Relationship between recombinant protein expression and host metabolome as determined by two-dimensional NMR spectroscopy

Metabolome analysis of metabolic burden in Escherichia coli caused by overexpression of green fluorescent protein and delta-rhodopsin

Overexpression of proteins by introducing a DNA vector is among the most important tools for the metabolic engineering of microorganisms such as Escherichia coli. Protein overexpression imposes a burden on metabolism because metabolic pathways must supply building blocks for protein and DNA synthesis. Different E. coli strains have distinct metabolic capacities. In this study, two proteins were overexpressed in four E. coli strains (MG1655(DE3), W3110(DE3), BL21star(DE3), and Rosetta(DE3)), and their effects on metabolic burden were investigated. Metabolomic analysis showed that E. coli strains overexpressing green fluorescent protein had decreased levels of several metabolites, with a positive correlation between the number of reduced metabolites and green fluorescent protein expression levels. Moreover, nucleic acid-related metabolites decreased, indicating a metabolic burden in the E. coli strains, and the growth rate and protein expression levels were improved by supplementation with the five nucleosides. In contrast, two strains overexpressing delta rhodopsin, a microbial membrane rhodopsin from Haloterrigena turkmenica, led to a metabolic burden and decrease in the amino acids Ala, Val, Leu, Ile, Thr, Phe, Asp, and Trp, which are the most frequent amino acids in the delta rhodopsin protein sequence. The metabolic burden caused by protein overexpression was influenced by the metabolic capacity of the host strains and the sequences of the overexpressed proteins. Detailed characterization of the effects of protein expression on the metabolic state of engineered cells using metabolomics will provide insights into improving the production of target compounds.

Designing a Secretory form of RTX-A as an Anticancer Toxin: An In Silico Approach

BACKGROUND

Cancer is a leading cause of death and a significant public health issue worldwide. Standard treatment methods such as chemotherapy, radiotherapy, and surgery are only sometimes effective. Therefore, new therapeutic approaches are needed for cancer treatment. Sea anemone actinoporins are pore-forming toxins (PFTs) with membranolytic activities. RTX-A is a type of PFT that interacts with membrane phospholipids, resulting in pore formation. The synthesis of recombinant proteins in a secretory form has several advantages, including protein solubility and easy purification. In this study, we aimed to discover suitable signal peptides for producing RTX-A in Bacillus subtilis in a secretory form.

METHODS

Signal peptides were selected from the Signal Peptide Web Server. The probability and secretion pathways of the selected signal peptides were evaluated using the SignalP server. ProtParam and Protein-sol were used to predict the physico-chemical properties and solubility. AlgPred was used to predict the allergenicity of RTX-A linked to suitable signal peptides. Non-allergenic, stable, and soluble signal peptides fused to proteins were chosen, and their secondary and tertiary structures were predicted using GOR IV and I-TASSER, respectively. The PROCHECK server performed the validation of 3D structures.

RESULTS

According to bioinformatics analysis, the fusion forms of OSMY_ECOLI and MALE_ECOLI linked to RTX-A were identified as suitable signal peptides. The final proteins with signal peptides were stable, soluble, and non-allergenic for the human body. Moreover, they had appropriate secondary and tertiary structures.

CONCLUSION

The signal above peptides appears ideal for rationalizing secretory and soluble RTX-A. Therefore, the signal peptides found in this study should be further investigated through experimental researches and patents.

In Search of Complementary Extraction Methods for Comprehensive Coverage of the Escherichia coli Metabolome

Escherichia coli is an invaluable research tool for many fields of biology, in particular for the production of recombinant enzymes. However, the activity of many such recombinant enzymes cannot be determined using standard biochemical assays, as often, the relevant substrates are not known, or the products produced are not detectable. Today, the biochemical footprints of such unknown enzyme activities can be revealed via the analysis of the metabolomes of the recombinant E. coli clones in which they are expressed, using sensitive technologies such as mass spectrometry. However, before any metabolites can be identified, it is necessary to achieve as high a coverage of the potential metabolites present within E. coli as possible. We have therefore analyzed a wide range of different extraction methods against the cell free extracts of various recombinant E. coli clones. The results were analyzed to determine the minimum number of extractions that achieved high recovery and coverage of metabolites. Two methods were selected for further analysis due to their ability to produce not only high numbers of ions, but also wide mass coverage and a high degree of complementarity. One extraction method uses acetonitrile and water, in a 4:1 ratio, which is then dried down and reconstituted in the chromatography running buffer prior to injection onto the chromatography column, and the other extraction method uses a combination of methanol, water and chloroform, in a 3:1:1 ratio, which is injected directly onto the chromatography column. These two extraction methods were shown to be complementary to each other, as regards the respective metabolites extracted, and to cover a large range of metabolites.

