Abstract
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.
Keywords
- network-based analysis
- drug targets
- flux balance analysis
- malaria
1. Introduction
Network-based analysis has become an important tool in biomedical research. It facilitates the investigation and understanding of a system as a whole, not only its single components. For this, first the networks need to be constructed and then investigated employing different analysis or modeling techniques. According to the applied methodological approaches to analyze these networks, one may distinguish cellular network models for signal transduction, gene regulation and metabolism. The network constructions based on information are compiled from databases and are assembled in an automated way often followed by manual refinement. Network-based models have been applied to study the cellular mechanisms of a large variety of diseases elucidating, for example, tumor growth, malfunctioning of the differentiation of immune cells, or identifying drug targets of invasive pathogens [1, 2]. To find drug targets for the treatment of malaria, metabolic and signaling networks have been constructed and intensively investigated. This chapter will introduce the reader into the basic principles of constructing and applying such cellular networks. It then leads through the application of these systems biology approaches to predict drug targets followed by a small section exemplarily showing an experimental validation for these predictions.
2. Construction of cellular networks
Proteins are involved in all cellular functions. These cellular processes can be put up as cellular networks, which describe associations among these proteins and other cellular compounds such as metabolites and nucleic acids. These cellular networks can conceptually be divided into three distinct parts: the cell signaling, the transcriptional regulatory network, and the metabolic network. The best observed and modeled network is the metabolic network while the complex system of signal transduction is rather captured statistically investigating the experimental information about proteins and their expressed genes of network models basing on protein-protein interactions [3]. The transcriptional regulatory network links transcriptional regulators to their target genes [4]. The simplest form of a network is a network represented by an undirected graph
R1: | A | ⇔ | B |
R2: | 2 B + C | → | E + F |
R3: | 2 E | → | B + D |
The stoichiometric matrix or the adjacency matrix containing stoichiometric coefficients of each reaction equation is
where the rows correspond to metabolites A, B, C, D, E, and F, and the columns correspond to reactions R1, R2, and R3, respectively. R1 is a reversible reaction. Metabolic networks for
3. Topological features for statistical analyses of cellular networks
Several computational techniques have been developed to identify essential genes and drug targets
3.1. Diameter and density of a network
The diameter of a network is the largest distance of all shortest paths between two nodes (reactions, signaling molecules) in the network. The density of a network is the ratio of the edges (links, connections) between two reactions divided by all possible edges of all reactions. These two properties can be used to determine the robustness of a network. In recent studies, a reaction was said to be essential if the mutated or targeted network showed a larger diameter after removing the reaction [12, 13].
3.2. Scale-freeness of networks
Networks can be distinguished by their degree distributions where the degree of a node
3.3. Clustering coefficient
The clustering coefficient is used to estimate the local density of links (edges) in the network. It describes the connectedness among neighbors and helps to estimate the probability of local alternative paths of signaling or metabolic fluxes (e.g., after targeting). The clustering coefficient of a node
3.4. Centrality
Descriptors for
3.5. Choke points and load points
In metabolic networks, Samal
3.6. Producibility (by deviations)
A reaction is determined to be potentially essential when basically the mutated network cannot yield the products of the reaction from upstream substrates of the reaction using other pathways linking the substrates to the products (see Figure 3). The percentage of the products that can be produced from the substrates, the so-called “producibility,” can be used to examine the essentiality of the observed reaction [13].
