Abstract Background The novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly after its identification in December 2019 to cause a global pandemic. The respiratory tract is the primary site of infection, and there is a large range in the severity of respiratory illnesses caused by the virus. Defining molecular and cellular factors for protection from severe disease and death has been a goal to better understand and to predict and mitigate the effects of SARS-CoV-2 and future coronaviruses. Objective Despite well-known susceptibilities to respiratory viral infections, respiratory allergy and allergic asthma have not been identified as risk factors for severe coronavirus disease 2019 (COVID-19) in most epidemiologic studies and may be protective. We sought to investigate associations between markers of type 2 (T2) immune responses with SARS-CoV-2 clinical outcomes and virus loads in a cohort of 1164 individuals hospitalized for COVID-19 from May 2020 to March 2021 as part of the IMPACC study. Methods We characterized the clinical outcomes, as defined by severity trajectory groups reflecting the degree of respiratory support required, virus loads, and antibody titers of COVID-19 infections in IMPACC participants in relation to molecular and cellular markers of T2 immune responses through multiple assays, including, (1) IL-4, IL-5, and IL-13 levels in serum Olink data, (2) T2 cellular signatures in blood cytometry by time of flight data, (3) relative quantification of T2 signaling gene pathways in airway RNA sequencing data, and/or (4) T2 pathways in peripheral blood mononuclear cell RNA sequencing data. We also investigated the outcomes of individuals with self-reported asthma and evidence of T2 immune responses. Results The diagnosis of asthma (odd ratio = 1.27), elevated serum T2 cytokine levels (median fold change = 1.06), and a higher frequency of T[H]2 cells (difference = +2%) were associated with less severe clinical disease during hospitalization. Distinct T2-related transcriptomic changes in nasal and blood samples were associated with reduced virus loads. This included the expression of T2-regulated genes implicated in T-/B-cell activation and apoptosis in nasal samples and the expression of T2-regulated genes implicated in myeloid differentiation and reactive oxygen species signaling in blood. Among these, several canonical T2-regulated genes that were increased in less severe disease were identified to have antiviral properties in large high-throughput screens. Conclusion T2 immune responses were associated with lower virus loads and more favorable clinical outcomes, suggesting that T2 inflammation related to asthma and allergic diseases may have a direct protective effect against SARS-CoV-2. Key words: Asthma, SARS-CoV-2, IMPACC __________________________________________________________________ Throughout the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there has been marked heterogeneity in disease severity. Higher airway virus loads and deficient antiviral interferon responses have been convincingly demonstrated to drive more severe disease; however, mechanisms of innate protection from SARS-CoV-2 remain poorly understood. Asthma is one of the most common chronic conditions, with an estimated prevalence of >300 million people globally.[113]^1 There are differing reports on the vulnerability of individuals with asthma to SARS-CoV-2 infection and its severity that are based on various local or national case series and analyses. At the start of the coronavirus disease 2019 (COVID-19) pandemic, asthma and allergies were not identified as comorbidities associated with severe SARS-CoV-2 infection.[114]^2^,[115]^3 There have been several meta-analyses conducted with similar, though not identical, results. Conclusions around the risk of asthma have been limited due to differences in how asthma has been defined, the inclusion of non–PCR-confirmed illnesses, and whether atopy was a concomitant comorbidity. The latter is notable because there are studies suggesting that allergic asthma may offer protection against severe SARS-CoV-2 infection, but not necessarily all asthma variants.[116]4, [117]5, [118]6, [119]7, [120]8 A meta-analysis by Liu et al of 131 studies suggested that asthma was not associated with a more severe disease course, hospitalization, or death.