A targeted proteomics toolkit for high-throughput absolute quantification of Escherichia coli proteins

Comparative label-free proteomics of the neonatal meningitis-causing Escherichia coli K1 IHE3034 and RS218 morphotypes

The proteome of two newborn meningitis Escherichia coli K1 (NMEC) morphotypes was examined via a label-free proteomics approach. Besides shared NMEC virulence factors, the two strains have different evolutionary strategies-strain IHE3034 tends to perform anaerobic respiration continuously, while strain RS218 maintains its filamentous morphotype due to active SOS response.

Comparison of Genome and Plasmid-Based Engineering of Multigene Benzylglucosinolate Pathway in Saccharomyces cerevisiae

Intake of brassicaceous vegetables such as cabbage is associated with numerous health benefits. The major defense compounds in the Brassicales order are the amino acid-derived glucosinolates that have been associated with the health-promoting effects.

On the potential of micro-flow LC-MS/MS in proteomics

INTRODUCTION

Due to its excellent sensitivity, nano-flow liquid chromatography tandem mass spectrometry (LC-MS/MS) is the mainstay in proteome research; however, this comes at the expense of limited throughput and robustness. In contrast, micro-flow LC-MS/MS enables high-throughput, robustness, quantitative reproducibility, and precision while retaining a moderate degree of sensitivity. Such features make it an attractive technology for a wide range of proteomic applications. In particular, large-scale projects involving the analysis of hundreds to thousands of samples.

AREAS COVERED

This review summarizes the history of chromatographic separation in discovery proteomics with a focus on micro-flow LC-MS/MS, discusses the current state-of-the-art, highlights advances in column development and instrumentation, and provides guidance on which LC flow best supports different types of proteomic applications.

EXPERT OPINION

Micro-flow LC-MS/MS will replace nano-flow LC-MS/MS in many proteomic applications, particularly when sample quantities are not limited and sample cohorts are large. Examples include clinical analyses of body fluids, tissues, drug discovery and chemical biology investigations, plus systems biology projects across all kingdoms of life. When combined with rapid and sensitive MS, intelligent data acquisition, and informatics approaches, it will soon become possible to analyze large cohorts of more than 10,000 samples in a comprehensive and fully quantitative fashion.

Increased carvone production in Escherichia coli by balancing limonene conversion enzyme expression via targeted quantification concatamer proteome analysis

(−)-Carvone is a monoterpenoid with a spearmint flavor. A sustainable biotechnological production process for (−)-carvone is desirable. Although all enzymes in (−)-carvone biosynthesis have been functionally expressed in Escherichia coli independently, the yield was low in previous studies. When cytochrome P450 limonene-6-hydroxylase (P450)/cytochrome P450 reductase (CPR) and carveol dehydrogenase (CDH) were expressed in a single strain, by-product formation (dihydrocarveol and dihydrocarvone) was detected. We hypothesized that P450 and CDH expression levels differ in E. coli. Thus, two strains independently expressing P450/CPR and CDH were mixed with different ratios, confirming increased carvone production and decreased by-product formation when CDH input was reduced. The optimum ratio of enzyme expression to maximize (−)-carvone production was determined using the proteome analysis quantification concatamer (QconCAT) method. Thereafter, a single strain expressing both P450/CPR and CDH was constructed to imitate the optimum expression ratio. The upgraded strain showed a 15-fold improvement compared to the initial strain, showing a 44 ± 6.3 mg/L (−)-carvone production from 100 mg/L (−)-limonene. Our study showed the usefulness of the QconCAT proteome analysis method for strain development in the industrial biotechnology field.

Betulinic Acid Affects the Energy-Related Proteomic Profiling in Pancreatic Ductal Adenocarcinoma Cells

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a 5-year survival rate of <8%. Therefore, finding new treatment strategies against PDAC cells is an imperative issue. Betulinic acid (BA), a plant-derived natural compound, has shown great potential to combat cancer owing to its versatile physiological functions. In this study, we observed the impacts of BA on the cell viability and migratory ability of PDAC cell lines, and screened differentially expressed proteins (DEPs) by an LC-MS/MS-based proteomics analysis. Our results showed that BA significantly inhibited the viability and migratory ability of PDAC cells under a relatively low dosage without affecting normal pancreatic cells. Moreover, a functional analysis revealed that BA-induced downregulation of protein clusters that participate in mitochondrial complex 1 activity and oxidative phosphorylation, which was related to decreased expressions of RNA polymerase mitochondrial (POLRMT) and translational activator of cytochrome c oxidase (TACO1), suggesting that the influence on mitochondrial function explains the effect of BA on PDAC cell growth and migration. In addition, BA also dramatically increased Apolipoprotein A1 (APOA1) expression and decreased NLR family CARD domain-containing protein 4 (NLRC4) expression, which may be involved in the dampening of PDAC migration. Notably, altered expression patterns of APOA1 and NLRC4 indicated a favorable clinical prognosis of PDAC. Based on these findings, we identified potential proteins and pathways regulated by BA from a proteomics perspective, which provides a therapeutic window for PDAC.

