Model-driven discovery of underground metabolic functions in Escherichia coli

Quantitative proteome analysis of an antibiotic resistant Escherichia coli exposed to tetracycline reveals multiple affected metabolic and peptidoglycan processes

Tetracyclines are among the most commonly used antibiotics administrated to farm animals for disease treatment and prevention, contributing to the worldwide increase in antibiotic resistance in animal and human pathogens. Although tetracycline mechanisms of resistance are well known, the role of metabolism in bacterial reaction to antibiotic stress is still an important assignment and could contribute to the understanding of tetracycline related stress response. In this study, spectral counts-based label free quantitative proteomics has been applied to study the response to tetracycline of the environmental-borne Escherichia coli EcAmb278 isolate soluble proteome. A total of 1484 proteins were identified by high resolution mass spectrometry at a false discovery rate threshold of 1%, of which 108 were uniquely identified under absence of tetracycline whereas 126 were uniquely identified in presence of tetracycline. These proteins revealed interesting difference in e.g. proteins involved in peptidoglycan-based cell wall proteins and energy metabolism. Upon treatment, 12 proteins were differentially regulated showing more than 2-fold change and p<0.05 (p value corrected for multiple testing). This integrated study using high resolution mass spectrometry based label-free quantitative proteomics to study tetracycline antibiotic response in the soluble proteome of resistant E. coli provides novel insight into tetracycline related stress.

SIGNIFICANCE

The lack of new antibiotics to fight infections caused by multidrug resistant microorganisms has motivated the use of old antibiotics, and the search for new drug targets. The evolution of antibiotic resistance is complex, but it is known that agroecosystems play an important part in the selection of antibiotic resistance bacteria. Tetracyclines are still used as phytopharmaceutical agents in crops, selecting resistant bacteria and changing the ecology of farm soil. Little is known about the metabolic response of genetically resistant populations to antibiotic exposure. Indeed, to date there are no quantitative tetracycline resistance studies performed with the latest generation of high resolution mass spectrometers allowing high mass accuracy in both MS and MS/MS scans. Here, we report the proteome profiling of a soil-borne Escherichia coli upon tetracycline stress, so that this new perspective could provide a broaden understanding of the metabolic responses of E. coli to a widely used antibiotic.

Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology, cancer biology, and biotechnology. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.

Structure and function of an ancestral-type β-decarboxylating dehydrogenase from Thermococcus kodakarensis

β-Decarboxylating dehydrogenases, which are involved in central metabolism, are considered to have diverged from a common ancestor with broad substrate specificity. In a molecular phylogenetic analysis of 183 β-decarboxylating dehydrogenase homologs from 84 species, TK0280 from Thermococcus kodakarensis was selected as a candidate for an ancestral-type β-decarboxylating dehydrogenase. The biochemical characterization of recombinant TK0280 revealed that the enzyme exhibited dehydrogenase activities toward homoisocitrate, isocitrate, and 3-isopropylmalate, which correspond to key reactions involved in the lysine biosynthetic pathway, tricarboxylic acid cycle, and leucine biosynthetic pathway, respectively. In T. kodakarensis, the growth characteristics of the KUW1 host strain and a TK0280 deletion strain suggested that TK0280 is involved in lysine biosynthesis in this archaeon. On the other hand, gene complementation analyses using Thermus thermophilus as a host revealed that TK0280 functions as both an isocitrate dehydrogenase and homoisocitrate dehydrogenase in this organism, but not as a 3-isopropylmalate dehydrogenase, most probably reflecting its low catalytic efficiency toward 3-isopropylmalate. A crystallographic study on TK0280 binding each substrate indicated that Thr71 and Ser80 played important roles in the recognition of homoisocitrate and isocitrate while the hydrophobic region consisting of Ile82 and Leu83 was responsible for the recognition of 3-isopropylmalate. These analyses also suggested the importance of a water-mediated hydrogen bond network for the stabilization of the β3-α4 loop, including the Thr71 residue, with respect to the promiscuity of the substrate specificity of TK0280.

