Reconstruction, modeling & analysis of Halobacterium salinarum R-1 metabolism

Application of coincidence index in the discovery of co-expressed metabolic pathways

Analyzing transcription data requires intensive statistical analysis to obtain useful biological information and knowledge. A significant portion of this data is affected by random noise or even noise intrinsic to the modeling of the experiment. Without robust treatment, the data might not be explored thoroughly, and incorrect conclusions could be drawn. Examining the correlation between gene expression profiles is one way bioinformaticians extract information from transcriptomic experiments. However, the correlation measurements traditionally used have worrisome shortcomings that need to be addressed. This paper compares five already published and experimented-with correlation measurements to the newly developed coincidence index, a similarity measurement that combines Jaccard and interiority indexes and generalizes them to be applied to vectors containing real values. We used microarray and RNA-Seq data from the archaeonHalobacterium salinarumand the bacteriumEscherichia coli, respectively, to evaluate the capacity of each correlation/similarity measurement. The utilized method explores the co-expressed metabolic pathways by measuring the correlations between the expression levels of enzymes that share metabolites, represented in the form of a weighted graph. It then searches for local maxima in this graph using a simulated annealing algorithm. We demonstrate that the coincidence index extracts larger, more comprehensive, and more statistically significant pathways for microarray experiments. In RNA-Seq experiments, the results are more limited, but the coincidence index managed the largest percentage of significant components in the graph.

Halomonas spp., as chassis for low-cost production of chemicals

Halomonas spp. are the well-studied platform organisms or chassis for next-generation industrial biotechnology (NGIB) due to their contamination-resistant nature combined with their fast growth property. Several Halomonas spp. have been studied regarding their genomic information and molecular engineering approaches. Halomonas spp., especially Halomonas bluephagenesis, have been engineered to produce various biopolyesters such as polyhydroxyalkanoates (PHA), proteins including surfactants and enzymes, small molecular compounds including amino acids and derivates, as well as organic acids. This paper reviews all the progress reported in the last 10 years regarding this robust microbial cell factory. KEY POINTS: • Halomonas spp. are robust chassis for low-cost production of chemicals • Genomic information of some Halomonas spp. has been revealed • Molecular tools and approaches for Halomonas spp. have been developed • Halomonas spp. are becoming more and more important for biotechnology.

Comprehensive Genome Analysis of Halolamina pelagica CDK2: Insights into Abiotic Stress Tolerance Genes

Halophilic archaeon Halolamina pelagica CDK2, showcasing plant growth-promoting properties and endurance towards harsh environmental conditions (high salinity, heavy metals, high temperature and UV radiation) was sequenced earlier. Pan-genome of Halolamina genus was created and investigated for strain-specific genes of CDK2, which might confer it with features helping it to withstand high abiotic stress. Pathways and subsystems in CDK2 were compared with other Halolamina strain CGHMS and analysed using KEGG and RAST. A genome-scale metabolic model was reconstructed from the genome of H. pelagica CDK2. Results implicated strain-specific genes like thermostable carboxypeptidase and DNA repair protein MutS which might protect the proteins and DNA from high temperature and UV denaturation respectively. A bifunctional trehalose synthase gene responsible for trehalose biosynthesis was also annotated specifying the need for low salt compatible solute strategy, the probable reason behind the ability of this haloarchaea to survive in a wide range of salt concentrations. A modified shikimate and mevalonate pathways were also identified in CDK2, along with many ABC transporters for metal uptakes like zinc and cobalt through pathway analysis. Probable employment of one multifunctional ABC transporter in place of two for similar metals (Nickel/cobalt and molybdenum/tungsten) might be employed as a strategy for energy conservation. The findings of the present study could be utilized for future research relating metabolic model for flux balance analysis and the genetic repertoire imparting resistance to harsh conditions can be transferred to crops for improving their tolerance to abiotic stresses.

Biofilms of Halobacterium salinarum as a tool for phenanthrene bioremediation

The use of hyperhalophilic microorganisms is emerging as a sustainable alternative to clean hydrocarbon-polluted hypersaline water bodies. In line with this practice, this work reports on the ability of the archaeon Halobacterium salinarum to develop biofilms on a solid surface conditioned by the presence of phenanthrene crystals, which results in the removal of the contaminating compound. The cell surface hydrophobicity does not change during the removal process and this organism is shown to constitutively produce a surfactant molecule with specific action on aromatic hydrocarbons, both indicating that phenanthrene removal might proceed through a non-contact mechanism. A new approach is presented to follow the process in situ through epifluorescence microscopy by monitoring phenanthrene auto-fluorescence.

Whole-genome comparison between the type strain of Halobacterium salinarum (DSM 3754T ) and the laboratory strains R1 and NRC-1

Halobacterium salinarum is an extremely halophilic archaeon that is widely distributed in hypersaline environments and was originally isolated as a spoilage organism of salted fish and hides. The type strain 91-R6 (DSM 3754T ) has seldom been studied and its genome sequence has only recently been determined by our group. The exact relationship between the type strain and two widely used model strains, NRC-1 and R1, has not been described before. The genome of Hbt. salinarum strain 91-R6 consists of a chromosome (2.17 Mb) and two large plasmids (148 and 102 kb, with 39,230 bp being duplicated). Cytosine residues are methylated (m4 C) within CTAG motifs. The genomes of type and laboratory strains are closely related, their chromosomes sharing average nucleotide identity (ANIb) values of 98% and in silico DNA-DNA hybridization (DDH) values of 95%. The chromosomes are completely colinear, do not show genome rearrangement, and matching segments show <1% sequence difference. Among the strain-specific sequences are three large chromosomal replacement regions (>10 kb). The well-studied AT-rich island (61 kb) of the laboratory strains is replaced by a distinct AT-rich sequence (47 kb) in 91-R6. Another large replacement (91-R6: 78 kb, R1: 44 kb) codes for distinct homologs of proteins involved in motility and N-glycosylation. Most (107 kb) of plasmid pHSAL1 (91-R6) is very closely related to part of plasmid pHS3 (R1) and codes for essential genes (e.g. arginine-tRNA ligase and the pyrimidine biosynthesis enzyme aspartate carbamoyltransferase). Part of pHS3 (42.5 kb total) is closely related to the largest strain-specific sequence (164 kb) in the type strain chromosome. Genome sequencing unraveled the close relationship between the Hbt. salinarum type strain and two well-studied laboratory strains at the DNA and protein levels. Although an independent isolate, the type strain shows a remarkably low evolutionary difference to the laboratory strains.

