Of the strategies entertained towards this goal, targeting host factors that are essential for the pathogen life cycle, rather than pathogen components directly, has recently received increasing attention [8–10]. While all pathogenic microbes experience interactions with their host organisms, viruses as obligatory parasites are directly dependent upon their host cell environment for replication, protein expression and assembly of progeny particles. It is anticipated that blocking one or more of these critical host components or cellular pathways will be resilient to the rapid development of viral resistance, since individual point mutations in viral components are unlikely to compensate for the loss of an essential host factor.

Indeed, the currently available data for the experimental use of host cyclin-dependent kinase (CDK) inhibitors to block HSV-1 and HIV-1 replication, for instance, has revealed a remarkably reduced frequency of viral escape from inhibition in tissue culture settings [11,12]. In contrast, single point mutations in viral components are fully sufficient to abrogate high-affinity binding of pathogendirected antivirals as demonstrated by numerous studies investigating the molecular mechanism of viral drug resistance [1]. Given that replication of related viral pathogens frequently depends on overlapping host cell pathways, host-directed antiviral strategies have high potential to move beyond the one-bug one-drug paradigm by broadening the pathogen target range of a chemical agent.

From a therapeutic perspective, the intricate and complex network of virus-host interactions yields a multitude of potential cellular targets for host-directed antivirals. In addition to the aforementioned role of CDK host protein kinases in HSV-1 and HIV-1 replication [11,12], some examples include regulatory kinases of the Abl [13] and Src [14] tyrosine kinase families, inhibition of which blocks poxvirus motility and maturation of West Nile virus particles, respectively. The Raf/MEK/ERK kinases of the mitogen-activated protein kinase (MAPK) cascades [15], when inhibited, induce nuclear retention of the influenza virus ribonucleoprotein complexes [16], preventing their export and ultimately influenza virion assembly. A further example includes inhibition of COX-2, a component of the eicosanoid biosynthesis pathway, which reduces yields of human cytomegalovirus progeny virus [17].

Clearly, the most desirable host target is essential for completion of the pathogen life cycle under investigation but at least temporarily dispensable for host cell survival, thus supporting the prospect that successful inhibition will combine a potent antiviral effect with manageable toxicity. Nevertheless, targeting host factors carries an inherently higher potential for undesirable drug-induced side effects than pathogen-directed antiviral therapies, particularly when the latter is highly selective. While host-directed therapies are being explored for the treatment of some major chronic viral infections such as HSV-1 and HIV-1 [11,12], they appear predestined for the therapy of infections by pathogens predominantly associated with severe acute disease, since anticipated treatment times and concomitant host exposure to the drug remain limited. On the other hand, chronic infections are conceivably treatable by host-protein targets where more than one gene pathway regulates the condition. In some such cases, sequential application of host-gene modifiers could control disease progression without undue side effects associated with chronic application of an anti-viral drug.

Hit candidates for host-directed drug development programs have resulted from a diverse set of experimental approaches. These can be grouped largely into knowledge-driven direct identification of individual targets, automated screening of chemical diversity sets with protocols specifically designed for the discovery of host-directed hits, and systems-wide screens for host factors essential for pathogen replication.

The evolving understanding of critical host-pathogen interactions through molecular virology research of individual viral families has made possible the direct selection of candidate host targets. With an arsenal of approved and experimental therapeutics for inhibition of many cellular pathways at hand, identified candidate targets may in many cases be immediately testable through repurposing of known drugs or commercially available experimental compounds with known bioactivity. The demonstration that Gleevec (Imatinib mesylate), an Abl tyrosine kinase inhibitor licensed for the treatment of several cancer forms [18], is a poxvirus blocker [13] and the repurposing of the MEK kinase inhibitor U0126 to block the Raf/MEK/ERK cascade for influenza virus inhibition [16,19] serve as cases in point. While the former originated from the insight that efficient vaccinia virus spread requires phosphorylation of the viral A36R protein by Abl and Src family tyrosine kinases [20,21], the latter was triggered by the observation that influenza virus infection induces the activation of MAPK family members [16,21].

The availability of large robotic capacities in both corporate and academic settings combined with the rapid design and production of small-molecule compound libraries in the past two decades has accelerated the pace of discovery of novel drug candidates via high-throughput screening exercises. Applied to the identification of antiviral hits with a host-directed activity profile, for instance, it should be feasible to derive suitable screening protocols based on the hypothesis that host-directed candidates will likely show some cellular interference and return a broadened pathogen target range. While the former will translate into a lower primary screening score represented by the selectivity index (CC₅₀/EC₅₀), the latter should result in efficient inhibition not only of the screening agent but also of pathogens of related viral families when assessed in counter-screening assays. When we explored the general feasibility of this approach conceptually using a ~140,000-entry diversity set, several chemical compound classes were identified that efficiently blocked replication of a panel of distinct members of the myxovirus families [22]. Significantly, a subset of these revealed a host-directed activity profile in secondary assays and counter-screening exercises.