A machine learning Automated Recommendation Tool for synthetic biology

Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstrate the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.

Investigation of the methylerythritol 4-phosphate pathway for microbial terpenoid production through metabolic control analysis

BACKGROUND

Terpenoids are of high interest as chemical building blocks and pharmaceuticals. In microbes, terpenoids can be synthesized via the methylerythritol phosphate (MEP) or mevalonate (MVA) pathways. Although the MEP pathway has a higher theoretical yield, metabolic engineering has met with little success because the regulation of the pathway is poorly understood.

RESULTS

We applied metabolic control analysis to the MEP pathway in Escherichia coli expressing a heterologous isoprene synthase gene (ispS). The expression of ispS led to the accumulation of isopentenyl pyrophosphate (IPP)/dimethylallyl pyrophosphate (DMAPP) and severely impaired bacterial growth, but the coexpression of ispS and isopentenyl diphosphate isomerase (idi) restored normal growth and wild-type IPP/DMAPP levels. Targeted proteomics and metabolomics analysis provided a quantitative description of the pathway, which was perturbed by randomizing the ribosome binding site in the gene encoding 1-deoxyxylulose 5-phosphate synthase (Dxs). Dxs has a flux control coefficient of 0.35 (i.e., a 1% increase in Dxs activity resulted in a 0.35% increase in pathway flux) in the isoprene-producing strain and therefore exerted significant control over the flux though the MEP pathway. At higher dxs expression levels, the intracellular concentration of 2-C-methyl-D-erythritol-2,4-cyclopyrophosphate (MEcPP) increased substantially in contrast to the other MEP pathway intermediates, which were linearly dependent on the abundance of Dxs. This indicates that 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (IspG), which consumes MEcPP, became saturated and therefore limited the flux towards isoprene. The higher intracellular concentrations of MEcPP led to the efflux of this intermediate into the growth medium.

DISCUSSION

These findings show the importance of Dxs, Idi and IspG and metabolite export for metabolic engineering of the MEP pathway and will facilitate further approaches for the microbial production of valuable isoprenoids.

Mevalonate Pathway Promiscuity Enables Noncanonical Terpene Production

Lepidoptera (butterflies and moths) make the six-carbon compounds homoisopentenyl pyrophosphate (HIPP) and homodimethylallyl pyrophosphate (HDMAPP) that are incorporated into 16, 17, and 18 carbon farnesyl pyrophosphate (FPP) analogues. In this work we heterologously expressed the lepidopteran modified mevalonate pathway, a propionyl-CoA ligase, and terpene cyclases in E. coli to produce several novel terpenes containing 16 carbons. Changing the terpene cyclase generated different novel terpene product profiles. To further validate the new compounds we confirmed ¹³C propionate was incorporated, and that the masses and fragmentation patterns were consistent with novel 16 carbon terpenes by GC-QTOF. On the basis of the available farnesyl pyrophosphate analogues lepidoptera produce, this approach should greatly expand the reachable biochemical space with applications in areas where terpenes have traditionally found uses.

Xenogeneic modulation of the ClpCP protease of Bacillus subtilis by a phage-encoded adaptor-like protein

Like eukaryotic and archaeal viruses, which coopt the host's cellular pathways for their replication, bacteriophages have evolved strategies to alter the metabolism of their bacterial host. SPO1 bacteriophage infection of Bacillus subtilis results in comprehensive remodeling of cellular processes, leading to conversion of the bacterial cell into a factory for phage progeny production. A cluster of 26 genes in the SPO1 genome, called the host takeover module, encodes for potentially cytotoxic proteins that specifically shut down various processes in the bacterial host, including transcription, DNA synthesis, and cell division. However, the properties and bacterial targets of many genes of the SPO1 host takeover module remain elusive. Through a systematic analysis of gene products encoded by the SPO1 host takeover module, here we identified eight gene products that attenuated B. subtilis growth. Of the eight phage gene products that attenuated bacterial growth, a 25-kDa protein called Gp53 was shown to interact with the AAA+ chaperone protein ClpC of the ClpCP protease of B. subtilis Our results further reveal that Gp53 is a phage-encoded adaptor-like protein that modulates the activity of the ClpCP protease to enable efficient SPO1 phage progeny development. In summary, our findings indicate that the bacterial ClpCP protease is the target of xenogeneic (dys)regulation by a SPO1 phage-derived factor and add Gp53 to the list of antibacterial products that target bacterial protein degradation and therefore may have utility for the development of novel antibacterial agents.

