Identification of sequential order of oxidative stress induced metabolic regulations in fibroblasts.

<p>Application of MNS algorithm for sequential data on metabolomics data from fibroblasts treated with increasing concentrations of H₂O₂. The figure shows segmentation results with increasing incluence of the neighborhood and sequential dependency: (a) sequence weight w_s = 0, neighborhood weight w_n = 0, (b) w_s = 0.15, w_n = 0.15, (c) w_s = 0.24, w_n = 0.24. In each time profile inset, the black line reports the log2 of the fold-change relative to the first time point (frame). With increasing dependency to the neighborhood and sequence, only the major known regulators involved in the metabolic reponse to oxidative stress remain as inferred regulatory sites. Furthermore, the algorithm correctly infers the sequential order of an initial activation of G6PD and inhibition of glycolytic flux (GAPDH) which is followed by a rerouting of flux into P5P, S7P, and E4P via PGD and back to upper glycolysis via TK and TA [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005577#pcbi.1005577.ref039" target="_blank">39</a>]. Abbreviations: Hexose P: hexose phosphates, GL6P: gluconolactone 6-phosphate, 6PG: 6-phospho gluconic acid, FBP: fructose bisphosphate, E4P: erythrose 4-phosphate, S7P: sedoheptulose 7-phosphate, P5P: pentose 5-phosphates, GAP/DHAP: glyceraldehyde 3-phosphate/dihydroxyacetone phosphate, 3-PGP: 3-phosphoglyceroyl phosphate, xPG: 2/3-Phosphoglyceric acid, PEP: phosphoenolpyruvate, LAC: lactic acid, CIT: citric acid, cAco: cis-aconitic acid, ICIT: isocitric acid, aKG: α-ketoglutaric acid, SUC: succinic acid, FUM: fumaric acid, MAL: Malic acid, OXA: oxaloacetic acid, G6PD: glucose-6-phosphate dehydrogenase, PGLS: 6-phosphogluconolactonase, PGD: phosphogluconate dehydrogenase, TK: transketolase, TA: transaldolase, PFK: phosphofructokinase, ALDO: aldolase, GAPDH: glyceraldehyde 3-phosphate dehydrogenase, PGK: phosphoglycerate kinase, ENO: enolase, PK: pyruvate kinase, LDH lactate dehydrogenase, CS: citrate synthetase, ACO: aconitase, IDH: isocitrate dehydrogenase, αKGDH: α- ketoglutarate dehydrogenase, SDH: succinate dehydrogenase, FH: fumarate hydratase, MDH: malate dehydrogenase.</p

Comparison of algorithms in the identification of the experimentally perturbed reactions in 647 <i>E</i>. <i>coli</i> enzyme knockout mutants.

<p>Significantly identified reactions were determined by a permutation test of the reaction labels with 1000 permutations and a p-value cutoff of 0.05.</p

Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data

<div><p>In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved) data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.</p></div

Identification of novel regulatory events in nucleotide metabolism mediated by MetR.

<p>(a) Change in nucleotide triphosphate (NTP), cyclic nucleotide monophosphates (cNMP), nucleotide monophosphates (NMP) and nucleoside metabolite levels comparing Δ<i>metR</i> knockout and wildtype <i>E</i>. <i>coli</i>. (b) Results of CyaA enzyme assays with 10 mM ATP as substrate in crude extracts of Δ<i>metR</i> knockout, <i>metR</i> overexpression and wildtype <i>E</i>. <i>coli</i>. (c) Known and potentially new interactions involved in the regulation of nucleotide metabolism. Our study suggests that MetR inhibits CyaA. This could be mediated through direct inhibition or indirect feedback for example to CRP, the known regulator of CyaA expression.</p

Parameter optimization for the accurate identification of experimentally perturbed reactions from metabolomics data.

<p>(a) Number of significantly identified reactions (p < 0.05) for each parameter combination for the reaction ranking based on maximum λ₁ or total numbers of identified fractures (Details see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005577#pcbi.1005577.s015" target="_blank">S1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005577#pcbi.1005577.s016" target="_blank">S2</a> Text, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005577#pcbi.1005577.s001" target="_blank">S1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005577#pcbi.1005577.s002" target="_blank">S2</a> Figs). Significantly identified reactions were classified into “exact” if the experimentally perturbed reaction was inferred by the algorithm or as “first neighbor” if one of the first neighbor reactions of the perturbed reaction was inferred. P-values are calculated by a permutation test of the reaction labels with 1000 permutations. (b) Influence of the models parameter settings on the significance of the inference of individually perturbed enzymes. Black box marks the best single parameter combination (3 hidden states, mean type: k-means, std type: all data). Certain perturbed enzyme knock-outs (e.g. shdB, aldA) cannot be identified with the best parameter combination but with others (e.g. 3 hidden states, mean type: quantile, std type: all data). (c) Number of significantly identified reactions (p < 0.05) for rank product combinations of different parameter settings. Three combinations of two predictors with different model parameterizations (black boxes) improve the significantly inferred enzymes to 14. P-values are calculated by a permutation test of the reaction labels with 1000 permutations.</p

Correlation analysis of metabolite responses with salt tolerance.

