Defining the Metabolome: Size, Flux, and Regulation
2015; Elsevier BV; Volume: 58; Issue: 4 Linguagem: Inglês
10.1016/j.molcel.2015.04.021
ISSN1097-4164
AutoresNicola Zamboni, Alan Saghatelian, Gary J. Patti,
Tópico(s)Bioinformatics and Genomic Networks
ResumoRenewed interest in metabolic research over the last two decades has inspired an explosion of technological developments for studying metabolism. At the forefront of methodological innovation is an approach referred to as "untargeted" or "discovery" metabolomics. The experimental objective of this technique is to comprehensively measure the entire metabolome, which constitutes a largely undefined set of molecules. Given its potential comprehensive coverage, untargeted metabolomics is often the first choice of experiments for investigators pursuing a metabolic research question. It is important to recognize, however, that untargeted metabolomics may not always be the optimal experimental approach. Conventionally, untargeted metabolomics only provides information about relative differences in metabolite pool sizes. Therefore, depending on the specific scientific question at hand, a complementary approach involving stable isotopes (such as metabolic flux analysis) may be better suited to provide biological insights. Unlike untargeted metabolomics, stable-isotope methods can provide information about differences in reaction rates. Renewed interest in metabolic research over the last two decades has inspired an explosion of technological developments for studying metabolism. At the forefront of methodological innovation is an approach referred to as "untargeted" or "discovery" metabolomics. The experimental objective of this technique is to comprehensively measure the entire metabolome, which constitutes a largely undefined set of molecules. Given its potential comprehensive coverage, untargeted metabolomics is often the first choice of experiments for investigators pursuing a metabolic research question. It is important to recognize, however, that untargeted metabolomics may not always be the optimal experimental approach. Conventionally, untargeted metabolomics only provides information about relative differences in metabolite pool sizes. Therefore, depending on the specific scientific question at hand, a complementary approach involving stable isotopes (such as metabolic flux analysis) may be better suited to provide biological insights. Unlike untargeted metabolomics, stable-isotope methods can provide information about differences in reaction rates. Which metabolic research questions are best tailored for each of the various experimental approaches available? What are the limitations of untargeted metabolomics? What are the challenges of metabolic flux analysis? How have each been successfully applied? Research investigators Gary Patti, Nicola Zamboni, and Alan Saghatelian address these questions in the vignettes that follow. First, Gary Patti discusses opportunities and challenges of untargeted metabolomics within the framework of potential unknown metabolites in "How Big Is the Metabolome? Opportunities and Challenges." Next, Nicola Zamboni describes the power of metabolic flux analysis and explores its most frequent limitations and pitfalls in "Modern Stable-Isotope Metabolic Flux Analysis." Finally, Alan Saghatelian contrasts different instrumentation platforms available for metabolite measurements and highlights some successful applications of both metabolomics and metabolic flux analysis in "Biological Lessons from Metabolite Profiling." In 1955, Donald Nicholson compiled all of the metabolic reactions known at that time into a single chart. This chart, which he drew by hand with stencils, provided the first perspective of a comprehensive cellular metabolome. The rendering displayed only about 20 metabolic pathways (Nicholson, 1970Nicholson S.D.D.E. An Introduction to Metabolic Pathways. Blackwell Scientific Publications, Oxford, Edinburgh1970Google Scholar). The majority of metabolic pathways taught in today's undergraduate biochemistry curriculum had been discovered and mapped onto comprehensive charts by the 1960s. There was a growing perception that the picture of the cellular metabolome was complete. In 1964, Nobel laureate Ernst Boris Chain offered his perspective on the greatest landmarks in biochemical research. He listed one major achievement as the elucidation of biochemical pathways (Chain, 1965Chain E.B. LANDMARKS AND PERSPECTIVES IN BIOCHEMICAL RESEARCH.BMJ. 1965; 1: 209-220Crossref PubMed Scopus (4) Google Scholar). Chain categorized the success of elucidating pathways into three historical eras: (1) the "pre-isotope era" in which the enzyme activities of pathways were determined in cell-free extracts, (2) the "isotope era" in which metabolite transformations were mapped with tracers, and (3) the "era of biochemical