Stephen S. Fong

Metabolic gene–deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes

Genome-scale metabolic models have a promising ability to describe cellular phenotypes accurately. Here we show that strains of Escherichia coli carrying a deletion of a single metabolic gene increase their growth rates (by 87% on average) during adaptive evolution and that the endpoint growth rates can be predicted computationally in 39 of 50 (78%) strains tested. These results show that computational models can be used to predict the eventual effects of genetic modifications.

Systems approach to refining genome annotation

Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.

Latent pathway activation and increased pathway capacity enable Escherichia coli adaptation to loss of key metabolic enzymes.

The ability of biological systems to adapt to genetic and environmental perturbations is a fundamental but poorly understood process at the molecular level. By quantifying metabolic fluxes and global mRNA abundance, we investigated the genetic and metabolic mechanisms that underlie adaptive evolution of four metabolic gene deletion mutants of Escherichia coli (Δpgi, Δppc, Δpta, and Δtpi) in parallel evolution experiments of each mutant. The initial response to the gene deletions was flux rerouting through local bypass reactions or normally latent pathways. The principal effect of evolution was improved capacity of already active pathways, whereas new flux distributions were not observed. Combinatorial changes in capacity and pathway activation, however, led to different intracellular flux states that enabled evolution in three of the four parallel cases tested. The molecular bases of the evolved phenotypes were then elucidated by global mRNA transcript analyses. Activation of latent pathways and flux changes in the tricarboxylic acid cycle were found to correlate well with molecular changes at the transcriptional level. Flux alterations in other central metabolic pathways, in contrast, were apparently not connected to changes in the transcriptional network. These results give new insight into the dynamics of the evolutionary process by demonstrating the flexibility of the metabolic network of E. coli to compensate for genetic perturbations and the utility of combining multiple high throughput data sets to differentiate between causal and noncausal mechanistic changes.

Description and Interpretation of Adaptive Evolution of Escherichia coli K-12 MG1655 by Using a Genome-Scale In Silico Metabolic Model

Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of alpha-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37 degrees C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.

Identification of Genome-Scale Metabolic Network Models Using Experimentally Measured Flux Profiles

Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.

Metabolic Burden: Cornerstones in Synthetic Biology and Metabolic Engineering Applications

Engineering cell metabolism for bioproduction not only consumes building blocks and energy molecules (e.g., ATP) but also triggers energetic inefficiency inside the cell. The metabolic burdens on microbial workhorses lead to undesirable physiological changes, placing hidden constraints on host productivity. We discuss cell physiological responses to metabolic burdens, as well as strategies to identify and resolve the carbon and energy burden problems, including metabolic balancing, enhancing respiration, dynamic regulatory systems, chromosomal engineering, decoupling cell growth with production phases, and co-utilization of nutrient resources. To design robust strains with high chances of success in industrial settings, novel genome-scale models (GSMs), (13)C-metabolic flux analysis (MFA), and machine-learning approaches are needed for weighting, standardizing, and predicting metabolic costs.

Metabolic engineering of Thermobifida fusca for direct aerobic bioconversion of untreated lignocellulosic biomass to 1-propanol

Biofuel production from renewable resources can potentially address lots of social, economic and environmental issues but an efficient production method has yet to be established. Combinations of different starting materials, organisms and target fuels have been explored with the conversion of cellulose to higher alcohols (1-propanol, 1-butanol) being one potential target. In this study we demonstrate the direct conversion of untreated plant biomass to 1-propanol in aerobic growth conditions using an engineered strain of the actinobacterium, Thermobifida fusca. Based upon computational predictions, a bifunctional butyraldehyde/alcohol dehydrogenase was added to T. fusca leading to 1-propanol production during growth on glucose, cellobiose, cellulose, switchgrass and corn stover. The highest 1-propanol titer (0.48g/L) was achieved for growth on switchgrass. These results represent the first demonstration of direct conversion of untreated lignocellulosic biomass to 1-propanol in an aerobic organism and illustrate the potential utility of T. fusca as an aerobic, cellulolytic bioprocess organism.

