Periodic Reporting for period 2 - RiPCoN (Rapid interaction profiling of 2019-nCoV for network-based deep drug-repurpose learning (DDRL))
Reporting period: 2021-04-01 to 2022-03-31
Fighting SARS-CoV-2 infections and treating both acute and more long- term (longCOVID) is made difficult by the poor understanding of how the virus amplifies inside the human body. All viruses enter cells, where they modulate normal cellular functions and reprogram the cell to make thousands of new viral particles that then spread the infection inside the body and ultimately in society. The virus does this reprogramming is with the help of its proteins that bind to human proteins inside the infected cells and change their function towards supporting the virus. Many anti-viral drugs for other viruses, e.g. HIV, interfere with this reprogramming and thereby reduce and ultimately stop viral spread in the body. The challenge for SARS-CoV-2 is the lack of knowledge about which human proteins are targeted by the virus and how exactly the reprogramming of human cells is achieved.
The goal of this project is to understand which human proteins are targeted by the virus and which human molecular networks are changed and how. This important information will lead to a much improved understanding of the biology of coronaviruses, including the more deadly SARS and MERS. More importantly, with the help of this network information we can then use artificial intelligence and deep neural networks to identify drugs that are already on the market that revert some of the changes the virus aims to make. Thereby it may be possible to find treatment options that do not require long and costly clinical safety testing and may therefore become available much quicker. Moreover, we expect that the interactions we find will help understand the acute and long-term symptoms of patients suffereing from COVID-19. By better understanding the molecular causes, we expect to help alleviate symptoms and help patients.
The goal of this project is to understand which human proteins are targeted by the virus and which human molecular networks are changed and how. This important information will lead to a much improved understanding of the biology of coronaviruses, including the more deadly SARS and MERS. More importantly, with the help of this network information we can then use artificial intelligence and deep neural networks to identify drugs that are already on the market that revert some of the changes the virus aims to make. Thereby it may be possible to find treatment options that do not require long and costly clinical safety testing and may therefore become available much quicker. Moreover, we expect that the interactions we find will help understand the acute and long-term symptoms of patients suffereing from COVID-19. By better understanding the molecular causes, we expect to help alleviate symptoms and help patients.
The experimental work started swiftly with project start. On the first day, the required nucleotide sequences were ordered. Upon delivery these were clones into universal entry plasmids, which we require for interactome analysis. the ORFs were further shared with international partners in the US, Canada and within Europe to accelerate COVID-19 research.
Afterwards interactome mapping was started such that initial interactome data were available within few months after project start. these were subsequently verified and confirmed. The inital network from this project was available in summer. In parallel we initiated a collaboration with partners in Toronto, Canada, and Boston, US to increase the coverage of the network. These partners are contibuting additional interactions and the validation of the integrated interacome dataset is ongoing.
The network shows a strong enrichment of proteins related to inflammation and viral life cycle, especially vesicle trafficking. The integration with available genetic data demonstrates that interactions and their local network neighborhood are linked to metabolic features related to COVID-19, namely glycogen and fatty acid metabolism, and to diseases of the immune system, metabolism and metabolic syndrome and to neurological phenotypes.
In addition to the SARS-CoV-2 – Human contactome mentioned above we also generated two more critical resources available to the scientific community: i) the pan-CoV-ORFeome collection from 7 coronaviridae and ii) the high-quality comprehensive pan-CoV-human interactome network map (manuscript in prep).
Importantly, the protein coding ORFs enable the expression of all viral proteins for subsequent use in protein microarray platform or ELISA-based screens for patient screening, identification of immunogenic epitopes and thus contributing to vaccine development.
On the computational drug repurposing aspect we focused our efforts in the use of small molecule bioactivity signatures, together with natural-language processing techniques to rank the likeliness of activity against SARS-CoV-2 of all those approved and experimental drugs that experts around the world had suggested to be potentially active. Indeed, we developed a tool to help prioritize these potential treatments, stratifying them according to the level of clinical evidence and suggested mechanism of action for the intended drugs. The rapid deployment of this tool was aimed at helping clinical researchers in their (almost blind) initial choices (Duran-Frigola et al. 2020 J Chem Inf Model). Additionally, as reported, we also implemented a collection of deep-networks to infer bioactivity signatures to any molecule of interest (i.e. the Signaturizers; Bertoni et al. 2021 Nat Commun).
Afterwards interactome mapping was started such that initial interactome data were available within few months after project start. these were subsequently verified and confirmed. The inital network from this project was available in summer. In parallel we initiated a collaboration with partners in Toronto, Canada, and Boston, US to increase the coverage of the network. These partners are contibuting additional interactions and the validation of the integrated interacome dataset is ongoing.
The network shows a strong enrichment of proteins related to inflammation and viral life cycle, especially vesicle trafficking. The integration with available genetic data demonstrates that interactions and their local network neighborhood are linked to metabolic features related to COVID-19, namely glycogen and fatty acid metabolism, and to diseases of the immune system, metabolism and metabolic syndrome and to neurological phenotypes.
In addition to the SARS-CoV-2 – Human contactome mentioned above we also generated two more critical resources available to the scientific community: i) the pan-CoV-ORFeome collection from 7 coronaviridae and ii) the high-quality comprehensive pan-CoV-human interactome network map (manuscript in prep).
Importantly, the protein coding ORFs enable the expression of all viral proteins for subsequent use in protein microarray platform or ELISA-based screens for patient screening, identification of immunogenic epitopes and thus contributing to vaccine development.
On the computational drug repurposing aspect we focused our efforts in the use of small molecule bioactivity signatures, together with natural-language processing techniques to rank the likeliness of activity against SARS-CoV-2 of all those approved and experimental drugs that experts around the world had suggested to be potentially active. Indeed, we developed a tool to help prioritize these potential treatments, stratifying them according to the level of clinical evidence and suggested mechanism of action for the intended drugs. The rapid deployment of this tool was aimed at helping clinical researchers in their (almost blind) initial choices (Duran-Frigola et al. 2020 J Chem Inf Model). Additionally, as reported, we also implemented a collection of deep-networks to infer bioactivity signatures to any molecule of interest (i.e. the Signaturizers; Bertoni et al. 2021 Nat Commun).
In summary, we generated a highly validated contactome map that provides direct interactions between SARS-CoV-2 and human target proteins in pathways and tissues relevant to COVID-19. Thereby, we establish paths of direct contacts between viral target proteins and proteins encoded from loci that modify the risk for critical COVID-19 illness and important comorbidities. Examining specific hypotheses for both viral and host proteins, we demonstrate that NSP14 activates the NF-κB pathway even beyond pathway activation by cytokines. We demonstrate that coding changes in SARS-CoV-2 strains perturb the intracellular interactome. We anticipate that these findings and the contactome resource will stimulate important research towards characterizing new viral strains, understanding the mechanism of COVID-19 symptoms and developing therapies for current and future pandemics.