genetics" where the expression of biosynthetic enzymes were manipulated to establish reaction sequences (Chain, 1965Chain E.B. LANDMARKS AND PERSPECTIVES IN BIOCHEMICAL RESEARCH.BMJ. 1965; 1: 209-220Crossref PubMed Scopus (4) Google Scholar). This perspective that the complete cellular metabolome had been elucidated did not evolve greatly until recently, when researchers began applying cutting-edge mass spectrometry to study metabolism comprehensively. The results have been unexpected. Thousands of signals can be detected from the metabolic extracts of biological samples whose masses do not match any of those predicted based on the conventional biochemical charts. Data from this new experimental approach, termed "metabolomics," have challenged the idea that the picture of cellular metabolism is complete. While it is unclear at this time precisely how many unknown metabolites are represented in metabolomic data, already metabolomic technologies have been applied to discover new metabolites and unexpected pathway fluxes that have important physiological relevance (Dang et al., 2009Dang L. White D.W. Gross S. Bennett B.D. Bittinger M.A. Driggers E.M. Fantin V.R. Jang H.G. Jin S. Keenan M.C. et al.Cancer-associated IDH1 mutations produce 2-hydroxyglutarate.Nature. 2009; 462: 739-744Crossref PubMed Scopus (2617) Google Scholar, Kalisiak et al., 2009Kalisiak J. Trauger S.A. Kalisiak E. Morita H. Fokin V.V. Adams M.W. Sharpless K.B. Siuzdak G. Identification of a new endogenous metabolite and the characterization of its protein interactions through an immobilization approach.J. Am. Chem. 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With the rise of metabolomics, a new era of biochemical discovery has begun. For the first time in several decades, excitement to discover new metabolites and pathways is at the forefront of biological research (McKnight, 2010McKnight S.L. On getting there from here.Science. 2010; 330: 1338-1339Crossref PubMed Scopus (86) Google Scholar). There are several technological platforms available for performing metabolomics, but liquid chromatography/mass spectrometry (LC/MS) is most commonly used for discovery (i.e., untargeted) experiments because thousands of signals are routinely detected from the metabolic extract of a biological sample (Patti et al., 2012bPatti G.J. Yanes O. Siuzdak G. Innovation: Metabolomics: the apogee of the omics trilogy.Nat. Rev. Mol. Cell Biol. 2012; 13: 263-269Crossref PubMed Scopus (1593) Google Scholar). The biggest challenge in performing discovery metabolomic experiments is translating these signals, termed features, into metabolite identities. While there are certainly more resources available now compared to 10 years ago, the process of making metabolite identifications is still low throughput. It is common to spend weeks to months analyzing each metabolomic dataset, and even then only a relatively small number (< 20) of metabolite identifications are often made. The possibility that hundreds or thousands of LC/MS signals might correspond to unknown metabolites that have yet to be described is one of the most exciting aspects of performing discovery metabolomics. This possibility is also the single reason that performing metabolomics is so challenging. For example, consider a situation where there is interest in identifying an ion with a mass-to-charge value of 808.118 as detected in negative-ionization mode. The first step would be searching the value of 808.118 in metabolomics databases such as METLIN and HMDB (Tautenhahn et al., 2012aTautenhahn R. Cho K. Uritboonthai W. Zhu Z. Patti G.J. Siuzdak G. An accelerated workflow for untargeted metabolomics using the METLIN database.Nat. Biotechnol. 2012; 30: 826-828Crossref PubMed Scopus (377) Google Scholar, Wishart et al., 2009Wishart D.S. Knox C. Guo A.C. Eisner R. Young N. Gautam B. Hau D.D. Psychogios N. Dong E. Bouatra S. et al.HMDB: a knowledgebase for the human metabolome.Nucleic Acids Res. 2009; 37: D603-D610Crossref PubMed Scopus (1497) Google Scholar). Given that modern mass spectrometers can routinely measure mass-to-charge ratios of metabolites with an error of 15 ppm or less, the databases would be searched for candidate compounds over the mass-to-charge interval 808.098–808.138. Currently, the only hit returned from this search is acetyl-CoA. Yet because there may be unknown metabolites with mass-to-charge values within this interval that have not been input into metabolomic databases, this result would not substantiate identifying the ion with a mass-to-charge value of 808.118 as acetyl-CoA. Without a complete parts list of the metabolome, accurate mass is never sufficient to confidently identify a metabolite. Instead, to reliably identify a metabolite, its mass-to-charge value, chromatographic retention time, isotopic pattern, and fragmentation data are generally