[121]^9 However, there was considerable difference regarding the prevalence of asthma in these studies. A second systematic review and meta-analysis of 51 studies by Sunjaya et al focused on whether patients with asthma had a high likelihood of being infected with SARS-CoV-2 virus and whether this was associated with poorer outcomes, such as hospitalization and intensive care unit admission.[122]^9^,[123]^10 In that review, the pooled prevalence of asthma was 8.08% (95% confidence interval, 6.87-9.30) among COVID-19–positive cases across America, Europe, and Asia. While there was a reduced risk of infection among those patients with asthma, there was no significant difference in the risk of hospitalization, intensive care admission, ventilator requirement, or mortality. There was significant heterogeneity across studies, which was mostly driven by age. Subgroup analyses by continent revealed a significant difference in risk of acquiring COVID-19, intensive care unit admission, ventilator requirement, and death between the continents. Overall, the risk of being infected with SARS-CoV-2 was reduced compared to the nonasthma group, but other outcomes were similar. Respiratory allergy or allergic asthma are characterized by chronic type 2 (T2) immune responses in the airways, driven by cytokines including IL-4, IL-5, and IL-13, produced in part by airway T[H]2 or group 2 innate lymphoid cells. Hypotheses suggested for mechanisms by which T2 immune responses may be protective have include reduced transcriptomic expression of the ACE2 gene coding for SARS-CoV-2 virus entry factor[124]^7^,[125]^8^,[126]^11 and greater mucus production, but prior studies have demonstrated that these cannot fully account for either the impaired virus entry or replication in airway epithelial cells.[127]^4^,[128]^12 Recent work has shown that IL-13 directly blocks SARS-CoV-2 replication in airway epithelium culture models, in part through inducing a relative dedifferentiation of ciliated epithelial cells required for efficient virus replication.[129]^5 We sought to determine whether T2 immune responses are associated with virus levels during acute COVID-19 disease and/or with protection from severe COVID-19; and to understand molecular mechanisms that may link T2 immune responses to virus levels and disease severity. We utilized the National Institute of Allergy and Infectious Diseases–National Institutes of Health prospective longitudinal cohort study of hospitalized COVID-19 patients in the United States, IMPACC (Immunophenotyping Assessment in a COVID-19 Cohort; [130]NCT04378777).[131]13, [132]14, [133]15, [134]16 IMPACC prospectively collected clinical and laboratory data along with a longitudinal biologic sampling of blood and respiratory secretions for in-depth immunologic and virologic testing with 1 year’s follow-up after hospital discharge. To investigate biomarkers and immune responses relevant to disease mechanisms, we used a systems biology approach including proteomic analysis of soluble markers in serum, transcriptional analysis by RNA sequencing (RNA-Seq) of peripheral blood mononuclear cells (PBMCs) and nasal swabs, and multidimensional single-cell analysis of whole blood by cytometry by time of flight (CyTOF). We found that elevated T2 inflammatory responses during SARS-CoV-2 infection were associated with lower virus loads and lesser disease severity, and that T2 inflammatory markers were generally stable throughout infection. Methods Study participants and sample collection Patients 18 years and older admitted to 20 US hospitals (affiliated with 15 academic institutions) between May 2020 and March 2021 were enrolled within 72 hours of hospital admission for COVID-19 infection. Only confirmed positive SARS-CoV-2 PCR and symptomatic cases attributable to COVID-19 infection were followed longitudinally.[135]^13 The work described was carried out in accordance with the code of ethics of the World Medical Association (Declaration of Helsinki) and is in line with the recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. National Institute of Allergy and Infectious Diseases staff conferred with the Department of Health and Human Services Office for Human Research Protections, who concurred that the study satisfied criteria for the public health surveillance exception [45CFR46.102(l)], and the IMPACC study team sent the study protocol, and participant information sheet for review, and assessment to institutional review boards (IRBs) at participating institutions. Twelve institutions elected to conduct the study as