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: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: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: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.