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Nucleic Acids Res. Nov 1, 2003; 31(21): 6306–6320.
PMCID: PMC275481

Transcription profiles of the bacterium Mycoplasma pneumoniae grown at different temperatures


Applying microarray technology, we have investigated the transcriptome of the small bacterium Mycoplasma pneumoniae grown at three different temperature conditions: 32, 37 and 32°C followed by a heat shock for 15 min at 43°C, before isolating the RNA. From 688 proposed open-reading frames, 676 were investigated and 564 were found to be expressed (P < 0.001; 606 with P < 0.01) and at least 33 (P < 0.001; 77 at P < 0.01) regulated. By quantitative real-time PCR of selected mRNA species, the expression data could be linked to absolute molecule numbers. We found M.pneumoniae to be regulated at the transcriptional level. Forty-seven genes were found to be significantly up-regulated after heat shock (P < 0.01). Among those were the conserved heat shock genes like dnaK, lonA and clpB, but also several genes coding for ribosomal proteins and 10 genes of unassigned functions. In addition, 30 genes were found to be down-regulated under the applied heat shock conditions. Further more, we have compared different methods of cDNA synthesis (random hexamer versus gene-specific primers, different RNA concentrations) and various normalization strategies of the raw microarray data.


Until now, 96 bacterial and 16 archaeal genomes have been completely sequenced (http://www.ncbi.nlm.nih.gov/PubMed/). The annotations of these sequences provide a fairly good description of the functional capacities of these organisms, but gaining a thorough understanding of the biology of these organisms requires more than the mere knowledge of gene functions. For instance, to comprehend the process of adaptation of an organism to changing environmental conditions demands a knowledge about the regulation of genes, operons and regulons, and about the coordination and interaction of numerous gene products. A prominent example of such a regulation is the bacterial response to high temperature, known as heat shock. Genes, which are up-regulated during heat shock are also known to be frequently involved in the bacterial response to other environmental stress conditions like UV exposure, osmotic shock or starvation conditions (1).

Only limited information on gene expression and regulation can be extracted directly from genome sequences. Nevertheless, such sequences can be exploited for working out new strategies and methods to monitor simultaneously and quantitatively the expression of the gene pool of an organism at the transcriptional (transcriptome) (2) and translational level (proteome) (3).

The most popular method for the detection and quantification of all mRNA species from an organism is the array-based technology. Derived from a known genome sequence, gene-specific probes can be synthesized, immobilized on a supportive surface and used in hybridization experiments either with labeled total RNA or with cDNA. The acquired signal intensities give a rough estimate of gene expression on the transcriptional level (410). However, this technology is prone to several errors and requires a careful examination of the collected raw data (1113). Moreover, it is yet unclear which statistical techniques are most suitable for the normalization and evaluation of microarray data.

The equivalent to a transcriptome analysis on the translational level is the proteome analysis. Aiming directly at the proteins, the proteome enables identification and quantification of single proteins, and provides an insight into the regulation of translation. At present, a frequently used method for protein analysis is two-dimensional (2D) gel electrophoresis with immobilized pH gradients (14), which allows the separation of complex protein mixtures into individual components, which can be characterized by mass spectrometry (1517). Since the separation of proteins is the bottleneck of this method, alternative approaches are explored to supplement 2D gel electrophoresis, e.g. multi-dimensional liquid chromatography (18). The main advantage of the mRNA analysis over the protein analysis is the direct and fast identification of all transcripts. Also, with specific probes, one can target and quantify individual RNA species within a crude mixture of RNAs, which is convenient when RNA samples are contaminated with RNAs from different organisms. Such a precise analysis is not yet feasible in the proteome analysis, which is limited by the power of resolution of the 2D gel electrophoresis, the sensitivity of mass spectral analyses and the lack of protein-specific probes which could be applied to a large number of proteins simultaneously.

