Computational identification of putative common genomic drug and vaccine targets in Mycoplasma genitalium

Mycoplasma genitalium is an obligate intracellular bacterium that is responsible for several sexually transmitted infections, including non-gonococcal urethritis in men and several inflammatory reproductive tract syndromes in women. Here, we applied subtractive genomics and reverse vaccinology approaches for in silico prediction of potential vaccine and drug targets against five strains of M. genitalium. We identified 403 genes shared by all five strains, from which 104 non-host homologous proteins were selected, comprising of 44 exposed/secreted/membrane proteins and 60 cytoplasmic proteins. Based on the essentiality, functionality, and structure-based binding affinity, we finally predicted 19 (14 novel) putative vaccine and 7 (2 novel) candidate drug targets. The docking analysis showed six molecules from the ZINC database as promising drug candidates against the identified targets. Altogether, both vaccine candidates and drug targets identified here may contribute to the future development of therapeutic strategies to control the spread of M. genitalium worldwide.     

Genome‐scale metabolic modeling reveals key features of a minimal gene set

Mesoplasma florum, a fast‐growing near‐minimal organism, is a compelling model to explore rational genome designs. Using sequence and structural homology, the set of metabolic functions its genome encodes was identified, allowing the reconstruction of a metabolic network representing ˜ 30% of its protein‐coding genes. Growth medium simplification enabled substrate uptake and product secretion rate quantification which, along with experimental biomass composition, were integrated as species‐specific constraints to produce the functional iJL208 genome‐scale model (GEM) of metabolism. Genome‐wide expression and essentiality datasets as well as growth data on various carbohydrates were used to validate and refine iJL208. Discrepancies between model predictions and observations were mechanistically explained using protein structures and network analysis. iJL208 was also used to propose an in silico reduced genome. Comparing this prediction to the minimal cell JCVI‐syn3.0 and its parent JCVI‐syn1.0 revealed key features of a minimal gene set. iJL208 is a stepping‐stone toward model‐driven whole‐genome engineering.     

Life on Arginine for Mycoplasma hominis: Clues from Its Minimal Genome and Comparison with Other Human Urogenital Mycoplasmas

Mycoplasma hominis is an opportunistic human mycoplasma. Two other pathogenic human species, M. genitalium and Ureaplasma parvum, reside within the same natural niche as M. hominis: the urogenital tract. These three species have overlapping, but distinct, pathogenic roles. They have minimal genomes and, thus, reduced metabolic capabilities characterized by distinct energy-generating pathways. Analysis of the M. hominis PG21 genome sequence revealed that it is the second smallest genome among self-replicating free living organisms (665,445 bp, 537 coding sequences (CDSs)). Five clusters of genes were predicted to have undergone horizontal gene transfer (HGT) between M. hominis and the phylogenetically distant U. parvum species. We reconstructed M. hominis metabolic pathways from the predicted genes, with particular emphasis on energy-generating pathways. The Embden–Meyerhoff–Parnas pathway was incomplete, with a single enzyme absent. We identified the three proteins constituting the arginine dihydrolase pathway. This pathway was found essential to promote growth in vivo. The predicted presence of dimethylarginine dimethylaminohydrolase suggested that arginine catabolism is more complex than initially described. This enzyme may have been acquired by HGT from non-mollicute bacteria. Comparison of the three minimal mollicute genomes showed that 247 CDSs were common to all three genomes, whereas 220 CDSs were specific to M. hominis, 172 CDSs were specific to M. genitalium, and 280 CDSs were specific to U. parvum. Within these species-specific genes, two major sets of genes could be identified: one including genes involved in various energy-generating pathways, depending on the energy source used (glucose, urea, or arginine) and another involved in cytadherence and virulence. Therefore, a minimal mycoplasma cell, not including cytadherence and virulence-related genes, could be envisaged containing a core genome (247 genes), plus a set of genes required for providing energy. For M. hominis, this set would include 247+9 genes, resulting in a theoretical minimal genome of 256 genes.     

