Integration of untargeted metabolomics with transcriptomics reveals active metabolic pathways
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While recent advances in metabolomic measurement technologies have been dramatic, extracting biological insight from complex metabolite profiles remains a challenge. We present an analytical strategy that uses data obtained from high resolution liquid chromatography–mass spectrometry and a bioinformatics toolset for detecting actively changing metabolic pathways upon external perturbation. We begin with untargeted metabolite profiling to nominate altered metabolites and identify pathway candidates, followed by validation of those pathways with transcriptomics. Using the model organisms Rhodospirillum rubrum and Bacillus subtilis, our results reveal metabolic pathways that are interconnected with methionine salvage. The rubrum-type methionine salvage pathway is interconnected with the active methyl cycle in which re-methylation, a key reaction for recycling methionine from homocysteine, is unexpectedly suppressed; instead, homocysteine is catabolized by the trans-sulfuration pathway. Notably, the non-mevalonate pathway is repressed, whereas the rubrum-type methionine salvage pathway contributes to isoprenoid biosynthesis upon 5′-methylthioadenosine feeding. In this process, glutathione functions as a coenzyme in vivo when 1-methylthio-d-xylulose 5-phosphate (MTXu 5-P) methylsulfurylase catalyzes dethiomethylation of MTXu 5-P. These results clearly show that our analytical approach enables unexpected metabolic pathways to be uncovered.
KeywordsActive pathway detection Isoprenoid biosynthesis Liquid chromatography–mass spectrometry Metabolomics Methionine salvage Quantitative real time polymerase chain reaction Transcriptomics
Active methyl cycle
Gene set enrichment analysis
Hit ratio per peak
Kyoto Encyclopedia of Gene and Genomes
Metabolite set enrichment analysis
- MTXu 5-P
Quantitative real time polymerase chain reaction
We have developed a systems biology approach that combines an automated liquid chromatography (LC)–mass spectrometry (MS)-based metabolomics pipeline (Raamsdonk et al. 2001) with transcriptomics and use it to construct a holistic picture of the methionine salvage metabolism in several model organisms. The methionine salvage pathway is universal and present in many organisms, from unicellular bacteria to plants and animals, with some variations (Albers 2009; Wray and Abeles 1995; Trackman and Abeles 1983). Two alternative methionine salvage pathways are known that convert 5′-methylthioadenosine (MTA), a by-product of S-adenosyl-l-methionine (SAM)-dependent polyamine biosynthesis, to methionine. The classical pathway, as determined in Bacillus subtilis (B. subtilis), is an eight-step pathway in which the carbon and sulfur skeleton of methionine is completely synthesized from the methylthioribose moiety of MTA (Ashida et al. 2003). The second pathway, recently reported by our group (Erb et al. 2012), was discovered in Rhodospirillum rubrum (R. rubrum) and involves a bifurcation of the methylthioribose molecule between methionine and isoprenoid biosynthesis. In R. rubrum, the pathway intermediate, methylthioxylulose-5-phosphate, is cleaved into methanethiol and the non-mevalonate isoprenoid precursor, 1-deoxy-d-xylulose 5-phosphate (DXP), in a novel enzymatic reaction that is dependent on 1-methylthio-d-xylulose 5-phosphate (MTXu 5-P) methylsulfurylase (Warlick et al. 2012). Whereas methanethiol can be further converted to methionine by the action of an O-acetyl-l-homoserine sulfhydrylase, the remaining carbon skeleton of MTA is channeled into isoprenoid biosynthesis.
In recent years, using metabolite profiling as a means to reveal novel aspects of cellular metabolism has gained popularity because it provides insight into complex regulatory processes as well as direct functional information on metabolic phenotypes (Fiehn et al. 2000; Zhao et al. 2013). Integration of metabolite profiles with transcriptomics data (Kresnowati et al. 2006; Bradley et al. 2009; Lei et al. 2011) or metabolic networks (Cakir et al. 2006) has advanced our understanding of biological systems to the next level. LC coupled to high resolution, accurate mass spectrometric platforms enables the detection of many thousands of features, but requires high throughput data handling methods to convert the raw data into biological discovery (Wei et al. 2011; Eliasson et al. 2012).
