Transport-controlled growth decoupling for self-induced protein expression with a glycerol-repressible genetic circuit.

Decoupling cell formation from recombinant protein synthesis is a potent strategy to intensify bioprocesses. Escherichia coli strains with mutations in the glucose uptake components lack catabolite repression, display low growth rate, no overflow metabolism, and high recombinant protein yields. Fast growth rates were promoted by the simultaneous consumption of glucose and glycerol, and this was followed by a phase of slow growth, when only glucose remained in the medium. A glycerol-repressible genetic circuit was designed to autonomously induce recombinant protein expression. The engineered strain bearing the genetic circuit was cultured in 3.9 g L-1 glycerol + 18 g L-1 glucose in microbioreactors with online oxygen transfer rate monitoring. The growth was fast during the simultaneous consumption of both carbon sources (C-sources), while expression of the recombinant protein was low. When glycerol was depleted, the growth rate decreased, and the specific fluorescence reached values 17% higher than those obtained with a strong constitutive promoter. Despite the relatively high amount of C-source used, no oxygen limitation was observed. The proposed approach eliminates the need for the substrate feeding or inducers addition and is set as a simple batch culture while mimicking fed-batch performance.

Increasing the Pentose Phosphate Pathway Flux to Improve Plasmid DNA Production in Engineered E. coli.

The demand of plasmid DNA (pDNA) as a key element for gene therapy products, as well as mRNA and DNA vaccines, is increasing together with the need for more efficient production processes. An engineered E. coli strain lacking the phosphotransferase system and the pyruvate kinase A gene has been shown to produce more pDNA than its parental strain. With the aim of improving pDNA production in the engineered strain, several strategies to increase the flux to the pentose phosphate pathway (PPP) were evaluated. The simultaneous consumption of glucose and glycerol was a simple way to increase the growth rate, pDNA production rate, and supercoiled fraction (SCF). The overexpression of key genes from the PPP also improved pDNA production in glucose, but not in mixtures of glucose and glycerol. Particularly, the gene coding for the glucose 6-phosphate dehydrogenase (G6PDH) strongly improved the SCF, growth rate, and pDNA production rate. A linear relationship between the G6PDH activity and pDNA yield was found. A higher flux through the PPP was confirmed by flux balance analysis, which also estimates relevant differences in fluxes of the tricarboxylic acid cycle. These results are useful for developing further cell engineering strategies to increase pDNA production and quality.

Doxorubicin-induced transcriptome meets interactome: identification of new drug targets.

The working mechanism of the chemotherapeutic drug doxorubicin, which is frequently used in cancer treatment, its effects on cell metabolism, and pathways activated solely by doxorubicin are not fully known. Understanding these principles is important both in improving existing therapies and in finding new drug targets. Here, I describe a systems-biology approach to find a generalizable working principle for doxorubicin by superimposition of human interactome over gene datasets commonly expressed among various cancer types. The common -in at least two different diseases-transcriptional response of distinctive cancer cell lines to doxorubicin was reflected via 199 significantly and differentially expressed genes, mostly related to the regulation of transcription. Then, by integrating with interactome data, an active network was constructed allowing detection of clusters. Since each cluster defines densely connected regions, another level of understanding of functional principles is provided. Significant clusters were associated with the linked transcription factors and transcriptional factor enrichment analysis within these regulatory networks led to the proposition of Pou5f1b, Znf428, Prmt3, Znf12, Erg, Tfdp1, Foxm1, and Cenpa as new drug targets in drug development that can be applied in different cancer types.

Fast Sampling of the Cellular Metabolome.

