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 Minist&#232;re de l&#39;Education Nationale, de la Recherche et de la Technologie.

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<li>Kruger, N. J., Ratcliffe, R. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Insights+into+plant+metabolic+networks+from+steady-state+metabolic+flux+analysis%5BTitle%5D)+AND+%22Biochimie%22%5BJournal%5D)">Insights into plant metabolic networks from steady-state metabolic flux analysis.</a> Biochimie. 91, 697-702 (2009).</li>
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The authors have nothing to disclose.

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...

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 &#945;-ketoglutarate10,11, with transaminases and deaminases. Through this network, amine groups from ammonium or other amino acids are gathered and &#945;-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, &#945;-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.

<table><tbody><tr><td>D-Glucose</td><td>PanReac</td><td>141341.0416</td><td></td></tr><tr><td>D-Fructose</td><td>PanReac</td><td>142728.0416</td><td></td></tr><tr><td>DL-Malic acid</td><td>Sigma Aldrich</td><td>M0875</td><td></td></tr><tr><td>Citric acid monohydrate</td><td>Sigma Aldrich</td><td>C7129</td><td></td></tr><tr><td>Potassium phosphate monobasic</td><td>Sigma Aldrich</td><td>P5379</td><td></td></tr><tr><td>Potassium sulfate</td><td>Sigma Aldrich</td><td>P0772</td><td></td></tr><tr><td>Magnesium sulfate heptahydrate</td><td>Sigma Aldrich</td><td>230391</td><td></td></tr><tr><td>Calcium chloride dihydrate</td><td>Sigma Aldrich</td><td>C7902</td><td></td></tr><tr><td>Sodium chloride</td><td>Sigma Aldrich</td><td>S9625</td><td></td></tr><tr><td>Ammonium chloride</td><td>Sigma Aldrich</td><td>A4514</td><td></td></tr><tr><td>Sodium hydroxide</td><td>Sigma Aldrich</td><td>71690</td><td></td></tr><tr><td>Manganese sulfate monohydrate</td><td>Sigma Aldrich</td><td>M7634</td><td></td></tr><tr><td>Zinc sulfate heptahydrate</td><td>Sigma Aldrich</td><td>Z4750</td><td></td></tr><tr><td>Copper (II) sulfate pentahydrate</td><td>Sigma Aldrich</td><td>C7631</td><td></td></tr><tr><td>Potassium iodine</td><td>Sigma Aldrich</td><td>P4286</td><td></td></tr><tr><td>Cobalt (II) chloride hexahydrate</td><td>Sigma Aldrich</td><td>C3169</td><td></td></tr><tr><td>Boric acid</td><td>Sigma Aldrich</td><td>B7660</td><td></td></tr><tr><td>Ammonium heptamolybdate</td><td>Sigma Aldrich</td><td>A7302</td><td></td></tr><tr><td>Myo-inositol</td><td>Sigma Aldrich</td><td>I5125</td><td></td></tr><tr><td>D-Pantothenic acid hemicalcium salt</td><td>Sigma Aldrich</td><td>21210</td><td></td></tr><tr><td>Thiamine, hydrochloride</td><td>Sigma Aldrich</td><td>T4625</td><td></td></tr><tr><td>Nicotinic acid</td><td>Sigma Aldrich</td><td>N4126</td><td></td></tr><tr><td>Pyridoxine</td><td>Sigma Aldrich</td><td>P5669</td><td></td></tr><tr><td>Biotine</td><td>Sigma Aldrich</td><td>B4501</td><td></td></tr><tr><td>Ergost&amp;eacute;rol</td><td>Sigma Aldrich</td><td>E6510</td><td></td></tr><tr><td>Tween 80</td><td>Sigma Aldrich</td><td>P1754</td><td></td></tr><tr><td>Ethanol absolute</td><td>VWR Chemicals</td><td>101074F</td><td></td></tr><tr><td>Iron (III) chloride hexahydrate</td><td>Sigma Aldrich</td><td>236489</td><td></td></tr><tr><td>L-Aspartic