Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets

Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity. We found that 39 cell types across eight kinds of organoids were significantly associated with COVID-19 outcomes. Notably, subset of lung mesenchymal stem cells increased proximity with fibroblasts predisposed to repair COVID-19-damaged lung tissue. Brain endothelial cell subset exhibited significant associations with severe COVID-19, and this cell subset showed a notable increase in cell-to-cell interactions with other brain cell types, including microglia. We repurposed 33 druggable genes, including IFNAR2, TYK2, and VIPR2, and their interacting drugs for COVID-19 in a cell-type-specific manner. Overall, our results showcase that host genetic determinants have cellular-specific contribution to COVID-19 severity, and identification of cell type-specific drug targets may facilitate to develop effective therapeutics for treating severe COVID-19 and its complications.

Mild asthma is not mild: risk factors and predictive biomarkers for severe acute exacerbations and progression in mild asthma

INTRODUCTION

Asthma is a common chronic respiratory disease characterized by chronic airway inflammation, airway hyperresponsiveness, reversible airflow limitation, and airway remodeling. Mild asthma is the most common type of asthma, but it is the most neglected. Sometimes mild asthma can lead to acute severe exacerbations or even death.

AREAS COVERED

This article reviews the epidemiology, risk factors, and possible predictors of acute severe exacerbations and disease progression in mild asthma to improve the understanding of mild asthma and its severe acute exacerbations and progression.

EXPERT OPINION

There is a necessity to improve asthma patient categorization and redefine mild asthma's concept to heighten patient and physician attention. Identifying mild asthma patients that are highly vulnerable to severe acute exacerbations and researching the mechanisms are future prioritizations.

Brain transcriptome-wide association study implicates novel risk genes underlying schizophrenia risk

BACKGROUND

To identify risk genes whose expression are regulated by the reported risk variants and to explore the potential regulatory mechanism in schizophrenia (SCZ).

METHODS

We systematically integrated three independent brain expression quantitative traits (eQTLs) (CommonMind, GTEx, and BrainSeq Phase 2, a total of 1039 individuals) and GWAS data (56 418 cases and 78 818 controls), with the use of transcriptome-wide association study (TWAS). Diffusion magnetic resonance imaging was utilized to quantify the integrity of white matter bundles and determine whether polygenic risk of novel genes linked to brain structure was present in patients with first-episode antipsychotic SCZ.

RESULTS

TWAS showed that eight risk genes (CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, PCDHA8, THOC7, and TYW5) reached transcriptome-wide significance (TWS) level. These findings were confirmed by an independent integrative approach (i.e. Sherlock). We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Gene expression analysis showed that several TWS genes (including CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, THOC7 and TYW5) were dysregulated in the dorsolateral prefrontal cortex of SCZ cases compared with controls. TWS genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and microglia. Finally, SCZ cases had a substantially greater TWS genes-based polygenic risk (PRS) compared to controls, and we showed that fractional anisotropy of the cingulum-hippocampus mediates the influence of TWS genes PRS on SCZ.

CONCLUSIONS

Our findings identified novel SCZ risk genes and highlighted the importance of the TWS genes in frontal-limbic dysfunctions in SCZ, indicating possible therapeutic targets.

Identification of MKNK1 and TOP3A as ovarian endometriosis risk-associated genes using integrative genomic analyses and functional experiments

The risk of endometriosis (EM), which is a common complex gynaecological disease, is related to genetic predisposition. However, it is unclear how genetic variants confer the risk of EM. Here, via Sherlock integrative analysis, we combined large-scale genome-wide association studies (GWAS) summary statistics on EM (N = 245,494) with a blood-based eQTL dataset (N = 1490) to identify EM risk-related genes. For validation, we leveraged two independent eQTL datasets (N = 769) for integration with the GWAS data. Thus, we prioritised 14 genes, including GIMAP4, TOP3A, and NMNAT3, which showed significant association with susceptibility to EM. We also utilised two independent methods, Multi-marker Analysis of GenoMic Annotation and S-PrediXcan, to further validate the EM risk-associated genes. Moreover, protein-protein interaction network analysis showed the 14 genes were functionally connected. Functional enrichment analyses further demonstrated that these genes were significantly enriched in metabolic and immune-related pathways. Differential gene expression analysis showed that in peripheral blood samples from patients with ovarian EM, TOP3A, MKNK1, SIPA1L2, and NUCB1 were significantly upregulated, while HOXB2, GIMAP5, and MGMT were significantly downregulated compared with their expression levels in samples from the controls. Immunohistochemistry further confirmed the increased expression levels of MKNK1 and TOP3A in the ectopic and eutopic endometrium compared to normal endometrium, while HOBX2 was downregulated in the endometrium of women with ovarian EM. Finally, in ex vivo functional experiments, MKNK1 knockdown inhibited ectopic endometrial stromal cells (EESCs) migration and invasion. TOP3A knockdown inhibited EESCs proliferation, migration, and invasion, while promoting their apoptosis. Convergent lines of evidence suggested that MKNK1 and TOP3A are novel EM risk-related genes.

COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data

Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from limited statistical power and in general, only a few genes can pass the conventional significance thresholds. This statistical limitation may be overcome by boosting weak association signals through integrating independent functional information such as molecular interactions. Additionally, the boosted results can be evaluated by various independent data for further connections to COVID-19. We present COVID-GWAB, a web-based tool to boost original GWAS signals from COVID-19 patients by taking the signals of the interactome neighbors. COVID-GWAB takes summary statistics from the COVID-19 HGI or user input data and reprioritizes candidate host genes for COVID-19 using HumanNet, a co-functional human gene network. The current version of COVID-GWAB provides the pre-processed data of releases 5, 6, and 7 of the HGI. Additionally, COVID-GWAB provides web interfaces for a summary of augmented GWAS signals, prediction evaluations by appearance frequency in COVID-19 literature, single-cell transcriptome data, and associated pathways. The web server also enables browsing the candidate gene networks.

[Markers of severity and predictors of response to treatment in severe asthma]

Asthma is a multifactorial disease with complex pathophysiology. Knowledge of its immunopathology and inflammatory mechanisms is progressing and has led to the development over recent years of increasingly targeted therapeutic strategies. The objective of this review is to pinpoint the different predictive markers of asthma severity and therapeutic response. Obesity, nasal polyposis, gastroesophageal reflux disease and intolerance to aspirin have all been considered as clinical markers associated with asthma severity, as have functional markers such as bronchial obstruction, low FEV1, small daily variations in FEV1, and high FeNO. While sinonasal polyposis and allergic comorbidities are associated with better response to omalizumab, nasal polyposis or long-term systemic steroid use are associated with better response to antibodies targeting the IL5 pathway. Elevated total IgE concentrations and eosinophil counts are classic biological markers regularly found in severe asthma. Blood eosinophils are predictive biomarkers of response to anti-IgE, anti-IL5, anti-IL5R and anti-IL4R biotherapies. Dupilumab is particularly effective in a subgroup of patients with marked type 2 inflammation (long-term systemic corticosteroid therapy, eosinophilia≥150/μl or FENO>20 ppb). Chest imaging may help to identify severe patients by seeking out bronchial wall thickening and bronchial dilation. Study of the patient's environment is crucial insofar as exposure to tobacco, dust mites and molds, as well as outdoor and indoor air pollutants (cleaning products), can trigger asthma exacerbation. Wider and more systematic use of markers of severity or response to treatment could foster increasingly targeted and tailored approaches to severe asthma.

The priming effect of diesel exhaust on native pollen exposure at the air-liquid interface

Pollen related allergic diseases have been increasing for decades. The reasons for this increase are unknown, but environmental pollution like diesel exhaust seem to play a role. While previous studies explored the effects of pollen extracts, we studied here for the first time priming effects of diesel exhaust on native pollen exposure using a novel experimental setup.

METHODS

Human bronchial epithelial BEAS-2B cells were exposed to native birch pollen (real life intact pollen, not pollen extracts) at the air-liquid interface (pollen-ALI). BEAS-2B cells were also pre-exposed in a diesel-ALI to diesel CAST for 2 h (a model for diesel exhaust) and then to pollen in the pollen-ALI 24 h later. Effects were analysed by genome wide transcriptome analysis after 2 h 25 min, 6 h 50 min and 24 h. Selected genes were confirmed by qRT-PCR.

RESULTS

Bronchial epithelial cells exposed to native pollen showed the highest transcriptomic changes after about 24 h. About 3157 genes were significantly up- or down-regulated for all time points combined. After pre-exposure to diesel exhaust the maximum reaction to pollen had shifted to about 2.5 h after exposure, plus the reaction to pollen was desensitised as only 560 genes were differentially regulated. Only 97 genes were affected synergistically. Of these, enrichment analysis showed that genes involved in immune and inflammatory response were involved.

CONCLUSION

Diesel exhaust seems to prime cells to react more rapidly to native pollen exposure, especially inflammation related genes, a factor known to facilitate the development of allergic sensitization. The marker genes here detected could guide studies in humans when investigating whether modern and outdoor diesel exhaust exposure is still detrimental for the development of allergic disease.

Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data

Recently, gene-based association studies have shown that integrating genome-wide association studies (GWAS) with expression quantitative trait locus (eQTL) data can boost statistical power and that the genetic liability of traits can be captured by polygenic risk scores (PRSs). In this paper, we propose a new gene-based statistical method that leverages gene-expression measurements and new PRSs to identify genes that are associated with phenotypes of interest. We used a generalized linear model to associate phenotypes with gene expression and PRSs and used a score-test statistic to test the association between phenotypes and genes. Our simulation studies show that the newly developed method has correct type I error rates and can boost statistical power compared with other methods that use either gene expression or PRS in association tests. A real data analysis figure based on UK Biobank data for asthma shows that the proposed method is applicable to GWAS.

