Observing local and global properties of metabolic pathways: ‘load points’ and ‘choke points’ in the metabolic networks

Network analysis of gut microbial communities reveal key genera for a multiple sclerosis cohort with Mycobacterium avium subspecies paratuberculosis infection

BACKGROUND

In gut ecosystems, there is a complex interplay of biotic and abiotic interactions that decide the overall fitness of an individual. Divulging the microbe-microbe and microbe-host interactions may lead to better strategies in disease management, as microbes rarely act in isolation. Network inference for microbial communities is often a challenging task limited by both analytical assumptions as well as experimental approaches. Even after the network topologies are obtained, identification of important nodes within the context of underlying disease aetiology remains a convoluted task. We therefore present a network perspective on complex interactions in gut microbial profiles of individuals who have multiple sclerosis with and without Mycobacterium avium subspecies paratuberculosis (MAP) infection. Our exposé is guided by recent advancements in network-wide statistical measures that identify the keystone nodes. We have utilised several centrality measures, including a recently published metric, Integrated View of Influence (IVI), that is robust against biases.

RESULTS

The ecological networks were generated on microbial abundance data (n = 69 samples) utilising 16 S rRNA amplification. Using SPIEC-EASI, a sparse inverse covariance estimation approach, we have obtained networks separately for MAP positive (+), MAP negative (-) and healthy controls (as a baseline). Using IVI metric, we identified top 20 keystone nodes and regressed them against covariates of interest using a generalised linear latent variable model. Our analyses suggest Eisenbergiella to be of pivotal importance in MS irrespective of MAP infection. For MAP + cohort, Pyarmidobacter, and Peptoclostridium were predominately the most influential genera, also hinting at an infection model similar to those observed in Inflammatory Bowel Diseases (IBDs). In MAP- cohort, on the other hand, Coprostanoligenes group was the most influential genera that reduces cholesterol and supports the intestinal barrier.

CONCLUSIONS

The identification of keystone nodes, their co-occurrences, and associations with the exposome (meta data) advances our understanding of biological interactions through which MAP infection shapes the microbiome in MS individuals, suggesting the link to the inflammatory process of IBDs. The associations presented in this study may lead to development of improved diagnostics and effective vaccines for the management of the disease.

Machine learning approach to gene essentiality prediction: a review

 

Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions.

SHORT ABSTRACT

Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

Opportunities at the Interface of Network Science and Metabolic Modeling

Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.

Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia-present in many filariae-which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

Computational Identification of Metabolic Pathways of Plasmodium falciparum using the k-Shortest Path Algorithm

Plasmodium falciparum, a malaria pathogen, has shown substantial resistance to treatment coupled with poor response to some vaccines thereby requiring urgent, holistic, and broad approach to prevent this endemic disease. Understanding the biology of the malaria parasite has been identified as a vital approach to overcome the threat of malaria. This study is aimed at identifying essential proteins unique to malaria parasites using a reconstructed iPfa genome-scale metabolic model (GEM) of the 3D7 strain of Plasmodium falciparum by filling gaps in the model with nineteen (19) metabolites and twenty-three (23) reactions obtained from the MetaCyc database. Twenty (20) currency metabolites were removed from the network because they have been identified to produce shortcuts that are biologically infeasible. The resulting modified iPfa GEM was a model using the k-shortest path algorithm to identify possible alternative metabolic pathways in glycolysis and pentose phosphate pathways of Plasmodium falciparum. Heuristic function was introduced for the optimal performance of the algorithm. To validate the prediction, the essentiality of the reactions in the reconstructed network was evaluated using betweenness centrality measure, which was applied to every reaction within the pathways considered in this study. Thirty-two (32) essential reactions were predicted among which our method validated fourteen (14) enzymes already predicted in the literature. The enzymatic proteins that catalyze these essential reactions were checked for homology with the host genome, and two (2) showed insignificant similarity, making them possible drug targets. In conclusion, the application of the intelligent search technique to the metabolic network of P. falciparum predicts potential biologically relevant alternative pathways using graph theory-based approach.