To determine the molecular target of host-directed compounds identified through screening of chemical libraries, a combination of traditional mechanism of action studies, genomics (i.e., gene microarrays), and/or proteomics (i.e., protein profiling) studies is conceivable. Target identification not only sets the stage for possible knowledge-based scaffold optimization through rationale design in conjunction with hit-to-lead chemistry or repurposing of known inhibitors with identical target profile, but also contributes to further elucidating critical pathogen host interactions and, thus, basic insight into pathogen biology.

A second unbiased approach for host-target identification centers on screens for host factors directly interacting with viral components or required for successful completion of the viral life cycle. Of these, avidity-based extracellular interaction screens (AVEXIS) of protein-protein contacts [23] and yeast two-hybrid screens [24,25] appear promising, although they remain inherently limited to specific pathogen factors selected as “baits”. In contrast, loss-of-function screens based on aptamers [26,27] or antisense RNA interference [28–31] and gain-of-function approaches utilizing expression libraries [32,33] afford a systems-wide view of host-factors essential for pathogen replication or boosting pathogen success, respectively. In recent years, groundbreaking “loss-of-function” antisense screens were carried out for major viral pathogens including influenza virus [34,35], human immunodeficiency virus (HIV) [36–39] West Nile virus [40] and hepatitis C virus [41]. These have transformed our understanding of the virus-host interplay.

Surprisingly, however, independent large-scale screens directed at influenza virus, for instance, returned very little redundancy for essential host factors identified. This suggests that the screening efforts still lack saturation, and that cross-study bioinformatics efforts for data mining would benefit significantly by commencing on a host cell pathway rather than at the individual protein level. When a set of identified pathways essential for virus replication emerges through bioinformatics selection, individual components can be subjected to secondary screens using known drugs or experimental inhibitors to short-list desirable individual targets. A proposed workflow for a bioinformatics lead-identification process is displayed in Figure 1. In the following, we will discuss in detail the current state of data mining approaches and how they have been, or could be, applied to screening results reported for HIV-1 and influenza A viruses.

For HIV, three independent siRNA studies were published in 2008 by Brass et al. [36], Konig et al. [95] and Zhou et al. [37]. All three siRNA studies utilized the National Center for Biotechnology Information (NCBI) database of HIV-1 and human protein interactions (currently 1443 proteins identified) to evaluate the overlap of hit genes with the curated virus-host interactions available in the NCBI database [61]. Figure 4 illustrates the total number of genes found as well as the pairwise overlap between genes in each study. A meta-analysis of these genome-wide studies was subsequently performed by Bushman et al. in 2009 [96].

Bushman et al. performed an overlap analysis/random distribution comparison based on these data and found associations that were statistically significant (p-values < 0.001). While one may safely assume that the hit genes are enriched with respect to independently identified and confirmed host factors required for HIV-1 replication, pairwise overlaps between the studies are low, ranging only from 3 to 6%. While these were still judged statistically significant (p-values < 0.024 for all pairs) [96], the overall very low redundancy suggests considerable experimental variability associated with each siRNA screen.

Variation in individual host factors could be accounted for by a number of factors, including: (a) high experimental variance of siRNA transfection efficiencies [42]; (b) harvest of cells at different time points post-infection; (c) the use of different analysis methods and filtering thresholds; (d) an inherent bias of individual assays towards specific stages of the viral life cycle [96]; and (e) overall moderate reproducibility of siRNA-based screens[97]. Some of these variances might be readily controlled by additional replicates examined per screen. For instance, only the study by Konig et al. performed the screen in duplicate. As a case in point, the experimental data showed large variances between the replicates: 24% of hit siRNAs (141) exhibit standard deviations greater than 25% of their median values. Furthermore, Bushman et al. demonstrated that adjusting the filtering thresholds in this study strongly influences the nature of the identified genes (shown Figure 1D of Bushman et al.) [96]. Other parameters, such as non-uniform harvesting time points, are inherent to the design of each individual study and cannot be standardized retroactively. Although capturing different stages of the viral life cycle in separate studies may ultimately be necessary to fully appreciate the scope of the host-pathogen interaction network, different analysis times should be considered as a major contributor to the low level of congruity between the currently available data.