Multivariate statistical methods are routinely used to analyze complex data to discover biological patterns (Fernie et al. 2004). However, converting this wealth of information into an understanding of biological function remains challenging. Even though pathway-level analysis has been applied to gene expression data (Tavazoie et al. 1999; Curtis et al. 2005), only recently have tools for analyzing metabolite profiles in the context of predefined biological metabolite sets been reported (Xia and Wishart 2010, 2011; Chagoyen and Pazos 2011; Kankainen et al. 2011). These approaches have been predominately applied to study human and mammalian metabolomics because many biological sets and their constituent metabolites are well defined for these species (Sreekumar et al. 2009; Deo et al. 2010; Putluri et al. 2011a, b). However, the application of these approaches to pathway discovery in bacteria is hampered by the following issues: (i) there is no reference metabolome that can be measured by a specific analytical platform; (ii) time-consuming data pre-processing is required to reduce false positives and false negatives in the peak detection process; (iii) detected peaks should be annotated in advance with high confidence before applying metabolite set enrichment analysis (MSEA), a counterpart of gene set enrichment analysis (GSEA) (Mootha et al. 2003; Subramanian et al. 2005); and (iv) it is not always obvious which metabolite sets (i.e., pathways) should be tested, especially when a goal is to discover the unexpected pathways in vivo.
Here, we integrated untargeted metabolomics with transcriptomics [quantitative real time polymerase chain reaction (qRT-PCR) and RNA sequencing (RNAseq)] to correlate specific changes in bacterial growth conditions and a genetic knockout with changes in the activities of metabolic pathways. We used the concept of seed metabolites, which we define as the metabolites showing higher abundance changes upon perturbation, unambiguous formula determination and search hits in a database. These seed metabolites and a dynamic build-up of metabolite sets were used for the detection of actively changing metabolic pathways observed in the raw LC–MS data. In this process, we used the refined mass spectral features from the LC–MS data, regardless of the metabolite annotations, as a reference metabolome for our enrichment analysis, which is based on the same procedure used in GSEA. Putative metabolite annotation of the seed metabolites was carried out with high confidence and high coverage, and with a low number of false positives. Metabolite annotation was followed by a dynamic build-up of metabolite sets based on the pathway information extracted from the seed metabolites. Detected active pathways were then validated with transcriptomics. We applied this analytical strategy to unravel metabolic pathways linked to methionine salvage in R. rubrum and B. subtilis. Our approach revealed elaborate metabolic strategies used by microbes to cope with stressful environments (e.g., MTA feeding) through the coordinated regulation of several expected and unexpected metabolic pathways.
2 Materials and methods
2.1 Bacterial strains and growth conditions
Bacillus subtilis 168 (a gift from the laboratory of G. Ordal) was grown aerobically in either Luria-Bertani broth (Albers 2009) or minimal medium at 37 °C as previously described (Sekowska and Danchin 2002). R. rubrum (DSM 467, ATCC 11170, American Type Culture Collection, Manassas, VA, USA) and its MTXu 5-P methylsulfurylase mutant (a gift from R. Tabita and Jaya Singh) were grown aerobically in the dark at 30 °C on 20–2,000 mL minimal medium with sulfate or MTA as the sole sulfur sources, as previously described (Erb et al. 2012). See the Supplementary methods for details.
