This protocol describes an experimental procedure to quantitatively and comprehensively investigate the metabolism of multiple nutrient sources. This workflow, based on a combination of isotopic tracer experiments and an analytical procedure, allows the fate of consumed nutrients and the metabolic origin of molecules synthetized by microorganisms to be determined.
Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations.
We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples–facilitated by a robot-assisted system for online monitoring of fermentations–and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.
Understanding how microbial metabolism operates is a key issue for the design of efficient strategies to improve fermentation processes and modulate the production of fermentative compounds. Advances in genomics and functional genomics in these last two decades largely contributed to extending knowledge of the topology of metabolic networks in many microorganisms. Access to this information led to the development of approaches that aim for a comprehensive overview of cellular function1. These methodologies often rely on a model-based interpretation of measurable parameters. These experimental data include, on one hand, metabolite uptake and production rates and, on the other hand, quantitative intracellular information that is obtained from isotope tracer experiments. These data provide essential information for the deduction of the in vivo activity of different pathways in a defined metabolic network2,3,4. Currently, the available analytical techniques only enable the accurate detection of labeling patterns of molecules when using a single-element isotope and possibly when co-labeling with two isotopic elements. Moreover, under most growth conditions, the carbon source only consists of one or two-compounds. Consequently, approaches based on 13C-isotopic tracers from carbon substrates were widely and successfully applied to develop a complete understanding of carbon metabolic network operations5,6,7,8.
In contrast, in many natural and industrial environments, the available nitrogen resource that supports microbial growth is often composed of a wide range of molecules. For example, during wine or beer fermentation, nitrogen is provided as a mixture of 18 amino acids and ammonium at variable concentrations9. This array of N compounds that are accessible for anabolism makes these complex media conditions greatly different from those commonly used for physiological studies, as the latter are achieved using a unique source of nitrogen, typically ammonium.
Overall, internalized nitrogen compounds may be directly incorporated into proteins or catabolized. The network structure of nitrogen metabolism in many microorganisms, including the yeast Saccharomyces cerevisiae, is very complex in accordance with the diversity of substrates. Schematically, this system is based on the combination of the central core of nitrogen metabolism which catalyzes the interconversion of glutamine, glutamate, and α-ketoglutarate10,11, with transaminases and deaminases. Through this network, amine groups from ammonium or other amino acids are gathered and α-keto acids released. These intermediates are also synthetized through central carbon metabolism (CCM)12,13. This large number of branched reactions and intermediates, involved in both the catabolism of exogenous nitrogen sources and the anabolism of proteinogenic amino acids, fulfills the anabolic requirements of the cells. The activity through these different interconnected routes also results in the excretion of metabolites. In particular, α-keto acids may be redirected through the Ehrlich pathway to produce higher alcohols and their acetate ester derivatives14, which are essential contributors to the sensory profiles of products. Subsequently, how nitrogen metabolism operates plays a key role in biomass production and the formation of volatile molecules (aroma).
The reactions, enzymes, and genes involved in nitrogen metabolism are extensively described in the literature. However, the issue of the distribution of multiple nitrogen sources throughout a metabolic network has not yet been addressed. There are two main reasons that explain this lack of information. First, in view of the important complexity of the nitrogen metabolism network, a large amount of quantitative data is required for a complete understanding of its operation that was unavailable until now. Second, many experimental constraints and limitations of analytical methods prevented the implementation of approaches that were previously used for the elucidation of CCM function.
To overcome these problems, we chose to develop a system-level approach that is based on the reconciliation of data from a series of isotopic tracer experiments. The workflow includes:
– A set of fermentations carried out under the same environmental conditions, while a different selected nutrient source (substrate) is labeled each time.
– A combination of analytical procedures (HPLC, GC-MS) for an accurate determination, at different stages of the fermentation, of the residual concentration of labeled substrate and the concentration and the isotopic enrichment of compounds that are derived from the catabolism of the labeled molecule, including derived biomass.
– A calculation of the mass and isotopic balance for each consumed labeled molecule and a further integrated analysis of the dataset to obtain a global overview of the management of multiple nutrient sources by microorganisms through the determination of flux ratios.
To apply this methodology, attention must be paid to the reproducible behavior of the strain/microorganism between cultures. Furthermore, samples from different cultures must be taken during the same well-defined fermentation progress. In the experimental work reported in this manuscript, a robot-assisted system is used for online monitoring of fermentations to account for these constraints.