public health surveillance, while 3 sites with prior IRB-approved biobanking protocols elected to integrate and conduct IMPACC under their institutional protocols (University of Texas, Austin, IRB 2020-04-0117; University of California, San Francisco, IRB 20-30497; Case Western Reserve University, IRB STUDY20200573) with informed consent requirements. Participants enrolled under the public health surveillance exclusion were provided information sheets describing the study, samples to be collected, and plans for data deidentification and use. Those who requested not to participate after reviewing the information sheet were not enrolled. In addition, participants did not receive compensation for study participation while inpatients, but they subsequently were offered compensation during outpatient follow-up. Sample processing and batch randomization Biological sample collection and processing followed a standard protocol used by every participating academic institution. The complete IMPACC sample processing protocol has been published previously.[136]^13 To mitigate potential batching effects, a randomization procedure was developed to help ensure that longitudinal samples from the same individuals were run on the same plates and were randomly distributed across the plates. We stratified this randomization by the maximum respiratory ordinal score during the 28 days after hospitalization and age (<65 years vs ≥65 years) with the representation of these strata across plates. In addition, we verified that race, ethnicity, sex, and site were well represented across the plates.[137]^14 Antibody titers Measurement of antibody levels against the recombinant receptor binding domain and full-length spike was performed by a research-grade ELISA as described.[138]^17^,[139]^18 More details are provided in this article’s Online Repository available at [140]www.jaci-global.org. Olink All samples were subjected to Olink multiplex assay with an inflammatory panel (Olink Bioscience) according to the manufacturer’s instructions. This inflammatory panel included 92 proteins associated with human inflammatory conditions. More details are provided in the Online Repository. Whole blood CyTOF Blood samples were collected at study time points and processed within 6 hours for multiparameter CyTOF as described previously.[141]^13 More details are provided in the Online Repository. PBMC transcriptomics PBMCs were isolated from whole blood using density gradient centrifugation with SepMate-50IVD or SepMate-15IVD isolation tubes. Whole blood was diluted with Dulbecco PBS, carefully layered over the density medium, and centrifuged to separate PBMCs from other blood components. The PBMC layer was then collected, washed, and resuspended for downstream bulk RNA-Seq processing. As described previously,[142]^13 RNA was extracted from cells (2.5 × 10^5 PBMCs) and extracted. Library preps were performed using the SMART-Seq v4 Ultra Low Input RNA Kit (Takara Bio) and sequenced on an Illumina NovaSeq 6000 at 100 bp, paired-end read length targeting ∼25 million reads per sample. More details are provided in the Online Repository. Raw counts were transformed to log[2] counts per millions using the function ‘voom’ implemented in the R package (R Project; [143]www.r-project.org) ‘limma.’ Nasal viral PCR Inferior nasal turbinate swabs were collected and placed in 1 mL of Zymo-DNA/RNA shield reagent (Zymo Research) as described previously.[144]^13 RNA was extracted, and CoV-2 was quantitated using the US Centers for Disease Control and Prevention’s quantitative real-time PCR assay (primers and probes from Integrated DNA Technologies [IDT]). More details are provided in the Online Repository. Nasal host transcriptomics From each nasal RNA sample, ribosomal depletion, cDNA synthesis, and library construction steps were performed using the Total Stranded RNA Prep with Ribo-Zero Plus kit following the manufacturer’s instructions (Illumina). Massively parallel sequencing by synthesis with fluorescently labeled, reversibly terminating nucleotides was carried out on the NovaSeq 6000 sequencer using S4 flow cells with target depth of 50 million 100 bp paired-end reads per sample (25 million read pairs). More details are provided in the Online Repository. Raw counts were transformed to log[2] counts per million using the function ‘voom’ implemented in the R package ‘limma.’ Clinical outcome: Trajectory groups We used previously defined clinical trajectory groups (TGs) in the full IMPACC cohort.