used together in combination (Patti et al., 2012bPatti G.J. Yanes O. Siuzdak G. Innovation: Metabolomics: the apogee of the omics trilogy.Nat. Rev. Mol. Cell Biol. 2012; 13: 263-269Crossref PubMed Scopus (1593) Google Scholar). The sum of this information is highly specific to a compound, and therefore it is unlikely that any two metabolites, known or unknown, produce the same set of data. Using retention time and fragmentation data to identify metabolites, however, leads to some major experimental challenges. The remainder of my discussion is dedicated to briefly describing some bioinformatic resources that have been developed to help with the process. One of the most widely used software packages for processing raw LC/MS-based metabolomic data is a program called XCMS. Just 5 years ago, installation and operation of XCMS required familiarity with the R programing language. Recently, however, a cloud-based version of XCMS was developed called XCMS Online that uses an intuitive graphical interface (Tautenhahn et al., 2012bTautenhahn R. Patti G.J. Rinehart D. Siuzdak G. XCMS Online: a web-based platform to process untargeted metabolomic data.Anal. Chem. 2012; 84: 5035-5039Crossref PubMed Scopus (833) Google Scholar). Metabolomic data are uploaded to the XCMS Online server much like files are uploaded as an attachment to an email. After the data are processed, the results are integrated with the METLIN database so that candidates for the metabolite identities of each signal (i.e., feature) are listed. The candidate list is produced on the basis of each feature's mass-to-charge value. As discussed above, the mass-to-charge value of a feature alone is insufficient to substantiate a metabolite identification. To facilitate metabolite assignments based on fragmentation data, a number of metabolomic databases such as METLIN, HMDB, and MassBank have begun incorporating experimental fragmentation data acquired from model standards (Horai et al., 2010Horai H. Arita M. Kanaya S. Nihei Y. Ikeda T. Suwa K. Ojima Y. Tanaka K. Tanaka S. Aoshima K. et al.MassBank: a public repository for sharing mass spectral data for life sciences.J. Mass Spectrom. 2010; 45: 703-714Crossref PubMed Scopus (1384) Google Scholar, Tautenhahn et al., 2012aTautenhahn R. Cho K. Uritboonthai W. Zhu Z. Patti G.J. Siuzdak G. An accelerated workflow for untargeted metabolomics using the METLIN database.Nat. Biotechnol. 2012; 30: 826-828Crossref PubMed Scopus (377) Google Scholar, Wishart et al., 2009Wishart D.S. Knox C. Guo A.C. Eisner R. Young N. Gautam B. Hau D.D. Psychogios N. Dong E. Bouatra S. et al.HMDB: a knowledgebase for the human metabolome.Nucleic Acids Res. 2009; 37: D603-D610Crossref PubMed Scopus (1497) Google Scholar). At this time, the METLIN database contains fragmentation data for more than 12,000 model standards. Current efforts are focused on further integrating XCMS Online and METLIN so that metabolite assignments can be made during data processing that are based on both mass-to-charge values and fragmentation data, an approach that has been referred to as autonomous metabolomics (Benton et al., 2015Benton H.P. Ivanisevic J. Mahieu N.G. Kurczy M.E. Johnson C.H. Franco L. Rinehart D. Valentine E. Gowda H. Ubhi B.K. et al.Autonomous metabolomics for rapid metabolite identification in global profiling.Anal. Chem. 2015; 87: 884-891Crossref PubMed Scopus (128) Google Scholar, Tautenhahn et al., 2012aTautenhahn R. Cho K. Uritboonthai W. Zhu Z. Patti G.J. Siuzdak G. An accelerated workflow for untargeted metabolomics using the METLIN database.Nat. Biotechnol. 2012; 30: 826-828Crossref PubMed Scopus (377) Google Scholar). In a typical discovery profiling experiment performed with LC/MS-based technologies, thousands of features are routinely detected from most biological samples. When the mass-to-charge ratios of each detected feature are searched against metabolomic databases, a surprisingly small fraction provide database hits. The challenge with interpreting this result with respect to the size of the unknown metabolome is that each feature detected does not necessarily correspond to a different metabolite. As a consequence, the number of "unknown" compounds detected is overestimated upon simple inspection. For example, naturally occurring isotopes can result in one metabolite species being detected as multiple features. Mostly, this results from the relatively high concentration of 13C that occurs naturally and results in a mass shift. Another reason a metabolite can produce multiple features is because metabolites can ionize as several adducts. That is, in addition to being detected as [M+H]+, metabolites might also be detected as [M+Na]+, [M+NH4]+, etc. Further, metabolites can sometimes fragment or form non-covalent interactions with other metabolites when entering the mass spectrometer. This too inflates the