Chronic viral infections are ubiquitous in humans, with individuals carrying multiple viruses that can reactivate during physiological stress, including severe illness1. Notably, SARS-CoV-2 infection has been shown to reactivate chronic viruses such as Epstein-Barr virus and cytomegalovirus, yet the full extent, temporal dynamics and immunological impact of viral reactivation in COVID-19 remain incompletely understood2-7. Here, leveraging multi-omic longitudinal data from 1,154 hospitalized patients with COVID-19 from the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study, we reveal significant reactivation of Herpesviridae and Anelloviridae during acute COVID-19, with distinct temporal dynamics for different viruses, and demonstrate that reactivation correlates with disease severity, host immune effects and clinical outcomes. Although our results do not establish causation between virus reactivation and clinical outcomes, we highlight the prevalence of chronic viral reactivation during acute COVID-19 and long COVID. Our findings challenge the prevailing view that chronic viral reactivation is primarily a consequence of immunosuppression, demonstrating that reactivations occur frequently in immunocompetent individuals during severe illness and in association with increased systemic inflammation. Additionally, we demonstrate persistence of viral reactivation in convalescence, and report an association of Anelloviridae with long COVID. This study provides immune, transcriptomic and metabolomic signatures of viral reactivation that could inform future strategies to prognosticate and treat acute COVID-19 and long COVID.

The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will develop long COVID is challenging due to the absence of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models may address this gap by leveraging clinical data to enhance diagnostic precision. Clinical data, including antibody titers and viral load measurements collected at the time of hospital admission, are used to predict the likelihood of acute COVID-19 progressing to long COVID. Machine learning models are trained and evaluated for predictive performance. Feature importance analysis is performed to identify the most influential predictors. The machine learning models achieve median AUROC values ranging from 0.64 to 0.66 and AUPRC values between 0.51 and 0.54, demonstrating predictive capabilities. Low antibody titers and high viral loads at hospital admission emerge as the strongest predictors of long COVID outcomes. Comorbidities—such as chronic respiratory, cardiac, and neurologic diseases—and female sex are also identified as significant risk factors. Machine learning models identify patients at risk for developing long COVID based on baseline clinical characteristics. These models guide early interventions, improve patient outcomes, and mitigate the long-term public health impacts of SARS-CoV-2. Long COVID, or post-acute sequelae of SARS-CoV-2, is a prolonged health condition that can occur after acute COVID-19 infection. However, the ability to predict who will develop long COVID remains limited due to the absence of clear tests or biomarkers. We looked at patients’ medical information, including the amount of virus in their body at hospital admission, and how strong their immune response was. Using computer programs that can find hidden patterns in large sets of data, we discovered that people with a weaker immune response, higher amounts of virus, certain long term health problems and women are more likely to develop long COVID. This study highlights that computer-based tools could help doctors identify high-risk patients early and provide care that may prevent long-term complications. Jayavelu, Samaha et al., apply machine learning models on hospital admission data, including antibody titers and viral load, to identify patients at high risk for Long COVID. Low antibody levels, high viral loads, chronic diseases, and female sex are key predictors, supporting early, targeted interventions.

Azithromycin is a widely used antibiotic and was frequently used to treat hospitalized patients during the COVID-19 pandemic. The impact of empiric azithromycin use on the respiratory microbiome in patients with viral respiratory infections is unclear. Here we used longitudinal metatranscriptomics on nasal swabs from a prospective multicentre cohort of 1,164 patients hospitalized for COVID-19. We compared the upper respiratory microbiome, resistome and systemic immune response in patients treated with azithromycin (n = 366) with those who received no antibiotics (n = 474) or other antibiotics (n = 324). We found that azithromycin altered microbiome composition and increased the expression and relative proportion of macrolide/lincosamide/streptogramin (MLS) resistance genes. These changes occurred after 1 day of exposure and persisted for over a week. MLS resistance gene expression was associated with commensals and potential pathogens, while there were no differences in host inflammatory gene expression in blood and airways. This demonstrates that empiric azithromycin treatment impacts the upper respiratory microbiome and resistome without apparent anti-inflammatory benefit.

