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Mass spectrometry based metabolomics and enzymatic assays for functional genomics
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Mass spectrometry based metabolomics and enzymatic assays for functional genomics Richard Baran, Wolfgang Reindl and Trent R Northen The exponential growth in the number of sequenced microorganisms versus the relative slow influx of direct biochemical characterization of microbes is limiting the utility of sequence information. High-throughput experimental approaches to functionally characterize microbial metabolism are urgently needed to leverage genome sequences for example: to understand host–microbe interactions, microbial communities, to utilize microbes for bioenergy, bioremediation, etc. Mass spectrometry based small molecule metabolite analysis is rapidly becoming a method of choice to meet these needs and enables multiple paths to discovering and validating the functional assignments. Approaches range from the targeted in vitro screening of small sets of metabolic transformations to define enzymatic activities to global metabolic profiling (metabolomics) to define metabolic pathways and gain insights into microbial responses to environmental and genetic perturbations. The combination of metabolite profiling with genome-scale models of metabolism and other -omic approaches provides opportunities to expand our understanding of microbial metabolic networks, stress responses, and to identify genes associated with specific enzymatic and regulatory activities. Address Department of GTL Bioenergy and Structural Biology, Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA Corresponding author: Northen, Trent R ([email protected])
Current Opinion in Microbiology 2009, 12:547–552 This review comes from a themed issue on Genomics Edited by Carmen Buchrieser and Stewart Cole Available online 18th August 2009 1369-5274/$ – see front matter Published by Elsevier Ltd. DOI 10.1016/j.mib.2009.07.004
Introduction The large number of sequenced microbial genomes coupled with the development of computational approaches for homology based functional assignments has allowed rapid annotation of genes (Figure 1) [1]. However, these annotations are often incorrect. The lower the sequence homology and/or the more phylogenetically divergent an organism is from a relevant model organism, the more error prone its annotation. This is www.sciencedirect.com
further complicated by the unavailability of gene sequences for a large number of enzymatic activities [2]. Correcting and identifying functional annotations to these genes remains a major challenge in microbial functional genomics. Metabolites are often the final downstream products or effects of gene expression (Figure 1) and therefore can be used to assign or validate functional annotations related to enzymatic activities. The high sensitivity of mass spectrometry (MS) coupled with its ability to detect and quantify a wide range of metabolites from complex mixtures (i.e. cellular extracts) makes it well suited for functional genomics. In a simple case, MS is used for the targeted analysis of products and reactants for single or small sets of enzymes (Figure 1). This is facilitated by the ability of MS to simultaneously monitor multiple endogenous substrates. The use of these MS-based ‘in vitro’ enzyme assays has expanded in scope and complexity since their first applications [3]. As the number of substrates and products increases this approach eventually results in the untargeted profiling of all metabolites (‘metabolomics’). These metabolomic approaches in particular have experienced considerable progress over recent years [4] and are now routinely providing new insights into microbial metabolic capabilities. Mass spectrometry based functional genomic studies can be arranged into two complimentary experimental goals (Figure 2): firstly, validating homology-based assignments and secondly, discovering and annotating new genes and alternative metabolic pathways. The scope of MS experiments designed to address these goals ranges from the very focused characterization of specific reactions (‘in vitro’ enzyme assays) to global metabolic profiling experiments. Selection of the appropriate MS methods depends upon the throughput and number of metabolites (reactions) to be characterized. The targeted ‘in vitro’ enzyme assays approaches are often higher throughput, less computationally demanding, and more sensitive. In contrast, global metabolic profiling approaches can address both goals simultaneously but the results are complex; often requiring the use of bioinformatics tools and metabolic network models. Overall, MS-based enzymatic activity assays and metabolomics have made numerous contributions to functional characterization of genes and metabolic characterization of microorganisms. Given the depth and breadth of the field the focus of this brief review is on the current literature and Current Opinion in Microbiology 2009, 12:547–552
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Figure 1
Metabolite analysis by mass spectrometry provides multiple approaches to functional genomics. Metabolites often represent the final downstream products or effects of gene expression. Mass spectrometry is widely used to analyze the metabolic network of microbes providing a powerful tool for the assignment of functions to genes. Abbreviations — G: gene; E: enzyme; S and P: substrate and product of an enzymatic reaction.
