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 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.
1. Cell culture (Figure 1)
2. Amino acid extraction
3. Amino acid derivatization and GC-MS conditions
Analysis of amino acids or charged/highly polar metabolites via GC requires that these metabolites be derivatized, so that the amino acids are volatile and can be separated by gas chromatography 2.
4. GC-MS data analysis
5. Pathway analysis using labeled amino acid data
By investigating only a few key amino acids produced from well-designed 13C tracer experiments, we may reveal several unique pathways or enzyme activities without performing sophisticated 13C-metabolic flux analysis of entire central metabolism.
6. Representative Results
Recent bioenergy studies have revived interests in using novel phototrophic microorganisms for bioenergy production and CO2 capture. In the past years, quite a few 13C-metabolism analyses, including advanced 13C-Metabolic Flux Analyses (13C-MFA), have been applied to investigate central metabolisms in phototrophic bacteria, because biochemical knowledge of the central metabolic pathways is not well-founded in these non-model organisms10, 11, 17-20. Here, we present an example of the discovery of an alternate isoleucine pathway in Cyanothece 51142 21. Cyanothece 51142 does not contain the enzyme (EC 4.3.1.19, threonine ammonia-lyase), which catalyzes conversion of threonine to 2-ketobutyrate in the typical isoleucine synthesis pathway. To resolve the isoleucine pathway, we grow Cyanothece 51142 (20 mL) in ASP2 medium 22 with 54 mM glycerol (2-13C, >98%). Cyanothece 51142 utilizes 2nd position labeled glycerol as the main carbon source. We observe that threonine and alanine have one labeled carbon, while isoleucine is labeled with three carbons. Therefore, synthesis in Cyanothece 51142 cannot be derived from the threonine route employed by most organisms (Figure 4). On the other hand, leucine and isoleucine have identical labeling patterns based on fragment (M-15)+ and fragment (M-159)+. For example, the isotopomer data from [M-15]+ (containing unfragmented amino acids) show identical labeling for leucine (M0=0.01, M1=0.03, M2=0.21, M3=0.69) and isoleucine (M0=0.01, M1=0.03, M2=0.24, M3=0.67). Thus leucine and isoleucine must be synthesized from the same precursors (i.e., pyruvate and acetyl-CoA). This observation is consistent with the labeled carbon transition in the citramalate pathway for isoleucine synthesis. To confirm this pathway, we search the Joint Genome Institute database and find the presence of a citramalate synthase CimA (cce_0248) in Cyanothece.
Figure 1.
The 13C-assisted pathway analysis steps.
Figure 2.
Amino acids used for acquiring the labeling pattern of their metabolic precursors. ACoA, acetyl-CoA; AKG, α-Ketoglutarate; C5P, ribose 5-phosphate; CIT, citrate; E4P, erythrose 4-phosphate; G6P, glucose 6-phosphate; OAA, oxaloacetate; PEP, phosphoenolpyruvate; PGA, 3-phosphoglycerate; PYR, pyruvate.
Figure 3. GC peaks for 16 amino acids. TBDMS derivatized amino acids are cracked by MS into two fragments: (M-57)+, containing the entire amino acid, and (M-159)+, which lacks the α carboxyl group of the amino acid. For leucine and isoleucine, the (M-57)+ was overlapped by other mass peaks. We suggest using fragment (M-15)+ to analyze the entire amino acid labeling. The (f302)+ group is detected in most amino acids, which contains only the first (α-carboxyl group) and second carbons in an amino acid backbone. Because this MS peak often has high noise-to-signal ratios, (f302)+ is not recommended for quantitatively analyzing the metabolic fluxes7.
Figure 4.
Labeling transitions in isoleucine pathways in Cyanothece 51142 (modified from our previous paper)21.
This protocol consists of feeding the cell with a labeled substrate and measuring the resulting isotopic labeling patterns in the amino acids via GC-MS. Since MS data (m/z ratios) give just the overall amount of labeling of MS ions, we have to assess the isotopomer distributions of amino acids by examining the m/z ratios of both unfragmented (M-57)+ and fragmented amino acids (i.e., (M-159)+ and (f302)+). Furthermore, we can perform several cell cultures with a chemically identical medium but substrates that have different labeling patterns (1st position labeled, 2nd position labeled, etc.). The labeling information about metabolites from these experiments can be integrated to decode the actual carbon transition routes through the central metabolic pathways.