A genome-scale metabolic network reconstruction of extremely halophilic bacterium Salinibacter ruber

A genome-scale metabolic network reconstruction of Salinibacter ruber DSM13855 is presented here. To our knowledge, this is the first metabolic model of an organism in the phylum Rhodothermaeota. This model, which will be called iMB631, was reconstructed based on genomic and biochemical data available on the strain Salinibacter ruber DSM13855. This network consists of 1459 reactions, 1363 metabolites and 631 genes. Model evaluation was performed based on existing biochemical data in the literature and also by performing laboratory experiments. For growth on different carbon sources, we show that iMB631 is able to correctly predict the growth in 91% of cases where growth has been observed experimentally and 83% of conditions in which S. ruber did not grow. The F-score was 93%, demonstrating a generally acceptable performance of the model. Based on the predicted flux distributions, we found that under certain autotrophic condition, a reductive tricarboxylic acid cycle (rTCA) has fluxes in all necessary reactions to support autotrophic growth. To include special metabolites of the bacterium, salinixanthin biosynthesis pathway was modeled based on the pathway proposed recently. For years, main glucose consumption pathway has been under debates in S. ruber. Using flux balance analysis, iMB631 predicts pentose phosphate pathway, rather than glycolysis, as the active glucose consumption method in the S. ruber.

Genome-scale metabolic model analysis indicates low energy production efficiency in marine ammonia-oxidizing archaea

Marine ammonia-oxidizing archaea (AOA) play an important role in the global nitrogen cycle by obtaining energy for biomass production from CO₂ via oxidation of ammonium. The isolation of Candidatus “Nitrosopumilus maritimus” strain SCM1, which represents the globally distributed AOA in the ocean, provided an opportunity for uncovering the contributions of those AOA to carbon and nitrogen cycles in ocean. Although several ammonia oxidation pathways have been proposed for SCM1, little is known about its ATP production efficiency. Here, based on the published genome of Nitrosopumilus maritimus SCM1, a genome-scale metabolic model named NmrFL413 was reconstructed. Based on the model NmrFL413, the estimated ATP/NH₄⁺ yield (0.149–0.276 ATP/NH₄⁺) is tenfold lower than the calculated theoretical yield of the proposed ammonia oxidation pathways in marine AOA (1.5–1.75 ATP/NH₄⁺), indicating a low energy production efficiency of SCM1. Our model also suggested the minor contribution of marine AOA to carbon cycle comparing with their significant contribution to nitrogen cycle in the ocean.

In a quest for engineering acidophiles for biomining applications: challenges and opportunities

Biomining with acidophilic microorganisms has been used at commercial scale for the extraction of metals from various sulfide ores. With metal demand and energy prices on the rise and the concurrent decline in quality and availability of mineral resources, there is an increasing interest in applying biomining technology, in particular for leaching metals from low grade minerals and wastes. However, bioprocessing is often hampered by the presence of inhibitory compounds that originate from complex ores. Synthetic biology could provide tools to improve the tolerance of biomining microbes to various stress factors that are present in biomining environments, which would ultimately increase bioleaching efficiency. This paper reviews the state-of-the-art tools to genetically modify acidophilic biomining microorganisms and the limitations of these tools. The first part of this review discusses resilience pathways that can be engineered in acidophiles to enhance their robustness and tolerance in harsh environments that prevail in bioleaching. The second part of the paper reviews the efforts that have been carried out towards engineering robust microorganisms and developing metabolic modelling tools. Novel synthetic biology tools have the potential to transform the biomining industry and facilitate the extraction of value from ores and wastes that cannot be processed with existing biomining microorganisms.

Constraint-based metabolic modelling of marine microbes and communities

Constraint-based metabolic modelling (CBMM) consists in the use of computational methods and tools to perform genome-scale simulations and predict metabolic features at the whole cellular level. This approach is rapidly expanding in microbiology, as it combines reliable predictive abilities with conceptually and technically simple frameworks. Among the possible outcomes of CBMM, the capability to i) guide a focused planning of metabolic engineering experiments and ii) provide a system-level understanding of (single or community-level) microbial metabolic circuits also represent primary aims in present-day marine microbiology. In this work we briefly introduce the theoretical formulation behind CBMM and then review the most recent and effective case studies of CBMM of marine microbes and communities. Also, the emerging challenges and possibilities in the use of such methodologies in the context of marine microbiology/biotechnology are discussed. As the potential applications of CBMM have a very broad range, the topics presented in this review span over a large plethora of fields such as ecology, biotechnology and evolution.

Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function

Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria.