most widely used approaches. However, we complement this with reference to reviews outside this scope and provide a perspective on the emerging developments and opportunities for exploiting mass spectrometry for microbial functional genomics. Figure 2
The two major applications of mass spectrometry for metabolic profiling in functional genomics. MS-based metabolic profiling is a valuable tool for (A) the validation of homology based metabolite assignments and (B) the discovery and annotation of currently undefined metabolic capabilities and pathways. Question-marks symbolize yet unknown metabolites, enzymes, or pathways. Current Opinion in Microbiology 2009, 12:547–552
In vitro enzymatic activity assays Mass spectrometry based in vitro enzymatic approaches have been used for decades. The methodological developments before 2005 and approaches for kinetic characterization of enzymes have been reviewed elsewhere [3]. Mass spectrometry requires gas phase ions and the most widely used ionization approaches are: electrospray ioniziation (ESI) a ‘soft’ approach (limited molecular fragmentation) and electron ionization (EI) which in contrast results in extensive fragmentation but has the advantage that it effectively ionizes most molecules. Typically, sample preparation steps are used to isolate the metabolites from other cellular materials such as solvent extraction. Simple mixtures can be directly infused into the mass spectrometer. However, for more complex samples such as whole cell extracts, ESI is typically coupled to liquid chromatography (LC–MS) or capillary electrophoresis (CE–MS) and EI is coupled to gas chromatography (GC–MS). This increases the number of metabolites detected by limiting the number of metabolites competing for ionization at any given time [3,4]. In contrast to these solution-based methods, which typically take several minutes per sample, high-throughput approaches using surface-based MS such as matrix-assisted laser desorption/ionization (MALDI) or matrix-less desorption/ionization can be used [3]. A recent advancement in surface-based methods is the Nimzyme approach [5] based on the related technology, nanostructure-initiator mass spectrometry (NIMS) [6] which utilizes fluorous phase interactions to immobilize www.sciencedirect.com
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enzyme substrates on the mass spectrometry surface. This ‘soft’ immobilization allows efficient desorption/ ionization while also allowing the use of surface washing steps to reduce signal suppression from complex biological samples. This is enabled by the preferential retention of the tagged products and reactants, is sensitive to subpicogram levels of enzyme, rapid (1 s/sample), detects both addition and cleavage reactions, and can measure activity directly from crude cell and microbial community lysates. Other recent advances in surface approaches include microfluidic networks and peptide arrays followed by MALDI-MS to assay kinase and phosphatase activities [7]. Electrowetting was utilized also by rapidly mixing droplets containing the enzyme, substrates, quenching and matrix solutions on an electrode array followed by MALDI-MS analysis [8]. The same group developed a different method, where single reaction droplet travels on the surface of porous silicon. Reactants, but no enzyme, are continuously left behind in the narrow pores. The trajectory is then analyzed by matrix-less desorption/ ionization followed by MS and corresponds to the time course of the reaction [9]. MS-based enzyme activity assays are applied routinely to different reactions and are especially useful when simultaneous monitoring of multiple substrates and products is desirable such as for glycosylhydrolase/transferases [10,11] or sesquiterpene synthases [12]. MS-based assays can be used for in vitro validation of specific enzymatic activities for putative enzymes [13–15], identification of substrates of putative enzymes [10,16], or inhibitor screening [17,18]. In vitro reconstitution of pathways and monitoring by MS were employed for the validation of discoveries of genes related to alternative D-glucarate degradation [19] and anaerobic oxidative degradation of L-ornithine [20].