<p>(A) Correlation of metabolite fold-changes in response to strong salt stress with IC₅₀ values of the different organisms was assessed by Pearson’s correlation coefficient R. For each metabolite, the upper quartile x_0.75 of absolute log₂ fold-changes across all species was plotted against R. Only metabolites detected in more than 10 species were considered. Metabolites with |R| > 0.5 and x_0.75 > 1 are highlighted in blue (anticorrelating metabolites) and pink (correlating metabolites), and the names of representative compounds are listed. The full correlation data is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s002" target="_blank">S2 Data</a>. (B) Visualization of metabolites correlating with salt tolerance on the KEGG metabolic pathway map using PathwayProjector [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.ref047" target="_blank">47</a>]. Color intensity of metabolites indicates strength of positive (pink) or negative (blue) correlation, and size indicates x_0.75. Key pathways are highlighted and labeled. (C) Correlation of fold-change with salt tolerance for selected compounds in lower glycolysis; (D) in cysteine and methionine metabolism; (E) in branched-chain amino acid metabolism; and (F) in heme biosynthesis. In panels C to F mean and standard deviation of four (microbes) or three (human cell lines) replicates are shown. Note that metabolite annotations are based on accurate mass and can be ambiguous; refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s001" target="_blank">S1 Data</a> for complete annotations.</p

Overview of the MNS toolbox.

<p>The MNS algorithm enables to predict sites and the sequential order of metabolic regulations from metabolomics data.</p

Phylogenetic relationship between species is insufficient to explain differences in metabolic salt stress responses.

<p>(A) Cladogram of analyzed species based on metabolite ion log₂ fold-changes upon exposure to low (IC₁₀, L), medium (IC₂₅, M) and high (IC₅₀, H) salt stress relative to unstressed controls (IC₀). Pairwise distances between samples were calculated using the Cityblock metric. Species labels are colored according to taxonomic classification as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.g001" target="_blank">Fig 1A</a>. (B) Correlation of pairwise distances between species based on metabolic salt stress responses with phylogenetic distances. Distances between metabolic responses to different salt stress severities were calculated based on metabolite ion log₂ fold-changes relative to IC₀ using the Cityblock metric, and phylogenetic distances based on the aligned small ribosomal subunit RNA sequences using the Jukes-Cantor measure. R indicates Pearson’s correlation coefficient.</p

Analysis of salt tolerance in fifteen diverse species.

<p>(A) Phylogenetic tree of analyzed species. Jukes-Cantor distances between bacteria are drawn to scale based on aligned 16S small ribosomal subunit RNA sequences. Distances between eukaryotes are not drawn to scale for visualization purposes. Organisms are colored based on taxonomic classification, and cell wall strengths and typical habitats are indicated. Further information about strains and cell lines is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s007" target="_blank">S1 Table</a>. (B) Sustained hyperosmotic salt tolerance based on growth inhibition experiments. Salt tolerance is expressed as mean and standard deviation (n = 2) of concentrations inhibiting growth rates by 10% (IC₁₀), 25% (IC₂₅) and 50% (IC₅₀) compared to unstressed conditions. Species are grouped according to taxonomic classification, and the colored horizontal bars indicate the average IC₅₀ of each taxonomic group. (C) Comparison of salt tolerance between species colonizing different habitats. (D) Comparison of salt tolerance between species with different cell wall strengths. Differences between groups in panels B to D were not statistically significant (P > 0.05, unpaired two-tailed <i>t</i>-tests) for all comparisons except those with human cell lines.</p

Hyperosmotic salt stress elicits complex and predominantly species-specific global metabolic responses.

<p>(A) Principal component analysis (PCA) was performed based on log₂ metabolite ion fold-changes upon low (IC₁₀, L), medium (IC₂₅, M) or high (IC₅₀, H) salt stress relative to unstressed controls. For each species the three stress intensity points are connected by triangular patches for visualization purposes. Patches and labels are colored according to taxonomic classification as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.g001" target="_blank">Fig 1A</a>. (B) Loading plot of metabolites underlying the PCA shown in panel A. Selected metabolites with large coefficients are highlighted. Note that metabolite annotations are based on accurate mass and can be ambiguous; refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148888#pone.0148888.s001" target="_blank">S1 Data</a> for complete annotations. (C) Numbers of strongly and significantly responding metabolites in each analyzed species, grouped either by the lowest stress intensity under which a change was observed (gray bars) or by change direction (magenta and blue bars). (D) Histogram of the number of species in which metabolite ions were affected by the individual salt stress intensities (black, dark gray and light gray curves) or by at least one stress intensity (magenta curve).</p