feature count independent of the number of metabolites present. Most features corresponding to isotopes, adducts, and fragments can be readily identified by using software programs such as CAMERA (Kuhl et al., 2012Kuhl C. Tautenhahn R. Böttcher C. Larson T.R. Neumann S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.Anal. Chem. 2012; 84: 283-289Crossref PubMed Scopus (710) Google Scholar). But even after these processing steps, the number of features detected can still be larger than the number of metabolites present due to artifacts associated with contaminants, chemical noise, and bioinformatic noise. To identify and remove these artifactual features, isotopic labeling methods such as credentialing and isotope-ratio outlier analysis have been developed (Mahieu et al., 2014Mahieu N.G. Huang X. Chen Y.J. Patti G.J. Credentialing features: a platform to benchmark and optimize untargeted metabolomic methods.Anal. Chem. 2014; 86: 9583-9589Crossref PubMed Scopus (63) Google Scholar, Stupp et al., 2013Stupp G.S. Clendinen C.S. Ajredini R. Szewc M.A. Garrett T. Menger R.F. Yost R.A. Beecher C. Edison A.S. Isotopic ratio outlier analysis global metabolomics of Caenorhabditis elegans.Anal. Chem. 2013; 85: 11858-11865Crossref PubMed Scopus (45) Google Scholar). In brief, identical samples with and without 13C labels are mixed. Features of biological origin consequently produce a unique isotopic pattern in the mass spectra, whereas artifactual features do not. Features with the appropriate isotopic patterns are said to be "credentialed." It is important to highlight that even when these extensive filtering strategies are applied to Escherichia coli, there remain hundreds of credentialed features whose mass-to-charge values do not return any matches in current metabolomics databases (Mahieu et al., 2014Mahieu N.G. Huang X. Chen Y.J. Patti G.J. Credentialing features: a platform to benchmark and optimize untargeted metabolomic methods.Anal. Chem. 2014; 86: 9583-9589Crossref PubMed Scopus (63) Google Scholar). Of course, it is possible that some credentialed features returning database hits are themselves unknowns that have matching mass-to-charge values but do not have matching structures. Alternatively, it may be that some credentialed features not returning database hits are known metabolites that have undergone an extracellular transformation (e.g., during the extraction process). Only after every credentialed feature is structurally characterized will a definitive calculation of the number of unknowns detected be possible. Certainly the results are expected to be at least partially organism specific. The credentialing experiments performed thus far, however, suggest that the number of unknown metabolites detected by LC/MS-based metabolomics is much smaller than the thousands of features without database hits appearing in the raw data prior to filtering. In 1955, Nicholson provided the first picture of the comprehensive cellular metabolome by constructing a chart connecting the ∼20 biochemical pathways that had then been characterized. By 1970, Nicholson's chart contained just over 400 metabolites. Today, comprehensive metabolic charts have expanded to now include more than four times as many metabolites (Ogata et al., 1999Ogata H. Goto S. Sato K. Fujibuchi W. Bono H. Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes.Nucleic Acids Res. 1999; 27: 29-34Crossref PubMed Scopus (3340) Google Scholar, Thiele et al., 2013Thiele I. Swainston N. Fleming R.M. Hoppe A. Sahoo S. Aurich M.K. Haraldsdottir H. Mo M.L. Rolfsson O. Stobbe M.D. et al.A community-driven global reconstruction of human metabolism.Nat. Biotechnol. 2013; 31: 419-425Crossref PubMed Scopus (715) Google Scholar). The introduction of new technologies for studying metabolism has raised the question of how complete even that picture is. Undoubtedly, advances in mass spectrometry-based metabolomics have reinvigorated research in metabolite discovery. The extent to which metabolomic technologies will impact the canonical view of comprehensive metabolism in textbooks remains to be seen, but it is interesting to consider that we have entered a new period of biochemical research. Recall that Chain organized the history of pathway discovery into three periods: the pre-isotope era, the isotope era, and the era of biochemical genetics. Building upon Chain's perspective, we might say that we have entered a fourth era of elucidating biochemical pathways: the "metabolomics era." In most cases, understanding how newly discovered metabolites integrate into our current comprehensive chart of metabolism will require using stable isotopes and metabolic flux analysis, as described below. While untargeted metabolite profiling approaches have yielded many important insights into a multitude of biological problems and may potentially lead to an update