Predicting synthetic rescues in metabolic networks

Network medicine: a network-based approach to human disease

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Array-based synthetic genetic screens to map bacterial pathways and functional networks in Escherichia coli

Cellular processes are carried out through a series of molecular interactions. Various experimental approaches can be used to investigate these functional relationships on a large-scale. Recently, the power of investigating biological systems from the perspective of genetic (gene-gene or epistatic) interactions has been evidenced by the ability to elucidate novel functional relationships. Examples of functionally related genes include genes that buffer each other's function or impinge on the same biological process. Genetic interactions have traditionally been investigated in bacteria by combining pairs of mutations (e.g., gene deletions) and assessing deviation of the phenotype of each double mutant from an expected neutral (or no interaction) phenotype. Fitness is a particularly convenient phenotype to measure: when the double mutant grows faster or slower than expected, the two mutated genes are said to show alleviating or aggravating interactions, respectively. The most commonly used neutral model assumes that the fitness of the double mutant is equal to the product of individual single mutant fitness. A striking genetic interaction is exemplified by the loss of two nonessential genes that buffer each other in performing an essential biological function: deleting only one of these genes produces no detectable fitness defect; however, loss of both genes simultaneously results in systems failure, leading to synthetic sickness or lethality. Systematic large-scale genetic interaction screens have been used to generate functional maps for model eukaryotic organisms, such as yeast, to describe the functional organization of gene products into pathways and protein complexes within a cell. They also reveal the modular arrangement and cross talk of pathways and complexes within broader functional neighborhoods (Dixon et al., Annu Rev Genet 43:601-625, 2009). Here, we present a high-throughput quantitative Escherichia coli Synthetic Genetic Array (eSGA) screening procedure, which we developed to systematically infer genetic interactions by scoring growth defects among large numbers of double mutants in a classic Gram-negative bacterium. The eSGA method exploits the rapid colony growth, ease of genetic manipulation, and natural efficient genetic exchange via conjugation of laboratory E. coli strains. Replica pinning is used to grow and mate arrayed sets of single gene mutant strains and to select double mutants en masse. Strain fitness, which is used as the eSGA readout, is quantified by the digital imaging of the plates and subsequent measuring and comparing single and double mutant colony sizes. While eSGA can be used to screen select mutants to probe the functions of individual genes, using eSGA more broadly to collect genetic interaction data for many combinations of genes can help reconstruct a functional interaction network to reveal novel links and components of biological pathways as well as unexpected connections between pathways. A variety of bacterial systems can be investigated, wherein the genes impinge on a essential biological process (e.g., cell wall assembly, ribosome biogenesis, chromosome replication) that are of interest from the perspective of drug development (Babu et al., Mol Biosyst 12:1439-1455, 2009). We also show how genetic interactions generated by high-throughput eSGA screens can be validated by manual small-scale genetic crosses and by genetic complementation and gene rescue experiments.

Array-based synthetic genetic screens to map bacterial pathways and functional networks in Escherichia coli