Genome-scale reconstruction of the metabolic network in Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is an endophytic bacterium capable of nitrogen fixation. This strain has been isolated from the rice rhizosphere and provides the plant with fixed nitrogen and phytohormones. These interesting features encouraged us to study the metabolism of this microorganism at the systems-level. In this work, we present the first genome-scale metabolic model (iPB890) for P. stutzeri, involving 890 genes, 1135 reactions, and 813 metabolites. A combination of automatic and manual approaches was used in the reconstruction process. Briefly, using the metabolic networks of Pseudomonas aeruginosa and Pseudomonas putida as templates, a draft metabolic network of P. stutzeri was reconstructed. Then, the draft network was driven through an iterative and curative process of gap filling. In the next step, the model was evaluated using different experimental data such as specific growth rate, Biolog substrate utilization data and other experimental observations. In most of the evaluation cases, the model was successful in correctly predicting the cellular phenotypes. Thus, we posit that the iPB890 model serves as a suitable platform to explore the metabolism of P. stutzeri.

Dynamic Metabolite Profiling in an Archaeon Connects Transcriptional Regulation to Metabolic Consequences

Previous work demonstrated that the TrmB transcription factor is responsible for regulating the expression of many enzyme-coding genes in the hypersaline-adapted archaeon Halobacterium salinarum via a direct interaction with a cis-regulatory sequence in their promoters. This interaction is abolished in the presence of glucose. Although much is known about the effects of TrmB at the transcriptional level, it remains unclear whether and to what extent changes in mRNA levels directly affect metabolite levels. In order to address this question, here we performed a high-resolution metabolite profiling time course during a change in nutrients using a combination of targeted and untargeted methods in wild-type and ΔtrmB strain backgrounds. We found that TrmB-mediated transcriptional changes resulted in widespread and significant changes to metabolite levels across the metabolic network. Additionally, the pattern of growth complementation using various purines suggests that the mis-regulation of gluconeogenesis in the ΔtrmB mutant strain in the absence of glucose results in low phosphoribosylpyrophosphate (PRPP) levels. We confirmed these low PRPP levels using a quantitative mass spectrometric technique and found that they are associated with a metabolic block in de novo purine synthesis, which is partially responsible for the growth defect of the ΔtrmB mutant strain in the absence of glucose. In conclusion, we show how transcriptional regulation of metabolism affects metabolite levels and ultimately, phenotypes.

A manual curation strategy to improve genome annotation: application to a set of haloarchael genomes

Genome annotation errors are a persistent problem that impede research in the biosciences. A manual curation effort is described that attempts to produce high-quality genome annotations for a set of haloarchaeal genomes (Halobacterium salinarum and Hbt. hubeiense, Haloferax volcanii and Hfx. mediterranei, Natronomonas pharaonis and Nmn. moolapensis, Haloquadratum walsbyi strains HBSQ001 and C23, Natrialba magadii, Haloarcula marismortui and Har. hispanica, and Halohasta litchfieldiae). Genomes are checked for missing genes, start codon misassignments, and disrupted genes. Assignments of a specific function are preferably based on experimentally characterized homologs (Gold Standard Proteins). To avoid overannotation, which is a major source of database errors, we restrict annotation to only general function assignments when support for a specific substrate assignment is insufficient. This strategy results in annotations that are resistant to the plethora of errors that compromise public databases. Annotation consistency is rigorously validated for ortholog pairs from the genomes surveyed. The annotation is regularly crosschecked against the UniProt database to further improve annotations and increase the level of standardization. Enhanced genome annotations are submitted to public databases (EMBL/GenBank, UniProt), to the benefit of the scientific community. The enhanced annotations are also publically available via HaloLex.

Carbohydrate metabolism in Archaea: current insights into unusual enzymes and pathways and their regulation

The metabolism of Archaea, the third domain of life, resembles in its complexity those of Bacteria and lower Eukarya. However, this metabolic complexity in Archaea is accompanied by the absence of many "classical" pathways, particularly in central carbohydrate metabolism. Instead, Archaea are characterized by the presence of unique, modified variants of classical pathways such as the Embden-Meyerhof-Parnas (EMP) pathway and the Entner-Doudoroff (ED) pathway. The pentose phosphate pathway is only partly present (if at all), and pentose degradation also significantly differs from that known for bacterial model organisms. These modifications are accompanied by the invention of "new," unusual enzymes which cause fundamental consequences for the underlying regulatory principles, and classical allosteric regulation sites well established in Bacteria and Eukarya are lost. The aim of this review is to present the current understanding of central carbohydrate metabolic pathways and their regulation in Archaea. In order to give an overview of their complexity, pathway modifications are discussed with respect to unusual archaeal biocatalysts, their structural and mechanistic characteristics, and their regulatory properties in comparison to their classic counterparts from Bacteria and Eukarya. Furthermore, an overview focusing on hexose metabolic, i.e., glycolytic as well as gluconeogenic, pathways identified in archaeal model organisms is given. Their energy gain is discussed, and new insights into different levels of regulation that have been observed so far, including the transcript and protein levels (e.g., gene regulation, known transcription regulators, and posttranslational modification via reversible protein phosphorylation), are presented.

Network motifs that recur across species, including gene regulatory and protein-protein interaction networks

Cellular molecules interact in complex ways, giving rise to a cell's functional outcomes. Conscientious efforts have been made in recent years to better characterize these patterns of interactions. It has been learned that many of these interactions can be represented abstractly as a network and within a network there in many instances are network motifs. Network motifs are subgraphs that are statistically overrepresented within networks. To date, specific network motifs have been experimentally identified across various species and also within specific, intracellular networks; however, motifs that recur across species and major network types have not been systematically characterized. We reason that recurring network motifs could potentially have important implications and applications for toxicology and, in particular, toxicity testing. Therefore, the goal of this study was to determine the set of intracellular, network motifs found to recur across species of both gene regulatory and protein-protein interaction networks. We report the recurrence of 13 intracellular, network motifs across species. Ten recurring motifs were found across both protein-protein interaction networks and gene regulatory networks. The significant pair motif was found to recur only in gene regulatory networks. The diamond and one-way cycle reversible step motifs were found to recur only in protein-protein interaction networks. This study is the first formal review of recurring, intracellular network motifs across species. Within toxicology, combining our understanding of recurring motifs with mechanism and mode of action knowledge could result in more robust and efficient toxicity testing models. We are sure that our results will support research in applying network motifs to toxicity testing.