of our perspective of the number of metabolites present in a cell, conventional untargeted metabolomic experiments generally only provide information about metabolite concentration. This is a static snapshot that cannot be translated into a dynamic map of metabolite traffic on biochemical routes. A more complete understanding of biochemical pathways can be obtained by using metabolic flux analysis. Metabolic fluxes are the in vivo velocities of metabolic reactions. These include the rate of transformation of intermediates by enzymes and of the transport of metabolites between compartments. In single cells or multicellular organisms, metabolic fluxes are an emerging property, as they depend on the systemic organization and interplay of enzymes, carriers, and substrates in the environment. Knowledge of metabolic fluxes is essential to unravel sites and mechanisms of metabolic regulation and thus augment our understanding of how metabolism is embedded in cellular decisions. In diseased cells, information on fluxes paves the road for identification of selective therapeutic targets. The frequent questions related to metabolic fluxes are (1) discovery of the catabolic fate of a given nutrient, (2) discovery of the biosynthetic origin of a given intracellular compound, (3) targeted analysis of fluxes at a specific metabolic node or reaction, or (4) determination of cellular balances for redox (i.e., NADPH and NADH) and energy (ATP) carriers. In all these quests, the method of choice is metabolic flux analysis with isotopic tracers (e.g., 13C and 2H). As any other reaction rate, metabolic fluxes cannot be measured directly. In cell cultures, but not in vivo, it is possible to measure the consumption rate of nutrients and secretion of metabolic byproducts by monitoring the time-dependent depletion or production, respectively, in spent medium. Intracellularly, however, metabolite levels remain constant in a dynamic equilibrium between producing and consuming reactions and do not inform on molecular flow. Instead, intracellular fluxes can be accessed by administering nutrients labeled with stable isotopes, typically 13C or 2H. Uptake and enzymatic transformation of labeled substrates propagates heavy isotopes through the metabolic network in a flux-dependent fashion. In some cases—but not always—the resulting labeling pattern of metabolites can be used to infer from which nutrient it originated or, if alternative pathways exist, through which enzymatic route. Two fundamental designs exist for metabolic flux analysis studies: stationary and non-stationary experiments. In stationary experiments, labeling patterns are evaluated at the end-point when they become invariant over time (i.e., an isotopic equilibrium was reached) (Zamboni et al., 2009Zamboni N. Fendt S.M. Rühl M. Sauer U. (13)C-based metabolic flux analysis.Nat. Protoc. 2009; 4: 878-892Crossref PubMed Scopus (409) Google Scholar). The main advantage of stationary experiments is that at isotopic steady-state the measured labeling patterns are fully independent of metabolite levels. The latter can be safely neglected in the calculation of fluxes from labeling data facilitating data acquisition and interpretation. On the downside, stationary isotopic experiments are only useful to resolve the relative contribution of different pathways to the synthesis of a common derivative, but are not suited to quantify the flux in a simple linear pathway. Stationary labeling experiments must last sufficiently long to attain isotopic stationarity. In peripheral pathways, it can take days. During this time, conditions and fluxes must be constant. In cell biology, stationary metabolic flux analysis is best suited to determine what nutrients contributed to the biosynthesis of intracellular metabolites. For this purpose, single nutrients are uniformly 13C labeled while all other substrates are provided with natural labeling. At isotopic steady state, the 13C-enrichment of each metabolite (i.e., its fractional labeling) indicates what ratio originated from the labeled substrate. Non-stationary labeling experiments, in contrast, focus on the kinetics of tracer propagation before isotopic equilibrium (Wiechert and Nöh, 2013Wiechert W. Nöh K. Isotopically non-stationary metabolic flux analysis: complex yet highly informative.Curr. Opin. Biotechnol. 