Adaptive Evolution of Thermotoga maritima Reveals Plasticity of the ABC Transporter Network.

Thermotoga maritima is a hyperthermophilic anaerobe that utilizes a vast network of ABC transporters to efficiently metabolize a variety of carbon sources to produce hydrogen. For unknown reasons, this organism does not metabolize glucose as readily as it does glucose di- and polysaccharides. The leading hypothesis implicates the thermolability of glucose at the physiological temperatures at which T. maritima lives. After a 25-day laboratory evolution, phenotypes were observed with growth rates up to 1.4 times higher than and glucose utilization rates exceeding 50% those of the wild type. Genome resequencing revealed mutations in evolved cultures related to glucose-responsive ABC transporters. The native glucose ABC transporter, GluEFK, has more abundant transcripts either as a result of gene duplication-amplification or through mutations to the operator sequence regulating this operon. Conversely, BglEFGKL, a transporter of beta-glucosides, is substantially downregulated due to a nonsense mutation to the solute binding protein or due to a deletion of the upstream promoter. Analysis of the ABC2 uptake porter families for carbohydrate and peptide transport revealed that the solute binding protein, often among the transcripts detected at the highest levels, is predominantly downregulated in the evolved cultures, while the membrane-spanning domain and nucleotide binding components are less varied. Similar trends were observed in evolved strains grown on glycerol, a substrate that is not dependent on ABC transporters. Therefore, improved growth on glucose is achieved through mutations favoring GluEFK expression over BglEFGKL, and in lieu of carbon catabolite repression, the ABC transporter network is modulated to achieve improved growth fitness.

Transcriptional regulation of the carbohydrate utilization network in Thermotoga maritima.

Hyperthermophilic bacteria from the Thermotogales lineage can produce hydrogen by fermenting a wide range of carbohydrates. Previous experimental studies identified a large fraction of genes committed to carbohydrate degradation and utilization in the model bacterium Thermotoga maritima. Knowledge of these genes enabled comprehensive reconstruction of biochemical pathways comprising the carbohydrate utilization network. However, transcriptional factors (TFs) and regulatory mechanisms driving this network remained largely unknown. Here, we used an integrated approach based on comparative analysis of genomic and transcriptomic data for the reconstruction of the carbohydrate utilization regulatory networks in 11 Thermotogales genomes. We identified DNA-binding motifs and regulons for 19 orthologous TFs in the Thermotogales. The inferred regulatory network in T. maritima contains 181 genes encoding TFs, sugar catabolic enzymes and ABC-family transporters. In contrast to many previously described bacteria, a transcriptional regulation strategy of Thermotoga does not employ global regulatory factors. The reconstructed regulatory network in T. maritima was validated by gene expression profiling on a panel of mono- and disaccharides and by in vitro DNA-binding assays. The observed upregulation of genes involved in catabolism of pectin, trehalose, cellobiose, arabinose, rhamnose, xylose, glucose, galactose, and ribose showed a strong correlation with the UxaR, TreR, BglR, CelR, AraR, RhaR, XylR, GluR, GalR, and RbsR regulons. Ultimately, this study elucidated the transcriptional regulatory network and mechanisms controlling expression of carbohydrate utilization genes in T. maritima. In addition to improving the functional annotations of associated transporters and catabolic enzymes, this research provides novel insights into the evolution of regulatory networks in Thermotogales.

The genome organization of Thermotoga maritima reflects its lifestyle.

