Oxysterols drive inflammation via GPR183 during influenza virus and SARS-CoV-2 infection

RationaleSevere viral respiratory infections are often characterized by extensive myeloid cell infiltration and activation and persistent lung tissue injury. However, the immunological mechanisms driving excessive inflammation in the lung remain elusive. ObjectivesTo 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. MethodsPreclinical murine models of influenza virus and SARS-CoV-2 infection. ResultsOxidized 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 infections. Both IAV and SARS-CoV-2 infections 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 oxidized cholesterols 25-hydroxycholesterol and 7,25-dihydroxycholesterol (7,25-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 also significantly attenuated the severity of SARS-CoV-2 infection by reducing weight loss and viral loads. ConclusionThis study demonstrates that oxysterols drive inflammation in the lung and provides the first preclinical evidence for therapeutic benefit of targeting GPR183 during severe viral respiratory infections. Author SummaryViral infections trigger oxysterol production in the lung, attracting macrophages via GPR183. Blocking GPR183 reduced inflammation and disease severity in SARS-CoV-2 infection, making GPR183 a putative target for therapeutic intervention.

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 govern this remain unknown. In this study, we investigated the landscape of cardiac tissues collected at rapid autopsy from SARS-CoV-2, pH1N1, and 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 {gamma}-H2Ax immunohistochemistry. In comparison, pH1N1 showed upregulation of Interferon-stimulated genes (ISGs), 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.

Quantifying the risks versus benefits of the Pfizer COVID-19 vaccine in Australia: a Bayesian network analysis

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 challenge for risk-benefit analysis of vaccination programs. 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, the probabilities of developing and dying from COVID-19-related myocarditis were 239-5847 and 1430-384,684 times higher (depending on age and sex), respectively, than developing vaccine-associated myocarditis. For one million people with this vaccine coverage, where transmission intensity was equivalent to 10% chance of infection over two 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. 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 Thromobocytopenia 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 the existing evidence on the risks versus benefits of the AZ vaccine. We developed a Bayesian network to consolidate the existing 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 structure of the model, relevant variables, data to be included, 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 a 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 easily adaptable to international settings by re-parameterising it with local data. This paper provides detailed description of the model-building methodology, which can 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 Thromobocytopenia Syndrome (TTS) has been associated with the AstraZencea (AZ) COVID-19 vaccine. 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 age-specific variation in rates of TTS, COVID-19, and CFR. 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 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. HIGHLIGHTSO_LIAZ vaccination risk-benefit analysis must consider age/community transmission level C_LIO_LIAZ vaccine benefits far outweigh risks in older age groups and during high transmission C_LIO_LIAZ vaccine-associated TTS lower fatality than COVID-related atypical blood clots C_LIO_LIBayesian networks utility for risk-benefit analysis of rapidly evolving situations C_LIO_LIBNs allow integrating multiple data sources when large datasets are not available C_LI

The role of pre-existing chronic disease in cardiac complications from SARS-CoV-2 infection: A systematic review and meta-analysis

ImportanceSARS-CoV-2 is associated with multiple direct and indirect effects to the heart. It is not yet well defined whether patient groups at increased risk of severe respiratory disease due to SARS-CoV-2 infection also experience a heightened incidence of cardiac complications. ObjectiveWe sought to analyse the role of pre-existing chronic disease (chronic respiratory illness, cardiovascular disease (CVD), hypertension and diabetes mellitus) in the development of cardiac complications from SARS-CoV-2. Data SourcesWe retrospectively investigated published (including pre-prints), publicly released, de-identified, data made available between Dec 1, 2019, and May 11, 2020. Information was accessed from PubMed, Embase, medRxiv and SSRN. Study Selection379 full-text articles were reviewed and 321 excluded for lack of original research, irrelevance to outcome, inappropriate cohort, or small patient numbers (case reports of <10 patients). Data were extracted from two studies and the remaining 56 contacted to request appropriate data, to which three responded with data contributions. A final of five studies were included. Data Extraction and SynthesisThis systematic review was conducted based on PRISMA and MOOSE statements. Included studies were critically appraised using Newcastle Ottawa Quality Assessment Scale (NOS). Data were extracted independently by multiple observers. A fixed-effects model was selected for the meta-analysis based on relatively low heterogeneity between the studies (I 2<50%). Main Outcome and MeasuresCardiac complications were determined via blood levels of cardiac biomarkers above the 99th percentile of the upper reference limit, abnormalities in electrocardiography, and/or abnormalities in echocardiography. ResultsSARS-CoV-2-infected patients who developed cardiac complications were, on average, 10 years older than those that did not. Pooled analyses showed the development of cardiac complications from SARS-CoV-2 was significantly increased in patients with underlying chronic respiratory illness (OR 2.88[1.45,5.71]), CVD (OR 5.12[3.09,8.48]), hypertension (OR 4.37[2.99,6.39]) and diabetes mellitus (OR 2.61[1.67,4.09]). Conclusions and RelevanceOlder age and pre-existing chronic respiratory illness, CVD, hypertension, and diabetes mellitus may represent prognostic factors for the development of additional cardiac complications in COVID-19, highlighting the need for a multidisciplinary approach to chronic disease patient management and providing justification for a larger scale observational study.

Children are unlikely to have been the primary source of household SARS-CoV-2 infections

The role of children in the spread of 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 paediatric 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 (SAR) in paediatric household contacts was assessed. The secondary attack rate in paediatric 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. 40-word summaryIn household transmission clusters of SARS-CoV-2 children are unlikely to be the index case. Children are also less likely than adults to be infected with SARS-CoV-2 from a family member.