Furthermore, it is essential to choose a set of labeled substrates (compound, nature, and position of the labeling) that is appropriate to address the scientific problem of the study. Here, 15N-labeled ammonium, glutamine, and arginine were selected as the three major nitrogen sources found in grape juice. This allowed assessing the pattern of nitrogen redistribution from consumed compounds to the proteinogenic amino acids. We also aimed to investigate the fate of the carbon backbone of the consumed amino acids and their contribution to the production of volatile molecules. To meet this objective, uniformly 13C-labeled leucine, isoleucine, threonine, and valine were included in the study as amino acids that are derived from major intermediates of the Ehrlich pathway.
Overall, we quantitatively explored how yeast manages a complex nitrogen resource by redistributing exogenous nitrogen sources to fulfill its anabolic requirements throughout fermentation while additionally removing the excess of carbon precursors as volatile molecules. The experimental procedure reported in this paper can be applied to investigate other multiple nutrient sources used by any other microorganism. It appears to be an appropriate approach for the analysis of the impact of genetic background or environmental conditions on the metabolic behavior of microorganisms.
1. Fermentation and Sampling
2. Quantification of Consumed Nitrogen Sources
3. Quantification of Proteinogenic Amino Acids
4. Measurement of Isotopic Enrichment of Proteinogenic Amino Acids
NOTE: For the measurement of isotopic enrichment of proteinogenic amino acids, use the labeled cell pellets. Three different agents are used for the derivatization step to quantify the isotopic enrichment of amino acids. The intensities of cluster ions are measured to estimate the labeling patterns of the amino acids. The signal from each cluster ion corresponds to the abundance of the mass isotopomers (m0 = without labeling, m+1 = 1 labeled atom, …) of an amino acid fragment. An example of a chromatogram that is obtained after the DMADMF procedure is provided in Figure 2.
5. Quantification and Isotopic Enrichment of Volatile Compounds
6. Calculations for an Integrated Analysis of the Dataset
Figure 3 presents a schematic diagram of the workflow that was implemented to investigate the management by yeast of the multiple nitrogen sources that are found during wine fermentation.
For different points of sampling, the biological parameters–growth characteristics, nitrogen consumption patterns, and the profile of proteinogenic amino acids–show a high reproducibility among fermentations (Figure 4). This consistency validates the relevance of the approach based on the combination of data that was generated during a set of 14 independent fermentations.
Figure 5 shows a comprehensive overview of the redistribution of nitrogen from the sources that are available in the grape juice to all the proteinogenic amino acids. This analysis is mainly supported by the results that were obtained from experiments carried out in the presence of 15N-labeled compounds. Combined with the determination of the mass and isotopic balances, the measurement of the isotopic enrichment of proteinogenic amino acids provided the first quantification of the contribution of nitrogen-originated arginine, glutamine, ammonium, and other sources to the amine groups of each of these compounds. The important redistribution of nitrogen that is underlined here reflects the substantial role of de novo synthesis of amino acids in sustaining yeast growth.
Furthermore, this study, comparing the amounts of labeled compounds in proteins with their consumption, allows the fraction of consumed N-containing molecules that are directly incorporated into proteins when they enter the cells to be assessed. Proteinogenic amino acids are differentiated according to their level of direct incorporation in the biomass; some of them are exclusively (Asp, Glu) or more than 80% de novo synthetized, while only small amounts of other compounds are generated by de novo synthesis. Interestingly, this last group includes lysine and histidine, which are the only amino acids in which all the consumed sources are directly incorporated.
Figure 5B also presents, for some amino acids, a comparison between the amount of consumed amino acid that is directly recovered into the biomass, calculated from data obtained in 15N-labeling experiments and measured experimentally in 13C-labeling experiments. The small differences between these values show the reliability and the robustness of the approach that was implemented in this study.
Figure 6 shows an example of quantitative partitioning (ratios) of fluxes through metabolic pathways that was obtained by implementing the workflow reported in this paper. This map was drawn by combining information from isotopic tracer experiments using 15N- and 13C-labeled substrates and describes the partitioning of the consumed aliphatic amino acids in the metabolic network that is involved in valine and leucine biosynthesis in yeast (for calculations, refer to Table 1 and Table 2). By measuring the production and isotopic enrichment of both proteinogenic amino acids and volatile compounds, we assessed the amount of these compounds that were synthetized from the carbon backbones of consumed U-C13-valine and U-C13-leucine, which corresponds to the labeled fraction of the compounds. Subsequently, we were able to determine the contribution of CCM to their formation (the unlabeled fraction of the compounds). Carbon mass balances set up using the workflow and the calculation procedure that is proposed here show that more than 96% of the consumed valine and leucine are recovered in their conversion products, namely, proteinogenic leucine and valine and volatile higher alcohols. This finding confirms the suitability of the reported approach for quantitative studies of metabolism. The integrated and comprehensive analysis of the dataset offers new insights in the fate of consumed amino acids. Surprisingly, a substantial fraction of valine and leucine are catabolized despite a considerable imbalance between the availability of these compounds in the medium and their content in biomass. However, the fraction of exogenous N-compounds directly incorporated into the proteins depends on the nature of the amino acid. Another key point concerns the metabolic origin of proteinogenic amino acids and volatile molecules. The analysis of the 13C-labeling pattern of proteinogenic leucine and valine reveals that the carbon skeleton of these amino acids mainly comes from precursors that were synthetized through the CCM. In line with this observation, the low incorporation of labeling in isobutanol and isoamyl alcohol demonstrates the very limited involvement of the catabolism of valine and leucine that were consumed by yeast in the formation of these higher alcohols.