Dendritic cells are essential for establishing thymic central tolerance; however, mechanisms supporting their homeostasis and activation remain unresolved. Through single-cell transcriptomics and functional assays, we identify seven thymic conventional dendritic cell (cDC) subsets and discriminate their abilities to present self-antigens and induce regulatory T cells. Mice blocked at different stages of T cell development revealed that CD4+ single-positive (CD4SP) and CD8SP thymocytes differentially support homeostasis and activation of type 1 cDCs (cDC1s) versus cDC2s/plasmacytoid DCs (pDCs), respectively. CD8SP thymocytes indirectly support pDC survival and cDC2 thymic migration, and they induce interferon signaling in cDCs, partly by promoting type 3 interferon expression by medullary thymic epithelial cells. By contrast, CD4SP thymocytes undergo cognate interactions with cDCs, inducing CD40 signaling required for activation of cDC1s. Activated cDC1s make nonredundant contributions to central tolerance. Together, this study comprehensively identifies distinct thymic DC subsets and elucidates requirements for cross-talk with thymocyte subsets that support their homeostasis, activation and function. Dendritic cells (DCs) are required to establish thymic central tolerance. Here Srinivasan et al. use single-cell transcriptomics to define thymic conventional dendritic cell (cDC) subsets and find that CD8+ single-positive thymocytes modulate the thymus environment to regulate plasmacytoid DC and cDC2 homeostasis and interferon signatures in DCs, while CD4+ single-positive thymocytes regulate cDC1 activation via cognate and CD40L–CD40 interactions.

Abstract: T-cell acute lymphoblastic leukemia (T-ALL) is a common pediatric malignancy, and central nervous system (CNS) involvement is associated with inferior prognoses. Here, we show that in the LIM-domain-only-2 transgenic and LN3 mouse models of T-ALL, leukemia blasts infiltrate the CNS, where they colocalize with an altered myeloid compartment. Notably, myeloid cells from the leukemic, but not healthy, CNS were sufficient to support T-ALL survival in vitro, and depletion of myeloid cells in vivo significantly reduced CNS leukemia burden. Among myeloid subsets, macrophages and monocytes from the leukemic CNS most robustly supported T-ALL in vitro. Both T-ALL blasts and leukemia-associated myeloid cells in the CNS expressed elevated levels of adhesion molecules, and inhibition of integrin-mediated adhesion or downstream signaling prevented myeloid cells from supporting survival of T-ALL cells in vitro. Single-cell transcriptomic analyses revealed that leukemia-associated myeloid cells in the CNS undergo transcriptional changes, increasing the expression of genes associated with oxidative phosphorylation while reducing inflammatory signatures. Given the clinical importance of T-ALL relapse in the CNS, we tested whether myeloid cells provide a supportive niche for chemotherapy resistance. T-ALL cells in the CNS that rebounded after chemotherapy retained myeloid dependence in vitro, and myeloid depletion improved responsiveness to chemotherapy, prolonging mouse survival. Altogether, these findings demonstrate that myeloid cells support T-ALL in the CNS and suggest that targeting myeloid cells or mechanisms by which they support T-ALL, in conjunction with conventional chemotherapeutic approaches, could improve current treatment outcomes.

The National Institutes of Health-funded IMPACC (IMmunoPhenotyping Assessment in a COVID-19 Cohort) evaluated longitudinal clinical and immunological features of human patients hospitalized for COVID-19. This study focuses on comparing the novel NULISAseq assay with the Olink platform using a subset of participants to assess their efficacy in predicting COVID-19 severity and understanding immune response dynamics. Our findings reveal that NULISAseq could provide superior detectability and dynamic range across various targets. Elastic net analysis demonstrated that specific proteins, including amphiregulin, effectively predict COVID-19 severity from sera at admission (samples drawn within 96 h of admission), with a test area under the curve of 0.84. Longitudinal analysis identified significant differences in multiple targets, including IL-5 and interferons, between low- and high-severity groups over time. Additionally, association rule mining suggested potential early markers predictive of later immune cell changes. These findings emphasize the potential of NULISAseq for comprehensive profiling, early prediction, and identification of targeted therapeutic interventions in COVID-19.