Epigenetic regulation of immune function in asthma

Asthma is a common complex respiratory disease characterized by chronic airway inflammation and partially reversible airflow obstruction resulting from genetic and environmental determinants. Because epigenetic marks influence gene expression and can be modified by both environmental exposures and genetic variation, they are increasingly recognized as relevant to the pathogenesis of asthma and may be a key link between environmental exposures and asthma susceptibility. Unlike changes to DNA sequence, epigenetic signatures are dynamic and reversible, creating an opportunity for not only therapeutic targets but may serve as biomarkers to follow disease course and identify molecular subtypes in heterogeneous diseases such as asthma. In this review, we will examine the relationship between asthma and 3 key epigenetic processes that modify gene expression: DNA methylation, modification of histone tails, and noncoding RNAs. In addition to presenting a comprehensive assessment of the existing epigenetic studies focusing on immune regulation in asthma, we will discuss future directions for epigenetic investigation in allergic airway disease.

Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene

BACKGROUND

Identifying the causal genes at the risk loci and elucidating their roles in schizophrenia (SCZ) pathogenesis remain significant challenges. To explore risk variants associated with gene expression in the human brain and to identify genes whose expression change may contribute to the susceptibility of SCZ, here we report a comprehensive integrative study on SCZ.

METHODS

We systematically integrated the genetic associations from a large-scale SCZ GWAS (N = 56,418) and brain expression quantitative trait loci (eQTL) data (N = 175) using a Bayesian statistical framework (Sherlock) and Summary data-based Mendelian Randomization (SMR). We also measured brain structure of 86 first-episode antipsychotic-naive schizophrenia patients and 152 healthy controls with the structural MRI.

RESULTS

Both Sherlock (P = 3. 38 × 10^-6) and SMR (P = 1. 90 × 10^-8) analyses showed that TYW5 mRNA expression was significantly associated with risk of SCZ. Brain-based studies also identified a significant association between TYW5 protein abundance and SCZ. The single-nucleotide polymorphism rs203772 showed significant association with SCZ and the risk allele is associated with higher transcriptional level of TYW5 in the prefrontal cortex. We further found that TYW5 was significantly upregulated in the brain tissues of SCZ cases compared with controls. In addition, TYW5 expression was also significantly higher in neurons induced from pluripotent stem cells of schizophrenia cases compared with controls. Finally, combining analysis of genotyping and MRI data showed that rs203772 was significantly associated with gray matter volume of the right middle frontal gyrus and left precuneus.

CONCLUSIONS

We confirmed that TYW5 is a risk gene for SCZ. Our results provide useful information toward a better understanding of the genetic mechanism of TYW5 in risk of SCZ.

Translational Analysis of Moderate to Severe Asthma GWAS Signals Into Candidate Causal Genes and Their Functional, Tissue-Dependent and Disease-Related Associations

Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including MUC5AC and STAT6. Gene expression analysis through publicly available datasets highlighted lung tissue cell specific expression, with both MUC5AC and SLC22A4 genes showing enriched expression in ciliated cells. Gene enrichment pathway and interaction analysis highlighted the dominance of the HLA-DQA1/A2/B1/B2 gene cluster across many immunological diseases including asthma, type I diabetes, and rheumatoid arthritis. Interaction and prediction analyses found IL33 and IL18R1 to be key co-localization partners for other genes, predicted that CD274 forms co-expression relationships with 13 other genes, including the HLA-DQA1/A2/B1/B2 gene cluster and that MUC5AC and IL37 are co-expressed. Drug interaction analysis revealed that 11 of the candidate genes have an interaction with available therapeutics. This study provides significant insight into these GWAS signals in the context of cell expression, function, and disease relationship with the view of informing future research and drug development efforts for moderate-severe asthma.

Integrating single-cell sequencing data with GWAS summary statistics reveals CD16+monocytes and memory CD8+T cells involved in severe COVID-19

Background

Understanding the host genetic architecture and viral immunity contributes to the development of effective vaccines and therapeutics for controlling the COVID-19 pandemic. Alterations of immune responses in peripheral blood mononuclear cells play a crucial role in the detrimental progression of COVID-19. However, the effects of host genetic factors on immune responses for severe COVID-19 remain largely unknown.

Methods

We constructed a computational framework to characterize the host genetics that influence immune cell subpopulations for severe COVID-19 by integrating GWAS summary statistics (N = 969,689 samples) with four independent scRNA-seq datasets containing healthy controls and patients with mild, moderate, and severe symptom (N = 606,534 cells). We collected 10 predefined gene sets including inflammatory and cytokine genes to calculate cell state score for evaluating the immunological features of individual immune cells.