Changing substrate specificity and iteration of amino acid chain elongation in glucosinolate biosynthesis through targeted mutagenesis of Arabidopsis methylthioalkylmalate synthase 1

Methylthioalkylmalate synthases catalyse the committing step of amino acid chain elongation in glucosinolate biosynthesis. As such, this group of enzymes plays an important role in determining the glucosinolate composition of Brassicaceae species, including Arabidopsis thaliana. Based on protein structure modelling of MAM1 from A. thaliana and analysis of 57 MAM sequences from Brassicaceae species, we identified four polymorphic residues likely to interact with the 2-oxo acid substrate. Through site-directed mutagenesis, the natural variation in these residues and the effect on product composition were investigated. Fifteen MAM1 variants as well as the native MAM1 and MAM3 from A. thaliana were characterised by heterologous expression of the glucosinolate chain elongation pathway in Escherichia coli. Detected products derived from leucine, methionine or phenylalanine were elongated with up to six methylene groups. Product profile and accumulation were changed in 14 of the variants, demonstrating the relevance of the identified residues. The majority of the single amino acid substitutions decreased the length of methionine-derived products, while approximately half of the substitutions increased the phenylalanine-derived products. Combining two substitutions enabled the MAM1 variant to increase the number of elongation rounds of methionine from three to four. Notably, characterisation of the native MAMs indicated that MAM1 and not MAM3 is responsible for homophenylalanine production. This hypothesis was confirmed by glucosinolate analysis in mam1 and mam3 mutants of A. thaliana.

Biosynthesis and secretion of the microbial sulfated peptide RaxX and binding to the rice XA21 immune receptor

The rice immune receptor XA21 is activated by the sulfated microbial peptide required for activation of XA21-mediated immunity X (RaxX) produced by Xanthomonas oryzae pv. oryzae (Xoo). Mutational studies and targeted proteomics revealed that the RaxX precursor peptide (proRaxX) is processed and secreted by the protease/transporter RaxB, the function of which can be partially fulfilled by a noncognate peptidase-containing transporter component B (PctB). proRaxX is cleaved at a Gly-Gly motif, yielding a mature peptide that retains the necessary elements for RaxX function as an immunogen and host peptide hormone mimic. These results indicate that RaxX is a prokaryotic member of a previously unclassified and understudied group of eukaryotic tyrosine sulfated ribosomally synthesized, posttranslationally modified peptides (RiPPs). We further demonstrate that sulfated RaxX directly binds XA21 with high affinity. This work reveals a complete, previously uncharacterized biological process: bacterial RiPP biosynthesis, secretion, binding to a eukaryotic receptor, and triggering of a robust host immune response.

Targeted Mass Spectrometry-Based Proteomics Tools for Strain Optimization

The goal of strain optimization is to create high-performance strains producing compounds of interest at a high titer, yield, and volumetric productivity. The effectiveness of strain optimization relies on methodologies used to aid optimization of native or novel pathways within cells. Many different factors, including mRNA abundance, protein abundance, and enzyme activity/stability, will contribute to the strain performance, which is not often evident by simply monitoring product titers. To this end, targeted proteomics tools utilizing LC-MS-MS (liquid chromatography coupled with tandem mass spectrometry) have recently been developed and can monitor protein levels at great sensitivities. Here, we describe all relevant aspects when developing proteomics tools for strain optimization.

Engineering Corynebacterium glutamicum to produce the biogasoline isopentenol from plant biomass hydrolysates

BACKGROUND

Many microbes used for the rapid discovery and development of metabolic pathways have sensitivities to final products and process reagents. Isopentenol (3-methyl-3-buten-1-ol), a biogasoline candidate, has an established heterologous gene pathway but is toxic to several microbial hosts. Reagents used in the pretreatment of plant biomass, such as ionic liquids, also inhibit growth of many host strains. We explored the use of Corynebacterium glutamicum as an alternative host to address these constraints.

