Metabolic Profiling: Disease and Xenobiotics

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Oxygen consumption rates were then normalized to protein concentration for each sample, which was determined using a modified Lowry procedure At this optical density, 2 ml of cells were harvested, washed with quarter-strength microbiological Ringer's solution, and resuspended in 10 ml of inoculation medium M9 salts. For carbon plates, the inoculation medium contained NH 4 Cl and glutamine, and for PM3B, it contained glucose and glutamine.

Cells were grown for 24 h, after which the optical density at and nm was recorded.

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The nm reading is a measurement of general turbidity, whereas nm is the absorption maximum of the reduced dye. We analyzed the endometabolome of the wild-type, rpoN , and complemented mutant on the PAO1 background. Briefly, 2. Afterward, all extracts were subjected to two freeze-thaw cycles and sonication. We derivatized the samples by methoximation followed by trimethylsilylation, following the method of Kind et al.

We measured gluconate production in longitudinal isolates taken from 16 patients, in which each set of strains from a single patient represented a clonal lineage Genotypes as assessed by random amplification of polymorphic DNA RAPD typing 28 of these isolates were available from a previous study We selected a total of 86 mutants, corresponding to 72 genes in total, as some genes were represented by more than one insertion supplemental Table S1.

We grew the bacterial strains in 1 ml of SCFM and sampled the medium after 24 h of growth for exometabolome profiling. Most of the strains alkalized the growth medium to some extent data not shown , and so there were pH-related shifts in resonance frequencies between spectra. Because of this, using simple peak integrals gave poor-quality data, and so we used a peak-fitting deconvolution method to provide the best output.

Metabolic Profiling: Disease and Xenobiotics By Martin Grootveld and Diana anderson | Souq - Egypt

We used the freely available R package BATMAN 19 , 20 to fit individual metabolites to the spectra, thus allowing for both peak overlap and peak shifting. We manually inspected all of the spectra to make sure there were no incorrectly fitted metabolites. The results were quantitatively comparable with the de facto gold standard Chenomx data when compared for a representative sample of spectra data not shown. We quantified 25 metabolites from the spectra, eight of which were excreted by some or all of the strains and 17 of which were present in the original medium and consumed to varying degrees.

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All of the data are available for downloading supplemental Table S2. We wanted to determine whether NMR exometabolome data would be useful for clustering knockout mutants of related function, and so carried out an initial analysis on whole profiles. Principal component analysis is a robust, unsupervised, multivariate technique that is often used for dimension reduction and data visualization.

Principal component analysis alone was sufficient to show that strain replicates clustered tightly together, indicating good biological reproducibility Fig. The majority of the mutants grew to similar final A as the wild type Fig. The two tpiA mutants and putative acoA mutant did not grow, whereas the rpoN , aceE , and aceF mutants had moderate to severe growth defects. PC1 could be interpreted as an overall growth axis. Functionally related P. A , principal component analysis score plot of all strains, PCs 1 and 2.

Metabolism of Xenobiotics

Different colors represent different mutant strains, and the lines connect individual points to strain centroids. B , linear discriminant analysis of dimension-reduced data PCs 2—10 inclusive. C , hierarchical cluster analysis of mean concentrations for two-component system mutants.

Metabolite changes in P. A , metabolite levels for all strains data normalized to medians. B , growth of all mutants as percent of the wild type. C , alanine utilization increases with extent of growth. Different mutant strains are indicated by different colors and are sorted clockwise according to increasing A The blue circle indicates the original level in SCFM, the red circle represents the mean final level, and the black circle the median final level across all strains.

D , aceE and aceF strains have high levels of pyruvate excretion. E , the rpoN strain produces high levels of gluconate and the cbrA strain the next highest. We followed the approach of Raamsdonk et al. Hence, if two strains cluster together following linear discriminant analysis, it can be interpreted as an unsupervised indication that the strains have a similar metabolism.


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Furthermore, this could be used to disentangle strain-specific changes from the overall effects of growth. Principal component analysis can be used as a dimension reduction preprocessing step for linear discriminant analysis, and excluding PC1 effectively removes the growth-related information. Using all the data as an input, the aceE and aceF strains were separated from the rest along axis 1, and rpoN was separated from the rest along axis 2 data not shown.

In addition, we focused on two-component systems. These bacterial regulatory systems prototypically comprise a membrane-localized histidine sensor kinase and a cytoplasmically localized response regulator They have low levels of cross-talk 33 , so they can be used for biological validation i. We clustered the two-component system mutants separately on the basis of Euclidean distance.

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Metabolic Profiling: Disease and Xenobiotics

Presumably the other genes were not expressed under these growth conditions. Together, all of these observations provide a biological validation that functionally related strains clustered together. We then examined the data in more depth to get a more specific idea of which metabolites were altered and for which strains Fig.

Alanine, lactate, phenylalanine, and glutamate were strongly negatively correlated with growth. This is obvious by eye when examining the data sorted by A e. Lactate is the major carbon source in SCFM, and lactate utilization and growth were strongly correlated across most strains.


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  • Some strains, however, most notably strains with mutations in genes encoding for quorum sensing molecule synthesis rhlI , lasI , pqsA , and pqsH , had inefficient lactate use. These strains consumed almost all the lactate from the medium but did not grow to the OD that would have been expected data not shown.

