESULTS

Meta-analysis of 4860 proteins identified 115 significantly (FDR<0.05) associated with asthma. Multiple signaling pathways related to airway inflammation and pulmonary injury were enriched (FDR<0.05) among these proteins. A proteomic score generated using machine learning provided predictive value for asthma (AUC = 0.77, 95% CI = 0.75-0.79 in training set; AUC = 0.72, 95% CI = 0.69-0.75 in validation set). Twenty proteins are targeted by approved or investigational drugs for asthma or other conditions, suggesting potential drug repurposing. The combined asthma-atopy phenotype showed significant associations with 20 proteins, including five not identified in the overall asthma analysis.

CONCLUSION

This first large-scale proteomics study identified over 100 plasma proteins associated with current asthma in adults. In addition to validating previous associations, we identified many novel proteins that could inform development of diagnostic biomarkers and therapeutic targets in asthma management.

Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease

Background

Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.

Methods

Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS.

Results

We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts.

Conclusions

In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma

BACKGROUND

Although inhaled corticosteroids (ICS) are the first-line therapy for patients with persistent asthma, many patients continue to have exacerbations. We developed machine learning models to predict the ICS response in patients with asthma.

METHODS

The subjects included asthma patients of European ancestry (n = 1371; 448 children; 916 adults). A genome-wide association study was performed to identify the SNPs associated with ICS response. Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest.

RESULTS

The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67-0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70-0.78; sensitivity: 0.70; specificity: 0.68). The genes contributing to the prediction of ICS response included those associated with ICS responses in asthma (TPSAB1, FBXL16), asthma symptoms and severity (ABCA7, CNN2, PTRN3, and BSG/CD147), airway remodeling (ELANE, FSTL3), mucin production (GAL3ST), leukotriene synthesis (GPX4), allergic asthma (ZFPM1, SBNO2), and others.

CONCLUSIONS

An accurate risk prediction of ICS response can be obtained using machine learning methods, with the potential to inform personalized treatment decisions. Further studies are needed to examine if the integration of richer phenotype data could improve risk prediction.

Widespread changes in gene expression accompany body size evolution in nematodes

Body size is a fundamental trait that drives multiple evolutionary and ecological patterns. Caenorhabditis inopinata is a fig-associated nematode that is exceptionally large relative to other members of the genus, including C. elegans. We previously showed that C. inopinata is large primarily due to postembryonic cell size expansion that occurs during the larval-to-adult transition. Here, we describe gene expression patterns in C. elegans and C. inopinata throughout this developmental period to understand the transcriptional basis of body size change. We performed RNA-seq in both species across the L3, L4, and adult stages. Most genes are differentially expressed across all developmental stages, consistent with C. inopinata's divergent ecology and morphology. We also used a model comparison approach to identify orthologs with divergent dynamics across this developmental period between the two species. This included genes connected to neurons, behavior, stress response, developmental timing, and small RNA/chromatin regulation. Multiple hypodermal collagens were also observed to harbor divergent developmental dynamics across this period, and genes important for molting and body morphology were also detected. Genes associated with TGF-β signaling revealed idiosyncratic and unexpected transcriptional patterns given their role in body size regulation in C. elegans. Widespread transcriptional divergence between these species is unexpected and may be a signature of the ecological and morphological divergence of C. inopinata. Alternatively, transcriptional turnover may be the rule in the Caenorhabditis genus, indicative of widespread developmental system drift among species. This work lays the foundation for future functional genetic studies interrogating the bases of body size evolution in this group.

Subconjunctival injection of human umbilical cord mesenchymal stem cells alleviates experimental allergic conjunctivitis via regulating T cell response

BACKGROUND

T helper 2 (Th2) cells are thought to play critical roles in allergic conjunctivitis (AC). They release inflammatory cytokines to promote an allergic response in AC. Due to individual heterogeneity and long-term chronic management, current therapies do not always effectively control AC. Mesenchymal stem cells (MSCs) have been shown to be effective in treating allergy-related disorders, but it is unclear how exactly the Th2-mediated allergic response is attenuated. This study aims to elucidate the therapeutic effect and mechanism of the human umbilical cord MSCs (hUCMSCs) in a mouse model of experimental AC (EAC).

METHODS

A mouse EAC model was established by inoculating short ragweed (SRW) pollen. After the SRW pollen challenge, the mice received a single subconjunctival or tail vein injection of 2 × 106 hUCMSCs, or subconjunctival injection of hUCMSCs conditioned medium (hUCMSC-CM), and dexamethasone eye drops was used as positive control; subsequent scratching behavior and clinical symptoms were assessed. Immunostaining and flow cytometry were carried out to show allergic reactions and the activation of CD4 + T cell subsets in the conjunctiva and cervical lymph nodes (CLNs). Gene expression was determined by RNA-seq and further verified by qRT-PCR and Western blot. Co-culture assays were performed to explore the regulatory role of hUCMSCs in the differentiation of CD4 + naive T cells (Th0) into Th2 cells.

RESULTS

Subconjunctival administration of hUCMSCs resulted in fewer instances of scratching and lower inflammation scores in EAC mice compared to the tail vein delivery, hUCMSC-CM and control groups. Subconjunctival administration of hUCMSCs reduced the number of activated mast cells and infiltrated eosinophils in the conjunctiva, as well as decreased the number of Th2 cells in CLNs. After pretreatment with EAC mouse serum in vitro to mimic the in vivo milieu, hUCMSCs were able to inhibit the differentiation of Th0 into Th2 cells. Further evidence demonstrated that repression of Th2 cell differentiation by hUCMSCs is mediated by CRISPLD2 through downregulation of STAT6 phosphorylation. Additionally, hUMCSCs were able to promote the differentiation of Th0 cells into regulatory T cells in CLNs of EAC mice.

CONCLUSIONS

Subconjunctival injection of hUCMSCs suppressed the Th2-allergic response and alleviated clinical symptoms. This study provides not only a potential therapeutic target for the treatment of AC but also other T cell-mediated diseases.

Pharmacogenetics of childhood uncontrolled asthma

INTRODUCTION

Asthma is a heterogeneous, multifactorial disease with multiple genetic and environmental risk factors playing a role in pathogenesis and therapeutic response. Understanding of pharmacogenetics can help with matching individualized treatments to specific genotypes of asthma to improve therapeutic outcomes especially in uncontrolled or severe asthma.