Cellular processes are carried out through a series of molecular interactions. Various experimental approaches can be used to investigate these functional relationships on a large-scale. Recently, the power of investigating biological systems from the perspective of genetic (gene-gene, or epistatic) interactions has been evidenced by the ability to elucidate novel functional relationships. Examples of functionally related genes include genes that buffer each other's function or impinge on the same biological process. Genetic interactions have traditionally been investigated in bacteria by combining pairs of mutations (for example, gene deletions) and assessing deviation of the phenotype of each double mutant from an expected neutral (or no interaction) phenotype. Fitness is a particularly convenient phenotype to measure: when the double mutant grows faster or slower than expected, the two mutated genes are said to show alleviating or aggravating interactions, respectively. The most commonly used neutral model assumes that the fitness of the double mutant is equal to the product of individual single mutant fitness. A striking genetic interaction is exemplified by the loss of two nonessential genes that buffer each other in performing an essential biological function: deleting only one of these genes produces no detectable fitness defect; however, loss of both genes simultaneously results in systems failure, leading to synthetic sickness or lethality. Systematic large-scale genetic interaction screens have been used to generate functional maps for model eukaryotic organisms, such as yeast, to describe the functional organization of gene products into pathways and protein complexes within a cell. They also reveal the modular arrangement and cross-talk of pathways and complexes within broader functional neighborhoods (Dixon et al. Annu Rev Genet 43:601-625, 2009). Here, we present a high-throughput quantitative Escherichia coli synthetic genetic array (eSGA) screening procedure, which we developed to systematically infer genetic interactions by scoring growth defects among large numbers of double mutants in a classic gram-negative bacterium. The eSGA method exploits the rapid colony growth, ease of genetic manipulation, and natural efficient genetic exchange via conjugation of laboratory E. coli strains. Replica pinning is used to grow and mate arrayed sets of single-gene mutant strains as well as to select double mutants en mass. Strain fitness, which is used as the eSGA readout, is quantified by the digital imaging of the plates and subsequent measuring and comparing single and double mutant colony sizes. While eSGA can be used to screen select mutants to probe the functions of individual genes; using eSGA more broadly to collect genetic interaction data for many combinations of genes can help reconstruct a functional interaction network to reveal novel links and components of biological pathways as well as unexpected connections between pathways. A variety of bacterial systems can be investigated, wherein the genes impinge on a essential biological process (e.g., cell wall assembly, ribosome biogenesis, chromosome replication) that are of interest from the perspective of drug development (Babu et al. Mol Biosyst 12:1439-1455, 2009). We also show how genetic interactions generated by high-throughput eSGA screens can be validated by manual small-scale genetic crosses and by genetic complementation and gene rescue experiments.

Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data

BACKGROUND

Saccharomyces cerevisiae is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., iMM904) remains significantly less predictive than the latest E. coli model (i.e., iAF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the E. coli model.

RESULTS

In this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model iMM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity) from 38.84% (87 out of 224) to 53.57% (120 out of 224) for the minimal medium and from 24.73% (45 out of 182) to 40.11% (73 out of 182) for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133) to 23.31% (31 out of 133) for the minimal medium and from 6.96% (8 out of 115) to 13.04% (15 out of 115) for the YP medium.

CONCLUSIONS

Overall, this study provides a roadmap for the computationally driven correction of multi-compartment genome-scale metabolic models and demonstrates the value of synthetic lethals as curation agents.

Emergent cooperation in microbial metabolism

Mixed microbial communities exhibit emergent biochemical properties not found in clonal monocultures. We report a new type of synthetic genetic interaction, synthetic mutualism in trans (SMIT), in which certain pairs of auxotrophic Escherichia coli mutants complement one another's growth by cross-feeding essential metabolites. We find significant metabolic synergy in 17% of 1035 such pairs tested, with SMIT partners identified throughout the metabolic network. Cooperative phenotypes show more growth on average by aiding the proliferation of their conjugate partner, thereby expanding the source of their own essential metabolites. We construct a quantitative, predictive, framework for describing SMIT interactions as governed by stoichiometric models of the metabolic networks of the interacting strains.

Network synchronization landscape reveals compensatory structures, quantization, and the positive effect of negative interactions

Synchronization, in which individual dynamical units keep in pace with each other in a decentralized fashion, depends both on the dynamical units and on the properties of the interaction network. Yet, the role played by the network has resisted comprehensive characterization within the prevailing paradigm that interactions facilitating pairwise synchronization also facilitate collective synchronization. Here we challenge this paradigm and show that networks with best complete synchronization, least coupling cost, and maximum dynamical robustness, have arbitrary complexity but quantized total interaction strength, which constrains the allowed number of connections. It stems from this characterization that negative interactions as well as link removals can be used to systematically improve and optimize synchronization properties in both directed and undirected networks. These results extend the recently discovered compensatory perturbations in metabolic networks to the realm of oscillator networks and demonstrate why "less can be more" in network synchronization.

