Computational Analysis of the Metabolic Network of Microorganisms to Detect Potential Drug Targets

Identifying essential genes in pathogens facilitates the identification of the corresponding proteins as potential drug targets and is the basis for understanding the minimum requirements for a synthetic cell. However, the experimental assessment of gene essentiality is resource-intensive and not feasible for all organisms, especially pathogens. Thus, the computational identification of new drug targets has become an important pursuit in biomedical research. In particular, essential metabolic enzymes have been successfully targeted by specific drugs. For directed drug development, the prediction of essential genes, especially in metabolic networks, is needed. In this thesis, I describe our development of a graph-based investigation tool aimed at finding possible deviations in a mutated network by knocking out particular reactions, and examining its producibility with a breadth-first search algorithm. We showed that this approach performed well at predicting new targets for antimalarial drugs. In addition, we analyzed the metabolic networks of bacteria and developed a machine learning approach based on various graph-based descriptors, including our own developed descriptor, that were potentially associated with the robustness and stabilization of metabolic networks. These descriptors were related to gene essentiality and included flux deviations, centrality and shortest paths. Besides these network topological features, we also used genomic and transcriptomic features, such as sequence characteristics and co-expression properties, as descriptors. The machine learning technique was developed to identify drug targets in metabolism. The metabolic networks of Escherichia coli, Pseudomonas aeruginosa and Salmonella typhimurium were analyzed. The well-studied metabolic network of Escherichia coli was used because it was an ideal model for formulating and validating our method. With publicly available genome-wide knockout screens, it was shown that topological, genomic and transcriptomic features describing the network are sufficient for defining drug targets. Furthermore, we tested our method across bacterial species and strains by using the experimental data from the genome-wide knockout screens of one bacterial organism to infer essential genes for another related bacterial organism. Our method is generic, and it enables the prediction of essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of the genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens are not available for the investigated organism

Identifying Antimalarial Drug Targets by Cellular Network Analysis

Malaria is one of the most deadly parasitic infectious diseases and identifying novel drug targets is mandatory for the development of new drugs. To find drug targets, metabolic and signaling networks have been constructed. These networks have been investigated by graph theoretical methods. Furthermore, mechanistic models have been set up based on stoichiometric equations. At equilibrium, production and consumption of internal metabolites need to be balanced leading to a large set of flux equations, and this can be used for metabolic flux simulations to identify drug targets. Analysis of flux variability and knockout simulations were applied to detect potential drug targets whose absence reduces the predicted biomass production and hence viability of the parasite in the host cell. Furthermore, not only the parasite was studied, but also the interaction between the host and the parasite, and, based on experimental expression data, stage-specific metabolic models of the parasite were developed, particularly during the red-blood cell stage. In this chapter, these various network-based approaches for drug target prediction will be explained and summarized

Identifying essential genes in bacterial metabolic networks with machine learning methods

<p>Abstract</p> <p>Background</p> <p>Identifying 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.</p> <p>Results</p> <p>We 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 <it>Salmonella typhimurium</it>. We compared our predictions to the viability of experimental knock-outs of <it>S. typhimurium </it>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.</p> <p>Conclusions</p> <p>Using 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.</p

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

Background: Computational identification of new drug targets is a major goal of pharmaceutical bioinformatics. Results: This 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. Conclusion: Our 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

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

Heterogeneous Network Model to Identify Potential Associations Between Plasmodium vivax and Human Proteins

Integration of multiple sources and data levels provides a great insight into the complex associations between human and malaria systems. In this study, a meta-analysis framework was developed based on a heterogeneous network model for integrating human-malaria protein similarities, a human protein interaction network, and a Plasmodium vivax protein interaction network. An iterative network propagation was performed on the heterogeneous network until we obtained stabilized weights. The association scores were calculated for qualifying a novel potential human-malaria protein association. This method provided a better performance compared to random experiments. After that, the stabilized network was clustered into association modules. The potential association candidates were then thoroughly analyzed by statistical enrichment analysis with protein complexes and known drug targets. The most promising target proteins were the succinate dehydrogenase protein complex in the human citrate (TCA) cycle pathway and the nicotinic acetylcholine receptor in the human central nervous system. Promising associations and potential drug targets were also provided for further studies and designs in therapeutic approaches for malaria at a systematic level. In conclusion, this method is efficient to identify new human-malaria protein associations and can be generalized to infer other types of association studies to further advance biomedical science

Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations of Plasmodium falciparum Genes

Functional annotation of unknown function genes reveals unidentified functions that can enhance our understanding of complex genome communications. A common approach for inferring gene function involves the ortholog-based method. However, genetic data alone are often not enough to provide information for function annotation. Thus, integrating other sources of data can potentially increase the possibility of retrieving annotations. Network-based methods are efficient techniques for exploring interactions among genes and can be used for functional inference. In this study, we present an analysis framework for inferring the functions of Plasmodium falciparum genes based on connection profiles in a heterogeneous network between human and Plasmodium falciparum proteins. These profiles were fed into a hybrid deep learning algorithm to predict the orthologs of unknown function genes. The results show high performance of the model’s predictions, with an AUC of 0.89. One hundred and twenty-one predicted pairs with high prediction scores were selected for inferring the functions using statistical enrichment analysis. Using this method, PF3D7_1248700 and PF3D7_0401800 were found to be involved with muscle contraction and striated muscle tissue development, while PF3D7_1303800 and PF3D7_1201000 were found to be related to protein dephosphorylation. In conclusion, combining a heterogeneous network and a hybrid deep learning technique can allow us to identify unknown gene functions of malaria parasites. This approach is generalized and can be applied to other diseases that enhance the field of biomedical science

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