Development of a rotor-gene real-time PCR assay for the detection and quantification of Mycoplasma genitalium

We developed and validated a real-time quantitative polymerase chain reaction (qPCR) assay to determine Mycoplasma genitalium bacterial load in endocervical swabs, based on amplification of the pdhD gene which encodes dihydrolipoamide dehydrogenase, using the Rotor-Gene platform. We first determined the qPCR assay sensitivity, limit of detection, reproducibility and specificity, and then determined the ability of the qPCR assay to quantify M. genitalium in stored endocervical specimens collected from Zimbabwean women participating in clinical research undertaken between 1999 and 2007. The qPCR assay had a detection limit of 300 genome copies/mL and demonstrated low intra- and inter-assay variability. The assay was specific for M. genitalium DNA and did not amplify the DNA from other mycoplasma and ureaplasma species. We quantified M. genitalium in 119 of 1600 endocervical swabs that tested positive for M. genitalium using the commercial Sacace M. genitalium real-time PCR, as well as 156 randomly selected swabs that were negative for M. genitalium by the same assay. The M. genitalium loads ranged between < 300 and 3,240,000 copies/mL. Overall, the qPCR assay demonstrated good range of detection, reproducibility and specificity and can be used for both qualitative and quantitative analyses of M. genitalium in endocervical specimens and potentially other genital specimens.     

Fumaric Acid Production in Saccharomyces cerevisiae by In Silico Aided Metabolic Engineering

Fumaric acid (FA) is a promising biomass-derived building-block chemical. Bio-based FA production from renewable feedstock is a promising and sustainable alternative to petroleum-based chemical synthesis. Here we report on FA production by direct fermentation using metabolically engineered Saccharomyces cerevisiae with the aid of in silico analysis of a genome-scale metabolic model. First, FUM1 was selected as the target gene on the basis of extensive literature mining. Flux balance analysis (FBA) revealed that FUM1 deletion can lead to FA production and slightly lower growth of S. cerevisiae. The engineered S. cerevisiae strain obtained by deleting FUM1 can produce FA up to a concentration of 610±31 mg L^–1 without any apparent change in growth in fed-batch culture. FT-IR and ¹H and ¹³C NMR spectra confirmed that FA was synthesized by the engineered S. cerevisiae strain. FBA identified pyruvate carboxylase as one of the factors limiting higher FA production. When the RoPYC gene was introduced, S. cerevisiae produced 1134±48 mg L^–1 FA. Furthermore, the final engineered S. cerevisiae strain was able to produce 1675±52 mg L^–1 FA in batch culture when the SFC1 gene encoding a succinate–fumarate transporter was introduced. These results demonstrate that the model shows great predictive capability for metabolic engineering. Moreover, FA production in S. cerevisiae can be efficiently developed with the aid of in silico metabolic engineering.     

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.     

Molecular Cloning,Expression, and Characterization of a Ca2+-Dependent,Membrane-Associated Nuclease of Mycoplasma genitalium