2.2 LC–Fourier transform (FT) MS metabolomics
Cell suspensions (OD600 = 6 for B. subtilis, OD578 = 6 for R. rubrum) were incubated at optimum growth temperature (temperature = 30 °C) in minimal media without a sulfur source (control samples) or with 1 mM MTA (feeding experiments). Sampling was carried out at various time points (B. subtilis: 0, 2, 5 and 15 min; R. rubrum: 0, 10 and 20 min) according to the strains. Then cells were pelleted and immediately frozen in liquid nitrogen. Metabolites were extracted from the frozen cell pellets by resuspending with 0.375 mL 10 mM ammonium bicarbonate buffer (pH 9.2) containing 90 % acetonitrile. LC–FTMS analysis was carried out using an 11T LTQ-FT Ultra mass spectrometer (Thermo-Fisher Scientific, Waltham, MA, USA) equipped with an Agilent 1200 HPLC system in negative mode (Agilent Technologies, Santa Clara, CA, USA) (Evans et al. 2011). See the Supplementary methods for details.
2.3 LC–FTMS data analysis
Data analysis was carried out with the analytical platform as described in the Supplementary methods. This discovery platform consists of various functional modules to cope with rising issues in untargeted metabolomics, including: data pre-processing, isotope pattern analysis, molecular formula determination, database searching and pathway activity profiling. Peaks were detected using XCMS (http://metlin.scripps.edu/xcms/). Peak lists were filtered to remove adducts and isotopic peaks. Molecular formulas were determined by comparing experimental isotopic patterns with theoretically predicted isotopic patterns modeled with Bayesian statistics. Seed metabolites were automatically detected based on raw LC–MS data. Both monoisotopic masses and the top three predicted molecular formulas were searched against the Kyoto Encyclopedia of Gene and Genomes (KEGG) (Kanehisa et al. 2012) database to putatively annotate peaks and to build-up metabolite sets using pathway information. Constructed metabolite sets (i.e., implicated pathways) were evaluated using the MSEA approach with a Kolmogorov–Smirnov running sum statistic (Mootha et al. 2003). Highly perturbed but not annotated peaks were listed for further identification experiments. See Supplementary methods for details.
2.4 Quantitative real time polymerase chain reaction (qRT-PCR)
qRT-PCR was performed as previously described (Pfaffl 2001). Cells were harvested at OD = 0.6, and RNA was extracted using an RNeasy protect kit (Qiagen, Gaithersburg, MD, USA) according to the manufacturer’s recommendations. Total RNA was resuspended in PCR-grade nuclease-free water, and RNA quality and concentration were estimated by optical density measurement using a Nanodrop 2000 spectrophotometer (Fisher Scientific, Pittsburgh, PA, USA). Each sample of 500 ng total RNA was reverse transcribed using a First Strand cDNA Synthesis Kit (Fermentas, Pittsburgh, PA, USA). Real-time PCR reactions were carried out on a LightCycler 480 (Roche, Indianapolis, IN, USA) using the SYBR Green detection format. Changes in the expression were calculated relative to the expression of 16SrRNA. After each PCR run, a melting curve analysis was carried out to control for production of primer dimers and/or non-specific PCR products. Expression levels of mRNA were estimated using external standard curves with serially diluted plasmids/PCR products with known concentrations for each target gene. Fold changes in mRNA expression during treatment were calculated using the crossing point (Cp) for each sample and the efficiency (Eff) of each transcript using the formula (Efftarget gene)ΔCp/(Effhousekeeping gene)ΔCp. The fold changes were estimated relative to 16SrRNA.
2.5 RNAseq and analysis
Subtraction of ribosomal RNAs from a 10 µg total RNA sample using an Ambion MICROBExpress Kit (Applied Biosystems, Foster City, CA, USA), as well as subsequent sequencing of the enriched mRNA fraction (Illumina, San Diego, CA, USA, 100 bp single end, directional RNAseq method) were performed at the W.M. Keck Center for Comparative and Functional Genomics (University of Illinois at Urbana-Champaign) using in-house protocols. The reads were on average between 70 and 80 nt, at a total of about 22 million reads for the RNA preparation from sulfate-grown cells and about 20 million reads for the RNA preparation from MTA-grown cells. RNAseq data were aligned against the R. rubrum genome (Accession number NC_007643) and analyzed using the CLC genomics workbench software, version 3.7 (CLC bio, Cambridge, MA, USA) according to the user’s manual. Briefly, short reads were aligned against the fully sequenced genome of R. rubrum to determine unique and total gene reads.