Central carbon metabolite profiling reveals vector-associated differences in the recombinant protein production host Escherichia coli BL21

The Gram-negative bacterium Escherichia coli is the most widely used host for recombinant protein production, both as an industrial expression platform and as a model system at laboratory scale. The recombinant protein production industry generates proteins with direct applications as biopharmaceuticals and in technological processes central to a plethora of fields. Despite the increasing economic significance of recombinant protein production, and the importance of E. coli as an expression platform and model organism, only few studies have focused on the central carbon metabolic landscape of E. coli during high-level recombinant protein production. In the present work, we applied four targeted CapIC- and LC-MS/MS methods, covering over 60 metabolites, to perform an in-depth metabolite profiling of the effects of high-level recombinant protein production in strains derived from E. coli BL21, carrying XylS/Pm vectors with different characteristics. The mass-spectrometric central carbon metabolite profiling was complemented with the study of growth kinetics and protein production in batch bioreactors. Our work shows the robustness in E. coli central carbon metabolism when introducing increased plasmid copy number, as well as the greater importance of induction of recombinant protein production as a metabolic challenge, especially when strong promoters are used.

Identification of interaction partners using protein aggregation and NMR spectroscopy

The interaction among proteins is one of the most fundamental methods of information transfer in the living system. Many methods have been developed in order to identify the interaction pairs or groups either in vivo or in vitro. The in vitro pulldown/coprecipitation assay directly observes the protein that binds to the target. This method involves electrophoresis, which is a technique of a low resolution as well as a low throughput. As a better alternative, we wish to propose a new method that is based on the NMR spectroscopy. This method utilizes the aggregation of the target protein and the concomitant signal disappearance of the interacting partner. The aggregation is accomplished by the elastin-like polypeptide, which is fused to the target. If a protein binds to this supramolecular complex, its NMR signal then becomes too broadened in order to be observed, which is the basic phenomenon of the NMR spectroscopy. Thus, the protein that loses its signal is the one that binds to the target. A compound that interferes with these types of bindings among the proteins can be identified by observing the reappearance of the protein signals with the simultaneous disappearance of the signals of the compound. This technique will be applied in order to find an interaction pair in the information transfer pathway as well as a compound that disrupts it. This proposed method should be able to work with a mixture of proteins and provide a higher resolution in order to find the binding partner in a higher throughput fashion.

Bacterial Growth Inhibition Screen (BGIS): harnessing recombinant protein toxicity for rapid and unbiased interrogation of protein function

In two proof-of-concept studies, we established and validated the Bacterial Growth Inhibition Screen (BGIS), which explores recombinant protein toxicity in Escherichia coli as a largely overlooked and alternative means for basic characterization of functional eukaryotic protein domains. By applying BGIS, we identified an unrecognized RNA-interacting domain in the DEK oncoprotein (this study) and successfully combined BGIS with random mutagenesis as a screening tool for loss-of-function mutants of the DNA modulating domain of DEK [1]. Collectively, our findings shed new light on the phenomenon of recombinant protein toxicity in E. coli. Given the easy and rapid implementation and wide applicability, BGIS will extend the repertoire of basic methods for the identification, analysis and unbiased manipulation of proteins.

Designing next generation recombinant protein expression platforms by modulating the cellular stress response in Escherichia coli

BACKGROUND

A cellular stress response (CSR) is triggered upon recombinant protein synthesis which acts as a global feedback regulator of protein expression. To remove this key regulatory bottleneck, we had previously proposed that genes that are up-regulated post induction could be part of the signaling pathways which activate the CSR. Knocking out some of these genes which were non-essential and belonged to the bottom of the E. coli regulatory network had provided higher expression of GFP and L-asparaginase.