In this study, we identified and characterized the enzymatic properties of MG_186, a calcium-dependent Mycoplasma genitalium nuclease. MG_186 displays the hallmarks of nucleases, as indicated by its amino acid sequence similarity to other nucleases. We cloned, UGA corrected, expressed, purified, and demonstrated that recombinant MG_186 (rMG_186) exhibits nuclease activity similar to that of typical sugar-nonspecific endonucleases and exonucleases. Biochemical characterization indicated that Ca²⁺ alone enhances its activity, which was inhibited by divalent cations, such as Zn²⁺ and Mn²⁺. Chelating agents EGTA and EDTA also inhibited nuclease activity. Mycoplasma membrane fractionation and Triton X-114 phase separation showed that MG_186 was a membrane-associated lipoprotein, and electron microscopy revealed its surface membrane location. Incubation of purified human endometrial cell nuclei with rMG_186 resulted in DNA degradation and morphological changes typical of apoptosis. Further, immunofluorescence analysis of rMG_186-treated nuclei indicated that morphological changes were linked to the disintegration of lamin and the internalization of rMG_186. Since M. genitalium has the capacity to invade eukaryotic cells and localize to the perinuclear and nuclear region of parasitized target cells, MG_186 has the potential to provide M. genitalium, which possesses the smallest genome of any self-replicating cell, with the ability to degrade host nucleic acids both as a source of nucleotide precursors for growth and for pathogenic purposes.Mycoplasma genitalium was first identified as a urogenital tract pathogen in men and subsequently implicated in a range of women pathologies, including pelvic inflammatory diseases, cervicitis, endometritis, salpingitis, and tubal factor infertility (5, 37, 40). In addition to its urogenital niche, M. genitalium has been detected in synovial and respiratory tract specimens (3, 39). M. genitalium DNA sequencing revealed a reduced genome size of 580 kb and a low GC content, along with 482 protein-encoding genes, of which 76 were categorized as hypothetical proteins (18). The streamlined genome of M. genitalium results in gene deficits that dramatically limit its biosynthetic capabilities, leading to a complete dependence on the host for metabolic precursors, such as nucleotides, amino acids, fatty acids, and sterols.Since M. genitalium, like most mollicutes, is unable to synthesize de novo purine and pyrimidine bases (27), it must scavenge nucleotides from the host in order to replicate and persist. Only Mycoplasma penetrans has an orotate-related pathway for converting carbamoyl-phosphate to uridine-5′-monophosphate (34). The importance of nucleases in the life cycle of mycoplasmas is reinforced by their detection in at least 20 Mycoplasma species (26). Purification of membrane-associated Ca²⁺/Mg²⁺-dependent M. penetrans and Mycoplasma hyorhinis nucleases and their relation to mycoplasma survival and pathogenesis have been reported (7, 8, 29, 30). Also, a membrane nuclease gene, mnuA, was identified and cloned from Mycoplasma pulmonis (20, 25). mnuA orthologous sequences were found in M. penetrans, Mycoplasma pneumoniae, Mycoplasma hyopneumoniae, Mycoplasma gallisepticum, and Ureaplasma urealyticum but not in M. genitalium. However, recent nuclease studies with M. hyopneumoniae (nuclease gene designated mhp379) revealed the existence of orthologous sequences in M. genitalium as well as in M. pneumoniae, M. pulmonis, M. gallisepticum, and Mycoplasma synoviae (35).M. genitalium was initially described as an extracellular pathogen. Subsequently, we reported that M. genitalium can be observed in the cytoplasmic and perinuclear regions of infected mammalian cells and can persist long-term within these compartments (4, 13, 24). The latter supports the contention that M. genitalium is capable of intracellular replication and survival. Furthermore, our recent evidence suggests that M. genitalium and its protein products are capable of intranuclear localization within infected endometrial cells (41). Therefore, understanding how M. genitalium overcomes its biosynthetic deficiencies and successfully parasitizes host tissues may provide insights into its biological uniqueness as the smallest pathogen capable of “independent” growth. In this report, we characterized a putative lipoprotein, MG_186, that retains the thermostable nuclease motif found in other bacterial nucleases. The gene encoding MG_186 was cloned and expressed in Escherichia coli, and the biochemical properties of purified recombinant MG_186 (rMG_186) nuclease protein were examined along with its impact on intact nuclei isolated from endometrial cells.     

Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.     

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens—specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.     

