Kitiporn Plaimas

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.

Identifying essential genes in bacterial metabolic networks with machine learning methods

BackgroundIdentifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.ResultsWe developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway.ConclusionsUsing elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism.

Machine learning based analyses on metabolic networks supports high-throughput knockout screens

BackgroundComputational identification of new drug targets is a major goal of pharmaceutical bioinformatics.ResultsThis paper presents a machine learning strategy to study and validate essential enzymes of a metabolic network. Each single enzyme was characterized by its local network topology, gene homologies and co-expression, and flux balance analyses. A machine learning system was trained to distinguish between essential and non-essential reactions. It was validated by a comprehensive experimental dataset, which consists of the phenotypic outcomes from single knockout mutants of Escherichia coli (KEIO collection). We yielded very reliable results with high accuracy (93%) and precision (90%). We show that topologic, genomic and transcriptomic features describing the network are sufficient for defining the essentiality of a reaction. These features do not substantially depend on specific media conditions and enabled us to apply our approach also for less specific media conditions, like the lysogeny broth rich medium.ConclusionOur analysis is feasible to validate experimental knockout data of high throughput screens, can be used to improve flux balance analyses and supports experimental knockout screens to define drug targets.

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

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.

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

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.

Identification of inflammatory bowel disease-related proteins using a reverse k-nearest neighbor search

Inflammatory bowel disease (IBD) is a chronic disease whose incidence and prevalence increase every year; however, the pathogenesis of IBD is still unclear. Thus, identifying IBD-related proteins is important for understanding its complex disease mechanism. Here, we propose a new and simple network-based approach using a reverse k-nearest neighbor ( R k NN ) search to identify novel IBD-related proteins. Protein-protein interactions (PPI) and Genome-Wide Association Studies (GWAS) were used in this study. After constructing the PPI network, the R k NN search was applied to all of the proteins to identify sets of influenced proteins among their k-nearest neighbors ( R k NNs ). An observed protein whose influenced proteins were mostly known IBD-related proteins was statistically identified as a novel IBD-related protein. Our method outperformed a random aspect, k NN search, and centrality measures based on the network topology. A total of 39 proteins were identified as IBD-related proteins. Of these proteins, 71% were reported at least once in the literature as related to IBD. Additionally, these proteins were found over-represented in the IBD pathway and enriched in importantly functional pathways in IBD. In conclusion, the R k NN search with the statistical enrichment test is a great tool to identify IBD-related proteins to better understand its complex disease mechanism.

DDA: A Novel Network-Based Scoring Method to Identify Disease–Disease Associations

Categorizing human diseases provides higher efficiency and accuracy for disease diagnosis, prognosis, and treatment. Disease-disease association (DDA) is a precious information that indicates the large-scale structure of complex relationships of diseases. However, the number of known and reliable associations is very small. Therefore, identification of DDAs is a challenging task in systems biology and medicine. Here, we developed a novel network-based scoring algorithm called DDA to identify the relationships between diseases in a large-scale study. Our method is developed based on a random walk prioritization in a protein-protein interaction network. This approach considers not only whether two diseases directly share associated genes but also the statistical relationships between two different diseases using known disease-related genes. Predicted associations were validated by known DDAs from a database and literature supports. The method yielded a good performance with an area under the curve of 71% and outperformed other standard association indices. Furthermore, novel DDAs and relationships among diseases from the clusters analysis were reported. This method is efficient to identify disease-disease relationships on an interaction network and can also be generalized to other association studies to further enhance knowledge in medical studies.

Molecular Karyotyping and Exome Analysis of Salt-Tolerant Rice Mutant from Somaclonal Variation