Improved network performance via antagonism: From synthetic rescues to multi-drug combinations

Recent research shows that a faulty or sub-optimally operating metabolic network can often be rescued by the targeted removal of enzyme-coding genes – the exact opposite of what traditional gene therapy would suggest. Predictions go as far as to assert that certain gene knockouts can restore the growth of otherwise nonviable gene-deficient cells. Many questions follow from this discovery: What are the underlying mechanisms? How generalizable is this effect? What are the potential applications? Here, I approach these questions from the perspective of compensatory perturbations on networks. Relations are drawn between such synthetic rescues and naturally occurring cascades of reaction inactivation, as well as their analogs in physical and other biological networks. I specially discuss how rescue interactions can lead to the rational design of antagonistic drug combinations that select against resistance and how they can illuminate medical research on cancer, antibiotics, and metabolic diseases.

Approximating spectral impact of structural perturbations in large networks

Determining the effect of structural perturbations on the eigenvalue spectra of networks is an important problem because the spectra characterize not only their topological structures, but also their dynamical behavior, such as synchronization and cascading processes on networks. Here we develop a theory for estimating the change of the largest eigenvalue of the adjacency matrix or the extreme eigenvalues of the graph Laplacian when small but arbitrary set of links are added or removed from the network. We demonstrate the effectiveness of our approximation schemes using both real and artificial networks, showing in particular that we can accurately obtain the spectral ranking of small subgraphs. We also propose a local iterative scheme which computes the relative ranking of a subgraph using only the connectivity information of its neighbors within a few links. Our results may not only contribute to our theoretical understanding of dynamical processes on networks, but also lead to practical applications in ranking subgraphs of real complex networks.

What dose metaphor?

The concept of hormesis, or low-dose U-shaped responses, is now well established in toxicology and pharmacology but requires development in medicine and therapeutics. In doing so, care must be taken to not confuse metaphorical and chemical uses of the term hormesis. Low dose, continuous adaptive responses are fundamentally different than conventional pharmacology, and they may improve the scientific underpinning for complementary medicine, nutrition and lifestyle therapies.

In silico feasibility of novel biodegradation pathways for 1,2,4-trichlorobenzene

Background

Bioremediation offers a promising pollution treatment method in the reduction and elimination of man-made compounds in the environment. Computational tools to predict novel biodegradation pathways for pollutants allow one to explore the capabilities of microorganisms in cleaning up the environment. However, given the wealth of novel pathways obtained using these prediction methods, it is necessary to evaluate their relative feasibility, particularly within the context of the cellular environment.

Results

We have utilized a computational framework called BNICE to generate novel biodegradation routes for 1,2,4-trichlorobenzene (1,2,4-TCB) and incorporated the pathways into a metabolic model for Pseudomonas putida. We studied the cellular feasibility of the pathways by applying metabolic flux analysis (MFA) and thermodynamic constraints. We found that the novel pathways generated by BNICE enabled the cell to produce more biomass than the known pathway. Evaluation of the flux distribution profiles revealed that several properties influenced biomass production: 1) reducing power required, 2) reactions required to generate biomass precursors, 3) oxygen utilization, and 4) thermodynamic topology of the pathway. Based on pathway analysis, MFA, and thermodynamic properties, we identified several promising pathways that can be engineered into a host organism to accomplish bioremediation.

Conclusions

This work was aimed at understanding how novel biodegradation pathways influence the existing metabolism of a host organism. We have identified attractive targets for metabolic engineers interested in constructing a microorganism that can be used for bioremediation. Through this work, computational tools are shown to be useful in the design and evaluation of novel xenobiotic biodegradation pathways, identifying cellularly feasible degradation routes.

Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network

Background

Constraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. However, metabolites are key players in the metabolic network and the current reaction-centric approach may not account for the effect of metabolite perturbation on the cellular physiology due to the inherent limitation in model formulation. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network.

