Integration of flux measurements and pharmacological controls to optimize stable isotope-resolved metabolomics workflows and interpretation

Stable isotope-resolved metabolomics (SIRM) provides information regarding the relative activity of numerous metabolic pathways and the contribution of nutrients to specific metabolite pools; however, SIRM experiments can be difficult to execute, and data interpretation is challenging. Furthermore, standardization of analytical procedures and workflows remain significant obstacles for widespread reproducibility. Here, we demonstrate the workflow of a typical SIRM experiment and suggest experimental controls and measures of cross-validation that improve data interpretation. Inhibitors of glycolysis and oxidative phosphorylation as well as mitochondrial uncouplers serve as pharmacological controls, which help define metabolic flux configurations that occur under well-controlled metabolic states. We demonstrate how such controls and time course labeling experiments improve confidence in metabolite assignments as well as delineate metabolic pathway relationships. Moreover, we demonstrate how radiolabeled tracers and extracellular flux analyses integrate with SIRM to improve data interpretation. Collectively, these results show how integration of flux methodologies and use of pharmacological controls increase confidence in SIRM data and provide new biological insights.


Stage 1 -Experimental design and execution.
Biological expertise and knowledge of biochemistry are required for designing and executing SIRM experiments. This involves identifying tracer(s) relevant to the pathway(s) of interest and planning experiments to derive clean results, the latter of which should strive to minimize factors that obfuscate interpretation. Considerations for metabolic steady state and the time required for isotope enrichment are particularly important.
A metabolic steady state is ideal for stable isotope tracing. Under conditions of a metabolic steady state, the intracellular metabolite concentrations and metabolic fluxes of a cell population are constant. Because progression of the cell cycle is coordinated with bioenergetic and biosynthetic demands [4][5][6][7] , differences in cell proliferation will affect substrate uptake and utilization, and thus metabolite labeling. The exponential phase of growth is commonly assumed to reflect a near steady state condition, if nutrient supply is maintained 2,8 . For controlling nutrient supply, a bioreactor (commonly called a nutrostrat) which adds fresh medium and removes waste products ensures that the extracellular environment is optimized for cell growth 9,10 . Often, experiments are performed at a metabolic pseudo-steady state, in which deviations in extracellular nutrient concentration and intracellular metabolite concentrations and fluxes are considered negligible over the course of the experiment. Many cell culture media compositions contain supraphysiologic substrate levels to prevent nutrient depletion over the course of culture. For example, incorporation of 13 C from 13 C 6 -glucose into glycolytic intermediates is expected to occur within minutes in most cell types, whereas tricarboxylic acid cycle (TCA) cycle intermediates may require a few hours for sufficient 13 C accumulation. Some biosynthetic pathway-derived intermediates and end products require longer time frames for sufficient label incorporation (Fig. 1B). Thus, at physiological concentrations, glucose would not become limiting if only 13 C enrichment in glycolytic intermediates are measured; however, higher concentrations of glucose may be required to prevent nutrient depletion for assessment of label incorporation into TCA cycle or biosynthetic pathway metabolites.
After provision of an isotopically labeled source, metabolite isotope incorporation will increase in a time-dependent manner. This phase of isotope labeling is referred to as dynamic labeling. After 13 C incorporation in a metabolite pool is saturated, isotopic steady state labeling is achieved (Fig. 1C). Importantly, the time required to achieve isotopic steady state is both metabolite-and tracer-dependent and is assessed empirically. Dynamic and steady state labeling are discussed in detail below. Stage 2 -Sample preparation, data acquisition, and data analysis. Proper sample preparation and data analysis are critical for reliable interpretations of SIRM experimental results. In this section, we discuss sample preparation, metabolite separation and detection by liquid chromatography mass spectrometry (LC-MS), and data processing and analysis.
Sample preparation. Rapid enzymatic inactivation and efficient metabolite extraction are crucial steps to provide a snap shot of isotopologue labeling. Collection vessels and solvents should be of the highest purity to prevent contamination, especially by typical metabolite contaminants such as lactate 11 and palmitate 12 . The most effective solvents for quenching are cold (-20 °C) acetonitrile or ultracold (−80 °C) methanol 13,14 . While methanol is sufficient to quench and extract a broad range of polar and some nonpolar metabolites 15 , maximal extraction of polar metabolites-including central carbon intermediates, amino acids and dipeptides, nucleosides and nucleotides, short-chain fatty acids, and some mono-and di-glycerides-can be achieved using 50-60% acetonitrile (v/v; acetonitrile/water) 13,14 ; for the empirical results presented below, a 60% acetonitrile solution was used. Extraction of more hydrophobic species and lipids may require solvents such as chloroform 16 or methyl tert-butyl ether 17 . or time-of-flight instruments empowers the LC-MS approach with the ability to perform SIRM in an untargeted manner. Knowledge of metabolite retention time, accurate m/z, MS/MS spectrum, and use of authentic standards improves peak assignment accuracy. Other techniques, such as gas chromatography mass spectrometry (GC-MS) [18][19][20][21] and capillary electrophoresis MS (CE-MS) 22 , can be used in targeted and non-targeted applications, and unit resolution triple quadrupole-based LC-MS is effective for targeted metabolomics methods 23,24 .
Spectrum deconvolution and metabolite assignment. Mass spectra generated for stable isotope-labeled metabolites differ significantly from those of unlabeled samples. Incorporation of heavy atoms (e.g. 13 C) from the stable isotope tracer increases the complexity of the mass spectrum and requires additional analytic steps. Several bioinformatics tools have been developed to analyze SIRM data, e.g., see [25][26][27][28][29][30] . Our group has used the following workflows for peak assignment: Premise 31,32 , MetSign 33 , and El-Maven/Polly Phi TM34,35 ; the last two of these software packages can facilitate large metabolomics databases or custom target lists. A key advantage of the El-Maven/ Polly Phi TM software is that it automatically calculates all possible isotopologue m/z ratios for heavy atoms specified by the user (e.g. 13 C, 15 N, etc.) and performs peak matching and assignments based on its algorithm and a set of user modifiable criteria. Metabolite peaks are assigned and confirmed using accurate m/z, retention time, and the MS/MS spectrum performed on peaks from unlabeled sample controls. These unlabeled controls are biological equivalent samples generated in the same experimental settings but without incubation with the labeled tracer. Unlabeled samples are important because: (1) they help define the sample's metabolome composition; (2) by absence of tracer atoms, they help validate isotopologue assignments; and (3) they can provide the relative concentration for a given metabolite.