Independent of redundancy between studies, the question remains of whether the gene hits represent bona fide host factors required for HIV replication or false positives that may have arisen from experimental variability. Equally important for hit confirmation is the organization of the data sets into groups by gene function and cellular pathways to illuminate distinct parts of the intricate host-pathogen interaction network. Using terms from the Gene Ontology (GO) database Brass et al. noted that 103 of their hit genes were assigned with 136 statistically significant (p-value < 0.05) biological processes [36]. In brief, the GO database is a consortium established to relate genes to one another in a fixed file format within three categories: biological processes, cellular components and molecular functions [44,63,98,99]. To reduce redundancy, these categories were clustered and manually curated. GO analysis yielded 17 enriched cellular functions in the Brass HIV study. Alternatively, the Zhou study used Ingenuity Pathway Analysis to determine enriched molecular functions and biological pathways. Thirty-two molecular functions were identified, and twelve biological processes were found to be statistically significant (p-value < 0.05).

In contrast, Konig and colleagues employed a multi-tiered bioinformatics approach to identify the host factors most important to HIV replication through the use of the Prolexys HyNet database [95]. This resulted in networks of 2468, 4080, and 2850 genes in the HIV, MLV, AAV and toxicity assays, respectively. Using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) [100], the Konig team extracted overrepresented functional clusters for all genes found in the three HIV siRNA studies. Filtering for significance (p-value < 0.06 based on a geometric mean for all the terms in a group), redundancy, biological relevance and specificity returned 24 functional groups (A listing of the overlapping pathways and those identified uniquely are presented in Figure 5), most of which contained genes that were identified in two or more studies. Although each study contains a significant number of genes that may be defined by molecular functions common to each of the three studies, consistent function identification across the siRNA screens is lacking due to the distinctions in each study’s bioinformatics methods. These functions may be defined differently between methods due to redundancies present in each database. Slightly different but biologically meaningless distinctions can arise thereby.

Although some well-documented host factors required for HIV replication (such as CD4, CXCR4, NFκB subunit RELA, activating kinases AKT1 and JAK1, TSG101, and various cofactors of Vpr, Vif, Tat, and Rev) [101–105] were identified in at least one of the three siRNA studies, a variety of other host factors known to engage with HIV (HLA-B57, HLA-C, PSIP1/LEDBF/p75, Sp1, cyclophilin A, ITGB1, ITGB2, and ITGB3) were not discovered [96,106–115]. Of these, the absence of the integration cofactor PSIP1/LEDBF/p75, HIV long terminal repeat transcription factor Sp1, HIV Gag binding protein cyclophilin A, and the three integrin proteins (ITBG1, ITBG2, ITBG3) is most noticeable. [108–112,114–117] These results suggest that even a combined host factor analysis is at risk of missing key host components required for viral replication. Furthermore, no single siRNA study thus far illuminates all relevant, and currently known, cellular factors associated with the pathogen.

The meta-analysis was extended by building an HIV-host factor interaction network of 1657 cellular proteins using an array of protein-protein interaction databases (BIND, HPRD, MINT, and Reactome) [96]. With MCODE’s graph theoretic clustering algorithm, clusters within this interactome map having different functions were identified. Of the 11 clusters found, 10 were associated with distinct cellular functions: the proteasome, transcription/RNA polymerase, the mediator complex, Tat activation/transcriptional elongation, RNA Binding/Splicing, BiP/GRP78/HSPA5 Chaperone, and CCT Chaperone. From a drug discovery perspective, however, small molecule testing or counter-screening with individual siRNAs against target candidates are required to validate individual pathways.

Lack of saturation in host genes identified through current siRNA screens, varying consistency and high overlap of genes in specific areas emerge as future challenges for application to system-wide drug discovery efforts.

In addition to application of system-wide siRNA screens to the HIV system, the technology was applied to the influenza virus. Major siRNA studies were reported by Hao et al. [118], Brass et al. [119], Shapira et al. [120], Konig et al. [34] and Karlas et al. [35]. Other types of screens were performed by Josset et al. [121], which identified a gene list based on gene expression response to influenza; and Coombs et al. [122], which performed a quantitative analysis of protein level changes in infected cells. While Hao and colleagues employed a Drosophila cell-based host system for their siRNA screens, both Konig and Karlas relied on human lung cells (A549) and the influenza A/WSN (H1N1) strain or a recombinant variant thereof. Brass and colleagues used a human osteosarcoma cell system (U2OS) and the influenza A/PR/8 (H1N1) strain. The Shapira study is unique in that it combined results for yeast two- hybrid analyses, genome-wide transcriptional gene expression profiling and siRNA screening. Unfortunately, a single publically available resource similar to the NIH/NIAID HIV-1 interaction database does not exist for influenza virus, although many distinct virus-host interactions have been described in the literature (reviewed in [123]).