3.7. Applying these topology-based methods to predict drug targets for Plasmodium spp.
The concept of choke points and load points was successfully applied to estimate the essentiality of an enzyme in
Protein-protein interactions were inferred by a high-throughput method (yeast-2-hybrid) and assembled for a signaling network of
Recently, Bhattacharyya and Chakrabarti analyzed a large-scale protein-protein interaction network of
Interactions between the human host and the parasite have been intensively studied [11–13, 33]. The comparison of several reconstructed network models has been performed to find the best suitable reconstruction for detecting drug targets
Chen
4. In silico modeling using flux balance analysis to identify drug targets
Flux balance analysis (FBA) is a computational approach to estimate the quantitative flux of metabolites through a mechanistic model of metabolism. Thereby, it is possible to predict the growth rate of an organism or the rate of production of an important metabolite [9, 34–36]. Biochemical stoichiometric equations are used to assemble a set of constraints to limit the feasible search space. The idea is that, at equilibrium, production and consumption of internal metabolites are balanced. This leads to a large set of equations in which the net production flux equals the net consumption flux for each internal metabolite. Additionally, allowable fluxes of any reaction are bounded at plausible maximum and minimum fluxes. Bounds may also be taken from the literature. These balances and bounds define the space of allowable flux distributions of a system, that is, the allowed combinations of fluxes for each reaction. To get a phenotype or modeling prediction from these constraints, an optimization criterion is put up. For example, in the case of predicting growth, the objective is to optimize biomass production which is the rate at which metabolic compounds are converted into the physiological portions of biomass constituents most importantly of nucleic acids, amino acids and lipids. Together with the constraints, this is mathematically formulated as a system of linear equations which is solved using linear programming based programs. Flux variability and knockout simulations are analyzed to detect potential drug targets whose absence reduces the biomass production and hence viability of the parasite in the host cell. By simulating a reconstructed metabolic network of an organism of interest, first a “wildtype” model is investigated and the growth rate of the wildtype under specific bounds (or conditions) obtained. Performing a single gene (or reaction) knockout/deletion under the same condition by limiting its corresponding fluxes to zero (knockout simulation), the fluxes are calculated simulating an organism effected to a drug (targeting the deleted enzyme) and the growth rate is compared to the wildtype. A knocked out gene (or reaction) is predicted to be essential under the given condition if the mutant model yields a much lower growth rate compared to the wildtype. Flux balance analysis is a widely used and well-established technique to assess the essentiality of genes and hence potential drug targets [9, 34–36]. The beauty of this approach is that it does not depend on specific enzymatic parameters for each enzyme like their Michaelis Menten constants, etc., but are rather basing on simple stoichiometric equations. To some extent, the only experimental parameters are the boundary conditions. The drawback is that often several solutions can come out which are mathematically equally good, but physiologically very different leading to follow-up analyses of each of these solutions. Nevertheless, the approach was used for several genome-scale metabolic network constructions, followed by flux simulations of the inner metabolites of
4.1. Flux balance analysis formulation
Let
where
where
4.1.1. Applying FBA to predict drug targets
FBA has been widely used to predict essential genes of the human malaria parasite
4.2. Finding multiple drug targets to treat a drug resistant Plasmodium strain
Recently, Phaiphinit et al. reconstructed the metabolic network of
FBA was used to get the flux distributions for the untreated and the treated conditions. For the untreated condition, the objective was to maximize the production rate of biomass according to Ref. [36], including the Na+/K+ ratio based potential at the ATPase, which plays an important role for the homeostasis of red blood cells [43, 44]. In the treated condition, the drug usually inhibits the detoxification process of the parasite harming the parasite due to the toxicity of free heme. Thus, during the treated condition, the (toxic) flux of heme production should be an additional objective to ensure that the toxic flux is not zero when identifying reactions or enzymes to be blocked during the treatment. The flux distributions of both models were then compared to obtain a list of candidate targets by the criteria that the reactions with zero fluxes in the treated condition but non-zero fluxes in the untreated condition could be potential targets for inhibiting heme detoxification.
With this method, 23 enzymes were identified as candidate targets, which mostly were in pyruvate metabolism and the citrate cycle. The optimal set of multiple targets for blocking the detoxification was a set of a heme ligase, adenosine transporter, myo-inositol 1-phosphate synthase, ferrodoxim reductase-like protein, and the guanine transporter. Purine transporters have been known as the major route of purine into the parasitized red blood cell. In the development of anti-malarial drugs, inhibitors targeting purine transport are of pharmaceutical interest and are investigated. Likewise, adenosine transport and its inhibitor have been studied in infected and uninfected human erythrocytes recently [45]. In summary, this shows an efficient way to identify useful target combinations in the development of novel antimalarial drugs [35].
5. Experimental validation, a case study
Typically, after the computational network analysis, a list of potential drug targets is assembled and needs to be validated experimentally. Exemplarily, in one study of a topological network analysis, 22 potential targets were proposed [26]. Using a refined network comprising also the host enzymes led to a refined set of the five potential drug targets (glutamyl–tRNA(gln) amidotransferase, hydroxyethylthiazole kinase, deoxyribose–phosphate aldolase, pseudouridylate synthase, and deoxyhypusine synthase) [46]. The next step was to find effective inhibitors to block these enzymes. Many reported inhibitors can be collected from databases like the Brenda Enzyme database [47], Drugbank [48], and from companies like Sigma (
6. Conclusions
Even though the number of deaths caused by malaria has diminished considerably, it is still a challenge to treat the effected patients and clear off the pathogen after infection. In particular, there are increasingly more strains getting resistant against common treatments, and hence there is a striking demand to find new targets for therapy.