acid</td><td>Sigma Aldrich</td><td>A9256</td><td></td></tr><tr><td>L-Glutamic acid</td><td>Sigma Aldrich</td><td>G1251</td><td></td></tr><tr><td>L-Alanine</td><td>Sigma Aldrich</td><td>A7627</td><td></td></tr><tr><td>L-Arginine</td><td>Sigma Aldrich</td><td>A5006</td><td></td></tr><tr><td>L-Cysteine</td><td>Sigma Aldrich</td><td>C7352</td><td></td></tr><tr><td>L-Glutamine</td><td>Sigma Aldrich</td><td>G3126</td><td></td></tr><tr><td>Glycine</td><td>Sigma Aldrich</td><td>G7126</td><td></td></tr><tr><td>L-Histidine</td><td>Sigma Aldrich</td><td>H8000</td><td></td></tr><tr><td>L-Isoleucine</td><td>Sigma Aldrich</td><td>I2752</td><td></td></tr><tr><td>L-Leucine</td><td>Sigma Aldrich</td><td>L8000</td><td></td></tr><tr><td>L-Lysine</td><td>Sigma Aldrich</td><td>L5501</td><td></td></tr><tr><td>L-Methionine</td><td>Sigma Aldrich</td><td>M9625</td><td></td></tr><tr><td>L-Phenylalanine</td><td>Sigma Aldrich</td><td>P2126</td><td></td></tr><tr><td>L-Proline</td><td>Sigma Aldrich</td><td>P0380</td><td></td></tr><tr><td>L-Serine</td><td>Sigma Aldrich</td><td>S4500</td><td></td></tr><tr><td>L-Threonine</td><td>Sigma Aldrich</td><td>T8625</td><td></td></tr><tr><td>L-Tryptophane</td><td>Sigma Aldrich</td><td>T0254</td><td></td></tr><tr><td>L-Tyrosine</td><td>Sigma Aldrich</td><td>T3754</td><td></td></tr><tr><td>L-Valine</td><td>Sigma Aldrich</td><td>V0500</td><td></td></tr><tr><td>&lt;sup&gt;13&lt;/sup&gt;C5-L-Valine</td><td>Eurisotop</td><td>CLM-2249-H-0.25</td><td></td></tr><tr><td>&lt;sup&gt;13&lt;/sup&gt;C6-L-Leucine</td><td>Eurisotop</td><td>CLM-2262-H-0.25</td><td></td></tr><tr><td>&lt;sup&gt;15&lt;/sup&gt;N-Ammonium chloride</td><td>Eurisotop</td><td>NLM-467-1</td><td></td></tr><tr><td>ALPHA-&lt;sup&gt;15&lt;/sup&gt;N-L-Glutamine</td><td>Eurisotop</td><td>NLM-1016-1</td><td></td></tr><tr><td>U-&lt;sup&gt;15&lt;/sup&gt;N4-L-Arginine</td><td>Eurisotop</td><td>NLM-396-PK</td><td></td></tr><tr><td>Ethyl acetate</td><td>Sigma Aldrich</td><td>270989</td><td></td></tr><tr><td>Ethyl propanoate</td><td>Sigma Aldrich</td><td>112305</td><td></td></tr><tr><td>Ethyl 2-methylpropanoate</td><td>Sigma Aldrich</td><td>246085</td><td></td></tr><tr><td>Ethyl butanoate</td><td>Sigma Aldrich</td><td>E15701</td><td></td></tr><tr><td>Ethyl 2-methylbutanoate</td><td>Sigma Aldrich</td><td>306886</td><td></td></tr><tr><td>Ethyl 3-methylbutanoate</td><td>Sigma Aldrich</td><td>8.08541.0250</td><td></td></tr><tr><td>Ethyl hexanoate</td><td>Sigma Aldrich</td><td>148962</td><td></td></tr><tr><td>Ethyl octanoate</td><td>Sigma Aldrich</td><td>W244910</td><td></td></tr><tr><td>Ethyl decanoate</td><td>Sigma Aldrich</td><td>W243205</td><td></td></tr><tr><td>Ethyl dodecanoate</td><td>Sigma Aldrich</td><td>W244112</td><td></td></tr><tr><td>Ethyl lactate</td><td>Sigma Aldrich</td><td>W244015</td><td></td></tr><tr><td>Diethyl succinate</td><td>Sigma Aldrich</td><td>W237701</td><td></td></tr><tr><td>2-methylpropyl acetate</td><td>Sigma Aldrich</td><td>W217514</td><td></td></tr><tr><td>2-methylbutyl acetate</td><td>Sigma Aldrich</td><td>W364401</td><td></td></tr><tr><td>3-methyl butyl acetate</td><td>Sigma Aldrich</td><td>287725</td><td></td></tr><tr><td>2-phenylethyl acetate</td><td>Sigma Aldrich</td><td>290580</td><td></td></tr><tr><td>2-methylpropanol</td><td>Sigma Aldrich</td><td>294829</td><td></td></tr><tr><td>2-methylbutanol</td><td>Sigma