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Background: The spread of artemisinin (ART)-resistant Plasmodium falciparum threatens the control of malaria. Mutations in the propeller domains of P. falciparum Kelch13 (k13) are strongly associated with ART resistance. Ferredoxin (Fd), a component of the ferredoxin/NADP+ reductase (Fd/FNR) redox system, is essential for isoprenoid precursor synthesis in the plasmodial apicoplast, which is important for K13-dependent hemoglobin trafficking and ART activation. Therefore, Fd is an antimalarial drug target and fd mutations may modulate ART sensitivity. We hypothesized that loss of Fd/FNR function enhances the effect of k13 mutation on ART resistance. Methods: In this study, methoxyamino chalcone (C3), an antimalarial compound that has been reported to inhibit the interaction of recombinant Fd and FNR proteins, was used as a chemical inhibitor of the Fd/FNR redox system. We investigated the inhibitory effects of dihydroartemisinin (DHA), C3, and iron chelators including deferiprone (DFP), 1-(N-acetyl-6-aminohexyl)-3-hydroxy-2-methylpyridin-4-one (CM1) and deferiprone-resveratrol hybrid (DFP-RVT) against wild-type (WT), k13 mutant, fd mutant, and k13 fd double mutant P. falciparum parasites. Furthermore, we investigated the pharmacological interaction of C3 with DHA, in which the iron chelators were used as reference ART antagonists. Results: C3 showed antimalarial potency similar to that of the iron chelators. As expected, combining DHA with C3 or iron chelators exhibited a moderately antagonistic effect. No differences were observed among the mutant parasites with respect to their sensitivity to C3, iron chelators, or the interactions of these compounds with DHA. Discussion: The data suggest that inhibitors of the Fd/FNR redox system should be avoided as ART partner drugs in ART combination therapy for treating malaria. [ABSTRACT FROM AUTHOR]

During the blood stages of malaria, several hundred parasite-encoded proteins are exported beyond the double-membrane barrier that separates the parasite from the host cell cytosol (1-6). These proteins have a variety of roles that are essential to virulence or parasite growth (7). There is keen interest in understanding how proteins are exported and whether common machineries are involved in trafficking the different classes of exported proteins (8,9). One potential trafficking machine is a protein complex known as the Plasmodium translocon of exported proteins (PTEX) (10). Although PTEX has been linked to the export of one class of exported proteins (10,11), there has been no direct evidence for its role and scope in protein translocation. Here we show, through the generation of two parasite lines defective for essential PTEX components (HSP101 or PTEX150), and analysis of a line lacking the non-essential component TRX2 (ref. 12), greatly reduced trafficking of all classes of exported proteins beyond the double membrane barrier enveloping the parasite. This includes proteins containing the PEXEL motif (RxLxE/Q/D) (1,2) and PEXEL-negative exported proteins (PNEPs) (6). Moreover, the export of proteins destined for expression on the infected erythrocyte surface, including the major virulence factor PfEMP1 in Plasmodium falciparum, was significantly reduced in PTEX knockdown parasites. PTEX function was also essential for blood-stage growth, because even a modest knockdown of PTEX components had a strong effect on the parasite's capacity to complete the erythrocytic cycle both in vitro and in vivo. Hence, as the only known nexus for protein export in Plasmodium parasites, and an essential enzymic machine, PTEX is a prime drug target., To address the role of PTEX in protein export directly, we examined parasite lines defective in PTEX components for their capacity to translocate exported proteins. Two PTEX components, TRX2 and [...]

Abstract Background Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD). Results In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles. Conclusion iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.

Abstract Background It is known that the highly pathogenic avian influenza A virus H5N1 binds strongly and with high specificity to the avian-type receptor by its hemagglutinin surface protein. This specificity is normally a barrier to viral transmission from birds to humans. However, strains may emerge with mutated hemagglutinin, potentially changing the receptor binding preference from avian to human-type. This hypothesis has been proven correct, since viral isolates from Vietnam and Thailand have been found which have increased selectivity toward the human cell receptor. The change in binding preference is due to mutation, which can be computationally modelled. The aim of this study is to further explore whether computational simulation could be used as a prediction tool for host type selectivity in emerging variants. Results Molecular dynamics simulation was employed to study the interactions between receptor models and hemagglutinin proteins from H5N1 strains A/Duck/Singapore/3/97, mutated A/Duck/Singapore/3/97 (Q222L, G224S, Q222L/G224S), A/Thailand/1(KAN-1)/2004, and mutated A/Thailand/1(KAN-1)/2004 (L129V/A134V). The avian receptor was represented by Siaα(2,3)Gal substructure and human receptor by Siaα(2,6)Gal. The glycoside binding conformation was monitored throughout the simulations since high selectivity toward a particular host occurs when the sialoside bound with the near-optimized conformation. Conclusion The simulation results showed all hemagglutinin proteins used the same set of amino acid residues to bind with the glycoside; however, some mutations alter linkage preferences. Preference toward human-type receptors is associated with a positive torsion angle, while avian-type receptor preference is associated with a negative torsion angle. According to the conformation analysis of the bound receptors, we could predict the relative selectivity in accordance with in vitro experimental data when disaccharides receptor analogs were used.