Metabolic profiling The power of MS becomes more prominent in untargeted ‘metabolic profiling’ or metabolomic approaches requiring the analysis of complex samples. Typical MS-based analytical methods for comprehensive metabolic profiling are GC–MS, LC–MS, and CE–MS [4]. However, no single method is suitable for all metabolites. ‘Multimode’ approaches combining multiple MS methods have been shown to increase the coverage of detected metabolites for biological samples [21] as does chemical modification to improve ionization [22]. A critical step in the interpretation of metabolic profiles is the identification of unknown metabolites. Toward this goal, MS approaches can provide detailed information on the accurate mass, isotopic profile, chromatographic retention times, and MS/MS fragmentation patterns [23,24]. Metabolites are ultimately identified by a comwww.sciencedirect.com
parison of these properties with metabolite standards [25]. Standardization of methods [26] and accumulation of data in metabolite databases greatly facilitate identifications and are a critical step in this developing field [27–29] (MassBank; URL: http://www.massbank.jp/). Identification of a large number of metabolites allows a comparison of experimental metabolite profiles against predicted metabolic capabilities of studied microorganisms from genome annotations, organism specific databases, that is BioCyc [30] or KEGG [31] or genome-scale models of metabolism [32,33]. For example, comparison of metabolite profiles of Bacillus subtilis [34] against metabolites present in the reconstructed genome-scale model showed a large number of metabolites unaccounted in the model [35]. Similar observations from the draft metabolic network reconstruction of Chlamydomonas reinhardtii [36] reveal the limitations of current homology based genome annotations and biochemical knowledge. Experimentally identified metabolites outside current genome annotations or genome-scale metabolic models point to additional metabolic capabilities. These identified metabolites or unidentified peaks can be linked to putative reactions if their levels correlate across different samples [37] or if the mass difference between them could correspond to common biochemical transformations or condensations among them [38,39]. Comparison of the composition of a complex mixture of metabolites before and following in vitro incubation with proteins of unknown function can lead to the identification of the enzymatic activity by detecting a reduction in substrate and/or formation of a new product. Recent examples include the identification of phosphatase and phosphotransferase activities for two proteins [40] and a novel 4-hydroxybutyrate dehydrogenase [41] from Escherichia coli. Incubation of human P450 cytochromes in vitro with tissue extracts lead to the untargeted identification of endogenous substrates [42]. In a related approach aimed at the identification of protein–metabolite interactions, immobilized proteins of interest are incubated with a complex mixture of metabolites, the proteins with bound metabolites are separated and the composition of the resulting mixture is compared to the original one to identify the bound metabolites [43]. Environmental or genetic perturbations may also lead to the appearance or disappearance of intermediates or products of perturbation-related pathways [44,45]. Comparison of metabolite profiles of wild type strains and strains with gene deletions contributed to the discovery of a novel bacterial type III polyketide synthase from Streptomyces coelicolor [46], orfamide A — a new lipopeptide from Pseudomonas fluorescens Pf-5 [47], elucidation of the S. coelicolor pathway to 2-undecylpyrrole [48], and the discovery of novel antibiotic production inducers from S. coelicolor [49]. Alternatively, deletion of a specific gene Current Opinion in Microbiology 2009, 12:547–552
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may lead to the accumulation of substrates of the corresponding enzyme as was shown in the identification of endogenous substrates for mammalian fatty acid amide hydrolase [50]. Integrating metabolic profiling with stable isotopelabeled substrates provides a global perspective on the fates of specific atoms within the metabolic network [51]. Presence of stable isotope labels in detected metabolites can be identified thanks to the characteristic shifts in the masses of corresponding ions in metabolite profiles. The labels can even be pinpointed to specific atoms within a molecule based on fragmentation spectra [52]. These stable isotope methods are used to quantitatively estimate the distribution of fluxes through the metabolic network [53] (and a review in this issue). From a functional genomics perspective, identification of a presence or an absence of a stable isotope label in a specific position within a molecule of a metabolite alone may be indicative of specific metabolic pathways, even if the metabolite is not directly involved in the pathway [54]. Using this approach, isoleucine was shown to be predominantly biosynthesized by an alternative pathway in Geobacter sulfurreducens [55] and glyoxylate regeneration via ethylmalonyl-CoA pathway was documented for Methylobacterium extorquens [56]. Global profiling and comparison of experimental stable isotope labeling patterns with theoretical ones can be used to help identify discrepancies and possible alternative pathways. This approach is greatly enabled through the development of genome-scale carbon-fate maps [57–59]. Such maps show promise in designing experiments to utilize specifically labeled substrates allowing discrimination between alternative metabolic pathways [60]. The use of libraries of gene deletion mutants can help identify differences in labeling patterns for mutants and may lead to the identification of metabolic or regulatory genes, provided experimental, analytical, and data analysis throughput are available. Combination of metabolic profiling with other -omic technologies provides a more comprehensive view on cellular physiology. For example, integrated metabolomic and transcriptomic studies show correlations between the levels of metabolites and biologically related genes [61]. Metabolomic and proteomic data recently supported the draft metabolic network reconstruction of C. reinhardtii [36]. Such integrated approaches may identify not only new enzymatic activities, but also new regulatory effects [62,63]. In future, the ability to perform large-scale multi-omic experiments [64,65] will allow more comprehensive functional genomic studies.