Aldrich</td><td>133051</td><td></td></tr><tr><td>3-methylbutanol</td><td>Sigma Aldrich</td><td>309435</td><td></td></tr><tr><td>Hexanol</td><td>Sigma Aldrich</td><td>128570</td><td></td></tr><tr><td>2-phenylethanol</td><td>Sigma Aldrich</td><td>77861</td><td></td></tr><tr><td>Propanoic acid</td><td>Sigma Aldrich</td><td>94425</td><td></td></tr><tr><td>Butanoic acid</td><td>Sigma Aldrich</td><td>19215</td><td></td></tr><tr><td>2-methylpropanoic acid</td><td>Sigma Aldrich</td><td>58360</td><td></td></tr><tr><td>2-methylbutanoic acid</td><td>Sigma Aldrich</td><td>193070</td><td></td></tr><tr><td>3-methylbutanoic acid</td><td>Sigma Aldrich</td><td>W310212</td><td></td></tr><tr><td>Hexanoic acid</td><td>Sigma Aldrich</td><td>153745</td><td></td></tr><tr><td>Octanoic acid</td><td>Sigma Aldrich</td><td>W279900</td><td></td></tr><tr><td>Decanoic acid</td><td>Sigma Aldrich</td><td>W236403</td><td></td></tr><tr><td>Dodecanoic acid</td><td>Sigma Aldrich</td><td>L556</td><td></td></tr><tr><td>Fermentor 1L</td><td>Legallais</td><td>AT1357</td><td>Fermenter handmade for fermentation</td></tr><tr><td>Disposable vacuum filtration system</td><td>Dominique Deutscher</td><td>029311</td><td></td></tr><tr><td>Fermenters (250 ml)</td><td>Legallais</td><td>AT1352</td><td>Fermenter handmade for fermentation</td></tr><tr><td>Sterile tubes</td><td>Sarstedt</td><td>62.554.502</td><td></td></tr><tr><td>Fermentation locks</td><td>Legallais</td><td>AT1356</td><td>Fermetation locks handmade for fermentation</td></tr><tr><td>BactoYeast Extract</td><td>Becton, Dickinson and Company</td><td>212750</td><td></td></tr><tr><td>BactoPeptone</td><td>Becton, Dickinson and Company</td><td>211677</td><td></td></tr><tr><td>Incubator shaker</td><td>Infors HT</td><td></td><td></td></tr><tr><td>Particle Counter</td><td>Beckman Coulter</td><td>6605697</td><td>Multisizer 3 Coulter Counter</td></tr><tr><td>Centrifuge</td><td>Jouan</td><td></td><td>GR412</td></tr><tr><td>Plate Butler Robotic system</td><td>Lab Services BV</td><td>PF0X-MA</td><td>Automatic instrument</td></tr><tr><td>Plate Butler Software</td><td>Lab Services BV</td><td></td><td>Robot monitor software</td></tr><tr><td>RobView</td><td></td><td></td><td>In-house developed calculation software</td></tr><tr><td>My SQL</td><td></td><td></td><td>International source database</td></tr><tr><td>Cimarec i Telesystem Multipoint Stirrers</td><td>Thermo Fisher Scientific</td><td>50088009</td><td>String Drive 60</td></tr><tr><td>BenchBlotter platform rocker</td><td>Dutscher</td><td>60903</td><td></td></tr><tr><td>Ammonia enzymatic kit</td><td>R-Biopharm AG</td><td>5390</td><td></td></tr><tr><td>Spectrophotometer cuvettes</td><td>VWR</td><td>634-0678</td><td></td></tr><tr><td>Spectrophotometer UviLine 9400</td><td>Secomam</td><td></td><td></td></tr><tr><td>Amino acids standards physiological - acidics and neutrals</td><td>Sigma Aldrich</td><td>A6407</td><td></td></tr><tr><td>Amino acids standards physiological - basics</td><td>Sigma Aldrich</td><td>A6282</td><td></td></tr><tr><td>Citrate lithium buffers - Ultra ninhydrin reagent</td><td>Biochrom</td><td>BC80-6000-06</td><td></td></tr><tr><td>Sulfosalycilic acid</td><td>Sigma Aldrich</td><td>S2130</td><td></td></tr><tr><td>Norleucine</td><td>Sigma Aldrich</td><td>N1398</td><td></td></tr><tr><td>Biochrom 30 