1. Starting, stopping, and processing time course CFME reactions for HPLC-RID quantification.
2. Preparing the HPLC system for metabolite detection.
3. Creating a method for the isocratic HPLC separation of organic fermentation products in the CDS.
4. Creating a sequence table for autosampling and start the HPLC-RID system for data acquisition.
5. Extracting and analyzing data post-run.
6. Starting, stopping, and processing time course isotope tracing CFME reactions for LC-MS/MS quantification.
7. Setting up the LC system for LC-MS/MS analysis.
8. Creating a method on LC-MS/MS data acquisition and interpretation software for the LC system linked to Fourier Transform and Ion Trap Mass Spectrometers.
9. Setting up a run sequence and starting the LC-MS/MS run.
10. Consolidating files and searching for tentative annotations on MZmine 2.53.
11. Calculating negative mode masses of 13C-labeled glucose-derived metabolites and searching for the m/z features of these analytes in filtered data.
To quantify the lysate-based cell-free synthesis of common fermentation products from glucose, lysates derived from strains grown in 2xYPTG media were fed 100 µM glucose as a primary carbon source8. Reactions were stopped over a 24 h time course by protein acidification. Filtered supernatants containing pyruvate, succinate, lactate, formate, acetate, and ethanol produced from glucose catabolism were loaded onto the autosampler module of a HPLC system equipped with a RID module. Vials with filtered mixtures of fermentative end-products and glucose at 1.17 µM, 2.34 µM, 4.69 µM, 9.38 µM, 18.75 µM, 37.50 µM, 75 µM, and 150 µM concentrations in S30 buffer were loaded onto the instrument as standards. Analytes were eluted isocratically from an HPLC column to the RID. Peaks for glucose, succinate, lactate, formate, acetate, and ethanol within the 1 to 150 µM range could be resolved by RID. Peak areas for glucose were derived by manual integration from the RID data for time course and standard curve samples. Extracted peak areas for succinate, lactate, formate, acetate, and ethanol were taken from automatically integrated signals. All standard curves (peak area vs. known concentration) had R2 values >0.99 and were linear throughout the range of concentrations used here.
Molar concentrations for all target analytes were calculated from their respective standard curves. Glucose was consumed within the first 3 h of the reaction and mainly fermented to lactate (Figure 2A,B). Ethanol accumulation also significantly occurred within the first 3 h of the reaction and stopped thereafter (Figure 2C). The observation of significant lactate and ethanol production with significant glucose consumption after 3 h was not unprecedented since lactate and ethanol production pathways allow the regeneration of 1 net mol NAD+ from glycolytic NADH required for continued glucose consumption through glycolysis (Figure 1). Lactate and ethanol can thus be considered as the major fermentation end-products in lysate-based cell-free glucose metabolism. Acetate was initially present in the reactions as a component of the S30 buffer and unexpectedly only accumulated due to metabolism after 6 h when glucose consumption had slowed down (Figure 2D). This result suggests that acetate fermentation does not necessarily enable rapid glycolytic flux in earlier time points. Meanwhile, formate and succinate were synthesized as minor fermentation products (Figure 2E,F). Altogether, the method enabled the absolute quantification of sugar substrate depletion and fermentative product formation in E. coli S30 lysates.
MS detection to profile lysate glucose metabolism specifically was applied here. Lysates derived from strains grown in 2xYPTG media were fed 13C6-glucose as a carbon source. CFME reactions were run in triplicate for 0 h, 1 h, 2 h, and 3 h. Samples from each timepoint were loaded on an LC system equipped with a reversed-phase column and coupled to Fourier transform and ion trap mass spectrometers. Negative ion mode spectra were obtained and processed to analyze organic acids, sugar phosphates, and amino acids. Calculated theoretical masses of 13C-labeled species belonging to central carbon metabolism were searched to identify specifically glucose-derived compounds. Based on the utilized source strain cultivation conditions and previous reports of active pathways in E. coli CFME, it is assumed here that the lysate proteome comprises a metabolic network that feeds glucose into glycolytic fermentation, the pentose phosphate pathway, and possibly amino acid anabolism5,6,7,8,14 (Figure 1). Therefore, the search was narrowed down to members of these pathways, of which 16 metabolites incorporating glucose-derived 13C labels were unambiguously annotated (Supplemental Table 1).