An in silico approach in identification of drug targets in Leishmania: A subtractive genomic and metabolic simulation analysis

Leishmaniasis is one of the major health issue in developing countries. The current therapeutic regimen for this disease is less effective with lot of adverse effects thereby warranting an urgent need to develop not only new and selective drug candidates but also identification of effective drug targets. Here we present subtractive genomics procedure for identification of putative drug targets in Leishmania. Comprehensive druggability analysis has been carried out in the current work for identified metabolic pathways and drug targets. We also demonstrate effective metabolic simulation methodology to pinpoint putative drug targets in threonine biosynthesis pathway. Metabolic simulation data from the current study indicate that decreasing flux through homoserine kinase reaction can be considered as a good therapeutic opportunity. The data from current study is expected to show new avenue for designing experimental strategies in search of anti-leishmanial agents.

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.

Identification of metabolic pathways using pathfinding approaches: a systematic review

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.

Ecological networking of cystic fibrosis lung infections

In the context of a polymicrobial infection, treating a specific pathogen poses challenges because of unknown consequences on other members of the community. The presence of ecological interactions between microbes can change their physiology and response to treatment. For example, in the cystic fibrosis lung polymicrobial infection, antimicrobial susceptibility testing on clinical isolates is often not predictive of antibiotic efficacy. Novel approaches are needed to identify the interrelationships within the microbial community to better predict treatment outcomes. Here we used an ecological networking approach on the cystic fibrosis lung microbiome characterized using 16S rRNA gene sequencing and metagenomics. This analysis showed that the community is separated into three interaction groups: Gram-positive anaerobes, Pseudomonas aeruginosa, and Staphylococcus aureus. The P. aeruginosa and S. aureus groups both anti-correlate with the anaerobic group, indicating a functional antagonism. When patients are clinically stable, these major groupings were also stable, however, during exacerbation, these communities fragment. Co-occurrence networking of functional modules annotated from metagenomics data supports that the underlying taxonomic structure is driven by differences in the core metabolism of the groups. Topological analysis of the functional network identified the non-mevalonate pathway of isoprenoid biosynthesis as a keystone for the microbial community, which can be targeted with the antibiotic fosmidomycin. This study uses ecological theory to identify novel treatment approaches against a polymicrobial disease with more predictable outcomes.

Systematic Identification of Anti-Fungal Drug Targets by a Metabolic Network Approach

New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness ("hubs"), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.

Computational methods to identify metabolic sub-networks based on metabolomic profiles

Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC₅₀ in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10⁻⁶ cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.

Comparative integrated omics: identification of key functionalities in microbial community-wide metabolic networks

BACKGROUND

Mixed microbial communities underpin important biotechnological processes such as biological wastewater treatment (BWWT). A detailed knowledge of community structure and function relationships is essential for ultimately driving these systems towards desired outcomes, e.g., the enrichment in organisms capable of accumulating valuable resources during BWWT.

METHODS

A comparative integrated omic analysis including metagenomics, metatranscriptomics and metaproteomics was carried out to elucidate functional differences between seasonally distinct oleaginous mixed microbial communities (OMMCs) sampled from an anoxic BWWT tank. A computational framework for the reconstruction of community-wide metabolic networks from multi-omic data was developed. These provide an overview of the functional capabilities by incorporating gene copy, transcript and protein abundances. To identify functional genes, which have a disproportionately important role in community function, we define a high relative gene expression and a high betweenness centrality relative to node degree as gene-centric and network topological features, respectively.

RESULTS

Genes exhibiting high expression relative to gene copy abundance include genes involved in glycerolipid metabolism, particularly triacylglycerol lipase, encoded by known lipid accumulating populations, e.g., CandidatusMicrothrix parvicella. Genes with a high relative gene expression and topologically important positions in the network include genes involved in nitrogen metabolism and fatty acid biosynthesis, encoded by Nitrosomonas spp. and Rhodococcus spp. Such genes may be regarded as 'keystone genes' as they are likely to be encoded by keystone species.

CONCLUSION

The linking of key functionalities to community members through integrated omics opens up exciting possibilities for devising prediction and control strategies for microbial communities in the future.

MetaNET--a web-accessible interactive platform for biological metabolic network analysis

Background

Metabolic reactions have been extensively studied and compiled over the last century. These have provided a theoretical base to implement models, simulations of which are used to identify drug targets and optimize metabolic throughput at a systemic level. While tools for the perturbation of metabolic networks are available, their applications are limited and restricted as they require varied dependencies and often a commercial platform for full functionality. We have developed MetaNET, an open source user-friendly platform-independent and web-accessible resource consisting of several pre-defined workflows for metabolic network analysis.