Results

Presented herein is a metabolite-centric approach of analyzing the metabolic network by including the turnover rate of metabolite, known as flux-sum, as key descriptive variable within the model formulation. By doing so, the effect of varying metabolite flux-sum on physiological change can be simulated by resorting to mixed integer linear programming. From the results, we could classify various metabolite types based on the flux-sum profile. Using the iAF1260 in silico metabolic model of Escherichia coli, we demonstrated that this novel concept complements the conventional reaction-centric analysis.

Conclusions

Metabolite flux-sum analysis elucidates the roles of metabolites in the network. In addition, this metabolite perturbation analysis identifies the key metabolites, implicating practical application which is achievable through metabolite flux-sum manipulation in the areas of biotechnology and biomedical research.

Evolutionary plasticity and innovations in complex metabolic reaction networks

Genome-scale metabolic networks are highly robust to the elimination of enzyme-coding genes. Their structure can evolve rapidly through mutations that eliminate such genes and through horizontal gene transfer that adds new enzyme-coding genes. Using flux balance analysis we study a vast space of metabolic network genotypes and their relationship to metabolic phenotypes, the ability to sustain life in an environment defined by an available spectrum of carbon sources. Two such networks typically differ in most of their reactions and have few essential reactions in common. Our observations suggest that the robustness of the Escherichia coli metabolic network to mutations is typical of networks with the same phenotype. We also demonstrate that networks with the same phenotype form large sets that can be traversed through single mutations, and that single mutations of different genotypes with the same phenotype can yield very different novel phenotypes. This means that the evolutionary plasticity and robustness of metabolic networks facilitates the evolution of new metabolic abilities. Our approach has broad implications for the evolution of metabolic networks, for our understanding of mutational robustness, for the design of antimetabolic drugs, and for metabolic engineering.

Understanding the fundamental processes that shape the evolution of bacterial organisms is of general interest to biology and may have important applications in medicine. We address the questions of how bacterial organisms acquire innovations, including drug resistance, allowing them to survive in new environments. We simulate the evolution of the metabolic network, the network of reactions that can occur inside a living organism. The metabolic network of an organism depends on the genes contained in its genome and can change by gaining genes from other organisms through horizontal gene transfer or loss of gene activity through mutations. Our observations suggest that the robustness to gene loss in Escherichia coli is typical of random viable metabolic networks of the same size. We also find that metabolic networks can change significantly without causing the loss of an organism's ability to survive in a given environment. This property allows organisms to explore a wide range of novel metabolic abilities and is the source of their ability to innovate. Finally we present a method to find reactions that are essential across all organisms. Drugs targeting such a reaction may avoid drug resistance mutations that bypass the reaction.

Carbohydrate metabolism and carbon fixation in Roseobacter denitrificans OCh114

The Roseobacter clade of aerobic marine proteobacteria, which compose 10–25% of the total marine bacterial community, has been reported to fix CO₂, although it has not been determined what pathway is involved. In this study, we report the first metabolic studies on carbohydrate utilization, CO₂ assimilation, and amino acid biosynthesis in the phototrophic Roseobacter clade bacterium Roseobacter denitrificans OCh114. We develop a new minimal medium containing defined carbon source(s), in which the requirements of yeast extract reported previously for the growth of R. denitrificans can be replaced by vitamin B₁₂ (cyanocobalamin). Tracer experiments were carried out in R. denitrificans grown in a newly developed minimal medium containing isotopically labeled pyruvate, glucose or bicarbonate as a single carbon source or in combination. Through measurements of ¹³C-isotopomer labeling patterns in protein-derived amino acids, gene expression profiles, and enzymatic activity assays, we report that: (1) R. denitrificans uses the anaplerotic pathways mainly via the malic enzyme to fix 10–15% of protein carbon from CO₂; (2) R. denitrificans employs the Entner-Doudoroff (ED) pathway for carbohydrate metabolism and the non-oxidative pentose phosphate pathway for the biosynthesis of histidine, ATP, and coenzymes; (3) the Embden-Meyerhof-Parnas (EMP, glycolysis) pathway is not active and the enzymatic activity of 6-phosphofructokinase (PFK) cannot be detected in R. denitrificans; and (4) isoleucine can be synthesized from both threonine-dependent (20% total flux) and citramalate-dependent (80% total flux) pathways using pyruvate as the sole carbon source.