Obtaining meaningful snapshots of the metabolome of microorganisms requires rapid sampling and immediate quenching of all metabolic activity, to prevent any changes in metabolite levels after sampling. Furthermore, a suitable extraction method is required ensuring complete extraction of metabolites from the cells and inactivation of enzymatic activity, with minimal degradation of labile compounds. Finally, a sensitive, high-throughput analysis platform is needed to quantify a large number of metabolites in a small amount of sample. An issue which has often been overlooked in microbial metabolomics is the fact that many intracellular metabolites are also present in significant amounts outside the cells and may interfere with the quantification of the endo metabolome. Attempts to remove the extracellular metabolites with dedicated quenching methods often induce release of intracellular metabolites into the quenching solution. For eukaryotic microorganisms, this release can be minimized by adaptation of the quenching method. For prokaryotic cells, this has not yet been accomplished, so the application of a differential method whereby metabolites are measured in the culture supernatant as well as in total broth samples, to calculate the intracellular levels by subtraction, seems to be the most suitable approach. Here we present an overview of different sampling, quenching, and extraction methods developed for microbial metabolomics, described in the literature. Detailed protocols are provided for rapid sampling, quenching, and extraction, for measurement of metabolites in total broth samples, washed cell samples, and supernatant, to be applied for quantitative metabolomics of both eukaryotic and prokaryotic microorganisms.

Integration of fluxome and transcriptome data in Saccharomyces cerevisiae offers unique features of doxorubicin and imatinib.

Improving the efficacy of drugs and developing new drugs are required to compensate for drug resistance. Therefore, it is critical to unveil the mode of action, which can be studied through the cellular response at genome-scale, of the existing drugs. Here, system-level response of Saccharomyces cerevisiae, a eukaryotic model microorganism, to two chemotherapy drugs doxorubicin and imatinib used against cancer are analysed. While doxorubicin is mainly known to interact with DNA through intercalation and imatinib is known to inhibit the activity of the tyrosine kinase enzyme, the exact mechanisms of action for both drugs have not been determined. The response of S. cerevisiae cells to long-term stress by these drugs under controlled aerobic conditions was investigated and analyzed by the genome-wide transcriptome and genome-wide fluxes. The classification of adverse and similar responses of a certain gene at a transcriptional versus flux level indicated the possible regulatory mechanisms under these different stress conditions. Most of the biochemical reactions were found to be regulated at a post-transcriptional or metabolic level, whereas fewer were regulated at a transcriptional level for both stress cases. Furthermore, disparately induced and repressed pathways in the metabolic network under doxorubicin and imatinib stress were identified. The glycolytic and pentose phosphate pathways responded similarly, whereas the purine-histidine metabolic pathways responded differently. Then, a comparison of differential fluxes and differentially co-expressed genes under doxorubicin and imatinib stress provided the potential common and unique features of these drugs. Analyzing such regulatory differences helps in resolving drug mechanisms and suggesting new drug targets.

Side effect prediction based on drug-induced gene expression profiles and random forest with iterative feature selection.

One in every ten drug candidates fail in clinical trials mainly due to efficacy and safety related issues, despite in-depth preclinical testing. Even some of the approved drugs such as chemotherapeutics are notorious for their side effects that are burdensome on patients. In order to pave the way for new therapeutics with more tolerable side effects, the mechanisms underlying side effects need to be fully elucidated. In this work, we addressed the common side effects of chemotherapeutics, namely alopecia, diarrhea and edema. A strategy based on Random Forest algorithm unveiled an expression signature involving 40 genes that predicted these side effects with an accuracy of 89%. We further characterized the resulting signature and its association with the side effects using functional enrichment analysis and protein-protein interaction networks. This work contributes to the ongoing efforts in drug development for early identification of side effects to use the resources more effectively.

Time-dependent re-organization of biological processes by the analysis of the dynamic transcriptional response of yeast cells to doxorubicin.

Doxorubicin is an efficient chemotherapeutic reagent in the treatment of a variety of cancers. However, its underlying molecular mechanism is not fully understood and several severe side effects limit its application. In this study, the dynamic transcriptomic response of Saccharomyces cerevisiae cells to a doxorubicin pulse in a chemostat system was investigated to reveal the underlying molecular mechanism of this drug. The clustering of differentially and significantly expressed genes (DEGs) indicated that the response of yeast cells to doxorubicin is time dependent and may be classified as short-term, mid-term and long-term responses. The cells have started to reorganize their response after the first minute following the injection of the pulse. A modified version of Weighted Gene Co-expression Network Analysis (WGCNA) was used to cluster the positively correlated co-expression profiles, and functional enrichment analysis of these clusters was carried out. DNA replication and DNA repair processes were significantly affected and induced 60 minutes after exposure to doxorubicin. The response to oxidative stress was not identified as a significant term. A transcriptional re-organization of the metabolic pathways seems to be an early event and persists afterwards. The present study reveals for the first time that the RNA surveillance pathway, which is a post-transcriptional regulatory pathway, may be implicated in the short-term reaction of yeast cells to doxorubicin. Integration with regulome revealed the dynamic re-organization of the transcriptomic landscape. Fhl1p, Mbp1p, and Mcm1p were identified as primary regulatory factors responsible for tuning the differentially expressed genes.

