A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality

Context-specific eQTLs reveal causal genes underlying shared genetic architecture of critically ill COVID-19 and idiopathic pulmonary fibrosis

Most genetic variants identified through genome-wide association studies (GWAS) are suspected to be regulatory in nature, but only a small fraction colocalize with expression quantitative trait loci (eQTLs, variants associated with expression of a gene). Therefore, it is hypothesized but largely untested that integration of disease GWAS with context-specific eQTLs will reveal the underlying genes driving disease associations. We used colocalization and transcriptomic analyses to identify shared genetic variants and likely causal genes associated with critically ill COVID-19 and idiopathic pulmonary fibrosis. We first identified five genome-wide significant variants associated with both diseases. Four of the variants did not demonstrate clear colocalization between GWAS and healthy lung eQTL signals. Instead, two of the four variants colocalized only in cell-type and disease-specific eQTL datasets. These analyses pointed to higher ATP11A expression from the C allele of rs12585036, in monocytes and in lung tissue from primarily smokers, which increased risk of IPF and decreased risk of critically ill COVID-19. We also found lower DPP9 expression (and higher methylation at a specific CpG) from the G allele of rs12610495, acting in fibroblasts and in IPF lungs, and increased risk of IPF and critically ill COVID-19. We further found differential expression of the identified causal genes in diseased lungs when compared to non-diseased lungs, specifically in epithelial and immune cell types. These findings highlight the power of integrating GWAS, context-specific eQTLs, and transcriptomics of diseased tissue to harness human genetic variation to identify causal genes and where they function during multiple diseases.

TOLLIP and MUC5B modulate the effect of ambient NO2 on respiratory symptoms in infancy

BACKGROUND

Current knowledge suggests that the gene region containing MUC5B and TOLLIP plays a role in airway defence and airway inflammation, and hence respiratory disease. It is also known that exposure to air pollution increases susceptibility to respiratory disease. We aimed to study whether the effect of air pollutants on the immune response and respiratory symptoms in infants may be modified by polymorphisms in MUC5B and TOLLIP genes.

METHODS

359 healthy term infants from the prospective Basel-Bern Infant Lung Development (BILD) birth cohort were included in the study. The main outcome was the score of weekly assessed respiratory symptoms in the first year of life. Using the candidate gene approach, we selected 10 single nucleotide polymorphisms (SNPs) from the MUC5B and TOLLIP regions. Nitrogen dioxide (NO2) and particulate matter ≤10 μm in aerodynamic diameter (PM10) exposure was estimated on a weekly basis. We used generalised additive mixed models adjusted for known covariates. To validate our results in vitro, cells from a lung epithelial cell line were downregulated in TOLLIP expression and exposed to diesel particulate matter (DPM) and polyinosinic-polycytidylic acid.

RESULTS

Significant interaction was observed between modelled air pollution (weekly NO2 exposure) and 5 SNPs within MUC5B and TOLLIP genes regarding respiratory symptoms as outcome: E.g., infants carrying minor alleles of rs5744034, rs3793965 and rs3750920 (all TOLLIP) had an increased risk of respiratory symptoms with increasing NO2 exposure. In vitro experiments showed that cells downregulated for TOLLIP react differently to environmental pollutant exposure with DPM and viral stimulation.

CONCLUSION

Our findings suggest that the effect of air pollution on respiratory symptoms in infancy may be influenced by the genotype of specific SNPs from the MUC5B and TOLLIP regions. For validation of the findings, we provided in vitro evidence for the interaction of TOLLIP with air pollution.

Complex patterns of multimorbidity associated with severe COVID-19 and long COVID

BACKGROUND

Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses are scarce but may help to understand severe COVID-19 among patients at supposedly low risk.

METHODS

We systematically harmonized >12 million primary care and hospitalisation health records from ~500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID.

RESULTS

Here we identify 679 diseases associated with an increased risk for severe COVID-19 (n = 672) and/or Long COVID (n = 72) that span almost all clinical specialties and are strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we establish consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This includes a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observe partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis or rheumatoid arthritis, possibly indicating a segregation of disease mechanisms.

CONCLUSIONS

Our results provide a unique reference that demonstrates how 1) complex co-occurrence of multiple - including non-fatal - conditions predispose to increased COVID-19 severity and 2) how incorporating the whole breadth of medical diagnosis can guide the interpretation of genetic risk loci.

Complex patterns of multimorbidity associated with severe COVID-19 and Long COVID

Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses, including common, but non-fatal diseases are scarce, but may help to understand severe COVID-19 among patients at supposedly low risk. Here, we systematically harmonized >12 million primary care and hospitalisation health records from ~500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID. We identified a total of 679 diseases associated with an increased risk for severe COVID-19 (n=672) and/or Long COVID (n=72) that spanned almost all clinical specialties and were strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we established consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This included a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observed partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis (e.g., MUC5B, NPNT, and PSMD3) or rheumatoid arthritis (e.g., TYK2), possibly indicating a segregation of disease mechanisms. Our results provide a unique reference that demonstrates how 1) complex co-occurrence of multiple - including non-fatal - conditions predispose to increased COVID-19 severity and 2) how incorporating the whole breadth of medical diagnosis can guide the interpretation of genetic risk loci.