13C6-glucose was observably consumed through glycolysis, as evidenced by the fluctuations in glycolytic intermediate abundances (Figure 3A–E). Consistent with the HPLC-RID data, glucose accumulated to 13C3-lactate and was also fermented to 13C3-succinate within the first 3 h of the reaction (Figure 4A,B). The formation of 13C3-succinate isotopologue supports the proposed model of lysate glucose metabolism (Figure 1), where succinate is likely to be generated by the carboxylation of 3-carbon phosphoenolpyruvate (PEP) molecule and not from the entry of a 2-carbon acetyl-CoA molecule to the TCA cycle. Activation of the TCA cycle has been assumed in previous CFME studies, but other 13C-labeled intermediates of TCA were not detected here8,19,21. 13C3-aspartate synthesis however occurred within the first h and was consumed, reinforcing the idea that PEP is directly converted to oxaloacetate (Figure 1, Figure 6C). The data are reflective of a lysate proteome from source strains harvested during fermentative growth on glucose-rich media (2xYPTG). This would further imply that the rest of the TCA enzymes not participating in succinate production form an oxidative TCA branch (Figure 1). None of the metabolites in this pathway, however, were detected, possibly because high concentrations of glutamate added to the CFME reaction as a salt counterion prevent the progression of this branch.
The HPLC-RID data is additionally complemented by the lack of 13C2-acetate detection within the 3 h reaction timeframe suggesting no build-up of acetate from glucose up to 3 h (Figure 2B). However, the direct precursor of acetate, acetyl-phosphate (acetyl-P), accumulated, suggesting that the Pta arm of the Pta-AckA pathway for acetate synthesis from acetyl-CoA is active (Figure 4C,D). The AckA catalyzed dephosphorylation of 13C2-acetyl-P to 13C2-acetate likely does not occur within this timeframe due to acetate being a major component of the S30 buffer used in the reactions (Figure 1, Figure 2B).
The incorporation of 13C6-glucose-derived carbons to sugar phosphates 6-phosphogluconolactone (6PGL), 6-phosphogluconate (6PG), ribulose-5-phosphate (Ru5P), and sedoheptulose-7-phosphate (S7P) was also observed (Figure 5). These results confirm the participation of the pentose phosphate pathway in lysate glucose metabolism and likely feeds 13C9-tyrosine synthesis, which has been suggested before by a proteomic study, while also providing a precursor for 13C5-histidine production (Figure 6A,B)7. Labeled phenylalanine and tryptophan were not observed here, and neither were most of the essential amino acids. However, this is not entirely surprising since amino acid anabolism is likely to be enriched in lysates derived from cells grown in nutrient-starved conditions or at the stationary phase7,22. Moreover, the data thus far suggests that intermediates of glycolysis and fermentation are funneled towards cofactor regenerating end reactions, which must preclude the synthesis of many amino acids derived from glyceraldehyde-3-phosphate, pyruvate, and acetyl-CoA (i.e., glycine, cysteine, serine, alanine, valine, leucine, and lysine) (Figure 1). As mentioned, 13C3-aspartate was produced within the first hour, whereas aspartate derived 13C-incorporating amino acids (i.e., threonine, isoleucine, methionine, and asparagine) were not observed possibly because glucose-derived aspartate participates in fermentation (Figure 1, Figure 6C). Lastly, flux towards labeled glutamate and amino acids derived from glutamate may have been impeded by high levels of glutamate in the reaction environment (Figure 1).
Figure 1: A putative metabolic model of lysates derived from E. coli BL21DE3-Star growing exponentially in high glucose concentrations. Intermediates and end-products of glycolysis (green), the pentose phosphate pathway (dark orange), and fermentative pathways (blue) from acetyl-CoA have been reported in lysate-based CFME. The presence of succinate fermentation implies the activation of the oxidative TCA branch (gray). Amino acid anabolism (gold) in lysates is not well-defined and is investigated here. Please click here to view a larger version of this figure.
Figure 2: HPLC-RID data for glucose consumption and fermentative end-product synthesis in CFME reactions prepared with E. coli crude extracts. (A) Glucose consumption and (B) lactate, (C) ethanol, (D) acetate, (E) formate, and (F) succinate production in CFME reactions were monitored over 24 h. Average mM concentrations and error bars (SE) quantified with standard curves are presented (n = 3). Please click here to view a larger version of this figure.
Figure 3: Time course trends of 13C6-glucose and 13C-labeled glycolytic intermediates in E. coli lysate CFME. Relative abundances of (A) 13C6-glucose, (B) 13C6-glucose-6-phosphate/fructose-6-phosphate, (C) 13C6-fructose-1,6-bisphosphate, (D) 13C3-glyceraldehyde-3-phosphate/dihydroxyacetone phosphate, and (E) 13C6-pyruvate in CFME reactions over 3 h. Raw peak areas extracted by mzMINE software were used to calculate averages and error bars (SE) for positive annotations (n = 3). Please click here to view a larger version of this figure.