Figure 1. Automated robotic system for monitoring high-throughput fermentations. (A) Robotic platform. Fermenters (a) are placed in support guides on magnetic stirring plates (b). A safety light curtain (c) protects the users. A robotic arm (d) moves the fermenters successively from their location on one of the 6 stirring plates to a precision balance (e) on the left of the worktable, to measure the weight every 4 hours. The robotic platform is located in a temperature-controlled room. (B) Schematic representation of the software architecture. A graphical interface enables the users to define the experimental settings. This information is transmitted to the control application that controls the robotic arm and records the different weight in different raw data.xlm files (1 file/fermentation). The calculation software then collects the xlm files and calculates, for each time point, the amount of CO2 that is released (expressed in g/L), which is proportional to the amount of sugars that have been consumed at this time, and the fermentation rate, which corresponds to the rate of CO2 production, in g CO2/L/h (proportional to the rate of sugar consumption). Data are stored in a relational database and visualized using a devoted graphical interface. Please click here to view a larger version of this figure.
Figure 2. GC-MS analysis of proteinogenic amino acids: example of alanine. (A) Chromatogram obtained after derivatization of proteinogenic amino acids using DMFDMA. (B) Mass spectra of derivatized alanine. Two main peaks that correspond to fragments with m0/z = 99 and m0/z = 158. (C) Derivatization reaction of alanine with DMFDMA. Please click here to view a larger version of this figure.
Figure 3. Workflow for the quantitative analysis of the metabolism of multiple nitrogen sources by yeast. This process includes (i) a set of 28 fermentations (7 nitrogen sources and fermentations with or without nitrogen compounds in duplicate); (ii) an analytical part that involves different procedures of extraction and derivatization of molecules and HPLC or GM-MS analyses and (iii) processing of raw data and an integrated analysis of the datasets. Please click here to view a larger version of this figure.
Figure 4. Reproducibility of biological parameters from a set of independent fermentations. (A-B) Mean values and standard deviations of dry weight and protein content that were obtained from the 14 independent fermentations carried out using unlabeled compounds. (C-D) Mean values and standard deviations of proteinogenic and consumed amino acids that were measured during 14 independent fermentations carried out using unlabeled compounds. CO2 production: 5 (green), 10 (blue), 40 (pink) and 90 (purple) g/L. Please click here to view a larger version of this figure.
Figure 5. Redistribution of nitrogen from the three major nitrogen sources to proteinogenic amino acids during fermentation. (A) Metabolic origin of proteinogenic amino acids: direct incorporation of consumed counterpart (blue) and de novo synthesis using nitrogen provided by consumed arginine (orange), glutamine (green), ammonium (pink) or other amino acids (purple). Expressed as a percentage of the total amount of each proteinogenic amino acid. (B) Comparison between the amount of consumed amino acid (blue) and the part of the consumed amino acid that is directly recovered in proteins, calculated from the data that were obtained in 15N tracer experiments (purple) or experimentally measured during fermentation with 13C-labeled molecules (pink). Please click here to view a larger version of this figure.
Figure 6. Quantitative analysis of valine and leucine metabolism. Partitioning of consumed leucine (green) and valine (blue) through the metabolic network when 40 g/L of CO2 are produced. The bars are proportional to the amount of each compound and are expressed in µM, including the fraction that was synthetized from central carbon metabolism (orange). Values in the regular font: amount in µM; values in the italic font: percentage of consumed valine (blue) or leucine (green) that was catabolized through the pathway. The calculations are provided in Table 1 and Table 2. Please click here to view a larger version of this figure.