Evolutionary constraints permeate large metabolic networks

BACKGROUND

Metabolic networks show great evolutionary plasticity, because they can differ substantially even among closely related prokaryotes. Any one metabolic network can also effectively compensate for the blockage of individual reactions by rerouting metabolic flux through other pathways. These observations, together with the continual discovery of new microbial metabolic pathways and enzymes, raise the possibility that metabolic networks are only weakly constrained in changing their complement of enzymatic reactions.

RESULTS

To ask whether this is the case, I characterized pairwise and higher-order associations in the co-occurrence of genes encoding metabolic enzymes in more than 200 completely sequenced representatives of prokaryotic genera. The majority of reactions show constrained evolution. Specifically, genes encoding most reactions tend to co-occur with genes encoding other reaction(s). Constrained reaction pairs occur in small sets whose number is substantially greater than expected by chance alone. Most such sets are associated with single biochemical pathways. The respective genes are not always tightly linked, which renders horizontal co-transfer of constrained reaction sets an unlikely sole cause for these patterns of association.

CONCLUSION

Even a limited number of available genomes suffices to show that metabolic network evolution is highly constrained by reaction combinations that are favored by natural selection. With increasing numbers of completely sequenced genomes, an evolutionary constraint-based approach may enable a detailed characterization of co-evolving metabolic modules.

Systems-level engineering of nonfermentative metabolism in yeast

We designed and experimentally validated an in silico gene deletion strategy for engineering endogenous one-carbon (C1) metabolism in yeast. We used constraint-based metabolic modeling and computer-aided gene knockout simulations to identify five genes (ALT2, FDH1, FDH2, FUM1, and ZWF1), which, when deleted in combination, predicted formic acid secretion in Saccharomyces cerevisiae under aerobic growth conditions. Once constructed, the quintuple mutant strain showed the predicted increase in formic acid secretion relative to a formate dehydrogenase mutant (fdh1 fdh2), while formic acid secretion in wild-type yeast was undetectable. Gene expression and physiological data generated post hoc identified a retrograde response to mitochondrial deficiency, which was confirmed by showing Rtg1-dependent NADH accumulation in the engineered yeast strain. Formal pathway analysis combined with gene expression data suggested specific modes of regulation that govern C1 metabolic flux in yeast. Specifically, we identified coordinated transcriptional regulation of C1 pathway enzymes and a positive flux control coefficient for the branch point enzyme 3-phosphoglycerate dehydrogenase (PGDH). Together, these results demonstrate that constraint-based models can identify seemingly unrelated mutations, which interact at a systems level across subcellular compartments to modulate flux through nonfermentative metabolic pathways.

Systems-level approaches for identifying and analyzing genetic interaction networks in Escherichia coli and extensions to other prokaryotes

Molecular interactions define the functional organization of the cell. Epistatic (genetic, or gene-gene) interactions, one of the most informative and commonly encountered forms of functional relationships, are increasingly being used to map process architecture in model eukaryotic organisms. In particular, 'systems-level' screens in yeast and worm aimed at elucidating genetic interaction networks have led to the generation of models describing the global modular organization of gene products and protein complexes within a cell. However, comparable data for prokaryotic organisms have not been available. Given its ease of growth and genetic manipulation, the Gram-negative bacterium Escherichia coli appears to be an ideal model system for performing comprehensive genome-scale examinations of genetic redundancy in bacteria. In this review, we highlight emerging experimental and computational techniques that have been developed recently to examine functional relationships and redundancy in E. coli at a systems-level, and their potential application to prokaryotes in general. Additionally, we have scanned PubMed abstracts and full-text published articles to manually curate a list of approximately 200 previously reported synthetic sick or lethal genetic interactions in E. coli derived from small-scale experimental studies.