The generation of genome-scale data is becoming more routine, yet the subsequent analysis of omics data remains a significant challenge. Here, an approach that integrates multiple omics datasets with bioinformatics tools was developed that produces a detailed annotation of several microbial genomic features. This methodology was used to characterize the genome of Thermotoga maritima--a phylogenetically deep-branching, hyperthermophilic bacterium. Experimental data were generated for whole-genome resequencing, transcription start site (TSS) determination, transcriptome profiling, and proteome profiling. These datasets, analyzed in combination with bioinformatics tools, served as a basis for the improvement of gene annotation, the elucidation of transcription units (TUs), the identification of putative non-coding RNAs (ncRNAs), and the determination of promoters and ribosome binding sites. This revealed many distinctive properties of the T. maritima genome organization relative to other bacteria. This genome has a high number of genes per TU (3.3), a paucity of putative ncRNAs (12), and few TUs with multiple TSSs (3.7%). Quantitative analysis of promoters and ribosome binding sites showed increased sequence conservation relative to other bacteria. The 5'UTRs follow an atypical bimodal length distribution comprised of "Short" 5'UTRs (11-17 nt) and "Common" 5'UTRs (26-32 nt). Transcriptional regulation is limited by a lack of intergenic space for the majority of TUs. Lastly, a high fraction of annotated genes are expressed independent of growth state and a linear correlation of mRNA/protein is observed (Pearson r = 0.63, p<2.2 × 10(-16) t-test). These distinctive properties are hypothesized to be a reflection of this organism's hyperthermophilic lifestyle and could yield novel insights into the evolutionary trajectory of microbial life on earth.

In silico method for modelling metabolism and gene product expression at genome scale.

Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

Predicting outcomes of steady-state ¹³C isotope tracing experiments using Monte Carlo sampling.

BACKGROUND: Carbon-13 (13C) analysis is a commonly used method for estimating reaction rates in biochemical networks. The choice of carbon labeling pattern is an important consideration when designing these experiments. We present a novel Monte Carlo algorithm for finding the optimal substrate input label for a particular experimental objective (flux or flux ratio). Unlike previous work, this method does not require assumption of the flux distribution beforehand. RESULTS: Using a large E. coli isotopomer model, different commercially available substrate labeling patterns were tested computationally for their ability to determine reaction fluxes. The choice of optimal labeled substrate was found to be dependent upon the desired experimental objective. Many commercially available labels are predicted to be outperformed by complex labeling patterns. Based on Monte Carlo Sampling, the dimensionality of experimental data was found to be considerably less than anticipated, suggesting that effectiveness of 13C experiments for determining reaction fluxes across a large-scale metabolic network is less than previously believed. CONCLUSIONS: While 13C analysis is a useful tool in systems biology, high redundancy in measurements limits the information that can be obtained from each experiment. It is however possible to compute potential limitations before an experiment is run and predict whether, and to what degree, the rate of each reaction can be resolved.

Adaptive laboratory evolution--harnessing the power of biology for metabolic engineering.

Adaptive laboratory evolution (ALE) strategies allow for the metabolic engineering of microorganisms by combining genetic variation with the selection of beneficial mutations in an unbiased fashion. These ALE strategies have been proven highly effective in the optimization of production strains. In contrast to rational engineering strategies and directed modification of specific enzymes, ALE has the advantage of letting nonintuitive beneficial mutations occur in many different genes and regulatory regions in parallel. So far, the majority of applications of ALE in metabolic engineering have used well-characterized platform organisms such as Saccharomyces cerevisiae and Escherichia coli; however, applications for other microorganisms are on the rise. This review will focus on current applications of ALE as a tool for metabolic engineering and discuss advancements and achievements that have been made in this field.

Deletion of genes encoding cytochrome oxidases and quinol monooxygenase blocks the aerobic-anaerobic shift in Escherichia coli K-12 MG1655.

The constitutive activation of the anoxic redox control transcriptional regulator (ArcA) in Escherichia coli during aerobic growth, with the consequent production of a strain that exhibits anaerobic physiology even in the presence of air, is reported in this work. Removal of three terminal cytochrome oxidase genes (cydAB, cyoABCD, and cbdAB) and a quinol monooxygenase gene (ygiN) from the E. coli K-12 MG1655 genome resulted in the activation of ArcA aerobically. These mutations resulted in reduction of the oxygen uptake rate by nearly 98% and production of d-lactate as a sole by-product under oxic and anoxic conditions. The knockout strain exhibited nearly identical physiological behaviors under both conditions, suggesting that the mutations resulted in significant metabolic and regulatory perturbations. In order to fully understand the physiology of this mutant and to identify underlying metabolic and regulatory reasons that prevent the transition from an aerobic to an anaerobic phenotype, we utilized whole-genome transcriptome analysis, (13)C tracing experiments, and physiological characterization. Our analysis showed that the deletions resulted in the activation of anaerobic respiration under oxic conditions and a consequential shift in the content of the quinone pool from ubiquinones to menaquinones. An increase in menaquinone concentration resulted in the activation of ArcA. The activation of the ArcB/ArcA regulatory system led to a major shift in the metabolic flux distribution through the central metabolism of the mutant strain. Flux analysis indicated that the mutant strain had undetectable fluxes around the tricarboxylic acid (TCA) cycle and elevated flux through glycolysis and anaplerotic input to oxaloacetate. Flux and transcriptomics data were highly correlated and showed similar patterns.

