The experimental work started swiftly with project start. On the first day, the required nucleotide sequences were ordered. Upon delivery, these were cloned 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 initial 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 contributing additional interactions and the validation of the integrated interactome 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).