[145]^14 Briefly, these TGs were identified using latent class modeling of a modified ordinal score reflecting respiratory support needs and discharge status. TG1 (n = 258) was characterized by relatively mild respiratory disease and a brief hospital stay with no limitations at hospital discharge; TG2 (n = 310) generally required more respiratory support than TG1 and had a longer length of hospital stay (LOS), but no limitations at discharge; TG3 (n = 276) was characterized by roughly similar respiratory support requirements and LOS as TG2 but generally had limitations at discharge. Two additional groups had overall high respiratory support requirements during their hospital stay: TG4 (n = 212) generally received more aggressive respiratory support and experienced a prolonged LOS, and TG5 (n = 108) was characterized by high respiratory support requirements and fatal illness by day 28. Statistical analysis Nonparametric tests (Kruskal-Wallis, Wilcoxon rank sum, and Spearman correlation) were used to test for association between features and outcome of interests (TGs, and asthma and virus load, respectively). A multivariate model with age, sex and enrollment sites was used to test for association between features and outcome of interest, adjusting for those covariates. Models also adjusted for administration of systemic corticosteroids when mentioned in the text; treatment with systemic corticosteroids was defined as patient receipt of enteral or parenteral corticosteroids at any point during the hospitalization. Benjamini-Hochberg adjustment was used to correct for multiple testing. Differential expression analysis and pathway enrichment analysis were done similarly with both RNA-Seq datasets. Linear models using gene expression as a dependent variable and the TGs as independent variables were fitted using the ‘limma’ framework. The ‘fgsea’ package was used to assess enrichment of T2 gene sets among genes associated with the TGs, ranked by the ‘limma’ moderated t statistic. The T2 gene sets were extracted from the Molecular Signatures Database v7.3 (MSigDB; [146]www.gsea-msigdb.org/gsea/msigdb) and complemented by gene sets identified in previous studies.[147]^19^,[148]^20 Cell deconvolution was done using the MuSiC method[149]^21 with reference cell subset expression profiles derived from two nasal single-cell RNA-Seq datasets.[150]^22^,[151]^23 Data sharing All underlying data were deposited in ImmPort under accession number SDY1760. Results Associations of self-reported asthma with COVID-19 outcomes Among the 1164 participants in the IMPACC cohort, 125 participants (10.7%) reported asthma without additional chronic pulmonary disease as a comorbidity, 185 (15.9%) reported having chronic pulmonary disease other than asthma, 49 (4.2%) reported having both asthma and other chronic pulmonary disease, and 805 (69.2%) had no reported pulmonary comorbidity disease at enrollment (see [152]Table E1 in the Online Repository available at [153]www.jaci-global.org). Notably, participants with asthma were significantly younger, were more likely female, and had fewer additional reported comorbidities at admission. Additionally, baseline laboratory measurements were notable for a lower frequency of lymphopenia, thrombocytopenia, and hepatic or renal dysfunction, and fewer asthmatic participants reported congestive heart failure, atrial fibrillation, or acute renal injury as complications during the 28 days after hospitalization compared to participants with other chronic pulmonary disease. In contrast, asthma participants had a significantly higher body mass index than participants with other chronic pulmonary disease. History of respiratory allergies was not systematically captured in our clinical database. Disease severity for the 1164 participants was previously characterized by longitudinal modeling of their respiratory ordinal score during the first 28 days after hospital admission, and each participant was assigned to a TG reflecting the severity of their acute disease course.