    The only other strains with equally inefficient lactate utilization were mutants deficient in RsmA and DksA, both negative regulators of quorum sensing 34 — Two of the excreted metabolites were present at very high levels in some strains: gluconate and pyruvate. Pyruvate was excreted by the aceE and aceF mutants, respectively, whereas gluconate was mainly produced by the rpoN , cbrA , and cbrB mutants and was highest in the rpoN mutant in particular Fig.

    We validated this observation independently by carrying out an enzymatic gluconate quantification of all samples. This confirmed the NMR results, with the rpoN strain samples again with the highest gluconate production data not shown. Gluconate production has not been described as an RpoN-dependent phenotype that we are aware of, and so we wanted to confirm that this was a genuine effect of the rpoN mutation and not a downstream effect of the transposon insertion or secondary mutations. We measured growth and gluconate for samples grown in standard shake flasks, both for the library rpoN transposon mutant and the PA14 wild type as well as for a clean in-frame rpoN deletion mutant in the PAO1 background and its isogenic parent strain.

    We supplemented the medium with 2 m m glutamine to make sure that any phenotype was not a simple consequence of glutamine auxotrophy Growth was decreased, and gluconate was elevated in both backgrounds Fig. RpoN deletion decreases growth but increases gluconate excretion in two different strain background A. B , glucose dehydrogenase but not gluconate dehydrogenase activity is deregulated in rpoN -mutant strains.

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    Filled bars , rpoN mutant; empty bars , wild type; ns , not significant. To further characterize the pleiotropic effects of the rpoN mutation, we analyzed the metabolic capacity of the rpoN mutant and its parent strain using the phenotypic microarray system from Biolog Technopath. This technology measures microbial activity using a redox-active dye in a well format, with each plate containing 95 different conditions We investigated activity on different sole carbon sources using glutamine as a nitrogen source and 95 different sole nitrogen sources using glucose as a carbon source.

    This confirmed the key role of rpoN in nitrogen metabolism, with the mutant only able to utilize 23 nitrogen sources, whereas the wild type utilized Surprisingly, the rpoN mutant exhibited a higher activity than the wild type on many of the carbon sources, with detectable activity on and 75 carbon sources, respectively supplemental Table S3.

    We also looked at the intracellular metabolome endometabolome of the rpoN clean deletion mutant, reanalyzed the supernatants exometabolome in more detail, and compared the profiles to the wild type and a complemented mutant strain. The metabolic effects of the rpoN mutation were both wide-ranging and growth phase-dependent Fig. The exometabolome data showed that organic acid production is altered in the rpoN mutant, which produced more gluconate but less acetate levels than the WT after 5 h. In addition, proline and serine utilization was reduced beyond what would be expected for the lower growth rate.

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    In contrast, glucose utilization was increased. After 24 h, most metabolites were used up, but the rpoN mutant failed to utilize all of the available histidine data not shown. The 6-phosphogluconate dehydratase edd mutant is the only mutant with a Crc-binding motif tested that gives the same gluconate accumulation phenotype as the rpoN mutant. Extracellular gluconate concentrations for mutant strains are given by the size of the red dot in a white square.

    Additionally, exo- and endometabolome changes for the rpoN mutant are shown simultaneously in the same pathway context. Key given in inset. Gene names are taken from the Pseudomonas genome database. An asterisk after the name indicates a predicted Crc-binding motif, taken from Browne et al. After 24 h of growth, more significant differences could be observed, with amino acid metabolism and the citric acid cycle particularly affected Fig.

    The rpoN mutant also contained significantly lower levels of disaccharides such as trehalose and sucrose. However, few metabolites showed consistent differences across both time-points. Only citrate and 4-hydroxyphenylacetate had consistently lower levels data not shown. The effect on 4-hydroxyphenylacetate suggests a possible role for RpoN in regulating the conversion of phenylalanine and tyrosine.

    Why do RpoN-deficient strains excrete gluconate? The presence of a compound in the culture medium can in general be the result of either the active excretion of the compound or the extracellular conversion of a substrate coupled to the inability to take up the resulting compound quickly enough. Both scenarios can be brought about by the deregulation of metabolic enzymes or metabolite transporters.

    To narrow down the reason for gluconate production by the rpoN mutant, we looked at the kinetics of gluconate production in shake flask culture. Gluconate production peaked at around 6—7 h in both SCFM as well as minimal glucose medium, with a much higher concentration on the glucose medium Fig.

    This could potentially be due to gluconate reuptake, but the NMR spectra of supernatants of both cultures showed the production of 2-ketogluconate at similar signal intensities data not shown. Therefore, a major part of the decrease in gluconate concentration in stationary phase was not due to direct uptake by the cell but to conversion to 2-ketogluconate.

    Glucose is oxidized to gluconate in the periplasm by a membrane-bound glucose dehydrogenase GDH , and further oxidation to 2-ketogluconate is carried out by a membrane-bound gluconate dehydrogenase To elucidate whether the increase in gluconate and 2-ketogluconate was caused by decreased uptake or by deregulation of the metabolic enzymes, we measured the activity of both GDH and gluconate dehydrogenase.

    Gluconate dehydrogenase was not altered, but GDH was clearly deregulated, with more than 5-fold higher activity for the rpoN mutant compared with the WT Fig. Extracellular gluconate peaks during growth and declines in stationary phase. A , synthetic cystic fibrosis medium. B , minimal medium with glucose as the sole carbon source.

    Line plots represent growth left axis and bars indicate extracellular gluconate production right axis. Error bars represent S. This does not, however, fully explain the gluconate accumulation, as GDH does not have an RpoN binding site 4.