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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.
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We describe an experimental setup and a currently running experiment for evaluating how physical interactions over time and between individuals affect the spread of epidemics. Our experiment involves the voluntary use of the Safe Blues Android app by participants at The University of Auckland (UoA) City Campus in New Zealand. The app spreads multiple virtual safe virus strands via Bluetooth depending on the social and physical proximity of the subjects. The evolution of the virtual epidemics is recorded as they spread through the population. The data is presented as a real-time (and historical) dashboard. A simulation model is applied to calibrate strand parameters. Participants locations are not recorded, but participants are rewarded based on the duration of participation within a geofenced area, and aggregate participation numbers serve as part of the data. Once the experiment is complete, the data will be made available as an open-source anonymized dataset. This paper outlines the experimental setup, software, subject-recruitment practices, ethical considerations, and dataset description. The paper also highlights current experimental results in view of the lockdown that started in New Zealand at 23:59 on August 17, 2021. The experiment was initially planned in the New Zealand environment, expected to be free of COVID and lockdowns after 2020. However, a COVID Delta strain lockdown shuffled the cards and the experiment is currently extended into 2022. Author summaryIn this paper, we describe the Safe Blues Android app experimental setup and a currently running experiment at the University of Auckland City Campus. This experiment is designed to evaluate how physical interactions over time and between individuals affect the spread of epidemics. The Safe Blues app spreads multiple virtual safe virus strands via Bluetooth based on the subjects unobserved social and physical proximity. The app does not record the participants locations, but participants are rewarded based on the duration of participation within a geofenced area, and aggregate participation numbers serve as part of the data. When the experiment is finished, the data will be released as an open-source anonymized dataset. The experimental setup, software, subject recruitment practices, ethical considerations, and dataset description are all described in this paper. In addition, we present our current experimental results in view of the lockdown that started in New Zealand at 23:59 on August 17, 2021. The information we provide here may be useful to other teams planning similar experiments in the future.
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Macrophages are a major source of pro-inflammatory cytokines in COVID-19. How macrophages sense the causative virus, SARS-CoV-2, to drive cytokine release is, however, unclear. Here, we show that human macrophages do not directly sense and respond to infectious SARS-CoV-2 virions because they lack sufficient ACE2 expression to support virus entry and replication. Over-expression of ACE2 in human macrophages permits SARS-CoV-2 entry and early-stage replication and facilitates macrophage pro-inflammatory and anti-viral responses. ACE2 over-expression does not, however, permit the release of newly synthesised virions from SARS-CoV-2-infected macrophages, consistent with abortive replication. Release of new, infectious SARS-CoV-2 virions from ACE2 over-expressing macrophages only occurred if anti-viral mediator induction was also blocked, indicating that macrophages restrict SARS-CoV-2 infection at two stages of the viral life cycle. These findings resolve the current controversy over macrophage-SARS-CoV-2 interactions and identify a signalling circuit that directly links macrophage recognition of SARS-CoV-2 to restriction of viral replication. One sentence summaryACE2 is necessary for SARS-CoV-2 infection and sensing by macrophages but not sufficient for productive viral replication.
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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.
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The >30 mutated residues in the Omicron spike protein have led to its rapid classification as a new SARS-CoV-2 variant of concern. As a result, Omicron may escape from the immune system, decreasing the protection provided by COVID-19 vaccines. Preliminary data shows a weaker neutralizing antibody response to Omicron compared to the ancestral SARS-CoV-2 virus, which can be increased after a booster vaccine. Here, we report that CD8+ T cells can recognize Omicron variant epitopes presented by HLA-A*02:01 in both COVID-19 recovered and vaccinated individuals, even 6 months after infection or vaccination. Additionally, the T cell response was stronger for Omicron variant epitopes after the vaccine booster. Altogether, T cells can recognize Omicron variants, especially in vaccinated individuals after the vaccine booster. One-Sentence SummaryCD8+ T cells response against Omicron variant epitopes is stronger after the vaccine booster.
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BackgroundRobust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness. MethodsWe conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients. FindingsWe show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression is associated with the presence of a high viral load. We further demonstrate that systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 severity. For clinical outcome prediction (e.g. respiratory failure), IFI27 expression displays a high positive (0.83) and negative (0.95) predictive value, outperforming all other known predictors of COVID-19 severity. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 swine influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients. InterpretationThese data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched the scientific literature using PubMed to identify studies that used the IFI27 biomarker to predict outcomes in COVID-19 patients. We used the search terms "IFI27", "COVID-19, "gene expression" and "outcome prediction". We did not identify any study that investigated the role of IFI27 biomarker in outcome prediction. Although ten studies were identified using the general terms of "gene expression" and "COVID-19", IFI27 was only mentioned in passing as one of the identified genes. All these studies addressed the broader question of the host response to COVID-19; none focused solely on using IFI27 to improve the risk stratification of infected patients in a pandemic. Added value of this studyHere, we present the findings of a multi-cohort study of the IFI27 biomarker in COVID-19 patients. Our findings show that the host response, as reflected by blood IFI27 gene expression, accurately predicts COVID-19 disease progression (positive and negative predictive values; 0.83 and 0.95, respectively), outperforming age, comorbidity, C-reactive protein and all other known risk factors. The strong association of IFI27 with disease severity occurs not only in SARS-CoV-2 infection, but also in other respiratory viruses with pandemic potential, such as the influenza virus. These findings suggest that host response biomarkers, such as IFI27, could help identify high-risk COVID-19 patients - those who are more likely to develop infection complications - and therefore may help improve patient triage in a pandemic. Implications of all the available evidenceThis is the first systemic study of the clinical role of IFI27 in the current COVID-19 pandemic and its possible future application in other respiratory virus pandemics. The findings not only could help improve the current management of COVID-19 patients but may also improve future pandemic preparedness.
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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.
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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
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Children typically experience more mild symptoms of COVID-19 when compared to adults. There is a strong body of evidence that children are also less susceptible to SARS-CoV-2 infection with the ancestral viral isolate. However, the emergence of SARS-CoV-2 variants of concern (VOCs) has been associated with an increased number of pediatric infections. Whether this is the result of widespread adult vaccination or fundamental changes in the biology of SARS-CoV-2 remains to be determined. Here, we use primary nasal epithelial cells from children and adults, differentiated at an air-liquid interface to show that the ancestral SARS-CoV-2 replicates to significantly lower titers in the nasal epithelial cells of children compared to those of adults. This was associated with a heightened antiviral response to SARS-CoV-2 in the nasal epithelial cells of children. Importantly, the Delta variant also replicated to significantly lower titres in the nasal epithelial cells of children. This trend was markedly less pronounced in the case of Omicron. It is also striking to note that, at least in terms of viral RNA, Omicron replicated better in pediatric NECs compared to both Delta and the ancestral virus. Taken together, these data show that the nasal epithelium of children supports lower infection and replication of ancestral SARS-CoV-2, although this may be changing as the virus evolves.
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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.
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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.