[154]^14 TG1 (n = 258) was characterized by relatively mild respiratory disease and a brief hospital stay with no limitations at hospital discharge; TG2 (n = 310) generally required more respiratory support than TG1 and had a longer LOS, but no limitations at discharge; and TG3 (n = 276) was characterized by roughly similar respiratory support requirements and LOS as TG2 but generally had limitations at discharge. Two additional groups had overall high respiratory support requirements during their hospital stay: TG4 (n = 212) generally received more aggressive respiratory support and experienced a prolonged LOS, and TG5 (n = 108) was characterized by high respiratory support requirements and fatal illness by day 28. Self-reported asthma without additional chronic pulmonary disease was most prevalent in TG1 compared to those in the more severe TGs, but this association among the 5 groups did not reach statistical significance (Fisher exact test, P = .070; [155]Fig 1). When comparing only the two most extreme groups, participants in TG1 versus those in TG5, all of whom died by day 28, asthma without additional chronic pulmonary disease was significantly more prevalent among participants in TG1 than TG5 (Fisher exact test, odds ratio = 3.94, 95% confidence intervals: [1.55, ∞], P = .0035), which also remained significant after adjusting for age, consistent with lower disease severity in those with asthma. Fig 1. [156]Fig 1 [157]Open in a new tab Asthma association with COVID-19 disease severity. Top, Bar plot showing proportion of participants with reported asthma as comorbidity (opaque) separated by TGs. Bottom, Heat map showing difference in observed number of participants with asthma to number of participants expected to have asthma (based on prevalence of asthmatic participants in IMPACC cohort), separated by TG. Blue–white–red gradient depicts number lower, similar, or greater than expected by chance alone. Associations of self-reported asthma with circulating cytokines T2 asthma is associated with an increased expression of circulating T2 cytokines in the blood.[158]^24 We therefore assessed the levels of IL-4, IL-5, and IL-13 cytokines in serum and evaluated their association with a reported history of asthma and with the clinical TGs. We observed that elevated expression of T2 cytokines was more pronounced in participants with asthma without additional chronic pulmonary disease, but those observations did not reach statistical significance (see [159]Fig E1, A, in the Online Repository available at [160]www.jaci-global.org, for Wilcoxon rank-sum test at visit 1: IL-4, P = .085; IL-5, P = .99; IL-13, P = .094). Levels of these cytokines did not change significantly over time during the first 28 days of hospitalization ([161]Fig E1, B). In addition, we did not observe a significant difference in the levels of T2 cytokines among participants who received systemic corticosteroids during hospitalization ([162]Fig E1, C and D), suggesting that corticosteroids did not affect the association between these cytokines and the TGs. These results suggest that self-reported asthma without additional chronic pulmonary disease is enriched for T2 asthma but does not perfectly align with the presence of T2 inflammation in this cohort. This is not surprising because a substantial fraction of asthma can be non-T2 in origin.[163]^25 Associations of serum T2 cytokine levels and COVID-19 outcomes We next directly assessed the association of T2 cytokines and clinical TGs. We observed significantly increased levels of IL-13 at the time of hospitalization (visit 1) among the less severe TGs (TG1-3), and nonsignificant but higher levels of IL-4 and IL-5 among the less severe TGs compared to the most severe groups (TG4-5; [164]Fig 2, A). The serum protein levels of these cytokines did not change significantly over time throughout illness (see [165]Fig E2, A, in the Online Repository available at [166]www.jaci-global.org). Fig 2. [167]Fig 2 [168]Open in a new tab T[H]2 cytokine levels by clinical TGs (A) IL-4, IL-5, and IL-13 NPX levels in serum at visit 1 in 5 TGs: TG1, n = 252; TG2, n = 297; TG3, n = 259; TG4, n = 199; TG5, n = 104. (B) IL-4 (n = 755), IL-5 (n = 755), and IL-13 (n = 755) NPX levels in serum at visit 1 as function of nasal virus load (C[t]); lower C[t] values indicate higher virus load. C[t], Cycle threshold; NPX, normalized protein expression. Furthermore, we tested the association of T2 cytokines with airway SARS-CoV-2 virus load and plasma anti–SARS-CoV-2 receptor binding domain IgG levels. We observed significant inverse correlations between serum protein levels of T2 cytokines and nasal virus load as measured by real-time quantitative PCR (IL-4: cor = −0.101, P = .0055; IL-5: cor = −0.087, P = .017; IL-13: cor = −0.063, P = .085) ([169]Fig 2, B, and [170]Fig E2, B) and positive correlations with antibody response against SARS-CoV-2 ([171]Fig E2, C and D) (IL-4: cor = 0.101, P = .0010; IL-5: cor = 0.093, P = .0026; IL-13: cor = 0.095, P = .0020). Associations of circulating T-cell populations and COVID-19 outcomes Previous analysis of the IMPACC cohort revealed that a decline in circulating T cells is associated with more severe disease trajectories.