Compounds | Amount per liter |
Glucose C6H12O6 | 240 g |
Malic acid C4H6O5 | 6 g |
Citric acid C6H8O7 | 6 g |
Potassium phosphate KH2PO4 | 0.75g |
Potassium sulfate K2SO4 | 0.5 g |
Magnesium sulfate MgSO4, 7H2O | 0.25 g |
Calcium chloride CaCl2, 2H2O | 0.155g |
Sodium chloride NaCl | 0.2g |
Myo-inositol | 20 mg |
Calcium pantothenate | 1.5 mg |
Thiamin hydrochloride | 0.223 mg |
Nicotinic acid | 2 mg |
Pyridoxine | 0.25 mg |
Biotin | 0.003 |
MnSO4·H2O | 4 mg |
ZnSO4·7H2O | 4 mg |
CuSO4·5H2O | 1 mg |
CoCl2·6H2O | 0.4 mg |
H3BO3 | 1 mg |
(NH4)6Mo7O24 | 1 mg |
Ergosterol | 3.75 mg |
Oleic acid | 1.25 µL |
Tween 80 | 125 µL |
Tyrosine | 8.6 mg |
Tryptophan | 84.4 mg |
Isoleucine | 15.4 mg |
Aspartate | 20.9 mg |
Glutamate | 56.7 mg |
Arginine | 176.2 mg |
Leucine | 22.8 mg |
Threonine | 35.7 mg |
Glycine | 8.6 mg |
Glutamine | 237.8 mg |
Alanine | 68.4 mg |
Valine | 20.9 mg |
Methionine | 14.8 mg |
Phenylalanine | 17.9 mg |
Serine | 37.0 mg |
Histidine | 15.4mg |
Lysine | 8.0 mg |
Cysteine | 6.2 mg |
Proline | 288.3 mg |
Ammonia chloride NH4Cl | 220 mg |
The pH of the medium was adjusted to 3.3 with NaOH 10 M. |
Table 1. Composition of the synthetic medium used in this study. This chemically defined medium mimics the composition of grape juice.
Elution buffer | Temperature | |
From 0 to 6 min | Lithium citrate, 200 mM, pH 2.8 | 32 °C |
From 6 to 38 min | Lithium citrate, 300 mM, pH 3 | 32 °C |
From 38 to 57 min | Lithium citrate, 500 mM, pH 3.15 | 64.5 °C |
From 57 to 83 min | Lithium citrate, 900 mM, pH 3.5 | 75 °C |
From 83 to 120 min | Lithium citrate, 1650 mM, pH 3.55 | 75 °C |
From 120 to 130 min | Lithium hydroxyde, 300 mM |
Table 2. Conditions used for the separation of amino acids by ion-exchange chromatography. Elution buffers with increased salt concentrations are used successively in combination with a temperature gradient to enable the separation of the amino acids.
Amino acids | Derivatizing reagent | RT (min) | Ion clusters (m/z) |
Alanine | ECFa | 3.86 | 116, 117, 118, 119 |
DMFDMAb | 6.37 | 99, 100, 101, 102 | |
DMFDMAb | 6.37 | 158, 159, 160, 161 | |
Glycine | ECFa | 4.19 | 102, 103, 104 |
ECFa | 4.19 | 175, 176, 177 | |
DMFDMAb | 6.61 | 85, 86, 87 | |
DMFDMAb | 6.61 | 144, 145, 146 | |
Valine | ECFa | 4.97 | 144, 145, 146, 147, 148, 149 |
DMFDMAb | 7.37 | 127, 128, 129, 130, 131, 132 | |
DMFDMAb | 7.37 | 143, 144, 145, 146, 147, 148 | |
DMFDMAb | 7.37 | 186, 187, 188, 189, 190, 191 | |
Leucine | ECFa | 5.67 | 158, 159, 160, 161, 162, 163, 164 |
Isoleucine | ECFa | 5.85 | 158, 159, 160, 161, 162, 163, 165 |
Threonine | ECFa | 6.48 | 146, 147, 148, 149, 150 |
ECFa | 6.48 | 175, 176, 177, 178, 179 | |
Serine | ECFa | 6.53 | 132, 133, 134, 135 |
ECFa | 6.53 | 175, 176, 177, 178 | |
Proline | ECFa | 6.83 | 142, 143, 144, 145, 146, 147 |
Aspartate | ECFa | 7.89 | 188, 189, 190, 191, 192 |
DMFDMAb | 11.77 | 115, 116, 117, 118, 119 | |
DMFDMAb | 11.77 | 216, 217, 218, 219, 220 | |
Glutamate | ECFa | 8.81 | 202, 203, 204, 205, 206, 207 |
DMFDMAb | 12.75 | 111, 112, 113, 114, 115, 116 | |
DMFDMAb | 12.75 | 143, 144, 145, 146, 147, 148 | |
DMFDMAb | 12.75 | 230, 231, 232, 233, 234, 235 | |
Phenylalanine | ECFa | 9.53 | 192, 193, 194, 195, 196, 197, 198, 199, 200, 201 |
DMFDMAb | 13.67 | 143, 144, 145, 146, 147, 148, 149, 150, 151, 152 | |
Lysine | ECFa | 11.95 | 156, 157, 158, 159, 160, 161, 162 |
Histidine | ECFa | 12.54 | 327, 328, 329, 330, 331, 332, 333 |
Arginine | BSTFAc | 18.8 | 174, 175, 176, 177, 178, 179 |
aECF, ethyl chloroformate ; bDMFDMA, (N,N)-dimethylformamide dibutyl acetal ; cBSTFA, (N,O-bis(trimethylsilyl)trifluoroacetamide). |
Table 3. Analytical parameters used for the determination of isotopic enrichments of amino acids using selected ion monitoring (SIM) mode. This table summarizes the chemical agents used for derivatization, the retention time of derivatized compounds and the cluster of ions obtained in MS (mode Electron Impact) for each amino acid.