Absolute flux
Rate of mass flow in a metabolic pathway. Rates are dependent on the availability of precursor metabolites and on enzymatic turnover. An absolute flux rate is generally described in terms of moles/unit time/unit mass or cell number.

Anaplerosis
Series of enzymatic reactions or pathways, excluding glucose oxidation, that replenish pools of metabolic intermediates in the Krebs cycle. Typical anaplerotic pathways include those regulated by pyruvate carboxylase, malic enzyme, transamination reactions between oxaloacetate or malate and their corresponding amino acids (e.g., aspartate, glutamate), formation of succinyl CoA from propionyl CoA precursors (e.g., branched chain amino acids, propionate, odd-carbon ketone bodies), etc.

Dynamic labeling
The phase of isotopic labeling in which metabolites are not saturated with isotope label. Measuring fractional enrichment during this phase of labeling delineates how fast a metabolite pool becomes labeled. Dynamic labeling patterns are dependent on the metabolite pool level and the turnover of a given metabolite, which is related to the flux of the metabolic pathway.

Fractional enrichment
The fraction of a metabolite pool that is enriched with an isotopic label.

Glucose oxidation
The complete catabolism of glucose. Glucose oxidation commences with the breakdown of glucose via glycolysis, which yields pyruvate and is oxidized further in mitochondria to provide energy for the cell.

Glycolysis
Metabolic pathway that converts glucose into pyruvate via a series of intermediate steps. Conversion of pyruvate to lactate regenerates NAD + and can allow glycolysis to continue.

Isobar
Atomic or molecular species with the same nominal mass as the target analyte. Isobaric compounds may be structural isomers or structurally unrelated compounds with the same nominal mass.

Isomer
Compound with the same molecular formula as a target analyte. Isomers have different arrangements of atoms, which can convey different properties.

Isotopic steady state
The phase of isotopic labeling in which metabolites are saturated with isotope. Defining the contribution of a substrate to a particular metabolite pool requires that the metabolite pool be in the isotopic steady state phase of labeling.

Isotopologue
A molecule that differs in its isotopic composition, with at least one atom having a different number of neutrons than the parent molecule. For example, glutamate with 1, 2, 3, 4, or 5 isotopic atoms (See Fig. 1D).

Isotopomer
An isotopomer is an isotopologue containing positional information of the isotopic atoms.

Krebs Cycle
A hub of metabolism located within mitochondria that has central importance for both energy production and biosynthesis. Also, called citric acid cycle and tricarboxylic acid cycle.

Metabolic steady state
The state or condition of metabolism in which the rates of substrate uptake and utilization are the same over time.
Under conditions of metabolic steady state, the metabolite levels remain constant.

