A new framework for host-pathogen interaction research

Abstract

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.

Keywords: COVID-19; COVID-19 cocktail; HPIPO framework; bioinformatics; coronavirus infectious disease ontology (CIDO); disease outcome; host-coronavirus interaction; host-pathogen interaction.

Figure 1

HPI Postulates. The network image is a symbol of the resulting huge interaction network we usually see. To better study such networks in host-pathogen interactions (HPIs) and the resulting disease outcomes, we propose four HPI postulates (HPIP), including HPI evolutionary dispositions (P1), HPI dynamic outcomes (P2), HPI roles (P3), and HPI checkpoints (P4), as the basic framework. The HPI postulates suggest the identification and definition of the roles of different nodes and edges in the network and how they are related to disease outcomes. Ontology supports such integrative knowledge representation and reasoning, leading to our proposed HPIPO framework.

Figure 2

Model of host-coronavirus interactions and associated disease outcomes. The viruses enter, survive in, replicate in host cells, and may have genetic variations during the process. After initial naïve acceptance of viral entry without triggering an immune reaction (a naïve response), the host initiates active innate and adaptive responses. The host disease outcome will be determined by the dynamic host-coronavirus interactions.

Figure 3

Ontological representation of coronaviruses, hosts, and phenotype outcomes of host-coronavirus interactions. (A) A general scheme. (B) The NCBITaxon taxonomical hierarchy of representative human coronaviruses. (C) The taxonomical hierarchy of representative hosts of coronaviruses. (D) Human Phenotype Ontology (HPO) hierarchy of representative human phenotypes commonly seen in COVID-19 patients. Representative comorbidity phenotypes and associated phenotype frequency in mild and severe COVID-19 patients are also represented. For example, “0.14, 0.30” in (D) indicates that superimposed hypertension is found in 14% of mild symptom patients and 30% of severe symptom patients. The results were summarized from reported literature (58, 59).

Figure 4

SPARQL query of CIDO for anticoronaviral chemicals/drugs out of host-coronavirus interactions. This SPARQL identified 125 biological processes having participant of proteins that are the targets of chemicals/drugs that inhibit coronaviral infection in vivo or in vitro, illustrating how the relations (red circles) and classes (blue circles) are associated and interlinked. The SPARQL was performed using Ontobee SPARQL endpoint (http://www.ontobee.org/sparql). The detailed information about these 125 biological processes and their associated proteins and chemicals/drugs is provided in Supplemental Figures 1, 2 .

Figure 5

Venn Diagram of potential COVID-19 drugs based on the HPI Postulate drug cocktail strategy. A total of 232 drugs were identified to have their protein targets involving coronavirus entry, coronavirus genome replication, and host cytokine activity against COVID-19. Two drugs (i.e., copper and artenimol) were shared to have protein targets involved in all three processes. The drug screening study was performed using the DrugXplore program (70).

Figure 6

Predicted human-coronavirus protein-protein interactions (PPIs) and their affected tissues. The predicted 1,001 interactions that involve 27 virus proteins and 233 human proteins are detailed in Supplemental File 3 . The predicted tissue specificity results are provided in the Supplemental File 4 . See the text for more details.

Figure 7

Verification of predicted human-coronavirus protein-protein interactions (PPIs) with experimentally validated PPIs. (A) Enriched GO terms of experimentally validated PPIs. (B) Enriched GO terms of our predicted PPIs. Our predicted PPIs ( Figure 6 ) have coherent informative GO term annotations with the experimentally validated PPIs.