The initial metabolome comparisons were of bacteria grown on glucose in minimal medium at three different growth rates. Exponential batch cultures (doubling time of 0.9 h) and glucose-limited chemostats set at D = 0.6 and 0.1 h−1 (doubling times of 1.15 and 7 h) were labelled with [U-14C]glucose and extracted for water-soluble metabolites. The 14C-labelled extracts were applied to silica TLC plates subjected to 2-D elution with different solvent systems in each dimension. The radioactive spots on the plates were visualized by phosphorimaging, as shown in Fig. .
In Fig. , with TLC plates developed with system A optimized for amino acid separation (22
), the resolved and unresolved spots represent all water-soluble carbon-containing compounds of the cell in the 2-D space analyzed. System A resolved not only amino acids but also sugars like glucose and trehalose, as well as metabolites like UDP-sugars. Depending on the sample, 60 to 70 spots could be visualized on the plates in Fig. . Comigration is likely to lead to an underestimate of spot numbers present, but the number of spots detected was still low relative to the 1,200 or so possible metabolites in a cell (20
). Judging by the amount of label at the origin (more than 45% of the total label on the plate), many metabolites, including most phosphorylated compounds, may have been unresolved in Fig. . Also, the low number of spots suggests that only the more abundant metabolites were labelled sufficiently to be detected by the method used. Many metabolic intermediates and signalling molecules, like cyclic AMP, are present at micromolar concentrations in a cell, and most of these are likely to be below the detection limit of the approach in Fig. . The detection limit could be estimated from the lack of a distinct galactose spot in samples such as in Fig. b, which contains ca. 0.3 mM endogenously produced intracellular galactose in independent enzymatic estimations (10
). Hence, only high-abundance metabolites present in millimolar concentration, but including most of the interesting stress metabolites of the cell, are labelled sufficiently to be seen in Fig. .
As only 40 to 60% of the extracted label was moved from the origin by the solvent system used for Fig. (and by many other systems tested), different solvent pairings were used to elute compounds not particularly well resolved in Fig. . An alternative means for resolving interesting stress metabolites like putrescine and glutathione as well as some nucleotides was with plates eluted in system B (Fig. ). The total number of individual spots (ca. 70) was not much greater than in Fig. , but the metabolites in the unseparated region near the origin in Fig. (system A) were better resolved in system B, and spots labelled 1 to 10 were altogether better separated. In total, we used four different pairs of solvents for resolving metabolites in each sample; 2-D analyses with the other systems (not shown in Fig. and ) were used to confirm identities and quantities of metabolites separated. Many metabolites stayed at the origin with system C, but it was useful for resolving the high-Rf spots seen with systems A and B. System D, like system B, was useful for sugar phosphates and nucleotides. Many of the spots in system D were less sharp, but the system also permitted good quantitation of glutamate and glutathione. Each of the metabolites analyzed below was resolved and identified (by comigration with standards) in at least two of these four elution systems.
The patterns shown in Fig. and were reproducible in each system, and quantitative data for identified spots (using ImageQuant software) were encouragingly similar for independently grown, independently extracted, and separately analyzed samples. As an indication of the reproducibility, the percentage of each of 10 identified metabolites as a proportion of the applied metabolome was estimated by ImageQuant densitometric analysis and compared in repeat experiments (Fig. ). This form of quantitation does not provide the absolute concentration of the metabolite in the cell, but the advantage of considering the proportion of the metabolome is that it avoids the problems associated with standardizing the harvesting and extraction conditions for different extracts. The data in Fig. , representing the means and standard deviations of four to six estimations for each of the 10 metabolites under each growth condition, suggest that the differences in pool sizes within samples obtained at a particular growth rate were small relative to differences between growth conditions. The identified spots, many of which significantly increase or decrease in intensity with various conditions, are numbered 1 to 10 in Fig. and . There were obvious trends in pool changes, increasing or decreasing with growth rate on glucose. The significance of these trends is considered in Discussion.
FIG. 3 Changes in pool sizes as determined by metabolome analysis. The spots corresponding to the compounds identified (a) and unidentified (b) in Fig. and were quantitated with ImageQuant software in four to six independent determinations. (more ...)
Differences in spot intensities were found with many compounds besides those identified as metabolites 1 to 10. The lettered spots in Fig. and did not comigrate with known standards but, as shown in Fig. b, also gave differences in intensity under different growth conditions. The trends shown by compounds such as spots A, B, S, and T were unlike those exhibited by the identified metabolites, in that these pools were either maximal or minimal at intermediate growth rates. The arrowed spot E is not included in Fig. quantitations, as this is one spot which did significantly vary in concentration between experiments. Although visual comparison of Fig. a, b, and c gives the impression that spot E is heavier in the batch sample, this pattern was not maintained in other experiments. This variability in spot E intensities was the exception rather than the rule in these studies.
Given the considerable change in metabolite pools at low growth rate, metabolome analysis was repeated with bacteria containing an rpoS
mutation. As shown in Fig. d and e, the changes in metabolite pools for some but not all compounds were sensitive to the rpoS
mutation. In general, the difference between wild type and mutant was more marked at the lower dilution rate; this was to be expected, as rpoS
expression is not significant at the higher dilution rate (13
). The trehalose spot disappeared in the rpoS
mutant at D
= 0.1 h−1
, but the glutamate pool size decreased at low growth rate despite the mutation. If anything, glutamate pools were even lower in the mutant. As discussed below, some but by no means all metabolic changes at slow growth rate are identifiably related to rpoS
As the growth states tested by metabolome analysis identified significant changes in glutamate pools, chemical amino acid analysis was also carried out to verify the data with extracts from bacteria grown on glucose. As shown in Fig. , the changes in the proportion of glutamate found by TLC analysis were mirrored by the quantitation of glutamate by using high- performance liquid chromatography (HPLC) methods. Also as found by metabolome analysis such as that for lysine, there was no great shift in the proportion of other amino acid pools (results not shown). There was one notable exception: the aspartate pool as measured after HPLC gave a large signal, particularly in the D = 0.1 h−1 samples. A high quantity of aspartate was not evident in the metabolome separations by 2-D TLC, and the aspartate spot was not prominent at low dilution rates in either the wild type or the rpoS mutant. Presumably, another unidentified cellular component present in the extract migrates like Asp under the separation conditions used in HPLC.
FIG. 4 Glutamate pools measured by HPLC analysis. Duplicate samples of the five types of cell extract obtained as for Fig. were analyzed for amino acids by using an AminoMate system. The mean of the glutamate quantities obtained for each type (more ...)