Metabolite pool
The reservoir of a metabolite upon which enzymes can operate. Metabolite pools may be compartmentalized in the cell (e.g., in the cytosol and/or mitochondrion); however, the total pool is commonly extracted for SIRM analysis.
Metabolic pseudo-steady state Assumption that the state of metabolism does not change under a given set of conditions.

Natural abundance
The abundance of isotopes of an element that are naturally found on a planet.

Relative flux
Relative activity of a metabolic pathway in experimental conditions compared with control conditions. The degree of isotopic enrichment in a metabolite, and the pattern of labeling, can be used to infer higher or lower metabolic pathway activity as long as the analysis is performed in the dynamic labeling phase.

Scrambling
Scrambling describes a hybrid metabolite labeling pattern resulting from a convergence of multiple pathways or several turns of a metabolic cycle (e.g., Krebs cycle). This complex isotopic labeling can obfuscate interpretation of SIRM data.

Stable isotope resolved metabolomics
An approach that uses nuclear magnetic resonance and/or mass spectrometry to determine the fate of individual atoms derived from stable isotope-enriched precursors (e.g., 13 C-glucose) in biological systems. By measuring the fractional enrichment of stably labeled metabolic intermediates and endproducts, it is used to deduce metabolic pathway and network activity. www.nature.com/scientificreports www.nature.com/scientificreports/ Natural abundance correction. After spectral deconvolution and assignment, the contribution of naturally occurring heavy isotopes (e.g. 2 H is 0.0115%, 13 C is 1.07% and 15 N is 0.368 36 ) is removed, yielding corrected peak intensities. By removing the natural abundance from spectral data, overestimation of fractional enrichment can be avoided. Natural abundance correction algorithms are a part of several software pipelines. For example, we have used online platforms based on a script described by Moseley et al. 37 as well as MetSign 33 . Here, we use the IsoCorrect module-part of a larger Elucidata PollyPhi ™ Platform 34 . A more detailed discussion of natural abundance correction can be found in the following reference 21 .
Data analysis and verification. Proper vetting of the assignments is essential to exclude inaccuracies and avoid incorrect data interpretation. The steps involved for data inspection require knowledge of analytical chemistry and metabolism. Data validation starts by setting up a priori criteria to maintain coherence within the dataset and eliminate possible user bias. The granularity of these criteria varies between experiments, and commonly encountered problems are summarized in Table 2. In general, replicate data are manually inspected for assignment errors and omissions. This allows the user to identify and address software-related issues quickly. Nevertheless, reexamination of chromatograms and mass spectra may be necessary. This is especially true when there is high intra-replicate variability, which is commonly caused by poor detection of low abundance metabolites. For poorly detected metabolites, increasing the starting material may be sufficient to obtain interpretable results.
Supplementary Fig. 1 illustrates the differences in chromatograms and raw mass spectra of citrate and α-ketoglutarate. High signal-to-noise chromatographic peaks were deconvoluted and assigned as citrate ( Supplementary Fig. 1A). The corresponding mass spectrum ( Supplementary Fig. 1C) revealed high abundance isotopologue peaks and undistorted labeling. In contrast, in this experiment, α-ketoglutarate signals were difficult to distinguish from instrument noise, increasing the likelihood for misassignment ( Supplementary Fig. 1B). The MS spectrum of α-ketoglutarate showed that while the two most abundant isotopologues (m + 0 and m + 5) were present, others were undetected ( Supplementary Fig. 1D). When peaks are below the software threshold of detection, metabolite peaks can be manually inspected, and assignments can be entered based on accurate retention times and masses. Removal of outliers is generally discouraged unless deemed statistically appropriate based on a priori criteria in the study design. For replicates with several poorly detected metabolites, the analyst may choose to reacquire the spectra or repeat the entire labeling experiment using more biological starting material.
Apart from analytical knowledge, expertise in metabolism, the integration of additional metabolic flux measurements, and the use of pharmacological controls may uncover additional data integrity issues. These frequently involve a mismatch in labeling patterns between a suspected metabolite and other pathway constituents. Such inconsistencies prompt additional steps to reexamine and evaluate assigned data. Furthermore, pharmacological controls that target critical steps in metabolic pathways can identify possible assignment issues, especially if the labeling patterns do not agree with textbook biochemistry. Finally, heavy isotope enrichment found in unlabeled samples that exceeds natural abundance suggests misassignments due to peak overlaps and requires intervention. Once assignments are corrected and possible outliers (e.g., undetected metabolites) are removed, they can be plotted and interpreted.