AREAS COVERED

In this review, we outline novel information about biology, pathways, and mechanisms related to interindividual variability in drug response (corticosteroids, bronchodilators, leukotriene modifiers, and biologics) for childhood asthma. We discuss candidate gene, genome-wide association studies and newer omics studies including epigenomics, transcriptomics, proteomics, and metabolomics as well as integrative genomics and systems biology methods related to childhood asthma. The articles were obtained after a series of searches, last updated November 2022, using database PubMed/CINAHL DB.

EXPERT OPINION

Implementation of pharmacogenetic algorithms can improve therapeutic targeting in children with asthma, particularly with severe or uncontrolled asthma who typically have challenges in clinical management and carry considerable financial burden. Future studies focusing on potential biomarkers both clinical and pharmacogenetic can help formulate a prognostic test for asthma treatment response that would represent true bench to bedside clinical implementation.

Differentially Expressed Heterogeneous Overdispersion Genes Testing for Count Data

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

Antagonizing cholecystokinin A receptor in the lung attenuates obesity-induced airway hyperresponsiveness

Obesity increases asthma prevalence and severity. However, the underlying mechanisms are poorly understood, and consequently, therapeutic options for asthma patients with obesity remain limited. Here we report that cholecystokinin-a metabolic hormone best known for its role in signaling satiation and fat metabolism-is increased in the lungs of obese mice and that pharmacological blockade of cholecystokinin A receptor signaling reduces obesity-associated airway hyperresponsiveness. Activation of cholecystokinin A receptor by the hormone induces contraction of airway smooth muscle cells. In vivo, cholecystokinin level is elevated in the lungs of both genetically and diet-induced obese mice. Importantly, intranasal administration of cholecystokinin A receptor antagonists (proglumide and devazepide) suppresses the airway hyperresponsiveness in the obese mice. Together, our results reveal an unexpected role for cholecystokinin in the lung and support the repurposing of cholecystokinin A receptor antagonists as a potential therapy for asthma patients with obesity.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Impact of adaptive filtering on power and false discovery rate in RNA-seq experiments

Background

In RNA-sequencing studies a large number of hypothesis tests are performed to compare the differential expression of genes between several conditions. Filtering has been proposed to remove candidate genes with a low expression level which may not be relevant and have little or no chance of showing a difference between conditions. This step may reduce the multiple testing burden and increase power.

Results

We show in a simulation study that filtering can lead to some increase in power for RNA-sequencing data, too aggressive filtering, however, can lead to a decline. No uniformly optimal filter in terms of power exists. Depending on the scenario different filters may be optimal. We propose an adaptive filtering strategy which selects one of several filters to maximise the number of rejections. No additional adjustment for multiplicity has to be included, but a rule has to be considered if the number of rejections is too small.

Conclusions

For a large range of simulation scenarios, the adaptive filter maximises the power while the simulated False Discovery Rate is bounded by the pre-defined significance level. Using the adaptive filter, it is not necessary to pre-specify a single individual filtering method optimised for a specific scenario.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04928-z.

Insights into functional connectivity in mammalian signal transduction pathways by pairwise comparison of protein interaction partners of critical signaling hubs

Growth factors and cytokines activate signal transduction pathways and regulate gene expression in eukaryotes. Intracellular domains of activated receptors recruit several protein kinases as well as transcription factors that serve as platforms or hubs for the assembly of multi-protein complexes. The signaling hubs involved in a related biologic function often share common interaction proteins and target genes. This functional connectivity suggests that a pairwise comparison of protein interaction partners of signaling hubs and network analysis of common partners and their expression analysis might lead to the identification of critical nodes in cellular signaling. A pairwise comparison of signaling hubs across several related pathways might reveal novel signaling modules. Analysis of protein interaction connectome by Venn (PIC-Venn) of transcription factors STAT1, STAT3, NFKB1, RELA, FOS, and JUN, and their common interaction network suggested that BRCA1 and TSC22D3 function as critical nodes in immune responses by connecting the signaling hubs into signaling modules. Transcriptional regulation of critical hubs may play a major role in the lung epithelial cells in response to SARS-CoV-2 and in COVID-19 patients. Mutations and differential expression levels of these critical nodes and modules in pathological conditions might deregulate signaling pathways and their target genes involved in inflammation. Biological connectivity emerges from the structural connectivity of interaction networks across several signaling hubs in related pathways. The main objectives of this study are to identify critical hubs, critical nodes, and modules involved in the signal transduction pathways of innate and adaptive immunity. Application of PIC-Venn to several signaling hubs might reveal novel nodes and modules that can be targeted by small regulatory molecules to simultaneously activate or inhibit cell signaling in health and disease.

CEBPD modulates the airway smooth muscle transcriptomic response to glucocorticoids

Background

CCAAT/Enhancer Binding Protein D (CEBPD), a pleiotropic glucocorticoid-responsive transcription factor, modulates inflammatory responses. Of relevance to asthma, expression of CEBPD in airway smooth muscle (ASM) increases with glucocorticoid exposure. We sought to characterize CEBPD-mediated transcriptomic responses to glucocorticoid exposure in ASM by measuring changes observed after knockdown of CEBPD and its impact on asthma-related ASM function.

Methods

Primary ASM cells derived from four donors were transfected with CEBPD or non-targeting (NT) siRNA and exposed to vehicle control, budesonide (100 nM, 18 h), TNFα (10 ng/ml, 18 h), or both budesonide and TNFα. Subsequently, RNA-Seq was used to measure gene expression levels, and pairwise differential expression results were obtained for exposures versus vehicle and knockdown versus control conditions. Weighted gene co-expression analysis was performed to identify groups of genes with similar expression patterns across the various experimental conditions (i.e., CEBPD knockdown status, exposures).

Results

CEBPD knockdown altered expression of 3037 genes under at least one exposure (q-value < 0.05). Co-expression analysis identified sets of 197, 152 and 290 genes that were correlated with CEBPD knockdown status, TNFα exposure status, and both, respectively. JAK-STAT signaling pathway genes, including IL6R and SOCS3, were among those influenced by both TNFα and CEBPD knockdown. Immunoblot assays revealed that budesonide-induced IL-6R protein expression and augmented IL-6-induced STAT3 phosphorylation levels were attenuated by CEBPD knockdown in ASM.

