GC-MS-based analysis of the metabolic response of Escherichia coli exposed to four different stress conditions reveals reduction of energy expensive pathways. Time-resolved response of E. coli to changing environmental conditions is more specific on the metabolite as compared with the transcript level. Cease of growth during stress response as compared with stationary phase response invokes similar transcript but dissimilar metabolite responses. Condition-dependent associations between metabolites and transcripts are revealed applying co-clustering and canonical correlation analysis., The response of biological systems to environmental perturbations is characterized by a fast and appropriate adjusting of physiology on every level of the cellular and molecular network. Stress response is usually represented by a combination of both specific responses, aimed at minimizing deleterious effects or repairing damage (e.g. protein chaperones under temperature stress) and general responses which, in part, comprise the downregulation of genes related to translation and ribosome biogenesis. This in turn is reflected by growth cessation or reduction observed under essentially all stress conditions and is an important strategy to adjust cellular physiology to the new condition. E. coli has been intensively investigated in relation to stress responses. Thus far, however, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. To better understand system response to perturbation, we designed a time-resolved experiment to compare and integrate metabolic and transcript changes of E. coli using four stress conditions including non-lethal temperature shifts, oxidative stress, and carbon starvation relative to cultures grown under optimal conditions covering both states before and directly after stress application, resumption of growth after stress-induced lag phase, and finally the stationary phase. Metabolic changes occurring after stress application were characterized by a reduction in metabolites of central metabolism (TCA cycle and glycolysis) as well as an increase in free amino acids. Whereas the latter is probably due to protein degradation and stalling of translation, the former supports and extends conclusions based on transcriptome data demonstrating a major decrease in energy-consuming processes as a general stress response. Further comparative analysis of the response on the metabolome and transcriptome, however, revealed in addition to these similarities major differences. Thus, the response on the metabolome displayed a significantly higher specificity towards the specific stress as compared with the transcriptome. Further, when comparing the metabolome of cells ceasing growth due to stress application with cells ceasing growth due to reaching stationary phase the metabolome response differed to a significant extent between both growth arrest phases, whereas the transcriptome response showed significant overlap again, suggesting that the response of E. coli on the metabolome level displays a higher level of significance as compared with the transcriptome level. Subsequently, both data sets were jointly analyzed using co-clustering and canonical correlation approaches to identify coordinated changes on the transcriptome and the metabolite level indicative metabolite–transcript associations. A first outcome of this study was that no association was preserved during all conditions analyzed but rather condition-specific associations were observed. One set of associations found was between metabolites from the oxidative pentose phosphate pathway such as glc-6-P, 6-P-gluconic acid, ribose-5-P, and E-4-P and metabolites from the glycolytic pathway (3PGA and PEP in addition to glc-6-P with two genes encoding pathway enzymes, that is rpe encoding ribulose phosphate 3-epimerase and pps encoding PEP synthase. A second example comprises metabolites of the TCA cycle such as pyruvic acid, 2-ketoglutaric acid, fumaric acid, malic acid, and succinic acid and the mqo gene encoding malate-quinone oxidoreductase (MQO). MQO catalyses the irreversible oxidation of malate to oxaloacetate that in turn regulates the activity of citrate synthase, which is a major rate determining enzyme of the TCA cycle. The strong association between mqo gene expression and multiple members of the TCA cycle as well as pyruvate suggest mqo expression to have a major function for the regulation of the TCA cycle, which need to be experimentally validated. Multiple further associations identified show on the one hand the power of integrative systems oriented approaches for developing new hypothesis, on the other hand their condition-dependent behavior shows the extreme flexibility of the biological systems studied thus requesting a much more intense effort toward parallel analysis of biological systems under several environmental conditions., Environmental fluctuations lead to a rapid adjustment of the physiology of Escherichia coli, necessitating changes on every level of the underlying cellular and molecular network. Thus far, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. Here, we incorporate the metabolite composition together with gene expression data to provide a more comprehensive insight on system level stress adjustments by describing detailed time-resolved E. coli response to five different perturbations (cold, heat, oxidative stress, lactose diauxie, and stationary phase). The metabolite response is more specific as compared with the general response observed on the transcript level and is reflected by much higher specificity during the early stress adaptation phase and when comparing the stationary phase response to other perturbations. Despite these differences, the response on both levels still follows the same dynamics and general strategy of energy conservation as reflected by rapid decrease of central carbon metabolism intermediates coinciding with downregulation of genes related to cell growth. Application of co-clustering and canonical correlation analysis on combined metabolite and transcript data identified a number of significant condition-dependent associations between metabolites and transcripts. The results confirm and extend existing models about co-regulation between gene expression and metabolites demonstrating the power of integrated systems oriented analysis.