Research Focus
Model-organism genomics in veterinary parasite drug-discovery

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A recent article about genomic filtering highlights exciting new opportunities for antiparasitic drug discovery resulting from major advances in genomic technologies. In this article, we discuss several approaches in which model-organism genomics and proteomics could be applied to the identification and validation of novel targets for antiparasitic drug discovery in veterinary medicine.

Section snippets

The drug-target challenge

Antiparasitic drug discovery is estimated to cost $25 million per year across the animal health industry, with only one or two classes of compound commercialized per decade and few mechanisms targeted by antiparasitic molecules. All new drug–target interactions must interfere with parasite survival, be selective (i.e. safe) and not be cross-resistant with known resistance mechanisms. The target must also be amenable to in vitro screening, ideally with a high-throughput screening

How closely do model organisms resemble target species?

A central assumption of the model-organism approach is that genetic model organisms produce information that is relevant to target species. Clearly, some aspects of parasite biology will not be highly conserved with free-living model organisms (e.g. mechanisms of immune evasion, and aspects of feeding and digestion). For many aspects of core biology, however, it is reasonable to anticipate a high degree of functional conservation [1]; indeed, the evidence of this is excellent in the cases of

Reverse genetics

A simple approach would be to exploit the genetic work that has gone into model organisms. In D. melanogaster, for example, tens of thousands of P element (transposon) insertions were generated and screened for lethality [9]. One could consider all genes flanking a recessive lethal P element insertion to be essential and, therefore, a valid target. This would identify ∼20% of the genes in the genome as targets; several start-up companies have adopted this approach, although there are drawbacks.

Bioinformatics

A second approach, using comparative genomics, was recently described as genomic filtering [14]. Searching expressed sequence tag (EST) datasets for genes that are represented widely across the nematode phylum but that lack vertebrate homologues, a subset of 1200 genes was selected from the C. elegans genome. This was reduced to 100 potential targets using reverse genetic data. It is impressive that such a small number of candidate genes can be chosen from a genome using simple criteria; this

Proteomics

Advances in bioinformatics, 2D electrophoresis [including 2D difference gel electrophoresis (DIGE)] and mass spectrometry enable more-accurate identification of proteins from femtomolar quantities of tissue. The measurement of conditional expression, abundance and posttranslational modification of proteins is now accessible to drug-discovery scientists. Does this information fit into the search for antiparasitic drug targets? Differential expression among tissues (CNS, muscle and gut) might

Chemical genetics

The approaches discussed can be described as ‘biology first’ because target identification is based on biological criteria. One disadvantage is the difficulty in determining whether gene function can be disrupted or altered by small molecules. An alternative approach is defined as ‘chemical first’, whereby antiparasitic molecules are used as probes to identify drug targets. Targets identified in this way are, by nature, susceptible to disruption by small molecules. One strategy is to isolate

Functional biology and integrative physiology

It is, perhaps, in functional biology and integrative physiology that genetic-model organisms have the most to offer. After identifying a manageable subset of genes, reverse genetics can be used, not as a simple screen for lethality but, instead, to provide the substrate for a phenotypic, ‘functional’ analysis. Following recent calls for a return to mechanism-based screening [20], it will be crucial to increase interactions between drug-discovery scientists and biologists who are interested in

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