The computational approaches introduced here show some convincing results. However, it needs to be shown that these predictions are experimentally confirmed and finally make their way from the bench to the bedside.
Various techniques of network-based analyses to identify potential drug targets of
Even though all these presented concepts have the very same aim to find a target, their results are quite heterogeneous lists of different predicted drug targets, some of them validated by experimental assays. As a future aspect, a data and method integration needs to be performed leading to a
References
- 1.
Ahn YY, et al., Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents. PLoS One, 2014. 9 (1): p. e8519. - 2.
Peng Q, Schork NJ, Utility of network integrity methods in therapeutic target identification. Front Genet, 2014. 5 : p. 12. - 3.
Acencio ML, Lemke N, Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information. BMC Bioinformatics, 2009. 10 : p. 290. - 4.
Luscombe NM, et al., Genomic analysis of regulatory network dynamics reveals large topological changes. Nature, 2004. 431 (7006): pp. 308–312. - 5.
König R, Eils R, Gene expression analysis on biochemical networks using the Potts spin model. Bioinformatics, 2004. 20 (10): pp. 1500–1505. - 6.
Barabasi AL, Oltvai ZN, Network biology: understanding the cell’s functional organization. Nat Rev Genet, 2004. 5 (2): pp. 101–113. - 7.
Karp PD, et al., Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res, 2005. 33 (19): pp. 6083–6089. - 8.
Ginsburg H, Progress in in silico functional genomics: the malaria metabolic pathways database. Trends Parasitol, 2006. 22 (6): pp. 238–240. - 9.
Huthmacher C, et al., Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis. BMC Syst Biol, 2010. 4 : p. 120. - 10.
Gursoy A, Keskin O, Nussinov R, Topological properties of protein interaction networks from a structural perspective. Biochem Soc Trans, 2008. 36 (Pt 6): pp. 1398–1403. - 11.
Swann J, et al., Systems analysis of host-parasite interactions. Wiley Interdiscip Rev Syst Biol Med, 2015. 7 (6): pp. 381–400. - 12.
Bhattacharyya M, Chakrabarti S, Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies. Malar J, 2015.14 : p. 70. - 13.
Fatumo S, et al., Comparing metabolic network models based on genomic and automatically inferred enzyme information from Plasmodium and its human host to define drug targets in silico. Infect Genet Evol, 2011. 11 (4): pp. 708–715. - 14.
Barabasi AL, Albert R, Emergence of scaling in random networks. Science, 1999. 286 (5439): pp. 509–512. - 15.
Almaas E, Biological impacts and context of network theory. J Exp Biol, 2007. 210 (9): pp. 1548–1558. - 16.
Milgram S, Small-world problem. Psychol Today, 1967. 1 (1): pp. 61–67. - 17.
Zhu X, Gerstein M, and Snyder M, Getting connected: analysis and principles of biological networks. Genes Dev, 2007. 21 (9): pp. 1010–1024. - 18.
Albert R, Jeong H, and Barabasi AL, Error and attack tolerance of complex networks. Nature, 2000. 406 (6794): pp. 378–382. - 19.
Wagner A, Fell DA, The small world inside large metabolic networks. Proc Biol Sci, 2001. 268 (1478): pp. 1803–1810. - 20.
Zur H, Ruppin E, and Shlomi T, iMAT: an integrative metabolic analysis tool. Bioinformatics, 2010. 26 (24): pp. 3140–3142. - 21.
Estrada E, Protein bipartivity and essentiality in the yeast protein-protein interaction network. J Proteome Res, 2006. 5 (9): pp. 2177–2184. - 22.
Samal A, et al., Low degree metabolites explain essential reactions and enhance modularity in biological networks. BMC Bioinformatics, 2006. 7 : p. 118. - 23.
Rahman SA, Schomburg D, Observing local and global properties of metabolic pathways: ‘load points’ and ‘choke points’ in the metabolic networks. Bioinformatics, 2006. 22 (14): pp. 1767–1774. - 24.
Yeh I, et al., Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res, 2004.14 (5): pp. 917–924. - 25.
Bonday ZQ, et al., Import of host delta-aminolevulinate dehydratase into the malarial parasite: identification of a new drug target. Nat Med, 2000. 6 (8): pp. 898–903. - 26.
Fatumo S, et al., Estimating novel potential drug targets of Plasmodium falciparum by analysing the metabolic network of knock-out strains in silico. Infect Genet Evol, 2009.9 (3): pp. 351–358. - 27.