Result

MetaNET is a web-accessible platform that incorporates a range of functions which can be combined to produce different simulations related to metabolic networks. These include (i) optimization of an objective function for wild type strain, gene/catalyst/reaction knock-out/knock-down analysis using flux balance analysis. (ii) flux variability analysis (iii) chemical species participation (iv) cycles and extreme paths identification and (v) choke point reaction analysis to facilitate identification of potential drug targets. The platform is built using custom scripts along with the open-source Galaxy workflow and Systems Biology Research Tool as components. Pre-defined workflows are available for common processes, and an exhaustive list of over 50 functions are provided for user defined workflows.

Conclusion

MetaNET, available at http://metanet.osdd.net, provides a user-friendly rich interface allowing the analysis of genome-scale metabolic networks under various genetic and environmental conditions. The framework permits the storage of previous results, the ability to repeat analysis and share results with other users over the internet as well as run different tools simultaneously using pre-defined workflows, and user-created custom workflows.

Inactivation of metabolic genes causes short- and long-range dys-regulation in Escherichia coli metabolic network

The metabolic network in E. coli can be severely affected by the inactivation of metabolic genes that are required to catabolize a nutrient (D-galactose). We hypothesized that the resulting accumulation of small molecules can yield local as well as systemic effects on the metabolic network. Analysis of metabolomics data in wild-type and D-galactose non-utilizing mutants, galT, galU and galE, reveal the large metabolic differences between the wild-type and the mutants when the strains were grown in D-galactose. Network mapping suggested that the enzymatic defects affected the metabolic modules located both at short- and long-ranges from the D-galactose metabolic module. These modules suggested alterations in glutathione, energy, nucleotide and lipid metabolism and disturbed carbon to nitrogen ratio in mutant strains. The altered modules are required for normal cell growth for the wild-type strain, explaining why the cell growth is inhibited in the mutants in the presence of D-galactose. Identification of these distance-based dys-regulations would enhance the systems level understanding of metabolic networks of microorganisms having importance in biomedical and biotechnological research.

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.

Discovery of anthelmintic drug targets and drugs using chokepoints in nematode metabolic pathways

Parasitic roundworm infections plague more than 2 billion people (1/3 of humanity) and cause drastic losses in crops and livestock. New anthelmintic drugs are urgently needed as new drug resistance and environmental concerns arise. A “chokepoint reaction” is defined as a reaction that either consumes a unique substrate or produces a unique product. A chokepoint analysis provides a systematic method of identifying novel potential drug targets. Chokepoint enzymes were identified in the genomes of 10 nematode species, and the intersection and union of all chokepoint enzymes were found. By studying and experimentally testing available compounds known to target proteins orthologous to nematode chokepoint proteins in public databases, this study uncovers features of chokepoints that make them successful drug targets. Chemogenomic screening was performed on drug-like compounds from public drug databases to find existing compounds that target homologs of nematode chokepoints. The compounds were prioritized based on chemical properties frequently found in successful drugs and were experimentally tested using Caenorhabditis elegans. Several drugs that are already known anthelmintic drugs and novel candidate targets were identified. Seven of the compounds were tested in Caenorhabditis elegans and three yielded a detrimental phenotype. One of these three drug-like compounds, Perhexiline, also yielded a deleterious effect in Haemonchus contortus and Onchocerca lienalis, two nematodes with divergent forms of parasitism. Perhexiline, known to affect the fatty acid oxidation pathway in mammals, caused a reduction in oxygen consumption rates in C. elegans and genome-wide gene expression profiles provided an additional confirmation of its mode of action. Computational modeling of Perhexiline and its target provided structural insights regarding its binding mode and specificity. Our lists of prioritized drug targets and drug-like compounds have potential to expedite the discovery of new anthelmintic drugs with broad-spectrum efficacy.

