key: cord-0927889-fobt3nti
authors: Jackson, Daniel J.; Busse, William W.; Bacharier, Leonard B.; Kattan, Meyer; O’Connor, George T.; Wood, Robert A.; Visness, Cynthia M.; Durham, Stephen R.; Larson, David; Esnault, Stephane; Ober, Carole; Gergen, Peter J.; Becker, Patrice; Togias, Alkis; Gern, James E.; Altman, Mathew C.
title: Association of Respiratory Allergy, Asthma and Expression of the SARS-CoV-2 Receptor, ACE2
date: 2020-04-22
journal: J Allergy Clin Immunol
DOI: 10.1016/j.jaci.2020.04.009
sha: b78283e820637a13eda5a3b69c081ccd0df26903
doc_id: 927889
cord_uid: fobt3nti

Underlying respiratory allergy and experimental allergen exposure reduce the expression of the SARS-CoV-2 receptor, ACE2, which could lead to reduced COVID-19 susceptibility.

illness, which is logical given that many respiratory viruses have been well established to cause more serious illnesses in those with chronic airway diseases such as asthma.

However, asthma and respiratory allergy have not been identified as significant risk factors for severe COVID-19 illness in case series from China.(2) These preliminary reports led us to question whether we could identify features of allergy and/or asthma that could be associated with potential for reduced COVID-19 illness severity. SARS-CoV-2 uses angiotensin-converting enzyme-2 (ACE2) as its cellular receptor, as do SARS-CoV and coronavirus NL63.(1) Higher ACE2 expression increases in vitro susceptibility to SARS-CoV, (3) and studies examining factors that impact ACE2 gene expression have revealed its upregulation is associated with smoking, diabetes, and hypertension, all associated with increased COVID-19 illness severity. (4) We hypothesized that one potential explanation for the unexpected observation that asthma and other allergic diseases may not be a risk factor for severe COVID-19 disease is a reduced ACE2 gene expression in airway cells and thus decreased susceptibility to infection. To test this hypothesis, we examined whether asthma and respiratory allergy are associated with reduced ACE2 expression in airway cells from three different cohorts of children and adults. In all three studies, total RNA was extracted from nasal or lower airway epithelial brush samples with RNA-sequencing performed independently for each study as previously described and provided in detail in the online supplement.(5) Differential expression of ACE2 was assessed using a weighted linear mixed effects model (limma) appropriate for RNA-seq data and empirical Bayes method.

Children at high risk for asthma based upon parental histories and living in urban neighborhoods were enrolled prenatally and followed prospectively in the Urban Environment and Childhood Asthma (URECA) cohort and 318 had nasal epithelial brushes obtained at 11 years of age. Prevalence of asthma was assessed at 10 years of age and atopic status was defined by allergic sensitization trajectories [no/minimal, low, medium, and high] as previously described.(6) Additional type 2 biomarkers, including fractional exhaled nitric oxide (FeNO), peripheral blood eosinophils, and total IgE, were evaluated using standard methods. In URECA, allergic sensitization was inversely related to ACE2 expression in nasal epithelium regardless of asthma status ( Figure 1A ). Within children with asthma, moderate allergic sensitization (fold change (FC)=0.70, p=4.2E-3) and high allergic sensitization (FC=0.54, p=6.4E-5) were associated with progressively greater reductions in ACE2 compared to children with asthma but no/minimal allergic sensitization ( Figure 1B ). ACE2 expression was also significantly inversely associated with type 2 biomarkers (Supplementary Table 1) including the number of positive allergen-specific IgE tests (beta coefficient -0.089, p=3.1E-5), total IgE (beta coefficient -0.31, p=5.1E-6), FeNO (beta coefficient -0.45, p=3.4E-3), and nasal epithelial IL13 expression (beta coefficient -0.123, p=8.6E-5).

ACE2 expression was not significantly correlated with peripheral blood eosinophils (beta coefficient -0.13, p=0.07). Although male sex has been associated with increased COVID-19 illness severity (2) , no sex-based differences in ACE2 expression were found in URECA. Of note, 10 participants reported nasal corticosteroid use at the time of nasal sampling and it was not associated with alterations in ACE2 expression.

