Short RNA half-lives in the slow-growing marine cyanobacterium Prochlorococcus
© Steglich et al.; licensee BioMed Central Ltd. 2010
Received: 26 March 2010
Accepted: 19 May 2010
Published: 19 May 2010
RNA turnover plays an important role in the gene regulation of microorganisms and influences their speed of acclimation to environmental changes. We investigated whole-genome RNA stability of Prochlorococcus, a relatively slow-growing marine cyanobacterium doubling approximately once a day, which is extremely abundant in the oceans.
Using a combination of microarrays, quantitative RT-PCR and a new fitting method for determining RNA decay rates, we found a median half-life of 2.4 minutes and a median decay rate of 2.6 minutes for expressed genes - twofold faster than that reported for any organism. The shortest transcript half-life (33 seconds) was for a gene of unknown function, while some of the longest (approximately 18 minutes) were for genes with high transcript levels. Genes organized in operons displayed intriguing mRNA decay patterns, such as increased stability, and delayed onset of decay with greater distance from the transcriptional start site. The same phenomenon was observed on a single probe resolution for genes greater than 2 kb.
We hypothesize that the fast turnover relative to the slow generation time in Prochlorococcus may enable a swift response to environmental changes through rapid recycling of nucleotides, which could be advantageous in nutrient poor oceans. Our growing understanding of RNA half-lives will help us interpret the growing bank of metatranscriptomic studies of wild populations of Prochlorococcus. The surprisingly complex decay patterns of large transcripts reported here, and the method developed to describe them, will open new avenues for the investigation and understanding of RNA decay for all organisms.
The rate of degradation of RNA is an important factor in the regulation of gene expression. It is well known that stress conditions, such as the presence of antibiotics, nutritional stress, and transitions in growth phase, cause a dramatic change in the rate of mRNA turnover for a subset of genes within a particular organism [1–3]. The stability of RNA encoded by certain genes can also be greatly affected by the growth rate of the cell [3, 4]. However, a genome-wide analysis showed that the half-lives of the vast majority of Escherichia coli transcripts do not differ with growth rate , suggesting an inherent median global half-life for a certain organism.
Whole genome half-life analyses comparing very different organisms, such as fast-growing bacteria and slower-growing eukaryotes, however, initially suggested that global RNA decay rates correlate with the intrinsic growth rate of the organism: ranging from minutes to hours in bacteria [5–7] and hours to days for eukaryotes [8–10]. The investigation of global RNA half-lives of archaea, which have intermediate growth rates, led to conflicting conclusions, with one study showing global half-lives similar to bacteria  and another showing considerably longer half-lives . To help resolve this issue we examined the global RNA half-live in the slow growing marine cyanobacterium Prochlorococcus MED4.
Prochlorococcus is an abundant component of the phytoplankton in the vast oligotrophic tropical and subtropical open oceans where it contributes a significant fraction of photosynthesis [13, 14]. Despite the high abundance of Prochlorococcus in these waters, it grows very slowly with growth rates of usually one division per day  and, at most, two divisions per day . Complete genome sequences of 12 cultured isolates of Prochlorococcus are now available [17–21] and reveal that genome reduction has left a minimal inventory of protein coding regulatory genes, but the regulatory capacity of Prochlorococcus has been complemented with numerous small non-coding RNAs (ncRNAs) [22, 23].
Changes in global gene expression profiles in the model Prochlorococcus strain MED4 have been studied under different light conditions , nitrogen and phosphorus depletion [25, 26] and during bacteriophage infection . In addition, metatranscriptomic data are currently being collected to characterize the physiological status of natural oceanic communities of which Prochlorococcus is often the dominant photosynthetic organism [28–31]. However, little is known about RNA stability in Prochlorococcus. This is of central importance if we are to understand the role RNA turnover plays in controlling gene expression.