Abstract Background Pyronaridine (PN) and chloroquine (CQ) are structurally related anti-malarial drugs with primarily the same mode of action. However, PN is effective against several multidrug-resistant lines of Plasmodium falciparum, including CQ resistant lines, suggestive of important operational differences between the two drugs. Methods Synchronized trophozoite stage cultures of P. falciparum strain K1 (CQ resistant) were exposed to 50% inhibitory concentrations (IC50) of PN and CQ, and parasites were harvested from culture after 4 and 24 hours exposure. Global transcriptional changes effected by drug treatment were investigated using DNA microarrays. Results After a 4 h drug exposure, PN induced a greater degree of transcriptional perturbation (61 differentially expressed features) than CQ (10 features). More genes were found to respond to 24 h treatments with both drugs, and 461 features were found to be significantly responsive to one or both drugs across all treatment conditions. Filtering was employed to remove features unrelated to primary drug action, specifically features representing genes developmentally regulated, secondary stress/death related processes and sexual stage development. The only significant gene ontologies represented among the 46 remaining features after filtering relate to host exported proteins from multi-gene families. Conclusions The malaria parasite's molecular responses to PN and CQ treatment are similar in terms of the genes and pathways affected. However, PN appears to exert a more rapid response than CQ. The faster action of PN may explain why PN is more efficacious than CQ, particularly against CQ resistant isolates. In agreement with several other microarray studies of drug action on the parasite, it is not possible, however, to discern mechanism of drug action from the drug-responsive genes.

Abstract Background The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis. Results A new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA. Conclusions The EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from http://www4a.biotec.or.th/GI/tools/ippca.

Abstract Background Non-random patterns of genetic variation exist among individuals in a population owing to a variety of evolutionary factors. Therefore, populations are structured into genetically distinct subpopulations. As genotypic datasets become ever larger, it is increasingly difficult to correctly estimate the number of subpopulations and assign individuals to them. The computationally efficient non-parametric, chiefly Principal Components Analysis (PCA)-based methods are thus becoming increasingly relied upon for population structure analysis. Current PCA-based methods can accurately detect structure; however, the accuracy in resolving subpopulations and assigning individuals to them is wanting. When subpopulations are closely related to one another, they overlap in PCA space and appear as a conglomerate. This problem is exacerbated when some subpopulations in the dataset are genetically far removed from others. We propose a novel PCA-based framework which addresses this shortcoming. Results A novel population structure analysis algorithm called iterative pruning PCA (ipPCA) was developed which assigns individuals to subpopulations and infers the total number of subpopulations present. Genotypic data from simulated and real population datasets with different degrees of structure were analyzed. For datasets with simple structures, the subpopulation assignments of individuals made by ipPCA were largely consistent with the STRUCTURE, BAPS and AWclust algorithms. On the other hand, highly structured populations containing many closely related subpopulations could be accurately resolved only by ipPCA, and not by other methods. Conclusion The algorithm is computationally efficient and not constrained by the dataset complexity. This systematic subpopulation assignment approach removes the need for prior population labels, which could be advantageous when cryptic stratification is encountered in datasets containing individuals otherwise assumed to belong to a homogenous population.

Background: Hypusination is an essential post-translational modification in eukaryotes. The two enzymes required for this modification, namely deoxyhypusine synthase (DHS) and deoxyhypusine hydrolase are also conserved. Plasmodium falciparum human malaria parasites possess genes for both hypusination enzymes, which are hypothesized to be targets of antimalarial drugs. Methods: Transgenic P. falciparum parasites with modification of the PF3D7_1412600 gene encoding PfDHS enzyme were created by insertion of the glmS riboswitch or the M9 inactive variant. The PfDHS protein was studied in transgenic parasites by confocal microscopy and Western immunoblotting. The biochemical function of PfDHS enzyme in parasites was assessed by hypusination and nascent protein synthesis assays. Gene essentiality was assessed by competitive growth assays and chemogenomic profiling. Results: Clonal transgenic parasites with integration of glmS riboswitch downstream of the PfDHS gene were established. PfDHS protein was present in the cytoplasm of transgenic parasites in asexual stages. The PfDHS protein could be attenuated fivefold in transgenic parasites with an active riboswitch, whereas PfDHS protein expression was unaffected in control transgenic parasites with insertion of the riboswitch-inactive sequence. Attenuation of PfDHS expression for 72 h led to a significant reduction of hypusinated protein; however, global protein synthesis was unaffected. Parasites with attenuated Pf DHS expression showed a significant growth defect, although their decline was not as rapid as parasites with attenuated dihydrofolate reductase-thymidylate synthase (Pf DHFR-TS) expression. Pf DHS-attenuated parasites showed increased sensitivity to N¹-guanyl-1,7-diaminoheptane, a structural analog of spermidine, and a known inhibitor of DHS enzymes. Discussion: Loss of PfDHS function leads to reduced hypusination, which may be important for synthesis of some essential proteins. The growth defect in parasites with attenuated Pf DHS expression suggests that this gene is essential. However, the slower decline of PfDHS mutants compared with PfDHFR-TS mutants in competitive growth assays suggests that PfDHS is less vulnerable as an antimalarial target. Nevertheless, the data validate PfDHS as an antimalarial target which can be inhibited by spermidine-like compounds. [ABSTRACT FROM AUTHOR]

