Transcriptomic profiling of cardiac tissues from SARS-CoV-2 patients identifies DNA damage.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known to present with pulmonary and extra-pulmonary organ complications. In comparison with the 2009 pandemic (pH1N1), SARS-CoV-2 infection is likely to lead to more severe disease, with multi-organ effects, including cardiovascular disease. SARS-CoV-2 has been associated with acute and long-term cardiovascular disease, but the molecular changes that govern this remain unknown. In this study, we investigated the host transcriptome landscape of cardiac tissues collected at rapid autopsy from seven SARS-CoV-2, two pH1N1, and six control patients using targeted spatial transcriptomics approaches. Although SARS-CoV-2 was not detected in cardiac tissue, host transcriptomics showed upregulation of genes associated with DNA damage and repair, heat shock, and M1-like macrophage infiltration in the cardiac tissues of COVID-19 patients. The DNA damage present in the SARS-CoV-2 patient samples, were further confirmed by γ-H2Ax immunohistochemistry. In comparison, pH1N1 showed upregulation of interferon-stimulated genes, in particular interferon and complement pathways, when compared with COVID-19 patients. These data demonstrate the emergence of distinct transcriptomic profiles in cardiac tissues of SARS-CoV-2 and pH1N1 influenza infection supporting the need for a greater understanding of the effects on extra-pulmonary organs, including the cardiovascular system of COVID-19 patients, to delineate the immunopathobiology of SARS-CoV-2 infection, and long term impact on health.

GPR183 antagonism reduces macrophage infiltration in influenza and SARS-CoV-2 infection.

RATIONALE: Severe viral respiratory infections are often characterised by extensive myeloid cell infiltration and activation and persistent lung tissue injury. However, the immunological mechanisms driving excessive inflammation in the lung remain poorly understood. OBJECTIVES: To identify the mechanisms that drive immune cell recruitment in the lung during viral respiratory infections and identify novel drug targets to reduce inflammation and disease severity. METHODS: Preclinical murine models of influenza virus and severe acute respiratory coronavirus 2 (SARS-CoV-2) infection. RESULTS: Oxidised cholesterols and the oxysterol-sensing receptor GPR183 were identified as drivers of monocyte-macrophage infiltration to the lung during influenza virus (IAV) and SARS-CoV-2 infection. Both IAV and SARS-CoV-2 infection upregulated the enzymes cholesterol 25-hydroxylase (CH25H) and cytochrome P450 family 7 subfamily member B1 (CYP7B1) in the lung, resulting in local production of the oxidised cholesterols 25-hydroxycholesterol (25-OHC) and 7α,25-dihydroxycholesterol (7α,25-OHC). Loss-of-function mutation of GPR183, or treatment with a GPR183 antagonist, reduced macrophage infiltration and inflammatory cytokine production in the lungs of IAV- or SARS-CoV-2-infected mice. The GPR183 antagonist significantly attenuated the severity of SARS-CoV-2 infection and viral loads. Analysis of single cell RNASeq data on bronchoalveolar lavage samples from healthy controls and COVID-19 patients with moderate and severe disease revealed that CH25H, CYP7B1 and GPR183 are significantly upregulated in macrophages during COVID-19. CONCLUSION: This study demonstrates that oxysterols drive inflammation in the lung via GPR183 and provides the first preclinical evidence for therapeutic benefit of targeting GPR183 during severe viral respiratory infections.

A Bayesian network analysis quantifying risks versus benefits of the Pfizer COVID-19 vaccine in Australia.