2013; 24: 979-986Crossref PubMed Scopus (77) Google Scholar). This relaxes most limitations of stationary experiments: labeling experiments can be as short as a few minutes, and it becomes possible to estimate fluxes within linear pathways. This increased power over stationary analyses comes with extra costs. First, many more time points, and thus more samples and more instrument time, are necessary to assemble the dataset. Second, data analysis is substantially more complex. Third and most important, non-stationary labeling transients depend both on metabolic fluxes and on intracellular metabolite concentration. Therefore, the latter have to be measured by canonical quantitative metabolomics and included in the interpretation. The labeling configuration of the tracer is a pivotal component of the labeling experiment. As mentioned before, 100% uniformly 13C-labeled substrates are preferred for identifying their intracellular fate in any cellular system. In microorganisms, the combination of uniformly 13C-labeled and unlabeled substrates has been largely used to assess multiple fluxes in central metabolism with a single experiment. The mix between labeled and unlabeled forms of the same substrate allows us to resolve alternative pathways that use different enzymes. The classical example is glucose catabolism through glycolysis or the pentose-phosphate pathway (PPP). With 100% 13C-glucose, the two pathways are indistinguishable because they both produce completely 13C-labeled pyruvate. If 50% uniformly 13C-labeled and 50% unlabeled glucose are mixed, catabolism through the transaldolase and transketolase in the PPP will combine labeled and unlabeled carbon backbones and lead to partly labeled pyruvate forms, which differentiate from the either completely 13C-labeled or unlabeled pyruvate molecules produced by glycolysis. In mammalian cells, however, there are many more interfering pathways and nutrients that prevent resolving several fluxes at once with generalist tracers. In contrast, positionally enriched tracers harboring stable isotopes only at specific positions are used to resolve important reactions. This strategy is particularly suited to assay decarboxylating enzymes if it is possible to label its substrate such as all label is lost as 13CO2. This ad hoc strategy is frequently used in the analysis of the oxidative PPP, pyruvate dehydrogenase, or oxidative TCA cycle. Non-radioactive isotopes are amenable with instrumentation platforms such as mass spectrometry and nuclear magnetic resonance (NMR), which are powerful tools for determining the isotopic patterns of numerous metabolites or macromolecules. The community is dominated by two types of instruments. Gas chromatography/mass spectrometry (GC/MS) is the most affordable technology and perfectly suited to analyze isotopic patterns in amino and organic acids, sugars, and fatty acids. GC/MS often adopts electron impact ionization, which intrinsically fragments analytes and therefore provides additional information on the localization of the isotopic label within the metabolite structure. In contrast, LC/MS uses a soft ionization technique that preserves molecular ions. Compared to GC/MS, it delivers less positional information but offers better coverage of endogenous metabolites. Therefore, LC/MS is preferred in non-stationary labeling experiments to monitor the isotopic transients in proximity of the important reactions and also quantify the levels of metabolites required for non-stationary data analysis. An ideally designed tracer experiment allows for verification of the working hypothesis through direct observation of the labeling data or the fractional labeling of a metabolite. Statistical significance is checked by simple univariate testing. This is a practical strategy when searching for the origin of a given metabolite, and the possible substrates or routes are characterized by different label content. It is also used in combination with selective inhibition of alternative pathways (i.e., isoenzymes) by gene knockout and knockdown or pharmacological inhibition, for which qualitative comparison of the resulting labeling patterns is sufficient to assess their relative contribution. An illustrative example is given by Son et al., 2013Son J. Lyssiotis C.A. Ying H. Wang X. Hua S. Ligorio M. Perera R.M. Ferrone C.R. Mullarky E. Shyh-Chang N. et al.Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway.Nature. 2013; 496: 101-105Crossref PubMed Scopus (1276) Google Scholar. Unfortunately, it is not always feasible to design such an explicit labeling experiment. Interpretation is complicated by the propagation and scrambling of tracer over the entire network following carbon fluxes. A metabolic alteration occurring in the early steps of tracer catabolism may affect the labeling patterns of all—close and distant—metabolites. The logical consequence is that a difference in labeling patterns could have been caused by upstream alterations in metabolism. An unbiased analysis of labeling patterns must consider the potential effect of distant pathways. This is particularly challenging for metabolic cycles, reversible reactions, and extensive compartmentalization that introduces numerous degrees of freedom. Already in mid-sized systems such as central carbon metabolism, this task exceeds human intuition
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