COVID-19 research priorities
Lead Research Organisation:
Earlham Institute
Department Name: UNLISTED
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Technical Summary
This project is addressing COVID-19 research priorities. It is supported by funding from the Genomics for Food security, Core Strategic Programme Grant.
The additional sub-objective (2.3.3 Impact of SARS-CoV-2 infection on host immune regulation) is based on key computational tool and database developments already carried out and published as part of the CSP (e.g., Csabai et al, Methods Mol Biol, 2018; Sudhakar et al, Autophagy, 2019; Andrighetti et al, Cells, 2020).
We will be applying modelling, AI, digital and data approaches to understanding of the COVID-19 pandemic and mitigating its effects.
Specifically, we will:
1. Analyse and integrate ‘omics (RNAseq and proteomics) data to create SARS-CoV-2 infected cell models, with a special focus on intestinal epithelial cells using already published and available datasets.
2. Create intercellular networks by integrating available and published singe-cell datasets to model how infected cells could rewire the normal immune regulation, focusing mostly on cytokine-based cell-cell communication.
3. Identify key cell types and pathways responsible for transducing the infection signal, and by combining this information with known disease-associated SNP datasets, determine risk groups for severe infection.
The additional sub-objective (2.3.3 Impact of SARS-CoV-2 infection on host immune regulation) is based on key computational tool and database developments already carried out and published as part of the CSP (e.g., Csabai et al, Methods Mol Biol, 2018; Sudhakar et al, Autophagy, 2019; Andrighetti et al, Cells, 2020).
We will be applying modelling, AI, digital and data approaches to understanding of the COVID-19 pandemic and mitigating its effects.
Specifically, we will:
1. Analyse and integrate ‘omics (RNAseq and proteomics) data to create SARS-CoV-2 infected cell models, with a special focus on intestinal epithelial cells using already published and available datasets.
2. Create intercellular networks by integrating available and published singe-cell datasets to model how infected cells could rewire the normal immune regulation, focusing mostly on cytokine-based cell-cell communication.
3. Identify key cell types and pathways responsible for transducing the infection signal, and by combining this information with known disease-associated SNP datasets, determine risk groups for severe infection.
Planned Impact
unavailable
People |
ORCID iD |
| Tamas Korcsmaros (Principal Investigator) |
Publications
Földvári-Nagy L
(2021)
On the role of bacterial metalloproteases in COVID-19 associated cytokine storm.
in Cell communication and signaling : CCS
Kapuy O
(2021)
Autophagy-dependent survival is controlled with a unique regulatory network upon various cellular stress events.
in Cell death & disease
Olbei M
(2021)
SARS-CoV-2 Causes a Different Cytokine Response Compared to Other Cytokine Storm-Causing Respiratory Viruses in Severely Ill Patients
in Frontiers in Immunology
Ostaszewski M
(2021)
COVID-19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms
in Molecular Systems Biology
Ostaszewski M
(2021)
COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms
in Molecular Systems Biology
Poletti M
(2022)
Mapping the epithelial-immune cell interactome upon infection in the gut and the upper airways.
in NPJ systems biology and applications
| Description | In the past year we applied our previous experience from multi-omics and network modelling to combat the COVID-19 pandemic. In the first effort, we carried out a large-scale literature curation on how the immune response given to SARS-CoV-2 differs from other viruses, published early last year (Olbei et al, Frontiers in Immunology, 2021). The paper is in the top 5% of articles in Frontiers in Immunology in terms of views, and has already been cited 36 times, including by a company. Since then, we developed a new computational tool and network resource called CytokineLink, published in August, aimed at helping the scientific community study inflammatory and infectious diseases such as COVID-19. CytokineLink introduced a novel concept of cytokine - cytokine interactions enabling the study of immune signalling events in a functional manner (Olbei et al, Cells, 2021). The results of this work have been presented at the ECCO conference as poster presentations, both in 2021 and 2022. Work is ongoing to improve the model combining it with the methods we utilised for ViralLink, a computational pipeline we published to investigate the effect of SARS-CoV-2 on intracellular signalling and regulation (Treveil et al, PLOS Computational Biology, 2021). We also actively contributed to the COVID Disease Map effort, a consortia of hundreds of researchers developing systems-level maps of SARS-CoV-2 (Ostaszewski et al, Molecular Systems Biology, 2021). |
| Exploitation Route | The ViralLink pipeline and the CytokineLink resource both provide easy access to carry out similar analysis we have already done. Our literature metaanalysis with the various coronaviruses (Olbei et al, Frontiers in Immunology, 2021) already attracted multiple users to use the collected data. |
| Sectors | Healthcare |
| Title | CytokineLink: an interactive cytokine communication map to analyse immune responses in inflammatory and infectious diseases |
| Description | In this project, we built a communication map between tissues and blood cells to show how cytokine feedback loops can build (and thus, possibly break) under pathophysiological conditions. We collated the most prevalent cytokines from literature, and assigned the proteins and their receptors to source tissue and blood cell types based on consensus RNA-Seq data from the Human Protein Atlas. To assign more confidence to the interactions, we integrated cytokine-cell interaction data from two systems immunology databases, immuneXpresso and ImmunoGlobe. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2021 |
| Provided To Others? | No |
| Impact | I gave a virtual poster presentation on the tool at the ISMB 2020 conference. |
| Description | COVID Disease Map Consortia |
| Organisation | University of Luxembourg |
| Department | Luxembourg Centre for Systems Biomedicine |
| Country | Luxembourg |
| Sector | Academic/University |
| PI Contribution | We provide resourcs (SignaLink, OmniPath and VIralLink) to the curators of the project. And we participate in a weekly TC among 150 scientists from all around the world. |
| Collaborator Contribution | They provided domain expertise to optimise our tool development, and feedback on our study design. |
| Impact | Ostaszewski et al, bioRXiv, 2021; doi: 10.1101/2020.10.26.356014 |
| Start Year | 2020 |