Stage 3 -Data interpretation.
After metabolites and isotopologues are assigned and quantified, fractional enrichment data are plotted for interpretation. An example of a fractional enrichment plot is shown in Fig. 1D.
Here, 13 C enrichment into glutamate is shown for cells provided with 13 C 6 -glucose. The degree of 13 C incorporation is indicated by the fractional (isotopic) enrichment values for each isotopologue (m + x). As mentioned earlier, the m + 0 isotopologue indicates the fraction of unlabeled glutamate, whereas each successive isotopologue shows the relative abundance of 13 C incorporated into 1, 2, 3, 4, or all 5 carbons of glutamate. For most LC-MS approaches, it is not possible to ascertain positional information of the labeled atom (i.e., distinguish isotopomers); however, strategic fragmentation and mathematical modeling strategies can provide position-specific enrichment information 38 .
Another way to examine metabolite labeling is by adding all isotopologues together, which provides the total isotopic enrichment (i.e. % metabolite enrichment) for a given metabolite pool. Importantly, when a time-course experiment is performed, plotting the isotopic enrichment is the only means by which dynamic and isotopic steady state may be distinguished. Shown in Supplementary Fig. 2 is a sampling of time-dependent isotopic enrichment in metabolites from several metabolic pathways from neonatal rat cardiomyocytes (NRCMs) incubated with 13 C 6 -glucose. Differences in pathway flux are readily appreciated by comparing enrichment in metabolites derived from slower pathways, such as the pentose phosphate pathway, versus metabolites in faster pathways, such as glycolysis. For example, 3′AMP and glutathione are in the dynamic labeling phase throughout the time course, whereas the 13 C label saturates the 3-phosphoglycerate (3PG) pool by at least 4 h. Metabolites associated with the Krebs cycle demonstrate intermediate labeling velocities, with the dynamic labeling phase occurring within 0-12 h of incubation with 13 C 6 -glucose.
Relative flux differences in metabolic pathways may be inferred from incorporation of isotopic tracer into metabolites during the dynamic labeling phase. Resolving changes in relative metabolic pathway flux can be assessed by direct interpretation of stable isotope labeling patterns 2 , without the need for formal 13 C flux analysis, which utilizes mathematical modeling [39][40][41] . For example, NRCMs incubated with 13 C 6 -glucose show purine (3′AMP) labeling in the dynamic phase through 18 h. Therefore, a hypothetical decrease in m+0 fractional enrichment and an increase in m + 5 labeling in purines in an experimental group within this timeframe would suggest higher rates of ribose incorporation and increased pathway flux.
The contribution of a tracer to a metabolite pool should be assessed after isotopic enrichment has reached steady state. For example, 13 Fig. 2).
Analysis of glycolysis: Although fractional enrichment plots of glycolytic intermediates can resolve changes in glycolysis, examining relative changes in glycolytic flux can be difficult. In NRCMs, glycolytic intermediates such as the glyceraldehyde 3-phosphate/dihydroxyacetone phosphate pool (GAP/DHAP), 3-phosphoglycerate (3PG), 2-phosphoglycerate (2PG), and phosphoenolpyruvate (PEP) show nearly complete saturation with label by 4 h ( Fig. 2A-C). In another experiment, NRCMs were incubated with medium containing 13 C 6 -glucose for 5 min (insets). Results from this experiment suggest that isotopic steady state is reached within minutes of 13 C 6 -glucose provision. Such rapid attainment of isotopic steady state makes the assessment of glycolytic flux by isotopic labeling difficult. In such cases, glycolytic rates can be measured by assessing the levels of 13 C 3 -lactate and 13 C 6 -glucose in the incubation medium 42 as well as by orthogonal methods, such as methods based on radiolabeled tracers ([5-3 H]-glucose) or extracellular flux analysis.
That 40-60% of the pyruvate and lactate pools were unlabeled suggests dilution by non-labeled substrates and is supported by the presence of unlabeled pyruvate in the medium. The presence of primarily the m + 3 isotopologue in these intermediates indicates that only 13 C 6 -labeled glucose was used for glycolysis. Similarly, the hexose 6-phosphate pool consisted of only m + 6 labeling, which indicates low levels of fructose bisphosphatase activity in NRCMs. The presence of other isotopologues, e.g., m + 3 labeling, in hexose 6-phosphate in cells incubated with only 13 C 6 -glucose could indicate gluconeogenic activity, but this isotopologue was not observed in these studies. Interestingly, in NRCMs, the hexose 6-phosphate pool had relatively modest labeling at the m + 6 isotopologue, which indicates unlabeled isomers of hexose 6-phosphate (Fig. 2C).