In silico and in vivo stability analysis of a heterologous biosynthetic pathway for 1,4-butanediol production in metabolically engineered E. coli

Recently, several approaches have been published in order to develop a functional biosynthesis route for the non-natural compound 1,4-butanediol (BDO) in E. coli using glucose as a sole carbon source or starting from xylose. Among these studies, there was reported as high as 18 g/L product concentration achieved by industrial strains, however BDO production varies greatly in case of the reviewed studies. Our motivation was to build a simple heterologous pathway for this compound in E. coli and to design an appropriate cellular chassis based on a systemic biology approach, using constraint-based flux balance analysis and bi-level optimization for gene knock-out prediction. Thus, the present study reports, at the "proof-of concept" level, our findings related to model-driven development of a metabolically engineered E. coli strain lacking key genes for ethanol, lactate and formate production (ΔpflB, ΔldhA and ΔadhE), with a three-step biosynthetic pathway. We found this strain to produce a limited quantity of 1,4-BDO (.89 mg/L BDO under microaerobic conditions and .82 mg/L under anaerobic conditions). Using glycerol as carbon source, an approach, which to our knowledge has not been tackled before, our results suggest that further metabolic optimization is needed (gene-introductions or knock-outs, promoter fine-tuning) to address the redox potential imbalance problem and to achieve development of an industrially sustainable strain. Our experimental data on culture conditions, growth dynamics and fermentation parameters can consist a base for ongoing research on gene expression profiles and genetic stability of such metabolically engineered E. coli strains.

Engineering Escherichia coli for production of 4-hydroxymandelic acid using glucose-xylose mixture

BACKGROUND

4-Hydroxymandelic acid (4-HMA) is a valuable aromatic fine chemical and widely used for production of pharmaceuticals and food additives. 4-HMA is conventionally synthesized by chemical condensation of glyoxylic acid with excessive phenol, and the process is environmentally unfriendly. Microbial cell factory would be an attractive approach for 4-HMA production from renewable and sustainable resources.

RESULTS

In this study, a biosynthetic pathway for 4-HMA production was constructed by heterologously expressing the fully synthetic 4-hydroxymandelic acid synthase (shmaS) in our L-tyrosine-overproducing Escherichia coli BKT5. The expression level of shmaS was optimized to improve 4-HMA production by fine tuning of four promoters of different strength combined with three plasmids of different copy number. Furthermore, two genes aspC and tyrB in the competitive pathway were deleted to block the formation of byproduct to enhance 4-HMA biosynthesis. The final engineered E. coli strain HMA15 utilized glucose and xylose simultaneously and produced 15.8 g/L of 4-HMA by fed-batch fermentation in 60 h.

CONCLUSIONS

Metabolically engineered E. coli strain for 4-HMA production was designed and constructed, and efficiently co-fermented glucose and xylose, the major components in the hydrolysate mixture of agricultural biomass. Our research provided a promising biomanufacturing route to produce 4-HMA from lignocellulosic biomass.

Systems biology of the structural proteome

BACKGROUND

The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology.

RESULTS

Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/).

CONCLUSIONS

Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism's genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.

Multiplex methods provide effective integration of multi-omic data in genome-scale models

BACKGROUND

Genomic, transcriptomic, and metabolic variations shape the complex adaptation landscape of bacteria to varying environmental conditions. Elucidating the genotype-phenotype relation paves the way for the prediction of such effects, but methods for characterizing the relationship between multiple environmental factors are still lacking. Here, we tackle the problem of extracting network-level information from collections of environmental conditions, by integrating the multiple omic levels at which the bacterial response is measured.

RESULTS

To this end, we model a large compendium of growth conditions as a multiplex network consisting of transcriptomic and fluxomic layers, and we propose a multi-omic network approach to infer similarity of growth conditions by integrating layers of the multiplex network. Each node of the network represents a single condition, while edges are similarities between conditions, as measured by phenotypic and transcriptomic properties on different layers of the network. We then fuse these layers into one network, therefore capturing a global network of conditions and the associated similarities across two omic levels. We apply this multi-omic fusion to an updated genome-scale reconstruction of Escherichia coli that includes underground metabolism and new gene-protein-reaction associations.

CONCLUSIONS

Our method can be readily used to evaluate and cross-compare different collections of conditions among different species. Acquiring multi-omic information on the topology of the space of experimental conditions makes it possible to infer the position and to build condition-specific models of untested or incomplete profiles for which experimental data is not available. Our weighted network fusion method for genome-scale models is freely available at https://github.com/maxconway/SNFtool .