Amino acids | Derivatizing reagent | RT (min) | Ion clusters (m/z) |
Alanine | ECFa | 3.86 | 116, 117, 118, 119 |
DMFDMAb | 6.37 | 99, 100, 101, 102 | |
DMFDMAb | 6.37 | 158, 159, 160, 161 | |
Glycine | ECFa | 4.19 | 102, 103, 104 |
ECFa | 4.19 | 175, 176, 177 | |
DMFDMAb | 6.61 | 85, 86, 87 | |
DMFDMAb | 6.61 | 144, 145, 146 | |
Valine | ECFa | 4.97 | 144, 145, 146, 147, 148, 149 |
DMFDMAb | 7.37 | 127, 128, 129, 130, 131, 132 | |
DMFDMAb | 7.37 | 143, 144, 145, 146, 147, 148 | |
DMFDMAb | 7.37 | 186, 187, 188, 189, 190, 191 | |
Leucine | ECFa | 5.67 | 158, 159, 160, 161, 162, 163, 164 |
Isoleucine | ECFa | 5.85 | 158, 159, 160, 161, 162, 163, 165 |
Threonin | ECFa | 6.48 | 146, 147, 148, 149, 150 |
ECFa | 6.48 | 175, 176, 177, 178, 179 | |
Serine | ECFa | 6.53 | 132, 133, 134, 135 |
ECFa | 6.53 | 175, 176, 177, 178 | |
Proline | ECFa | 6.83 | 142, 143, 144, 145, 146, 147 |
Aspartate | ECFa | 7.89 | 188, 189, 190, 191, 192 |
DMFDMAb | 11.77 | 115, 116, 117, 118, 119 | |
DMFDMAb | 11.77 | 216, 217, 218, 219, 220 | |
Glutamate | ECFa | 8.81 | 202, 203, 204, 205, 206, 207 |
DMFDMAb | 12.75 | 111, 112, 113, 114, 115, 116 | |
DMFDMAb | 12.75 | 143, 144, 145, 146, 147, 148 | |
DMFDMAb | 12.75 | 230, 231, 232, 233, 234, 235 | |
Phenylalanine | ECFa | 9.53 | 192, 193, 194, 195, 196, 197, 198, 199, 200, 201 |
DMFDMAb | 13.67 | 143, 144, 145, 146, 147, 148, 149, 150, 151, 152 | |
Lysine | ECFa | 11.95 | 156, 157, 158, 159, 160, 161, 162 |
Histidine | ECFa | 12.54 | 327, 328, 329, 330, 331, 332, 333 |
Arginine | BSTFAc | 18.8 | 174, 175, 176, 177, 178, 179 |
Table 4. Analytical parameters used for the determination of isotopic enrichments and quantification of volatile compounds using selected ion monitoring (SIM) mode. This table summarizes the retention time and the cluster of ions obtained in MS (mode Electron Impact) for each volatile compound.
Table 5. Processing of raw data obtained from the quantification of residual amino acids Dataset contains data from measurements of initial and residual amino acids. These values are used to calculate the amount of consumed amino acid (by subtraction). Means and standard deviations are calculated for further calculations. Please click here to download this table.
Table 6. Processing of raw data obtained from the quantification of proteinogenic amino acids. This dataset contains data on the composition in amino acids of biomass hydrolysates (A) and the fraction of proteins in biomass (B). These values are used to assess the mass percentage of each amino acid in proteins (C). Means and standard deviations are calculated for further calculations. Please click here to download this table.
Table 7. Proteinogenic amino acids. This spreadsheet is used to calculate the concentration (in mM) of proteinogenic amino acids from data summarized in Table 5 and Table 6. Means and standard deviations are calculated for further calculations. Please click here to download this table.
Table 8. Processing of raw data obtained from the quantification of higher alcohols. Dataset includes data from the measurements of volatile compound concentrations (A) and converts data expressed in units of mg/L to µM (B). Means and standard deviations are calculated for further calculations. Please click here to download this table.