Conclusions

CEBPD modulates glucocorticoid responses in ASM, in part via modulation of IL-6 receptor signaling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02119-1.

m6A echoes with DNA methylation: Coordinated DNA methylation and gene expression data analysis identified critical m6A genes associated with asthma

Asthma is a chronic inflammatory disease that involves complex gene-environment interactions. Methylation of nucleotides, such as 5-methylcytosine (5mC) in DNA and N6-methyladenosine (m6A) in mRNA, carries important information for gene regulation. Our study screened m6A genes and genes associated with asthma from the Gene Expression Omnibus (GEO) databases GSE63383, GSE119580, GSE38003, GSE34313, GSE13168, and GSE35643. GSE52778, GSE35643, GSE40996, and GSE64744), and DNA methylation data from GSE85568 and GSE146377. We screened out 6 m6A related genes (FTO, IGF2BP2, RBM15, RBMX, WTAP, and YTHDC1) that were significantly dysregulated in asthma or proinflammatory conditions. A correlation study showed a high correlation between m6A genes and gene pairs such as WTAP, IL7R, and TLR2; RBMX, SLC22A4, IL33, TNC, FLG, and IL6R (|r| ≥ 0.8). Following DNA methylation dataset analysis, we proposed several DNA methylation-m6A modification asthma-related gene axes such as cg19032951/cg15153914-IGF2BP2-SMAD3. Interestingly, several target genes, such as SMAD3, possess the ability to participate in DNA methylation processes, which may reciprocally regulate the expression of m6A genes and form a closed-loop regulation axis. Some classic DNA methylation-related genes, such as TET1, UHRF1, and ZBTB4, were also involved. We identified an integrated profile of m6A gene expression in asthma and proposed a novel potential interplay between DNA methylation and m6A modification in asthma pathogenesis. Using the CMAP database, we found that resveratrol may target these dysregulated m6A genes, and therefore may serve as a potential therapeutic agent for asthma.

Pathways linked to unresolved inflammation and airway remodelling characterize the transcriptome in two independent severe asthma cohorts

Background and objective

Severe asthma (SA) is a heterogeneous disease. Transcriptomic analysis contributes to the understanding of pathogenesis necessary for developing new therapies. We sought to identify and validate mechanistic pathways of SA across two independent cohorts.

Methods

Transcriptomic profiles from U‐BIOPRED and Australian NOVocastrian Asthma cohorts were examined and grouped into SA, mild/moderate asthma (MMA) and healthy controls (HCs). Differentially expressed genes (DEGs), canonical pathways and gene sets were identified as central to SA mechanisms if they were significant across both cohorts in either endobronchial biopsies or induced sputum.

Results

Thirty‐six DEGs and four pathways were shared across cohorts linking to tissue remodelling/repair in biopsies of SA patients, including SUMOylation, NRF2 pathway and oxidative stress pathways. MMA presented a similar profile to HCs. Induced sputum demonstrated IL18R1 as a shared DEG in SA compared with healthy subjects. We identified enrichment of gene sets related to corticosteroid treatment; immune‐related mechanisms; activation of CD4⁺ T cells, mast cells and IL18R1; and airway remodelling in SA.

Conclusion

Our results identified differentially expressed pathways that highlight the role of CD4⁺ T cells, mast cells and pathways linked to ongoing airway remodelling, such as IL18R1, SUMOylation and NRF2 pathways, as likely active mechanisms in the pathogenesis of SA.

Transcriptome analysis from endobronchial biopsies and induced sputum from two independent cohorts of adults with severe asthma (SA) (U‐BIOPRED and Australian NOVocastrian Asthma cohort) demonstrated shared differentially expressed pathways previously linked to persistent unresolved inflammation and novel mechanisms of airway remodelling, which may represent potential novel mechanistic pathways involved in the pathogenesis of SA.

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Th1 cytokines synergize to change gene expression and promote corticosteroid insensitivity in pediatric airway smooth muscle

Background

Corticosteroids remain a key therapy for treating children with asthma. Patients with severe asthma are insensitive, resistant, or refractory to corticosteroids and have poorly controlled symptoms that involve airway inflammation, airflow obstruction, and frequent exacerbations. While the pathways that mediate corticosteroid insensitivity in asthma remain poorly defined, recent studies suggest that enhanced Th1 pathways, mediated by TNFα and IFNγ, may play a role. We previously reported that the combined effects of TNFα and IFNγ promote corticosteroid insensitivity in developing human airway smooth muscle (ASM).

Methods

To further understand the effects of TNFα and IFNγ on corticosteroid sensitivity in the context of neonatal and pediatric asthma, we performed RNA sequencing (RNA-seq) on human pediatric ASM treated with fluticasone propionate (FP), TNFα, and/or IFNγ.

Results

We found that TNFα had a greater effect on gene expression (~ 1000 differentially expressed genes) than IFNγ (~ 500 differentially expressed genes). Pathway and transcription factor analyses revealed enrichment of several pro-inflammatory responses and signaling pathways. Interestingly, treatment with TNFα and IFNγ augmented gene expression with more than 4000 differentially expressed genes. Effects of TNFα and IFNγ enhanced several pro-inflammatory genes and pathways related to ASM and its contributions to asthma pathogenesis, which persisted in the presence of corticosteroids. Co-expression analysis revealed several gene networks related to TNFα- and IFNγ-mediated signaling, pro-inflammatory mediator production, and smooth muscle contractility. Many of the co-expression network hubs were associated with genes that are insensitive to corticosteroids.

Conclusions

Together, these novel studies show the combined effects of TNFα and IFNγ on pediatric ASM and implicate Th1-associated cytokines in promoting ASM inflammation and hypercontractility in severe asthma.

Multiomics Analysis Identifies BIRC3 as a Novel Glucocorticoid Response-Associated Gene

BACKGROUND

Inhaled corticosteroid (ICS) response among patients with asthma is influenced by genetics, but biologically actionable insights based on associations have not been found. Various glucocorticoid response omics datasets are available to interrogate their biological effects.