Compensatory ability to null mutation in metabolic networks

Robustness is an inherent property of biological system. It is still a limited understanding of how it is accomplished at the cellular or molecular level. To this end, this article analyzes the impact degree of each reaction to others, which is defined as the number of cascading failures of following and/or forward reactions when an initial reaction is deleted. By analyzing more than 800 organism's metabolic networks, it suggests that the reactions with larger impact degrees are likely essential and the universal reactions should also be essential. Alternative metabolic pathways compensate null mutations, which represents that average impact degrees for all organisms are small. Interestingly, average impact degrees of archaea organisms are smaller than other two categories of organisms, eukayote and bacteria, indicating that archaea organisms have strong robustness to resist the various perturbations during the evolution process. The results show that scale-free feature and reaction reversibility contribute to the robustness in metabolic networks. The optimal growth temperature of organism also relates the robust structure of metabolic network.

Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets

Mortality due to multidrug-resistant Staphylococcus aureus infection is predicted to surpass that of human immunodeficiency virus/AIDS in the United States. Despite the various treatment options for S. aureus infections, it remains a major hospital- and community-acquired opportunistic pathogen. With the emergence of multidrug-resistant S. aureus strains, there is an urgent need for the discovery of new antimicrobial drug targets in the organism. To this end, we reconstructed the metabolic networks of multidrug-resistant S. aureus strains using genome annotation, functional-pathway analysis, and comparative genomic approaches, followed by flux balance analysis-based in silico single and double gene deletion experiments. We identified 70 single enzymes and 54 pairs of enzymes whose corresponding metabolic reactions are predicted to be unconditionally essential for growth. Of these, 44 single enzymes and 10 enzyme pairs proved to be common to all 13 S. aureus strains, including many that had not been previously identified as being essential for growth by gene deletion experiments in S. aureus. We thus conclude that metabolic reconstruction and in silico analyses of multiple strains of the same bacterial species provide a novel approach for potential antibiotic target identification.

GrowMatch: an automated method for reconciling in silico/in vivo growth predictions

Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data. This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment (GNG inconsistencies) or the model predicts no growth when the experiment reveals growth (NGG inconsistencies). Here we propose an optimization-based framework, GrowMatch, to automatically reconcile GNG predictions (by suppressing functionalities in the model) and NGG predictions (by adding functionalities to the model). We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli (iAF1260) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90.6% to 96.7%. Specifically, we were able to suggest consistency-restoring hypotheses for 56/72 GNG mutants and 13/38 NGG mutants. GrowMatch resolved 18 GNG inconsistencies by suggesting suppressions in the mutant metabolic networks. Fifteen inconsistencies were resolved by suppressing isozymes in the metabolic network, and the remaining 23 GNG mutants corresponding to blocked genes were resolved by suitably modifying the biomass equation of iAF1260. GrowMatch suggested consistency-restoring hypotheses for five NGG mutants by adding functionalities to the model whereas the remaining eight inconsistencies were resolved by pinpointing possible alternate genes that carry out the function of the deleted gene. For many cases, GrowMatch identified fairly nonintuitive model modification hypotheses that would have been difficult to pinpoint through inspection alone. In addition, GrowMatch can be used during the construction phase of new, as opposed to existing, genome-scale metabolic models, leading to more expedient and accurate reconstructions.

A genome-scale metabolic reconstruction of Mycoplasma genitalium, iPS189

With a genome size of ∼580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms.

There is growing interest in elucidating the minimal number of genes needed for life. This challenge is important not just for fundamental but also practical considerations arising from the need to design microorganisms exquisitely tuned for particular applications. The genome of the pathogen Mycoplasma genitalium is believed to be a close approximation to the minimal set of genes required for bacterial growth. In this paper, we constructed a genome-scale metabolic model of M. genitalium that mathematically describes a unified characterization of its biochemical capabilities. The model accounts for 189 of the 482 genes listed in the latest genome annotation. We used computational tools during the process to bridge network gaps in the model and restore consistency with experimental data that determined which gene deletions led to cell death (i.e., are essential). We achieved 87% correct model predictions for essential genes and 89% for non-essential genes. We subsequently used the metabolic model to determine components that must be part of the growth medium. The approaches and tools described here provide a roadmap for the automated metabolic reconstruction of other organisms. This task is becoming increasingly critical as genome sequencing for new organisms is proceeding at an ever-accelerating pace.