Table 9. Processing of raw data obtained from the determination of isotopic enrichments of proteinogenic amino acids using 15N-labeled substrates.
Means and standard deviations are calculated for further calculations. Please click here to download this table.
Table 10. Processing of raw data obtained from the determination of isotopic enrichments of proteinogenic amino acids and volatile compounds using 13C-labeled substrates.
Means and standard deviations are calculated for further calculations. Please click here to download this table.
Quantifying the partitioning of compounds through metabolic networks using isotopic tracer experiments is a promising approach for understanding the operation of microbial metabolism. This methodology, while successfully applied with one or two labeled substrates, cannot currently be implemented to study metabolism of various sources using multiple labeled elemental isotopes (i.e., more than two substrates). Indeed, the available analytical techniques enable the accurate determination of the labeling patterns of proteinogenic amino acids and molecules exclusively when using isotopes of a single element and possibly when co-labeling with two elements. Consequently, to address these limitations and assess the management of multiple nutrient sources by microorganisms, we chose to repeat a set of cultures under the same environmental conditions while labeling a selected substrate with 13C or 15N isotopes. Then, further combination of the specific information that is provided by each 13C or 15N tracer experiment offers a quantitative extended view of the metabolism of multiple sources.
The achievement of the reported approach relies on the implementation of a reproducible series of cultures under standard conditions and on the collection of samples from a population of cells in a defined physiological state that is the same for all cultures (cell growth, consumption of substrate, etc.). These conditions must be satisfied so that data obtained from independent experiments can be mixed and analyzed together. Satisfying these constraints requires accurate and frequent monitoring of the microbial activity which was carried out, in the experimental work reported here, using a robot-assisted system for online monitoring of yeast fermentation activity. However, more conventional methods for microorganism cultivation and monitoring, such as the determination of cell growth by measuring the optical density, can be used to normalize the sampling procedure.
Another prerequisite for carrying out this methodology is to have a clear vision of the metabolic pathways that are involved in the network to be investigated. This knowledge is essential for choosing the set of labeled substrates that are the most appropriate for dealing with the issue being studied. The labeled compounds as well as the nature and position of the labeling (13C, 15N, or others) within the molecules must be selected suitably in order to i) recover all the labels provided by the substrates in the compounds that are derived from their catabolism and are further analyzed in the procedure and ii) obtain redundant data that demonstrate the accuracy of the methodology and the validity of the findings. The procedure described here also includes an important number of analyses that could be time-consuming and costly in human and financial resources. Moreover, some analytical constraints can limit the use of this methodology. First, users should ensure that all the analytical methods that are required for the quantification of the compounds produced during microbial metabolism and for the determination of their isotopic enrichment are available. Second, this approach only applies to assessing the metabolism of substrates for which all the conversion compounds are produced in sufficient amounts to be accurately quantified.
In this paper, we applied this workflow to explore the management of multiple nitrogen sources by yeast during wine fermentation. This report offered new insights on yeast metabolism, including the substantial catabolism of most consumed amino acids, combined with their low direct incorporation into proteins, and the major contribution of CCM to supply precursors that are further used for both the de novo synthesis of proteinogenic amino acids and the formation of volatile molecules.
More broadly, the approach that is described here can be used for quantifying the partitioning of multiple substrates through the metabolic network of any microorganism. It will allow researchers to elucidate the fate of all consumed compounds after they enter the cells and to address the identification of the metabolic origin of precursors and products. This information can be useful for comparing the metabolic activity of strains that have different genetic backgrounds or grow under different conditions and, consequently, for designing rational strategies to improve fermentation processes.
The authors have nothing to disclose.
We thank Jean-Roch Mouret, Sylvie Dequin and Jean-Marie Sabalyrolles for contributing to the conception of the robotic-assisted fermentation system and Martine Pradal, Nicolas Bouvier and Pascale Brial for their technical support. Funding for this project was provided by the Ministère de l'Education Nationale, de la Recherche et de la Technologie.