OBJECTIVE

We sought to identify functionally relevant ICS response genetic associations by integrating complementary multiomics datasets.

METHODS

Variants with p-values<10^-4 from a previous ICS response genome-wide association study were re-ranked based on integrative scores determined from: i) glucocorticoid receptor (GR)- and ii) RNA polymerase II (RNAP II)-binding regions inferred from ChIP-Seq data for three airway cell types, iii) glucocorticoid response element (GRE) motifs, iv) differentially expressed genes in response to glucocorticoid exposure according to 20 transcriptomic datasets, and v) expression quantitative trait loci (eQTLs) from GTEx. Candidate variants were tested for association with ICS response and asthma in six independent studies.

RESULTS

Four variants had significant (q-value<0.05) multiomics integrative scores. These variants were in a locus consisting of 52 variants in high LD (r²≥0.8) near GR-binding sites by the gene BIRC3. Variants were also BIRC3 eQTLs in lung, and two were within/near putative GRE motifs. BIRC3 had increased RNAP II occupancy and gene expression with glucocorticoid exposure in two ChIP-Seq and 13 transcriptomic datasets. Some BIRC3 variants in the 52-variant locus were associated (p-value<0.05) with ICS response in three independent studies and others with asthma in one study.

CONCLUSION

BIRC3 should be prioritized for further functional studies of ICS response.

CLINICAL IMPLICATION

Genetic variation near BIRC3 may influence ICS response in people with asthma.

Pharmacogenetic studies of long-acting beta agonist and inhaled corticosteroid responsiveness in randomised controlled trials of individuals of African descent with asthma

BACKGROUND

Pharmacogenetic studies in asthma cohorts, primarily made up of White people of European descent, have identified loci associated with response to inhaled beta agonists and corticosteroids (ICSs). Differences exist in how individuals from different ancestral backgrounds respond to long-acting beta agonist (LABA) and ICSs. Therefore, we sought to understand the pharmacogenetic mechanisms regulating therapeutic responsiveness in individuals of African descent.

METHODS

We did ancestry-based pharmacogenetic studies of children (aged 5-11 years) and adolescents and adults (aged 12-69 years) from the Best African Response to Drug (BARD) trials, in which participants with asthma uncontrolled with low-dose ICS (fluticasone propionate 50 μg in children, 100 μg in adolescents and adults) received different step-up combination therapies. The hierarchal composite outcome of pairwise superior responsiveness in BARD was based on asthma exacerbations, a 31-day difference in annualised asthma-control days, or a 5% difference in percentage predicted FEV1. We did whole-genome admixture mapping of 15 159 ancestral segments within 312 independent regions, stratified by the two age groups. The two co-primary outcome comparisons were the step up from low-dose ICS to the quintuple dose of ICS (5 × ICS: 250 μg twice daily in children and 500 μg twice daily in adolescents and adults) versus double dose (2-2·5 × ICS: 100 μg twice daily in children, 250 μg twice daily in adolescents and adults), and 5 × ICS versus 100 μg fluticasone plus a LABA (salmeterol 50 μg twice daily). We used a genome-wide significance threshold of p<1·6 × 10-4, and tested for replication using independent cohorts of individuals of African descent with asthma.

FINDINGS

We included 249 unrelated children and 267 unrelated adolescents and adults in the BARD pharmacogenetic analysis. In children, we identified a significant admixture mapping peak for superior responsiveness to 5 × ICS versus 100 μg fluticasone plus salmeterol on chromosome 12 (odds ratio [ORlocal African] 3·95, 95% CI 2·02-7·72, p=6·1 × 10-5) fine mapped to a locus adjacent to RNFT2 and NOS1 (rs73399224, ORallele dose 0·17, 95% CI 0·07-0·42, p=8·4 × 10-5). In adolescents and adults, we identified a peak for superior responsiveness to 5 × ICS versus 2·5 × ICS on chromosome 22 (ORlocal African 3·35, 1·98-5·67, p=6·8 × 10-6) containing a locus adjacent to TPST2 (rs5752429, ORallele dose 0·21, 0·09-0·52, p=5·7 × 10-4). We replicated rs5752429 and nominally replicated rs73399224 in independent African American cohorts.

INTERPRETATION

BARD is the first genome-wide pharmacogenetic study of LABA and ICS response in clinical trials of individuals of African descent to detect and replicate genome-wide significant loci. Admixture mapping of the composite BARD trial outcome enabled the identification of novel pharmacogenetic variation accounting for differential therapeutic responses in people of African descent with asthma.

FUNDING

National Institutes of Health, National Heart, Lung, and Blood Institute.

Network and co-expression analysis of airway smooth muscle cell transcriptome delineates potential gene signatures in asthma

Airway smooth muscle (ASM) is known for its role in asthma exacerbations characterized by acute bronchoconstriction and remodeling. The molecular mechanisms underlying multiple gene interactions regulating gene expression in asthma remain elusive. Herein, we explored the regulatory relationship between ASM genes to uncover the putative mechanism underlying asthma in humans. To this end, the gene expression from human ASM was measured with RNA-Seq in non-asthmatic and asthmatic groups. The gene network for the asthmatic and non-asthmatic group was constructed by prioritizing differentially expressed genes (DEGs) (121) and transcription factors (TFs) (116). Furthermore, we identified differentially connected or co-expressed genes in each group. The asthmatic group showed a loss of gene connectivity due to the rewiring of major regulators. Notably, TFs such as ZNF792, SMAD1, and SMAD7 were differentially correlated in the asthmatic ASM. Additionally, the DEGs, TFs, and differentially connected genes over-represented in the pathways involved with herpes simplex virus infection, Hippo and TGF-β signaling, adherens junctions, gap junctions, and ferroptosis. The rewiring of major regulators unveiled in this study likely modulates the expression of gene-targets as an adaptive response to asthma. These multiple gene interactions pointed out novel targets and pathways for asthma exacerbations.