Introduction:The coronavirus disease 2019 (COVID-19) pandemic threatened public health and placed a significant burden on medical resources. The Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study collected clinical, demographic, blood cytometry, serum receptor-binding domain (RBD) antibody titers, metabolomics, targeted proteomics, nasal metagenomics, Olink, nasal viral load, autoantibody, SARS-CoV-2 antibody titers, and nasal and peripheral blood mononuclear cell (PBMC) transcriptomics data from patients hospitalized with COVID-19. The aim of this study is to select baseline biomarkers and build predictive models for 28-day in-hospital COVID-19 severity and mortality with most predictive variables while prioritizing routinely collected variables. Methods:We analyzed 1102 hospitalized COVID-19 participants. We used the lasso and forward selection to select top predictors for severity and mortality, and built predictive models based on balanced training data. We then validated the models on testing data. Results:Severity was best predicted by the baseline SpO2/FiO2 ratio obtained from COVID-19 patients (test AUC: 0.874). Adding patient age, BMI, FGF23, IL-6, and LTA to the disease severity prediction model improves the test AUC by an additional 3%. The clinical mortality prediction model using SpO2/FiO2 ratio, age, and BMI resulted in a test AUC of 0.83. Adding laboratory results such as TNFRSF11B and plasma ribitol count increased the prediction model by 3.5%. The severity and mortality prediction models developed outperform the Sequential Organ Failure Assessment (SOFA) score among inpatients and perform similarly to the SOFA score among ICU patients. Conclusion:This study identifies clinical data and laboratory biomarkers of COVID-19 severity and mortality using machine learning models. The study identifies SpO2/FiO2 ratio to be the most important predictor for both severity and mortality. Several biomarkers were identified to modestly improve the predictions. The results also provide a baseline of SARS-CoV-2 infection during the early stages of the coronavirus emergence and can serve as a baseline for future studies that inform how the genetic evolution of the coronavirus affects the host response to new variants.

Elevated circulating endothelial cells (CECs), released from monolayers after insult, have been implicated in worse outcomes in ARDS and COVID-19, however there is no consensus proteomic phenotype that define CECs. We queried whether a transcriptomic approach would alternatively support the presence of endothelial cells in circulation and correlate with worsening respiratory failure. To test whether elevated endothelial cell signatures (ECS) in circulation plays a role in worse respiratory outcomes, we used unsupervised bulk-transcriptome deconvolution to quantify ECS

T cells in the perinatal thymus have distinct phenotypes and functions that may be instructed by age-specific features of the microenvironment. We evaluated molecular and cellular profiles of thymic stromal cells, including thymic epithelial cells (TECs), mesenchyme, endothelium, and hematopoietic antigen presenting cells (hAPCs), from birth through one-month of age in mice. Single-cell transcriptional profiling, flow cytometry, and immunohistochemistry revealed coordinated stromal changes accompanied by altered thymocyte differentiation at defined transitional ages during the shift from perinatal growth to juvenile homeostasis, which was mirrored in humans. These analyses link diminished IGF2 expression by mesenchymal cells with activation of the RB pathway in TECs at the transition. Moreover, a coordinated increase in type I interferon signaling in stroma across the transition is associated with altered antigen processing and presentation signatures in TECs and hAPCs. Collectively, these datasets provide a resource to interrogate thymic stroma across the perinatal to juvenile transition. ### Competing Interest Statement Laura Hale served on the scientific advisory board of Sumitomo Pharma America in October 2024.

BACKGROUNDFollowing SARS-CoV-2 infection, approximately 10%-35% of patients with COVID-19 experience long COVID (LC), in which debilitating symptoms persist for at least 3 months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities.METHODSWe utilized machine learning methods on biologic analytes provided over 12 months after hospital discharge from more than 500 patients with COVID-19 in the IMPACC cohort to identify a multiomics "recovery factor," trained on patient-reported physical function survey scores. Immune profiling data included PBMC transcriptomics, serum O-link and plasma proteomics, plasma metabolomics, and blood mass cytometry by time of flight (CyTOF) protein levels. Recovery factor scores were tested for association with LC, disease severity, clinical parameters, and immune subset frequencies. Enrichment analyses identified biologic pathways associated with recovery factor scores.RESULTSParticipants with LC had lower recovery factor scores compared with recovered participants. Recovery factor scores predicted LC as early as hospital admission, irrespective of acute COVID-19 severity. Biologic characterization revealed increased inflammatory mediators, elevated signatures of heme metabolism, and decreased androgenic steroids as predictive and ongoing biomarkers of LC. Lower recovery factor scores were associated with reduced lymphocyte and increased myeloid cell frequencies. The observed signatures are consistent with persistent inflammation driving anemia and stress erythropoiesis as major biologic underpinnings of LC.CONCLUSIONThe multiomics recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.TRIAL REGISTRATIONClinicalTrials.gov NCT04378777.FUNDINGNational Institute of Allergy and Infectious Diseases (NIAID), NIH (3U01AI167892-03S2, 3U01AI167892-01S2, 5R01AI135803-03, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI057229-18, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07S1, 3U19AI089992-09, 3U19AI128913-03, and 5T32DA018926-1, 3U19AI1289130, U19AI128913-04S1, R01AI122220); NIH (UM1TR004528); and National Science Foundation (NSF) (DMS2310836).