RESULTS

We chose the best performing double knockout E. coli BW25113ΔelaAΔcysW and demonstrated its ability to enhance the expression of the toxic Rubella E1 glycoprotein by 2.5-fold by tagging it with sfGFP at the C-terminal end to better quantify expression levels. Transcriptomic analysis of this hyper-expressing mutant showed that a significantly lower proportion of genes got down-regulated post induction, which included genes for transcription, translation, protein folding and sorting, ribosome biogenesis, carbon metabolism, amino acid and ATP synthesis. This down-regulation which is a typical feature of the CSR was clearly blocked in the double knockout strain leading to its enhanced expression capability. Finally, we supplemented the expression of substrate uptake genes glpK and glpD whose down-regulation was not prevented in the double knockout, thus ameliorating almost all the negative effects of the CSR and obtained a further doubling in recombinant protein yields.

CONCLUSION

The study validated the hypothesis that these up-regulated genes act as signaling messengers which activate the CSR and thus, despite having no casual connection with recombinant protein synthesis, can improve cellular health and protein expression capabilities. Combining gene knockouts with supplementing the expression of key down-regulated genes can counter the harmful effects of CSR and help in the design of a truly superior host platform for recombinant protein expression.

Kinetic study and optimization of recombinant human tumor necrosis factor-alpha (rhTNF-α) production in Escherichia coli

Tumor necrosis factor-alpha (TNF-α) is an inflammatory cytokine that plays a major role in immune regulation, homeostatic function, and cellular organization. The present study was undertaken to overproduce recombinant human TNF-α (rhTNF-α) in Escherichia coli (E.coli) in high cell density culture. The use of a codon-optimized gene and strong promoter-based (T7) expression system, choice of Terrific Broth (TB) as medium, and subsequent optimization of culture conditions in shake flasks resulted in production of 0.95 g/L insoluble rhTNF-α comprising upto 50% of total cellular protein (TCP) The protein yield further increased upto 1.26 g/L in 1 L TB medium batch culture in bioreactor with the controlled temperature, pH, and dissolved oxygen. In a series of chemostats operated at dilution rates of 0.2 h^-1, 0.3 h^-1, 0.4 h^-1 and 0.5 h^-1 the specific growth rate (μ) positively correlated with specific yield (Y_p/x) and a maximum yield of 164 mg/g DCW was obtained at μ = 0.4 h^-1 within 4 h post-induction. A fed-batch cultivation in TB with an exponential feeding profile (μ = ∼0.4 h^-1) of concentrated feed resulted in an accumulation of 5.5 g/L of rhTNF-α within 14 h of cultivation which accounted for ∼29% of TCP.

Identification of bioactive metabolites using activity metabolomics

The metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other 'omics' levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites - which we term activity metabolomics - is already having a broad impact on biology.

Comparative analysis of 1H NMR and 1H-13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth

In nuclear magnetic resonance (NMR) metabolomics, most of the studies have been focused on the analysis of one-dimensional proton (1D ¹H) NMR, whereas the analysis of other nuclei, such as ¹³C, or other NMR experiments are still underrepresented. The preference of 1D ¹H NMR metabolomics lies on the fact that it has good sensitivity and a short acquisition time, but it lacks spectral resolution because it presents a high degree of overlap. In this study, the growth metabolism of yeast ( Saccharomyces cerevisiae) was analyzed by 1D ¹H NMR and by two-dimensional (2D) ¹H-¹³C heteronuclear single quantum coherence (HSQC) NMR spectroscopy, leading to the detection of more than 50 metabolites with both analytical approaches. These two analyses allow for a better understanding of the strengths and intrinsic limitations of the two types of NMR approaches. The two data sets (1D and 2D NMR) were investigated with PCA, ASCA, and PLS DA chemometric methods, and similar results were obtained regardless of the data type used. However, data-analysis time for the 2D NMR data set was substantially reduced when compared with the data analysis of the corresponding ¹H NMR data set because, for the 2D NMR data, signal overlap was not a major problem and deconvolution was not required. The comparative study described in this work can be useful for the future design of metabolomics workflows, to assist in the selection of the most convenient NMR platform and to guide the posterior data analysis of biomarker selection.