Global Transcriptional Programs Reveal a Carbon Source Foraging Strategy by Escherichia coli

Abstract

By exploring global gene expression of Escherichia coli growing on six different carbon sources, we discovered a striking genome transcription pattern: as carbon substrate quality declines, cells systematically increase the number of genes expressed. Gene induction occurs in a hierarchical manner and includes many factors for uptake and metabolism of better but currently unavailable carbon sources. Concomitantly, cells also increase their motility. Thus, as the growth potential of the environment decreases, cells appear to devote progressively more energy on the mere possibility of improving conditions. This adaptation is not what would be predicated by classic regulatory models alone. We also observe an inverse correlation between gene activation and rRNA synthesis suggesting that reapportioning RNA polymerase (RNAP) contributes to the expanded genome activation. Significant differences in RNAP distribution in vivo, monitored using an RNAP-green fluorescent protein fusion, from energy-rich and energy-poor carbon source cultures support this hypothesis. Together, these findings represent the integration of both substrate-specific and global regulatory systems, and may be a bacterial approximation to metazoan risk-prone foraging behavior. By exploring global gene expression of Escherichia coli growing on six different carbon sources, we discovered a striking genome transcription pattern: as carbon substrate quality declines, cells systematically increase the number of genes expressed. Gene induction occurs in a hierarchical manner and includes many factors for uptake and metabolism of better but currently unavailable carbon sources. Concomitantly, cells also increase their motility. Thus, as the growth potential of the environment decreases, cells appear to devote progressively more energy on the mere possibility of improving conditions. This adaptation is not what would be predicated by classic regulatory models alone. We also observe an inverse correlation between gene activation and rRNA synthesis suggesting that reapportioning RNA polymerase (RNAP) contributes to the expanded genome activation. Significant differences in RNAP distribution in vivo, monitored using an RNAP-green fluorescent protein fusion, from energy-rich and energy-poor carbon source cultures support this hypothesis. Together, these findings represent the integration of both substrate-specific and global regulatory systems, and may be a bacterial approximation to metazoan risk-prone foraging behavior. Bacteria Prepare for Better Times♦Journal of Biological ChemistryVol. 280Issue 16PreviewThe ideal food source for E. coli is glucose. But what happens when only lesser sugars are available? Mingzhu Liu and colleagues have answered this question by comparing the gene expression profiles of E. coli grown on glucose and five poorer carbon sources. Their results were surprising. Rather than maximizing expression of genes needed to utilize their current food source, the bacteria started expressing genes needed to break down other better carbon sources. Full-Text PDF Open Access Jacob and Monod originally studied Escherichia coli gene regulation using carbon catabolism as the experimental system (specifically lactose). They elucidated that genes needed to metabolize lactose are specifically induced by that substrate (1Jacob F. Monod J. J. Mol. Biol. 1961; 3: 318-356Crossref PubMed Scopus (2977) Google Scholar). Over the ensuing 40 years this model has been refined and extended to many substrates and it is now generally accepted that virtually all carbohydrate catabolic genes can be regulated by substrate-specific induction (2Bruckner R. Titgemeyer F. FEMS Microbiol. Lett. 2002; 209: 141-148Crossref PubMed Scopus (356) Google Scholar). This mechanism is attractive because it promotes efficient use of cellular resources, and energy need not be wasted producing enzymes and transporters for substrates unless they are available. In addition to these highly specific mechanisms, cells also have several levels of global regulation. A prime example is carbon catabolite repression, a multifactorial system that blocks expression of alternative carbon utilization pathways when glucose is present (3Saier Jr., M.H. Biotechnol. Bioeng. 1998; 58: 170-174Crossref PubMed Scopus (72) Google Scholar). Relief from carbon catabolite repression, among other effects, activates the cyclic AMP receptor protein (CRP), 1The abbreviations used are: CRP, cyclic AMP receptor protein; RT, reverse transcription; MOPS, 4-morpholinepropanesulfonic acid; DAPI, 4′,6-diamidino-2-phenylindole; AI-2, autoinducer-2; RNAP, RNA polymerase; GFP, green fluorescent protein. 1The abbreviations used are: CRP, cyclic AMP receptor protein; RT, reverse transcription; MOPS, 4-morpholinepropanesulfonic acid; DAPI, 4′,6-diamidino-2-phenylindole; AI-2, autoinducer-2; RNAP, RNA polymerase; GFP, green fluorescent protein. a global transcription factor that positively regulates most carbon catabolic pathways including those for glucose (4Crasnier M. Res. Microbiol. 