Malate/lactate dehydrogenase in mollicutes: evidence for a multienzyme protein

The malate (MDH) and lactate (LDH) dehydrogenases belong to the homologous class of 2-ketoacid dehydrogenases. The specificity for their respective substrates depends on residues differing at two or three regions within each molecule. Theoretical peptide-mass fingerprinting and PROSITE analysis of nine MDH and six LDH molecules were used to describe conserved sites related to function. A unique LDH is described which probably also confers MDH activity within the 580 kbp genome of Mycoplasma genitalium (class: Mollicutes). A single hydrophilic arginine residue was found in the active site of the M. genitalium LDH enzyme, differing from an hydrophobic residue normally present in these molecules. The effect of this residue may be to alter active site substrate specificity, allowing the enzyme to perform two closely related tasks. Evidence for a single gene affording dual enzymatic function is discussed in terms of genome size reduction in the simplest of free-living organisms. Since Mollicutes are thought to lack enzymes of the tricarboxylic acid cycle that would otherwise bind and interact with MDH in bacterial species possessing this pathway, active site modification of M. genitalium LDH is the sole requirement for MDH activity of this molecule. The closely related helical Mollicute, Spiroplasma melliferum, was shown to possess two distinct gene products for MDH/LDH activity.     

Comparative Analysis of Yeast Metabolic Network Models Highlights Progress,Opportunities for Metabolic Reconstruction

We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.     

CONSTRICTOR: Constraint Modification Provides Insight into Design of Biochemical Networks

Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. Here, we present Constrictor, a computational framework that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. Constrictor identifies engineering interventions by classifying the reactions in the metabolic model depending on the extent to which their flux must be decreased to achieve the overproduction target. The optimal inhibition of various reaction pathways is determined by restricting the flux through targeted reactions below the steady state levels of a baseline strain. Constrictor generates unique in silico strains, each representing an “expression state”, or a combination of gene expression levels required to achieve the overproduction target. The Constrictor framework is demonstrated by studying overproduction of ethylene in Escherichia coli network models iAF1260 and iJO1366 through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as combinations of reactions reveals in silico mutants that are predicted to have as high as 25% greater theoretical ethylene yields than the baseline strain during simulated exponential growth. Altering the degree of restriction reveals a large distribution of ethylene yields, while analysis of the expression states that return lower yields provides insight into system bottlenecks. Finally, we demonstrate the ability of Constrictor to scan networks and provide targets for a range of possible products. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels and provide non-intuitive strategies for metabolic engineering.     

Treatment of Mycoplasma genitalium. Observations from a Swedish STD Clinic

Objectives

To evaluate therapy for Mycoplasma genitalium infection with doxycycline or azithromycin 1 g compared to five days of azithromycin (total dose 1.5 g).

Methods

A retrospective case study was performed among patients attending the STD-clinic in Falun, Sweden 1998–2005. All patients with a positive PCR test for M. genitalium were routinely offered a test of cure (toc). Response to doxycycline for 9 days, azithromycin 1 g single dose and extended azithromycin (500 mg on day 1 followed by 250 mg o.d. for 4 days) was determined. In patients with treatment failure after azithromycin, macrolide resistance was monitored before and after treatment. Furthermore, the rate of macrolide resistance was monitored for positive specimens available from 2006–2011.

Results

The eradication rate after doxycycline was 43% (48% for women and 38% for men), for azithromycin 1 g 91% (96% for women and 88% for men) and for extended azithromycin 99% (100% for women and 93% for men). Macrolide resistance developed in 7/7 examined (100%) of those testing positive after azithromycin 1 g, but in none of those treated with extended azithromycin. Macrolide resistance before treatment increased from 0% in 2006 and 2007 to 18% in 2011.

Conclusions

These findings confirm the results from other studies showing that doxycycline is inefficient in eradicating M. genitalium. Although azithromycin 1 g was not significantly less efficient than extended dosage, it was associated with selection of macrolide resistant M. genitalium strains and should not be used as first line therapy for M. genitalium. Monitoring of M. genitalium macrolide resistance should be encouraged.     