[172]^15 We found that both CD4 and CD8 T-cell frequencies, as well as eosinophils, are higher in participants with less severe disease (TG1-3) and decline with severity ([173]Fig 3, A). Thus, we further considered whether differences in CD4 T helper cell subtype frequencies correlated with a less severe disease trajectory. We assessed the frequency of T[H]1, T[H]2, and T[H]17 subsets in blood by CyTOF using expression of chemokine receptors commonly used to stratify CD4 T helper cell subsets (see [174]Fig E3, A and B, in the Online Repository available at [175]www.jaci-global.org). At visit 1, the frequency of Th1 (TG1, 0.126; TG2, 0.127; TG3, 0.114; TG4, 0.091; TG5, 0.093), T[H]2 (TG1, 0.141; TG2, 0.159; TG3, 0.136; TG4, 0.121; TG5, 0.125), and T[H]17 (TG1, 0.131; TG2, 0.125; TG3, 0.105; TG4, 0.097; TG5, 0.086) subsets were higher in the mild and moderate TGs (TG1-3) and progressively lower with increasing disease severity (TG4-5; [176]Fig 3, B, Wilcoxon rank-sum test TG1-3 vs TG4-5 T[H]1: P = 7.2e-12; T[H]2: difference = +2%, P = 3.0e-4; T[H]17: P = 1.8e-4). This suggests that differentiation of T helper cells, including toward a T[H]2 phenotype, is associated with less severe COVID-19 disease. Fig 3. [177]Fig 3 [178]Open in a new tab Elevated frequency of differentiated T helper cell subsets is associated with milder disease course. (A) Box plots of frequency of CD4^+ T cells, CD8^+ T cells, and eosinophils out of total CD45^+ blood cells in each TG at visit 1 (TG1, n = 171; TG2, n = 212; TG3, n = 190; TG4, n = 133; TG5, n = 58). (B) Jitter plot of frequencies of Th1, T[H]2, and T[H]17 subsets relative to parent CD4^+ T helper cell subset (CD4^+CD45RO^+CD45RA^−) as function of TGs at visit 1. (A and B) For statistical testing, Kruskal-Wallis test was used to test differences between TGs. Notably, T[H]2 cells were more elevated among asthmatic participants compared to nonasthmatic participants ([179]Fig E3, C and D). In addition, we did not observe a significant difference in the frequency of T helper cell subsets among participants that received corticosteroids, similar to the observation noted previously for T2 cytokines ([180]Fig E3, E and F). These results suggest that a higher frequency of T[H]2 cells is associated with less severe COVID-19 disease. Associations of PBMC and nasal gene expression patterns and COVID-19 outcomes RNA-Seq of PBMC and nasal swabs was performed, and the relative expression of known gene sets related to T2 signaling was assessed among TGs and in relation to virus loads. In the PBMC RNA-Seq, we observed a significant enrichment of T2 gene sets in the least severe TG, relative to all other TGs at visit 1 (TG1; see [181]Fig E4, A, in the Online Repository available at [182]www.jaci-global.org). However, in the nasal RNA-Seq data, the majority of T2 gene sets were relatively low in the least severe TG (TG1) and higher in the more severe TGs ([183]Fig E4, B). Only five T2-related gene sets were associated with less severe disease in both PBMC RNA-Seq and nasal RNA-Seq, being consistent across tissues and corroborating the T2 cytokines and T[H]2 subset association with less severe disease. This includes genes positively correlated with IL4 and IL13 gene expression levels, genes with a putative binding site of STAT6 (signal transducer and activator of transcription 6), and genes associated with T2-type asthma previously identified. Relating these gene sets to virus load, we found significant inverse associations between multiple known T2 gene sets and nasal virus load in both the PBMC and nasal RNA-Seq datasets ([184]Fig 4). This included T2 regulated genes implicated in unfolded protein response shared between the PBMC and nasal transcriptomics. In PBMC, we saw several T2 target genes regulating myeloid differentiation and reactive oxygen species signaling. In the upper airway, T2 target genes known to activate T lymphocytes were most associated with lower virus load. This included genes coding for the transcription factor FOXO3 (forkhead box protein O3), known to limit sequelae of antiviral responses.