3.1 Analytical strategy
3.2 Data pre-processing
A basic and mandatory step in metabolite profiling is to reduce data complexity caused by adducts, isotopologues, multimers and signals arising from chemical and electronic noise. Currently, this feature refinement is carried out through either a manual or semi-automated process that mostly deals with the features of biological interest and not the complete set of detected features (Dunn 2008). While there are several publicly available LC–MS based bioinformatics tools for peak detection and alignment, including XCMS (Smith et al. 2006) and MZmine (Katajamaa and Oresic 2005), there are few public tools for this error-prone and time-consuming raw data refinement process (Alonso et al. 2011).
Distribution of putatively annotated peaks according to the time points (negative mode, 100–1,000 m/z)
R. rubrum (min)
B. subtilis (min)
No. of features
Invalid RT (e.g. 120 s ≤ RT ≤ 2,030 s)
No. of artifact features (e.g., adducts and multimers)
No. of non-biological signals (i.e., mass distribution filter)
145 (6.3 %)a
347 (8.3 %)a
704 (14.2 %)a
672 (15.2 %)a
620 (14.3 %)a
No. of isotopes
Total no. of eliminated features
No. of refined features for further analysis
1,082 (47.1 %)a
1,733 (41.2 %)a
2,562 (48.4 %)a
2,200 (49.8 %)a
2,133 (49.4 %)a
No. of predicted molecular formulas
270 (11.8 %)a
366 (8.7 %)a
252 (5.1 %)a
240 (5.4 %)a
225 (5.2 %)a
3.3 Seed metabolites and dynamic build-up of metabolite sets
A challenge in metabolite profiling is the effort to automate the annotation of the detected peaks with high confidence, high coverage and low false positives in order to identify active pathways and extract biological insights. A simple search against publicly available databases with only nominal mass information produces many false positives. As an alternative, accurate mass and elemental composition constraints are used together to define metabolites by determining their molecular formulas (Bocker et al. 2008, 2009; Rogers et al. 2009; Brown et al. 2011). However, only a small portion of the LC–MS data contains this useful information, so the problem of limited coverage remains to be solved. Furthermore, since metabolite profiling is a snapshot of metabolism at a specific time point, not all of the possible metabolites are detected, and slightly different subsets of metabolites of a metabolic pathway can be observed even under the same experimental conditions. To apply enrichment analysis to detect unexpectedly interwoven pathways, biologically meaningful metabolite sets should be constructed exhaustively.
3.4 Actively changing metabolic pathways detected by the enrichment analysis
Dynamically constructed metabolite sets (i.e., implicated pathways) were further curated into subcategories manually and evaluated by the enrichment analysis process to detect interconnected pathways upon metabolic perturbation with MTA, a key metabolite in bacterial methionine salvage pathways.
In R. rubrum, a total of 53 implicated pathways were dynamically constructed. Among them, eight were considered as active pathways using the enrichment analysis approach (Fig. 4b). As expected, the purine salvage pathway (p-value = 1.35 × 10−4 at 20 min), the rubrum-type methionine salvage pathway (p-value = 6.05 × 10−5 at 20 min), and the non-mevalonate isoprenoid pathway (p-value = 1.68 × 10−3 at 20 min) were clearly affected by MTA feeding. Thus, the unexpected link of MTA metabolism with the isoprenoid pathway in R. rubrum, as recently published by our group (Erb et al. 2012), was re-confirmed by this analysis, establishing the MTA-isoprenoid shunt as an essential part of the novel MTA recycling strategy in R. rubrum. Note that isoprenoid biosynthesis is not affected in B. subtilis upon MTA feeding, which is in line with the classical methionine pathway in this organism. The fold changes and p-values of detected metabolites in the isoprenoid biosynthesis of R. rubrum were compared with those of B. subtilis, with results shown in Supplementary Material Table S4.