The World Health Organization estimates that 2.9 million people are infected with parasitic roundworms, causing high-morbidity and mortality rates, developmental delays in children, and low productivity of affected individuals. The agricultural industry experiences drastic losses in crop and livestock due to parasitic worm infections. Therefore, there is an urgent need to identify new targets and drugs to fight parasitic nematode infection. This study identified metabolic chokepoint compounds that were either produced or consumed by a single reaction and elucidated the chokepoint enzyme that drives the reaction. If the enzyme that catalyzes that reaction is blocked, a toxic build-up of a compound or lack of compound necessary for subsequent reaction will occur, potentially causing adverse effects to the parasite organism. Compounds that target some of the chokepoint enzymes were tested in C. elegans and several compounds showed efficacy. One drug-like compound, Perhexiline, showed efficacy in two different parasitic worms and yielded expected physiological effects, indicating that this drug-like compound may have efficacy on a pan-phylum level through the predicted mode of action. The methodology to find and prioritize metabolic chokepoint targets and prioritize compounds could be applied to other parasites.

DRUG TARGET PRIORITIZATION IN PLASMODIUM FALCIPARUM THROUGH METABOLIC NETWORK ANALYSIS, AND INHIBITOR DESIGNING USING VIRTUAL SCREENING AND DOCKING APPROACH

The genome sequence of Plasmodium falciparum reveals that many metabolic pathways are unique as compared to its human host. Metabolic Network Analysis was carried out to find the essential enzymes critical for the survival of the pathogen. In the present study, choke point and load point analysis was used to locate putative targets. The identified targets were further checked to confirm that no alternate pathway or human homolog exists. Among the top 15 enzymes obtained from this analysis, we have selected P. falciparum orotidine-5'-monophosphate decarboxylase (PfODCase) enzyme as it is sequentially and structurally different from that of humans, for searching novel inhibitors. A five-point 3D pharmacophore was generated for the crystal structure of PfODCase complexes with uridine-5'-monophosphate (U5P). The binding site environment shows three H-bond acceptors, one H-bond donor and one negative ionizable feature. This pharmacophore model was used as a 3D query to perform virtual screening experiments against 2,664,779 standard lead compounds obtained from the freely available ZINC database. Top 10 hits obtained from virtual screening were selected for molecular docking experiments against PfODCase in order to verify their results and to have a better insight into their binding modes. Here, docking of U5P with PfODCase is used as a control. We have identified six compounds, among them, few are U5P analogs and others are novel ones with diverse scaffolds. The key residues: Lys42, Asp20, Lys72, Ser127, Ala184, Gln185 and Arg203 at the main binding pocket of PfODCase are responsible for better stability of diverse ligands. These compounds according to their free energy of binding could serve as potent leads for designing novel inhibitors against malarial ODCase enzyme.

Clostridium-DT(DB): a comprehensive database for potential drug targets of Clostridium difficile

Clostridium difficile is considered to be one of the most important causes of health care-associated infections currently. The prevalence and severity of C. difficile infection have increased significantly worldwide in the past decade which has led to the increased research interest. Here, using comparative genomics strategy coupled with bioinformatics tools we have identified potential drug targets in C. difficile and determined their three-dimensional structures in order to develop a database, named Clostridium-DT(DB). Currently, the database comprises the potential drug targets with their structural information from three strains of C. difficile, namely hypervirulent PCR-ribotype 027 strain R20291, PCR-ribotype 012 strain 630, and PCR-ribotype 027 strain CD196.

In silico systems biology approaches for the identification of antimicrobial targets

Classical antibiotic discovery efforts have relied mainly on molecular library screening coupled with target-based lead optimization. The conventional approaches are unable to tackle the emergence of antibiotic resistance and are failing to provide understanding of multiple mechanisms behind drug actions and the off-target effects. These insufficiencies have prompted researchers to focus on a multidisciplinary approach of systems biology-based antibiotic discovery. Systems biology is capable of providing a big-picture view for therapeutic targets through interconnected networks of biochemical reactions derived from both experimental and computational techniques. In this chapter, we have compiled software tools and databases that are typically used for target identification through in silico analyses. We have also identified enzyme- and broad-spectrum metabolite-based drug targets that have emerged through in silico systems microbiology.

Software applications toward quantitative metabolic flux analysis and modeling

Metabolites and their pathways are central for adaptation and survival. Metabolic modeling elucidates in silico all the possible flux pathways (flux balance analysis, FBA) and predicts the actual fluxes under a given situation, further refinement of these models is possible by including experimental isotopologue data. In this review, we initially introduce the key theoretical concepts and different analysis steps in the modeling process before comparing flux calculation and metabolite analysis programs such as C13, BioOpt, COBRA toolbox, Metatool, efmtool, FiatFlux, ReMatch, VANTED, iMAT and YANA. Their respective strengths and limitations are discussed and compared to alternative software. While data analysis of metabolites, calculation of metabolic fluxes, pathways and their condition-specific changes are all possible, we highlight the considerations that need to be taken into account before deciding on a specific software. Current challenges in the field include the computation of large-scale networks (in elementary mode analysis), regulatory interactions and detailed kinetics, and these are discussed in the light of powerful new approaches.