Vitreoscilla Haemoglobin: A Tool to Reduce Overflow Metabolism.

Overflow metabolism is a phenomenon extended in nature, ranging from microbial to cancer cells. Accumulation of overflow metabolites pose a challenge for large-scale bioprocesses. Yet, the causes of overflow metabolism are not fully clarified. In this work, the underlying mechanisms, reasons and consequences of overflow metabolism in different organisms have been summarized. The reported effect of aerobic expression of Vitreoscilla haemoglobin (VHb) in different organisms are revised. The use of VHb to reduce overflow metabolism is proposed and studied through flux balance analysis in E. coli at a fixed maximum substrate and oxygen uptake rates. Simulations showed that the presence of VHb increases the growth rate, while decreasing acetate production, in line with the experimental measurements. Therefore, aerobic VHb expression is considered a potential tool to reduce overflow metabolism in cells.

Insights Into the Mechanism of Anticancer Drug Imatinib Revealed Through Multi-Omic Analyses in Yeast.

Imatinib mesylate is a receptor tyrosine kinase inhibitor drug with broad applications in cancer therapeutics, for example, in chronic myeloid leukemia and gastrointestinal stromal tumors. In this study, new multi-omics findings in yeast on the mechanism of imatinib are reported, using the model organism Saccharomyces cerevisiae. Whole-genome analysis of the transcriptional response of yeast cells following long-term exposure to imatinib, flux-balance analysis (FBA), and modular analysis of protein/protein interaction network consisting of proteins encoded by differentially expressed genes (DEGs) were performed. DEGs indicated that carbon, nitrogen, starch, sucrose, glyoxylate/dicarboxylate metabolism, valine and leucine degradation, and tricarboxylic acid cycle (TCA) were significantly upregulated. By contrast, ribosome biogenesis, pentose/glucuronate interconversion, tryptophan/pyruvate metabolic pathways, and meiosis were significantly downregulated. FBA revealed that a large set of metabolic pathways was altered by imatinib to compensate cancer-associated metabolic changes. Integration of transcriptome and interactome (protein/protein interactions) data helped to identify the core regulatory genes and pathways through elucidation of the active subnetworks. It appears that imatinib may also contribute to antitumoral immune response in the tumor microenvironment and most of the metabolic rearrangements are at posttranscriptional level. Furthermore, additional support for possible contribution of thiamine/pyridoxal phosphate biosynthesis and mitogen-activated protein kinase pathway to drug resistance is noted. This report advances multi-omics understanding of the mechanism of imatinib, and by extension, offers new molecular avenues toward precision medicine and discovery of novel drug targets in cancer therapeutics.

Doxorubicin induces an extensive transcriptional and metabolic rewiring in yeast cells.

Doxorubicin is one of the most effective chemotherapy drugs used against solid tumors in the treatment of several cancer types. Two different mechanisms, (i) intercalation of doxorubicin into DNA and inhibition of topoisomerase II leading to changes in chromatin structure, (ii) generation of free radicals and oxidative damage to biomolecules, have been proposed to explain the mode of action of this drug in cancer cells. A genome-wide integrative systems biology approach used in the present study to investigate the long-term effect of doxorubicin in Saccharomyces cerevisiae cells indicated the up-regulation of genes involved in response to oxidative stress as well as in Rad53 checkpoint sensing and signaling pathway. Modular analysis of the active sub-network has also revealed the induction of the genes significantly associated with nucleosome assembly/disassembly and DNA repair in response to doxorubicin. Furthermore, an extensive re-wiring of the metabolism was observed. In addition to glycolysis, and sulfate assimilation, several pathways related to ribosome biogenesis/translation, amino acid biosynthesis, nucleotide biosynthesis, de novo IMP biosynthesis and one-carbon metabolism were significantly repressed. Pentose phosphate pathway, MAPK signaling pathway biological processes associated with meiosis and sporulation were found to be induced in response to long-term exposure to doxorubicin in yeast cells.