Figure 4: Time course trends of intermediates and end-products in 13C6-glucose fermentation in E. coli lysate CFME. Relative abundances of (A) 13C3-lactate, (B) 13C3-succinate, (C) 13C2-acetyl-phosphate, and (D) 13C2-acetyl-CoA in CFME reactions over 3 h. Raw peak areas extracted by mzMINE software were used to calculate averages and error bars (SE) for positive annotations (n = 3). Please click here to view a larger version of this figure.
Figure 5: Time course trends of 13C6-glucose derived pentose phosphate pathway intermediates in E. coli lysate CFME. Relative abundances of (A) 13C6-6-phosphogluconolactone, (B) 13C6-6-phosphogluconate, (C) 13C5-ribulose-5-phosphate, and (D) 13C7-sedoheptulose-7-phosphate over 3 h. Raw peak areas extracted by mzMINE software were used to calculate averages and error bars (SE) for positive annotations (n = 3). Please click here to view a larger version of this figure.
Figure 6: Time course trends of detected 13C6-glucose derived amino acids in E. coli lysate CFME. Relative abundances of (A) 13C9-tyrosine, (B) 13C5-histidine, and (C) 13C3-aspartate over 3 h. Raw peak areas extracted by mzMINE software were used to calculate averages and error bars (SE) for positive annotations (n = 3). Please click here to view a larger version of this figure.
Supplemental Figure 1: Representative HPLC-RID chromatogram showing peaks for major fermentative products in a CFME reaction incubated at 37 °C for 24 h. Glucose, succinate, lactate, formate, acetate, and ethanol peaks are sufficiently distinguishable by their retention times on an HPLC column during isocratic elution with 5 mM sulfuric acid solvent. Please click here to download this File.
Supplemental Figure 2: Representative mass spectra for 13C-labeled metabolites, specifically (A) lactate,(B) glucose, and (C) 6-phosphogluconate (6PG) in a CFME reaction incubated at 37 °C for 1 h. Please click here to download this File.
Supplemental Table 1: List of detected 13C-labeled metabolites, retention times (aligned across samples using MZmine), theoretical fully 13C-labeled negative mode m/z values, m/z values of detected features, and calculated mass errors. Please click here to download this Table.
0.22 μm centrifuge tube filters (spin columns) | Corning Costar | 8160 | |
1.5 mL microcentrifuge tubes | VWR | 87003-294 | |
1260 Infinity Binary LC Pump | Agilent | G1312B | HPLC-RID system |
1260 Infinity High Performance Degasser | Agilent | G4225A | HPLC-RID system |
1260 Infinity Refractive Index Detector | Agilent | G7162A | HPLC-RID system |
1260 Infinity Standard Autosampler | Agilent | G1329B | HPLC-RID system |
13C6-glucose | Sigma-Aldrich | 389374 | CFME reaction mix component (LC-MS/MS) |
500 mL 0.20 μm pore (PES membrane) filter | VWR | 10040-436 | |
Acetonitrile (LC/MS grade) | Fisher Scientific | A955 | Solvent preparation for LC-MS/MS |
Adenosine triphosphate | Sigma-Aldrich | A7699 | CFME reaction mix component |
Aminex HPX 87-H column | Bio-rad | 1250140 | Chromatography column for HPLC-RID |
Ammonium acetate (LC/MS grade) | Fisher Scientific | A11450 | Solvent preparation for LC-MS/MS |
Ammonium glutamate | Sigma-Aldrich | G1376 | CFME reaction mix component |
Autosampler vial caps (yellow, snap) | Thermo Scientific | C4011-50Y | Sample storage/delivery for LC-MS/MS |
Autosampler vials (0.30 mL, polypropylene) | Wheaton | W225181 | Sample storage/delivery for LC-MS/MS |
Benchtop microcentrifuge | Fisher Scientific | 13-100-675 | |
Bis-Tris | Sigma-Aldrich | B9754 | CFME reaction mix component |
Coenzyme A (CoA) | Sigma-Aldrich | C4282 | CFME reaction mix component |
D-dextrose (Glucose) | VWR | BDH9230 | CFME reaction mix component |
Dipotassium phosphate | Sigma-Aldrich | P8281 | CFME reaction mix component |
Ethanol | Fisher Scientific | BP2818100 | Dissolved in S30 buffer for standard curve solution preparation (HPLC-RID) |
Formic acid (LC/MS grade) | Thermo Scientific | 85178 | Solvent preparation for LC-MS/MS |
Fused silica (internal diameter of 100 μm, external diameter of 375 μm) | Polymicro Technologies | WM22005-ND | Chromatography column for LC-MS/MS |
Glacial acetic acid | Sigma-Aldrich | A6283 | S30 buffer ingredient |
Isopropanol (LC/MS grade) | Fisher Scientific | A461 | Solvent preparation for LC-MS/MS |
Kinetex 5 μm C18 stationary phase (100 Å) | Phenomenex | N/A; special order | Chromatography column for LC-MS/MS |
LTQ Orbitrap Velos Pro Mass Spectrometer | ThermoFisher Scientific | N/A; special order | Mass spectrometer for LC-MS/MS |