Structural and dynamical analysis of biological networks

Biological networks are currently being studied with approaches derived from the mathematical and physical sciences. Their structural analysis enables to highlight nodes with special properties that have sometimes been correlated with the biological importance of a gene or a protein. However, biological networks are dynamic both on the evolutionary time-scale, and on the much shorter time-scale of physiological processes. There is therefore no unique network for a given cellular process, but potentially many realizations, each with different properties as a consequence of regulatory mechanisms. Such realizations provide snapshots of a same network in different conditions, enabling the study of condition-dependent structural properties. True dynamical analysis can be obtained through detailed mathematical modeling techniques that are not easily scalable to full network models.

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria

Background

Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed.

Methods

A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed.

Results

The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets.

Conclusions/Significance

UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar.

THE IMPORTANCE OF INHIBITORS FOR THE SIMULATION OF METABOLIC PROCESSES: IN SILICOZn2+ INHIBITION OF m-ACONITASE FROM ANALYSIS OF GLYCOLYSIS AND KREBS CYCLE KINETIC MODELS

Metal ions have a major effect on the metabolic processes in cells either as inhibitors or as integral components of enzymes. The inhibition of enzymes can take place either through the inhibition of gene expression or through inhibition of protein function. A particularly interesting example of the effect of a metal ion on metabolism is the observed inhibition of Krebs cycle and alteration of energy metabolism by zinc (II) cations. In this particular case metal ion inhibition of enzyme is linked to one of the major functions of prostate cells of accumulation and excretion of citrate. Experimental results have shown that increase in concentration of zinc (II) in prostate cells effectively blocks progression of a part of the Krebs cycle leading to change in the concentration of several metabolites with largest effect in the accumulation of citrate. Based on previously reported experimental results, several distinct mechanisms for zinc (II) inhibition of Krebs cycle were proposed. In order to determine the precise mechanism of inhibition in this system, a mathematical model of glycolysis and Krebs cycle was constructed. Three different types of inhibition were analyzed, including competitive and uncompetitive inhibition as well as inhibition through the alteration of the expression level of m-aconitase. The effects of different inhibition models on metabolite concentrations were investigated as a time course simulation of the system of reactions. Several kinetic parameters in the model were optimized in order to best resemble experimental measurements. The simulation shows that only competitive inhibition leads to an agreement with experimental data.

Enhancers regulate progression of development in mammalian cells

During development and differentiation of an organism, accurate gene regulation is central for cells to maintain and balance their differentiation processes. Transcriptional interactions between cis-acting DNA elements such as promoters and enhancers are the basis for precise and balanced transcriptional regulation. We identified modules of combinations of binding sites in proximal and distal regulatory regions upstream of all transcription start sites (TSSs) in silico and applied these modules to gene expression time-series of mouse embryonic development and differentiation of human stem cells. In addition to tissue-specific regulation controlled by combinations of transcription factors (TFs) binding at promoters, we observed that in particular the combination of TFs binding at promoters together with TFs binding at the respective enhancers regulate highly specifically temporal progression during development: whereas 40% of TFs were specific for time intervals, 79% of TF pairs and even 97% of promoter-enhancer modules showed specificity for single time intervals of the human stem cells. Predominantly SP1 and E2F contributed to temporal specificity at promoters and the forkhead (FOX) family of TFs at enhancer regions. Altogether, we characterized three classes of TFs: with binding sites being enriched at the TSS (like SP1), depleted at the TSS (like FOX), and rather uniformly distributed.

Path finding methods accounting for stoichiometry in metabolic networks

Graph-based methods have been widely used for the analysis of biological networks. Their application to metabolic networks has been much discussed, in particular noting that an important weakness in such methods is that reaction stoichiometry is neglected. In this study, we show that reaction stoichiometry can be incorporated into path-finding approaches via mixed-integer linear programming. This major advance at the modeling level results in improved prediction of topological and functional properties in metabolic networks.

