Host-coronavirus interactions
Here we collect, model, represent, and analyze experimentally verified host-coronavirus interactions. This project is a collaboratove project with the Ontology of Host-Pathogen Interactions (OHPI): https://github.com/OHPI/ohpi.
Our study is based on a list of HPI-Outcome postulates. Assuming we know the causal relation between a microbe and a disease, we target to explain how the host-pathogen interaction (HPI) is causally associated with an outcome. Like Koch’s postulates, our HPI-Outcome postulates include four criteria: (i) HPI motivation: The host (or pathogen) reacts to the pathogen (or host) to achieve their best possible outcome. Such phenomena explain the root motivation of the HPI and their associated outcomes. (ii) HPI-outcome causality: The host or pathogen outcomes are determined by the HPIs at the molecular and cellular level. As a result, the host or pathogen’s genetic and phenotypic profiles (such as the direct and co-morbid susceptibility factors of the host) may affect such HPIs and thus disease outcomes. (iii) HPI role: The HPIs involves a network of molecular interactions at the interface of host and pathogen or within either the host and pathogen, and different molecules and interactions play different roles in determining disease outcomes. (iv) HPI interruption: An internal or external interruption of one or more H-P interactions critical to the HPI-outcome network may change the outcome. The external interruption can arise from drug or vaccine administration.
The HPI-outcome postulates provide a set of very basic rules to guide our study of specific host-coronavirus interaction (HCI) mechanisms to explain COVID-19 and other coronavirus disease outcomes.
- Anthony Huffman
- Edison Ong
- Oliver He
Yu H, Li L, Huang H, Wang Y, Liu Y, Ong E, Huffman A, Zeng T, Zhang J, Li P, Liu Z, Zhang X, Ye X, Handelman SK, Higgins G, Omenn GS, Athey B, Hur J, Chen L, He Y. Ontology-based systematic classification and analysis of coronaviruses, hosts, and host-coronavirus interactions towards deep understanding of COVID-19. arXiv e-prints. 2020. arXiv:2006.00639.