Cell landscape atlas for patients with chronic thromboembolic pulmonary hypertension after pulmonary endarterectomy constructed using single-cell RNA sequencing

This study aimed to construct an atlas of the cell landscape and comprehensively characterize the cellular repertoire of the pulmonary endarterectomized tissues of patients with chronic thromboembolic pulmonary hypertension (CTEPH). Five pulmonary endarterectomized tissues were collected. 10× Genomics single-cell RNA sequencing was performed, followed by the identification of cluster marker genes and cell types. Gene Ontology (GO) enrichment analysis was conducted. Seventeen cell clusters were characterized, corresponding to 10,518 marker genes, and then classified into eight cell types, including fibroblast/smooth muscle cell, endothelial cell, T cell/NK cell, macrophage, mast cell, cysteine rich secretory protein LCCL domain containing 2 (CRISPLD2)+ cell, cancer stem cell, and undefined. The specific marker genes of fibroblast/smooth muscle cell, endothelial cell, T cell/NK cell, macrophage, mast cell, and cancer stem cell were significantly enriched for multiple functions associated with muscle cell migration, endothelial cell migration, T cell activation, neutrophil activation, erythrocyte homeostasis, and tissue remodeling, respectively. No functions were significantly enriched for the marker gene of CRISPLD2+ cell. Our study, for the first time, provides an atlas of the cell landscape of the pulmonary endarterectomized tissues of CTEPH patients at single-cell resolution, which may serve as a valuable resource for further elucidation of disease pathophysiology.

Drug Repurposing to Treat Glucocorticoid Resistance in Asthma

Corticosteroid resistance causes significant morbidity in asthma, and drug repurposing may identify timely and cost-effective adjunctive treatments for corticosteroid resistance. In 95 subjects from the Childhood Asthma Management Program (CAMP) and 19 subjects from the Severe Asthma Research Program (SARP), corticosteroid response was measured by the change in percent predicted forced expiratory volume in one second (FEV₁). In each cohort, differential gene expression analysis was performed comparing poor (resistant) responders, defined as those with zero to negative change in FEV₁, to good responders, followed by Connectivity Map (CMap) analysis to identify inversely associated (i.e., negatively connected) drugs that reversed the gene expression profile of poor responders to resemble that of good responders. Mean connectivity scores weighted by sample size were calculated. The top five drug compound candidates underwent in vitro validation in NF-κB-based luciferase reporter A549 cells stimulated by IL-1β ± dexamethasone. In CAMP and SARP, 134 and 178 respective genes were differentially expressed in poor responders. CMap analysis identified 46 compounds in common across both cohorts with connectivity scores < −50. γ-linolenic acid, ampicillin, exemestane, brinzolamide, and INCA-6 were selected for functional validation. γ-linolenic acid, brinzolamide, and INCA-6 significantly reduced IL-1β induced luciferase activity and potentiated the anti-inflammatory effect of dexamethasone in A549/NF-κB-luc reporter cells. These results demonstrate how existing drugs, including γ-linolenic acid, brinzolamide, and INCA-6, may be repurposed to improve corticosteroid response in asthmatics.

Differential gene expression and Ingenuity Pathway Analysis of bronchoalveolar lavage cells from horses with mild/moderate neutrophilic or mastocytic inflammation on BAL cytology

Mild to moderate equine asthma syndrome (mEAS) affects horses of all ages and breeds. To date, the etiology and pathophysiology of mEAS are active areas of research, and it remains incompletely understood whether mEAS horses with different immune cell 'signatures' on BAL cytology represent different phenotypes, distinct pathobiological mechanisms (endotypes), varied environmental conditions, disease severity, genetic predispositions, or all of the above. In this descriptive study, we compared gene expression data from BAL cells isolated from horses with normal BALF cytology (n = 5), to those isolated from horses with mild/moderate neutrophilic inflammation (n = 5), or mild/moderate mastocytic inflammation (n = 5). BAL cell protein lysates were analyzed for cytokine/chemokine levels using Multiplex Bead Immunoassay, and for select proteins using immunoblot. The transcriptome, determined by RNA-seq and analyzed with DEseq2, contained 20, 63, and 102 significantly differentially expressed genes in horses with normal vs. neutrophilic, normal vs. mastocytic, and neutrophilic vs. mastocytic BALF cytology, respectively. Pathway analyses revealed that BAL-isolated cells from horses with neutrophilic vs. normal cytology showed enrichment in inflammation pathways, and horses with mastocytic vs. normal cytology showed enrichment in pathways involved in fibrosis and allergic reaction. BAL cells from horses with mastocytic mEAS, compared to neutrophilic mEAS, showed enrichment in pathways involved in alteration of tissue structures. Cytokine analysis determined that IL-1β was significantly different in the lysates from horses with neutrophilic inflammation compared to those with normal or mastocytic BAL cytology. Immunoblot revealed significant difference in the relative level of MMP2 in horses with neutrophilic vs. mastocytic mEAS. Upregulation of mRNA transcripts involved in the IL-1 family cytokine signaling axis (IL1a, IL1b, and IL1R2) in neutrophilic mEAS, as well as KIT mRNA in mastocytic mEAS, are novel, potentially clinically relevant, findings of this study. These findings further inform our understanding of inflammatory cell subtypes in mEAS.

Modified Significance Analysis of Microarrays in Heterogeneous Diseases

Significance analysis of microarrays (SAM) provides researchers with a non-parametric score for each gene based on repeated measurements. However, it may lose certain power in general statistical tests to correctly detect differentially expressed genes (DEGs) which violate homogeneity. Monte Carlo simulation shows that the “half SAM score” can maintain type I error rates of about 0.05 based on assumptions of normal and non-normal distributions. The author found 265 DEGs using the half SAM scoring, more than the 119 DEGs detected by SAM, with the false discovery rate controlled at 0.05. In conclusion, the author recommends the half SAM scoring method to detect DEGs in data that show heterogeneity.