AAA</td><td>Biochrom</td><td></td><td></td></tr><tr><td>EZChrom Elite</td><td>Biochrom</td><td></td><td>Instrument control and Data analysis software</td></tr><tr><td>Ultropac 8 resin Lithium</td><td>Biochrom</td><td>BC80-6002-47</td><td>Lithium High Resolution Physiological Column</td></tr><tr><td>Filter Millex GV</td><td>Merck Millipore</td><td>SLGVX13NL</td><td>Millex GV 13mm (pore size 0.22 &amp;micro;m)</td></tr><tr><td>Membrane filter PALL</td><td>VWR</td><td>514-4157</td><td>Supor-450 47mm 0.45&amp;micro;m</td></tr><tr><td>Vacuum pump Millivac Mini</td><td>Millipore</td><td>XF5423050</td><td></td></tr><tr><td>Aluminium smooth weigh dish 70mm</td><td>VWR</td><td>611-1380</td><td></td></tr><tr><td>Precision balance</td><td>Mettler</td><td></td><td>Specifications AE163</td></tr><tr><td>Dimethyl sulfoxid dried</td><td>Merck</td><td>1029310161</td><td>(max. 0.025% H&lt;sub&gt;2&lt;/sub&gt;O) SeccoSolv</td></tr><tr><td>Combustion oven</td><td>Legallais</td><td></td><td></td></tr><tr><td>Pierce BCA protein assay kit</td><td>Interchim</td><td>UP40840A</td><td></td></tr><tr><td>Formic acid</td><td>Fluka</td><td>94318</td><td></td></tr><tr><td>Hydrogen peroxide</td><td>Sigma Aldrich</td><td>H1009</td><td></td></tr><tr><td>Hydrochloric Acid Fuming 37% Emsure</td><td>Merck</td><td>1003171000</td><td>Grade ACS,ISO,Reag. Ph Eur</td></tr><tr><td>Lithium acetate buffer</td><td>Biochrom</td><td>80-2038-10</td><td></td></tr><tr><td>Commercial solution of hydrolyzed amino acids</td><td>Sigma Aldrich</td><td>AAS18</td><td></td></tr><tr><td>L-Methionine sulfone</td><td>Sigma Aldrich</td><td>M0876</td><td></td></tr><tr><td>L-Cysteic acid monohydrate</td><td>Sigma Aldrich</td><td>30170</td><td></td></tr><tr><td>Pyrex glass culture tubes</td><td>Sigma Aldrich</td><td>Z653586</td><td></td></tr><tr><td>Pyridine</td><td>Acros Organics</td><td>131780500</td><td>99% Extrapure</td></tr><tr><td>Ethyl chloroformate</td><td>Sigma Aldrich</td><td>23131</td><td></td></tr><tr><td>Dichloromethane</td><td>Sigma Aldrich</td><td>32222</td><td></td></tr><tr><td>Vials</td><td>Sigma Aldrich</td><td>854165</td><td></td></tr><tr><td>Microinserts for 1.5ml vials</td><td>Sigma Aldrich</td><td>SU860066</td><td></td></tr><tr><td>GC/MS</td><td>Agilent Technologies</td><td>5890 GC/5973 MS</td><td></td></tr><tr><td>Chemstation</td><td>Agilent Technologies</td><td></td><td>Instrument control and data analysis software</td></tr><tr><td>Methanol</td><td>Sigma Aldrich</td><td>34860</td><td>Chromasolv, for HPLC</td></tr><tr><td>Acetonitrile</td><td>Sigma Aldrich</td><td>34998</td><td>ChromasolvPlus, for HPLC</td></tr><tr><td>N,N-Dimethylformamide dimethyl acetal</td><td>Sigma Aldrich</td><td>394963</td><td></td></tr><tr><td>BSTFA</td><td>Sigma Aldrich</td><td>33024</td><td></td></tr><tr><td>DB-17MS column</td><td>Agilent Technologies</td><td>122-4731</td><td>30m*0.25mm*0.15&amp;micro;m</td></tr><tr><td>Sodium sulfate, anhydrous</td><td>Sigma Aldrich</td><td>238597</td><td></td></tr><tr><td>Technical nitrogen</td><td>Air products</td><td>14629</td><td></td></tr><tr><td>Zebron ZB-WAX column</td><td>Phenomenex</td><td>7HG-G007-11</td><td>30m*0.25mm*0.25&amp;micro;m</td></tr><tr><td>Helium BIP</td><td>Air products</td><td>26699</td><td></td></tr><tr><td>Glass Pasteur pipettes</td><td>VWR</td><td>612-1702</td><td></td></tr></tbody></table>