3.5 Validation by qRT-PCR
Subsets of genes detected in each active pathway have been manually validated upon MTA perturbation. Selected genes and primers are listed in Supplementary Material Table S6. After feeding MTA, gene expression levels were monitored at selected time points by qRT-PCR. In B. subtilis, genes of the classical, subtilis-type methionine and purine salvage pathways were investigated based on their activation observed upon MTA perturbation (Supplementary Material Table S7). In line with the metabolomics results, genes in the subtilis-type methionine salvage pathway were highly up-regulated by MTA perturbation (BSU27270 = +5.9, BSU13560 = +4.4, BSU13550 = +9.5 and BSU13620 = +5.4). However, genes in the purine salvage pathway were not induced by MTA feeding, even though RNAs were detected under the growth conditions, suggesting that accumulation of metabolites in these pathways in vivo is not related to up-regulation of transcription.
As listed in Supplementary Material Table S8, in R. rubrum, genes in the rubrum-type methionine salvage pathway (e.g., Rru_A0361, Rru_A0360, Rru_A1998, Rru_A2000, Rru_A0774 and Rru_A0784) and in the isoprenoid pathway (e.g., Rru_A1592 and Rru_A0263), were highly up-regulated upon MTA feeding. Up-regulation of the MTXu 5-P methylsulfurylase gene (Rru_A2000, fold change = +3.1 at 20 min) confirms its critical role in the novel MTA isoprenoid shunt. Similarly, the gene encoding the methylthioribulose-1-phosphate isomerase, a RuBisCO-like protein that provides the substrate for the MTXu 5-P methylsulfurylase, was also highly affected (Rru_A1998, fold change = +4.3 at 20 min) upon MTA perturbation. Notably, 1-deoxy-d-xylulose-5-phosphate synthase (dxs, Rru_A2619, fold change = −8.8 at 20 min), which converts d-glyceraldehyde-3-phosphate into DXP, was suppressed by MTA-feeding. Since this gene was expressed prior to MTA perturbation, the qRT-PCR results suggest that the non-mevalonate pathway is the major route to isoprenoid biosynthesis under physiological conditions. However, upon MTA perturbation, cells suppressed the use of the non-mevalonate pathway and instead activated the rubrum-type methionine salvage pathway to channel the carbon skeleton of methylthioxylulose-5-phosphate into DXP, indicating that the MTA-isoprenoid shunt is able to contribute significantly to DXP synthesis in R. rubrum. Expression levels of genes were stabilized to approximately basal levels after 60 min. Although the isoprenoid pathway intermediates were not affected in B. subtilis upon MTA perturbation, two genes of the non-mevalonate isoprenoid pathway were analyzed in their expression pattern to rule out the possibility that isoprenoid genes are induced in B. subtilis. Expression of the two marker genes was not induced, in line with the idea that methionine salvage and isoprenoid biosynthesis are indeed unlinked in B. subtilis (BSU24270 = +1.4, BSU16550 = −2.9) (see Supplementary Material Table S7).
In contrast to the genes involved in methionine salvage and isoprenoid biosynthesis in R. rubrum, genes of the purine salvage pathway (Rru_A2483, Rru_A0149 and Rru_A0607) and de novo purine biosynthesis (Rru_A2168, Rru_A1963, Rru_A0299 and Rru_A3655) were expressed constitutively. As observed for B. subtilis, these genes are apparently expressed under physiological conditions and are not affected by MTA perturbation.
Our qRT-PCR data show that the active pathways detected using our metabolomics analysis platform are reliable, whether they are accompanied by a change of gene expression upon MTA feeding, or by consistent expression of genes under growth conditions before MTA is added (e.g., de novo purine biosynthesis). qRT-PCR allows sensitive and specific assays for targeted genes, but it covers only a limited number of genes in the whole genome. We also felt it was important to clarify both the role of the trans-sulfuration pathway and the sources and sinks of glutathione in the context of R. rubrum MTA feeding; this required that the expression patterns of many more genes be checked. Hence, an RNAseq experiment was conducted for R. rubrum.