Genome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced Perturbations.

Cells respond to environmental and/or genetic perturbations in order to survive and proliferate. Characterization of the changes after various stimuli at different -omics levels is crucial to comprehend the adaptation of cells to the changing conditions. Genome-wide quantification and analysis of transcript levels, the genes affected by perturbations, extends our understanding of cellular metabolism by pointing out the mechanisms that play role in sensing the stress caused by those perturbations and related signaling pathways, and in this way guides us to achieve endeavors, such as rational engineering of cells or interpretation of disease mechanisms. Saccharomyces cerevisiae as a model system has been studied in response to different perturbations and corresponding transcriptional profiles were followed either statically or/and dynamically, short and long term. This review focuses on response of yeast cells to diverse stress inducing perturbations, including nutritional changes, ionic stress, salt stress, oxidative stress, osmotic shock, and to genetic interventions such as deletion and overexpression of genes. It is aimed to conclude on common regulatory phenomena that allow yeast to organize its transcriptomic response after any perturbation under different external conditions.

Comparative fluxome and metabolome analysis for overproduction of succinate in Escherichia coli.

An aerobic succinate-producing Escherichia coli mutant was compared to its wild-type by quantitatively analyzing both the metabolome and fluxome, during glucose-limited steady-state and succinate excess dynamic conditions, in order to identify targets for further strain engineering towards more efficient succinate production. The mutant had four functional mutations under the conditions investigated: increased expression of a succinate exporter (DcuC), deletion of a succinate importer (Dct), deletion of succinate dehydrogenase (SUCDH) and expression of a PEP carboxylase (PPC) with increased capacity due to a point mutation. The steady-state and dynamic patterns of the intracellular metabolite levels and fluxes in response to changes were used to locate the quantitative differences in the physiology/metabolism of the mutant strain. Unexpectedly the mutant had a higher energy efficiency, indicated by a much lower rate of oxygen consumption, under glucose-limited conditions, caused by the deletion of the transcription factors IclR and ArcA. Furthermore the mutant had a much lower uptake capacity for succinate (26-fold) and oxygen (17-fold under succinate excess) compared to the wild-type strain. The mutant strain produced 7.9 mmol.CmolX(-1).h(-1) succinate during chemostat cultivation, showing that the choice of the applied genetic modifications was a successful strategy. Furthermore, the applied genetic modifications resulted in multiple large changes in metabolite levels (FBP, pyruvate, 6PG, NAD(+) /NADH ratio, α-ketogluarate) corresponding to large changes in fluxes. Compared to the wild-type a considerable flux shift occurred from the tricarboxylic acid (TCA) cycle to the oxidative part of the pentose phosphate pathway, including an inversion of the pyruvate kinase flux. The mutant responded very differently to excess of succinate, with a remarkable possible reversal of the TCA cycle. The mutant and the wild-type both showed homeostatic behaviour with respect to the energy charge. In contrast, large changes in redox ratios (NAD(+) /NADH) occurred in the wild-type, while the mutant showed even larger changes. This large redox change can be associated to the reversal of flux directions. The observed large flexibility in the central metabolism following genetic (deletions) and environmental (substrate excess) perturbations of the mutant, indicates that introducing a more efficient succinate exporter could result in an even higher succinate production rate.

Effect of growth rate on plasmid DNA production and metabolic performance of engineered Escherichia coli strains.

Two engineered Escherichia coli strains, designated VH33 and VH34, were compared to their parent strain W3110 in chemostat mode during plasmid DNA (pDNA) production. In strain VH33 the glucose uptake system was modified with the aim of reducing overflow metabolism. The strain VH34 has an additional deletion of the pyruvate kinase A gene (pykA) to increase pDNA formation. pDNA formation rates as well as kinetic and stoichiometric parameters were investigated in dependence of the growth rate within a range from 0.02 to 0.25 h(-1). Differences between strains were found in terms of the biomass yields on nitrogen and oxygen, as well as on the cell maintenance coefficients. The deletion of pykA led to a significantly increased pDNA yield and productivity. At an optimal growth rate of 0.20 h(-1) it was nearly 60% higher than that of W3110 and VH33. Metabolic fluxes calculated by metabolite balance analysis showed differences mainly in reactions catalyzed by pyruvate kinase and glucose 6-phosphate dehydrogenase. The obtained data are useful for the design of cultivation schemes for pDNA production by E. coli.