Figure 3
Application of mass spectrometry to screen for specific activities in microbial communities. Mass spectrometry can be used to study metabolism in complex microbial communities. There is a broad range of possible applications, for example mass spectrometry can be used to study (A) the lignocelluloses degrading capabilities of a microbial community, (B) the metabolic role of gut microbes or (C) various host– pathogen interactions.
enzymatic activities and metabolic networks. Recent developments in MS-based metabolomic and activity profiling approaches are being used to address these needs by rapidly defining functional gene annotations. These efforts are enabled through the interplay of MS with bioinformatics, development of new MS approaches, and the development of metabolite databases. These are converging to provide new insights into microbial metabolism. We anticipate that these capabilities coupled with both the increased focus on microbial communities and integration with other ‘-omics’ technologies provide tremendous opportunities for ‘meta-metabolomic’ studies of microbial communities (Figure 3) [66] and will provide new insights into the complex interactions between microbes and other higher organisms [67,68].
Acknowledgements We gratefully acknowledge support from the Department of Energy [DEAC02-05CH11231]. We thank Gary Siuzdak and Steven Yannone for their insightful comments, and Manfred Auer and Bernhard Knierim for providing SEM images.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as:
Conclusions The rapid expansion of sequenced microorganisms coupled with the critical need for improved understanding of microbial metabolism and stress responses demands high-throughput methods for functionally characterizing Current Opinion in Microbiology 2009, 12:547–552
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Mass spectrometry based metabolomics and enzymatic assays for functional genomics Richard Baran, Wolfgang Reindl and Trent R Northen The exponential growth in the number of sequenced microorganisms versus the relative slow influx of direct biochemical characterization of microbes is limiting the utility of sequence information. High-throughput experimental approaches to functionally characterize microbial metabolism are urgently needed to leverage genome sequences for example: to understand host–microbe interactions, microbial communities, to utilize microbes for bioenergy, bioremediation, etc. Mass spectrometry based small molecule metabolite analysis is rapidly becoming a method of choice to meet these needs and enables multiple paths to discovering and validating the functional assignments. Approaches range from the targeted in vitro screening of small sets of metabolic transformations to define enzymatic activities to global metabolic profiling (metabolomics) to define metabolic pathways and gain insights into microbial responses to environmental and genetic perturbations. The combination of metabolite profiling with genome-scale models of metabolism and other -omic approaches provides opportunities to expand our understanding of microbial metabolic networks, stress responses, and to identify genes associated with specific enzymatic and regulatory activities. Address Department of GTL Bioenergy and Structural Biology, Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA Corresponding author: Northen, Trent R ([email protected])
Current Opinion in Microbiology 2009, 12:547–552 This review comes from a themed issue on Genomics Edited by Carmen Buchrieser and Stewart Cole Available online 18th August 2009 1369-5274/$ – see front matter Published by Elsevier Ltd. DOI 10.1016/j.mib.2009.07.004
Introduction The large number of sequenced microbial genomes coupled with the development of computational approaches for homology based functional assignments has allowed rapid annotation of genes (Figure 1) [1]. However, these annotations are often incorrect. The lower the sequence homology and/or the more phylogenetically divergent an organism is from a relevant model organism, the more error prone its annotation. This is www.sciencedirect.com
further complicated by the unavailability of gene sequences for a large number of enzymatic activities [2]. Correcting and identifying functional annotations to these genes remains a major challenge in microbial functional genomics. Metabolites are often the final downstream products or effects of gene expression (Figure 1) and therefore can be used to assign or validate functional annotations related to enzymatic activities. The high sensitivity of mass spectrometry (MS) coupled with its ability to detect and quantify a wide range of metabolites from complex mixtures (i.e. cellular extracts) makes it well suited for functional genomics. In a simple case, MS is used for the targeted analysis of products and reactants for single or small sets of enzymes (Figure 1). This is facilitated by the ability of MS to simultaneously