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Published December 4, 2015 | Published + Supplemental Material
Journal Article Open

Defining the Schistosoma haematobium kinome enables the prediction of essential kinases as anti-schistosome drug targets

Abstract

The blood fluke Schistosoma haematobium causes urogenital schistosomiasis, a neglected tropical disease (NTD) that affects more than 110 million people. Treating this disease by targeted or mass administration with a single chemical, praziquantel, carries the risk that drug resistance will develop in this pathogen. Therefore, there is an imperative to search for new drug targets in S. haematobium and other schistosomes. In this regard, protein kinases have potential, given their essential roles in biological processes and as targets for drugs already approved by the US Food and Drug Administration (FDA) for use in humans. In this context, we defined here the kinome of S. haematobium using a refined bioinformatic pipeline. We classified, curated and annotated predicted kinases, and assessed the developmental transcription profiles of kinase genes. Then, we prioritised a panel of kinases as potential drug targets and inferred chemicals that bind to them using an integrated bioinformatic pipeline. Most kinases of S. haematobium are very similar to those of its congener, S. mansoni, offering the prospect of designing chemicals that kill both species. Overall, this study provides a global insight into the kinome of S. haematobium and should assist the repurposing or discovery of drugs against schistosomiasis.

Additional Information

© 2015 Macmillan Publishers Limited. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ Received: 06 May 2015; Accepted: 26 August 2015; Published online: 04 December 2015. TThis project was funded by the National Health and Medical Research Council (NHMRC) of Australia and the Australian Research Council (ARC), and supported by a Victorian Life Sciences Computation Initiative (VLSCI) grant number VR0007 on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government. Other support from the Australian Academy of Science, the Australian-American Fulbright Commission, Alexander von Humboldt Foundation and Melbourne Water Corporation (R.B.G.) is gratefully acknowledged. A.J.S. is a recipient of a Melbourne International Research Scholarships (MIRS) and a Melbourne International Fee Remission Scholarship (MIFRS) from the University of Melbourne. N.D.Y. is an NHMRC Early Career Research (ECR) Fellow. We thank Stefano Mangiola for help with preliminary data analysis. Author Contributions: A.J.S., R.B.G. and N.D.Y. were involved in the experimental design. A.J.S., R.B.G. and N.D.Y. were responsible for writing and editing of the manuscript. A.J.S. and N.D.Y. conducted bioinformatic analyses. Other authors (A.R.J., P.W.S., P.T., P.R.B. and A.H.) contributed to the writing of the manuscript. The authors declare no competing financial interests.

Attached Files

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