D-Glucose | PanReac | 141341.0416 | |
D-Fructose | PanReac | 142728.0416 | |
DL-Malic acid | Sigma Aldrich | M0875 | |
Citric acid monohydrate | Sigma Aldrich | C7129 | |
Potassium phosphate monobasic | Sigma Aldrich | P5379 | |
Potassium sulfate | Sigma Aldrich | P0772 | |
Magnesium sulfate heptahydrate | Sigma Aldrich | 230391 | |
Calcium chloride dihydrate | Sigma Aldrich | C7902 | |
Sodium chloride | Sigma Aldrich | S9625 | |
Ammonium chloride | Sigma Aldrich | A4514 | |
Sodium hydroxide | Sigma Aldrich | 71690 | |
Manganese sulfate monohydrate | Sigma Aldrich | M7634 | |
Zinc sulfate heptahydrate | Sigma Aldrich | Z4750 | |
Copper (II) sulfate pentahydrate | Sigma Aldrich | C7631 | |
Potassium iodine | Sigma Aldrich | P4286 | |
Cobalt (II) chloride hexahydrate | Sigma Aldrich | C3169 | |
Boric acid | Sigma Aldrich | B7660 | |
Ammonium heptamolybdate | Sigma Aldrich | A7302 | |
Myo-inositol | Sigma Aldrich | I5125 | |
D-Pantothenic acid hemicalcium salt | Sigma Aldrich | 21210 | |
Thiamine, hydrochloride | Sigma Aldrich | T4625 | |
Nicotinic acid | Sigma Aldrich | N4126 | |
Pyridoxine | Sigma Aldrich | P5669 | |
Biotine | Sigma Aldrich | B4501 | |
Ergostérol | Sigma Aldrich | E6510 | |
Tween 80 | Sigma Aldrich | P1754 | |
Ethanol absolute | VWR Chemicals | 101074F | |
Iron (III) chloride hexahydrate | Sigma Aldrich | 236489 | |
L-Aspartic acid | Sigma Aldrich | A9256 | |
L-Glutamic acid | Sigma Aldrich | G1251 | |
L-Alanine | Sigma Aldrich | A7627 | |
L-Arginine | Sigma Aldrich | A5006 | |
L-Cysteine | Sigma Aldrich | C7352 | |
L-Glutamine | Sigma Aldrich | G3126 | |
Glycine | Sigma Aldrich | G7126 | |
L-Histidine | Sigma Aldrich | H8000 | |
L-Isoleucine | Sigma Aldrich | I2752 | |
L-Leucine | Sigma Aldrich | L8000 | |
L-Lysine | Sigma Aldrich | L5501 | |
L-Methionine | Sigma Aldrich | M9625 | |
L-Phenylalanine | Sigma Aldrich | P2126 | |
L-Proline | Sigma Aldrich | P0380 | |
L-Serine | Sigma Aldrich | S4500 | |
L-Threonine | Sigma Aldrich | T8625 | |
L-Tryptophane | Sigma Aldrich | T0254 | |
L-Tyrosine | Sigma Aldrich | T3754 | |
L-Valine | Sigma Aldrich | V0500 | |
13C5-L-Valine | Eurisotop | CLM-2249-H-0.25 | |
13C6-L-Leucine | Eurisotop | CLM-2262-H-0.25 | |
15N-Ammonium chloride | Eurisotop | NLM-467-1 | |
ALPHA-15N-L-Glutamine | Eurisotop | NLM-1016-1 | |
U-15N4-L-Arginine | Eurisotop | NLM-396-PK | |
Ethyl acetate | Sigma Aldrich | 270989 | |
Ethyl propanoate | Sigma Aldrich | 112305 | |
Ethyl 2-methylpropanoate | Sigma Aldrich | 246085 | |
Ethyl butanoate | Sigma Aldrich | E15701 | |
Ethyl 2-methylbutanoate | Sigma Aldrich | 306886 | |
Ethyl 3-methylbutanoate | Sigma Aldrich | 8.08541.0250 | |
Ethyl hexanoate | Sigma Aldrich | 148962 | |
Ethyl octanoate | Sigma Aldrich | W244910 | |
Ethyl decanoate | Sigma Aldrich | W243205 | |
Ethyl dodecanoate | Sigma Aldrich | W244112 | |
Ethyl lactate | Sigma Aldrich | W244015 | |
Diethyl succinate | Sigma Aldrich | W237701 | |
2-methylpropyl acetate | Sigma Aldrich | W217514 | |
2-methylbutyl acetate | Sigma Aldrich | W364401 | |
3-methyl butyl acetate | Sigma Aldrich | 287725 | |
2-phenylethyl acetate | Sigma Aldrich | 290580 | |
2-methylpropanol | Sigma Aldrich | 294829 | |
2-methylbutanol | Sigma Aldrich | 133051 | |
3-methylbutanol | Sigma Aldrich | 309435 | |
Hexanol | Sigma Aldrich | 128570 | |
2-phenylethanol | Sigma Aldrich | 77861 | |
Propanoic acid | Sigma Aldrich | 94425 | |
Butanoic acid | Sigma Aldrich | 19215 | |
2-methylpropanoic acid | Sigma Aldrich | 58360 | |
2-methylbutanoic acid | Sigma Aldrich | 193070 | |
3-methylbutanoic acid | Sigma Aldrich | W310212 | |
Hexanoic acid | Sigma Aldrich | 153745 | |
Octanoic acid | Sigma Aldrich | W279900 | |