Results and discussion
Determination of RNA half-lives and decay rates
We examined the half-lives of known and predicted mRNAs and non-coding RNAs in Prochlorococcus MED4 at single-gene resolution using high density Affymetrix microarrays . Rifampicin, which prevents initiation of new transcripts by binding to the β subunit of RNA polymerase , was added to triplicate cultures. Samples were harvested at 0 minutes (before rifampicin addition), and 2.5, 5, 10, 20, 40 and 60 minutes after rifampicin addition. As shown previously in a similar microarray experiment for E. coli , the decay of RNA does not always follow an exponential curve, which deems it necessary to adjust and improve existing methods for the calculation and description of RNA decay. Thus, we applied two different approaches: the so-called 'twofold' decay step method as proposed previously by Selinger et al.  in order to determine the RNA half-life; and a new method developed here based on fitting the decay profile to two distinct phases to derive the decay rate (see Materials and methods). The latter method was more accurate to describe decay patterns of genes that displayed two distinct decay phases: either a fast decay followed by a slow decay; or an apparent initial period of constant expression or even increase in expression prior to the decay. Notably, large differences between the two methods were observed only for genes with a delayed onset of degradation or for genes with very stable half-lives (Additional file 1). For the determination of global half-lives and decay rates we excluded genes with low expression signals below a set threshold, resulting in data for 1,102 genes (including protein-, ribosomal-, tRNA, ncRNA and antisense RNA (asRNA) coding genes).
Genome-wide RNA decay
Comparison of decay rates and half-lives of 17 selected genes determined from microarray data and qRT-PCR
Expression at time 0 [log2]
Decay rate [min]
Decay rate [min]
High rates of RNA turnover are likely to facilitate the rapid adaptation of Prochlorococus to environmental change in the oceans and may help compensate for its minimal regulatory capacity. This is even more pronounced in relation to their slow growth as the rapid metabolic response achieved relative to growth rate would be considerably greater than for fast growing organisms. Furthermore, the fast recycling of nucleotides through rapid RNA turnover may help save resources and compensate for the scarcity of nutrients like phosphorus and nitrogen in the nutrient poor oligotrophic waters in which Prochlorococcus is so abundant.
Correlation of RNA stability and gene product function
Decay rates of expressed ncRNAs and asRNAs
Decay rate [min]
rnpB (RNase P sRNA)
ffs (SRP RNA)
ssrS (6S RNA;Yfr7)
Operon decay profiles
Thus, we have observed several intriguing genome-wide RNA decay patterns for genes organized in operons. These include: increased stability once decay begins, delayed onset of decay and increased transcript levels after rifampicin addition, as a function of distance from the transcription start site. Although these patterns were not apparent in a similar study of the Sulfolobus archaea , they are not restricted to Prochlorococcus. As mentioned above, Selinger et al.  reported increased stability with distance from the transcription start site for many operons. They also found an increase in transcript levels after rifampicin addition for a single operon in E. coli - that of the tdc operon. Furthermore, several studies have documented segmental differences in RNA half-lives along the atp operon in E. coli with very unstable transcripts for the first two genes (atp1 and atpB), and longer half-lives for the more distal ones [44–46]. Lastly, Ziemke et al.  measured translation rates of the ATPase subunits after rifampicin treatment by pulse chase experiments and observed an initial induction in signal intensity, which became more pronounced with increasing distance from the promoter. Despite the differences in methodology between the E. coli and the Prochlorococcus studies, these combined findings suggest that the correlation between decay patterns and position from the transcription start site may be a general phenomenon for genes organized in operons, at least for the eubacteria.
Rate of RNA polymerase transcription
The fast RNA turnover we found for Prochlorococcus made us wonder whether both RNA transcription and RNA degradation are more rapid in this organism relative to other bacteria. The time taken to achieve peak expression between different probes within a single gene can be used to estimate the transcription rate of RNA polymerase. The average polymerase rate of elongation was estimated to be 7.7 (standard error ± 1.1) and 10.3 (standard error ± 3.0) nucleotides per second based on half-lives and decay rates, respectively, with the median in vivo velocity of the polymerase estimated to be 4.8 and 4.5 nucleotides per second for the two methods, respectively. The average rate of transcription in Prochlorococcus MED4 is remarkably slower than that reported for E. coli of 65 to more than 400 nucleotides per second and an average rate of 91 nucleotides per second . However, elongation rates reported by Dennis et al.  are derived from ribosomal RNA operons, which show a general greater average rate than that of mRNA transcripts . The slow rate of transcription in Prochlorococcus MED4 might be in close correlation with the difference in growth rate of the organisms, differences between the composition of the RNA polymerase complex found in cyanobacteria and other eubacteria , or differences in methodology used to estimate these rates. However, slow elongation rates might - together with the fact that a high density microarray was used in this study - explain why type I and II operon profiles could be observed.