workflow based on the combination of isotopic tracer experiments to investigate microbial metabolism of multiple nutrient sources

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&#8211;facilitated by a robot-assisted system for online monitoring of fermentations&#8211;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.

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&#8211;growth characteristics, nitrogen consumption patterns, and the profile of proteinogenic amino acids&#8211;show a high reproducibility among fermentations (Figure 4). This co...

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Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

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 ...

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Microbial Metabolism

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9.3K Views.  Université de Montpellier. Dit protocol beschrijft een experimentele procedure voor het kwantitatief en uitgebreid onderzoek naar het metabolisme van meerdere nutriënten bronnen. Deze werkstroom, gebaseerd op een combinatie van isotopische tracerproeven en een analytische procedure, kan het lot van verbruikte voedingsstoffen en de metabole oorsprong van moleculen synthetized door micro-organismen te bepalen.

Video: Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Measuring Fluxes of Mineral Nutrients and Toxicants in Plants with Radioactive Tracers

Unidirectional influx and efflux of nutrients and toxicants, and their resultant net fluxes, are central to the nutrition and toxicology of plants. Radioisotope tracing is a major technique used to measure such fluxes, both within plants, and between plants and their environments. Flux data obtained with radiotracer protocols can help elucidate the capacity, mechanism, regulation, and energetics of transport systems for specific mineral nutrients or toxicants, and can provide insight into compartmentation and turnover rates of subcellular mineral and metabolite pools. Here, we describe two major radioisotope protocols used in plant biology: direct influx (DI) and compartmental analysis by tracer efflux (CATE). We focus on flux measurement of potassium (K+) as a nutrient, and ammonia/ammonium (NH3/NH4+) as a toxicant, in intact seedlings of the model species barley (Hordeum vulgare L.). These protocols can be readily adapted to other experimental systems (e.g., different species, excised plant material, and other nutrients/toxicants). Advantages and limitations of these protocols are discussed.

In planta measurement of nutrient and toxicant fluxes is essential to the study of plant nutrition and toxicity. Here, we cover radiotracer protocols for influx and efflux determination in intact plant roots, using potassium (K+) and ammonia/ammonium (NH3/NH4+) fluxes as examples. Advantages and limitations of such techniques are discussed.

Unidirectional influx and efflux of nutrients and toxicants, and their resultant net fluxes, are central to the nutrition and toxicology of plants. ...

Environment

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. Moreover, even cultures of a single cell type may contain cells in distinct metabolic states, such as resting or cycling cells. Methods for measuring metabolic activities of such subpopulations are valuable tools for understanding cellular metabolism. Complex cell populations are most commonly separated using a cell sorter, and subpopulations isolated by this method can be analyzed by metabolomics methods. However, a problem with this approach is that the cell sorting procedure subjects cells to stresses that may distort their metabolism.
To overcome these issues, we reasoned that the mass isotopomer distributions (MIDs) of metabolites from cells cultured with stable isotope-labeled nutrients are likely to be more stable than absolute metabolite concentrations, because MIDs are formed over longer time scales and should be less affected by short-term exposure to cell sorting conditions. Here, we describe a method based on this principle, combining cell sorting with liquid chromatography-high resolution mass spectrometry (LC-HRMS). The procedure involves analyzing three types of samples: (1) metabolite extracts obtained directly from the complex population; (2) extracts of "mock sorted" cells passed through the cell sorter instrument without gating any specific population; and (3) extracts of the actual sorted populations. The mock sorted cells are compared against direct extraction to verify that MIDs are indeed not altered by the cell sorting procedure itself, prior to analyzing the actual sorted populations. We show example results from HeLa cells sorted according to cell cycle phase, revealing changes in nucleotide metabolism.

This article describes a method for studying cellular metabolism in complex communities of multiple cell types, using a combination of stable isotope tracing, cell sorting to isolate specific cell types, and mass spectrometry.

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. ...