monitor multiple endogenous substrates. The use of these MS-based ‘in vitro’ enzyme assays has expanded in scope and complexity since their first applications [3]. As the number of substrates and products increases this approach eventually results in the untargeted profiling of all metabolites (‘metabolomics’). These metabolomic approaches in particular have experienced considerable progress over recent years [4] and are now routinely providing new insights into microbial metabolic capabilities. Mass spectrometry based functional genomic studies can be arranged into two complimentary experimental goals (Figure 2): firstly, validating homology-based assignments and secondly, discovering and annotating new genes and alternative metabolic pathways. The scope of MS experiments designed to address these goals ranges from the very focused characterization of specific reactions (‘in vitro’ enzyme assays) to global metabolic profiling experiments. Selection of the appropriate MS methods depends upon the throughput and number of metabolites (reactions) to be characterized. The targeted ‘in vitro’ enzyme assays approaches are often higher throughput, less computationally demanding, and more sensitive. In contrast, global metabolic profiling approaches can address both goals simultaneously but the results are complex; often requiring the use of bioinformatics tools and metabolic network models. Overall, MS-based enzymatic activity assays and metabolomics have made numerous contributions to functional characterization of genes and metabolic characterization of microorganisms. Given the depth and breadth of the field the focus of this brief review is on the current literature and Current Opinion in Microbiology 2009, 12:547–552
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Figure 1
Metabolite analysis by mass spectrometry provides multiple approaches to functional genomics. Metabolites often represent the final downstream products or effects of gene expression. Mass spectrometry is widely used to analyze the metabolic network of microbes providing a powerful tool for the assignment of functions to genes. Abbreviations — G: gene; E: enzyme; S and P: substrate and product of an enzymatic reaction.
most widely used approaches. However, we complement this with reference to reviews outside this scope and provide a perspective on the emerging developments and opportunities for exploiting mass spectrometry for microbial functional genomics. Figure 2
The two major applications of mass spectrometry for metabolic profiling in functional genomics. MS-based metabolic profiling is a valuable tool for (A) the validation of homology based metabolite assignments and (B) the discovery and annotation of currently undefined metabolic capabilities and pathways. Question-marks symbolize yet unknown metabolites, enzymes, or pathways. Current Opinion in Microbiology 2009, 12:547–552
In vitro enzymatic activity assays Mass spectrometry based in vitro enzymatic approaches have been used for decades. The methodological developments before 2005 and approaches for kinetic characterization of enzymes have been reviewed elsewhere [3]. Mass spectrometry requires gas phase ions and the most widely used ionization approaches are: electrospray ioniziation (ESI) a ‘soft’ approach (limited molecular fragmentation) and electron ionization (EI) which in contrast results in extensive fragmentation but has the advantage that it effectively ionizes most molecules. Typically, sample preparation steps are used to isolate the metabolites from other cellular materials such as solvent extraction. Simple mixtures can be directly infused into the mass spectrometer. However, for more complex samples such as whole cell extracts, ESI is typically coupled to liquid chromatography (LC–MS) or capillary electrophoresis (CE–MS) and EI is coupled to gas chromatography (GC–MS). This increases the number of metabolites detected by limiting the number of metabolites competing for ionization at any given time [3,4]. In contrast to these solution-based methods, which typically take several minutes per sample, high-throughput approaches using surface-based MS such as matrix-assisted laser desorption/ionization (MALDI) or matrix-less desorption/ionization can be used [3]. A recent advancement in surface-based methods is the Nimzyme approach [5] based on the related technology, nanostructure-initiator mass spectrometry (NIMS) [6] which utilizes fluorous phase interactions to immobilize www.sciencedirect.com