Thymic dendritic cells (DCs) are critical mediators of central tolerance, cooperating with medullary thymic epithelial cells (mTECs) and B cells to establish T-cell self-tolerance to the proteome. The DC compartment is highly heterogeneous and is comprised of three major subsets, plasmacytoid dendritic cells (pDCs) and two conventional dendritic cell (cDC) subsets, cDC1 and cDC2. Thymic cDC1 and cDC2 arise from distinct progenitors and access the thymus at different stages of their differentiation, but both become activated by cellular and secreted cues received within the sterile thymus environment. Activated cDC1s and cDC2s have been implicated in presenting distinct types of self-antigens to induce central tolerance. Thus, understanding how the distinct cDC subsets are regulated within the thymus environment will provide important insights into mechanisms governing self-tolerance. Furthermore, the thymic DC compartment undergoes age-associated compositional and transcriptional changes that likely impact the efficiency and quality of central tolerance established over the lifespan. Here, we review recent findings from our lab and others on mechanisms regulating thymic DC activation, the distinct roles of thymic DC subsets in central tolerance, and age-associated changes in thymic DCs that could impact T-cell selection.

Perinatal T cells have distinctive phenotypes and functions that may be due in part to age-associated features of stromal cells in the perinatal thymus. We identify age-associated changes in mouse thymic epithelial cells, mesenchyme, endothelium, and hematopoietic antigen-presenting cells from birth to one month of age using single-cell transcriptional profiling, flow cytometry, and imaging. Coordinated cellular and molecular changes occur at 7-14 days of age, designated "transitional ages," as thymus growth switches to homeostasis. E2F target gene expression declines, and the expression of type I interferon response genes increases across diverse cell types at transitional ages. Alterations in thymic stromal cells coincide with elevated markers of thymocyte self-reactivity and enhanced Treg suppressive phenotypes and function. The integrated results reveal coordinated remodeling of multiple stromal cell types during the perinatal to juvenile transition, which likely impacts T cell differentiation. These datasets provide a resource for the investigation of the perinatal thymus environment.

Following SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which often debilitating symptoms persist for at least three months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities. We utilized machine learning methods on biologic analytes and patient reported outcome surveys provided over 12 months after hospital discharge from >500 hospitalized COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor". IMPACC participants who experienced LC had lower recovery factor scores compared to participants without LC. Biologic characterization revealed increased levels of plasma proteins associated with inflammation, elevated transcriptional signatures of heme metabolism, and decreased androgenic steroids in LC patients. The recovery factor was also associated with altered circulating immune cell frequencies. Notably, recovery factor scores were predictive of LC occurrence in patients as early as hospital admission, irrespective of acute disease severity. Thus, the recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.

BACKGROUND:Defining the kinetics of SARS-CoV-2 antibody responses is critical for informing the management of reinfections, vaccinations, and therapeutics of Coronavirus disease 2019 (COVID-19). METHODS:Using four antibody assays, we evaluated antibody titers against SARS-CoV-2 nucleocapsid (N), spike (S), and receptor binding domain (RBD) in 98 convalescent participants with varying COVID-19 disease severities (asymptomatic, mild, moderate or severe) at 1, 3, 6, and 12-months post-SARS-CoV-2-positive PCR and in 17 non-vaccinated, non-infected controls. RESULTS:Increasing acute COVID-19 disease severity correlates with higher anti-N and anti-RBD titers throughout 12 months post-infection. Anti-N and anti-RBD titers decline over time in all participants, except for increased anti-RBD titers post-vaccination, with hospitalized participants exhibiting faster decay rates. Less than 50% of participants retain anti-N titers above controls at 12 months, with non-hospitalized participants falling below controls sooner. Nearly all participants maintain anti-RBD titers above controls for 12 months, suggesting long-term protection against severe reinfections. Nonetheless, by 6 months, few participants retain >50% of their initial 1-month anti-N or anti-RBD titers. Notably, vaccine-induced anti-RBD titers are higher in non-hospitalized participants. Lastly, early convalescent titers correlate with age but not with Post-Acute Sequelae of SARS-CoV-2 infection (PASC) status or steroid use. CONCLUSION:Hospitalized participants initially develop higher anti-SARS-CoV-2 antibody titers that decline faster relative to non-hospitalized participants. While anti-N titers fall below control levels in some participants, anti-RBD titers remain above controls over 12 months, demonstrating long-lived antibody responses known to protect against severe disease. These findings advance our understanding of COVID-19 antibody dynamics.