3.6 Assembling missing pieces of the puzzle by RNAseq
The RNAseq (Supplementary Material Table S9) clearly reconfirmed our qRT-PCR results for R. rubrum, even though there were sensitivity differences between the two techniques. In addition, RNAseq provided some clues for the role of trans-sulfuration and for the multiple fates of methanethiol liberated during rubrum-type MTA salvage. RNAseq showed that glutathione metabolism was perturbed by MTA feeding, as evidenced by NADPH-glutathione reductase (Rru_A0682, fold change = +2.4) and glutathione S-transferase (Rru_A0332, fold change = +3.7). In line with the metabolomics data from this study (i.e., glutathione–methylthiol adduct), these gene expression patterns describe well the biochemistry of glutathione. Cysteine can also be metabolized to hydrogen sulfide and pyruvate by cystathionine β-synthase (CBS) and cystathionine γ-lyase (CGL) (Singh et al. 2009). Although metabolites in pyruvate metabolism were not detected via our LC–MS based metabolomics, there were several up-regulated genes in this pathway, including hydroxyacylglutathione hydrolase (Rru_A2371, fold change = +2.74), acetate kinase (Rru_A2998, fold change = +2.51), aldehyde dehydrogenase (Rru_A0931, fold change = +1.69), acetyl-CoA acetyltransferase (Rru_A0274, fold change = + 1.65) and formate acetyltransferase (Rru_A3000, fold change = +1.84).
We describe an analytical strategy that combines untargeted metabolomics and transcriptomics (i.e., a combination of targeted qRT-PCR and RNAseq) to decipher intertwined metabolic pathways of universal application. The LC–MS platform we developed facilitates the data analysis and includes several critical functional modules: data processing, isotope pattern analysis, molecular formula determination, database searching and pathway activity profiling. Together with the concepts of seed metabolites and the dynamic build-up of metabolite sets, this platform enables a streamlined process for detecting interconnected metabolic pathways from raw LC–MS data. Implicated pathways and their constituent metabolites are automatically annotated with high confidence and coverage by integrating different sources of information. Statistical evaluation using MSEA confers high confidence to the annotations of perturbed metabolites and their respective pathways.
In Fig. 6b, we emphasize an unusual branch point in these coordinated pathways in which SAM is metabolized to SAH but not MTA. As accumulation of SAH can inhibit SAM-dependent methyltransferases; continuous depletion of SAH to homocysteine and adenosine is essential for maintaining normal methylation of DNA, RNA, proteins and other small molecules (Hoffman et al. 1980; James et al. 2002). In addition, it is known that up-regulated SAH can in turn up-regulate CBS and/or γ-cystathionase, and down-regulate 5,10-methylenetetrahydrofolate reductase (MTHFR). These regulatory functions can act in concert to reduce methionine re-methylation and expedite homocysteine removal in an attempt to normalize one-carbon flow (i.e., methyl group transfer). Homocysteine is catabolized to cysteine through cystathionine, and further metabolized into other important biological compounds such as pyruvate or glutathione, the latter being a reducing agent that protects the cell from oxidative stress. Since reducing equivalents in the form of reduced sulfur is necessary for the MTXu 5-P methylsulfurylase catalyzed dethiomethylation of MTXu 5-P, glutathione might be activated in vivo. Indeed, we detected the glutathione-methylthiol adduct via metabolomics, indicating that glutathione functions as a coenzyme in vivo in this process. In line with the metabolomics results, transcriptomics suggested that glutathione is tightly regulated to act in the metabolism of methylthiol released from MTXu 5-P. Glutathione is provided dynamically by NADPH-glutathione reductase from oxidized glutathione, and glutathione–methylthiol formation can be catalyzed by glutathione S-transferase (Rru_A0332). This adduct can be metabolized through the detoxification process by γ-glutamyl transpeptidase (Rru_A0385) and leucyl aminopeptidase (Rru_A0454), which were not induced but constitutively expressed in our RNAseq experiment. Also, cysteine from the trans-sulfuration can be catabolized into pyruvate metabolism and the TCA cycle, as evidenced by the RNAseq and metabolomics data.