Collectively, while Prochlorococcus has a more rapid RNA turnover, it has remarkably slower rates of RNA transcription relative to other bacteria.
The global mRNA half-life of 2.4 minutes reported here for Prochlorococcus is the shortest measured for any organism, and is the first reported for a cyanobacterium. Prochlorococcus grows photoautotrophically and energy is often found in surplus relative to nutrients such as nitrogen and phosphorus, which are vanishingly scarce in the oligotrophic oceans. A rapid RNA turn-over strategy might be advantageous for the recycling of nucleotides to synthesize novel mRNAs, allowing a very rapid response to changing environmental conditions by adjusting transcript amounts on a short time scale - especially in light of the slow growth rate of this organism. Furthermore, we have detected unusual kinetics of RNA degradation for large transcripts and operons in Prochlorococcus, which are likely to exist in other bacteria. The complex patterns of large transcript decay reported here indicate that longer half-lives with distance from the promoter are due to a combination of both a delayed onset of decline and a slower decay rate once degradation begins. This would enable more extensive translation of this portion of an operon and may counter, in part, lower transcript levels that often result from reduced transcription of genes positioned far from the promoter.
Materials and methods
Culture and experimental growth conditions
Prochlorococcus MED4 was grown at 21°C in Sargasso seawater-based Pro99 medium  under 30 μmol quanta m-2 s-1 continuous cool white light with a growth rate of 0.325 day-1. Triplicate cultures were divided into seven 30 ml subcultures each and 1.9 ml rifampicin added to a final concentration of 150 μg/ml. Rifampicin was dissolved at a concentration of 2.5 mg/ml in Pro99 medium (the limit of its solubility in aqueous solution) to avoid potential negative impacts of organic solvents on Prochlorococcus growth. For sampling time point 0 minutes only 1.9 ml Pro99 medium was added. Cells were harvested after 0, 2.5, 5, 10, 20, 40 and 60 minutes of rifampicin treatment by rapid filtration onto Supor-450 membranes. Filters were immersed in 2 ml RNA resuspension buffer (10 mM sodium acetate pH 5.2, 200 mM sucrose, 5 mM EDTA), snap frozen in liquid nitrogen and subsequently stored at -80°C. The filtration was started 45 s before the respective sampling points to account for the time needed for filtration and storage of filters in liquid nitrogen.
We recently found that DMSO does not negatively affect Prochlorococcus growth and carried out a limited comparison of expression profiles for cells treated with rifampicin dissolved in water and DMSO. Expression profiles and half-life measures were similar irrespective of the solution used to dissolve the rifampicin (Additional file 7).
Total RNA was extracted from cells on filters using a hot-phenol method described previously [24, 50]. Total nucleic acids (12 μg) were treated with 6 U DNase (DNA-free, Ambion, Austin, TX, USA) for 60 minutes at 37°C. RNA was precipitated with 1/10 volume 3 M sodium acetate (pH 5.2), 3 volumes ethanol and resuspended in H2O at a concentration of approximately 1 μg/μl RNA.
RNA (300 ng) were DNAse-treated and reverse-transcribed using QuantiTect reverse transcriptase (Qiagen, Hilden, Germany). Samples were DNAse-treated for 2 minutes at 42°C using 2 μl 7 × gDNA wipeout buffer followed by the reverse transcription in a final volume of 20 μl (containing 1 × Quantiscript RT buffer, Mg2+, dNTPs, RT primer mix and RNAse inhibitor). Reactions were incubated at 42°C for 15 minutes. The enzyme was inactivated at 95°C for 3 minutes.
qPCR was performed in an Applied Biosystems 7500 Fast Real-Time PCR system using the ABI Power SYBR Green PCR reagents (Foster City, CA, USA). Each 15 μl reaction contained SYBR® Green 1 Dye, AmpliTaq Gold® DNA Polymerase LD, dNTPs with dUTP/dTTP blend, ROX reference, optimized buffer components and 4.5 μl of the reverse transcription reaction in varying dilutions and different primer concentrations (Additional file 8). The reactions were incubated for 2 minutes at 50°C and then 10 minutes at 95°C followed by 40 cycles of 15 s at 95°C, 30 s at 59°C and 30 s at 72°C. After the last cycle, the PCR products were subjected to heat denaturation over a temperature gradient from 60°C to 95°C at 0.03°C s-1. All reactions were performed in triplicates for three biological replicates (that is, nine RT-PCR in total). All samples were tested for the presence of residual DNA during quantitative real-time PCR with an RT-minus control.