Decanoic acid | Sigma Aldrich | W236403 | |
Dodecanoic acid | Sigma Aldrich | L556 | |
Fermentor 1L | Legallais | AT1357 | Fermenter handmade for fermentation |
Disposable vacuum filtration system | Dominique Deutscher | 029311 | |
Fermenters (250 ml) | Legallais | AT1352 | Fermenter handmade for fermentation |
Sterile tubes | Sarstedt | 62.554.502 | |
Fermentation locks | Legallais | AT1356 | Fermetation locks handmade for fermentation |
BactoYeast Extract | Becton, Dickinson and Company | 212750 | |
BactoPeptone | Becton, Dickinson and Company | 211677 | |
Incubator shaker | Infors HT | ||
Particle Counter | Beckman Coulter | 6605697 | Multisizer 3 Coulter Counter |
Centrifuge | Jouan | GR412 | |
Plate Butler Robotic system | Lab Services BV | PF0X-MA | Automatic instrument |
Plate Butler Software | Lab Services BV | Robot monitor software | |
RobView | In-house developed calculation software | ||
My SQL | International source database | ||
Cimarec i Telesystem Multipoint Stirrers | Thermo Fisher Scientific | 50088009 | String Drive 60 |
BenchBlotter platform rocker | Dutscher | 60903 | |
Ammonia enzymatic kit | R-Biopharm AG | 5390 | |
Spectrophotometer cuvettes | VWR | 634-0678 | |
Spectrophotometer UviLine 9400 | Secomam | ||
Amino acids standards physiological – acidics and neutrals | Sigma Aldrich | A6407 | |
Amino acids standards physiological – basics | Sigma Aldrich | A6282 | |
Citrate lithium buffers – Ultra ninhydrin reagent | Biochrom | BC80-6000-06 | |
Sulfosalycilic acid | Sigma Aldrich | S2130 | |
Norleucine | Sigma Aldrich | N1398 | |
Biochrom 30 AAA | Biochrom | ||
EZChrom Elite | Biochrom | Instrument control and Data analysis software | |
Ultropac 8 resin Lithium | Biochrom | BC80-6002-47 | Lithium High Resolution Physiological Column |
Filter Millex GV | Merck Millipore | SLGVX13NL | Millex GV 13mm (pore size 0.22 µm) |
Membrane filter PALL | VWR | 514-4157 | Supor-450 47mm 0.45µm |
Vacuum pump Millivac Mini | Millipore | XF5423050 | |
Aluminium smooth weigh dish 70mm | VWR | 611-1380 | |
Precision balance | Mettler | Specifications AE163 | |
Dimethyl sulfoxid dried | Merck | 1029310161 | (max. 0.025% H2O) SeccoSolv |
Combustion oven | Legallais | ||
Pierce BCA protein assay kit | Interchim | UP40840A | |
Formic acid | Fluka | 94318 | |
Hydrogen peroxide | Sigma Aldrich | H1009 | |
Hydrochloric Acid Fuming 37% Emsure | Merck | 1003171000 | Grade ACS,ISO,Reag. Ph Eur |
Lithium acetate buffer | Biochrom | 80-2038-10 | |
Commercial solution of hydrolyzed amino acids | Sigma Aldrich | AAS18 | |
L-Methionine sulfone | Sigma Aldrich | M0876 | |
L-Cysteic acid monohydrate | Sigma Aldrich | 30170 | |
Pyrex glass culture tubes | Sigma Aldrich | Z653586 | |
Pyridine | Acros Organics | 131780500 | 99% Extrapure |
Ethyl chloroformate | Sigma Aldrich | 23131 | |
Dichloromethane | Sigma Aldrich | 32222 | |
Vials | Sigma Aldrich | 854165 | |
Microinserts for 1.5ml vials | Sigma Aldrich | SU860066 | |
GC/MS | Agilent Technologies | 5890 GC/5973 MS | |
Chemstation | Agilent Technologies | Instrument control and data analysis software | |
Methanol | Sigma Aldrich | 34860 | Chromasolv, for HPLC |
Acetonitrile | Sigma Aldrich | 34998 | ChromasolvPlus, for HPLC |
N,N-Dimethylformamide dimethyl acetal | Sigma Aldrich | 394963 | |
BSTFA | Sigma Aldrich | 33024 | |
DB-17MS column | Agilent Technologies | 122-4731 | 30m*0.25mm*0.15µm |
Sodium sulfate, anhydrous | Sigma Aldrich | 238597 | |
Technical nitrogen | Air products | 14629 | |
Zebron ZB-WAX column | Phenomenex | 7HG-G007-11 | 30m*0.25mm*0.25µm |
Helium BIP | Air products | 26699 | |
Glass Pasteur pipettes | VWR | 612-1702 |