Computational biology approaches for selecting host-pathogen drug targets

Antiviral Therapy

Antiviral therapy is one of the most exciting aspects of virology, since it has successfully employed basic science to generate very effective treatments for serious viral infections. Table 1 lists selected examples of those human viral diseases for which there are established antiviral drugs. Therapy for human immunodeficiency virus (HIV) infection has demonstrated that the potential impact antivirals can have on a lethal, chronic infection with lifesaving therapy administered to more than 12 million individuals by 2015. This dramatic advance is about to be recapitulated for the treatment of hepatitis C virus (HCV) infection. The development of new antiviral drugs is very much a work in progress, with active drug discovery programs for filoviruses, coronaviruses, dengue, and others.

Experimental approaches to phenotypic diversity in infection

Microbial infections are burdening human health, even after the advent of antibiotics, vaccines and hygiene. Thus, infection biology has aimed at the molecular understanding of the pathogen-host interaction. This has revealed key virulence factors, host cell signaling pathways and immune responses. However, our understanding of the infection process is still incomplete. Recent evidence suggests that phenotypic diversity can have important consequences for the infection process. Diversity arises from the formation of distinct subpopulations of pathogen cells (with distinct virulence factor expression patterns) and host cells (with distinct response capacities). For technical reasons, such phenotypic diversity has often been overlooked. We are highlighting several striking examples and discuss the experimental approaches available for analyzing the different subpopulations. Single cell reporters and approaches from systems biology do hold much promise.

A review on computational systems biology of pathogen-host interactions

Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.

Comparative transcriptomics reveals striking similarities between the bovine and feline isolates of Tritrichomonas foetus: consequences for in silico drug-target identification

BACKGROUND

Few, if any, protozoan parasites are reported to exhibit extreme organ tropism like the flagellate Tritrichomonas foetus. In cattle, T. foetus infects the reproductive system causing abortion, whereas the infection in cats results in chronic large bowel diarrhoea. In the absence of a T. foetus genome, we utilized a de novo approach to assemble the transcriptome of the bovine and feline genotype to identify host-specific adaptations and virulence factors specific to each genotype. Furthermore, a subset of orthologs was used to characterize putative druggable targets and expose complications of in silico drug target mining in species with indefinite host-ranges.

RESULTS

Illumina RNA-seq reads were assembled into two representative bovine and feline transcriptomes containing 42,363 and 36,559 contigs, respectively. Coding and non-coding regions of the genome libraries revealed striking similarities, with 24,620 shared homolog pairs reduced down to 7,547 coding orthologs between the two genotypes. The transcriptomes were near identical in functional category distribution; with no indication of selective pressure acting on orthologs despite differences in parasite origins/host. Orthologs formed a large proportion of highly expressed transcripts in both genotypes (bovine genotype: 76%, feline genotype: 56%). Mining the libraries for protease virulence factors revealed the cysteine proteases (CP) to be the most common. In total, 483 and 445 bovine and feline T. foetus transcripts were identified as putative proteases based on MEROPS database, with 9 hits to putative protease inhibitors. In bovine T. foetus, CP8 is the preferentially transcribed CP while in the feline genotype, transcription of CP7 showed higher abundance. In silico druggability analysis of the two genotypes revealed that when host sequences are taken into account, drug targets are genotype-specific.

CONCLUSION

Gene discovery analysis based on RNA-seq data analysis revealed prominent similarities between the bovine and feline T. foetus, suggesting recent adaptation to their respective host/niche. T. foetus represents a unique case of a mammalian protozoan expanding its parasitic grasp across distantly related host lineages. Consequences of the host-range for in silico drug targeting are exposed here, demonstrating that targets of the parasite in one host are not necessarily ideal for the same parasite in another host.

Host response to respiratory bacterial pathogens as identified by integrated analysis of human gene expression data

Respiratory bacterial pathogens are one of the leading causes of infectious death in the world and a major health concern complicated by the rise of multi-antibiotic resistant strains. Therapeutics that modulate host genes essential for pathogen infectivity could potentially avoid multi-drug resistance and provide a wider scope of treatment options. Here, we perform an integrative analysis of published human gene expression data generated under challenges from the gram-negative and Gram-positive bacteria pathogens, Pseudomonas aeruginosa and Streptococcus pneumoniae, respectively. We applied a previously described differential gene and pathway enrichment analysis pipeline to publicly available host mRNA GEO datasets resulting from exposure to bacterial infection. We found 72 canonical human pathways common between four GEO datasets, representing P. aeruginosa and S. pneumoniae. Although the majority of these pathways are known to be involved with immune response, we found several interesting new interactions such as the SUMO1 pathway that might have a role in bacterial infections. Furthermore, 36 host-bacterial pathways were also shared with our previous results for respiratory virus host gene expression. Based on our pathway analysis we propose several drug-repurposing opportunities supported by the literature.