CD16A is an activating Fc receptor on NK cells that mediates antibody-dependent cellular cytotoxicity (ADCC), a key mechanism in antiviral immunity. However, the role of NK cell-mediated ADCC in SARS-CoV-2 infection remains unclear, particularly whether it limits viral spread and disease severity or contributes to the immunopathogenesis of COVID-19. We hypothesized that the high-affinity CD16AV176 polymorphism influences these outcomes. Using an in vitro reporter system, we demonstrated that CD16AV176 is a more potent and sensitive activator than the common CD16AF176 allele. To assess its clinical relevance, we analyzed 1,027 patients hospitalized with COVID-19 from the Immunophenotyping Assessment in a COVID-19 cohort (IMPACC), a comprehensive longitudinal dataset with extensive transcriptomic, proteomic, and clinical data. The high-affinity CD16AV176 allele was associated with a significantly reduced risk of ICU admission, mechanical ventilation, and severe disease trajectories. Lower anti-SARS-CoV-2 IgG titers were correlated to CD16AV176; however, there was no difference in viral load across CD16A genotypes. Proteomic analysis revealed that participants homozygous for CD16AV176 had lower levels of inflammatory mediators. These findings suggest that CD16AV176 enhances early NK cell-mediated immune responses, limiting severe respiratory complications in COVID-19. This study identifies a protective genetic factor against severe COVID-19, informing future host-directed therapeutic strategies.

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 TH2 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.

Azithromycin is often prescribed unnecessarily for respiratory infections, many of which are viral. During the COVID-19 pandemic, its use was widespread, in part due to alleged therapeutic benefits, which have since been disproven. Here, we sought to understand the impact of azithromycin exposure on the respiratory microbiome, antimicrobial resistome, and host immune response in a prospective multicenter cohort of 1164 patients hospitalized for SARS-CoV-2 infection. Using longitudinal nasal metatranscriptomics, we compared patients treated with azithromycin (n=366, 31.4%) to those who received no antibiotics (n=474, 40.7%) or antibiotics other than azithromycin (n=324, 27.8%). We found that azithromycin treatment altered the community composition of the nasal microbiome, reducing bacterial relative abundance, increasing fungal relative abundance, and increasing potentially pathogenic taxa such as Klebsiellaand Staphylococcus. Azithromycin treatment was most notably associated with increases in the number of detectably expressed macrolide/lincosamide/streptogramin (MLS) antimicrobial resistance genes, as well as their relative proportion in the resistome, with changes observable after one day of exposure. Of the MLS resistance genes, the expression of ermC, msrA and ermX increased the most in patients receiving azithromycin. Correlation analyses demonstrated that MLS resistance gene expression was significantly associated with the abundance of several taxa, including both commensal (e.g., Dolosigranulum, Corynebacterium) and potentially pathogenic genera (e.g., Streptococcus, Staphylococcus). Assessment of the peripheral blood and upper airway host transcriptome demonstrated no differences in the expression of inflammatory genes. Taken together, our findings demonstrate that azithromycin treatment in COVID-19 leads to dysbiosis of the upper respiratory microbiome and changes in the expression of MLS resistance genes, without apparent anti-inflammatory benefit.

MHC class I polypeptide-related sequence B (MICB) is a ligand for NKG2D. We have shown NK cells are central to lung transplant acute lung injury (ALI) via NKG2D activation, and increased MICB in bronchoalveolar lavage predicts ALI severity. Separately, we found a MICB polymorphism (MICBG406A) is associated with decreased ALI risk. We hypothesized this polymorphism would protect against severe SARS-CoV-2 respiratory disease. We analyzed 1,036 patients hospitalized with SARS-CoV-2 infection from IMPACC. Associations between MICBG406A and outcomes were determined by linear regression or Cox proportional hazards models. We also measured immune profiles of peripheral blood and the upper and lower airway. We identified 560 major allele homozygous patients, and 426 and 50 with 1 or 2 copies of the variant allele, respectively. MICBG406A conferred reduced odds of severe COVID-19. MICBG406A homozygous participants demonstrated 34% reduced cumulative odds for mechanical ventilation or death and 43% reduced risk for mortality. Patients with MICBG406A variant alleles had reduced soluble inflammatory mediators and differential regulation of multiple immune pathways. These findings demonstrate a potentially novel association between increasing MICBG406A variant allele copies and reduced COVID-19 severity, independent of SARS-CoV-2 viral burden and humoral immunity, suggesting the NKG2D-ligand pathway as an intervention target.