Changes in substrate availability in Escherichia coli lead to rapid metabolite, flux and growth rate responses.

The interactions between the intracellular metabolome, fluxome and growth rate of Escherichia coli after sudden glycolytic/gluconeogenic substrate shifts are studied based on pulses of different substrates to an aerobic glucose-limited steady-state (dilution rate=0.1h(-1)). After each added glycolytic (glucose) and gluconeogenic (pyruvate and succinate) substrate pulse, no by-products were secreted and a pseudo steady state in flux and metabolites was achieved in about 30-40s. In the pulse experiments a large oxygen uptake capacity of the cells was observed. The in vivo dynamic responses showed massive reorganization and flexibility (1/100-14-fold change) of extra/intracellular metabolic fluxes, matching with large changes in the concentrations of intracellular metabolites, including reversal of reaction rate for pseudo/near equilibrium reactions. The coupling of metabolome and fluxome could be described by Q-linear kinetics. Remarkably, the three different substrate pulses resulted in a very similar increase in growth rate (0.13-0.3h(-1)). Data analysis showed that there must exist as yet unknown mechanisms which couple the protein synthesis rate to changes in central metabolites.

Fast sampling of the cellular metabolome.

Obtaining meaningful snapshots of the metabolome of microorganisms requires rapid sampling and immediate quenching of all metabolic activity, to prevent any changes in metabolite levels after sampling. Furthermore, a suitable extraction method is required ensuring complete extraction of metabolites from the cells and inactivation of enzymatic activity, with minimal degradation of labile compounds. Finally a sensitive, high-throughput analysis platform is needed to quantify a large number of metabolites in a small amount of sample. An issue which has often been overlooked in microbial metabolomics is the fact that many intracellular metabolites are also present in significant amounts outside the cells, and may interfere with the endometabolome measurements. Attempts to remove the extracellular metabolites with dedicated quenching methods often induce release of intracellular metabolites into the quenching solution. For eukaryotic microorganisms, leakage can be minimized by adaptation of the quenching method. For prokaryotic cells this had not yet been accomplished, so the application of a differential method whereby metabolites are measured in the culture supernatant as well as in total broth samples, to calculate the intracellular levels by subtraction, seems to be the most suitable approach. Here we present an overview of different sampling, quenching, and extraction methods developed for microbial metabolomics, described in the literature. Detailed protocols are provided for rapid sampling, quenching, and extraction for measurement of metabolites in total broth samples, washed cell samples and supernatant, to be applied for quantitative metabolomics of both eukaryotic and prokaryotic microorganisms.

Escherichia coli responds with a rapid and large change in growth rate upon a shift from glucose-limited to glucose-excess conditions.

Glucose pulse experiments at seconds time scale resolution were performed in aerobic glucose-limited Escherichia coli chemostat cultures. The dynamic responses of oxygen-uptake and growth rate at seconds time scale were determined using a new method based on the dynamic liquid-phase mass balance for oxygen and the pseudo-steady-state ATP balance. Significant fold changes in metabolites (10-1/10) and fluxes (4-1/4) were observed during the short (200 s) period of glucose excess. During glucose excess there was no secretion of by-products and the increased glucose uptake rate led within 40s to a 3.7 fold increase in growth rate. Also within 40-60s a new pseudo-steady-state was reached for both metabolite levels and fluxes. Flux changes of reactions were strongly correlated to the concentrations of involved compounds. Surprisingly the 3.7 fold increase in growth rate and hence protein synthesis rate was not matched by a significant increase in amino acid concentrations. This poses interesting questions for the kinetic factors, which drive protein synthesis by ribosomes.

Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry.