Background. Sugarcane is an important global food crop and energy resource. To facilitate the sugarcane improvement program, genome and gene information are important for studying traits at the molecular level. Most currently available transcriptome data for sugarcane were generated using second-generation sequencing platforms, which provide short reads. The de novo assembled transcripts from these data are limited in length, and hence may be incomplete and inaccurate, especially for long RNAs. Methods. We generated a transcriptome dataset of leaf tissue from a commercial Thai sugarcane cultivar Khon Kaen 3 (KK3) using PacBio RS II single-molecule long-read sequencing by the Iso-Seq method. Short-read RNA-Seq data were generated from the same RNA sample using the Ion Proton platform for reducing base calling errors. Results. A total of 119,339 error-corrected transcripts were generated with the N50 length of 3,611 bp, which is on average longer than any previously reported sugarcane transcriptome dataset. 110,253 sequences (92.4%) contain an open reading frame (ORF) of at least 300 bp long with ORF N50 of 1,416 bp. The mean lengths of 5' and 3' untranslated regions in 73,795 sequences with complete ORFs are 1,249 and 1,187 bp, respectively. 4,774 transcripts are putatively novel full-length transcripts which do not match with a previous Iso-Seq study of sugarcane. We annotated the functions of 68,962 putative full-length transcripts with at least 90% coverage when compared with homologous protein coding sequences in other plants. Discussion. The new catalog of transcripts will be useful for genome annotation, identification of splicing variants, SNP identification, and other research pertaining to the sugarcane improvement program. The putatively novel transcripts suggest unique features of KK3, although more data from different tissues and stages of development are needed to establish a reference transcriptome of this cultivar. [ABSTRACT FROM AUTHOR]

Background: DNA gel electrophoresis is a molecular biology technique for separating different sizes of DNA fragments. Applications of DNA gel electrophoresis include DNA fingerprinting (genetic diagnosis), size estimation of DNA, and DNA separation for Southern blotting. Accurate interpretation of DNA banding patterns from electrophoretic images can be laborious and error prone when a large number of bands are interrogated manually. Although many bio-imaging techniques have been proposed, none of them can fully automate the typing of DNA owing to the complexities of migration patterns typically obtained. Results: We developed an image-processing tool that automatically calls genotypes from DNA gel electrophoresis images. The image processing workflow comprises three main steps: 1) lane segmentation, 2) extraction of DNA bands and 3) band genotyping classification. The tool was originally intended to facilitate large-scale genotyping analysis of sugarcane cultivars. We tested the proposed tool on 10 gel images (433 cultivars) obtained from polyacrylamide gel electrophoresis (PAGE) of PCR amplicons for detecting intron length polymorphisms (ILP) on one locus of the sugarcanes. These gel images demonstrated many challenges in automated lane/band segmentation in image processing including lane distortion, band deformity, high degree of noise in the background, and bands that are very close together (doublets). Using the proposed bio-imaging workflow, lanes and DNA bands contained within are properly segmented, even for adjacent bands with aberrant migration that cannot be separated by conventional techniques. The software, called GELect, automatically performs genotype calling on each lane by comparing with an all-banding reference, which was created by clustering the existing bands into the non-redundant set of reference bands. The automated genotype calling results were verified by independent manual typing by molecular biologists. Conclusions: This work presents an automated genotyping tool from DNA gel electrophoresis images, called GELect, which was written in Java and made available through the imageJ framework. With a novel automated image processing workflow, the tool can accurately segment lanes from a gel matrix, intelligently extract distorted and even doublet bands that are difficult to identify by existing image processing tools. Consequently, genotyping from DNA gel electrophoresis can be performed automatically allowing users to efficiently conduct large scale DNA fingerprinting via DNA gel electrophoresis. The software is freely available from http://www.biotec.or.th/gi/tools/gelect. [ABSTRACT FROM AUTHOR]