Analysis of Krebs cycle activity: As expected, Krebs cycle intermediates in NRCMs demonstrate time-dependent 13 C labeling (Fig. 3), from which several conclusions may be drawn. The labeling patterns in Krebs cycle intermediates are suggestive of both pyruvate dehydrogenase-mediated and anaplerotic incorporation of the 13 C atoms. Enrichment in m + 2 isotopologues indicate the first turn of the Krebs cycle (i.e. m + 2). The m + 4, m + 5, and m + 6 isotopologue labeling in citrate and aconitate may indicate the 2 nd and 3 rd turns of the Krebs cycle, with label saturation apparent in the m + 4 isotopologues by 12 h. Labeling the cells with 13 C 6 -glucose for 5 min led to approximately 20% 13 C enrichment (m + 2) in citrate and aconitate; however, little to no labeling was found in α-ketoglutarate (Fig. 3, insets) or any other Krebs cycle metabolite (data not shown). These data suggest that incorporation of label into citrate is faster than that through "downstream" metabolites, which is consistent with relatively higher metabolic needs for citrate due to fatty acid synthesis.
Product-precursor relationships are also important to consider. For example, by 4 h, >70% of the citrate pool was labeled (at m + 1, m + 2, m + 3, m + 4, or m + 5), yet pyruvate (see Fig. 2C) showed only 40% labeling (at m + 3). This could indicate the presence of more than one pool of pyruvate, with the labeled pyruvate pool contributing more to the Krebs cycle than the unlabeled pool; however, the Krebs cycle carries large flux, and given that the levels of unlabeled pyruvate in the medium would progressively decrease over time, earlier times of labeling may be informative. Indeed, a 5 min labeling study revealed lower fractional enrichment in citrate than in pyruvate ( Supplementary Fig. 3), which refutes the idea that two pools of pyruvate contribute to citrate formation in NRCMs. These data exemplify the importance of time course data for delineating product-precursor relationships and for interpreting metabolic pathway activities.
The fact that glutamate and glutathione (GSH) were labeled suggests that NRCMs use glucose as a carbon source for glutathione synthesis. Glutamine was not labeled, which is expected given that unlabeled glutamine is provided in relatively high amounts in the culture medium (Fig. 3). The m + 3 labeling, e.g., in 13 C 3 -malate and -fumarate, may also indicate anaplerotic activity 43,44 , likely through either pyruvate carboxylase 44 or malic enzyme 45 . The kinetics of labeling in these isotopologues, when compared to that of the PDH-supported labeling (m + 2), indicate that these carboxylation reactions are generally slower than pyruvate decarboxylation reactions in NRCMs. Indeed, 5 min of culture of NRCMs with 13 C 6 -glucose led to labeling of citrate at only m + 2 and did not label malate (Supplementary Fig. 3C).
While the labeling patterns of Krebs cycle intermediates and associated metabolites (e.g., aspartate, glutamate) are generally consistent, questions arise with regard to succinate and alanine. Succinate was almost completely  www.nature.com/scientificreports www.nature.com/scientificreports/ devoid of 13 C incorporation derived from PDH-mediated Krebs cycle activity. Nevertheless, the fact that independent studies [e.g. 44 ,] show relatively low levels of 13 C incorporation into succinate compared with other Krebs cycle intermediates suggest that these data may be valid; they might be explained by different intracellular pools of succinate, in which a relatively minor succinate pool is used for fumarate production. Surprisingly, alanine labeling is most similar to that of malate and aspartate (Fig. 3) and does not follow that of pyruvate (see Fig. 2C). This would suggest that, in NRCMs, alanine does not arise by alanine transamination of pyruvate but rather via aspartate decarboxylase. Conclusive evidence for this mechanism could be provided by interventions that target aspartate decarboxylase directly.
Importance of complementary metabolic assays and pharmacological controls: The preceding data illustrate how isotopologue pattern matching and time course information can impart confidence or skepticism in stable isotope metabolomics data as well as give rise to new, interesting questions. While technical controls-including proper retention time and mass values according to metabolite standards-provide confidence in metabolite assignment and isotopologue quantification, the metabolome consists of many similar metabolites that may have nearly identical masses and may be difficult to resolve by chromatography. While some newer technologies (e.g., differential mobility spectrometry) can address some of these challenges 38,46 , additional methods to assess the veracity of stable isotope metabolomics data and to standardize workflows within and between laboratories could increase the reproducibility, certainty, and usefulness of SIRM results.
We gain additional confidence in SIRM data by including pharmacological controls in experimental design. By predicting 13 C labeling based on the metabolic steps and pathways known to be affected by pharmacological compounds, these experimental groups could be used as positive or negative controls. To examine this, we treated NRCMs with koningic acid (KA)-a known glyceraldehyde 3-phosphate dehydrogenase inhibitor; rotenone-a commonly used inhibitor of mitochondrial Complex I; oligomycin-a commonly used inhibitor of Complex V; or FCCP-an uncoupler that stimulates mitochondrial oxygen consumption (Fig. 4A). We measured mitochondria and glycolytic activity with acute (17 or 100 min: Protocol I) and chronic (12 h: Protocol II, III) treatments with each compound (Fig. 4B,C). As expected, both acute and