[185]^26 Interestingly, some of the known T2 target genes, including DAZAP2, NR4A2, and JAK1, have been identified as restriction factors of SARS-CoV-2 in large CRISPR (clustered regularly interspaced short palindromic repeat) and cDNA screenings. Fig 4. [186]Fig 4 [187]Open in a new tab Transcriptional profile of T2 gene signatures. (A) Scaled expression of T2-related genes identified in MSigDB database and measured by PBMC RNA-Seq. Genes were separated into 6 clusters based on enrichment analysis. Nasal virus load is presented as column annotation. Samples (columns) are ordered by increasing levels of T2 gene signatures. (B) Scaled expression of T2-related genes identified in MSigDB database and measured by nasal RNA-Seq. To investigate if there are any differences in cellular composition in nasal epithelium (nasal swab samples), we performed cell deconvolution of bulk RNA-Seq data using known cell type markers previously derived from upper airway single-cell RNA-Seq studies (see [188]Fig E5, A, in the Online Repository available at [189]www.jaci-global.org).[190]^22^,[191]^23 Overall, we observed higher inferred frequencies of B cells and goblet cells among the less severe TGs (TG1-3) and lower inferred frequencies of macrophages compared to the more severe TGs (TG4-5) ([192]Fig E5, B). B cells are known to produce IL-4 and their activity is promoted by IL-4, while IL-13 is important for goblet cell maturation and mucin production. Macrophage activity has been described to be negatively regulated by IL-4. Taken together, the results of the deconvolution suggest that the activity of T2 cytokines in the nasal compartment is also associated with a less severe response to virus in hospitalized COVID-19 participants. Discussion In this study of 1164 adults hospitalized with COVID-19 during the first year of the pandemic, we observed that multiple T2 inflammatory markers were associated with lower virus loads and/or lesser disease severity. Those T2 markers were identified in serum (IL-4, IL-5, IL-13), peripheral blood cells (T[H]2 cells, T2 target genes), and in the nasal compartment (T2 target genes). Asthma has not been associated with differential risk of SARS-CoV-2 infection, and in this study, the levels of the T2 cytokines in serum are at levels comparable to noninfected individuals. This suggests that T2 inflammation did not prevent infection but was rather associated with better virus control and/or reduced virus spread, and ultimately lesser disease severity resulting from the virus. Whether mediators of T2 inflammation both in the tissue and systematically are necessary for the antiviral effect and better clinical outcomes is not clear. However, T2 inflammation in diseases such as allergic rhinitis and allergic asthma is generally present both systemically and in the airway.[193]^24 Several T2 target genes have been identified in genome-wide screens as antiviral markers.[194]27, [195]28, [196]29 This includes genes such as DAZAP2, NR4A2, JAK1, ETV6, and ARF6. Here we identified DAZAP2, NR4A2, and JAK1 in the transcriptomic analysis. DAZAP2 has been identified previously as a flavivirus RNA-binding factor, and CRISPR knockout of DAZAP2 reduced SARS-CoV-2 virus replication without perturbation of virus entry in A549 cells.[197]^30 Notably, T2 inflammation driven by IL-13 has been repeatedly demonstrated to markedly decrease virus replication in airway epithelium culture models. This appears to be mediated through a few mechanisms, including a relative dedifferentiation of multiciliated cells that blocks the ability of the virus to replicate, an increase in goblet cells and mucus production that may inhibit virus entry and spread through the respiratory epithelium,[198]^4^,[199]^5^,[200]^12 and a reduction in angiotensin-converting enzyme receptor expression.