Plant genome-scale modeling and implementation

Considerable progress has been made in plant genome-scale metabolic reconstruction and modeling in recent years. Such reconstructions made it possible to explore metabolic phenotypes through appropriate model formulation and optimization methods. As a result, plant genome-scale modeling has increasingly attracted interest from the plant research community. In this chapter, the first generation of plant genome-scale metabolic reconstructions is presented, along with the important concepts behind model and constraint formulation. A brief protocol describing the use of constraint-based reconstruction and analysis (COBRA) Toolbox in flux simulation and model modification is provided. This is followed by a presentation of metabolic constraints required to generate fluxes in AraGEM using COBRA that describe photosynthesis, photorespiration, and respiration, respectively. Overall, plant genome-scale modeling is a powerful approach that is accessible and readily adopted.

Simple topological properties predict functional misannotations in a metabolic network

Motivation: Misannotation in sequence databases is an important obstacle for automated tools for gene function annotation, which rely extensively on comparison with sequences with known function. To improve current annotations and prevent future propagation of errors, sequence-independent tools are, therefore, needed to assist in the identification of misannotated gene products. In the case of enzymatic functions, each functional assignment implies the existence of a reaction within the organism’s metabolic network; a first approximation to a genome-scale metabolic model can be obtained directly from an automated genome annotation. Any obvious problems in the network, such as dead end or disconnected reactions, can, therefore, be strong indications of misannotation.

Results: We demonstrate that a machine-learning approach using only network topological features can successfully predict the validity of enzyme annotations. The predictions are tested at three different levels. A random forest using topological features of the metabolic network and trained on curated sets of correct and incorrect enzyme assignments was found to have an accuracy of up to 86% in 5-fold cross-validation experiments. Further cross-validation against unseen enzyme superfamilies indicates that this classifier can successfully extrapolate beyond the classes of enzyme present in the training data. The random forest model was applied to several automated genome annotations, achieving an accuracy of in most cases when validated against recent genome-scale metabolic models. We also observe that when applied to draft metabolic networks for multiple species, a clear negative correlation is observed between predicted annotation quality and phylogenetic distance to the major model organism for biochemistry (Escherichia coli for prokaryotes and Homo sapiens for eukaryotes).

Contact:j.pinney@imperial.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

An optimization rule for in silico identification of targeted overproduction in metabolic pathways

In an extension of previous work, here we introduce a second-order optimization method for determining optimal paths from the substrate to a target product of a metabolic network, through which the amount of the target is maximum. An objective function for the said purpose, along with certain linear constraints, is considered and minimized. The basis vectors spanning the null space of the stoichiometric matrix, depicting the metabolic network, are computed, and their convex combinations satisfying the constraints are considered as flux vectors. A set of other constraints, incorporating weighting coefficients corresponding to the enzymes in the pathway, are considered. These weighting coefficients appear in the objective function to be minimized. During minimization, the values of these weighting coefficients are estimated and learned. These values, on minimization, represent an optimal pathway, depicting optimal enzyme concentrations, leading to overproduction of the target. The results on various networks demonstrate the usefulness of the methodology in the domain of metabolic engineering. A comparison with the standard gradient descent and the extreme pathway analysis technique is also performed. Unlike the gradient descent method, the present method, being independent of the learning parameter, exhibits improved results.

Network motifs provide signatures that characterize metabolism

Motifs are repeating patterns that determine the local properties of networks. In this work, we characterized all 3-node motifs using enzyme commission numbers of the International Union of Biochemistry and Molecular Biology to show that motif abundance is related to biochemical function. Further, we present a comparative analysis of motif distributions in the metabolic networks of 21 species across six kingdoms of life. We found the distribution of motif abundances to be similar between species, but unique across cellular organelles. Finally, we show that motifs are able to capture inter-species differences in metabolic networks and that molecular differences between some biological species are reflected by the distribution of motif abundances in metabolic networks.

Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico

BACKGROUND

Genome-scale metabolic networks and flux models are an effective platform for linking an organism genotype to its phenotype. However, few modeling approaches offer predictive capabilities to evaluate potential metabolic engineering strategies in silico.

RESULTS

A new method called "flux balance analysis with flux ratios (FBrAtio)" was developed in this research and applied to a new genome-scale model of Clostridium acetobutylicum ATCC 824 (iCAC490) that contains 707 metabolites and 794 reactions. FBrAtio was used to model wild-type metabolism and metabolically engineered strains of C. acetobutylicum where only flux ratio constraints and thermodynamic reversibility of reactions were required. The FBrAtio approach allowed solutions to be found through standard linear programming. Five flux ratio constraints were required to achieve a qualitative picture of wild-type metabolism for C. acetobutylicum for the production of: (i) acetate, (ii) lactate, (iii) butyrate, (iv) acetone, (v) butanol, (vi) ethanol, (vii) CO2 and (viii) H2. Results of this simulation study coincide with published experimental results and show the knockdown of the acetoacetyl-CoA transferase increases butanol to acetone selectivity, while the simultaneous over-expression of the aldehyde/alcohol dehydrogenase greatly increases ethanol production.

