The dynamic architecture of the metabolic switch in Streptomyces coelicolor
- Kay Nieselt†1Email author,
- Florian Battke†1,
- Alexander Herbig†1,
- Per Bruheim2, 3,
- Alexander Wentzel2, 3,
- Øyvind M Jakobsen3,
- Håvard Sletta3,
- Mohammad T Alam4,
- Maria E Merlo4, 5,
- Jonathan Moore11,
- Walid AM Omara10,
- Edward R Morrissey11,
- Miguel A Juarez-Hermosillo11,
- Antonio Rodríguez-García7, 13,
- Merle Nentwich8,
- Louise Thomas9,
- Mudassar Iqbal11,
- Roxane Legaie11,
- William H Gaze10,
- Gregory L Challis6,
- Ritsert C Jansen4,
- Lubbert Dijkhuizen5,
- David A Rand11,
- David L Wild11,
- Michael Bonin12,
- Jens Reuther8,
- Wolfgang Wohlleben8,
- Margaret CM Smith9,
- Nigel J Burroughs11,
- Juan F Martín7, 13,
- David A Hodgson10,
- Eriko Takano5,
- Rainer Breitling4,
- Trond E Ellingsen3 and
- Elizabeth MH Wellington10Email author
© Nieselt et al; licensee BioMed Central Ltd. 2010
Received: 28 May 2009
Accepted: 6 January 2010
Published: 6 January 2010
During the lifetime of a fermenter culture, the soil bacterium S. coelicolor undergoes a major metabolic switch from exponential growth to antibiotic production. We have studied gene expression patterns during this switch, using a specifically designed Affymetrix genechip and a high-resolution time-series of fermenter-grown samples.
Surprisingly, we find that the metabolic switch actually consists of multiple finely orchestrated switching events. Strongly coherent clusters of genes show drastic changes in gene expression already many hours before the classically defined transition phase where the switch from primary to secondary metabolism was expected. The main switch in gene expression takes only 2 hours, and changes in antibiotic biosynthesis genes are delayed relative to the metabolic rearrangements. Furthermore, global variation in morphogenesis genes indicates an involvement of cell differentiation pathways in the decision phase leading up to the commitment to antibiotic biosynthesis.
Our study provides the first detailed insights into the complex sequence of early regulatory events during and preceding the major metabolic switch in S. coelicolor, which will form the starting point for future attempts at engineering antibiotic production in a biotechnological setting.
The switch from primary metabolism (exponential growth) to secondary metabolism (stationary growth) upon nutrient starvation is commonly found in most microorganisms . The phenomenon has been known for a long time, but new details of function and regulation of the "metabolic switch" continue to emerge as we begin to apply postgenomic technology to the analysis. Understanding the switch to secondary metabolism is of major importance in biotechnology, where it can contribute to the optimized production of commercially relevant secondary metabolites, such as antibiotics.
Here we have used the soil bacterium Streptomyces coelicolor, the model organism of the antibiotics producing genus Streptomyces, to dissect its metabolic switch in unprecedented detail. Reproducible growth of the filamentous Streptomyces species has been a challenge, and especially producing the same quantity of antibiotics in each fermentation has been a major hurdle for conducting systems biology experiments in these species. Some short gene expression time series have been reported [2–5], the largest being a study by Lian et al. , with 13 time points at 1- to 3-hour intervals from 15 hours to 42 hours comparing the S. coelicolor M145 wild type to a pleiotropic regulator mutant, but these experiments were conducted in shaker flasks, where cultivation conditions, such as pH and dissolved oxygen, were not held constant, resulting in irreproducible fluctuations in growth from run to run.
Unsupervised clustering analysis revealed a strong structure in the data: on the one hand, when clustering the samples, adjacent time points cluster closely together (Figure 1B), and the strongest change in gene expression occurs between time points 35 and 36 hours; here the two samples are clearly separated on the clustering tree despite being just 1 hour apart along the growth curve. This interval coincides with the moment when phosphate in the medium is depleted and corresponds to the classical metabolic switch. On the other hand, when clustering the gene expression profiles, several major groups of genes emerge as showing highly correlated gene expression patterns. A more detailed analysis of this correlation structure identified a very fine-grained dynamic pattern of gene expression. In addition to two large groups of genes that showed consistent gradual increase or decrease of expression levels, respectively, we observed several clusters that showed a more complex, transient upregulation. Detailed analysis of each of these clusters revealed that they consist of biologically coherent groups of genes, often dominated by a few large operons, and reveal an unexpectedly complex series of switching events well before and after the classical metabolic switch.
The expression measurements were validated by qRT-PCR using six genes that showed highly characteristic and very different profiles. The resulting time course profiles are highly correlated to those obtained by the microarray experiments (Pearson correlation between 0.74 and 0.93; see Additional File 1). To verify reproducibility, the fermentation was repeated for a total of four biological replicates and microarray data collected for 8 time points in each of the fermentations. An evaluation of the variance between the biological replicates revealed an average standard deviation across all replicates of less than 6% for biomass concentration and remaining phosphate levels in the growth medium, and less than 10% for the production levels of undecylprodigiosin and actinorhodin. Similar correlations were seen for groups of differentially expressed genes, with between-fermentor correlations for individual clusters of genes ranging from 0.48 to 0.94 (average 0.72), indicating excellent synchronicity between replicates.
In addition to the genes discussed here, most of which have been studied in detail before, there is a large number of unannotated genes showing the same strong expression switches. For all genes, the complete expression profiles have been deposited in GEO for further exploration (http://www.ncbi.nlm.nih.gov/geo/: GSE18489, GPL9417, GSM460281-460312).