[201]^8 T-cell lymphopenia is associated with increasing COVID-19 severity in our studies[202]^15 and in published work,[203]^31 but specific T-cell subsets may play differential roles in contributing to disease severity. Our data from blood CyTOF indicate that Th1, T[H]2, and T[H]17 T helper subsets are all found at elevated frequencies in participants with less severe disease. T[H]2-related cytokines (IL-4, IL-5, IL-13) but not Th1-related cytokines (IFN-γ) were also associated with less severe disease, supporting our finding that T2 immunity is associated with less severe COVID-19 disease among hospitalized individuals. Steroids administered during hospitalizations did not significantly alter the associations of T2 markers with virus load or disease severity, suggesting that the effect of T2 markers cannot be explained solely by the treatment those participants received. Similarly, when adjusting for asthma, T2 markers remained significantly associated with virus load and disease severity, suggesting that the medications asthmatic participants receive before SARS-CoV-2 infection cannot totally explain our observations. We did not fully capture the history of allergic asthma and allergic rhinitis, so we cannot conclusively link the T2 inflammation in this cohort to those diseases. We only measured T2 cytokine levels in circulation but not in the respiratory system, where the initial steps of the infection occur. Our nasal transcriptomic data do support a beneficial role of T2 cytokines in hospitalized COVID-19 patients. It would be important to confirm at the protein level the T2 cytokine involvement in moderate COVID-19 disease. Deconvolution of bulk RNA-Seq is not sensitive enough to infer frequency of T[H]2 cells in the upper respiratory tract, and our study did not collect nasal samples, so we were not able to measure the frequency of T[H]2 cells in that compartment. We did observe a negative association of T2 cytokine target genes and nasal virus load, and we hypothesize that there are direct antiviral effects of T2 cytokines on virus replication in the host respiratory system; however, additional cell and animal model experiments are necessary to confirm the mechanistic basis of our results. In summary, we found that elevated T2 inflammatory signatures, both systemically and in the upper airway, during SARS-CoV-2 infection were associated with lower virus loads, higher anti–SARS-CoV-2 antibody titers, and lesser disease severity; we also found that T2 inflammatory markers were generally stable during infection. This novel finding, in conjunction with published epidemiologic and in vitro literature relating T2 inflammation with COVID-19 disease and SARS-CoV replication, leads us to conclude that individuals with allergies and asthma with a T2 endotype likely have a level of intrinsic protection against severe SARS-CoV-2 infection. Disclosure statement Funded by the National Institutes of Health (NIH; 5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-18); the National Institute of Allergy and Infectious Diseases, NIH (3U19AI1289130, U19AI128913-04S1, and R01AI122220); and the National Science Foundation (DMS2310836). Disclosure of potential conflict of interest: C. B. Cairns serves as consultant to bioMérieux; and is funded by a grant from the Bill & Melinda Gates Foundation. C. S. Calfee has received research funding from the National Institutes of Health, the US Food and Drug Administration, the Department of Defense, Roche-Genentech, and Quantum Leap Healthcare Collaborative; and has performed consulting services for Janssen, Vasomune, Gen1e Life Sciences, NGMBio, and Cellenkos. L. N. Geng has received research funding paid to her institution from Pfizer. F. Krammer reports that the Icahn School of Medicine at Mount Sinai has filed patent applications relating to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serologic assays and NDV-based SARS-CoV-2 vaccines listing him as coinventor (Mount Sinai has spun off a company, Kantaro, to market serologic tests for SARS-CoV-2); has consulted for Merck and Pfizer (before 2020) and is currently consulting for Pfizer, Seqirus, 3rd Rock Ventures, Merck, and Avimex; and reports that his laboratory is also collaborating with Pfizer on animal models of SARS-CoV-2. O. Levy is a named inventor on patents held by Boston Children’s Hospital relating to vaccine adjuvants and human in vitro platforms that model vaccine action; his laboratory has received research support from GSK. G. A. McComsey has received research grants from Rehdhill, Cognivue, Pfizer, and Genentech; and has served as a research consultant for Gilead, Merck, Viiv/GSK, and Jenssen. E. Melamed has received research funding from Babson Diagnostics; has received honoraria from the Multiple Sclerosis Association of America; and has served on advisory boards of Genentech, Horizon, Teva, and Viela Bio. V. Simon is coinventor of a patent filed relating to SARS-CoV-2 serologic assays. The rest of the authors declare that they have no relevant conflicts of interest. Acknowledgments