Biochemistry

A Tandem Liquid Chromatography&#8211;Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the abundances of key metabolites to which regulatory factors respond, adjusting cellular metabolism. In recent years, a number of proteins and RNA have emerged as important regulators of virulence gene expression. For example, the CodY protein responds to levels of branched-chain amino acids and GTP and is widely conserved in low G+C Gram-positive bacteria. As a global regulator in Staphylococcus aureus, CodY controls the expression of dozens of virulence and metabolic genes. We hypothesize that S. aureus uses CodY, in part, to alter its metabolic state in an effort to adapt to nutrient-limiting conditions potentially encountered in the host environment. This manuscript describes a method for extracting and analyzing metabolites from S. aureus using liquid chromatography coupled with mass spectrometry, a protocol that was developed to test this hypothesis. The method also highlights best practices that will ensure rigor and reproducibility, such as maintaining biological steady state and constant aeration without the use of continuous chemostat cultures. Relative to the USA200 methicillin-susceptible S. aureus isolate UAMS-1 parental strain, the isogenic codY mutant exhibited significant increases in amino acids derived from aspartate (e.g., threonine and isoleucine) and decreases in their precursors (e.g., aspartate and O-acetylhomoserine). These findings correlate well with transcriptional data obtained with RNA-seq analysis: genes in these pathways were up-regulated between 10- and 800-fold in the codY null mutant. Coupling global analyses of the transcriptome and the metabolome can reveal how bacteria alter their metabolism when faced with environmental or nutritional stress, providing potential insight into the physiological changes associated with nutrient depletion experienced during infection. Such discoveries may pave the way for the development of novel anti-infectives and therapeutics.

Here we describe a protocol for the extraction of metabolites from Staphylococcus aureus and their subsequent analysis via liquid chromatography and mass spectrometry.

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the ...

Fluorescently Labeled Bacteria as a Tracer to Reveal Novel Pathways of Organic Carbon Flow in Aquatic Ecosystems

Elucidating trophic interactions, such as predation and its effects, is a frequent task for many researchers in ecology. The study of microbial communities has many limitations, and determining a predator, prey, and predatory rates is often difficult. Presented here is an optimized method based on the addition of fluorescently labelled prey as a tracer, which allows for reliable quantitation of the grazing rates in aquatic predatory eukaryotes and estimation of nutrient transfer to higher trophic levels.

Presented here is a protocol for a single-cell, epifluorescence microscopy-based technique to quantify grazing rates in aquatic predatory eukaryotes with high precision and taxonomic resolution.

Elucidating trophic interactions, such as predation and its effects, is a frequent task for many researchers in ecology. The study of microbial ...

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

Understanding the metabolic consequences of microbial interactions that occur during infection presents a unique challenge to the field of biomedical imaging. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry represents a label-free, in situ imaging modality capable of generating spatial maps for a wide variety of metabolites. While thinly sectioned tissue samples are now routinely analyzed via this technology, imaging mass spectrometry analyses of non-traditional substrates, such as bacterial colonies commonly grown on agar in microbiology research, remain challenging due to the high water content and uneven topography of these samples. This paper demonstrates a sample preparation workflow to allow for imaging mass spectrometry analyses of these sample types. This process is exemplified using bacterial co-culture macrocolonies of two gastrointestinal pathogens: Clostridioides difficile and Enterococcus faecalis. Studying microbial interactions in this well-defined agar environment is also shown to complement tissue studies aimed at understanding microbial metabolic cooperation between these two pathogenic organisms in mouse models of infection. Imaging mass spectrometry analyses of the amino acid metabolites arginine and ornithine are presented as representative data. This method is broadly applicable to other analytes, microbial pathogens or diseases, and tissue types where a spatial measure of cellular or tissue biochemistry is desired.

Investigation of Microbial Cooperation via Imaging Mass Spectrometry Analysis of Bacterial Colonies Grown on Agar and in Tissue During Infection

A novel sample preparation method is demonstrated for the analysis of agar-based, bacterial macrocolonies via matrix-assisted laser desorption/ionization imaging mass spectrometry.

Understanding the metabolic consequences of microbial interactions that occur during infection presents a unique challenge to the field of biomedical ...

Stable Isotopic Profiling of Intermediary Metabolic Flux in Developing and Adult Stage Caenorhabditis elegans

Stable isotopic profiling has long permitted sensitive investigations of the metabolic consequences of genetic mutations and/or pharmacologic therapies in cellular and mammalian models. Here, we describe detailed methods to perform stable isotopic profiling of intermediary metabolism and metabolic flux in the nematode, Caenorhabditis elegans. Methods are described for profiling whole worm free amino acids, labeled carbon dioxide, labeled organic acids, and labeled amino acids in animals exposed to stable isotopes either from early development on nematode growth media agar plates or beginning as young adults while exposed to various pharmacologic treatments in liquid culture. Free amino acids are quantified by high performance liquid chromatography (HPLC) in whole worm aliquots extracted in 4% perchloric acid. Universally labeled 13C-glucose or 1,6-13C2-glucose is utilized as the stable isotopic precursor whose labeled carbon is traced by mass spectrometry in carbon dioxide (both atmospheric and dissolved) as well as in metabolites indicative of flux through glycolysis, pyruvate metabolism, and the tricarboxylic acid cycle. Representative results are included to demonstrate effects of isotope exposure time, various bacterial clearing protocols, and alternative worm disruption methods in wild-type nematodes, as well as the relative extent of isotopic incorporation in mitochondrial complex III mutant worms (isp-1(qm150)) relative to wild-type worms. Application of stable isotopic profiling in living nematodes provides a novel capacity to investigate at the whole animal level real-time metabolic alterations that are caused by individual genetic disorders and/or pharmacologic therapies.