Aerobic fermentation of D-glucose by an evolved cytochrome oxidase-deficient Escherichia coli strain.

Fermentation of glucose to D-lactic acid under aerobic growth conditions by an evolved Escherichia coli mutant deficient in three terminal oxidases is reported in this work. Cytochrome oxidases (cydAB, cyoABCD, and cbdAB) were removed from the E. coli K12 MG1655 genome, resulting in the ECOM3 (E. coli cytochrome oxidase mutant) strain. Removal of cytochrome oxidases reduced the oxygen uptake rate of the knockout strain by nearly 85%. Moreover, the knockout strain was initially incapable of growing on M9 minimal medium. After the ECOM3 strain was subjected to adaptive evolution on glucose M9 medium for 60 days, a growth rate equivalent to that of anaerobic wild-type E. coli was achieved. Our findings demonstrate that three independently adaptively evolved ECOM3 populations acquired different phenotypes: one produced lactate as a sole fermentation product, while the other two strains exhibited a mixed-acid fermentation under oxic growth conditions with lactate remaining as the major product. The homofermenting strain showed a D-lactate yield of 0.8 g/g from glucose. Gene expression and in silico model-based analyses were employed to identify perturbed pathways and explain phenotypic behavior. Significant upregulation of ygiN and sodAB explains the remaining oxygen uptake that was observed in evolved ECOM3 strains. E. coli strains produced in this study showed the ability to produce lactate as a fermentation product from glucose and to undergo mixed-acid fermentation during aerobic growth.

Systematic condition-dependent annotation of metabolic genes.

The task of deriving a functional annotation for genes is complex as their involvement in various processes depends on multiple factors such as environmental conditions and genetic backup mechanisms. This study employs a large-scale model of the metabolism of Saccharomyces cerevisiae to investigate the function of yeast genes and derive a condition-dependent annotation (CDA) for their involvement in major metabolic processes under various genetic and environmental conditions. The resulting CDA is validated on a large scale and is shown to be superior to the corresponding Gene Ontology (GO) annotation, by showing that genes annotated with the same CDA term tend to be more coherently conserved in evolution and display greater expression coherency than those annotated with the same GO term. The CDA gives rise to new kinds of functional condition-dependent metabolic pathways, some of which are described and further examined via substrate auxotrophy measurements of knocked-out strains. The CDA presented is likely to serve as a new reference source for metabolic gene annotation.

Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae.

We describe the use of model-driven analysis of multiple data types relevant to transcriptional regulation of metabolism to discover novel regulatory mechanisms in Saccharomyces cerevisiae. We have reconstructed the nutrient-controlled transcriptional regulatory network controlling metabolism in S. cerevisiae consisting of 55 transcription factors regulating 750 metabolic genes, based on information in the primary literature. This reconstructed regulatory network coupled with an existing genome-scale metabolic network model allows in silico prediction of growth phenotypes of regulatory gene deletions as well as gene expression profiles. We compared model predictions of gene expression changes in response to genetic and environmental perturbations to experimental data to identify potential novel targets for transcription factors. We then identified regulatory cascades connecting transcription factors to the potential targets through a systematic model expansion strategy using published genome-wide chromatin immunoprecipitation and binding-site-motif data sets. Finally, we show the ability of an integrated metabolic and regulatory network model to predict growth phenotypes of transcription factor knockout strains. These studies illustrate the potential of model-driven data integration to systematically discover novel components and interactions in regulatory and metabolic networks in eukaryotic cells.

The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes.

The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.