Systematic approaches towards the development of host-directed antiviral therapeutics

Abstract

Since the onset of antiviral therapy, viral resistance has compromised the clinical value of small-molecule drugs targeting pathogen components. As intracellular parasites, viruses complete their life cycle by hijacking a multitude of host-factors. Aiming at the latter rather than the pathogen directly, host-directed antiviral therapy has emerged as a concept to counteract evolution of viral resistance and develop broad-spectrum drug classes. This approach is propelled by bioinformatics analysis of genome-wide screens that greatly enhance insights into the complex network of host-pathogen interactions and generate a shortlist of potential gene targets from a multitude of candidates, thus setting the stage for a new era of rational identification of drug targets for host-directed antiviral therapies. With particular emphasis on human immunodeficiency virus and influenza virus, two major human pathogens, we review screens employed to elucidate host-pathogen interactions and discuss the state of database ontology approaches applicable to defining a therapeutic endpoint. The value of this strategy for drug discovery is evaluated, and perspectives for bioinformatics-driven hit identification are outlined.

Keywords: HIV; Influenza virus; RNAi; antiviral; bioinformatics; genome-wide screening; pathway analysis; siRNA; target identification.

Figure 1

RNAi-Based Lead Identification Workflow. Aberrant expressed genes identified in the RNAi screen are categorized into clusters based on biological function. Members of the largest clusters are literature and database-mined for known small molecule modulators. Candidate inhibitors are subjected to biotesting for hit confirmation. This review focuses on the ability of bioinformatics methods to identify potential medicinal lead compounds.

Figure 2

Network Association Map of RNAi screening results generated by the Brass et al. influenza virus infection. The list of perturbed host cell genes were complied in STRING and illustrated as nodes above. Lines between different nodes (edges) represent protein interactions that are either known experimentally (purple) or predicted computationally (yellow). Significant nodes such as those shown around COPA and CRNLK1 suggest these pathways to be critical for the viral life cycle.

Figure 3

Gene interaction map overlapped with Tamoxifen via the STITCH database. The latter also connects ovals to one another suggesting that these molecules display similar biological behavior towards the same target. Edges refer to interactions as determined by experiment (purple), manual curation (cyan) or computationally predictions (yellow).

Figure 4

Illustration of the pairwise overlap between hit genes in the three HIV siRNA studies and the NCBI database. Circle areas are proportional to the number of genes. For clarity, three-way and higher overlaps are not shown.

Figure 5

Comparison of HIV-dependent host functions identified by Bushman et al. [96], Brass et al. [36], Konig et al. [95] and Zhou et al. [37]. Grey boxes indicate functions unique to an individual study.

Figure 6

Small molecule (ovals) identification of gene products (spheres) associated with translation initiation. Green edges represent protein-ligand interactions. These compounds have not been reported previously to interfere with influenza infection, although quercetin has been demonstrated to attenuate HCV, however through a different host factor [126].