Stable Isotopic Profiling of Intermediary Metabolic Flux in Developing and Adult Stage Caenorhabditis elegans

Stable isotopic profiling by gas chromatography mass spectrometric analysis of intermediary metabolic flux is described in the nematode, Caenorhabditis elegans. Methods are detailed for assessing isotopic enrichment in carbon dioxide, organic acids, and amino acids following isotope exposure either during development on agar plates or during adulthood in liquid culture.

Stable isotopic profiling has long permitted sensitive investigations of the metabolic consequences of genetic mutations and/or pharmacologic therapies in ...

Biology

Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids

Microbes have complex metabolic pathways that can be investigated using biochemistry and functional genomics methods. One important technique to examine cell central metabolism and discover new enzymes is 13C-assisted metabolism analysis 1. This technique is based on isotopic labeling, whereby microbes are fed with a 13C labeled substrates. By tracing the atom transition paths between metabolites in the biochemical network, we can determine functional pathways and discover new enzymes. 
As a complementary method to transcriptomics and proteomics, approaches for isotopomer-assisted analysis of metabolic pathways contain three major steps 2. First, we grow cells with 13C labeled substrates. In this step, the composition of the medium and the selection of labeled substrates are two key factors. To avoid measurement noises from non-labeled carbon in nutrient supplements, a minimal medium with a sole carbon source is required. Further, the choice of a labeled substrate is based on how effectively it will elucidate the pathway being analyzed. Because novel enzymes often involve different reaction stereochemistry or intermediate products, in general, singly labeled carbon substrates are more informative for detection of novel pathways than uniformly labeled ones for detection of novel pathways3, 4. Second, we analyze amino acid labeling patterns using GC-MS. Amino acids are abundant in protein and thus can be obtained from biomass hydrolysis. Amino acids can be derivatized by N-(tert-butyldimethylsilyl)-N-methyltrifluoroacetamide (TBDMS) before GC separation. TBDMS derivatized amino acids can be fragmented by MS and result in different arrays of fragments. Based on the mass to charge (m/z) ratio of fragmented and unfragmented amino acids, we can deduce the possible labeled patterns of the central metabolites that are precursors of the amino acids. Third, we trace 13C carbon transitions in the proposed pathways and, based on the isotopomer data, confirm whether these pathways are active 2. Measurement of amino acids provides isotopic labeling information about eight crucial precursor metabolites in the central metabolism. These metabolic key nodes can reflect the functions of associated central pathways.
13C-assisted metabolism analysis via proteinogenic amino acids can be widely used for functional characterization of poorly-characterized microbial metabolism1. In this protocol, we will use Cyanothece 51142 as the model strain to demonstrate the use of labeled carbon substrates for discovering new enzymatic functions.

Metabolic Pathway Confirmation and Discovery Through 13C-labeling of Proteinogenic Amino Acids

13C-isotope labeling is a useful technique for determining the cell central metabolism for various types of microorganisms. After cells have been cultured with a specific labeled substrate, GC-MS measurement can reveal functional metabolic pathways based on unique labeling patterns in proteinogenic amino acids.

Microbes have complex metabolic pathways that can be investigated using biochemistry and functional genomics methods. One important technique to examine ...

Comprehensive Protocol for Cell Identification and Metabolic Analysis Using OPTIR-FISH

Optical Photothermal Infrared-Fluorescence In Situ Hybridization (OPTIR-FISH)

Understanding the metabolic activities of individual cells within complex communities is critical for unraveling their role in human disease. Here, we present a comprehensive protocol for simultaneous cell identification and metabolic analysis with the OPTIR-FISH platform by combining rRNA-tagged FISH probes and isotope-labeled substrates. Fluorescence imaging provides cell identification by the specific binding of rRNA-tagged FISH probes, while OPTIR imaging provides metabolic activities within single cells by isotope-induced red shift on OPTIR spectra. Using bacteria cultured with 13C-glucose as a test bed, the protocol outlines microbial culture with isotopic labeling, fluorescence in situ hybridization (FISH), sample preparation, optimization of the OPTIR-FISH imaging setup, and data acquisition. We also demonstrate how to perform image analysis and interpret spectral data at the single-cell level with high throughput. This protocol&#39;s standardized and detailed nature will greatly facilitate its adoption by researchers from diverse backgrounds and disciplines within the broad single-cell metabolism research community.