For pathway analysis, the choice of a labeled substrate is important. In general, singly labeled carbon substrates are easier to use in tracing the fate of the labeled carbon when 13C percolates through central pathways, while multiple-carbon labeled substrates may confound carbon tracing. Also, singly labeled substrates are more informative to elucidate unique molecule structures in metabolites than uniformly labeled substrates.4 For example, the (Re)-type citrate synthase shows different reaction stereochemistry from normal citrate synthase, and thus causes citrate to have different molecular chirality. On the other hand, substrates are different in their suitability to detect their associated pathways. Glucose is best for detecting the split ratio between the glycolysis and pentose phosphate pathways, while pyruvate or acetate are best for analyzing the TCA cycle and some amino acid pathways. Therefore, it is necessary to use different substrates to investigate the overall picture of cell metabolisms.
13C-isotope labeling is a useful technique for determining functional pathways in microorganisms. However, this technique has several limitations. First, it is suited only for analysis of carbon metabolism using organic substrates, as it cannot directly resolve metabolism in autotrophic metabolisms if CO2 is used as the sole carbon source. Autotrophic cultures using CO2 label all amino acids to the same extent as the input 12CO2/13CO2 mixture 23. This makes pathway analysis difficult, as metabolism analysis has to be inferred from a rearrangement of 13C carbons in metabolites by different metabolic pathways. Second, this paper presents solely qualitative results discriminating between “active” and “non-active” pathways. Precise quantification of metabolism requires a sophisticated modeling approach (i.e., 13C-MFA) to decipher metabolic fluxes from isotopomer data. Third, the scope of 13C-metabolism analysis is limited by technical challenges in determining low abundance and unstable metabolites. Broader metabolic network can be probed by analyzing free metabolites besides amino acids. Measurement of free metabolites requires both highly-efficient metabolite extraction methods and highly-sensitive analytical platforms. LC-MS, FT-ICR MS, and CE-MS have been used for identifying the labeling patterns of free metabolites, and provide more insight into cell metabolisms2. Fourth, 13C-assisted pathway analysis is best done in minimal medium, because addition of non-labeled nutrient supplements leads to falsely lower labeling concentrations and make quantitative 13C-MFA studies not so straightforward. Also, cells may utilize exogenous amino acids extensively for protein synthesis, and thus give very weak labeling signals for these proteinogenic amino acids 24. If a rich medium is required to grow cells, measurement of intracellular metabolites, instead of amino acids, can effectively reduce the interference in labeling data that arises from exogenous non-labeled carbon nutrients.
Finally, an increasing number of genome sequences for non-model microbial species are being published each year. However, functional characterization of these species has lagged far behind the pace of genomic sequencing. 13C-labeling approaches can play important roles in the confirmation and discovery of metabolic pathways in many non-model organisms. Furthermore, the labeling information can be integrated with metabolic modeling (13C-MFA) to decipher absolute carbon fluxes in microorganisms 25. Therefore, this technique can be widely used in analyzing biological systems related to biofuel, ecological and medical applications.
The authors have nothing to disclose.
This study was supported by an NSF Career Grant (MCB0954016) and a DOE Bioenergy Research Grant (DEFG0208ER64694).
Name of the reagent | Company | Catalogue number | Comments (optional) |
---|---|---|---|
TBDMS | Sigma-Aldrich | 19915 | – |
THF | Sigma-Aldrich | 34865 | – |
Labeled carbon substrate | Cambridge Isotope Laboratories | Depend on the experimental requirement | Website: http://www.isotope.com |
Gas chromatograph | Agilent Technologies | Hewlett-Packard, model 7890A | – |
GC Columns | J&W Scientific, Folsom, CA | DB5 (30m) | – |
Mass spectrometer | Agilent Technologies | 5975C | – |
Reacti-Vap Evaporator | Thermo Scientific | TS-18825 | For drying amino acid samples |