Results

We found that 34 risk genes were significantly associated with severe COVID-19, and the number of highly expressed genes increased with the severity of COVID-19. Three cell subtypes that are CD16+monocytes, megakaryocytes, and memory CD8+T cells were significantly enriched by COVID-19-related genetic association signals. Notably, three causal risk genes of CCR1, CXCR6, and ABO were highly expressed in these three cell types, respectively. CCR1⁺CD16+monocytes and ABO⁺ megakaryocytes with significantly up-regulated genes, including S100A12, S100A8, S100A9, and IFITM1, confer higher risk to the dysregulated immune response among severe patients. CXCR6⁺ memory CD8+ T cells exhibit a notable polyfunctionality including elevation of proliferation, migration, and chemotaxis. Moreover, we observed an increase in cell-cell interactions of both CCR1⁺ CD16+monocytes and CXCR6⁺ memory CD8+T cells in severe patients compared to normal controls among both PBMCs and lung tissues. The enhanced interactions of CXCR6⁺ memory CD8+T cells with epithelial cells facilitate the recruitment of this specific population of T cells to airways, promoting CD8+T cell-mediated immunity against COVID-19 infection.

Conclusions

We uncover a major genetics-modulated immunological shift between mild and severe infection, including an elevated expression of genetics-risk genes, increase in inflammatory cytokines, and of functional immune cell subsets aggravating disease severity, which provides novel insights into parsing the host genetic determinants that influence peripheral immune cells in severe COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01021-1.

Single cell sequencing analysis identifies genetics-modulated ORMDL3+ cholangiocytes having higher metabolic effects on primary biliary cholangitis

BACKGROUND

Primary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear.

RESULTS

We constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P < 0.05). The risk gene of ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). The ORMDL3⁺ cholangiocytes have prominently higher metabolism activity score than ORMDL3^- cholangiocytes (P = 1.38 × 10^-15). Compared with ORMDL3^- cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3⁺ cholangiocytes (FDR < 0.05), and these significant genes were associated with autoimmune diseases-related functional terms or pathways. The ORMDL3⁺ cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3^- cholangiocytes, the VEGF signaling pathway is specific for ORMDL3⁺ cholangiocytes to interact with other cell populations.

CONCLUSIONS

To the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3⁺ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC.

Integrative genomics analysis reveals a 21q22.11 locus contributing risk to COVID-19

The systematic identification of host genetic risk factors is essential for the understanding and treatment of coronavirus disease 2019 (COVID-19). By performing a meta-analysis of two independent genome-wide association summary datasets (N = 680 128), a novel locus at 21q22.11 was identified to be associated with COVID-19 infection (rs9976829 in IFNAR2-IL10RB, odds ratio = 1.16, 95% confidence interval = 1.09-1.23, P = 2.57 × 10-6). The rs9976829 represents a strong splicing quantitative trait locus for both IFNAR2 and IL10RB genes, especially in lung tissue (P = 1.8 × 10-24). Integrative genomics analysis of combining genome-wide association study with expression quantitative trait locus data showed the expression variations of IFNAR2 and IL10RB have prominent effects on COVID-19 in various types of tissues, especially in lung tissue. The majority of IFNAR2-expressing cells were dendritic cells (40%) and plasmacytoid dendritic cells (38.5%), and IL10RB-expressing cells were mainly nonclassical monocytes (29.6%). IFNAR2 and IL10RB are targeted by several interferons-related drugs. Together, our results uncover 21q22.11 as a novel susceptibility locus for COVID-19, in which individuals with G alleles of rs9976829 have a higher probability of COVID-19 susceptibility than those with non-G alleles.

Machine learning in asthma research: moving toward a more integrated approach

Introduction: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favored the adoption of modern analytical and computational methodologies in every area of medicine. Over the past decades, asthma research has been characterized by a shift in the way studies are conducted and data are analyzed. Motivated by the assumptions that 'data will speak for themselves', hypothesis-driven approaches have been replaced by data-driven hypotheses-generating methods to explore hidden patterns and underlying mechanisms. However, even with all the advancement in technologies and the new important insight that we gained to understand and characterize asthma heterogeneity, very few research findings have been translated into clinically actionable solutions.Areas covered: To investigate some of the fundamental analytical approaches adopted in the current literature and appraise their impact and usefulness in medicine, we conducted a bibliometric analysis of big data analytics in asthma research in the past 50 years.Expert opinion: No single data source or methodology can uncover the complexity of human health and disease. To fully capitalize on the potential of 'big data', we will have to embrace the collaborative science and encourage the creation of integrated cross-disciplinary teams brought together around technological advances.