Transcriptome-Level Interactions between Budesonide and Formoterol Provide Insight into the Mechanism of Action of Inhaled Corticosteroid/Long-Acting β 2-Adrenoceptor Agonist Combination Therapy in Asthma

In 2019, the Global Initiative for Asthma treatment guidelines were updated to recommend that inhaled corticosteroid (ICS)/long-acting β ₂-adrenoceptor agonist (LABA) combination therapy should be a first-in-line treatment option for asthma. Although clinically superior to ICS, mechanisms underlying the efficacy of this combination therapy remain unclear. We hypothesized the existence of transcriptomic interactions, an effect that was tested in BEAS-2B and primary human bronchial epithelial cells (pHBECs) using formoterol and budesonide as representative LABA and ICS, respectively. In BEAS-2B cells, formoterol produced 267 (212 induced; 55 repressed) gene expression changes (≥2/≤0.5-fold) that were dominated by rapidly (1 to 2 hours) upregulated transcripts. Conversely, budesonide induced 370 and repressed 413 mRNAs, which occurred predominantly at 6-18 hours and was preceded by transcripts enriched in transcriptional regulators. Significantly, genes regulated by both formoterol and budesonide were over-represented in the genome; moreover, budesonide plus formoterol induced and repressed 609 and 577 mRNAs, respectively, of which ∼one-third failed the cutoff criterion for either treatment alone. Although induction of many mRNAs by budesonide plus formoterol was supra-additive, the dominant (and potentially beneficial) effect of budesonide on formoterol-induced transcripts, including those encoding many proinflammatory proteins, was repression. Gene ontology analysis of the budesonide-modulated transcriptome returned enriched terms for transcription, apoptosis, proliferation, differentiation, development, and migration. This "functional" ICS signature was augmented in the presence of formoterol. Thus, LABAs modulate glucocorticoid action, and comparable transcriptome-wide interactions in pHBECs imply that such effects may be extrapolated to individuals with asthma taking combination therapy. Although repression of formoterol-induced proinflammatory mRNAs should be beneficial, the pathophysiological consequences of other interactions require investigation. SIGNIFICANCE STATEMENT: In human bronchial epithelial cells, formoterol, a long-acting β ₂-adrenoceptor agonist (LABA), enhanced the expression of inflammatory genes, and many of these changes were reduced by the glucocorticoid budesonide. Conversely, the ability of formoterol to enhance both gene induction and repression by budesonide provides mechanistic insight as to how adding a LABA to an inhaled corticosteroid may improve clinical outcomes in asthma.

The functions of CAP superfamily proteins in mammalian fertility and disease

BACKGROUND

Members of the cysteine-rich secretory proteins (CRISPS), antigen 5 (Ag5) and pathogenesis-related 1 (Pr-1) (CAP) superfamily of proteins are found across the bacterial, fungal, plant and animal kingdoms. Although many CAP superfamily proteins remain poorly characterized, over the past decade evidence has accumulated, which provides insights into the functional roles of these proteins in various processes, including fertilization, immune defence and subversion, pathogen virulence, venom toxicology and cancer biology.

OBJECTIVE AND RATIONALE

The aim of this article is to summarize the current state of knowledge on CAP superfamily proteins in mammalian fertility, organismal homeostasis and disease pathogenesis.

SEARCH METHODS

The scientific literature search was undertaken via PubMed database on all articles published prior to November 2019. Search terms were based on following keywords: 'CAP superfamily', 'CRISP', 'Cysteine-rich secretory proteins', 'Antigen 5', 'Pathogenesis-related 1', 'male fertility', 'CAP and CTL domain containing', 'CRISPLD1', 'CRISPLD2', 'bacterial SCP', 'ion channel regulator', 'CatSper', 'PI15', 'PI16', 'CLEC', 'PRY proteins', 'ASP proteins', 'spermatogenesis', 'epididymal maturation', 'capacitation' and 'snake CRISP'. In addition to that, reference lists of primary and review article were reviewed for additional relevant publications.

OUTCOMES

In this review, we discuss the breadth of knowledge on CAP superfamily proteins with regards to their protein structure, biological functions and emerging significance in reproduction, health and disease. We discuss the evolution of CAP superfamily proteins from their otherwise unembellished prokaryotic predecessors into the multi-domain and neofunctionalized members found in eukaryotic organisms today. At least in part because of the rapid evolution of these proteins, many inconsistencies in nomenclature exist within the literature. As such, and in part through the use of a maximum likelihood phylogenetic analysis of the vertebrate CRISP subfamily, we have attempted to clarify this confusion, thus allowing for a comparison of orthologous protein function between species. This framework also allows the prediction of functional relevance between species based on sequence and structural conservation.

WIDER IMPLICATIONS

This review generates a picture of critical roles for CAP proteins in ion channel regulation, sterol and lipid binding and protease inhibition, and as ligands involved in the induction of multiple cellular processes.

ideal: an R/Bioconductor package for interactive differential expression analysis

BACKGROUND

RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking.

RESULTS

We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility.

CONCLUSION

ideal is distributed as an R package in the Bioconductor project ( http://bioconductor.org/packages/ideal/ ), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.

Dexamethasone rescues TGF-β1-mediated β2-adrenergic receptor dysfunction and attenuates phosphodiesterase 4D expression in human airway smooth muscle cells

Glucocorticoids (GCs) and β2-adrenergic receptor (β2AR) agonists improve asthma outcomes in most patients. GCs also modulate gene expression in human airway smooth muscle (HASM), thereby attenuating airway inflammation and airway hyperresponsiveness that define asthma. Our previous studies showed that the pro-fibrotic cytokine, transforming growth factor- β1 (TGF-β1) increases phosphodiesterase 4D (PDE4D) expression that attenuates agonist-induced levels of intracellular cAMP. Decreased cAMP levels then diminishes β2 agonist-induced airway relaxation. In the current study, we investigated whether glucocorticoids reverse TGF-β1-effects on β2-agonist-induced bronchodilation and modulate pde4d gene expression in HASM. Dexamethasone (DEX) reversed TGF-β1 effects on cAMP levels induced by isoproterenol (ISO). TGF-β1 also attenuated G protein-dependent responses to cholera toxin (CTX), a Gαs stimulator downstream from the β2AR receptor. Previously, we demonstrated that TGF-β1 treatment increased β2AR phosphorylation to induce hyporesponsiveness to a β2 agonist. Our current data shows that expression of grk2/3, kinases associated with attenuation of β2AR function, are not altered with TGF-β1 stimulation. Interestingly, DEX also attenuated TGF-β1-induced pde4d gene expression. These data suggest that steroids may be an effective therapy for treatment of asthma patients whose disease is primarily driven by elevated TGF-β1 levels.