CONCLUSIONS

FBrAtio is a promising new method for constraining genome-scale models using internal flux ratios. The method was effective for modeling wild-type and engineered strains of C. acetobutylicum.

A systems biology approach to drug targets in Pseudomonas aeruginosa biofilm

Antibiotic resistance is an increasing problem in the health care system and we are in a constant race with evolving bacteria. Biofilm-associated growth is thought to play a key role in bacterial adaptability and antibiotic resistance. We employed a systems biology approach to identify candidate drug targets for biofilm-associated bacteria by imitating specific microenvironments found in microbial communities associated with biofilm formation. A previously reconstructed metabolic model of Pseudomonas aeruginosa (PA) was used to study the effect of gene deletion on bacterial growth in planktonic and biofilm-like environmental conditions. A set of 26 genes essential in both conditions was identified. Moreover, these genes have no homology with any human gene. While none of these genes were essential in only one of the conditions, we found condition-dependent genes, which could be used to slow growth specifically in biofilm-associated PA. Furthermore, we performed a double gene deletion study and obtained 17 combinations consisting of 21 different genes, which were conditionally essential. While most of the difference in double essential gene sets could be explained by different medium composition found in biofilm-like and planktonic conditions, we observed a clear effect of changes in oxygen availability on the growth performance. Eight gene pairs were found to be synthetic lethal in oxygen-limited conditions. These gene sets may serve as novel metabolic drug targets to combat particularly biofilm-associated PA. Taken together, this study demonstrates that metabolic modeling of human pathogens can be used to identify oxygen-sensitive drug targets and thus, that this systems biology approach represents a powerful tool to identify novel candidate antibiotic targets.

MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases

BACKGROUND

Increasingly, metabolite and reaction information is organized in the form of genome-scale metabolic reconstructions that describe the reaction stoichiometry, directionality, and gene to protein to reaction associations. A key bottleneck in the pace of reconstruction of new, high-quality metabolic models is the inability to directly make use of metabolite/reaction information from biological databases or other models due to incompatibilities in content representation (i.e., metabolites with multiple names across databases and models), stoichiometric errors such as elemental or charge imbalances, and incomplete atomistic detail (e.g., use of generic R-group or non-explicit specification of stereo-specificity).

DESCRIPTION

MetRxn is a knowledgebase that includes standardized metabolite and reaction descriptions by integrating information from BRENDA, KEGG, MetaCyc, Reactome.org and 44 metabolic models into a single unified data set. All metabolite entries have matched synonyms, resolved protonation states, and are linked to unique structures. All reaction entries are elementally and charge balanced. This is accomplished through the use of a workflow of lexicographic, phonetic, and structural comparison algorithms. MetRxn allows for the download of standardized versions of existing genome-scale metabolic models and the use of metabolic information for the rapid reconstruction of new ones.

CONCLUSIONS

The standardization in description allows for the direct comparison of the metabolite and reaction content between metabolic models and databases and the exhaustive prospecting of pathways for biotechnological production. This ever-growing dataset currently consists of over 76,000 metabolites participating in more than 72,000 reactions (including unresolved entries). MetRxn is hosted on a web-based platform that uses relational database models (MySQL).

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.

Ecological aspects of the distribution of different autotrophic CO2 fixation pathways

Autotrophic CO(2) fixation represents the most important biosynthetic process in biology. Besides the well-known Calvin-Benson cycle, five other totally different autotrophic mechanisms are known today. This minireview discusses the factors determining their distribution. As will be made clear, the observed diversity reflects the variety of the organisms and the ecological niches existing in nature.

Metabolic reconstruction of the archaeon methanogen Methanosarcina Acetivorans

BACKGROUND

Methanogens are ancient organisms that are key players in the carbon cycle accounting for about one billion tones of biological methane produced annually. Methanosarcina acetivorans, with a genome size of ~5.7 mb, is the largest sequenced archaeon methanogen and unique amongst the methanogens in its biochemical characteristics. By following a systematic workflow we reconstruct a genome-scale metabolic model for M. acetivorans. This process relies on previously developed computational tools developed in our group to correct growth prediction inconsistencies with in vivo data sets and rectify topological inconsistencies in the model.

RESULTS

The generated model iVS941 accounts for 941 genes, 705 reactions and 708 metabolites. The model achieves 93.3% prediction agreement with in vivo growth data across different substrates and multiple gene deletions. The model also correctly recapitulates metabolic pathway usage patterns of M. acetivorans such as the indispensability of flux through methanogenesis for growth on acetate and methanol and the unique biochemical characteristics under growth on carbon monoxide.

CONCLUSIONS

Based on the size of the genome-scale metabolic reconstruction and extent of validated predictions this model represents the most comprehensive up-to-date effort to catalogue methanogenic metabolism. The reconstructed model is available in spreadsheet and SBML formats to enable dissemination.

Genome-scale reconstruction of metabolic network for a halophilic extremophile, Chromohalobacter salexigens DSM 3043

BACKGROUND

Chromohalobacter salexigens (formerly Halomonas elongata DSM 3043) is a halophilic extremophile with a very broad salinity range and is used as a model organism to elucidate prokaryotic osmoadaptation due to its strong euryhaline phenotype.

RESULTS

C. salexigens DSM 3043's metabolism was reconstructed based on genomic, biochemical and physiological information via a non-automated but iterative process. This manually-curated reconstruction accounts for 584 genes, 1386 reactions, and 1411 metabolites. By using flux balance analysis, the model was extensively validated against literature data on the C. salexigens phenotypic features, the transport and use of different substrates for growth as well as against experimental observations on the uptake and accumulation of industrially important organic osmolytes, ectoine, betaine, and its precursor choline, which play important roles in the adaptive response to osmotic stress.