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enzyme substrates on the mass spectrometry surface. This ‘soft’ immobilization allows efficient desorption/ ionization while also allowing the use of surface washing steps to reduce signal suppression from complex biological samples. This is enabled by the preferential retention of the tagged products and reactants, is sensitive to subpicogram levels of enzyme, rapid (1 s/sample), detects both addition and cleavage reactions, and can measure activity directly from crude cell and microbial community lysates. Other recent advances in surface approaches include microfluidic networks and peptide arrays followed by MALDI-MS to assay kinase and phosphatase activities [7]. Electrowetting was utilized also by rapidly mixing droplets containing the enzyme, substrates, quenching and matrix solutions on an electrode array followed by MALDI-MS analysis [8]. The same group developed a different method, where single reaction droplet travels on the surface of porous silicon. Reactants, but no enzyme, are continuously left behind in the narrow pores. The trajectory is then analyzed by matrix-less desorption/ ionization followed by MS and corresponds to the time course of the reaction [9]. MS-based enzyme activity assays are applied routinely to different reactions and are especially useful when simultaneous monitoring of multiple substrates and products is desirable such as for glycosylhydrolase/transferases [10,11] or sesquiterpene synthases [12]. MS-based assays can be used for in vitro validation of specific enzymatic activities for putative enzymes [13–15], identification of substrates of putative enzymes [10,16], or inhibitor screening [17,18]. In vitro reconstitution of pathways and monitoring by MS were employed for the validation of discoveries of genes related to alternative D-glucarate degradation [19] and anaerobic oxidative degradation of L-ornithine [20].
Metabolic profiling The power of MS becomes more prominent in untargeted ‘metabolic profiling’ or metabolomic approaches requiring the analysis of complex samples. Typical MS-based analytical methods for comprehensive metabolic profiling are GC–MS, LC–MS, and CE–MS [4]. However, no single method is suitable for all metabolites. ‘Multimode’ approaches combining multiple MS methods have been shown to increase the coverage of detected metabolites for biological samples [21] as does chemical modification to improve ionization [22]. A critical step in the interpretation of metabolic profiles is the identification of unknown metabolites. Toward this goal, MS approaches can provide detailed information on the accurate mass, isotopic profile, chromatographic retention times, and MS/MS fragmentation patterns [23,24]. Metabolites are ultimately identified by a comwww.sciencedirect.com
parison of these properties with metabolite standards [25]. Standardization of methods [26] and accumulation of data in metabolite databases greatly facilitate identifications and are a critical step in this developing field [27–29] (MassBank; URL: http://www.massbank.jp/). Identification of a large number of metabolites allows a comparison of experimental metabolite profiles against predicted metabolic capabilities of studied microorganisms from genome annotations, organism specific databases, that is BioCyc [30] or KEGG [31] or genome-scale models of metabolism [32,33]. For example, comparison of metabolite profiles of Bacillus subtilis [34] against metabolites present in the reconstructed genome-scale model showed a large number of metabolites unaccounted in the model [35]. Similar observations from the draft metabolic network reconstruction of Chlamydomonas reinhardtii [36] reveal the limitations of current homology based genome annotations and biochemical knowledge. Experimentally identified metabolites outside current genome annotations or genome-scale metabolic models point to additional metabolic capabilities. These identified metabolites or unidentified peaks can be linked to putative reactions if their levels correlate across different samples [37] or if the mass difference between them could correspond to common biochemical transformations or condensations among them [38,39]. Comparison of the composition of a complex mixture of metabolites before and following in vitro incubation with proteins of unknown function can lead to the identification of the enzymatic activity by detecting a reduction in substrate and/or formation of a new product. Recent examples include the identification of phosphatase and phosphotransferase activities for two proteins [40] and a novel 4-hydroxybutyrate dehydrogenase [41] from Escherichia coli. Incubation of human P450 cytochromes in vitro with tissue extracts lead to the untargeted identification of endogenous substrates [42]. In a related approach aimed at the identification of protein–metabolite interactions, immobilized proteins of interest are incubated with a complex mixture of metabolites, the proteins with bound metabolites are separated and the composition of the resulting mixture is compared to the original one to identify the bound metabolites [43]. Environmental or genetic perturbations may also lead to the appearance or disappearance of intermediates or products of perturbation-related pathways [44,45]. Comparison of metabolite profiles of wild type strains and strains with gene deletions contributed to the discovery of a novel bacterial type III polyketide synthase from Streptomyces coelicolor [46], orfamide A — a new lipopeptide from Pseudomonas fluorescens Pf-5 [47], elucidation of the S. coelicolor pathway to 2-undecylpyrrole [48], and the discovery of novel antibiotic production inducers from S. coelicolor [49]. Alternatively, deletion of a specific gene Current Opinion in Microbiology 2009, 12:547–552