Regulation of gene expression is known to take place at the level of transcription and translation. Therefore, to understand the regulation of gene expression of an organism, it is necessary to study both processes. However, the complexity of such an analysis grows considerably with the number of expressed genes. For this reason, we have chosen the simple bacterium Mycoplasma pneumoniae as a model organism. The genome of M.pneumoniae has been completely sequenced (19). It has a small genome size of only 816 kb and was originally proposed to code for 677 open-reading frames (ORFs). In the course of the recent re-annotation, the number of ORFs increased from 677 to 688, one ORF being dismissed and 12 added (20).

Mycoplasma pneumoniae is a human pathogen causing an atypical pneumonia (21,22). It is considered to be a parasite of the respiratory tract, colonizing the surface of the epithelial cells. Recently, several reports were published indicating that M.pneumoniae may also enter the host cells, although growth of these bacteria inside the cell has not been well documented (23). Mycoplasma pneumoniae is host dependent in nature, but can be grown in the laboratory without the presence of host cells in rich medium including horse or pig serum.

There is a lot yet to be learned about gene expression regulation in M.pneumoniae. In both annotations of the genome, only one sigma factor could be identified (19,20). Bornberg-Bauer and Weiner (24) found a weak homology in a further protein (MPN626) to sigma D factor of Bacillus subtilis, but its putative role remains unclear. Mycoplasma pneumoniae possesses only a limited number of transcriptional regulation elements common for the bacterial world. Therefore, one of the primary questions asked was whether transcriptional regulation exists at all in this organism. The answer obtained with the applied microarray technology was clear and revealed the identification of numerous genes which were up- and down-regulated during heat shock conditions. Thus, we laid the foundation for the identification of regulated genes at altered growth conditions and for further investigation of regulatory elements in M.pneumoniae.


Organism, growth conditions and RNA isolation

Mycoplasma pneumoniae M129 was grown at 37°C in cell culture flasks (150 cm2) containing 100 ml of modified Hayflick medium (25) supplemented with 20% horse serum. After 96 h, surface-attached cells were washed twice with phosphate-buffered saline (PBS; 0.15 M NaCl, 10 mM sodium phosphate, pH 7.4) and immediately lysed in the cultivation flask by adding RLT buffer from the RNeasy Midi RNA-extraction System (Qiagen). This RNA isolation method was used for RNA extraction as it removes most RNAs smaller than 200 bases, thus preventing the synthesis of cDNA from tRNAs. For cell lysis, 2 ml of RLT buffer in the presence of 20 µl of β-mercaptoethanol was used per cultivation flask. The lysate was sonicated twice for 5 s to shear genomic DNA and diluted with an equal volume of RLT buffer. The diluted lysate was mixed with an equal volume of 70% ethanol and was immediately loaded onto an RNeasy Midi column (Qiagen). The column was washed twice with 3 ml of RW-1 wash buffer and twice with 3 ml of RPE wash buffer. RNA was eluted from the column with 1200 µl of the elution buffer supplied with the kit. All centrifugation steps were carried out at 4000 r.p.m. (Hettich Rotana/R centrifuge) at room temperature. The eluate was ethanol precipitated and the RNA was resuspended in 150 µl of H2O/DEPC. The RNA solution was mixed with 5 µl of 10× DNAse buffer (1 M NaAc, 50 mM MgSO4) and 30 U of RNAse-free DNAse I (Roche) and incubated for 30 min at 25°C, followed by a phenol/chloroform extraction and an ethanol precipitation. Finally, the RNA was then resuspended in 50 µl aliquots and stored at –70°C. For quantitative PCR, the total RNA was isolated using the guanidinium isothiocyanate/phenol extraction method. The reagents were obtained from Roth (Roti-Quick® kit, catalog no. A979.1) and preparation was done following the manufacturer’s instructions. For a single 100 ml cell-culture flask of M.pneumoniae, 2 ml of solution A, 2.6 ml of solution B and 2 ml of solution C were used. The RNA was intensively washed with 70% ethanol, dried, suspended in DEPC-treated water and stored at –70°C.

