The candidate gene approach in asthma: what happens with the neighbours?

Single-cell profiles reveal distinctive immune response in atopic dermatitis in contrast to psoriasis

BACKGROUND

Understanding the complex orchestrated inflammation in atopic dermatitis (AD), one of the most common chronic inflammatory diseases worldwide, is essential for therapeutic approaches. However, a comparative analysis on the single-cell level of the inflammation signatures correlated with the severity is missing so far.

METHODS

We applied single-cell RNA and T-cell receptor (TCR) sequencing on immune cells enriched from skin biopsies and matched blood samples of AD in comparison with psoriasis (PS) patients.

RESULTS

Clonally propagated skin-derived T cells showed disease-specific TCR motifs shared between patients which was more pronounced in PS compared to AD. The disease-specific T-cell clusters were mostly of a Th2/Th22 sub-population in AD and Th17/Tc17 in PS, and their numbers were associated with severity scores in both diseases. Herein, we provide for the first time a list that associates cell type-specific gene expression with the severity of the two most common chronic inflammatory skin diseases. Investigating the cell signatures in the patients´ PBMCs and skin stromal cells, a systemic involvement of type-3 inflammation was clearly detectable in PS circulating cells, while in AD inflammatory signatures were most pronounced in fibroblasts, pericytes, and keratinocytes. Compositional and functional analyses of myeloid cells revealed the activation of antiviral responses in macrophages in association with disease severity in both diseases.

CONCLUSION

Different disease-driving cell types and subtypes which contribute to the hallmarks of type-2 and type-3 inflammatory signatures and are associated with disease activities could be identified by single-cell RNA-seq and TCR-seq in AD and PS.

Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative

The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.

Disentangling the effects of traits with shared clustered genetic predictors using multivariable Mendelian randomization

When genetic variants in a gene cluster are associated with a disease outcome, the causal pathway from the variants to the outcome can be difficult to disentangle. For example, the chemokine receptor gene cluster contains genetic variants associated with various cytokines. Associations between variants in this cluster and stroke risk may be driven by any of these cytokines. Multivariable Mendelian randomization is an extension of standard univariable Mendelian randomization to estimate the direct effects of related exposures with shared genetic predictors. However, when genetic variants are clustered, due to being located in a single genetic region, a Goldilocks dilemma arises: including too many highly-correlated variants in the analysis can lead to ill-conditioning, but pruning variants too aggressively can lead to imprecise estimates or even lack of identification. We propose multivariable methods that use principal component analysis to reduce many correlated genetic variants into a smaller number of orthogonal components that are used as instrumental variables. We show in simulations that these methods result in more precise estimates that are less sensitive to numerical instability due to both strong correlations and small changes in the input data. We apply the methods to demonstrate the most likely causal risk factor for stroke at the chemokine gene cluster is monocyte chemoattractant protein-1.

The central role of IL-33/IL-1RL1 pathway in asthma: From pathogenesis to intervention

Interleukin-33 (IL-33), a member of the IL-1 family, and its cognate receptor, Interleukin-1 receptor like-1 (IL-1RL1 or ST2), are susceptibility genes for childhood asthma. In response to cellular damage, IL-33 is released from barrier tissues as an 'alarmin' to activate the innate immune response. IL-33 drives type 2 responses by inducing signalling through its receptor IL-1RL1 in several immune and structural cells, thereby leading to type 2 cytokine and chemokine production. IL-1RL1 gene transcript encodes different isoforms generated through alternative splicing. Its soluble isoform, IL-1RL1-a or sST2, acts as a decoy receptor by sequestering IL-33, thereby inhibiting IL1RL1-b/IL-33 signalling. IL-33 and its receptor IL-1RL1 are therefore considered as putative biomarkers or targets for pharmacological intervention in asthma. This review will provide an overview of the genetics and biology of the IL-33/IL-1RL1 pathway in the context of asthma pathogenesis. It will discuss the potential and complexities of targeting the cytokine or its receptor, how genetics or biomarkers may inform precision medicine for asthma targeting this pathway, and the possible positioning of therapeutics targeting IL-33 or its receptor in the expanding landscape of novel biologicals applied in asthma management.

Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering

Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-regulated genes that covary in all of the samples or in only a subset of the samples. Our biclustering method, BicMix, allows overcomplete representations of the data, computational tractability, and joint modeling of unknown confounders and biological signals. Compared with related biclustering methods, BicMix recovers latent structure with higher precision across diverse simulation scenarios as compared to state-of-the-art biclustering methods. Further, we develop a principled method to recover context specific gene co-expression networks from the estimated sparse biclustering matrices. We apply BicMix to breast cancer gene expression data and to gene expression data from a cardiovascular study cohort, and we recover gene co-expression networks that are differential across ER+ and ER- samples and across male and female samples. We apply BicMix to the Genotype-Tissue Expression (GTEx) pilot data, and we find tissue specific gene networks. We validate these findings by using our tissue specific networks to identify trans-eQTLs specific to one of four primary tissues.