chronic treatment with FCCP increased oxygen consumption rate (OCR); rotenone decreased OCR both acutely and chronically (Fig. 4D,F). Oligomycin initially decreased OCR by 50%, which is consistent with our previous studies (Fig. 4D) 42,47,48 ; however, upon sustained (12 h) exposure, OCR rebounded to baseline levels (Fig. 4F). This is expected given the propensity of oligomycin to increase Δp and thereby promote proton leakage or electron slippage 49 . While KA had no effect on OCR (Fig. 4D,F), KA acutely decreased the extracellular acidification rate (ECAR), whereas oligomycin and rotenone increased ECAR (Fig. 4E). Radiolabeled tracing experiments using [5-3 H]-glucose (Protocol III; Fig. 4C) provided absolute flux measurements and demonstrated sustained changes in glucose utilization caused by each pharmacological compound (Fig. 4H). Importantly, the pharmacological agents did not affect protein content significantly ( Supplementary Fig. 4), which suggests minimal changes in cell viability.
We next examined 13 C metabolite labeling in NRCMs cultivated with 13 C 6 -glucose for 12 h in the absence or presence of each pharmacological compound (Fig. 5A). As shown in Fig. 5B, compounds that increased glycolytic activity (i.e., FCCP, oligomycin, and rotenone) robustly increased the fractional enrichment of 13 C-glucose-derived carbon into hexose 6-phosphate. That KA also augmented 13 C enrichment into the hexose phosphate pool and that all glycolytic intermediates indicate nearly maximal 13 C enrichment appear at first inconsistent; however, analysis of relative pool size, knowledge of biochemistry, and additional experiments can provide an explanation. Because KA inhibits GAPDH, crossover theorem [50][51][52][53] predicts that the concentrations of metabolites should increase before the inhibition site and decrease after the inhibition site. Indeed, hexose 6-phosphate and GAP/DHAP levels were remarkably elevated, and levels of metabolites after the GAPDH reaction (e.g., 3PG) were diminished (Fig. 5C). Moreover, because high levels of G6P promote glycogen synthesis 54 and inhibit glycogen breakdown 55 , the high level of 13 C enrichment that occurs in the hexose 6-phosphate pool with KA treatment is likely due to diminished G6P dilution by the unlabeled glycogen pool.
In separate experiments, we also evaluated 3 H-2-deoxyglucose (2DG) uptake, which provides a measure of glucose uptake, in NRCMs treated with KA and FCCP; treatment of NRCMs with unlabeled 2DG (100 mM) was used as a control. As predicted from the labeling patterns of hexose 6-phosphate in Fig. 5, FCCP increased glucose uptake in NRCMs; however, KA did not affect glucose uptake ( Supplementary Fig. 5), which suggests that in KA-treated cells glucose-derived intermediates upstream of the GAPDH reaction are likely shunted to ancillary pathways. Such complementary assays help to better define changes in metabolism, especially when the metabolites of interest are assessed at the isotopic steady state phase of labeling.
Analysis of 13 C incorporation into Krebs cycle metabolites provides information on mitochondrial activity. As expected from the mitochondrial OCR measurements (see Fig. 4), FCCP led to higher 13 C incorporation into Krebs cycle intermediates, with the exception of α-ketoglutarate, which showed less 13 C incorporation with FCCP (Fig. 6A). Reanalysis of the α-ketoglutarate spectral data indicated a poor signal-to-noise ratio ( Supplementary  Fig. 1), which diminishes confidence in its adequate detection in this set of experiments. The markedly higher fractional enrichment of the m + 6 isotopologues of citrate and aconitate and the m + 4 isotopologues of succinate, fumarate, and malate could indicate more turns of the Krebs cycle; however, it is important to consider other explanations as well. Longer periods of labeling can increase the possibility of tracer scrambling, which can occur after the tracer has been extensively recycled, or promote more isotope labeling due to less dilution by the unlabeled pool of metabolite precursors. Although increased m + 6 and m + 4 labeling could suggest more turns of the Krebs cycle, it is possible that the same number of turns are occurring with less input from other carbon-donating sources or that there is more input from 13 C-labeled metabolic products (e.g., 13 C 3 -lactate or labeled non-essential amino acids); however, integration of pharmacological controls in labeling studies and OCR measurements help resolve these issues. For example, in FCCP-treated NRCMs, complementary data showing higher 13 C enrichment (Fig. 6) and lower pool size of several Krebs cycle intermediates (Supplementary Fig. 6) as well as higher OCR (Fig. 4F)  www.nature.com/scientificreports www.nature.com/scientificreports/ FCCP also increased 13 C incorporation into glutathione (GSH) and alanine, indicating that mitochondrial uncoupling also increases the biosynthetic rates of GSH and alanine. The observation that chronic oligomycin treatment did not markedly affect 13 C labeling of Krebs cycle intermediates (Fig. 6) is not surprising, as OCR was unaffected after 12 h of incubation. As expected based on the OCR data, the Complex I inhibitor rotenone diminished fractional enrichment in all Krebs cycle intermediates as well as in GSH and alanine (Fig. 6A). Collectively, these data illustrate the fractional enrichment patterns that occur in uncoupled mitochondria and when complex I is inhibited.
