We are using an interdisciplinary approach to develop improved computational methods that (1) aid prioritization of the most promising infectious disease drug/vaccine/diagnostic targets. (2) Improve our understanding of the evolution of infectious disease, using this knowledge to develop more sustainable approaches for infectious disease control.

Computational Tools We’ve Developed

Currently Funded Projects

Our Research Approach

Our approaches holistically target the three major players resulting in infectious disease: (1) pathogen virulence; (2) host immune system failure/over-activity; and (3) changes in environment/social factors.

Pathogen-targeted infectious disease therapies – a more evolutionary approach: One understudied approach to better controlling pathogens attempts to “convert” a bacterial pathogen to a non-pathogen, using “anti-virulence” approaches that target disease-causing proteins/toxins in a bacteria. We hypothesize that drugs that target such toxins will have less selection for antimicrobial resistance, since the drugs do not kill the microorganism, but rather put evolutionary pressure on the microorganism to “disarm” and not cause disease (i.e., a bacteria that does not contain/require the toxin would survive the drug treatment, or at least the toxin would be targeted, not the bacteria, making production of the toxin by the bacteria useless, thus removing selective pressure). Such therapies may be able to capitalize on common themes in microbial disease mechanisms. To date we have been successful in developing and using computational (microbial bioinformatics) approaches to identify such drug targets in bacteria, then using additional cheminformatics/chemical systems biology/chemical docking technology to identify small molecules, including known drugs, that inhibit the bacterial protein targets. The predicted inhibitors are then validated in our lab using protein- and cell-based assays, plus worm infection models. Our group has shown, for example, that a drug currently used to treat bone density in post menopausal women, Raloxifene, can be potentially repurposed as an anti-infective to treat certain notoriously antibiotic resistant infections (Pseudomonas aeruginosa). Most recently, we are using a unique “Indel-targeting” approach to identify drug targets not normally considered by Pharma (see Summaries of Proposed Research, below). These targets are so highly evolutionarily conserved and essential that drug resistance may be more difficult to develop (or at least this approach provides a new pipeline of novel classes of drugs while anti-virulence approaches are further developed). We continue to develop PSORTb, the most precise predictor of bacterial protein subcellular localization which is used to aid identification of cell surface/secreted proteins of interest as more “accessible” drug targets/vaccine components.

Human-targeted infectious disease therapies – Immune-modulation: In addition to targeting the disease-causing microbe with therapies, we can also increase chances for treatment success by potentially boosting the human immune system against the infectious agent. To this end we have developed InnateDB, a database and systems biology resource that facilitates more network-based analyses of molecules involved in the human innate immune response. InnateDB has enabled us to identify, in a more whole-system based manner, how the immune system is perturbed and fails when confronted with certain disease-causing bacteria. It has allowed us to identify ways in which we could successfully perturb this complex immune network to boost the innate immune system’s ability to defeat a microbe or at least reduce damaging inflammation that can result. For example, aided by our bioinformatics approach, we recently identified a new therapy that dampens down damaging inflammation in cases of severe malaria, working synergistically with anti-malarial drugs to significantly boost survival. This therapy, initially demonstrated to be effective in a mouse infection model, is now being further tested.

Identifying environment/social factors in disease outbreak occurrence. In addition to targeting the bacterium and the host with therapies, comprehensive infectious disease control must also tackle the role of the environment and social factors in causing disease spread. Through a combined microbiology, genomic, epidemiology, and social networking approach, we are developing approaches, encompassed under the new fields of genomic epidemiology and metagenomics, to improve tracking of infectious disease-causing bacteria in people and the environment, coupling this with social network data to identify any social or environmental causes of a disease outbreak. For example, through our first combined use of genomic epidemiology and social network analysis, we identified increased crack cocaine use in a community as likely playing a role in precipitating an outbreak of tuberculosis in a medium-sized BC town. In another project, we are identifying new metagenomics markers of fecally-contaminated water in the environment that should improve our ability to identify agricultural and human sources of waterborne disease.

In addition, reflecting our interest in enabling our bioinformatics methods for broader use, we have aided researchers from other fields (environmental toxicology, allergy and asthma research, are key examples).