Correlation between Genomic Variants and Worldwide COVID-19 Epidemiology

COVID-19 is a systemic disease caused by the etiologic agent SARS-CoV-2, first reported in Hubei Province in Wuhan, China, in late 2019. The SARS-CoV-2 virus has evolved over time with distinct transmissibility subvariants from ancestral lineages. The clinical manifestations of the disease vary according to their severity and can range from asymptomatic to severe. Due to the rapid evolution to a pandemic, epidemiological studies have become essential to understand and effectively combat COVID-19, as the incidence and mortality of this disease vary between territories and populations. This study correlated epidemiological data on the incidence and mortality of COVID-19 with frequencies of important SNPs in GWAS studies associated with the susceptibility and mortality of this disease in different populations. Our results indicated significant correlations for 11 genetic variants (rs117169628, rs2547438, rs2271616, rs12610495, rs12046291, rs35705950, rs2176724, rs10774671, rs1073165, rs4804803 and rs7528026). Of these 11 variants, 7 (rs12046291, rs117169628, rs1073165, rs2547438, rs2271616, rs12610495 and rs35705950) were positively correlated with the incidence rate, these variants were more frequent in EUR populations, suggesting that this population is more susceptible to COVID-19. The rs2176724 variant was inversely related to incidence rates; therefore, the higher the frequency of the allele is, the lower the incidence rate. This variant was more frequent in the AFR population, which suggests a protective factor against SARS-CoV-2 infection in this population. The variants rs10774671, rs4804803, and rs7528026 showed a significant relationship with mortality rates. SNPs rs10774671 and rs4804803 were inversely related to mortality rates and are more frequently present in the AFR population. The rs7528026 variant, which is more frequent in the AMR population, was positively related to mortality rates. The study has the potential to identify and correlate the genetic profile with epidemiological data, identify populations that are more susceptible to severe forms of COVID-19, and relate them to incidence and mortality.

Immunology of SARS-CoV-2 Infection

According to the data from the World Health Organization, about 800 million of the world population had contracted coronavirus infection caused by SARS-CoV-2 by mid-2023. Properties of this virus have allowed it to circulate in the human population for a long time, evolving defense mechanisms against the host immune system. Severity of the disease depends largely on the degree of activation of the systemic immune response, including overstimulation of macrophages and monocytes, cytokine production, and triggering of adaptive T- and B-cell responses, while SARS-CoV-2 evades the immune system actions. In this review, we discuss immune responses triggered in response to the SARS-CoV-2 virus entry into the cell and malfunctions of the immune system that lead to the development of severe disease.

Leveraging the Million Veteran Program Infrastructure and Data for a Rapid Research Response to COVID-19

Background

The Veterans Health Administration Office of Research and Development (ORD) played a key role in the federal government's response to the COVID-19 pandemic. The ORD effectively leveraged existing resources to answer questions related to the SARS-CoV-2 virus and COVID-19.

Observations

When the COVID-19 pandemic hit in 2020, the Million Veteran Program (MVP), one of the largest genomic cohorts in the world, extended the centralized recruitment and enrollment infrastructure to develop a COVID-19 research volunteer registry to assist enrollment in the vaccine and treatment trials in which the US Department of Veterans Affairs (VA) participated. In addition, the MVP allowed for new data collection and a large genomic cohort to understand host contributions to COVID-19. This article describes ways the MVP contributed to the VA's rapid research response to COVID-19. Several host genetic factors believed to play a role in the development and severity of COVID-19 were identified. Furthermore, existing MVP partnerships with other federal agencies, particularly with the Department of Energy, were leveraged to improve understanding and management of COVID-19.

Conclusions

A previously established enterprise approach and research infrastructure were essential to the VA's successful and timely COVID-19 research response. This infrastructure not only supported rapid recruitment in vaccine and treatment trials, but also leveraged the unique MVP and VA electronic health record data to drive rapid scientific discovery and inform clinical operations. Extending the models that VA research applied to the federal government at large and establishing centralized resources for shared or federated data analyses across federal agencies will better equip the nation to respond to future public health crises.

LY6E is a pan-coronavirus restriction factor in the respiratory tract

LY6E is an antiviral restriction factor that inhibits coronavirus spike-mediated fusion, but the cell types in vivo that require LY6E for protection from respiratory coronavirus infection are unknown. Here we used a panel of seven conditional Ly6e knockout mice to define which Ly6e-expressing cells confer control of airway infection by murine coronavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Loss of Ly6e in Lyz2-expressing cells, radioresistant Vav1-expressing cells and non-haematopoietic cells increased susceptibility to murine coronavirus. Global conditional loss of Ly6e expression resulted in clinical disease and higher viral burden after SARS-CoV-2 infection, but little evidence of immunopathology. We show that Ly6e expression protected secretory club and ciliated cells from SARS-CoV-2 infection and prevented virus-induced loss of an epithelial cell transcriptomic signature in the lung. Our study demonstrates that lineage confined rather than broad expression of Ly6e sufficiently confers resistance to disease caused by murine and human coronaviruses.