Progesterone Receptor Signaling Selectively Modulates Cytokine-Induced Global Gene Expression in Human Cervical Stromal Cells

Preterm birth (PTB) is the leading cause of morbidity and mortality in infants <1 year of age. Intrauterine inflammation is a hallmark of preterm and term parturition; however, this alone cannot fully explain the pathobiology of PTB. For example, the cervix undergoes a prolonged series of biochemical and biomechanical events, including extracellular matrix (ECM) remodeling and mechanochemical changes, culminating in ripening. Vaginal progesterone (P₄) prophylaxis demonstrates great promise in preventing PTB in women with a short cervix (<25 mm). We used a primary culture model of human cervical stromal fibroblasts to investigate gene expression signatures in cells treated with interleukin-1β (IL-1β) in the presence or absence of P₄ following 17β-estradiol (17β-E₂) priming for 7–10 days. Microarrays were used to measure global gene expression in cells treated with cytokine or P₄ alone or in combination, followed by validation of select transcripts by semiquantitative polymerase chain reactions (qRT-PCR). Primary/precursor (MIR) and mature microRNAs (miR) were quantified by microarray and NanoString^® platforms, respectively, and validated by qRT-PCR. Differential gene expression was computed after data normalization followed by pathway analysis using Kyoto Encyclopedia Genes and Genomes (KEGG), Panther, Gene Ontology (GO), and Ingenuity Pathway Analysis (IPA) upstream regulator algorithm tools. Treatment of fibroblasts with IL-1β alone resulted in the differential expression of 1432 transcripts (protein coding and non-coding), while P₄ alone led to the expression of only 43 transcripts compared to untreated controls. Cytokines, chemokines, and their cognate receptors and prostaglandin endoperoxide synthase-2 (PTGS-2) were among the most highly upregulated transcripts following either IL-1β or IL-1β + P₄. Other prominent differentially expressed transcripts were those encoding ECM proteins, ECM-degrading enzymes, and enzymes involved in glycosaminoglycan (GAG) biosynthesis. We also detected differential expression of bradykinin receptor-1 and -2 transcripts, suggesting (prominent in tissue injury/remodeling) a role for the kallikrein–kinin system in cervical responses to cytokine and/or P₄ challenge. Collectively, this global gene expression study provides a rich database to interrogate stromal fibroblasts in the setting of a proinflammatory and endocrine milieu that is relevant to cervical remodeling/ripening during preparation for parturition.

Insights into glucocorticoid responses derived from omics studies

Glucocorticoid drugs are commonly used in the treatment of several conditions, including autoimmune diseases, asthma and cancer. Despite their widespread use and knowledge of biological pathways via which they act, much remains to be learned about the cell type-specific mechanisms of glucocorticoid action and the reasons why patients respond differently to them. In recent years, human and in vitro studies have addressed these questions with genomics, transcriptomics and other omics approaches. Here, we summarize key insights derived from omics studies of glucocorticoid response, and we identify existing knowledge gaps related to mechanisms of glucocorticoid action that future studies can address.

gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

g:Profiler ( https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

g:Profiler ( https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

Single-cell analyses identify dysfunctional CD16+ CD8 T cells in smokers

Tobacco smoke exposure contributes to the global burden of communicable and chronic diseases. To identify the immune cells affected by smoking, we use single-cell RNA sequencing on peripheral blood from smokers and nonsmokers. Transcriptomes reveal a subpopulation of FCGR3A (CD16)-expressing natural killer (NK)-like CD8 T lymphocytes that increase in smokers. Mass cytometry confirms elevated CD16⁺ CD8 T cells in smokers. Inferred as highly differentiated by pseudotime analysis, NK-like CD8 T cells express markers that are characteristic of effector memory re-expressing CD45RA T (T_EMRA) cells. Indicative of immune aging, smokers’ CD8 T cells are biased toward differentiated cells, and smokers have fewer naive cells than nonsmokers. DNA methylation-based models show that smoking dose is associated with accelerated aging and decreased telomere length, a biomarker of T cell senescence. Immune aging accompanies T cell senescence, which can ultimately lead to impaired immune function. This suggests a role for smoking-induced, senescence-associated immune dysregulation in smoking-mediated pathologies.

Smoking shifts the composition of CD8 T cells from naive to differentiated states

NK-like CD16⁺ CD8 T_EMRA cells are elevated in smokers and express GZMB and PRF1

DNA methylation links smoking dose with age acceleration and shortened telomeres

CD8 T, CD4 T, NKT, NK, and monocytes express senescence-linked genes in smokers

Important lessons learned from studies on the pharmacology of glucocorticoids in human airway smooth muscle cells: Too much of a good thing may be a problem

Glucocorticoids (GCs) are the treatment of choice for chronic inflammatory diseases such as asthma. Despite proven effective anti-inflammatory and immunosuppressive effects, long-term and/or systemic use of GCs can potentially induce adverse effects. Strikingly, some recent experimental evidence suggests that GCs may even exacerbate some disease outcomes. In asthma, airway smooth muscle (ASM) cells are among the targets of GC therapy and have emerged as key contributors not only to bronchoconstriction, but also to airway inflammation and remodeling, as implied by experimental and clinical evidence. We here will review the beneficial effects of GCs on ASM cells, emphasizing the differential nature of GC effects on pro-inflammatory genes and on other features associated with asthma pathogenesis. We will also summarize evidence describing how GCs can potentially promote pro-inflammatory and remodeling features in asthma with a specific focus on ASM cells. Finally, some of the possible solutions to overcome these unanticipated effects of GCs will be discussed.