The real-time PCR data were analyzed using 7500 Fast Real-Time PCR system sequence detection software version 1.4. Data were plotted asnormalized reporter signal, representing the level of fluorescence detected during the PCR process after subtraction of background noise versus cycle number. A threshold was set manually in the middle of the linear phase of the amplification curve. The Ct value (threshold cycle) is defined as the cycle in which an increase in reporter signal (fluorescence) crosses the threshold. The average of Ct values of the triplicate PCR reactions is labeled dCt. The change in geneX cDNA relative to the endogenous standard (RNase P sRNA, rnpB) was determined by 2- [dCt(geneX)-dCt(rnpB)], summarized as 2-ddCt.
cDNA synthesis, labeling and microarray hybridization
Labeling, hybridization, staining and scanning were carried out according to Affymetrix protocols for E. coli  and  using 2.5 μg of total RNA on an Affymetrix high density array MD4-9313 made for Prochlorococcus MED4. The custom array covers all gene coding regions with a probe pair (match and mismatch) every 80 bases and every 45 bases in intergenic regions in both sense and antisense orientations. Microarray data have been deposited in NCBI's Gene Expression Omnibus (GEO) under accession number GSE17075 .
Most normalization methods of microarray data assume that the expression levels of only a subset of genes differ between single arrays. Since our experiment clearly violates this assumption, we performed a systematic comparison of different schemes to select an optimal one. As quality criterion, the Spearman correlation with quantitative RT-PCR data for the 17 genes was used (Additional file 2). In particular, we compared microarray data derived by either Microarray Array Suite (MAS) or robust multi-array analysis methods (as implemented in the Bioconductor package affy). Additionally, different normalization approaches were performed: scaling to the same medium intensity of all genes; scaling to the same medium intensity of spike controls; scaling to the same medium intensity of RNA genes (assumed to be particularly stable); and no subsequent scaling. Remarkably, the robust multi-array analysis processed microarray data with no subsequent scaling achieved the highest concordance with the quantitative PCR standard (that is, a mean Spearman correlation coefficient of 0.83). This shows that the single microarray measurements were highly consistent, and that subsequent scaling introduced experimental variability rather than reducing it.
RNA half-life and polymerase transcription rate calculations
This model is based on the fit of two successive exponential decays to the time series. Thus, we fitted the first decay exponential to the expression values from t = 0 minutes to t = x minutes and the second exponential decay to the expression values from t = x minutes to t = 60 minutes. To choose the time point x (dividing the time series into the two phases), we repeatedly performed the fitting for all possible time points for x and chose the fit with minimal mean square error of the logged data. In cases where the time point of maximal expression was not t = 0 minutes, we used the last time point with maximal expression as the initial time point for the first exponential decay. Thus, the decay rates were calculated relative to the time point of maximal expression. This allows distinguishing effectively between half-life time and decay rate in the calculations.
The rate of RNA polymerase transcription was assessed for expressed genes with a length of at least 2 kb by first calculating the distance between every probe of a probe set and the first probe of this set. The calculated half-life time of every probe of a set was then subtracted from the first probe of the set. The distance(s) and the difference of the half-life between the probes (t) were used to calculate the rate of transcription (v) as a function of v = s/t. Polymerase transcription rates for all of the single probes were averaged and the mean as well as the median were calculated.
Soft clustering was applied to distinguish different expression profiles as implemented in the Bionconductor Mfuzz package and described previously . In brief, the cluster parameter m was set to 2. The number of clusters was chosen to maximize the functional enrichment of gene clusters.
We thank Katharina Kienzler for performing quantitative real-time PCR analyses. The research was supported by the DFG (SPP 1258) and GIF (young investigator grant 2167-1743.9/2007) to CS, by a DOE - GTL grant, an NSF grant and a Gordon and Betty Moore Foundation Investigatorship to SWC, an ISF Morasha grant (1504/06) to DL and a FCT grant (IBB/CBME, LA, FEDER/POCI 2010) to MF. DL is a Shillman Fellow.
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