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Estimating novel potential drug targets of Plasmodium falciparum by analysing the metabolic network of knock-out strains in silico.

Malaria is one of the worlds most common and serious diseases causing death of about 3 million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Biomedical research could enable treating the disease by effectively and specifically targeting essential enzymes of this parasite. However, the parasite has developed resistance to existing drugs making it indispensable to discover new drugs. We have established a simple computational tool which analyses the topology of the metabolic network of P. falciparum to identify essential enzymes as possible drug targets. We investigated the essentiality of a reaction in the metabolic network by deleting (knocking-out) such a reaction in silico. The algorithm selected neighbouring compounds of the investigated reaction that had to be produced by alternative biochemical pathways. Using breadth first searches, we tested qualitatively if these products could be generated by reactions that serve as potential deviations of the metabolic flux. With this we identified 70 essential reactions. Our results were compared with a comprehensive list of 38 targets of approved malaria drugs. When combining our approach with an in silico analysis performed recently [Yeh, I., Hanekamp, T., Tsoka, S., Karp, P.D., Altman, R.B., 2004. Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res. 14, 917-924] we could improve the precision of the prediction results. Finally we present a refined list of 22 new potential candidate targets for P. falciparum, half of which have reasonable evidence to be valid targets against micro-organisms and cancer.

Genetic characterization of Greek population isolates reveals strong genetic drift at missense and trait-associated variants

Isolated populations are emerging as a powerful study design in the search for low-frequency and rare variant associations with complex phenotypes. Here we genotype 2,296 samples from two isolated Greek populations, the Pomak villages (HELIC-Pomak) in the North of Greece and the Mylopotamos villages (HELIC-MANOLIS) in Crete. We compare their genomic characteristics to the general Greek population and establish them as genetic isolates. In the MANOLIS cohort, we observe an enrichment of missense variants among the variants that have drifted up in frequency by more than fivefold. In the Pomak cohort, we find novel associations at variants on chr11p15.4 showing large allele frequency increases (from 0.2% in the general Greek population to 4.6% in the isolate) with haematological traits, for example, with mean corpuscular volume (rs7116019, P=2.3 × 10−26). We replicate this association in a second set of Pomak samples (combined P=2.0 × 10−36). We demonstrate significant power gains in detecting medical trait associations.

H3Africa: a tipping point for a revolution in bioinformatics, genomics and health research in Africa

BackgroundA multi-million dollar research initiative involving the National Institutes of Health (NIH), Wellcome Trust and African scientists has been launched. The initiative, referred to as H3Africa, is an acronym that stands for Human Heredity and Health in Africa. Here, we outline what this initiative is set to achieve and the latest commitments of the key players as at October 2013.FindingsThe initiative has so far been awarded over

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74 million in research grants. During the first set of awards announced in 2012, the NIH granted

Comparing metabolic network models based on genomic and automatically inferred enzyme information from Plasmodium and its human host to define drug targets in silico

5 million a year for a period of five years, while the Wellcome Trust doled out at least

Computational and experimental analysis identified 6-diazo-5-oxonorleucine as a potential agent for treating infection by Plasmodium falciparum

12 million over the period to the research consortium. This was in addition to Wellcome Trust’s provision of administrative support, scientific consultation and advanced training, all in collaboration with the African Society for Human Genetics. In addition, during the second set of awards announced in October 2013, the NIH awarded to the laudable initiative 10 new grants of up to

Computational biology and bioinformatics in Nigeria.

17 million over the next four years.ConclusionsH3Africa is poised to transform the face of research in genomics, bioinformatics and health in Africa. The capacity of African scientists will be enhanced through training and the better research facilities that will be acquired. Research collaborations between Africa and the West will grow and all stakeholders, including the funding partners, African scientists, scientists across the globe, physicians and patients will be the eventual winners.

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1 International Institute of Tropical Agriculture, Nairobi, Kenya, 2 Faculty of Life Sciences, The University of Manchester, Manchester, United Kingdom, 3 Department of Computer and Information Sciences, Covenant University, Ota, Nigeria, 4 Institute of Bioinformatics, Johannes Kepler University, Linz, Austria, 5 Moroccan Society for Bioinformatics Institute, Morocco, 6 South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa, 7 University of Cape Town, Cape Town, South Africa, 8 University of Notre Dame, South Bend, Indiana, United States of America, 9 Biotechnology Unit, University of Buea, Buea, South West Region, Cameroon, 10 International Livestock Research Institute, Nairobi, Kenya, 11 Biosciences Eastern and Central Africa, Nairobi, Kenya, 12 International Center of Insect Physiology and Ecology, Nairobi, Kenya, 13 Bioinformatics Organization, Hudson, Massachusetts, United States of America, 14 Bioinformatics Team, Center for Development of Advanced Computing, Pune University Campus, Pune, India, 15 Harvard School of Public Health, Boston, Massachusetts, United States of America

Aligning Multiple Sequences with Genetic Algorithm

Plasmodium falciparum causes the most severe malaria pathogen and has developed resistance to existing drugs making it indispensable to discover new drugs. In order to predict drug targets in silico, a useful model for the metabolism is needed. However, automatically reconstructed network models typically cover more non-confirmed enzymes than confirmed enzymes of known gene products. Furthermore, it needs to be considered that the parasite takes advantage of the metabolism of the host. We compared several reconstructed network models and aimed to find the best suitable reconstruction for detecting drug targets in silico. We computationally reconstructed the metabolism based on automatically inferred enzymes and compared this with a reconstructed model that was based only on enzymes whose coding genes are known. Additionally, we tested if integrating enzymes of the host cell is beneficial for such an analysis. We employed several well established criteria for defining essential enzymes including chokepoints, betweenness centrality (or load-points), connectivity and the diameter of the networks. Comparing the modeling results with a comprehensive list of known drug targets for P. falciparum, showed that we had the best discovery success with a network model consisting only of enzymes from the parasite alone which coding genes were known.

Development of Bioinformatics Infrastructure for Genomics Research in H3Africa

Plasmodium falciparum (PF) is the most severe malaria parasite. It is developing resistance quickly to existing drugs making it indispensable to discover new drugs. Effective drugs have been discovered targeting metabolic enzymes of the parasite. In order to predict new drug targets, computational methods can be used employing database information of metabolism. Using this data, we performed recently a computational network analysis of metabolism of PF. We analyzed the topology of the network to find reactions which are sensitive against perturbations, i.e., when a single enzyme is blocked by drugs. We now used a refined network comprising also the host enzymes which led to a refined set of the five targets glutamyl-tRNA (gln) amidotransferase, hydroxyethylthiazole kinase, deoxyribose-phophate aldolase, pseudouridylate synthase, and deoxyhypusine synthase. It was shown elsewhere that glutamyl-tRNA (gln) amidotransferase of other microorganisms can be inhibited by 6-diazo-5-oxonorleucine. Performing a half maximal inhibitory concentration (IC50) assay, we showed, that 6-diazo-5-oxonorleucine is also severely affecting viability of PF in blood plasma of the human host. We confirmed this by an in vivo study observing Plasmodium berghei infected mice.