Discovery of New Modules in Metabolic Biology Using ChemoMetabolomics

The use of small molecules to transiently modulate protein function circumvents the limitations of classical genetic approaches (Dobson, 2004; Hicks and Raikhel, 2012). These approaches are limited by functional gene redundancy because mutation of a single gene can result in a lack of phenotype due to compensation (Borevitz and Ecker, 2004). Alternatively, when an essential gene is mutated, embryo lethality can result (Candela et al., 2011). These obstacles can be circumvented by a chemical genomics approach that uses small molecules to temporarily modulate protein function. Small molecules often bind to proteins, transiently inhibiting proper function. This marriage of synthetic chemistry and biology, coupled with the grand scale of genomics, is a powerful approach that produces large amounts of useful phenotypic data prior to genetics (Hicks and Raikhel, 2012). Linking this to metabolomics studies will facilitate the detection of metabolic modulators, the dissection of the cross talk in the metabolic network, and the development of hypotheses on how changes in metabolism affect developmental or cellular responses.

Forward chemical genomic screens can uncover new specific reagents that can target transitory compartments to dissect their functions in a systematic manner. Such screens were performed in plants and resulted in the dissection of pathways including cellulose biosynthesis (DeBolt et al., 2007; Yoneda et al., 2007), callose activation (Zabotina et al., 2008), pathogen defense (Serrano et al., 2007), and the elusive identification of the abscisic acid receptor (Park et al., 2009). Furthermore, chemical screens probing the endomembrane system led to the identification of Sortins (Zouhar et al., 2004; Chanda et al., 2009), Gravacin (Surpin et al., 2005; Rojas-Pierce et al., 2007), and Endosidins (Robert et al., 2008; Drakakaki et al., 2011), which were used to elucidate endomembrane trafficking within the cell. Hitherto, chemical genomics has only rarely led to new insights or to the identification of target proteins involved in basic cellular metabolism as in a few case studies of the plant cell wall (Desprez et al., 2002), herbicide targets (Grossmann et al., 2012), and phenolics (Rosado et al., 2011). Nevertheless, there are great prospects to identify and dissect novel aspects of metabolism, particularly in multicellular organisms. Certainly, a large portion of the metabolome awaits discovery as our knowledge regarding the complex metabolic network and intracellular trafficking of metabolites is considerably limited in most organisms.

Metabolomics, a recently established “omics” approach, allows a broad detection of hundreds, if not thousands, of small molecules in a single analytical run of a given sample (Saito and Matsuda, 2010). Despite the frequently ambiguous identification of metabolites due to the lack of reference compounds and intrinsic limitations of mass spectrometry data interpretation, this technology facilitates new insights into currently unexplored metabolic pathways (Mintz-Oron et al., 2008; Hall and Hardy, 2012). The powerful feature of metabolomics is in its ability to perform nontargeted analysis, which permits the detection and to a certain extent identification of “unexpected” metabolites that were not initially “under the spotlight.”

The development of metabolomics approaches was largely driven by quantum leaps in analytical chemistry tools that allow for the separation and detection of metabolites (Allwood et al., 2011). The most widespread technology used currently in metabolomics assays is liquid chromatography-mass spectrometry (Lei et al., 2011; Moing et al., 2011). Instruments with rapid and high-resolution separation capacity through ultra-pressure chromatography are typically coupled with mass spectrometry detectors with extreme resolution and accurate mass determination capabilities. Complementary technologies include gas chromatography-mass spectrometry (Almstetter et al., 2012) and NMR (Kim et al., 2011; Ward et al., 2011). Another key feature of these cutting-edge analytical tools is their versatility in examining any given extract from bacteria to fungi, plants, and mammalian cells. Nevertheless, to date, no extraction method nor any technological platform exists that enables the measurement of all endogenous metabolites in a single extract.

Plants are especially appropriate targets for metabolomics approaches because they produce a wide range of metabolites (Clardy and Walsh, 2004). Several of these metabolites belong to unique chemical classes and are of great importance in our diet and daily life. The most widespread use of metabolomics technologies is currently in functional genomics, which links genetic and metabolic changes (Fiehn et al., 2000). This can be observed in the influence of abiotic/biotic stresses on metabolism (Obata and Fernie, 2012), or as an analysis tool employed in metabolic engineering approaches (Wu and Chappell, 2008). Furthermore, metabolomics becomes an integral part of systems biology where changes at the metabolome, transcriptome, and proteome levels are analyzed in an integrated fashion, as exemplified by Liberman et al. (2012). Recent advancements in the isolation of individual cell populations (e.g. through laser capture microdissection and fluorescence-activated cell sorting) can now be coupled to metabolomics analysis for a comprehensive investigation of the time- and space-resolved distribution of analytes (Rogers et al., 2012). Finally, given that metabolism is highly compartmentalized, more and more attention is also paid to the study of metabolism at a subcellular level (Krueger et al., 2011).