To further investigate the actively changing metabolic pathways affected by glutathione and MTXu 5-P methylsulfurylase in R. rubrum, metabolite profiles of glutathione-related compounds in the wild-type organism were compared with those of an MTXu 5-P methylsulfurylase mutant (Rru_A2000, Fig. 5). Specifically, MTXu 5-P cleaved the pathway intermediate, methylthioxylulose-5-phosphate, into DXP and the methanethiol–glutathione adduct under the function of MTXu 5-P methylsulfurylase. In line with the critical function of MTXu 5-P methylsulfurylase, analysis of the MTXu 5-P methylsulfurylase mutant showed that only the purine salvage pathway was active, whereas other pathways in the mutant organism that are active in the wild type did not change relative to the control upon MTA-perturbation.
Interconnections identified using our approaches were also supported by metabolite–metabolite correlation analyses. Pearson correlation coefficients of percentage changes in metabolite abundances were clustered by agglomerative hierarchical clustering, clearly showing a high correlation between metabolites in each active pathway in R. rubrum (Supplementary Material Fig. S5). In addition, a strong correlation between interconnected active pathways could be observed; the rubrum-type methionine salvage pathway was strongly intertwined with the purine salvage pathway, de novo purine biosynthesis pathway, glutathione pathway, TCA cycle and the AMC. Also, there was a moderate correlation between the isoprenoid pathway and other active pathways (e.g., the methionine salvage, de novo purine biosynthesis, purine salvage, glutathione metabolism, butanoate metabolism, TCA cycle and the AMC). Metabolites in sulfur metabolism showed an anti-correlation with the other active pathways. When MTA was used as the sole sulfur source, no sulfate source could be provided through conventional sulfur metabolism; hence, the down-regulation of metabolites in that pathway was expected and mirrored the effect from transcriptomics. In B. subtilis, although metabolites in each pathway showed a strong correlation, there was only a weak correlation between the classical methionine salvage pathway and purine salvage pathway (Supplementary Material Fig. S6).
In addition, our analyses showed that there are two types of regulation, metabolic (i.e., changes in the metabolite levels) and hierarchical (i.e., transcription, translation and post-translational modification) (ter Kuile and Westerhoff 2001), that are induced by feeding MTA in R. rubrum. In R. rubrum the rubrum-type methionine salvage pathway, isoprenoid pathway, the AMC and the trans-sulfuration pathway were regulated hierarchically. In contrast, the purine salvage pathway, de novo purine biosynthesis pathway, and sulfur metabolism could be classified into metabolic regulation since corresponding genes were not induced by MTA feeding. Only the abundances of metabolites were changed upon MTA perturbation. In B. subtilis the subtilis-type methionine salvage pathway corresponded to hierarchical regulation. The purine salvage and de novo purine biosynthesis pathways were regulated only at the metabolite level.
These results show that our analytical strategy is useful for uncovering novel pathways, and that the addition of gene expression patterns provide complementary data to gain greater insight into bacterial metabolism. Therefore, untargeted metabolomics greatly aids the discovery of novel pathways. We expect our analytical pipeline to be applicable to many other systems and useful for uncovering a range of unexpected pathways.
We thank Heidi J. Imker for fruitful suggestions. The project described was supported by the Enzyme Function Initiative funded by the National Institutes of General Medical Sciences Award No. U54GM093342 and a fellowship to Tobias J. Erb from the Deutsche Forschungsgemeinschaft (ER 646/1-1). The content is solely the responsibility of the authors and does not necessarily represent the official views of the award agency.
Conflict of interest
The authors declare no competing interests.
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