Decoding asthma: translating genetic variation in IL33 and IL1RL1 into disease pathophysiology

Asthma is a complex disease that results from the interaction between genetic predisposition and environmental factors. Recently, genome-wide association studies have identified a number of genes that significantly contribute to asthma. Two of these genes, IL33 and IL-1 receptor-like 1 (IL1RL1), act in one signal transduction pathway. IL33 encodes a cytokine released on damage of cells, whereas IL1RL1 encodes part of the IL-33 receptor complex. Recent progress made in functional studies in human subjects and mouse models of allergic airway disease indicate a central role of IL-33 signaling in driving TH2 inflammation, which is central to eosinophilic allergic asthma. Here, IL-33 acts on cells of both the adaptive and innate immune systems. Very recently, a novel population of IL-33-responsive innate immune cells, the type 2 innate lymphoid cells, was found to produce hallmark TH2 cytokines, such as IL-5 and IL-13. The relevance of these cells for asthma is underscored by the identification of retinoic acid-related orphan receptor α(RORA), the gene encoding the transcription factor critical for their differentiation, as another asthma gene in genome-wide association studies. This review describes the mechanisms through which genetic variation at the IL33 and IL1RL1 loci translates into increased susceptibility for asthma. We propose that genetic variation associated with asthma at the IL33 and IL1RL1 loci can be dissected into independent signals with distinct functional consequences for this pathway that is central to asthma pathogenesis.

Common SNPs/haplotypes in IL18R1 and IL18 genes are associated with variations in humoral immunity to smallpox vaccination in Caucasians and African Americans

BACKGROUND

Identifying genetic factors that influence poxvirus immunity across races may assist in the development of better vaccines and approaches for vaccine development.

METHODS

We performed an extensive candidate-gene genetic screen (across 32 cytokine and cytokine receptor genes) in a racially diverse cohort of 1056 healthy adults after a single dose of smallpox vaccine. Associations between single-nucleotide polymorphisms (SNPs)/haplotypes and vaccinia virus-specific neutralizing antibodies were assessed using linear regression methodologies.

RESULTS

The combined analysis identified 63 associations between candidate SNPs and antibody levels after smallpox vaccination with P < .05. Thirty-one of these were within the IL18R1 and IL18 genes. Five IL18R1 SNPs, including a coding synonymous polymorphism rs1035130 (Phe251Phe) and 2 promoter SNPs (rs6710885, rs2287037), all in linkage disequilibrium, were associated with significant variations in antibody levels in both Caucasians (P ≤ .016) and African Americans (P ≤ .025). Similarly, associations with 2 intronic IL18 SNPs (rs2043055 and rs5744280) were consistent in the Caucasian (P ≤ .023) and African American samples (P ≤ .014). Haplotype analysis revealed highly significant associations between IL18R1 haplotypes and vaccinia virus-specific antibody levels (P < .001, by combined analysis) that were consistent across races.

CONCLUSIONS

Our study provides evidence for IL18 and IL18R1 genes as plausible genes regulating the humoral immune response to smallpox vaccine in both Caucasians and African Americans.

Interleukin-1 receptor-like 1 polymorphisms are associated with serum IL1RL1-a, eosinophils, and asthma in childhood

BACKGROUND

IL-1 receptor-like 1 (IL1RL1) is a membrane receptor involved in T(H)2 inflammatory responses and eosinophilia. Single nucleotide polymorphisms (SNPs) in IL1RL1 have been associated with blood eosinophil counts in a genome-wide association study and with asthma in family-based and case-control studies.

OBJECTIVE

We assessed in the prospective birth cohort Prevention and Incidence of Asthma and Mite Allergy (PIAMA) whether IL1RL1 SNPs associate with levels of its soluble transcript IL1RL1 (IL1RL1-a) in serum, blood eosinophil counts, and asthma prevalence from birth to age 8 years, and whether IL1RL1-a serum levels associate with blood eosinophil counts.

METHODS

Fifteen IL1RL1 SNPs were genotyped. Serum IL1RL1-a levels were measured in 2 independent subsets within PIAMA, at 4 and 8 years. Blood eosinophil counts were measured in 4-year-old children.

RESULTS

In 2 independent subsets of children, 13 of 15 SNPs were associated with serum IL1RL1-a levels at ages 4 and 8 years with a consistent direction of effect for each allele. Rs11685480 allele A and rs1420102 allele A were significantly associated with lower numbers of blood eosinophils. In the total cohort, rs1041973 allele A was associated with a decreased risk of developing asthma (odds ratio, 0.70; 95% CI, 0.54-0.90). Rs1420101, recently identified in a genome-wide association study in the Icelandic population, was not associated with asthma in this study. IL1RL1-a levels were not associated with eosinophil counts.

CONCLUSION

We demonstrate that IL1RL1 polymorphisms are associated with serum IL1RL1-a, blood eosinophils, and asthma in childhood.