Combining metabolomics with chemical genomics, also referred to as “ChemoMetabolomics,” is a promising future application of both technologies. In such experiments, samples derived from cell cultures or whole organisms that were treated with a library of chemicals would be subjected to metabolomics analyses as, for example, those employing high-resolution mass spectrometry instruments. As compared with genetic approaches, ChemoMetabolomics, as well as chemical genomics, has the advantage of a time-resolved conditional disturbance, whereby the samples are exposed to a given chemical treatment over different time periods. This allows for the identification of perturbations, which when constitutively active (i.e. in a mutant) are lethal and thus not accessible for analysis. Examination of the metabolic response of the biological system to the chemical treatment can result in a number of potential outcomes (Fig. 1). A first obvious one is the identification of lead compounds that represent a starting point for the development of highly potent inhibitors, or modulators, of a metabolic pathway. Depending on the intermediate metabolites that can be identified in a given pathway, it might even be possible to predict the target protein/enzyme due to the specific change of metabolites in this putative pathway. Furthermore, whereas established target-oriented high-throughput setups that use in vitro systems monitor only one enzymatic reaction per experiment, the proposed approach facilitates screening products of many enzymatic reactions at a time. Moreover, a predicted change in the metabolic profile upon chemical treatment can be used to identify a chemical that targets a given enzyme. Leads identified in this way may be useful as inhibitors, blocking or enhancing certain specific biochemical pathways (e.g. resulting in the discovery of new herbicides, or compounds that boost a biosynthetic pathway of interest). A second, more complex outcome is the reconstruction of metabolic networks based on the perturbation data obtained using a large number of chemicals. Once a chemical affecting multiple metabolic pathways is identified, this will open the way to dissect the exceedingly intricate cross talk in the metabolic network. A third obvious outcome is based on the integrated analysis of metabolic changes and corresponding alterations in developmental or cellular processes, thus allowing for the development of a hypothesis regarding the effect of changes in metabolism on developmental or cellular responses. As a consequence, applying chemicals that could perturb vesicular-based metabolite trafficking will enable new insights into where and how metabolites are delivered to their site of action and storage.

Figure 1.

Work flow of a ChemoMetabolomics study. In a ChemoMetabolomics study, plants or cell cultures are treated with a chemical(s) prior to metabolomics analyses. Metabolic data outputs such as the one obtained by high-resolution liquid chromatography-mass spectrometry are then processed by multivariant analysis, for example, to identify target metabolic pathways and new modules in the metabolic network.

Whereas carrying out metabolomics assays undoubtedly has multiple advantages, largely due to the quantity and diversity of metabolite coverage and the “untargeted approach,” it might not always be the preferred method for metabolite analysis. As in other technologies, increasing metabolite coverage decreases capabilities in terms of sensitivity and quantification (Sweetlove et al., 2003). Moreover, the capacity to accurately and unambiguously identify metabolites is reduced to a great extent (Fernie et al., 2011). In some cases, a particular section of the metabolic network consisting of several metabolites that are structurally or functionally/biologically related (e.g. carotenoids, vitamins, hormones, and amino acids) are of interest. In this view, one might choose to utilize the “metabolite profiling” approach in ChemoMetabolomics, in which defined, well-established procedures are used to extract, separate, detect, quantify, and identify the metabolites in a targeted manner (Gu et al., 2012).

An important part of data handling and quality control procedures includes attempts to identify the metabolic “fate” of the chemical used for treatment, and subsequently filtering out its derivatives, which in most cases form through endogenous metabolism and can be the primary cause of the metabolic phenotype observed. A next step in ChemoMetabolomics would consist of genetic screens to isolate mutants that are hypersensitive or resistant to the chemical of interest, which will facilitate the location of the component(s)/target(s). Another approach for identifying targets is analyzing the similarity between metabolic profiles obtained from genetic mutants to those obtained from ChemoMetabolomics, which will also allow for the identification of the target protein and pathway of a given chemical. The output of such experiments could also be integrated and mutually queried with additional data derived from various chemical screens at the developmental and cell biological levels. The generation and mining of such a compendium of data sets could be an excellent instrument to probe as yet undiscovered links between metabolic and developmental programs and to obtain a more detailed view of biological processes at the system level. Our labs have just recently initiated ChemoMetabolomics experiments with the most encouraging preliminary data. It is expected that once established, this concept will open new frontiers in metabolic studies by leveraging on the gradually vanishing boundary between chemistry and biology.