Heat shock

Mycoplasma pneumoniae was grown at 32°C in the same medium as described above. After 144 h, 50 ml of the 100 ml medium was removed and either heated to 45°C or left at 32°C. The heated medium was mixed with the remaining 50 ml of medium in the culture flask and placed in a 43°C water bath for 15 min. Adherent cells were washed twice with 1× PBS (32°C for cells grown at 32°C and 37°C for heat shock-treated cells or cells grown at 37°C) and RNA was extracted as described above.

Primer database and synthesis of ORF-specific PCR products

A database was designed which contains approximately 7000 M.pneumoniae-specific primers. All primers, which were used for the synthesis of ORF-specific PCR products as well as for cDNA synthesis, were between 17 and 22 nt in length. The length of the PCR products varied from 250 to 450 bp; exceptions were ORFs shorter than 250 bp. The PCR products started >45 bp downstream of the start codon of the postulated ORF unless either repetitive DNA sequences were located within this region or the primers had more than one binding site within a region. The primer database can be found at http://mol2.zmbh.uni-heidelberg.de/mycodb/.

PCR products were evaluated by visual inspection of ethidium bromide stained DNA after gel electrophoresis in a 2% agarose gel. A successfully synthesized PCR product had to fulfill the following criteria: (i) the PCR should produce only one DNA fragment, (ii) the fragment had to be of the expected size and (iii) the concentration of the PCR products should be similar.

Membrane preparation

The PCR products were diluted 1:5 with TE buffer (10 mM Tris–HCl, pH 8, 0.1 mM EDTA), which was stained with bromphenol blue and transferred into 384 well plates. Positively charged nylon membranes (Amersham) 7.3 × 11.5 cm were equilibrated for 5 min in a denaturing solution (0.5 M NaOH, 1.5 M NaCl) and then placed onto a precut 3MM Whatman paper soaked with the same solution.

The PCR products were transferred onto the membrane with a BioGrid robot (BioRobotics Ltd). Each PCR product was spotted in a diagonally positioned doublet for an internal control. Every spot was loaded 10 times with ~0.05 µl per loading. After the spotting, the membrane was briefly placed on a 3MM Whatman soaked with a neutralizing solution (0.5 M Tris–HCl, pH 7.2, 1.5 M NaCl, 1 mM EDTA) before it was air dried. DNA was immobilized by automatic UV crosslinking (Stratalinker; 120 mJ).

cDNA synthesis for microarray probes

For gene-specific cDNA synthesis used in microarray analysis, 10 µg of total RNA and 676 ORF-specific primers (0.5 pmol per primer) were annealed in a programmable thermocycler. The primers used for cDNA synthesis were the reverse primers of each ORF used in the PCR synthesis of ORF-specific probes.

The samples were kept at 75°C for 1 min and then cooled down over a period of 8 min to 43°C. Then, 20 µmol each of dCTP, dGTP and dTTP, 120 pmol of dATP, 100 µCi of [α-33P]dATP (Amersham-Pharmacia), 50 U of RNAse inhibitor, MMuLV RT buffer and 60 U of MMuLV reverse transcriptase was added. Reverse transcription took place in a final volume of 100 µl at 37°C for 1 h. RNA was hydrolyzed for 30 min at 68°C in the presence of 4.5 µl of 2 N NaOH, 1 µl of 10% SDS and 1 µl of 500 mM EDTA pH 8.0. The probe was neutralized with 3 µl of 2 N HCl and 10 µl of 1 M Tris–HCl pH 7.4. Labeled cDNA was separated from the unincorporated nucleotides by a small Sephadex G100 column. The incorporation of 33P-labeled dATP was measured in scintillation enhancer fluid (Quicksafe A, Zinsser Analytic).