The Pfizer COVID-19 vaccine is associated with increased myocarditis incidence. Constantly evolving evidence regarding incidence and case fatality of COVID-19 and myocarditis related to infection or vaccination, creates challenges for risk-benefit analysis of vaccination. Challenges are complicated further by emerging evidence of waning vaccine effectiveness, and variable effectiveness against variants. Here, we build on previous work on the COVID-19 Risk Calculator (CoRiCal) by integrating Australian and international data to inform a Bayesian network that calculates probabilities of outcomes for the delta variant under different scenarios of Pfizer COVID-19 vaccine coverage, age groups (≥12 years), sex, community transmission intensity and vaccine effectiveness. The model estimates that in a population where 5% were unvaccinated, 5% had one dose, 60% had two doses and 30% had three doses, there was a substantially greater probability of developing (239-5847 times) and dying (1430-384,684 times) from COVID-19-related than vaccine-associated myocarditis (depending on age and sex). For one million people with this vaccine coverage, where transmission intensity was equivalent to 10% chance of infection over 2 months, 68,813 symptomatic COVID-19 cases and 981 deaths would be prevented, with 42 and 16 expected cases of vaccine-associated myocarditis in males and females, respectively. These results justify vaccination in all age groups as vaccine-associated myocarditis is generally mild in the young, and there is unequivocal evidence for reduced mortality from COVID-19 in older individuals. The model may be updated to include emerging best evidence, data pertinent to different countries or vaccines and other outcomes such as long COVID.

Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine.

Uncertainty surrounding the risk of developing and dying from Thrombosis and Thrombocytopenia Syndrome (TTS) associated with the AstraZeneca (AZ) COVID-19 vaccine may contribute to vaccine hesitancy. A model is urgently needed to combine and effectively communicate evidence on the risks versus benefits of the AZ vaccine. We developed a Bayesian network to consolidate evidence on risks and benefits of the AZ vaccine, and parameterised the model using data from a range of empirical studies, government reports, and expert advisory groups. Expert judgement was used to interpret the available evidence and determine the model structure, relevant variables, data for inclusion, and how these data were used to inform the model. The model can be used as a decision-support tool to generate scenarios based on age, sex, virus variant and community transmission rates, making it useful for individuals, clinicians, and researchers to assess the chances of different health outcomes. Model outputs include the risk of dying from TTS following the AZ COVID-19 vaccine, the risk of dying from COVID-19 or COVID-19-associated atypical severe blood clots under different scenarios. Although the model is focused on Australia, it can be adapted to international settings by re-parameterising it with local data. This paper provides detailed description of the model-building methodology, which can be used to expand the scope of the model to include other COVID-19 vaccines, booster doses, comorbidities and other health outcomes (e.g., long COVID) to ensure the model remains relevant in the face of constantly changing discussion on risks versus benefits of COVID-19 vaccination.

Risk-benefit analysis of the AstraZeneca COVID-19 vaccine in Australia using a Bayesian network modelling framework.

Thrombosis and Thrombocytopenia Syndrome (TTS) has been associated with the AstraZencea (AZ) COVID-19 vaccine (Vaxzevria). Australia has reported low TTS incidence of < 3/100,000 after the first dose, with case fatality rate (CFR) of 5-6%. Risk-benefit analysis of vaccination has been challenging because of rapidly evolving data, changing levels of transmission, and variation in rates of TTS, COVID-19, and CFR between age groups. We aim to optimise risk-benefit analysis by developing a model that enables inputs to be updated rapidly as evidence evolves. A Bayesian network was used to integrate local and international data, government reports, published literature and expert opinion. The model estimates probabilities of outcomes under different scenarios of age, sex, low/medium/high transmission (0.05%/0.45%/5.76% of population infected over 6 months), SARS-CoV-2 variant, vaccine doses, and vaccine effectiveness. We used the model to compare estimated deaths from AZ vaccine-associated TTS with i) COVID-19 deaths prevented under different scenarios, and ii) deaths from COVID-19 related atypical severe blood clots (cerebral venous sinus thrombosis & portal vein thrombosis). For a million people aged ≥ 70 years where 70% received first dose and 35% received two doses, our model estimated < 1 death from TTS, 25 deaths prevented under low transmission, and > 3000 deaths prevented under high transmission. Risks versus benefits varied significantly between age groups and transmission levels. Under high transmission, deaths prevented by AZ vaccine far exceed deaths from TTS (by 8 to > 4500 times depending on age). Probability of dying from COVID-related atypical severe blood clots was 58-126 times higher (depending on age and sex) than dying from TTS. To our knowledge, this is the first example of the use of Bayesian networks for risk-benefit analysis for a COVID-19 vaccine. The model can be rapidly updated to incorporate new data, adapted for other countries, extended to other outcomes (e.g., severe disease), or used for other vaccines.