Neutralism and selectionism: a network-based reconciliation

Neutralism and selectionism are extremes of an explanatory spectrum for understanding patterns of molecular evolution and the emergence of evolutionary innovation. Although recent genome-scale data from protein-coding genes argue against neutralism, molecular engineering and protein evolution data argue that neutral mutations and mutational robustness are important for evolutionary innovation. Here I propose a reconciliation in which neutral mutations prepare the ground for later evolutionary adaptation. Key to this perspective is an explicit understanding of molecular phenotypes that has only become accessible in recent years.

Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.

Spontaneous reaction silencing in metabolic optimization

Metabolic reactions of single-cell organisms are routinely observed to become dispensable or even incapable of carrying activity under certain circumstances. Yet, the mechanisms as well as the range of conditions and phenotypes associated with this behavior remain very poorly understood. Here we predict computationally and analytically that any organism evolving to maximize growth rate, ATP production, or any other linear function of metabolic fluxes tends to significantly reduce the number of active metabolic reactions compared to typical nonoptimal states. The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organism to grow at all. We show that this massive spontaneous reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity. Our results help explain existing experimental data on intracellular flux measurements and the usage of latent pathways, shedding new light on microbial evolution, robustness, and versatility for the execution of specific biochemical tasks. In particular, the identification of optimal reaction activity provides rigorous ground for an intriguing knockout-based method recently proposed for the synthetic recovery of metabolic function.

Cellular growth and other integrated metabolic functions are manifestations of the coordinated interconversion of a large number of chemical compounds. But what is the relation between such whole-cell behaviors and the activity pattern of the individual biochemical reactions? In this study, we have used flux balance-based methods and reconstructed networks of Helicobacter pylori, Staphylococcus aureus, Escherichia coli, and Saccharomyces cerevisiae to show that a cell seeking to optimize a metabolic objective, such as growth, has a tendency to spontaneously inactivate a significant number of its metabolic reactions, while all such reactions are recruited for use in typical suboptimal states. The mechanisms governing this behavior not only provide insights into why numerous genes can often be disabled without affecting optimal growth but also lay a foundation for the recently proposed synthetic rescue of metabolic function in which the performance of suboptimally operating cells can be enhanced by disabling specific metabolic reactions. Our findings also offer explanation for another experimentally observed behavior, in which some inactive reactions are temporarily activated following a genetic or environmental perturbation. The latter is of utmost importance given that nonoptimal and transient metabolic behaviors are arguably common in natural environments.

The art of community detection

Networks in nature possess a remarkable amount of structure. Via a series of data-driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman, introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection.

Cascading failure and robustness in metabolic networks

We investigate the relationship between structure and robustness in the metabolic networks of Escherichia coli, Methanosarcina barkeri, Staphylococcus aureus, and Saccharomyces cerevisiae, using a cascading failure model based on a topological flux balance criterion. We find that, compared to appropriate null models, the metabolic networks are exceptionally robust. Furthermore, by decomposing each network into rigid clusters and branched metabolites, we demonstrate that the enhanced robustness is related to the organization of branched metabolites, as rigid cluster formations in the metabolic networks appear to be consistent with null model behavior. Finally, we show that cascading in the metabolic networks can be described as a percolation process.

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded over the past 15 years. In particular, Escherichia coli metabolic network reconstruction has reached the genome scale and been utilized to address a broad spectrum of basic and practical applications in five main categories: metabolic engineering, model-directed discovery, interpretations of phenotypic screens, analysis of network properties and studies of evolutionary processes. Spurred on by these accomplishments, the field is expected to move forward and further broaden the scope and content of network reconstructions, develop new and novel in silico analysis tools, and expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.