A Causal Atlas on Comorbidities in Idiopathic Pulmonary Fibrosis: A Bidirectional Mendelian Randomization Study

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease with a high burden of both pulmonary and extrapulmonary comorbidities.

RESEARCH QUESTION

Do these comorbidities have causal relationships with IPF?

STUDY DESIGN AND METHODS

We searched PubMed to pinpoint possible IPF-related comorbid conditions. Bidirectional Mendelian randomization (MR) was performed using summary statistics from the largest genome-wide association studies for these diseases to date in a two-sample setting. Findings were verified using multiple MR approaches under different model assumptions, replication datasets for IPF, and secondary phenotypes.

RESULTS

A total of 22 comorbidities with genetic data available were included. Bidirectional MR analyses showed convincing evidence for two comorbidities and suggestive evidence for four comorbidities. Gastroesophageal reflux disease, VTE, and hypothyroidism were associated causally with an increased risk of IPF, whereas COPD was associated causally with a decreased risk of IPF. For the reverse direction, IPF showed causal associations with a higher risk of lung cancer, but a reduced risk of hypertension. Follow-up analyses of pulmonary function parameters and BP measures supported the causal effect of COPD on IPF and the causal effect of IPF on hypertension.

INTERPRETATION

The present study suggested the causal associations between IPF and certain comorbidities from a genetic perspective. Further research is needed to understand the mechanisms of these associations.

[SARS-CoV-2-Infection and Interstitial Lung Disease: Position paper of the German Respiratory Society]

The SARS-CoV-2 pandemic had a tremendous impact on diagnosis and treatment of interstitial lung diseases (ILD). Especially in the early phase of the pandemic, when the delta variant was prevailling, a huge number of viral pneumonias were observed, which worsened pre-existing, triggered de novo occurence or discovery of previously subclincal interstitial lung diseases. The effect of SARS-CoV-2 infection - without or with accompanying viral pneumonia - on the further development of pre-existing ILD as well of new pulmonary inflitrates and consolidiations is difficult to predict and poses a daily challenge to interdisciplinary ILD boards. This position paper of the German Respiratory Society (DGP e.V.) provides answers to the most pressing questions based on current knowledge.

Diagnosis and Stratification of COVID-19 Infections Using Differential Plasma Levels of D-Dimer: A Two-Center Study from Saudi Arabia

Background: D-dimer, generated upon the degradation of fibrin, is extensively used to detect thrombosis in various diseases. It is also explored as a marker for thrombosis in cases with COVID-19 disease. Few studies have confirmed its utility as a marker for assessing disease severity. Objectives: The current research was undertaken to determine the role of D-dimer in patients with COVID-19 and to investigate any association with the progression and severity of the disease in the Saudi population. Methods: Clinical indices in confirmed COVID-19 patients were collected from tertiary care hospitals in Aljouf and Qassim regions. The plasma D-dimer levels were quantified directly in the samples collected from COVID-19 patients (n = 148) using an immunofluorescence assay, and the data were presented in Fibrinogen Equivalent Units (mg/L). The collected data of D-dimer were analyzed based on COVID-19 severity, age, and the gender of patients. Results: The findings show that the plasma D-dimer concentrations were significantly (p = 0.0027) elevated in COVID-19 cases (n = 148), compared to in the normal healthy uninfected controls (n = 309). Moreover, the D-dimer levels were analyzed according to the severity of the disease in the patients. The data revealed that D-dimer concentrations were significantly increased in patients with mild infection to moderate disease, and the levels were the highest in patients with severe COVID-19 disease (p < 0.05). Our analysis demonstrates that the D-dimer levels have no association with the age or gender of COVID-19 patients (p > 0.05) in the study population. Conclusions: D-dimer can serve as a biomarker not only for the detection of COVID-19 infection, but also for determining the severity of infection of COVID-19 disease.

Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors

The clinical course and outcome of COVID-19 are highly variable, ranging from asymptomatic infections to severe disease and death. Understanding the risk factors of severe COVID-19 is relevant both in the clinical setting and at the epidemiological level. Here, we provide an overview of host, viral and environmental factors that have been shown or (in some cases) hypothesized to be associated with severe clinical outcomes. The factors considered in detail include the age and frailty, genetic polymorphisms, biological sex (and pregnancy), co- and superinfections, non-communicable comorbidities, immunological history, microbiota, and lifestyle of the patient; viral genetic variation and infecting dose; socioeconomic factors; and air pollution. For each category, we compile (sometimes conflicting) evidence for the association of the factor with COVID-19 outcomes (including the strength of the effect) and outline possible action mechanisms. We also discuss the complex interactions between the various risk factors.