Readme

Readm

glucose_time_course/lipidA

glucose_time_course/lipid

glucose_time_course/phospholipid

glucose_time_course/phospholipi

glucose_time_course/RNA

glucose_time_course/RN

data_summary

data_summar

Flagellar genes and other energy intensive processes were down-regulated while stress-response genes were up-regulated.

<p>Fitting the mRNA and protein profiles allowed us to estimate the underlying dynamics and differential regulation of each gene, sorting them into high confidence categories describing their behavior. Genes were put into categories based upon whether they were up-regulated, down-regulated, transiently up-regulated, or transiently down-regulated. The mRNA or proteins in each category were then tested for enrichment of GO terms. (A, B) The average of the mRNAs in a given enriched GO term that were down- and up-regulated, respectively. (C, D) The average of the proteins in a given enriched term GO term that were down- and up-regulated, respectively. Amine biosynthesis was also enriched for mRNAs that were transiently up-regulated (not plotted) however no other terms for either mRNA or protein were enriched for the transiently up- or down-regulated categories. All functional clustering of GO enrichment terms for all categories, for both protein and mRNA, are provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004400#pcbi.1004400.s014" target="_blank">S3</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004400#pcbi.1004400.s015" target="_blank">S4</a> Files, respectively. As a complementary approach we took the average of all proteins in a given pathway. (E, F) The average protein levels in the KEGG pathway, for KEGG pathways that changed significantly. All the other terms showed no significant change.</p

flux

flu

glucose_time_course/Protein

glucose_time_course/Protei

mRNA levels within an operon correlated strongly whereas protein levels generally did not.

<p>(A, B) Histograms of the median pairwise correlation coefficient between all possible pairs of mRNA and protein profiles, respectively, within an operon. (C) 2D Histogram of the pairwise correlation between proteins in the same operon (<i>y</i>-axis) and the inter-gene distance between the protein coding regions (<i>x</i>-axis). Darker colors represent higher correlation. Proteins that had a smaller inter-gene distance were more likely to have correlated profiles. (D) Example of mRNAs in the same operon that were highly correlated. (E, F) Examples of proteins in the same operon that were poorly and highly correlated, respectively.</p

Overview of experimental design.

<p>Measurements of RNA, protein, lipids, and metabolic flux were taken under uniform growth and environmental conditions. (A) Long-term stationary phase experiment. The <i>E</i>. <i>coli</i> B REL606 strain was taken from a freezer stock and revived (day –2), diluted and regrown to precondition it to culture conditions for 24 h (day –1), and diluted then into several individual cultures to initiate the experiment. (B) The OD₆₀₀ (blue curve) was measured to assess growth and optimal collection of time points. Nine time points were selected for this experiment, spanning three hours to two weeks. Cell viability was accessed at each time point by determining the number of colony forming units (CFU, purple curve). (C) For each sample an aliquot was removed from the culture for each experiment to be done, spun down, flash frozen, and used to measure RNA via RNA-seq, protein via LC/MS, lipids via MALDI-TOF MS and ESI MS, and metabolic flux via GC-MS. Metabolic flux samples were grown separately under identical conditions excepting the labeled U-¹³C glucose. Raw RNA and protein counts, calculated flux ratios, raw phospholipid MS peaks, and lipid A peaks for all time points are available in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004400#pcbi.1004400.s012" target="_blank">S1 File</a>.</p