LPT123‐TC171 is a salt‐tolerant (ST) and drought‐tolerant (DT) rice line that was selected from somaclonal variation of the original Leuang Pratew 123 (LPT123) rice cultivar. The objective of this study was to identify the changes in the rice genome that possibly lead to ST and/or DT characteristics. The genomes of LPT123 and LPT123‐TC171 were comparatively studied at the four levels of whole chromosomes (chromosome structure including telomeres, transposable elements, and DNA sequence changes) by using next‐generation sequencing analysis. Compared with LPT123, the LPT123‐TC171 line displayed no changes in the ploidy level, but had a significant deficiency of chromosome ends (telomeres). The functional genome analysis revealed new aspects of the genome response to the in vitro cultivation condition, where exome sequencing revealed the molecular spectrum and pattern of changes in the somaclonal variant compared with the parental LPT123 cultivar. Mutation detection was performed, and the degree of mutations was evaluated to estimate the impact of mutagenesis on the protein functions. Mutations within the known genes responding to both drought and salt stress were detected in 493 positions, while mutations within the genes responding to only salt stress were found in 100 positions. The possible functions of the mutated genes contributing to salt or drought tolerance were discussed. It was concluded that the ST and DT characteristics in the somaclonal variegated line resulted from the base changes in the salt‐ and drought‐responsive genes rather than the changes in chromosome structure or the large duplication or deletion in the specific region of the genome.

OsNucleolin1-L Expression in Arabidopsis Enhances Photosynthesis via Transcriptome Modification under Salt Stress Conditions

OsNUC1 encodes rice nucleolin, which has been shown to be involved in salt stress responses. Expression of the full-length OsNUC1 gene in Arabidopsis resulted in hypersensitivity to ABA during germination. Transcriptome analysis of the transgenic lines, in comparison with the wild type, revealed that the RNA abundance of >1,900 genes was significantly changed under normal growth conditions, while under salt stress conditions the RNAs of 999 genes were found to be significantly regulated. Gene enrichment analysis showed that under normal conditions OsNUC1 resulted in repression of genes involved in photosynthesis, while in salt stress conditions OsNUC1 increased expression of the genes involved in the light-harvesting complex. Correspondingly, the net rate of photosynthesis of the transgenic lines was increased under salt stress. Transgenic rice lines with overexpression of the OsNUC1-L gene were generated and tested for photosynthetic performance under salt stress conditions. The transgenic rice lines treated with salt stress at the booting stage had a higher photosynthetic rate and stomatal conductance in flag leaves and second leaves than the wild type. Moreover, higher contents of Chl a and carotenoids were found in flag leaves of the transgenic rice. These results suggest a role for OsNUC1 in the modification of the transcriptome, especially the gene transcripts responsible for photosynthesis, leading to stabilization of photosynthesis under salt stress conditions.

In silico multiple-targets identification for heme detoxification in the human malaria parasite Plasmodium falciparum.

Detoxification of hemoglobin byproducts or free heme is an essential step and considered potential targets for anti-malaria drug development. However, most of anti-malaria drugs are no longer effective due to the emergence and spread of the drug resistant malaria parasites. Therefore, it is an urgent need to identify potential new targets and even for target combinations for effective malaria drug design. In this work, we reconstructed the metabolic networks of Plasmodium falciparum and human red blood cells for the simulation of steady mass and flux flows of the parasites metabolites under the blood environment by flux balance analysis (FBA). The integrated model, namely iPF-RBC-713, was then adjusted into two stage-specific metabolic models, which first was for the pathological stage metabolic model of the parasite when invaded the red blood cell without any treatment and second was for the treatment stage of the parasite when a drug acted by inhibiting the hemozoin formation and caused high production rate of heme toxicity. The process of identifying target combinations consisted of two main steps. Firstly, the optimal fluxes of reactions in both the pathological and treatment stages were computed and compared to determine the change of fluxes. Corresponding enzymes of the reactions with zero fluxes in the treatment stage but non-zero fluxes in the pathological stage were predicted as a preliminary list of potential targets in inhibiting heme detoxification. Secondly, the combinations of all possible targets listed in the first step were examined to search for the best promising target combinations resulting in more effective inhibition of the detoxification to kill the malaria parasites. Finally, twenty-three enzymes were identified as a preliminary list of candidate targets which mostly were in pyruvate metabolism and citrate cycle. The optimal set of multiple targets for blocking the detoxification was a set of heme ligase, adenosine transporter, myo-inositol 1-phosphate synthase, ferrodoxim reductase-like protein and guanine transporter. In conclusion, the method has shown an effective and efficient way to identify target combinations which are obviously useful in the development of novel antimalarial drug combinations.