IAR22_Influenza_Recon2 - A collection of cell-type specific constraint-based metabolic influenza infection models
Ontology highlight
ABSTRACT: A collection of constraint-based metabolic models of human host cells infected with SARS-CoV-2 based that were generated based on gene-expression data.
Project description:A collection of cell-type specific constraint-based metabolic models of human BALF2 cells (bronchoalveolar Lavage fluid cells) infected with SARS-CoV-2 based that were generated based on gene-expression data using a manually curated version of Recon 2 as base model.
Project description:A collection of cell-type specific constraint-based metabolic models of human cells infected with SARS-CoV-2 based that were generated from a curated version of Recon 2 based on gene-expression data.
Project description:A collection of cell-type specific constraint-based metabolic models of human H1299 cells (human non-small cell lung carcinoma cell line) infected with SARS-CoV-2 based that were generated based on gene-expression data.
Project description:A collection of cell-type specific constraint-based metabolic models of Calu-3 cells (a human lung epithelial cancer cell line) infected with SARS-CoV-2 based that were generated based on gene-expression data.
Project description:A collection of cell-type specific constraint-based metabolic models of human intestine cells infected with SARS-CoV-2 based that were generated based on gene-expression data.
Project description:A collection of constraint-based metabolic models of bronchoalveolar lavage fluid epithelia cells infected with SARS-CoV-2 based that were generated based on gene-expression data.
Project description:Healthcare workers were recruited at St Bartholomew’s Hospital, London, UK in the week of lockdown in the United Kingdom (between 23rd and 31st March 2020). Participants underwent weekly evaluation using a questionnaire and biological sample collection (including serological assays) for up to 16 weeks when attending for work and self-declared as fit to attend work at each visit, with further follow up samples collected at 24 weeks. Blood RNA sequencing data was to be used to identify host-response biomarkers of early SARS-CoV-2 infection, to evaluate existing blood transcriptomic signatures of viral infection, and to describe the underlying biology during SARS-CoV-2 infection. This submission includes a total of 172 blood RNA samples from 99 participants. Of these, 114 samples (including 16 convalescent samples collected 6 months after infection) were obtained from 41 SARS-CoV-2 cases, with the remaining 58 from uninfected controls. Participants with available blood RNA samples who had PCR-confirmed SARS-CoV-2 infection during follow-up were included as ‘cases’. Those without evidence of SARS-CoV-2 infection on nasopharyngeal swabs and who remained seronegative by both Euroimmun anti S1 spike protein and Roche anti nucleocapsid protein throughout follow-up were included as uninfected controls. ‘Cases’ include all available RNA samples, including convalescent samples at week 24 of follow-up for a subset of participants. For uninfected controls, we included baseline samples only. Sample class denotes weekly interval to positive SARS-CoV-2 PCR; non-infected controls (NIC); convalescent samples (Conv)_.
Project description:We combined RT-LAMP with deep sequencing to detect as few as 5–10 virions of SARS-CoV-2 in unprocessed human saliva. Based on a multi-dimensional barcoding strategy, COV-ID can be used to test thousands of samples overnight in a single sequencing run with limited labor and laboratory equipment. The sequencing-based readout allows COV-ID to detect multiple amplicons simultaneously, including key controls such as host transcripts and artificial spike-ins, as well as multiple pathogens. Here we demonstrate this flexibility by simultaneous detection of 4 amplicons in contrived saliva samples: SARS-CoV-2, influenza A, human STATHERIN, and an artificial SARS spike-in. The approach was validated on clinical saliva samples, where it showed 100% agreement with RT-qPCR. COV-ID can also be performed directly on saliva adsorbed on filter paper, simplifying collection logistics and sample handling.
Project description:To explore the relationship between SARS-CoV-2 infection in different time before operation and postoperative main complications (mortality, main pulmonary and cardiovascular complications) 30 days after operation; To determine the best timing of surgery after SARS-CoV-2 infection.
Project description:Seasonal infection rates of individual viruses are influenced by synergistic or inhibitory interactions between coincident viruses. Endemic patterns of SARS-CoV-2 and influenza infection overlap seasonally in the Northern hemisphere and may be similarly influenced. We explored the immunopathologic basis of SARS-CoV-2 and influenza A (H1N1) interactions in Syrian hamsters. H1N1 given 48 hours prior to SARS-CoV-2 profoundly mitigated weight loss and lung pathology compared to SARS-CoV-2 infection alone. This was accompanied by normalization of granulocyte dynamics and accelerated antigen presenting populations in bronchoalveolar lavage and blood. Using nasal transcriptomics, we identified rapid upregulation of innate and antiviral pathways induced by H1N1 by the time of SARS-CoV-2 inoculation in 48 hour dual infected animals. Dual infected animals also experienced significant transient downregulation of mitochondrial and viral replication pathways. By quantitative RT-PCR, we confirmed reduced SARS-CoV-2 viral load and lower cytokine levels throughout disease course in lung of dual infected animals. Our data confirm that H1N1 infection induces rapid and transient gene expression that is associated with mitigation of SARS-CoV-2 pulmonary disease. These protective responses are likely to begin in the upper respiratory tract shortly after infection. On a population level, interaction between these two viruses may influence their relative seasonal infection rates.