Model-guided metabolic gene knockout of gnd for enhanced succinate production in Escherichia coli from glucose and glycerol substrates

The metabolic role of 6-phosphogluconate dehydrogenase (gnd) under anaerobic conditions with respect to succinate production in Escherichia coli remained largely unspecified. Herein we report what are to our knowledge the first metabolic gene knockout of gnd to have increased succinic acid production using both glucose and glycerol substrates in E. coli. Guided by a genome scale metabolic model, we engineered the E. coli host metabolism to enhance anaerobic production of succinic acid by deleting the gnd gene, considering its location in the boundary of oxidative and non-oxidative pentose phosphate pathway. This strategy induced either the activation of malic enzyme, causing up-regulation of phosphoenolpyruvate carboxylase (ppc) and down regulation of phosphoenolpyruvate carboxykinase (ppck) and/or prevents the decarboxylation of 6 phosphogluconate to increase the pool of glyceraldehyde-3-phosphate (GAP) that is required for the formation of phosphoenolpyruvate (PEP). This approach produced a mutant strain BMS2 with succinic acid production titers of 0.35 g l(-1) and 1.40 g l(-1) from glucose and glycerol substrates respectively. This work further clearly elucidates and informs other studies that the gnd gene, is a novel deletion target for increasing succinate production in E. coli under anaerobic condition using glucose and glycerol carbon sources. The knowledge gained in this study would help in E. coli and other microbial strains development for increasing succinate production and/or other industrial chemicals.

Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli

Recent insights suggest that non-specific and/or promiscuous enzymes are common and active across life. Understanding the role of such enzymes is an important open question in biology. Here we develop a genome-wide method, PROPER, that uses a permissive PSI-BLAST approach to predict promiscuous activities of metabolic genes. Enzyme promiscuity is typically studied experimentally using multicopy suppression, in which over-expression of a promiscuous 'replacer' gene rescues lethality caused by inactivation of a 'target' gene. We use PROPER to predict multicopy suppression in Escherichia coli, achieving highly significant overlap with published cases (hypergeometric p = 4.4e-13). We then validate three novel predicted target-replacer gene pairs in new multicopy suppression experiments. We next go beyond PROPER and develop a network-based approach, GEM-PROPER, that integrates PROPER with genome-scale metabolic modeling to predict promiscuous replacements via alternative metabolic pathways. GEM-PROPER predicts a new indirect replacer (thiG) for an essential enzyme (pdxB) in production of pyridoxal 5'-phosphate (the active form of Vitamin B6), which we validate experimentally via multicopy suppression. We perform a structural analysis of thiG to determine its potential promiscuous active site, which we validate experimentally by inactivating the pertaining residues and showing a loss of replacer activity. Thus, this study is a successful example where a computational investigation leads to a network-based identification of an indirect promiscuous replacement of a key metabolic enzyme, which would have been extremely difficult to identify directly.

Model-driven discovery of synergistic inhibitors against E. coli and S. enterica serovar Typhimurium targeting a novel synthetic lethal pair, aldA and prpC

Mathematical models of biochemical networks form a cornerstone of bacterial systems biology. Inconsistencies between simulation output and experimental data point to gaps in knowledge about the fundamental biology of the organism. One such inconsistency centers on the gene aldA in Escherichia coli: it is essential in a computational model of E. coli metabolism, but experimentally it is not. Here, we reconcile this disparity by providing evidence that aldA and prpC form a synthetic lethal pair, as the double knockout could only be created through complementation with a plasmid-borne copy of aldA. Moreover, virtual and biological screening against the two proteins led to a set of compounds that inhibited the growth of E. coli and Salmonella enterica serovar Typhimurium synergistically at 100-200 μM individual concentrations. These results highlight the power of metabolic models to drive basic biological discovery and their potential use to discover new combination antibiotics.

Assessing the Metabolic Diversity of Streptococcus from a Protein Domain Point of View

Understanding the diversity and robustness of the metabolism of bacteria is fundamental for understanding how bacteria evolve and adapt to different environments. In this study, we characterised 121 Streptococcus strains and studied metabolic diversity from a protein domain perspective. Metabolic pathways were described in terms of the promiscuity of domains participating in metabolic pathways that were inferred to be functional. Promiscuity was defined by adapting existing measures based on domain abundance and versatility. The approach proved to be successful in capturing bacterial metabolic flexibility and species diversity, indicating that it can be described in terms of reuse and sharing functional domains in different proteins involved in metabolic activity. Additionally, we showed striking differences among metabolic organisation of the pathogenic serotype 2 Streptococcus suis and other strains.

What is the difference between the breakpoint graph and the de Bruijn graph?

The breakpoint graph and the de Bruijn graph are two key data structures in the studies of genome rearrangements and genome assembly. However, the classical breakpoint graphs are defined on two genomes (represented as sequences of synteny blocks), while the classical de Bruijn graphs are defined on a single genome (represented as DNA strings). Thus, the connection between these two graph models is not explicit. We generalize the notions of both the breakpoint graph and the de Bruijn graph, and make it transparent that the breakpoint graph and the de Bruijn graph are mathematically equivalent. The explicit description of the connection between these important data structures provides a bridge between two previously separated bioinformatics communities studying genome rearrangements and genome assembly.