Genome‐scale metabolic model integrated with RNAseq data to identify metabolic states of Clostridium thermocellum

Constraint-based genome-scale metabolic models are becoming an established tool for using genomic and biochemical information to predict cellular phenotypes. While these models provide quantitative predictions for individual reactions and are readily scalable for any biological system, they have inherent limitations. Using current methods, it is difficult to computationally elucidate a specific network state that directly depicts an in vivo state, especially in the instances where the organism might be functionally in a suboptimal state. In this study, we generated RNA sequencing data to characterize the transcriptional state of the cellulolytic anaerobe, Clostridium thermocellum, and algorithmically integrated these data with a genome-scale metabolic model. The phenotypes of each calculated metabolic flux state were compared to 13 experimentally determined physiological parameters to identify the flux mapping that best matched the in vitro growth of C. thermocellum. By this approach we found predicted fluxes for 88 reactions to be changed between the best solely computational prediction (flux balance analysis) and the best experimentally derived prediction. The alteration of these 88 reaction fluxes led to a detailed network-wide flux mapping that was able to capture the suboptimal cellular state of C. thermocellum.

Metabolic Characterization of Escherichia coli Strains Adapted to Growth on Lactate

ABSTRACT In comparison with intensive studies of genetic mechanisms related to biological evolutionary systems, much less analysis has been conducted on metabolic network responses to adaptive evolution that are directly associated with evolved metabolic phenotypes. Metabolic mechanisms involved in laboratory evolution of Escherichia coli on gluconeogenic carbon sources, such as lactate, were studied based on intracellular flux states determined from 13C tracer experiments and 13C-constrained flux analysis. At the end point of laboratory evolution, strains exhibited a more than doubling of the average growth rate and a 50% increase in the average biomass yield. Despite different evolutionary trajectories among parallel evolved populations, most improvements were obtained within the first 250 generations of evolution and were generally characterized by a significant increase in pathway capacity. Partitioning between gluconeogenic and pyruvate catabolic flux at the pyruvate node remained almost unchanged, while flux distributions around the key metabolites phosphoenolpyruvate, oxaloacetate, and acetyl-coenzyme A were relatively flexible over the course of evolution on lactate to meet energetic and anabolic demands during rapid growth on this gluconeogenic carbon substrate. There were no clear qualitative correlations between most transcriptional expression and metabolic flux changes, suggesting complex regulatory mechanisms at multiple levels of genetics and molecular biology. Moreover, higher fitness gains for cell growth on both evolutionary and alternative carbon sources were found for strains that adaptively evolved on gluconeogenic carbon sources compared to those that evolved on glucose. These results provide a novel systematic view of the mechanisms underlying microbial adaptation to growth on a gluconeogenic substrate.

Exploring biodiversity for cellulosic biofuel production.

Industrial production of solvents such as EtOH and BuOH from cellulosic biomass has the potential to provide a sustainable energy source that is relatively cheap, abundant, and environmentally sound, but currently production costs are driven up by expensive enzymes, which are necessary to degrade cellulose into fermentable sugars. These costs could be significantly reduced if a microorganism could be engineered to efficiently and quickly convert cellulosic biomass directly to product in a one‐step process. There is a large amount of biodiversity in the number of existing microorganisms that naturally possess the enzymes necessary to convert cellulose to usable sugars, and many of these microorganisms can directly ferment sugars to EtOH or other solvents. Currently, the vast majority of cellulolytic organisms are poorly understood and have complex metabolic networks. In this review, we survey the current state of knowledge on different cellulases and metabolic capabilities found in various cellulolytic microorganisms. We also propose that the use of large‐scale metabolic models (and associated analyses) is potentially an ideal means for improving our understanding of basic metabolic network function and directing metabolic engineering efforts for cellulolytic microorganisms.