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  1. In silico identification and validation of a novel hypothetical protein in Cryptosporidium hominis and virtual screening of inhibitors as therapeutics
    Shrivastava AK, Kumar S, Sahu PS, Mahapatra RK
    Parasitol Res, 2017 May;116(5):1533-1544.
    PMID: 28389892 DOI: 10.1007/s00436-017-5430-1
    Computational approaches to predict structure/function and other biological characteristics of proteins are becoming more common in comparison to the traditional methods in drug discovery. Cryptosporidiosis is a major zoonotic diarrheal disease particularly in children, which is caused primarily by Cryptosporidium hominis and Cryptosporidium parvum. Currently, there are no vaccines for cryptosporidiosis and recommended drugs are ineffective. With the availability of complete genome sequence of C. hominis, new targets have been recognized for the development of effective and better drugs and/or vaccines. We identified a unique hypothetical protein (TU502HP) in the C. hominis genome from the CryptoDB database. A three-dimensional model of the protein was generated using the Iterative Threading ASSEmbly Refinement server through an iterative threading method. Functional annotation and phylogenetic study of TU502HP protein revealed similarity with human transportin 3. The model is further subjected to a virtual screening study form the ZINC database compound library using the Dock Blaster server. A docking study through AutoDock software reported N-(3-chlorobenzyl)ethane-1,2-diamine as the best inhibitor in terms of docking score and binding energy. The reliability of the binding mode of the inhibitor is confirmed by a complex molecular dynamics simulation study using GROMACS software for 10 ns in the water environment. Furthermore, antigenic determinants of the protein were determined with the help of DNASTAR software. Our findings report a great potential in order to provide insights in the development of new drug(s) or vaccine(s) for treatment and prophylaxis of cryptosporidiosis among humans and animals.
    Matched MeSH terms: Genome, Protozoan/genetics
  2. Fulltext Updates on k13 mutant alleles for artemisinin resistance in Plasmodium falciparum
    Zaw MT, Emran NA, Lin Z
    J Microbiol Immunol Infect, 2018 Apr;51(2):159-165.
    PMID: 28711439 DOI: 10.1016/j.jmii.2017.06.009
    BACKGROUND: In the fight against malaria caused by Plasmodium falciparum, the successes achieved by artemisinin were endangered by resistance of the parasites to the drug. Whole genome sequencing approach on artemisinin resistant parasite line discovered k13 gene associated with drug resistance. In vitro and in vivo studies indicated mutations in the k13 gene were linked to the artemisinin resistance.

    METHODOLOGY: The literatures published after April, 2015 up to December, 2016 on k13 mutant alleles for artemisinin resistance in Plasmodium falciparum and relevant literatures were comprehensively reviewed.

    RESULTS: To date, 13 non-synonymous mutations of k13 gene have been observed to have slow parasite clearance. Worldwide mapping of k13 mutant alleles have shown mutants associated with artemisinin resistance were confined to southeast Asia and China and did not invade to African countries. Although in vitro ring stage survival assay of 0-3 h was a recently developed assay, it was useful for rapid detection of artemisinin resistance associated k13 allelic marker in the parasite. Recently, dissemination of k13 mutant alleles was recommended to be investigated by identity of haplotypes. Significant characteristics of well described alleles in the reports were mentioned in this review for the benefit of future studies.

    CONCLUSION: According to the updates in the review, it can be concluded artemisinin resistance does not disseminate to India and African countries within short period whereas regular tracking of these mutants is necessary.

    Matched MeSH terms: Genome, Protozoan/genetics
  3. Fulltext A molecular barcode to inform the geographical origin and transmission dynamics of Plasmodium vivax malaria
    Diez Benavente E, Campos M, Phelan J, Nolder D, Dombrowski JG, Marinho CRF, et al.
    PLoS Genet, 2020 02;16(2):e1008576.
    PMID: 32053607 DOI: 10.1371/journal.pgen.1008576
    Although Plasmodium vivax parasites are the predominant cause of malaria outside of sub-Saharan Africa, they not always prioritised by elimination programmes. P. vivax is resilient and poses challenges through its ability to re-emerge from dormancy in the human liver. With observed growing drug-resistance and the increasing reports of life-threatening infections, new tools to inform elimination efforts are needed. In order to halt transmission, we need to better understand the dynamics of transmission, the movement of parasites, and the reservoirs of infection in order to design targeted interventions. The use of molecular genetics and epidemiology for tracking and studying malaria parasite populations has been applied successfully in P. falciparum species and here we sought to develop a molecular genetic tool for P. vivax. By assembling the largest set of P. vivax whole genome sequences (n = 433) spanning 17 countries, and applying a machine learning approach, we created a 71 SNP barcode with high predictive ability to identify geographic origin (91.4%). Further, due to the inclusion of markers for within population variability, the barcode may also distinguish local transmission networks. By using P. vivax data from a low-transmission setting in Malaysia, we demonstrate the potential ability to infer outbreak events. By characterising the barcoding SNP genotypes in P. vivax DNA sourced from UK travellers (n = 132) to ten malaria endemic countries predominantly not used in the barcode construction, we correctly predicted the geographic region of infection origin. Overall, the 71 SNP barcode outperforms previously published genotyping methods and when rolled-out within new portable platforms, is likely to be an invaluable tool for informing targeted interventions towards elimination of this resilient human malaria.
    Matched MeSH terms: Genome, Protozoan/genetics*
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