Cardiovascular disease in SARS-CoV-2 infection.

Pre-existing cardiovascular disease (CVD) increases the morbidity and mortality of COVID-19 and is strongly associated with poor disease outcomes. However, SARS-CoV-2 infection can also trigger de novo acute and chronic cardiovascular disease. Acute cardiac complications include arrhythmia, myocarditis and heart failure, which are significantly associated with higher in-hospital mortality. The possible mechanisms by which SARS-CoV-2 causes this acute cardiac disease include direct damage caused by viral invasion of cardiomyocytes as well as indirect damage through systemic inflammation. The long-term cardiac complications associated with COVID-19 are incompletely characterised and thought to include hypertension, arrhythmia, coronary atherosclerosis and heart failure. Although some cardiac-related symptoms can last over 6 months, the effect of these complications on long-term patient health remains unclear. The risk factors associated with long-term cardiovascular disease remain poorly defined. Determining which patients are most at-risk of long-term cardiovascular disease is vital so that targeted follow-up and patient care can be provided. The aim of this review was to summarise the current evidence of the acute and long-term cardiovascular consequences of SARS-CoV-2 infection and the mechanisms by which SARS-CoV-2 may cause cardiovascular disease.

A Meta-analysis on the Role of Children in Severe Acute Respiratory Syndrome Coronavirus 2 in Household Transmission Clusters.

The role of children in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains highly controversial. To address this issue, we performed a meta-analysis of the published literature on household SARS-CoV-2 transmission clusters (n = 213 from 12 countries). Only 8 (3.8%) transmission clusters were identified as having a pediatric index case. Asymptomatic index cases were associated with a lower secondary attack in contacts than symptomatic index cases (estimate risk ratio [RR], 0.17; 95% confidence interval [CI], 0.09-0.29). To determine the susceptibility of children to household infections the secondary attack rate in pediatric household contacts was assessed. The secondary attack rate in pediatric household contacts was lower than in adult household contacts (RR, 0.62; 95% CI, 0.42-0.91). These data have important implications for the ongoing management of the COVID-19 pandemic, including potential vaccine prioritization strategies.

A meta-analysis on the role of pre-existing chronic disease in the cardiac complications of SARS-CoV-2 infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been associated with multiple direct and indirect cardiovascular complications. We sought to analyze the association of host co-morbidities (chronic respiratory illnesses, cardiovascular disease [CVD], hypertension or diabetes mellitus [DM]) with the acute cardiovascular complications associated with SARS-CoV-2 infection. Individual analyses of the majority of studies found median age was higher by ~10 years in patients with cardiovascular complications. Pooled analyses showed development of SARS-CoV-2 cardiovascular complications was significantly increased in patients with chronic respiratory illness (odds ratio (OR): 1.67 [1.48, 1.88]), CVD (OR: 3.37 [2.57, 4.43]), hypertension (OR: 2.68 [2.11, 3.41]), DM (OR: 1.60 [1.31, 1.95]) and male sex (OR: 1.31 [1.21, 1.42]), findings that were mostly conserved during sub-analysis of studies stratified into global geographic regions. Age, chronic respiratory illness, CVD, hypertension, DM, and male sex may represent prognostic factors for the development of cardiovascular complications in COVID-19 disease, highlighting the need for a multidisciplinary approach to chronic disease patient management.