1996; 147: 479-482Crossref PubMed Scopus (22) Google Scholar). Such global mechanisms have the advantage of responding to many different conditions and can potentiate a spectrum of transcriptional outcomes. While much is known about how individual substrate-specific and global systems regulate a limited set of operons under a defined condition, much less is known regarding how the two coordinate genome-wide transcription under a range of conditions. In this paper, we report the findings from a global gene expression study of E. coli growing on a series of six carbon sources. A transcription pattern emerges from these profiles that expands hierarchically as the growth rate declines. Inspection of the up-regulated genes suggests that, instead of maximizing energy conservation through strict adherence to substrate-specific induction, cells growing on poor substrates devote progressively more of their limited reserves to expand their genome transcription needed to broaden a search for alternative energy sources. Bacterial Growth—MG1655 cells were grown overnight in MOPS minimal medium (5Neidhardt F.C. Bloch P.L. Smith D.F. J. Bacteriol. 1974; 119: 736-747Crossref PubMed Google Scholar) supplemented with 0.1% glucose. Overnight cultures were then diluted 1:50 (or 1:107 for the deeper adapted experiments) into the same base medium with either glucose, glycerol, succinate, l-alanine, acetate, or l-proline as the carbon source and grown aerobically at 37 °C in shake flasks. The carbon compound concentration in each case was adjusted so that the number of carbon atoms was equivalent to that of 0.1% glucose. Sample Preparation for Microarray Analysis—Detailed protocols for RNA preparation and labeling can be found at www.genome.wisc.edu/functional/protocols.htm. Briefly, at an A600 of 0.2 (∼6 generations except for deeper adapted cultures, ∼30), 15-ml samples were mixed with 30 ml of RNAprotect bacterial reagent (Qiagen), pelleted, and stored at –80 °C. Total RNA was then isolated using MasterPure kits (Epicenter Technologies). cDNA was synthesized and labeled using a protocol similar to that described by Rosenow et al. (6Rosenow C. Saxena R.M. Durst M. Gingeras T.R. Nucleic Acids Res. 2001; 29: E112Crossref PubMed Scopus (64) Google Scholar). Briefly, 10 μg of total RNA was reverse transcribed with 1,200 units of Superscript II (Invitrogen) using 500 ng of random hexamers at 42 °C for 90 min. The reaction buffer was used according to the manufacturer's recommendations. Remaining RNA was removed with 2 units of RNase H and 1 μg of RNase A for 10 min at 37 °C. Synthesized cDNA was then purified with QiaQuick (Qiagen) and fragmented to 50–100 bp with 0.2 unit of DNase I (Epicenter) for 10 min at 37 °C. The fragmented cDNA was 3′-end-labeled with 25 μm biotin-N6-ddATP (Applied Biosystems, Foster City, CA) using 50 units of terminal transferase (New England Biolabs, Beverly, MA) at 37 °C for 2 h. The labeled cDNA was hybridized to Affymetrix GeneChip® E. coli antisense genome arrays as recommended by the manufacturer (www.affymetrix.com). Following a 16-h hybridization at 45 °C, the array was washed and stained with streptavidin-phycoerythrin (Molecular Probes) using an antibody intermediate to enhance the signal. The arrays were read at 570 nm with a resolution of 3 μm using a Gene-Array® confocal laser scanner (Affymetrix). Washing and scanning were automated by a GeneChip® Fluidics Station controlled by Affymetrix® Microarray Suite 5.0 software (Affymetrix). For each carbon source, two biological replicates were done except glucose which was replicated five times. Image Processing and Data Analysis—Image analysis was carried out by Affymetrix® Microarray Suite 5.0 software. This calculates a detection call, detection p value and signal (background-subtracted and adjusted for noise) for each gene. Those values are then imported into a relational database, converted to log2 values, and averaged for each gene across replicates. The correlation coefficient of log2 values between any two replicates was greater than 0.95. Genes were considered up-regulated relative to glucose if they increased at least 3-fold in signal intensity and the signal intensity in experiment had a log2 value of at least 8.5. Conversely, genes with at least a 3-fold reduction in signal intensity and a log2 signal intensity in glucose of at least 8.5 were deemed down-regulated. The higher log2 intensity values were used to limit the analysis to those genes for which we have a high degree of confidence in their level of expression. Note, the use of “up-regulation” and “down-regulation” refers only to the change in measured RNA abundance and has no regulatory implication. Real-time Quantitative RT-PCR—rRNA leader sequence RNAs were quantified by real-time PCR in an ABI Prism 7700 DNA analyzer (Applied Biosystems), and the QuantiTect SYBR green RT-PCR kit (Qiagen) was used according to the manufacturer's protocol. A 115-base pair segment was amplified using forward primer, 5′-TGACACGGAACAACGGCAAACACG-3′, and reverse primer, 5′-TGCATAATACGCCTTCCCGCTACA-3′. This fragment assesses transcripts originating from the P1 promoter of three rrn operons (A–C). PCR cycling conditions were 95 °C for 10 min, 40 cycles of denaturation at 94 °C for 15 s, and extension at 60 °C for 1 min. Relative gene expression data analysis was carried out with the standard curve method (7Winer J. Jung C.K. Shackel I. Williams P.M. Anal. Biochem. 1999; 270: 41-49Crossref PubMed Scopus (1221) Google Scholar). The frr gene was chosen as the internal control because of its consistent expression levels across conditions (see www.genome.wisc.edu). Measurement of σS and σE Protein Levels—Cells were harvested at the appropriate times and lysed in 1× SDS lysis buffer (3% (w/v) SDS, 0.06 m Tris-HCl (pH = 6.8), 5%(v/v) β-mercaptoethanol, 5% (v/v) glycerol). Extracts were separated by SDS-polyacrylamide gel electrophoresis and then transferred electrophoretically to a nitrocellulose membrane. Blots were probed with specific mouse monoclonal antibodies NT73, 1RS1, and 1RE53 (8Anthony L.C. Foley K.M. Thompson N.E. Burgess R.R. Methods Enzymol. 