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may lead to the accumulation of substrates of the corresponding enzyme as was shown in the identification of endogenous substrates for mammalian fatty acid amide hydrolase [50]. Integrating metabolic profiling with stable isotopelabeled substrates provides a global perspective on the fates of specific atoms within the metabolic network [51]. Presence of stable isotope labels in detected metabolites can be identified thanks to the characteristic shifts in the masses of corresponding ions in metabolite profiles. The labels can even be pinpointed to specific atoms within a molecule based on fragmentation spectra [52]. These stable isotope methods are used to quantitatively estimate the distribution of fluxes through the metabolic network [53] (and a review in this issue). From a functional genomics perspective, identification of a presence or an absence of a stable isotope label in a specific position within a molecule of a metabolite alone may be indicative of specific metabolic pathways, even if the metabolite is not directly involved in the pathway [54]. Using this approach, isoleucine was shown to be predominantly biosynthesized by an alternative pathway in Geobacter sulfurreducens [55] and glyoxylate regeneration via ethylmalonyl-CoA pathway was documented for Methylobacterium extorquens [56]. Global profiling and comparison of experimental stable isotope labeling patterns with theoretical ones can be used to help identify discrepancies and possible alternative pathways. This approach is greatly enabled through the development of genome-scale carbon-fate maps [57–59]. Such maps show promise in designing experiments to utilize specifically labeled substrates allowing discrimination between alternative metabolic pathways [60]. The use of libraries of gene deletion mutants can help identify differences in labeling patterns for mutants and may lead to the identification of metabolic or regulatory genes, provided experimental, analytical, and data analysis throughput are available. Combination of metabolic profiling with other -omic technologies provides a more comprehensive view on cellular physiology. For example, integrated metabolomic and transcriptomic studies show correlations between the levels of metabolites and biologically related genes [61]. Metabolomic and proteomic data recently supported the draft metabolic network reconstruction of C. reinhardtii [36]. Such integrated approaches may identify not only new enzymatic activities, but also new regulatory effects [62,63]. In future, the ability to perform large-scale multi-omic experiments [64,65] will allow more comprehensive functional genomic studies.
Figure 3
Application of mass spectrometry to screen for specific activities in microbial communities. Mass spectrometry can be used to study metabolism in complex microbial communities. There is a broad range of possible applications, for example mass spectrometry can be used to study (A) the lignocelluloses degrading capabilities of a microbial community, (B) the metabolic role of gut microbes or (C) various host– pathogen interactions.
enzymatic activities and metabolic networks. Recent developments in MS-based metabolomic and activity profiling approaches are being used to address these needs by rapidly defining functional gene annotations. These efforts are enabled through the interplay of MS with bioinformatics, development of new MS approaches, and the development of metabolite databases. These are converging to provide new insights into microbial metabolism. We anticipate that these capabilities coupled with both the increased focus on microbial communities and integration with other ‘-omics’ technologies provide tremendous opportunities for ‘meta-metabolomic’ studies of microbial communities (Figure 3) [66] and will provide new insights into the complex interactions between microbes and other higher organisms [67,68].
Acknowledgements We gratefully acknowledge support from the Department of Energy [DEAC02-05CH11231]. We thank Gary Siuzdak and Steven Yannone for their insightful comments, and Manfred Auer and Bernhard Knierim for providing SEM images.
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Conclusions The rapid expansion of sequenced microorganisms coupled with the critical need for improved understanding of microbial metabolism and stress responses demands high-throughput methods for functionally characterizing Current Opinion in Microbiology 2009, 12:547–552
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