RESULTS

We found C. glutamicum ATCC 13032 to be tolerant to both the final product, isopentenol, as well to three classes of ionic liquids. A heterologous mevalonate-based isopentenol pathway was engineered in C. glutamicum. Targeted proteomics for the heterologous pathway proteins indicated that the 3-hydroxy-3-methylglutaryl-coenzyme A reductase protein, HmgR, is a potential rate-limiting enzyme in this synthetic pathway. Isopentenol titers were improved from undetectable to 1.25 g/L by combining three approaches: media optimization; substitution of an NADH-dependent HmgR homolog from Silicibacter pomeroyi; and development of a C. glutamicum ∆poxB ∆ldhA host chassis.

CONCLUSIONS

We describe the successful expression of a heterologous mevalonate-based pathway in the Gram-positive industrial microorganism, C. glutamicum, for the production of the biogasoline candidate, isopentenol. We identified critical genetic factors to harness the isopentenol pathway in C. glutamicum. Further media and cultivation optimization enabled isopentenol production from sorghum biomass hydrolysates.

Viscous control of cellular respiration by membrane lipid composition

Lipid composition determines the physical properties of biological membranes and can vary substantially between and within organisms. We describe a specific role for the viscosity of energy-transducing membranes in cellular respiration. Engineering of fatty acid biosynthesis in Escherichia coli allowed us to titrate inner membrane viscosity across a 10-fold range by controlling the abundance of unsaturated or branched lipids. These fluidizing lipids tightly controlled respiratory metabolism, an effect that can be explained with a quantitative model of the electron transport chain (ETC) that features diffusion-coupled reactions between enzymes and electron carriers (quinones). Lipid unsaturation also modulated mitochondrial respiration in engineered budding yeast strains. Thus, diffusion in the ETC may serve as an evolutionary constraint for lipid composition in respiratory membranes.

Restoration of biofuel production levels and increased tolerance under ionic liquid stress is enabled by a mutation in the essential Escherichia coli gene cydC

BACKGROUND

Microbial production of chemicals from renewable carbon sources enables a sustainable route to many bioproducts. Sugar streams, such as those derived from biomass pretreated with ionic liquids (IL), provide efficiently derived and cost-competitive starting materials. A limitation to this approach is that residual ILs in the pretreated sugar source can be inhibitory to microbial growth and impair expression of the desired biosynthetic pathway.

RESULTS

We utilized laboratory evolution to select Escherichia coli strains capable of robust growth in the presence of the IL, 1-ethyl-3-methyl-imidizolium acetate ([EMIM]OAc). Whole genome sequencing of the evolved strain identified a point mutation in an essential gene, cydC, which confers tolerance to two different classes of ILs at concentrations that are otherwise growth inhibitory. This mutation, cydC-D86G, fully restores the specific production of the bio-jet fuel candidate D-limonene, as well as the biogasoline and platform chemical isopentenol, in growth medium containing ILs. Similar amino acids at this position in cydC, such as cydC-D86V, also confer tolerance to [EMIM]OAc. We show that this [EMIM]OAc tolerance phenotype of cydC-D86G strains is independent of its wild-type function in activating the cytochrome bd-I respiratory complex. Using shotgun proteomics, we characterized the underlying differential cellular responses altered in this mutant. While wild-type E. coli cannot produce detectable amounts of either product in the presence of ILs at levels expected to be residual in sugars from pretreated biomass, the engineered cydC-D86G strains produce over 200 mg/L D-limonene and 350 mg/L isopentenol, which are among the highest reported titers in the presence of [EMIM]OAc.

CONCLUSIONS

The optimized strains in this study produce high titers of two candidate biofuels and bioproducts under IL stress. Both sets of production strains surpass production titers from other IL tolerant mutants in the literature. Our application of laboratory evolution identified a gain of function mutation in an essential gene, which is unusual in comparison to other published IL tolerant mutants.

A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Prediction of Hopeless Peptides Unlikely to be Selected for Targeted Proteome Analysis

In targeted proteomics using liquid chromatography-tandem triple quadrupole mass spectrometry (LC/MS/MS) in the selected reaction monitoring (SRM) mode, selecting the best observable or visible peptides is a key step in the development of SRM assay methods of target proteins. A direct comparison of signal intensities among all candidate peptides by brute-force LC/MS/MS analysis is a concrete approach for peptide selection. However, the analysis requires an SRM method with hundreds of transitions. This study reports on the development of a method for predicting and identifying hopeless peptides to reduce the number of candidate peptides needed for brute-force experiments. Hopeless peptides are proteotypic peptides that are unlikely to be selected for targets in SRM analysis owing to their poor ionization characteristics. Targeted proteomics data from Escherichia coli demonstrated that the relative ionization efficiency between two peptides could be predicted from sequences of two peptides, when a multivariate regression model is used. Validation of the method showed that >20% of the candidate peptides could be successfully eliminated as hopeless peptides with a false positive rate of less than 2%.