Moritz E, et al., The efficacy of inhibitors involved in spermidine metabolism in Plasmodium falciparum ,Anopheles stephensi andTrypanosoma evansi . Parasitol Res, 2004.94 (1): pp. 37–48. - 28.
Suthram S, Sittler T, Ideker T, The Plasmodium protein network diverges from those of other eukaryotes. Nature, 2005. 438 (7064): pp. 108–112. - 29.
Kelley BP, et al., PathBLAST: a tool for alignment of protein interaction networks. Nucleic Acids Res, 2004. 32 (Web Server issue): pp. W83–W88. - 30.
Scheibel LW, et al., Calcium and calmodulin antagonists inhibit human malaria parasites ( Plasmodium falciparum ): implications for drug design. Proc Natl Acad Sci U S A, 1987.84 (20): pp. 7310–7314. - 31.
Sanchez CP, et al., Evidence for a substrate specific and inhibitable drug efflux system in chloroquine resistant Plasmodium falciparum strains. Biochemistry, 2004.43 (51): pp. 16365–16373. - 32.
Hoppe HC, et al., Antimalarial quinolines and artemisinin inhibit endocytosis in Plasmodium falciparum. Antimicrob Agents Chemother, 2004. 48 (7): pp. 2370–2378. - 33.
Chen Y, Xu R, Network-based gene prediction for Plasmodium falciparum malaria towards genetics-based drug discovery. BMC Genomics, 2015.16 (Suppl 7): p. S9. - 34.
Orth JD, Thiele I, Palsson BO, What is flux balance analysis? Nat Biotechnol, 2010. 28 (3): pp. 245–248. - 35.
Phaiphinit S, et al., In silico multiple-targets identification for heme detoxification in the human malaria parasite Plasmodium falciparum . Infect Genet Evol, 2016.37 : pp. 237–244. - 36.
Plata G, et al., Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network. Mol Syst Biol, 2010.6 : p 408. - 37.
Plaimas K, Computational Analysis of the Metabolic Network of Microorganisms to Detect Potential Drug Targets, Doctoral thesis, Heidelberg University, 2011. - 38.
Dholakia N, Dhandhukia P, Roy N, Screening of potential targets in Plasmodium falciparum using stage-specific metabolic network analysis. Mol Divers, 2015.19 (4): pp. 991–1002. - 39.
Shlomi T, et al., Network-based prediction of human tissue-specific metabolism. Nat Biotechnol, 2008. 26 (9): pp. 1003–1110. - 40.
Magni G, et al., NAD(P) biosynthesis enzymes as potential targets for selective drug design. Curr Med Chem, 2009. 16 (11): pp. 1372–1390. - 41.
Sorci L, et al., Targeting NAD biosynthesis in bacterial pathogens: structure-based development of inhibitors of nicotinate mononucleotide adenylyltransferase NadD. Chem Biol, 2009. 16 (8): pp. 849–861. - 42.
Li Z, Wang RS, Zhang XS, Two-stage flux balance analysis of metabolic networks for drug target identification. BMC Syst Biol, 2011. 5 (Suppl 1): p. S11. - 43.
Mauritz JM, et al., The homeostasis of Plasmodium falciparum -infected red blood cells. PLoS Comput Biol, 2009.5 (4): p. e1000339. - 44.
Wiback SJ, Palsson BO, Extreme pathway analysis of human red blood cell metabolism. Biophys J, 2002. 83 (2): pp. 808–818. - 45.
Quashie NB, Ranford-Cartwright LC, de Koning HP, Uptake of purines in Plasmodium falciparum -infected human erythrocytes is mostly mediated by the human equilibrative nucleoside transporter and the human facilitative nucleobase transporter. Malar J, 2010.9 : p. 36. - 46.
Plaimas K, et al., Computational and experimental analysis identified 6-diazo-5-oxonorleucine as a potential agent for treating infection by Plasmodium falciparum . Infect Genet Evol, 2013.20 : pp. 389–395. - 47.
Schomburg I, Chang A, Placzek S, Sohngen C, Rother M, Lang M, Munaretto C, Ulas S, Stelzer M, Grote A, et al., BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification: new options and contents in BRENDA. Nucleic Acids Res, 2013. 41 (Database issue): pp. D764–D772. - 48.
Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M, DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008. 36 (Database issue): pp. D901–D906. - 49.
Jahn D, Kim YC, Ishino Y, Chen MW, Soll D. Purification and functional characterization of the Glu–tRNA(Gln) amidotransferase from Chlamydomonas reinhardtii. J Biol Chem, 1990. 265 : pp. 8059–8064.