Magnesium acetate | Sigma-Aldrich | M5661 | S30 buffer ingredient |
Magnesium glutamate | Sigma-Aldrich | 49605 | CFME reaction mix component |
Methanol (LC/MS grade) | Fisher Scientific | A456 | Solvent preparation for LC-MS/MS |
NAD+ | Sigma-Aldrich | N0632 | CFME reaction mix component |
Nanospray Ionization Source | ThermoFisher Scientific/Proxeon | ES071 (newest model) | Mass spectrometer for LC-MS/MS |
OpenLab CDS (Online) Software | Agilent | Version 2.15.26 | Chromatography Data System for acquiring and analyzing HPLC data |
Orbitrap Velos Pro LTQ Tune Plus Software | Thermo | Version 2.7 | Software for tuning the LC-MS/MS system |
Potassium acetate | Sigma-Aldrich | P1190 | S30 buffer ingredient |
Potassium glutamate | Sigma-Aldrich | G1501 | CFME reaction mix component |
Refrigerated centrifuge | Eppendorf | 5415 C | |
Screw caps (with septa, 9 mm) | Supelco | 29315-U | Sample storage/delivery for HPLC-RID |
Screwthread glass vials (2 mL) | Supelco | 29376-U | Sample storage/delivery for HPLC-RID |
Sodium acetate | Sigma-Aldrich | 241245 | Dissolved in S30 buffer for standard curve solution preparation (HPLC-RID) |
Sodium formate | Sigma-Aldrich | 247596 | Dissolved in S30 buffer for standard curve solution preparation (HPLC-RID) |
Sodium lactate | Sigma-Aldrich | 71716 | Dissolved in S30 buffer for standard curve solution preparation (HPLC-RID) |
Succinic acid | Sigma-Aldrich | 398055 | Dissolved in S30 buffer for standard curve solution preparation (HPLC-RID) |
Sulfuric acid | Sigma-Aldrich | 258105 | Solvent preparation for HPLC-RID |
Trichloroacetic acid | Sigma-Aldrich | T6399 | |
Tris-acetate | GoldBio | T-090-100 | S30 buffer ingredient |
Ultimate 3000 LC with autosampler | Dionex | Solvent Rack: SRD-3600 | Liquid chromatography system for LC-MS/MS analysis |
Ultimate 3000 LC with autosampler | Dionex | Rapid Separation Binary Pump: HPG-3400RS | Liquid chromatography system for LC-MS/MS analysis |
Ultimate 3000 LC with autosampler | Dionex | Rapid Separation Well Plate Autosampler: WPS-3000TRS | Liquid chromatography system for LC-MS/MS analysis |
Water (LC/MS grade) | Fisher Scientific | W6500 | Solvent preparation for LC-MS/MS |
Xcalibur Software | Thermo | Version 3.0.63 | Data acquisition and interpretation software for LC-MS/MS |
Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell's survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. Here, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.
Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell's survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. Here, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.
Engineering cellular metabolism for targeted biosynthesis can require extensive design-build-test-learn (DBTL) cycles as the engineer works around the cell's survival requirements. Alternatively, carrying out DBTL cycles in cell-free environments can accelerate this process and alleviate concerns with host compatibility. A promising approach to cell-free metabolic engineering (CFME) leverages metabolically active crude cell extracts as platforms for biomanufacturing and for rapidly discovering and prototyping modified proteins and pathways. Realizing these capabilities and optimizing CFME performance requires methods to characterize the metabolome of lysate-based cell-free platforms. That is, analytical tools are necessary for monitoring improvements in targeted metabolite conversions and in elucidating alterations to metabolite flux when manipulating lysate metabolism. Here, metabolite analyses using high-performance liquid chromatography (HPLC) coupled with either optical or mass spectrometric detection were applied to characterize metabolite production and flux in E. coli S30 lysates. Specifically, this report describes the preparation of samples from CFME lysates for HPLC analyses using refractive index detection (RID) to quantify the generation of central metabolic intermediates and by-products in the conversion of low-cost substrates (i.e., glucose) to various high-value products. The analysis of metabolite conversion in CFME reactions fed with 13C-labeled glucose through reversed-phase liquid chromatography coupled to tandem mass spectrometry (MS/MS), a powerful tool for characterizing specific metabolite yields and lysate metabolic flux from starting materials, is also presented. Altogether, applying these analytical methods to CFME lysate metabolism enables the advancement of these systems as alternative platforms for executing faster or novel metabolic engineering tasks.