Gas fermentation: cellular engineering possibilities and scale up

Low carbon fuels and chemicals can be sourced from renewable materials such as biomass or from industrial and municipal waste streams. Gasification of these materials allows all of the carbon to become available for product generation, a clear advantage over partial biomass conversion into fermentable sugars. Gasification results into a synthesis stream (syngas) containing carbon monoxide (CO), carbon dioxide (CO2), hydrogen (H2) and nitrogen (N2). Autotrophy-the ability to fix carbon such as CO2 is present in all domains of life but photosynthesis alone is not keeping up with anthropogenic CO2 output. One strategy is to curtail the gaseous atmospheric release by developing waste and syngas conversion technologies. Historically microorganisms have contributed to major, albeit slow, atmospheric composition changes. The current status and future potential of anaerobic gas-fermenting bacteria with special focus on acetogens are the focus of this review.

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

Background

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.

Results

The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and ¹³C Metabolic Flux Analysis. Moreover, it introduces the capability to use ¹³C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale ¹³C Metabolic Flux Analysis (2S-¹³C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user’s specific needs.

Conclusions

jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1615-y) contains supplementary material, which is available to authorized users.

Cell-Free Mixing of Escherichia coli Crude Extracts to Prototype and Rationally Engineer High-Titer Mevalonate Synthesis

Cell-free metabolic engineering (CFME) is advancing a powerful paradigm for accelerating the design and synthesis of biosynthetic pathways. However, as most cell-free biomolecule synthesis systems to date use purified enzymes, energy and cofactor balance can be limiting. To address this challenge, we report a new CFME framework for building biosynthetic pathways by mixing multiple crude lysates, or extracts. In our modular approach, cell-free lysates, each selectively enriched with an overexpressed enzyme, are generated in parallel and then combinatorically mixed to construct a full biosynthetic pathway. Endogenous enzymes in the cell-free extract fuel high-level energy and cofactor regeneration. As a model, we apply our framework to synthesize mevalonate, an intermediate in isoprenoid synthesis. We use our approach to rapidly screen enzyme variants, optimize enzyme ratios, and explore cofactor landscapes for improving pathway performance. Further, we show that genomic deletions in the source strain redirect metabolic flux in resultant lysates. In an optimized system, mevalonate was synthesized at 17.6 g·L^-1 (119 mM) over 20 h, resulting in a volumetric productivity of 0.88 g·L^-1·hr^-1. We also demonstrate that this system can be lyophilized and retain biosynthesis capability. Our system catalyzes ∼1250 turnover events for the cofactor NAD⁺ and demonstrates the ability to rapidly prototype and debug enzymatic pathways in vitro for compelling metabolic engineering and synthetic biology applications.

Mass-Spectrometry-Based Quantification of Protein-Bound Fatty Acid Synthesis Intermediates from Escherichia coli

The production of fatty acids from simple nutrients occurs via a complex biosynthetic pathway with dozens of intermediate compounds and multiple branch points. Despite its importance for microbial physiology and biotechnology, critical aspects of fatty acid biosynthesis, especially dynamics of in vivo regulation, remain poorly characterized. We have developed a liquid chromatography/mass spectroscopy (LC-MS) method for relative quantification of fatty acid synthesis intermediates in Escherichia coli, a model organism for studies of fatty acid metabolism. The acyl carrier protein, a vehicle for the substrates and intermediates of fatty acid synthesis, is extracted from E. coli, proteolytically digested, resolved using reverse-phase LC, and detected using electrospray ionization coupled with a tandem MS. Our method reliably resolves 21 intermediates of fatty acid synthesis, with an average relative standard deviation in ratios of individual acyl-ACP species to total ACP concentrations of 20%. We demonstrate that fast sampling and quenching of cells is essential to accurately characterize intracellular concentrations of ACP species. We apply our method to examine the rapid response of fatty acid metabolism to the antibiotic cerulenin. We anticipate that our method will enable the characterization of in vivo regulation and kinetics of microbial fatty acid synthesis at unprecedented detail and will improve integration of fatty acid synthesis into models of microbial metabolism.