2003; 370: 181-192Crossref PubMed Scopus (18) Google Scholar), which detect the RNA polymerase (RNAP) β′ subunit, σS and σE, respectively. The blot was rinsed three times with TBST buffer (10 mm Tris-HCl (pH 7.4), 150 mm NaCl, and 0.1% Tween 20) and then incubated with goat anti-mouse IgG horseradish peroxidase-labeled secondary antibody for 1 h at room temperature. The blots were again washed three times with TBST buffer and proteins visualized with SuperSignal® West Femto Maximum Sensitivity Substrate (Pierce). CRP-binding Site Analysis—Seventy known CRP-binding sequences (9Salgado H. Gama-Castro S. Martinez-Antonio A. Diaz-Peredo E. F. M. A. C. J. Nucleic Acids Res. PubMed Google M. M. J. A. C. Gama-Castro S. Nucleic Acids Res. 2002; PubMed Scopus Google Scholar) were used as for S. J. Mol. Biol. PubMed Scopus Google Scholar). The sequence is base in and two and were as the 500 base pair of each in the three (see were using the cells Mol. Microbiol. 2003; PubMed Scopus Google Scholar) were grown in either glucose or supplemented MOPS minimal an A600 of of were removed from the and to a concentration of were for 1 h at room and in 1 ml of 1× the on the 15 of a 10 of was The of cells was on using was in a II with a and a were with a at The were with of were as described Mol. Microbiol. 2003; PubMed Scopus Google Scholar) to cells glucose or Briefly, in each of at each we measured the intensity of each and its The differences in were used to a level as described by R. I. 3: Scopus Google Scholar). This level is a of the level the of and the can be from described the was by the by the and by the of the of This a of the by and level on a the of different carbon on E. coli global gene we cells aerobically in MOPS minimal medium (5Neidhardt F.C. Bloch P.L. Smith D.F. J. Bacteriol. 1974; 119: 736-747Crossref PubMed Google Scholar) with either glucose, glycerol, succinate, l-alanine, acetate, or l-proline as the carbon carbon are of quality as defined by the growth which range from in glucose to in were from each at and total RNA was and hybridized to Affymetrix E. coli profiles of the five alternative carbon were with the growing glucose, to genes abundance on the quality of the carbon we were an adapted than a and cultures were also diluted 1:107 into the medium that generations were needed to the same profiles from these cultures were to the samples are the of the The analysis two the number of up-regulated genes in each alternative carbon source the number of and this is with growth rate the up-regulated genes are in a hierarchical manner with to growth rate the genes at higher growth are a of those in growing This suggests a system is across carbon sources. and cultures have similar growth and 40 up-regulated The set of up-regulated genes in is and includes all 40 genes in the The two with the growth and have similar profiles their different and both profiles of the genes in the data set including the 40 the we the up-regulated genes as three set 1 set 2 set 3 set 1 the that is up-regulated in all five alternative carbon set 2 those up-regulated in acetate, and only and set 3 includes genes that are only up-regulated in the two carbon sources, and genes are in across the generally increase with growth rate so the between the three is to genes different induction not because they are on in Genes for genes for specifically the carbon compound are they only a of the total genes in each a number of and catabolic genes for carbon not present in the medium are progressively induced in each set is a but not activation of transporters for growth with or better than the source While activation of pathways for unavailable substrates has been in carbon source expression C. 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This to in each using A of with carbon quality was from cells in glucose to highly cells in acetate, in at This with the of suggests that cells search for better conditions as the quality of the The of suggests this search may be an of as of the and genes in set 2 carbon utilization and a of the The is up-regulated as is a alternative factor of both proteins in acetate, and of these two factors is for a of systems for cellular adaptation to in R. J. Mol. Microbiol. Biotechnol. 2002; Google Microbiol. PubMed Scopus Google R. Microbiol. Mol. Biol. 2002; of PubMed Scopus Google Scholar). 3 the number and abundance of of the up-regulated genes in the three is of of by in is known to positively control most carbon catabolic pathways when the carbon source from glucose. how control may be the up-regulated we a from CRP-binding (9Salgado H. Gama-Castro S. Martinez-Antonio A. Diaz-Peredo E. F. M. A. C. J. Nucleic Acids Res. PubMed Google M. M. J. A. C. Gama-Castro S. 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Microbiol. 2003; PubMed Scopus Google Scholar). grown cells were and more than those grown in A and The distribution of RNAP in glucose grown cells was with that in as by the increased number of in the and In of the relative cellular by the suggests the may be more in cells and and and the degree of of the RNAP distribution in from different we quantified the intensity in the signal individual using analysis R. I. 3: Scopus Google Scholar) as described by and Mol. Microbiol. 2003; PubMed Scopus Google Scholar). This analysis has been used to in cells A. Jacob E. Anal. Google S. 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