Disruption of cell wall fatty acid biosynthesis in Mycobacterium tuberculosis using a graph theoretic approach

Fatty acid biosynthesis of Mycobacterium tuberculosis was analyzed using graph theory and influential (impacting) proteins were identified. The graphs (digraphs) representing this biological network provide information concerning the connectivity of each protein or metabolite in a given pathway, providing an insight into the importance of various components in the pathway, and this can be quantitatively analyzed. Using a graph theoretic algorithm, the most influential set of proteins (sets of {1, 2, 3}, etc.), which when eliminated could cause a significant impact on the biosynthetic pathway, were identified. This set of proteins could serve as drug targets. In the present study, the metabolic network of Mycobacterium tuberculosis was constructed and the fatty acid biosynthesis pathway was analyzed for potential drug targeting. The metabolic network was constructed using the KEGG LIGAND database and subjected to graph theoretical analysis. The nearness index of a protein was used to determine the influence of the said protein on other components in the network, allowing the proteins in a pathway to be ordered according to their nearness indices. A method for identifying the most strategic nodes to target for disrupting the metabolic networks is proposed, aiding the development of new drugs to combat this deadly disease.

Structural analysis of metabolic networks based on flux centrality

Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell's biochemistry. We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network.

Graph methods for the investigation of metabolic networks in parasitology

Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

Identifying essential genes in bacterial metabolic networks with machine learning methods

Background

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.

Results

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 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.

Conclusions

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.

Molecular networks for the study of TCM pharmacology

To target complex, multi-factorial diseases more effectively, there has been an emerging trend of multi-target drug development based on network biology, as well as an increasing interest in traditional Chinese medicine (TCM) that applies a more holistic treatment to diseases. Thousands of years' clinic practices in TCM have accumulated a considerable number of formulae that exhibit reliable in vivo efficacy and safety. However, the molecular mechanisms responsible for their therapeutic effectiveness are still unclear. The development of network-based systems biology has provided considerable support for the understanding of the holistic, complementary and synergic essence of TCM in the context of molecular networks. This review introduces available sources and methods that could be utilized for the network-based study of TCM pharmacology, proposes a workflow for network-based TCM pharmacology study, and presents two case studies on applying these sources and methods to understand the mode of action of TCM recipes.

Pathway Tools version 13.0: integrated software for pathway/genome informatics and systems biology

Pathway Tools is a production-quality software environment for creating a type of model-organism database called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc integrates the evolving understanding of the genes, proteins, metabolic network and regulatory network of an organism. This article provides an overview of Pathway Tools capabilities. The software performs multiple computational inferences including prediction of metabolic pathways, prediction of metabolic pathway hole fillers and prediction of operons. It enables interactive editing of PGDBs by DB curators. It supports web publishing of PGDBs, and provides a large number of query and visualization tools. The software also supports comparative analyses of PGDBs, and provides several systems biology analyses of PGDBs including reachability analysis of metabolic networks, and interactive tracing of metabolites through a metabolic network. More than 800 PGDBs have been created using Pathway Tools by scientists around the world, many of which are curated DBs for important model organisms. Those PGDBs can be exchanged using a peer-to-peer DB sharing system called the PGDB Registry.

Predicting cell cycle regulated genes by causal interactions

The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first.

Hierarchical coordination of periodic genes in the cell cycle of Saccharomyces cerevisiae

Background

Gene networks are a representation of molecular interactions among genes or products thereof and, hence, are forming causal networks. Despite intense studies during the last years most investigations focus so far on inferential methods to reconstruct gene networks from experimental data or on their structural properties, e.g., degree distributions. Their structural analysis to gain functional insights into organizational principles of, e.g., pathways remains so far under appreciated.

Results

In the present paper we analyze cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions and not just associations or correlations between genes, and a list of known periodic genes. No further data are used. Partitioning the transcriptional regulatory network according to a graph theoretical property leads to a hierarchy in the network and, hence, in the information flow allowing to identify two groups of periodic genes. This reveals a novel conceptual interpretation of the working mechanism of the cell cycle and the genes regulated by this pathway.

Conclusion

Aside from the obtained results for the cell cycle of yeast our approach could be exemplary for the analysis of general pathways by exploiting the rich causal structure of inferred and/or curated gene networks including protein or signaling networks.