Watanabe et al. summarized five of the six systematic studies reported above and performed bioinformatics analysis on the 1,449 identified genes required for influenza replication [123]. Much like the Bushman et al. analysis of HIV host factors [96], 128 genes were found in multiple screens when pairwise comparisons were performed. The highest pairwise overlap (32 genes) is found in the Konig et al. and Karlas et al. studies, possibly due to the overlaps in host cell type (A549 cells) and influenza virus strain (A/WSN (H1N1)). This lack of overlap is also illustrated in the pairwise analysis given in Table S1. Unlike the meta-analysis for the HIV studies discussed above, this pairwise comparison lacked random distribution simulations, preventing the assessment of statistical significance. Nevertheless, the observed low overlap rate most likely results from factors similar to those discussed above for the HIV siRNA studies, i.e., different harvest times, detection thresholds and host cell lines, coupled with the additional complication of variability introduced through the use of different viral strains.

As described in the HIV siRNA analyses, each study examining influenza virus infections performed individual bioinformatics analyses on siRNA screening results. A summary of these bioinformatics data along with the methodology is reviewed by Min [124]. In unique congruence, three of the influenza virus studies explored the use of known small-molecule inhibitors to obtain independent proof-of-concept for the importance of cellular targets identified by bioinformatics for virus replication [34,35,121].

Konig reported six compounds with EC₅₀ values ranging from 0.5 to 30 μM target FRAP1, HSP90AA1, TUBB, FGFR4, GSK3B, or ANPEP. Of these, FRAP1, TUBB, FGFR4 and GSK3B home to the same GO Term cluster, protein kinase activity, recommending it as a potentially rich source for influenza virus inhibitors. The cytosolic chaperone Hsp90AAP1 was identified in a separate GO Term cluster; interestingly, previous reports have already established a link to influenza virus [125] and HCV [126] replication. The Karlas study reported another efficacious small molecule inhibitor, TG003, which targets the CDC-like kinase 1 (CLK1). CLK1 was retrieved from the Spliceosome GO term cluster, where it ranked seventh in significance among the list of enriched cellular components.

While the above studies identified potential host factor targets through GO term enrichment and then followed up with small molecules available for viral testing, the Josset project searched Connectivity Map with 20 of the most perturbed genes from the 300 initially identified. Of the eight compounds available through commercial vendors, six attenuated influenza virus replication with EC₅₀ values ranging from 5.8 to 30 μM. In-house analysis of the 20 genes used to identify these compounds revealed that they are significantly enriched in metabolic processes. Similar to previous studies, the Josset report does not explicitly identify a host pathway essential for viral replication based on the small molecule inhibition studies. To appreciate the full potential of this approach for antiviral drug development, it may be informative to collect all known inhibitors of a particular host pathway and determine the complete extent of virus inhibition.

Watanabe et al. performed a meta-analysis of the siRNA results using the set of 128 genes found in two or more studies [123]. The major gene categories were determined through PANTHER, a database that also utilizes GO terms to organize gene lists. Several molecular functions were found significant: nucleic acid-binding proteins, kinases, transcription factors, ribosomal proteins, hydrogen transporters and proteins related to mRNA splicing. Biological processes found to be consequential were protein metabolism and modification, signal transduction, protein phosphorylation, nucleoside, nucleotide and nucleic acid metabolism and intracellular transport. Reactome analysis tagged as significant eukaryotic translation initiation, regulation of gene expression, processing of capped intron-containing pre-mRNAs and Golgi-to-ER retrograde transport. This set of 128 genes was further integrated with the viral protein interaction partners determined by Konig and Shapira, resulting in a network of virus-host interactions. Based on this map, MCODE further identified translation initiation, mRNA processing and proton-transport as crucial. Accordingly, mining of the top MCODE cluster in Figure 6 predicts that compounds such as spectoinomycin, emetine and quercetin will interfere with influenza virus replication.

Successful outcomes for bioinformatics searches predominantly depend on the accuracy of tabulated database interactions. As detailed below, use of different databases may alter the profile of pathways that are enriched from the same gene list. In such cases, users are obligated to formulate a realistic biological interpretation of the relational data to ensure identification of meaningful candidate compounds for an antiviral drug program.