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. 2015 Aug 14;11(8):e1004400.
doi: 10.1371/journal.pcbi.1004400. eCollection 2015 Aug.

Controlled Measurement and Comparative Analysis of Cellular Components in E. Coli Reveals Broad Regulatory Changes in Response to Glucose Starvation

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Free PMC article

Controlled Measurement and Comparative Analysis of Cellular Components in E. Coli Reveals Broad Regulatory Changes in Response to Glucose Starvation

John R Houser et al. PLoS Comput Biol. .
Free PMC article

Abstract

How do bacteria regulate their cellular physiology in response to starvation? Here, we present a detailed characterization of Escherichia coli growth and starvation over a time-course lasting two weeks. We have measured multiple cellular components, including RNA and proteins at deep genomic coverage, as well as lipid modifications and flux through central metabolism. Our study focuses on the physiological response of E. coli in stationary phase as a result of being starved for glucose, not on the genetic adaptation of E. coli to utilize alternative nutrients. In our analysis, we have taken advantage of the temporal correlations within and among RNA and protein abundances to identify systematic trends in gene regulation. Specifically, we have developed a general computational strategy for classifying expression-profile time courses into distinct categories in an unbiased manner. We have also developed, from dynamic models of gene expression, a framework to characterize protein degradation patterns based on the observed temporal relationships between mRNA and protein abundances. By comparing and contrasting our transcriptomic and proteomic data, we have identified several broad physiological trends in the E. coli starvation response. Strikingly, mRNAs are widely down-regulated in response to glucose starvation, presumably as a strategy for reducing new protein synthesis. By contrast, protein abundances display more varied responses. The abundances of many proteins involved in energy-intensive processes mirror the corresponding mRNA profiles while proteins involved in nutrient metabolism remain abundant even though their corresponding mRNAs are down-regulated.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of experimental design.
Measurements of RNA, protein, lipids, and metabolic flux were taken under uniform growth and environmental conditions. (A) Long-term stationary phase experiment. The E. coli 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 S1 File.
Fig 2
Fig 2. K-means clustering of mRNA and protein profiles in long-term stationary phase revealed trends in transcriptional and post-transcriptional regulation.
mRNA and protein profiles, normalized to each molecule’s maximum value, were clustered by geometric distance using K-means clustering with 15 and 25 clusters, respectively. The cluster centroids were then plotted as heat maps with darker blue representing higher RNA or protein levels. (A) mRNA levels were largely shut off upon entry to stationary phase with some mRNA being transiently up-regulated during the transition between exponential and stationary growth. (B) Protein levels showed a much wider range of behaviors with some being up- or down-regulated for the duration of the experiment as well as for a short period of time during the transition from exponential to stationary growth. (C) Histogram of the correlation coefficients between individual protein levels and the time integral of mRNA expression. This model tests the limit of slow protein degradation, where protein levels are proportional to the cumulative sum of their respective transcripts. Approximately 15% of the protein levels integrated their transcript’s response over the entire duration of the experiment (with ρ>0.70). (D) Histogram of the correlation coefficient between relative protein levels and their corresponding (relative) transcripts. This model tests to what extent protein levels are proportional to their respective mRNA levels. Approximately 20% of the protein levels were proportional to their transcript’s response over the entire duration of the experiment (with ρ>0.70). (E) 2-D histogram of the correlation coefficient of protein vs. mRNA (on the x-axis) and the protein vs. the time integral of mRNA (y-axis). Darker colors indicate more genes in the given bin. We observed a strong anti-correlation between the two measures of dynamic correlation, indicating that these two quantities were largely mutually exclusive. (F) The correlation between all mRNA and protein levels for a single time point was strongest at 3 h (Spearman correlation coefficient ~0.71). Normalized mRNA and protein levels, both relative and absolute, used to generate the above figure are provided in S2 File.
Fig 3
Fig 3. mRNA levels within an operon correlated strongly whereas protein levels generally did not.
(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 (y-axis) and the inter-gene distance between the protein coding regions (x-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.
Fig 4
Fig 4. Flagellar genes and other energy intensive processes were down-regulated while stress-response genes were up-regulated.
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 S3 and S4 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.
Fig 5
Fig 5. Lipid A and phospholipids were modified starting at 6 hours and these modifications continued to accumulate for two weeks.
Lipid A and phospholipids were extracted from all samples for analysis by negative ion MALDI-TOF and ESI-MS mass spectrometry, respectively, and the 6 h and two week representative samples are shown in this figure. (A) Activation of the acyl-transferase PagP adds a C16 chain to lipid A on the 2-position primary acyl chain, resulting in a m/z of ~2035. (B) Modification of phospholipids by cyclopropanation of one unsaturated double bond is catalyzed by CFA synthase. Transcripts of CFA synthase increased at late times (green) consistent with modification of PE whereas the level of CFA protein stayed relatively flat at late time points. (C) As represented here by the 6 h sample, lipid A from all samples collected between 3 and 48 h contained one major peak at ~1797 m/z corresponding to wild type, hexa-acylated lipid A. As illustrated on the right by the two week sample, the one and two week (168 and 336 h) time points showed the addition of the C16 chain to lipid A. (D) Phosphatidylethanolamine (PE) is shown and similar results were also obtained for phosphatidylglycerol (PG). The phospholipid profiles of the samples remained relatively consistent with wild-type E. coli phospholipid profiles until hour 8, when a gradual increase in a peak ~702.5 m/z began. This peak became the predominant species by two weeks. Its mass corresponds to the cyclopropanation of one unsaturated double bond within a PE molecule containing acyl chains totaling 33 carbons distributed between the two acyl chains.
Fig 6
Fig 6. Summary of key results.
(A) Cells are starved around 6–8 h after the initiation of growth, and the cell viability (and OD600) remained constant up until at least one week. At two weeks, however, there was a decrease of 38% in viability. (B) The relative fraction of rRNA (compared to all RNA) stayed fairly constant through the entire time course, as did the fraction of tRNA (in non-rRNA depleted samples). On the other hand, relative levels of mRNA decreased upon entry to stationary phase perhaps as a strategy for reducing overall protein synthesis. As a reference we also analyzed RNA fractions for the rRNA-depleted samples, to demonstrate that this reduction of mRNA levels was not simply due to the low relative counts of mRNA compared to rRNA in the nondepleted samples. Note that the rRNA-depleted samples still contained residual amounts of rRNA, as shown. (C) Phospholipids and lipid A were modified in a manner consistent with the activation of stress responses. Modifications began early in the stationary phase and slowly increased during the time course for up to two weeks. (D) Transcriptional changes (measured by mRNAs) separated into at least two temporal domains, before and after stationary phase. A possible third region corresponded to late transcriptional changes observed at two weeks. All changes in regulation had begun by 10 h. At this point stress response genes were up-regulated and energy intensive processes were down-regulated. (E) Approximately 20% of the measured protein levels were proportional to their transcript levels over time while 15% of the protein levels integrated their transcript’s response over the entire duration of the experiment. This observation highlights that differences in post-transcriptional regulation, such as protein degradation, cause differences in regulation between mRNAs and their expressed proteins.

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