CONCLUSIONS

This work presents the first comprehensive genome-scale metabolic model of a halophilic bacterium. Being a useful guide for identification and filling of knowledge gaps, the reconstructed metabolic network iOA584 will accelerate the research on halophilic bacteria towards application of systems biology approaches and design of metabolic engineering strategies.

Flux-based analysis of sulfur metabolism in desulfurizing strains of Rhodococcus erythropolis

Rhodococcus erythropolis has been studied widely for potential applications in biodesulfurization. Previous works have been largely experimental with an emphasis on the characterization and genetic engineering of desulfurizing strains for improved biocatalysis. A systems modeling approach that can complement these experimental efforts by providing useful insights into the complex interactions of desulfurization reactions with various other metabolic activities is absent in the literature. In this work, we report the first attempt at reconstructing a flux-based model to analyze sulfur utilization by R. erythropolis. The model includes the 4S pathway for dibenzothiophene (DBT) desulfurization. It predicts closely the growth rates reported by two independent experimental studies, and gives a clear and comprehensive picture of the pathways that assimilate the sulfur from DBT into biomass. In addition, it successfully elucidates that sulfate promotes higher cell growth than DBT and its presence in the medium reduces DBT desulfurization rates. A study using eight carbon sources suggests that ethanol and lactate yield higher cell growth and desulfurization rates than citrate, fructose, glucose, gluconate, glutamate, and glycerol.

Characterization of growth and metabolism of the haloalkaliphile Natronomonas pharaonis

Natronomonas pharaonis is an archaeon adapted to two extreme conditions: high salt concentration and alkaline pH. It has become one of the model organisms for the study of extremophilic life. Here, we present a genome-scale, manually curated metabolic reconstruction for the microorganism. The reconstruction itself represents a knowledge base of the haloalkaliphile's metabolism and, as such, would greatly assist further investigations on archaeal pathways. In addition, we experimentally determined several parameters relevant to growth, including a characterization of the biomass composition and a quantification of carbon and oxygen consumption. Using the metabolic reconstruction and the experimental data, we formulated a constraints-based model which we used to analyze the behavior of the archaeon when grown on a single carbon source. Results of the analysis include the finding that Natronomonas pharaonis, when grown aerobically on acetate, uses a carbon to oxygen consumption ratio that is theoretically near-optimal with respect to growth and energy production. This supports the hypothesis that, under simple conditions, the microorganism optimizes its metabolism with respect to the two objectives. We also found that the archaeon has a very low carbon efficiency of only about 35%. This inefficiency is probably due to a very low P/O ratio as well as to the other difficulties posed by its extreme environment.

Extremophiles are organisms that thrive in physically or geochemically extreme conditions that are detrimental, even lethal, to the majority of life on Earth. Natronomonas pharaonis is one that has been able to adapt to both high salt concentration and an alkaline pH. In this study, we investigate the chemical reactions that occur within the microorganism, collectively referred to as its metabolic network, that allow it to convert the nutrients in its environment to biomass and energy. Specifically, we reconstructed the network by collecting evidence for the existence of reactions from the literature, and then supplemented them with computational approaches, for example by searching the genome of Natronomonas pharaonis for genes that could potentially encode analogs of known enzymes from other organisms. Finally, with the network in hand, we developed a computational model which we used to simulate growth. Among other results, we found indications that Natronomonas pharaonis regulates its metabolism such that energy production and growth are maximized. Despite this however, we also found that Natronomonas pharaonis is only able to incorporate a very small fraction of the total carbon that it consumes (approximately 35%), likely in no small part due to the difficulties posed by its environment.

Transcriptional control by two leucine-responsive regulatory proteins in Halobacterium salinarum R1

Background

Archaea combine bacterial-as well as eukaryotic-like features to regulate cellular processes. Halobacterium salinarum R1 encodes eight leucine-responsive regulatory protein (Lrp)-homologues. The function of two of them, Irp (OE3923F) and lrpA1 (OE2621R), were analyzed by gene deletion and overexpression, including genome scale impacts using microarrays.

Results

It was shown that Lrp affects the transcription of multiple target genes, including those encoding enzymes involved in amino acid synthesis, central metabolism, transport processes and other regulators of transcription. In contrast, LrpA1 regulates transcription in a more specific manner. The aspB3 gene, coding for an aspartate transaminase, was repressed by LrpA1 in the presence of L-aspartate. Analytical DNA-affinity chromatography was adapted to high salt, and demonstrated binding of LrpA1 to its own promoter, as well as L-aspartate dependent binding to the aspB3 promoter.

Conclusion

The gene expression profiles of two archaeal Lrp-homologues report in detail their role in H. salinarum R1. LrpA1 and Lrp show similar functions to those already described in bacteria, but in addition they play a key role in regulatory networks, such as controlling the transcription of other regulators. In a more detailed analysis ligand dependent binding of LrpA1 was demonstrated to its target gene aspB3.

Large scale physiological readjustment during growth enables rapid, comprehensive and inexpensive systems analysis

Background

Rapidly characterizing the operational interrelationships among all genes in a given organism is a critical bottleneck to significantly advancing our understanding of thousands of newly sequenced microbial and eukaryotic species. While evolving technologies for global profiling of transcripts, proteins, and metabolites are making it possible to comprehensively survey cellular physiology in newly sequenced organisms, these experimental techniques have not kept pace with sequencing efforts. Compounding these technological challenges is the fact that individual experiments typically only stimulate relatively small-scale cellular responses, thus requiring numerous expensive experiments to survey the operational relationships among nearly all genetic elements. Therefore, a relatively quick and inexpensive strategy for observing changes in large fractions of the genetic elements is highly desirable.