Reverse transcriptase (RT)–PCR

Total RNA was isolated using the Qiagen RNeasy Mini Kit (see Organism, growth conditions and RNA isolation). For cDNA synthesis, 5 µg of RNA was mixed with a primer mix containing 688 ORF-specific primers (new primer mix generated for experiments which include the additional 12 genes) (1 pmol/primer) at a volume of 29 µl. This mix was denatured for 1 min at 75°C, followed by a gradual cooling from 75 to 42°C in 6 s steps. Twenty-one microliters of enzyme mix was added when the temperature reached 42°C. The enzyme mix contained 5 µl of DTT, 10 µl of SSII 5× buffer,1 µl of dNTP (10 mM), 1 µl of RNase inhibitor (40 U), 1 µl of (200 U) SuperScript™ II (Life Technologies), 3 µl of ddH2O. Reverse transcription was carried out at 42°C for 50 min.

PCR protocol. Each reaction was done in a final volume of 50 µl containing 0.1 µl of cDNA, 5 µl of Taq 10× buffer, 1 µl of dNTP (10 mM), 30 pmol/primer, 0.5 µl of Taq polymerase (2.5 U). The following cycles were applied to all PCRs: 2 min at 92°C; 24 cycles of: 20 s at 92°C; 30 s at 56°C; 45 s at 72°C. Five microliter PCR mixtures were loaded on a 1.5% agarose gel.

Hybridization procedure

Before the first hybridization, the membrane was washed twice with a boiling 0.1% SDS solution. Hybridization was carried out in 100 ml hybridization bottles (Mini Hybridization Oven; Appligene). Membranes were prehybridized twice for 2 h in 3.0 ml of hybridization buffer (5× SSC, 1% SDS) at 60°C. Hybridization was carried out overnight in 3–5 ml of buffer and complemented with 2–3 × 106 c.p.m. of labeled cDNA. After hybridization, the membrane was washed three times for 20 min at 60°C with the following buffers: 2× SSC, 0.1% SDS; 1× SSC, 0.1% SDS; and 0.1× SSC, 0.1% SDS, respectively. The membrane was placed under a PhosphorImager screen for 3–5 days and then scanned at 88u (high pixel resolution) on a PhosphorImager (Molecular Dynamics). Immediately after scanning, the membrane was stripped two times with a boiling solution of 0.1% SDS and rehybridized or dried for storage.

Image analysis, quantification and data analysis

For data acquisition, the imaging program VisualGrid was used (VisualGrid®, implemented by Markus Kietzmann with contributions from David Bancroft and Igor Ivanov. Copyrighted and licensed by GPC Biotech AG 1998–2000). The software measured both pixel density as well as background signals. A standard deviation of each duplicated signal was calculated. In case the standard deviation exceeded 30%, it was checked whether an increased signal was due to high radioactive background within the measured field. If such was the case, the higher signal was substituted by the low signal. An average of the duplicates was calculated and the quantile 20 of the background calculated by VisualGrid was subtracted. Furthermore, an average of 41 signals derived from unspotted fields was subtracted from the calculated signal.

Data normalization

Two normalization procedures were applied to the collected raw data. The results from the different normalization methods were compared with each other. In the first application, each data point from a hybridization experiment was divided by the sum of all signals from that membrane. In the second normalization procedure, the mean background was subtracted from the data points and the result was logarithmized. From each derived data point the mean signal from the membrane was subtracted and the result was divided by the standard error of the signals.

Normalization against genomic DNA

Genomic DNA of M.pneumoniae was highly purified by a CsCl density-centrifuged gradient and ethanol precipitation. The DNA was then quantified and mechanically further disrupted by pipeting and vortexing. A standard amount of 10 µg of DNA was used for a nick-translation (Roche) using 33P-labeled dATP and dCTP and the labeled DNA was further disrupted by sonication. A standard hybridization was done as described before. This experiment aimed to gain an estimate of the hybridization ability of the PCR product.