It is evident from our data that gene expression during the metabolic switch is far more dynamic than initially expected. Our densely sampled time-series allows identification of rapid complex expression changes of biological significance, as well the prediction of central regulatory relationships.
Bacterial strain and cultivation conditions
Experiments were performed using S. coelicolor A3(2) strain M145 . Cultivations were performed in 3-liter fermentors (Applikon) with an initial culture volume of 1.8 liter. The growth medium used was based on ion-free water and consisted of Na-glutamate, 55.2 g/l; glucose, 40 g/l; MgSO4, 2.0 mM; phosphate, 4.6 mM; supplemented minimal medium trace element solution , 8 ml/l and TMS1, 5.6 ml/l. TMS1 consisted of FeSO4 × 7 H2O, 5 g/l; CuSO4 × 5 H2O, 390 mg/l; ZnSO4 × 7 H2O, 440 mg/l; MnSO4 × H2O, 150 mg/l; Na2MoO4 × 2 H2O, 10 mg/l; CoCl2 × 6 H2O, 20 mg/l, and HCl, 50 ml/l. Furthermore, the pH was adjusted to 7.0 by addition of 2 M NaOH and 1.8 ml Clerol FBA 622 fermentation defoamer (Diamond Shamrock Scandinavia) were added to the growth medium before inoculation, while additional 0.5 ml were added after 58 hours. For the inoculum, 109 CFU of S. coelicolor spores (typically 1 ml of a thawed spore-stock in 20% (v/v) glycerol) were germinated for 5 hours at 30°C and 250 rpm in 250 ml baffled shake-flasks with 2 grams of 3 mm glass beads containing 50 ml 2× YT medium . The germinated spores were harvested by centrifugation (3200 × g, 15°C, 5 min) and re-suspended in 5 ml ion-free water. Each fermentor (1.8 liter growth medium) was inoculated with 4.5 ml germinated spores suspension. Throughout the fermentation trial, pH 7.0 was maintained by automatic addition of 2 M HCl (typically 150 ml per fermentor). Dissolved oxygen was maintained at a minimum of 50% by automatic adjustment of the agitation speed and a constant aeration rate of 0.9 l/min air. The agitation speed range was from approximately 300 rpm (set minimum) to 1050 rpm. Dissolved oxygen, agitation speed and CO2 evolution was measured and logged online, while samples for the determination of cell dry weight, levels of growth medium components and secondary metabolites were taken throughout the fermentation trial. Samples for transcriptome analysis were taken hourly from 20 to 44 hours, and every two hours from 44 to 60 hours: 3 × 4 ml culture sample were applied in parallel onto three 0.45 μm nitrocellulose filters (Millipore) connected to vacuum. The biomass on each filter was immediately washed twice with 4 ml double-autoclaved ion-free water pre-heated to 30°C, before the filters were collected in a 50 ml plastic tube, frozen in liquid nitrogen and stored at -80°C until RNA isolation. The net culture volume reduction over the time-course of the fermentation was typically 930 ml.
Analyses of nutrient levels and secondary metabolites
Levels of phosphate, glucose and glutamate were measured spectrophotometrically by using the Spectroquant Phosphate test kit, the Lactose/D-glucose test kit (both R-Biopharm), and the L-glutamate Bioassay kit (USBiological), respectively, following the manufacturer's instructions. Extracellular actinorhodin (γ-actinorhodin) levels were determined spectrophotometrically at 608 nm after adjusting the supernatants to pH 12 with NaOH; undecylprodigiosin levels were determined spectrophotometrically at 530 nm after acidified methanol extraction from the mycelium . Total blue pigments levels were determined by extraction of whole culture samples with 1 M KOH (final concentration) and spectrophotometric measurement at 640 nm .
RNA isolation and transcriptome analysis
RNA was isolated from each sample using phenol extraction and the RNeasy Midi Kit (Qiagen). Affymetrix genechip arrays were designed by Affymetrix with 13 25 bp-oligos targeting each coding sequence. The array contained a total of 226,576 perfect match probes (oligos of length 25 bp), including 8205 probe sets targetting coding sequences, 10834 intergenic probe sets (sense and antisense), and 3671 probe sets targetting predicted non-coding RNAs. Biotinylated cDNA was prepared after fragmentation according to the standard Affymetrix protocol from 3 μg total RNA. 3 μg of cDNA were hybridized for 16 hr at 50°C and arrays were scanned using the Affymetrix GeneChip Scanner 3000 7G. Gene expression values were normalized using RMA  and clustered based on pair-wise Pearson correlation. Details on the array layout and R scripts  used for the analysis are available from the authors. All expression data have been deposited in GEO (accession numbers GSE18489, GPL9417, GSM460281-460312). In-depth analysis was performed within the visual analysis framework Mayday .
qRT-PCR validation of expression
qRT-PCR was performed in 384-well format with the LightCycler 480 System (Roche) and the QuantiTect SYBR Green PCR Kit (Qiagen). The principal sigma-like transcriptional factor of S. coelicolor (hrdB) was used as reference (housekeeping) gene.
We are very grateful to Mervyn Bibb for his generous support with the Affymetrix custom microarray design. We acknowledge the excellent technical help of K. Klein, S. Poths, M. Walter, A. Øverby and E. Hansen. This project was supported by grants of the ERA-NET SySMO Project [GEN2006-27745-E/SYS]: (P-UK-01-11-3i) and the Research Council of Norway [project no. 181840/I30].
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