Results

We have discovered in the model organism Halobacterium salinarum NRC-1 that batch culturing in complex medium stimulates meaningful changes in the expression of approximately two thirds of all genes. While the majority of these changes occur during transition from rapid exponential growth to the stationary phase, several transient physiological states were detected beyond what has been previously observed. In sum, integrated analysis of transcript and metabolite changes has helped uncover growth phase-associated physiologies, operational interrelationships among two thirds of all genes, specialized functions for gene family members, waves of transcription factor activities, and growth phase associated cell morphology control.

Conclusions

Simple laboratory culturing in complex medium can be enormously informative regarding the activities of and interrelationships among a large fraction of all genes in an organism. This also yields important baseline physiological context for designing specific perturbation experiments at different phases of growth. The integration of such growth and perturbation studies with measurements of associated environmental factor changes is a practical and economical route for the elucidation of comprehensive systems-level models of biological systems.

BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions

BACKGROUND

Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use.

DESCRIPTION

We describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest.

CONCLUSIONS

BiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at http://bigg.ucsd.edu.

Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology

Driven by advancements in high-throughput biological technologies and the growing number of sequenced genomes, the construction of in silico models at the genome scale has provided powerful tools to investigate a vast array of biological systems and applications. Here, we review comprehensively the uses of such models in industrial and medical biotechnology, including biofuel generation, food production, and drug development. While the use of in silico models is still in its early stages for delivering to industry, significant initial successes have been achieved. For the cases presented here, genome-scale models predict engineering strategies to enhance properties of interest in an organism or to inhibit harmful mechanisms of pathogens. Going forward, genome-scale in silico models promise to extend their application and analysis scope to become a trans-formative tool in biotechnology.

Applications of genome-scale metabolic reconstructions

The availability and utility of genome-scale metabolic reconstructions have exploded since the first genome-scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high-throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis-driven discovery, (4) interrogation of multi-species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome-scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.

Life-style changes of a halophilic archaeon analyzed by quantitative proteomics

Quantitative proteomics based on isotopic labeling has become the method of choice to accurately determine changes in protein abundance in highly complex mixtures. Isotope-coded protein labeling (ICPL), which is based on the nicotinoylation of proteins at lysine residues and free N-termini was used as a simple, reliable and fast method for the comparative analysis of three different cellular states of the halophilic archaeon Halobacterium salinarum through pairwise comparison. The labeled proteins were subjected to SDS-PAGE, in-gel digested and the proteolytic peptides were separated by LC and analyzed by MALDI-TOF/TOF MS. Automated quantitation was performed by comparing the MS peptide signals of (12)C and (13)C nicotinoylated isotopic peptide pairs. The transitions between (i) aerobic growth in complex versus synthetic medium and (ii) aerobic versus anaerobic/phototrophic growth, both in complex medium, provide a wide span in nutrient and energy supply for the cell and thus allowed optimal studies of proteome changes. In these two studies, 559 and 643 proteins, respectively, could be quantified allowing a detailed analysis of the adaptation of H. salinarum to changes of its living conditions. The subtle cellular response to a wide variation of nutrient and energy supply demonstrates a fine tuning of the cellular protein inventory.

Metabolic network discovery through reverse engineering of metabolome data

Reverse engineering of high-throughput omics data to infer underlying biological networks is one of the challenges in systems biology. However, applications in the field of metabolomics are rather limited. We have focused on a systematic analysis of metabolic network inference from in silico metabolome data based on statistical similarity measures. Three different data types based on biological/environmental variability around steady state were analyzed to compare the relative information content of the data types for inferring the network. Comparing the inference power of different similarity scores indicated the clear superiority of conditioning or pruning based scores as they have the ability to eliminate indirect interactions. We also show that a mathematical measure based on the Fisher information matrix gives clues on the information quality of different data types to better represent the underlying metabolic network topology. Results on several datasets of increasing complexity consistently show that metabolic variations observed at steady state, the simplest experimental analysis, are already informative to reveal the connectivity of the underlying metabolic network with a low false-positive rate when proper similarity-score approaches are employed. For experimental situations this implies that a single organism under slightly varying conditions may already generate more than enough information to rightly infer networks. Detailed examination of the strengths of interactions of the underlying metabolic networks demonstrates that the edges that cannot be captured by similarity scores mainly belong to metabolites connected with weak interaction strength. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0156-4) contains supplementary material, which is available to authorized users.

A single transcription factor regulates evolutionarily diverse but functionally linked metabolic pathways in response to nutrient availability

During evolution, enzyme-coding genes are acquired and/or replaced through lateral gene transfer and compiled into metabolic pathways. Gene regulatory networks evolve to fine tune biochemical fluxes through such metabolic pathways, enabling organisms to acclimate to nutrient fluctuations in a competitive environment. Here, we demonstrate that a single TrmB family transcription factor in Halobacterium salinarum NRC-1 globally coordinates functionally linked enzymes of diverse phylogeny in response to changes in carbon source availability. Specifically, during nutritional limitation, TrmB binds a cis-regulatory element to activate or repress 113 promoters of genes encoding enzymes in diverse metabolic pathways. By this mechanism, TrmB coordinates the expression of glycolysis, TCA cycle, and amino-acid biosynthesis pathways with the biosynthesis of their cognate cofactors (e.g. purine and thiamine). Notably, the TrmB-regulated metabolic network includes enzyme-coding genes that are uniquely archaeal as well as those that are conserved across all three domains of life. Simultaneous analysis of metabolic and gene regulatory network architectures suggests an ongoing process of co-evolution in which TrmB integrates the expression of metabolic enzyme-coding genes of diverse origins.