Quantitative PCR

Quantitative PCR (Q-PCR) allows a precise quantification of the amount of DNA present in a certain probe. We applied this method to determine the absolute copy number of mRNA molecules in Mycoplasma cells and correlate the numbers with the signal strength derived from microarrays.

For Q-PCR, the 5700 series PCR detection unit and SYBR green master mix (Applied Biosystems) were used. For each experimental set-up, a standard curve was prepared, consisting of five dilutions of the PCR products, ranging from ~1 × 106 to 100 copies of DNA. From each of the cDNA probes and the negative controls, 1 µl was amplified in a 50 µl reaction. The results were evaluated using the software provided by the manufacturer of the instrument (Applied Biosystems).

cDNA synthesis for Q-PCR

For Q-PCR, the following procedure has been applied: each reaction was carried out in 25 µl with 150 ng of total RNA isolation, 50 U of AMV reverse transcriptase (RT) (Roche), 1 µl of AMV RT buffer, 1.4 mM dNTP, 1 µl of random hexanucleotide primer set (Roche) and 50 U of RNAse inhibitor (Roche). Before adding the AMV RT, the mixture was heated for 10 min at 60°C, then cooled on ice. Next, AMV RT was added and the mixture was transferred to 42°C for 45 min. Enzymes were heat inactivated by heating for 10 min at 95°C, the reaction volume was set to 200 µl by adding DEPC-treated water, and the cDNA was stored at –20°C. For each of the probes, negative controls were prepared as described, with the omission of the AMV RT.

Determination of cell density

Two methods were applied for the determination of cell density (color changing test and Q-PCR). For the color changing test, serial 1:10 dilutions of M.pneumoniae cultures were done, beginning at the time of inoculation. Samples were taken in 24 h intervals and cell density was determined based on the last dilution in which color change of the medium, due to cell growth, could be observed (CCU).

Secondly, genome equivalents were determined by Q-PCR in three different PCRs. Mycoplasma pneumoniae cells taken from one culture were washed with 1× PBS and suspended in 1 ml of 1× PBS. The suspension was subsequently sonicated three times for 15 s and diluted 1:100. From this dilution, 1 µl was taken for Q-PCRs corresponding to genome positions located within the gene MPN434.

Mycoplasma pneumoniae transcriptome web site

All results and additional data related to the M.pneumoniae transcriptome can be retrieved from the web address http://www.zmbh.uni-heidelberg.de/M_pneumoniae/transcriptome/.


Experimental set-up and standard transcription profile

The aims of our study were: (i) to monitor simultaneously and semi-quantitatively all mRNA species synthesized in M.pneumoniae cells grown under standard laboratory conditions, i.e. rich medium and a growth temperature of 37°C; and (ii) to investigate regulation of gene expression at the transcriptional level during heat shock. Microarray technology served as the basis for these investigations and the results of selected candidates were confirmed by Q-PCR and RT–PCR. Furthermore, our approach involved determining and quantifying cell numbers as well as copy numbers of RNA species.

Although M.pneumoniae grows at 37°C under standard laboratory conditions and probably also in its natural environment, we chose 32°C as a reference temperature for comparison with heat shock conditions, because pilot experiments indicated that 37°C seems already to be a kind of heat shock for certain genes. Therefore, to get clearer differences of temperature-induced expression signals, we used total RNA from cells grown at 32°C as a reference for the effect of heat shock. A temperature shift from 37 to 43°C gave similar but less diverging results (data not shown).

First, we established the transcription profiles from each of the 677 ORFs proposed in the original publication (Fig. (Fig.11).

Figure 1
DNA array of the entire set of 677 postulated ORFs of M.pneumoniae hybridized with radioactively labeled cDNA probes generated from RNA which was extracted from cells grown at 37°C for 96 h. Each ORF was spotted in doublets.