Pharmacogenetics of Pediatric Asthma: Current Perspectives

Asthma is a chronic respiratory disease that affects 339 million people worldwide and has a considerable impact on the pediatric population. Asthma symptoms can be controlled by pharmacological treatment. However, some patients do not respond to therapy and continue suffering from symptoms, which impair the quality of life of patients and limit their daily activity. Genetic variation has been shown to have a role in treatment response. The aim of this review is to update the main findings described in pharmacogenetic studies of pediatric asthma published from January 1, 2018 to December 31, 2019. During this period, the response to short-acting beta-agonists and inhaled corticosteroids in childhood asthma has been evaluated by eleven candidate-gene studies, one meta-analysis of a candidate gene, and six pharmacogenomic studies. The findings have allowed validating the association of genes previously related to asthma treatment response (ADRB2, GSDMB, FCER2, VEGFA, SPAT2SL, ASB3, and COL2A1), and identifying novel associations (PRKG1, DNAH5, IL1RL1, CRISPLD2, MMP9, APOBEC3B-APOBEC3C, EDDM3B, and BBS9). However, some results are not consistent across studies, highlighting the need to conduct larger studies in diverse populations with more homogeneous definitions of treatment response. Once stronger evidence was established, genetic variants will have the potential to be applied in clinical practice as biomarkers of treatment response enhancing asthma management and improving the quality of life of asthma patients.

RASflow: an RNA-Seq analysis workflow with Snakemake

BACKGROUND

With the cost of DNA sequencing decreasing, increasing amounts of RNA-Seq data are being generated giving novel insight into gene expression and regulation. Prior to analysis of gene expression, the RNA-Seq data has to be processed through a number of steps resulting in a quantification of expression of each gene/transcript in each of the analyzed samples. A number of workflows are available to help researchers perform these steps on their own data, or on public data to take advantage of novel software or reference data in data re-analysis. However, many of the existing workflows are limited to specific types of studies. We therefore aimed to develop a maximally general workflow, applicable to a wide range of data and analysis approaches and at the same time support research on both model and non-model organisms. Furthermore, we aimed to make the workflow usable also for users with limited programming skills.

RESULTS

Utilizing the workflow management system Snakemake and the package management system Conda, we have developed a modular, flexible and user-friendly RNA-Seq analysis workflow: RNA-Seq Analysis Snakemake Workflow (RASflow). Utilizing Snakemake and Conda alleviates challenges with library dependencies and version conflicts and also supports reproducibility. To be applicable for a wide variety of applications, RASflow supports the mapping of reads to both genomic and transcriptomic assemblies. RASflow has a broad range of potential users: it can be applied by researchers interested in any organism and since it requires no programming skills, it can be used by researchers with different backgrounds. The source code of RASflow is available on GitHub: https://github.com/zhxiaokang/RASflow.

CONCLUSIONS

RASflow is a simple and reliable RNA-Seq analysis workflow covering many use cases.

Facilitating Analysis of Publicly Available ChIP-Seq Data for Integrative Studies

ChIP-Seq, a technique that allows for quantification of DNA sequences bound by transcription factors or histones, has been widely used to characterize genome-wide DNA-protein binding at baseline and induced by specific exposures. Integrating results of multiple ChIP-Seq datasets is a convenient approach to identify robust DNA- protein binding sites and determine their cell-type specificity. We developed brocade, a computational pipeline for reproducible analysis of publicly available ChIP-Seq data that creates R markdown reports containing information on datasets downloaded, quality control metrics, and differential binding results. Glucocorticoids are commonly used anti-inflammatory drugs with tissue-specific effects that are not fully understood. We demonstrate the utility of brocade via the analysis of five ChIP-Seq datasets involving glucocorticoid receptor (GR), a transcription factor that mediates glucocorticoid response, to identify cell type-specific and shared GR binding sites across the five cell types. Our results show that brocade facilitates analysis of individual ChIP-Seq datasets and comparative studies involving multiple datasets.

Airway Smooth Muscle-Specific Transcriptomic Signatures of Glucocorticoid Exposure

Glucocorticoids, commonly used asthma controller medications, decrease symptoms in most patients, but some remain symptomatic despite high-dose treatment. The physiological basis underlying the glucocorticoid response, especially in asthma patients with severe, refractory disease, is not fully understood. We sought to identify differences between the transcriptomic response of airway smooth muscle (ASM) cells derived from donors with fatal asthma and donors without asthma to glucocorticoid exposure and to compare ASM-specific changes with those observed in other cell types. In cells derived from nine donors with fatal asthma and eight donors without asthma, RNA sequencing was used to measure ASM transcriptome changes after exposure to budesonide (100 nM 24 h) or control vehicle (DMSO). Differential expression results were obtained for this dataset, as well as 13 publicly available glucocorticoid-response transcriptomic datasets corresponding to seven cell types. Specific genes were differentially expressed in response to glucocorticoid exposure (7,835 and 6,957 in ASM cells derived from donors with fatal asthma and donors without asthma, respectively; adjusted P value < 0.05). Transcriptomic changes in response to glucocorticoid exposure were similar in ASM derived from donors with fatal asthma and donors without asthma, with enriched ontological pathways that included cytokine- and chemokine-related categories. A comparison of glucocorticoid-induced changes in the nonasthma ASM transcriptome with those observed in six other cell types showed that ASM has a distinct glucocorticoid-response signature that is also present in ASM cells from donors with fatal asthma.

Expression of SMARCD1 interacts with age in association with asthma control on inhaled corticosteroid therapy

Background

Global gene expression levels are known to be highly dependent upon gross demographic features including age, yet identification of age-related genomic indicators has yet to be comprehensively undertaken in a disease and treatment-specific context.

Methods

We used gene expression data from CD4+ lymphocytes in the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), an open-access collection of subjects participating in genetic studies of asthma with available gene expression data. Replication population participants were Puerto Rico islanders recruited as part of the ongoing Genes environments & Admixture in Latino Americans (GALA II), who provided nasal brushings for transcript sequencing. The main outcome measure was chronic asthma control as derived by questionnaires. Genomic associations were performed using regression of chronic asthma control score on gene expression with age in years as a covariate, including a multiplicative interaction term for gene expression times age.

Results

The SMARCD1 gene (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily D member 1) interacted with age to influence chronic asthma control on inhaled corticosteroids, with a doubling of expression leading to an increase of 1.3 units of chronic asthma control per year (95% CI [0.86, 1.74], p = 6 × 10^− 9), suggesting worsening asthma control with increasing age. This result replicated in GALA II (p = 3.8 × 10^− 8). Cellular assays confirmed the role of SMARCD1 in glucocorticoid response in airway epithelial cells.

Conclusion

Focusing on age-dependent factors may help identify novel indicators of asthma medication response. Age appears to modulate the effect of SMARCD1 on asthma control with inhaled corticosteroids.