Metabolic network models describing growth of Escherichia coli on glucose, glycerol and acetate were derived from a genome scale model of E. coli. One of the uncertainties in the metabolic networks is the exact stoichiometry of energy generating and consuming processes. Accurate estimation of biomass and product yields requires correct information on the ATP stoichiometry. The unknown ATP stoichiometry parameters of the constructed E. coli network were estimated from experimental data of eight different aerobic chemostat experiments carried out with E. coli MG1655, grown at different dilution rates (0.025, 0.05, 0.1, and 0.3 h(-1)) and on different carbon substrates (glucose, glycerol, and acetate). Proper estimation of the ATP stoichiometry requires proper information on the biomass composition of the organism as well as accurate assessment of net conversion rates under well-defined conditions. For this purpose a growth rate dependent biomass composition was derived, based on measurements and literature data. After incorporation of the growth rate dependent biomass composition in a metabolic network model, an effective P/O ratio of 1.49 +/- 0.26 mol of ATP/mol of O, K(X) (growth dependent maintenance) of 0.46 +/- 0.27 mol of ATP/C-mol of biomass and m(ATP) (growth independent maintenance) of 0.075 +/- 0.015 mol of ATP/C-mol of biomass/h were estimated using a newly developed Comprehensive Data Reconciliation (CDR) method, assuming that the three energetic parameters were independent of the growth rate and the used substrate. The resulting metabolic network model only requires the specific rate of growth, micro, as an input in order to accurately predict all other fluxes and yields.

Catching prompt metabolite dynamics in Escherichia coli with the BioScope at oxygen rich conditions.

The design and application of a BioScope, a mini plug-flow reactor for carrying out pulse response experiments, specifically designed for Escherichia coli is presented. Main differences with the previous design are an increased volume-specific membrane surface for oxygen transfer and significantly decreased sampling intervals. The characteristics of the new device (pressure drop, residence time distribution, plug-flow behavior and O2 mass transfer) were determined and evaluated. Subsequently, 2.8 mM glucose perturbation experiments on glucose-limited aerobic E. coli chemostat cultures were carried out directly in the chemostat as well as in the BioScope (for two time frames: 8 and 40 s). It was ensured that fully aerobic conditions were maintained during the perturbation experiments. To avoid metabolite leakage during quenching, metabolite quantification (glycolytic and TCA-cycle intermediates and nucleotides) was carried out with a differential method, whereby the amounts measured in the filtrate were subtracted from the amounts measured in total broth. The dynamic metabolite profiles obtained from the BioScope perturbations were very comparable with the profiles obtained from the chemostat perturbation. This agreement demonstrates that the BioScope is a promising device for studying in vivo kinetics in E. coli that shows much faster response (< 10 s) in comparison with eukaryotes.

Fast dynamic response of the fermentative metabolism of Escherichia coli to aerobic and anaerobic glucose pulses.

The response of Escherichia coli cells to transient exposure (step increase) in substrate concentration and anaerobiosis leading to mixed-acid fermentation metabolism was studied in a two-compartment bioreactor system consisting of a stirred tank reactor (STR) connected to a mini-plug-flow reactor (PFR: BioScope, 3.5 mL volume). Such a system can mimic the situation often encountered in large-scale, fed-batch bioreactors. The STR represented the zones of a large-scale bioreactor that are far from the point of substrate addition and that can be considered as glucose limited, whereas the PFR simulated the region close to the point of substrate addition, where glucose concentration is much higher than in the rest of the bioreactor. In addition, oxygen-poor and glucose-rich regions can occur in large-scale bioreactors. The response of E. coli to these large-scale conditions was simulated by continuously pumping E. coli cells from a well stirred, glucose limited, aerated chemostat (D = 0.1 h(-1)) into the mini-PFR. A glucose pulse was added at the entrance of the PFR. In the PFR, a total of 11 samples were taken in a time frame of 92 s. In one case aerobicity in the PFR was maintained in order to evaluate the effects of glucose overflow independently of oxygen limitation. Accumulation of acetate and formate was detected after E. coli cells had been exposed for only 2 s to the glucose-rich (aerobic) region in the PFR. In the other case, the glucose pulse was also combined with anaerobiosis in the PFR. Glucose overflow combined with anaerobiosis caused the accumulation of formate, acetate, lactate, ethanol, and succinate, which were also detected as soon as 2 s after of exposure of E. coli cells to the glucose and O(2) gradients. This approach (STR-mini-PFR) is useful for a better understanding of the fast dynamic phenomena occurring in large-scale bioreactors and for the design of modified strains with an improved behavior under large-scale conditions.