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow

Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology; provides molecular insight into how the host organism microenvironment changes due to pathway engineering; and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proof of concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity. The contributed workflow is available as an open-source tool in the form of iPython notebooks.

Designing biomedical proteomics experiments: state-of-the-art and future perspectives

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters.

Absolute quantification of proteins in the fatty acid biosynthetic pathway using protein standard absolute quantification

With worldwide attention on renewable energy and climate change, metabolic engineering of the fatty acid biosynthetic pathway has become an active area of research, with a view to enhance production of biofuels. Indeed, this pathway has already been extensively studied in Escherichia coli. Nevertheless, little is known about the absolute abundance of the enzymes involved, information that may be valuable for engineering, such as the optimal molar ratios of different proteins. In this study, we use protein standard absolute quantification (PSAQ) to measure the absolute abundance of proteins that catalyze fatty acid biosynthesis in E. coli. In addition, the changes of protein abundance were analyzed by comparing the differences between high-yield and the background strain. Our work highlights opportunities to enhance fatty acid production by measuring protein molar ratios and identifying catalytic and regulatory bottlenecks. More importantly, our results provide evidence that PSAQ is a generally valuable tool to investigate metabolic pathways.

Supernatant protein biomarkers of red blood cell storage hemolysis as determined through an absolute quantification proteomics technology

BACKGROUND

Laboratory technologies have highlighted the progressive accumulation of the so-called "storage lesion," a wide series of alterations to stored red blood cells (RBCs) that may affect the safety and effectiveness of the transfusion therapy. New improvements in the field are awaited to ameliorate this lesion, such as the introduction of washing technologies in the cell processing pipeline. Laboratory studies that have tested such technologies so far rely on observational qualitative or semiquantitative techniques.

STUDY DESIGN AND METHODS

A state-of-the-art quantitative proteomics approach utilizing quantitative concatamers (QconCAT) was used to simultaneously monitor fluctuations in the abundance of 114 proteins in AS-3 RBC supernatants (n = 5; 11 time points, including before and after leukoreduction, at 3 hours, on Days 1 and 2, and weekly sampling from Day 7 through Day 42).

RESULTS

Leukoreduction-dependent depletion of plasma proteins was observed at the earliest time points. A subset of proteins showed very high linear correlation (r(2)  > 0.9) not only with storage time, but also with absolute levels of hemoglobin α1 and β, a proxy for RBC hemolysis and vesiculation. Linear regression was performed to describe the temporal relationship between these proteins. Our findings suggest a role for supernatant glyceraldehyde-3-phosphate dehydrogenase; peroxiredoxin-1, -2, and -6; carbonic anhydrase-1 and -2; selenium binding protein-1; biliverdin reductase; aminolevulinate dehydratase; and catalase as potential biomarkers of RBC quality during storage.

CONCLUSION

A targeted proteomics technology revealed novel biomarkers of the RBC storage lesion and promises to become a key analytical readout for the development and testing of alternative cell processing strategies.

Methods and applications of absolute protein quantification in microbial systems

In the last years the scientific community faced an increased need to provide high-quality data on the concentration of single proteins within a cell. Especially against the background of the fast evolving field of systems biology this does not only apply for a few proteins but preferably for the whole proteome of the organism. Therefore there has been a rapid development from pure identification of proteins via characterization of changes between different conditions by relative protein quantification towards determination of absolute protein amounts for hundreds of protein species in a cell. This review aims for discussion of different small-scale and large-scale approaches for absolute protein quantification in bacterial cells to picture biological processes and explore life in deeper detail. The presented advantages and limitations of various methods may provide interested researchers help to appraise available methods, select the most appropriate technique and avoid common pitfalls during determination of protein concentration in a complex sample.

Analytics for Metabolic Engineering

Realizing the promise of metabolic engineering has been slowed by challenges related to moving beyond proof-of-concept examples to robust and economically viable systems. Key to advancing metabolic engineering beyond trial-and-error research is access to parts with well-defined performance metrics that can be readily applied in vastly different contexts with predictable effects. As the field now stands, research depends greatly on analytical tools that assay target molecules, transcripts, proteins, and metabolites across different hosts and pathways. Screening technologies yield specific information for many thousands of strain variants, while deep omics analysis provides a systems-level view of the cell factory. Efforts focused on a combination of these analyses yield quantitative information of dynamic processes between parts and the host chassis that drive the next engineering steps. Overall, the data generated from these types of assays aid better decision-making at the design and strain construction stages to speed progress in metabolic engineering research.