Exploring the druggable proteome of Candida species through comprehensive computational analysis

Candida albicans and non-albicans Candida spp. are major cause of systemic mycoses. Antifungal drugs such as azoles and polyenes are not efficient to successfully eradicate Candida infection owing to their fungistatic nature or low bioavailability. Here, we have adopted a comprehensive computational workflow for identification, prioritization and validation of targets from proteomes of Candida albicans and Candida tropicalis. The protocol involves identification of essential drug-target candidates using subtractive genomics, protein-protein interaction network properties and systems biology based methods. The essentiality of the novel metabolic and non-metabolic targets was established by performing in silico gene knockouts, under aerobic as well as anaerobic conditions, and in vitro drug inhibition assays respectively. Deletion of twelve genes that are involved in amino acid, secondary metabolite, and carbon metabolism showed zero growth in metabolic model under simulated conditions. The algorithm, used in this study, can be downloaded from http://pbit.bicnirrh.res.in/offline.php and executed locally.     

Use of the Riboflavin Synthase Gene (ribC) as a Model for Development of an Essential Gene Disruption and Complementation System for Haemophilus influenzae

We have developed a system for rapid and reliable assessment of gene essentiality in Haemophilus influenzae Rd strain KW20. We constructed two “suicide” complementation vectors (pASK5 and pASK6) containing 5′ and 3′ regions of the nonessential ompP1 gene flanking a multiple cloning site and a selectable marker (a chloramphenicol resistance gene or a tetracycline resistance cassette). Transformation of H. influenzae with the complementation constructs directs chromosomal integration of a gene of interest into the ompP1 locus, where the strong, constitutive ompP1 promoter drives its expression. This single-copy, chromosome-based complementation system is useful for confirming the essentiality of disrupted genes of interest. It allows genetic analysis in a background free of interference from any upstream or downstream genetic elements and enables conclusive assignment of essentiality. We validated this system by using the riboflavin synthase gene (ribC), a component of the riboflavin biosynthetic pathway. Our results confirmed the essentiality of ribC for survival of H. influenzae Rd strain KW20 and demonstrated that a complementing copy of ribC placed under control of the ompP1 promoter reverses the lethal phenotype of a strain with ribC deleted.     

Exploring root symbiotic programs in the model legume Medicago truncatula using EST analysis

We report on a large-scale expressed sequence tag (EST) sequencing and analysis program aimed at characterizing the sets of genes expressed in roots of the model legume Medicago truncatula during interactions with either of two microsymbionts, the nitrogen-fixing bacterium Sinorhizobium meliloti or the arbuscular mycorrhizal fungus Glomus intraradices. We have designed specific tools for in silico analysis of EST data, in relation to chimeric cDNA detection, EST clustering, encoded protein prediction, and detection of differential expression. Our 21 473 5′- and 3′-ESTs could be grouped into 6359 EST clusters, corresponding to distinct virtual genes, along with 52 498 other M.truncatula ESTs available in the dbEST (NCBI) database that were recruited in the process. These clusters were manually annotated, using a specifically developed annotation interface. Analysis of EST cluster distribution in various M.truncatula cDNA libraries, supported by a refined R test to evaluate statistical significance and by ‘electronic northern’ representation, enabled us to identify a large number of novel genes predicted to be up- or down-regulated during either symbiotic root interaction. These in silico analyses provide a first global view of the genetic programs for root symbioses in M.truncatula. A searchable database has been built and can be accessed through a public interface.     

New methods for tightly regulated gene expression and highly efficient chromosomal integration of cloned genes for Methanosarcina species