Phosphate-dependent behavior of the archaeon Halobacterium salinarum strain R1

Phosphate is essential for life on earth, since it is an integral part of important biomolecules. The mechanisms applied by bacteria and eukarya to combat phosphate limitation are fairly well understood. However, it is not known how archaea sense phosphate limitation or which genes are regulated upon limitation. We conducted a microarray analysis to explore the phosphate-dependent gene expression of Halobacterium salinarum strain R1. We identified a set of 17 genes whose transcript levels increased up to several hundredfold upon phosphate limitation. Analysis of deletion mutants showed that this set of genes, the PHO stimulon, is very likely independent of signaling via two-component systems. Our experiments further indicate that PHO stimulon induction might be dependent on the intracellular phosphate concentration, which turned out to be subject to substantial changes. Finally, the study revealed that H. salinarum exhibits a phosphate-directed chemotaxis, which is induced by phosphate starvation.

Systems analysis of bioenergetics and growth of the extreme halophile Halobacterium salinarum

Halobacterium salinarum is a bioenergetically flexible, halophilic microorganism that can generate energy by respiration, photosynthesis, and the fermentation of arginine. In a previous study, using a genome-scale metabolic model, we have shown that the archaeon unexpectedly degrades essential amino acids under aerobic conditions, a behavior that can lead to the termination of growth earlier than necessary. Here, we further integratively investigate energy generation, nutrient utilization, and biomass production using an extended methodology that accounts for dynamically changing transport patterns, including those that arise from interactions among the supplied metabolites. Moreover, we widen the scope of our analysis to include phototrophic conditions to explore the interplay between different bioenergetic modes. Surprisingly, we found that cells also degrade essential amino acids even during phototropy, when energy should already be abundant. We also found that under both conditions considerable amounts of nutrients that were taken up were neither incorporated into the biomass nor used as respiratory substrates, implying the considerable production and accumulation of several metabolites in the medium. Some of these are likely the products of forms of overflow metabolism. In addition, our results also show that arginine fermentation, contrary to what is typically assumed, occurs simultaneously with respiration and photosynthesis and can contribute energy in levels that are comparable to the primary bioenergetic modes, if not more. These findings portray a picture that the organism takes an approach toward growth that favors the here and now, even at the cost of longer-term concerns. We believe that the seemingly “greedy” behavior exhibited actually consists of adaptations by the organism to its natural environments, where nutrients are not only irregularly available but may altogether be absent for extended periods that may span several years. Such a setting probably predisposed the cells to grow as much as possible when the conditions become favorable.

Living cells can produce usable energy through various means. For example, animals derive energy, through respiration, from nutrients that they consume, and plants from light using photosynthesis. The particular microorganism that we study, Halobacterium salinarum, is a model organism for the archaeal domain of life. It is bioenergetically flexible in that it can perform both respiration and photosynthesis and in addition can also derive energy using fermentation. Accordingly, it is a good model system for investigating the interplay between different energy generating mechanisms. In this study, we investigate these relationships as well as how energy production is linked to the other processes involved in growth, including the consumption of nutrients and the production of cellular material. Because Halobacterium salinarum thrives in salt-saturated solutions, such as those that may be found in salt lakes and solar salterns, our study yields insight on how these cellular processes operate in environments that are lethal to most life on Earth.

Identification of the arginine/ornithine antiporter ArcD from Halobacterium salinarum

This paper identifies the first arginine/ornithine antiporter ArcD from the domain of archea. The functional role of ArcD is demonstrated by transport assays with radioactive labelled arginine, by its necessity to enable arginine fermentation under anaerobic growth conditions and by the consumption of arginine from the medium during growth. All three experimentally observables are severely disturbed when the deletion strain DeltaArcD is used. The isolated protein is verified by mass spectrometry and reconstituted in vesicles. The proteoliposomes are attached to a membrane and capacitive currents are recorded which appear upon initiation of the transport process by change from arginine-free to arginine-containing buffer. This clearly demonstrates that the purified 34kD protein is the functional unit.

Genome information management and integrated data analysis with HaloLex

HaloLex is a software system for the central management, integration, curation, and web-based visualization of genomic and other -omics data for any given microorganism. The system has been employed for the manual curation of three haloarchaeal genomes, namely Halobacterium salinarum (strain R1), Natronomonas pharaonis, and Haloquadratum walsbyi. HaloLex, in particular, enables the integrated analysis of genome-wide proteomic results with the underlying genomic data. This has proven indispensable to generate reliable gene predictions for GC-rich genomes, which, due to their characteristically low abundance of stop codons, are known to be hard targets for standard gene finders, especially concerning start codon assignment. The proteomic identification of more than 600 N-terminal peptides has greatly increased the reliability of the start codon assignment for Halobacterium salinarum. Application of homology-based methods to the published genome of Haloarcula marismortui allowed to detect 47 previously unidentified genes (a problem that is particularly serious for short protein sequences) and to correct more than 300 start codon misassignments.

Metabolism of halophilic archaea

In spite of their common hypersaline environment, halophilic archaea are surprisingly different in their nutritional demands and metabolic pathways. The metabolic diversity of halophilic archaea was investigated at the genomic level through systematic metabolic reconstruction and comparative analysis of four completely sequenced species: Halobacterium salinarum, Haloarcula marismortui, Haloquadratum walsbyi, and the haloalkaliphile Natronomonas pharaonis. The comparative study reveals different sets of enzyme genes amongst halophilic archaea, e.g. in glycerol degradation, pentose metabolism, and folate synthesis. The carefully assessed metabolic data represent a reliable resource for future system biology approaches as it also links to current experimental data on (halo)archaea from the literature.