The glycolysis inhibitor, KA, remarkably diminished 13 C incorporation into Krebs cycle intermediates, indicating that it decreases glucose oxidation in NRCMs (Fig. 6A). That 13 C 6 -glucose-derived carbon labeled approximately 80% of the Krebs cycle intermediate pool (e.g., see citrate in Supplementary Fig. 2) indicates that glucose is a primary nutrient for mitochondrial oxidation in cultured NRCMs; that KA generally diminished the levels of Krebs cycle intermediates (see Supplementary Fig. 6) further supports the concept that cultured NRCMs rely www.nature.com/scientificreports www.nature.com/scientificreports/ on glucose for mitochondrial respiration. Interestingly, mitochondrial respiration data indicate that KA does not markedly inhibit basal OCR (Fig. 4), which suggests inherent flexibility in recruiting other nutrients (e.g., unlabeled pyruvate) to support mitochondrial respiration when glucose is limiting or glucose catabolism is inhibited. www.nature.com/scientificreports www.nature.com/scientificreports/ Biosynthetic pathway activity: In addition to catabolic pathways, SIRM provides information of how nucleotides, UDP-HexNAc (comprising UDP-GlcNAc and UDP-GalNAc), amino acids, and glycerophospholipids are built, which delineates anabolic pathway activity in cells or tissues 31,42,56 . As an example, we show in Fig. 7 fractional enrichment data in the pyrimidine, uridine monophosphate (UMP), and in the endproduct of the hexosamine biosynthetic pathway (HBP), UDP-HexNAc. The former is synthesized via the pentose phosphate pathway and from aspartate-derived carbons (Fig. 7A), and the latter is synthesized from carbons derived from not only F6P, but from acetyl CoA and UDP as well (Fig. 7D). In NRCMs, both UMP and UDP-HexNAc appear to remain in the dynamic phases of labeling through 12 h of 13 C 6 -glucose provision (Fig. 7B,E).
The UMP labeling profile indicates that all inhibitors diminish incorporation of glucose-derived carbon into pyrimidines, with FCCP inhibiting glucose carbon incorporation into the ribose ring (m + 5). The labeling patterns of the m + 6, m + 7, and m + 8 isotopologues of UMP in FCCP-treated cells are consistent with that of the m + 2, m + 3, and m + 4 isotopologues of asparate, respectively (see Fig. 6). The pool size of UMP was modestly lower in FCCP-treated NRCMs (Supplementary Fig. 7).
Exposure of NRCMs to KA markedly diminished glucose-derived carbon incorporation into UMP (Fig. 7B), and it diminished UMP pool size (Supplementary Fig. 7A). Although KA did not affect ribose incorporation into UMP (m + 5), it markedly diminished incorporation from aspartate (see m + 6, m + 7, m + 8 isotopologues). Because KA markedly inhibited glucose oxidation and diminished the overall Krebs cycle intermediate pool size (Supplementary Fig. 6), these data indicate that KA inhibited pyrimidine biosynthesis by decreasing glucose oxidation and aspartate synthesis.
The 13 C labeling patterns and pool size in UDP-HexNAc from KA-exposed NRCMs suggest that although inhibition of GAPDH drives F6P-derived carbon into the HBP, UDP-HexNAc biosynthesis is limited by availability of Krebs cycle-derived products. This conclusion is supported by the fact that KA increased 13 C enrichment in the m + 6 isotopologue, yet diminished 13 C incorporation into the ribose of UDP (m + 5 isotopologue) and into those isotopologues built from Krebs cycle-derived products (m + 7-m + 16 isotopologues). Furthermore, KA remarkably decreased UDP-HexNAc pool size. Oligomycin and rotenone treatment did not have large effects on UMP or UDP-HexNAc pool size or 13 C enrichment. Collectively, these findings illustrate critical relationships between catabolic and anabolic pathways and show that mitochondrial uncoupling and glucose oxidation affect the rates of pyrimidine and UDP-HexNAc biosynthesis.
Summary: The findings of this study illustrate how to integrate SIRM with the use of pharmacological controls and complementary flux analyses, which collectively provide a more comprehensive view of metabolism. The pharmacological controls used here provide the experimenter the ability to appraise confidence in fractional enrichment data. An example shown here is α-ketoglutarate, the enrichment pattern of which led to its spectral reanalysis and exclusion from interpretation. The combined use of time course data and metabolic inhibitors and activators can be used to assess the relative difference in pathway activity. We provide evidence that, in NRCMs, glucose oxidation occurs at a higher rate than anaplerotic activity from pyruvate carboxylase or malic enzyme. Moreover, we provide tangible examples of how labeling at isotopic steady state does not provide an index of flux. Additionally, we found that, in NRCMs, alanine may be synthesized via an aspartate decarboxylase. The data also provide unique insights into how glycolysis, glucose oxidation, and mitochondrial uncoupling regulate ancillary biosynthetic pathway activity. Although these findings may be specific to the cultured NRCM system used here, they provide evidence that may extend to other in vitro or to in vivo systems.