A highly efficient method for chromosomal integration of cloned DNA into Methanosarcina spp. was developed utilizing the site-specific recombination system from the Streptomyces phage φC31. Host strains expressing the φC31 integrase gene and carrying an appropriate recombination site can be transformed with non-replicating plasmids carrying the complementary recombination site at efficiencies similar to those obtained with self-replicating vectors. We have also constructed a series of hybrid promoters that combine the highly expressed M. barkeri PmcrB promoter with binding sites for the tetracycline-responsive, bacterial TetR protein. These promoters are tightly regulated by the presence or absence of tetracycline in strains that express the tetR gene. The hybrid promoters can be used in genetic experiments to test gene essentiality by placing a gene of interest under their control. Thus, growth of strains with tetR-regulated essential genes becomes tetracycline-dependent. A series of plasmid vectors that utilize the site-specific recombination system for construction of reporter gene fusions and for tetracycline regulated expression of cloned genes are reported. These vectors were used to test the efficiency of translation at a variety of start codons. Fusions using an ATG start site were the most active, whereas those using GTG and TTG were approximately one half or one fourth as active, respectively. The CTG fusion was 95% less active than the ATG fusion.     

An Anaerobic-Type α-Ketoglutarate Ferredoxin Oxidoreductase Completes the Oxidative Tricarboxylic Acid Cycle of Mycobacterium tuberculosis

Aerobic organisms have a tricarboxylic acid (TCA) cycle that is functionally distinct from those found in anaerobic organisms. Previous reports indicate that the aerobic pathogen Mycobacterium tuberculosis lacks detectable α-ketoglutarate (KG) dehydrogenase activity and drives a variant TCA cycle in which succinyl-CoA is replaced by succinic semialdehyde. Here, we show that M. tuberculosis expresses a CoA-dependent KG dehydrogenase activity, albeit one that is typically found in anaerobic bacteria. Unlike most enzymes of this family, the M. tuberculosis KG: ferredoxin oxidoreductase (KOR) is extremely stable under aerobic conditions. This activity is absent in a mutant strain deleted for genes encoding a previously uncharacterized oxidoreductase, and this strain is impaired for aerobic growth in the absence of sufficient amounts of CO₂. Interestingly, inhibition of the glyoxylate shunt or exclusion of exogenous fatty acids alleviates this growth defect, indicating the presence of an alternate pathway that operates in the absence of β-oxidation. Simultaneous disruption of KOR and the first enzyme of the succinic semialdehyde pathway (KG decarboxylase; KGD) results in strict dependence upon the glyoxylate shunt for growth, demonstrating that KG decarboxylase is also functional in M. tuberculosis intermediary metabolism. These observations demonstrate that unlike most organisms M. tuberculosis utilizes two distinct TCA pathways from KG, one that functions concurrently with β-oxidation (KOR-dependent), and one that functions in the absence of β-oxidation (KGD-dependent). As these pathways are regulated by metabolic cues, we predict that their differential utilization provides an advantage for growth in different environments within the host.     

BioMog: A Computational Framework for the De Novo Generation or Modification of Essential Biomass Components

The success of genome-scale metabolic modeling is contingent on a model''s ability to accurately predict growth and metabolic behaviors. To date, little focus has been directed towards developing systematic methods of proposing, modifying and interrogating an organism''s biomass requirements that are used in constraint-based models. To address this gap, the biomass modification and generation (BioMog) framework was created and used to generate lists of biomass components de novo, as well as to modify predefined biomass component lists, for models of Escherichia coli (iJO1366) and of Shewanella oneidensis (iSO783) from high-throughput growth phenotype and fitness datasets. BioMog''s de novo biomass component lists included, either implicitly or explicitly, up to seventy percent of the components included in the predefined biomass equations, and the resulting de novo biomass equations outperformed the predefined biomass equations at qualitatively predicting mutant growth phenotypes by up to five percent. Additionally, the BioMog procedure can quantify how many experiments support or refute a particular metabolite''s essentiality to a cell, and it facilitates the determination of inconsistent experiments and inaccurate reaction and/or gene to reaction associations. To further interrogate metabolite essentiality, the BioMog framework includes an experiment generation algorithm that allows for the design of experiments to test whether a metabolite is essential. Using BioMog, we correct experimental results relating to the essentiality of thyA gene in E. coli, as well as perform knockout experiments supporting the essentiality of protoheme. With these capabilities, BioMog can be a valuable resource for analyzing growth phenotyping data and component of a model developer''s toolbox.