Author Spotlight: Integrated OPTIR-FISH for Single-Cell Metabolic and Identity Analysis in Complex Environments

Here, we present a protocol using optical photothermal infrared-fluorescence in situ hybridization (OPTIR-FISH), also known as mid-infrared photothermal-FISH (MIP-FISH), to identify individual cells and understand their metabolism. This methodology can be applied broadly for diverse applications, including mapping cellular metabolism with single-cell resolution.

Understanding the metabolic activities of individual cells within complex communities is critical for unraveling their role in human disease. Here, we ...

PET-CT Scanning for Multi-Tracer Study of Cerebral Oxygen and Glucose Metabolism

Multi-Tracer Studies of Brain Oxygen and Glucose Metabolism Using a Time-of-Flight Positron Emission Tomography-Computed Tomography Scanner

The authors have developed a paradigm using positron emission tomography (PET) with multiple radiopharmaceutical tracers that combines measurements of cerebral metabolic rate of glucose (CMRGlc), cerebral metabolic rate of oxygen (CMRO2), cerebral blood flow (CBF), and cerebral blood volume (CBV), culminating in estimates of brain aerobic glycolysis (AG). These in vivo estimates of oxidative and non-oxidative glucose metabolism are pertinent to the study of the human brain in health and disease. The latest positron emission tomography-computed tomography (PET-CT) scanners provide time-of-flight (TOF) imaging and critical improvements in spatial resolution and reduction of artifacts. This has led to significantly improved imaging with lower radiotracer doses.
Optimized methods for the latest PET-CT scanners involve administering a sequence of inhaled 15O-labeled carbon monoxide (CO) and oxygen (O2), intravenous 15O-labeled water (H2O), and 18F-deoxyglucose (FDG)-all within 2-h or 3-h scan sessions that yield high-resolution, quantitative measurements of CMRGlc, CMRO2, CBF, CBV, and AG. This methods paper describes practical aspects of scanning designed for quantifying brain metabolism with tracer kinetic models and arterial blood samples and provides examples of imaging measurements of human brain metabolism.

Quantitative measurements of oxygen and glucose metabolism by PET are established technologies, but details of practical protocols are sparsely described in the literature. This paper presents a practical protocol successfully implemented on a state-of-the-art positron emission tomography-computed tomography scanner.

The authors have developed a paradigm using positron emission tomography (PET) with multiple radiopharmaceutical tracers that combines measurements of ...

Neuroscience

Using Microtiter Dish Radiolabeling for Multiple In Vivo Measurements Of Escherichia coli (p)ppGpp Followed by Thin Layer Chromatography

The (p)ppGpp nucleotide functions as a global regulator in bacteria in response to a variety of physical and nutritional stress. It has a rapid onset, in seconds, which leads to accumulation of levels that approach or exceed those of GTP pools. Stress reversal occasions a rapid disappearance of (p)ppGpp, often with a half-life of less than a minute. The presence of (p)ppGpp results in alterations of cellular gene expression and metabolism that counter the damaging effects of stress. Gram-negative and Gram-positive bacteria have different response mechanisms, but both depend on (p)ppGpp concentration. In any event, there is a need to simultaneously monitor many radiolabeled bacterial cultures at time intervals that may vary from 10 seconds to hours during critical stress transition periods. This protocol addresses this technical challenge. The method takes advantage of temperature- and shaker-controlled microtiter dish incubators that allow parallel monitoring of growth (absorbance) and rapid sampling of uniformly phosphate-radiolabeled cultures to resolve and quantitate nucleotide pools by thin-layer chromatography on PEI-cellulose. Small amounts of sample are needed for multiple technical and biological replicates of analyses. Complex growth transitions, such as diauxic growth and rapid (p)ppGpp turnover rates can be quantitatively assessed by this method.

Using Microtiter Dish Radiolabeling for Multiple In Vivo Measurements Of Escherichia coli pppGpp Followed by Thin Layer Chromatography

The growth of radiolabeled bacterial cultures in microtiter dishes facilitates high throughput sampling that allows multiple technical and biological replicate assays of nucleotide pool abundance, including that of (p)ppGpp. The effects of growth transitions provoked by sources of physiological stress as well as recovery from stress can be monitored.

The (p)ppGpp nucleotide functions as a global regulator in bacteria in response to a variety of physical and nutritional stress. It has a rapid onset, in ...

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

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