Materials and Methods
Materials. DMEM growth medium containing l-glutamine, D-glucose, and sodium pyruvate was purchased

Rodent models & primary cell isolation. All procedures were approved by the University of Louisville
Institutional Animal Care and Use Committee and were in accordance with NIH guidelines. The euthanasia procedures were consistent with the AVMA Guidelines for the Euthanasia. Neonatal rat cardiomyocytes (NRCMs) were isolated from 1-to 2-day-old Sprague-Dawley rats as previously described [57][58][59] . Briefly, neonatal rat hearts were excised, rinsed, and minced in calcium-and bicarbonate-free Hank's balanced salt solution containing HEPES (pH 7.0). Cells were dissociated by stepwise trypsin digestion, after which the trypsin in the dissociated cell mixture was quenched with FBS and centrifuged at 180 g for 5 min. The pellet was then resuspended in warm DMEM (25 mM glucose, 4 mM glutamine, 1 mM sodium pyruvate) supplemented with 5% FBS, 1% penicillin/ streptomycin, 0.1 mM BrdU, and 2 µg/mL vitamin B12 and preplated in 10 cm dishes for 1 h to allow for fibroblast adherence and removal. The non-adherent cardiomyocytes were then collected and plated at the desired cell density, as outlined below.
Cell culture. NRCMs were plated into tissue culture treated 6-well dishes (1.7 × 10 6 cells/well) or XF24 cell culture microplates (7.5 × 10 4 cells/well) in DMEM (25 mM glucose, 4 mM glutamine, 1 mM sodium pyruvate) supplemented with 5% FBS, 1% penicillin/streptomycin, 0.1 mM BrdU, and 2 µg/mL vitamin B12. The following day (day 1), media was replaced with the same plating media. On day 4, culture media was changed to media lacking BrdU. On day 5, media was replaced to serum-free media for at least 18 h prior to experimental treatments.   Fig. 4) for 12 h in medium containing 25 mM 13 C 6 -glucose. Intracellular metabolites were then extracted and subjected to LC/MS for SIRM analysis; (B) Fractional enrichment plots of glycolytic intermediates; and (C) Relative pool size for indicated glycolytic intermediates. n = 3-6 replicates per group, pooled from NRCMs isolated from three independent litters. **p < 0.01, ****p < 0.0001 vs. Ctrl.
Unlabeled samples were analyzed in full MS scan and data-dependent MS/MS modes, while the labeled specimens were only analyzed in full MS mode. The LC methods and electrospray ionization conditions were set as follows: for full MS scan, scan range Stable isotope data analyses. Full MS.raw files were first converted to.mzML format with msConvert tool, a part of an open-source ProteoWizard suite, described in detail by Adusumilli and Mallick 62 . Isotopologue peak deconvolution and assignments were performed using El-MAVEN. Peaks were assigned using a metabolite