coli cultures. Figure 2 represents those metabolite molarity calculator profiles that showed significant uncorrelated patterns among cultures and the estimated pairwise Pearson’s correlation coefficients. Figure 2 Metabolic patterns of the W3110 (represented by full diamonds and dashed lines) and ΔrelA (represented by open circles and red lines) E. coli cultures that presented low pairwise correlation coefficients (r < 0.6). The error bars shown ... As illustrated in Figure 2, only Inhibitors,research,lifescience,medical one metabolite (succinate, succ) was found to have negatively correlated profiles, which means that the intracellular levels of this metabolite followed

an opposite pattern in both E. coli strains. However, six other metabolites showed poorly correlated patterns that are essentially caused by discrepancies at lower dilution rates (i.e., dilution rates of 0.1 and 0.05 h−1). Most of these uncorrelated profiles are associated with fatty acids, denoting Inhibitors,research,lifescience,medical that the coordination of fatty acids biosynthetic activities is somehow affected by the relA gene mutation. Inhibitors,research,lifescience,medical To understand how these specific metabolic alterations are related to changes in biochemical activities, metabolite profiles were translated into metabolic pathway activities. Two enrichment analyses were performed: the Metabolite Biological Role (MBRole) a web-server tool that uses biological and chemical annotations from

different databases to highlight the biological role of metabolomics data; and Pathway Activity Profiling (PAPi), an algorithm that uses the metabolite

Inhibitors,research,lifescience,medical profiles and KEGG database to compare the activities of metabolic pathways between different experimental conditions. While MBRole highlights metabolic activities that are over-represented in the metabolomics data, PAPi used the DAPT secretase price quantification of metabolite levels to determine pathways activity measured by the Activity Score (AS). In both analyses, pathways like “Aminoacyl-tRNA biosynthesis,” “ABC transporters,” “Citrate cycle (TCA cycle),” “Alanine, aspartate and glutamate metabolism” and “Fatty acid biosynthesis” were highlighted (see Tables S3 and S4). However, Inhibitors,research,lifescience,medical PAPi showed that, particularly at the dilution rate of 0.1 h−1, pathways such as “Aminoacyl-tRNA biosynthesis,” “ABC transporters,” “Nicotinate and nicotinamide metabolism,” “Sphingolipid metabolism” and “Sulfur metabolism” presented higher activity scores in the E. coli W3110 culture, whereas pathways of “Biosynthesis of unsaturated GSK-3 fatty acids” and “Alanine, aspartate and glutamate metabolism” showed lower activity scores. Clearly, metabolic pathway activities involving amino and fatty acids seem to be the most affected by the relA gene mutation in these experiments. To illustrate these differences, metabolite profiles were also represented in the E. coli metabolic map that includes these major metabolic pathways (Figure 3). Figure 3 Representation of metabolic profiles on the metabolic map of E. coli.