Bridging the past and the future of virology: surface plasmon resonance as a powerful tool to investigate virus/host interactions

Despite decades of antiviral drug research and development, viruses still remain a top global healthcare problem. Compared to eukaryotic cells, viruses are composed by a limited numbers of proteins that, nevertheless, set up multiple interactions with cellular components, allowing the virus to take control of the infected cell. Each virus/host interaction can be considered as a therapeutical target for new antiviral drugs but, unfortunately, the systematic study of a so huge number of interactions is time-consuming and expensive, calling for models overcoming these drawbacks. Surface plasmon resonance (SPR) is a label-free optical technique to study biomolecular interactions in real time by detecting reflected light from a prism-gold film interface. Launched 20 years ago, SPR has become a nearly irreplaceable technology for the study of biomolecular interactions. Accordingly, SPR is increasingly used in the field of virology, spanning from the study of biological interactions to the identification of putative antiviral drugs. From the literature available, SPR emerges as an ideal link between conventional biological experimentation and system biology studies functional to the identification of highly connected viral or host proteins that act as nodal points in virus life cycle and thus considerable as therapeutical targets for the development of innovative antiviral strategies.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Gastrointestinal weight-loss surgery: glimpses at the molecular level

Pharmacotherapy for obesity remains a key challenge, and gastrointestinal weight-loss surgery remains a preferred option to help reduce excess body weight along with resolution of several comorbidities associated with obesity. This offers a unique opportunity to study the underlying mechanisms of gastro-intestinal weight-loss surgery to develop effective and less invasive long-term therapeutic interventions potentially mimicking the benefits of gastrointestinal weight-loss surgery. Here, we present an integrative analysis of currently available human transcriptomics data sets pre- and post-surgery and propose a computational biology strategy for selecting putative drug targets. We anticipate that approaches similar to the one that we outline here, would help elucidate underlying mechanisms that result in metabolic improvements and provide guidance on pharmaceutical targets to develop effective and less invasive therapies for obesity and related comorbidities.

Identification of common biological pathways and drug targets across multiple respiratory viruses based on human host gene expression analysis

BACKGROUND

Pandemic and seasonal respiratory viruses are a major global health concern. Given the genetic diversity of respiratory viruses and the emergence of drug resistant strains, the targeted disruption of human host-virus interactions is a potential therapeutic strategy for treating multi-viral infections. The availability of large-scale genomic datasets focused on host-pathogen interactions can be used to discover novel drug targets as well as potential opportunities for drug repositioning.

METHODS/RESULTS

In this study, we performed a large-scale analysis of microarray datasets involving host response to infections by influenza A virus, respiratory syncytial virus, rhinovirus, SARS-coronavirus, metapneumonia virus, coxsackievirus and cytomegalovirus. Common genes and pathways were found through a rigorous, iterative analysis pipeline where relevant host mRNA expression datasets were identified, analyzed for quality and gene differential expression, then mapped to pathways for enrichment analysis. Possible repurposed drugs targets were found through database and literature searches. A total of 67 common biological pathways were identified among the seven different respiratory viruses analyzed, representing fifteen laboratories, nine different cell types, and seven different array platforms. A large overlap in the general immune response was observed among the top twenty of these 67 pathways, adding validation to our analysis strategy. Of the top five pathways, we found 53 differentially expressed genes affected by at least five of the seven viruses. We suggest five new therapeutic indications for existing small molecules or biological agents targeting proteins encoded by the genes F3, IL1B, TNF, CASP1 and MMP9. Pathway enrichment analysis also identified a potential novel host response, the Parkin-Ubiquitin Proteasomal System (Parkin-UPS) pathway, which is known to be involved in the progression of neurodegenerative Parkinson's disease.

CONCLUSIONS

Our study suggests that multiple and diverse respiratory viruses invoke several common host response pathways. Further analysis of these pathways suggests potential opportunities for therapeutic intervention.