We also evaluated 24 adult participants with allergic rhinitis to cat, without asthma symptoms in the prior year, who were enrolled in a study where they underwent nasal cat allergen challenge (NAC) and exposure to cat allergen through an environmental exposure chamber (EEC) as previously described. An additional cohort of 23 adult participants with mild asthma, not treated with asthma controller therapy, underwent segmental allergen bronchoprovocation to dust mite, ragweed, or cat, as previously described. 2) Here, we report that respiratory allergy and controlled allergen exposures are each associated with significant reductions in ACE2 expression. ACE2 expression was lowest in those with both high levels of allergic sensitization and asthma. Importantly, non-atopic asthma was not associated with reduced ACE2 expression. Given that ACE2 serves as the receptor for SARS-CoV-2, our findings suggest a potential mechanism of reduced COVID-19 severity in patients with respiratory allergies. However, it is likely that additional factors beyond ACE2 expression modulate the response to COVID-19 in allergic individuals, and elucidation of these factors may also provide important insights into COVID-19 disease pathogenesis.

Strengths of our study include carefully phenotyped cohorts of children and adults. Further, the allergen challenge studies included both upper and lower airway samples, with each demonstrating a consistent impact on ACE2 expression. Limitations include lack of clinical information to directly link ACE2 expression to SARS-CoV-2 infection and illness severity in our study populations. In addition, we do not have data on the ACE2 protein levels to confirm the gene expression data, though previous work suggests a direct association between ACE2 mRNA levels and ACE2 protein levels in the lung. (8) It is important to note that early data in the US suggest a higher rate of asthma in patients hospitalized for severe COVID-19 illness, but do not specify whether asthma was allergic or not, an important differentiation that relates to our findings, nor the potential presence of other co-morbidities, such as obesity, that have been identified as risk factors for COVID-19 illness. In all three studies, total RNA was extracted from epithelial brush samples preserved in RLT buffer (Qiagen, MD, USA). Samples were thawed, vortexed, and quick-spun, and the supernatant transferred to fresh tubes. The samples were then spun through a Qiashredder column (Qiagen) and extracted using RNeasy mini kits (Qiagen) with 25 ul elution volumes following the manufacturer's protocol. In the cat allergy upper airway challenge study, sequencing libraries were constructed from total RNA using TruSeq RNA Sample Preparation Kits v2 (Illumina). In the URECA and adult asthma studies, sequencing libraries were constructed from total RNA using SMART-Seq v4 Ultra Low Input RNA Kit (Takara). For each study, libraries were clustered onto a flowcell using a cBOT amplification system with a HiSeq SR v4 Cluster Kit (Illumina). Single-read sequencing was carried out on a HiSeq2500 sequencer (Illumina), using a HiSeq SBS v4 Kit to generate 58-base reads, with a target of approximately 10 million reads per sample. Sample for each study was processed and sequenced independently.

Reads were processed using workflows managed on the Galaxy platform. Reads were trimmed by 1 base at the 3' end, and then trimmed from both ends until base calls had a minimum quality score of at least 30 (Galaxy FASTQ Trimmer tool v1.0.0). FastqMcf (v1.1.2) was used to remove any remaining adapter sequence. To align the trimmed reads, we used the STAR aligner with the GRCh38 reference genome and gene annotations from ensembl release 91. Gene counts were generated using HTSeq-count (v0.4.1). For quality control, samples were kept that had counts >1 million, percent of reads aligned >80% and median CV coverage <1. Genes were filtered to include those that had a trimmed mean of M values (TMM) normalization count of at least 1 in at least 10% of libraries and were classified as protein coding using BioMart (1) . Counts were transformed to log2 counts per million along with observations level weights using voomWithQualityWeights from the limma R package(2) to create a weighted gene expression matrix suitable for downstream analyses.

Differential expression of ACE2 was assessed independently in each dataset using a weighted linear mixed effects model (limma) appropriate for RNA-seq data and empirical Bayes method (2, 3) . Mixed-effects linear regression models were used including relevant clinical or technical variables (for URECA, cytologically determined cell percentages in the brush and the clinical site; for the upper airway challenge study, processing batch; for the adult asthma study no fixed effects were included) and a random effect of participant in both of the airway challenge studies. pvalues <0.05 were considered statistically significant.

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IL-13-induced airway mucus production is attenuated by MAPK13 inhibition

We searched NCBI's Gene Expression Omnibus for the terms "IL13" and "epithelial" subset to organism homo sapiens.(4) From this we identified two studies investigating the effects of IL-13 stimulation on human airway epithelial cells grown at air liquid interface that had repeated measures in the IL-13 stimulation and unstimulated groups. GSE110799 has the study design: "Human nasal epithelial cells isolated from nasal turbinates were cultured in air-liquid interface (ALI) until the full differentiation was complete. Differentiated cells at ALI-D47 were incubated with 100 ng/mL of IL-13 for 3 days." GSE37693 has the study design: "RNA was isolated from primary culture airway epithelial cells grown at air-liquid interface, treated with or without IL-13 for 21 days".(5) Differential expression analysis was performed using GEO2R, which performs voom and limma (2, 3) in the NCBI GEO browser.

Supplementary Figure 1