Genes coding for RNA only, like the abundant ribosomal RNA or tRNA, were excluded from the analysis for technical reasons. The cDNA was synthesized from total M.pneumoniae RNA by priming with 676 gene-specific oligonucleotides. Since one of the originally proposed 677 ORFs was dismissed (20), we used only 676 primers. The individual signals obtained after exposing the nylon membrane to a PhosphorImager were inspected and expressed as a fraction of the total hybridization signal. Figure Figure22 presents the data from a selected array of ORFs as the average of various independent gene expression measurements for three growth conditions. Pooling the results from the four independently established transcription profiles of cells grown at 37°C, signals from more than 600 genes were significantly above background (606 for P < 0.01, 564 for P < 0.001; see Table Table1).1). Among the genes which showed no significant expression (transcription) according to our rules, 31 (43%) were without assigned function and for 21 (30%) genes that showed no significant transcriptional expression, proteins were identified in the proteome analysis of M.pneumoniae (36).

Figure 2
A series of 25 ORFs (MPN507–MPN531) and their hybridization signals. The signals were derived from three temperature experiments where M.pneumoniae was incubated for 96 h at 37°C, 144 h at 32°C or exposed to a heat shock of 43°C ...
Table 1.
Genes with significant differences (P < 0.01) in expression between the 32°C and heat shock profiles

The additional 12 genes (MPN069, MPN242, MPN254, MPN270, MPN272, MPN296, MPN377, MPN388, MPN418, MPN482, MPN495, MPN605) (20), which were identified after the original publication appeared, were tested on separate nylon membranes. Since the genes were spotted on a new array set, the data for these genes could not be evaluated together with the data discussed in this paper. However, we found a positive transcription, based on signal above background and controls, for 11 of the 12 new genes. The transcription signal for MPN418 (hypothetical protein) fell below the background signal. MPN069 (50s ribosomal protein L33) and MPN377 (hypothetical protein) showed high expression signals.

For comparative studies and to show regulation of gene expression in M.pneumoniae by heat shock, transcription profiles were established from cells grown at 32°C and compared with those derived from cells grown at 32°C but exposed for 15 min to a temperature of 43°C before RNA isolation. Altogether, 47 genes were up-regulated after exposure of the bacterium to 43°C (Figs (Figs33 and and44 and Table Table1).1). Among the up-regulated candidates were ubiquitous conserved genes coding for heat shock proteins like DnaK, GroEL and LonA. In addition, unexpected genes, which were not known to code for products involved in a heat shock response, like those coding for various ribosomal proteins, proteases and functionally unassigned lipoproteins, were also heat shock induced. Besides an up-regulation, we also observed the down-regulation of 30 genes upon a temperature shift to 43°C (Fig. (Fig.33 and Table Table1).1). Down-regulation was also perceived in genes of unassigned function. A further comparison of the genes with the highest expression level at each of the three selected temperatures revealed that certain genes like MPN053 (phosphocarrier protein HPr), MPN624 (ribosomal protein L28), MPN665 (elongation factor Tu), MPN024 (DNA-directed RNA polymerase delta subunit) or MPN331 (trigger factor) were constitutively highly expressed at all three temperatures (Table (Table2).2). The results of the comparisons among the various experimental conditions were very similar for the two statistical tests applied. If compared using the t-test, 133 genes showed significant difference in expression in the heat shock profile with P < 0.05 and 19 with P < 0.001. In the Mann–Whitney U-test, the numbers were 135 and 33, respectively (see Table Table11 for details). A transcriptome map of M.pneumoniae based on the above described results is shown in Figure Figure4.4. The ORF, which was removed from the original annotation, was negative in the transcriptome.

Figure 3
Correlation between expression at 32°C and expression in heat shock. Gray circles, genes that are not significantly expressed in either of the tested experimental conditions; black circles, genes that are significantly expressed, but do not show ...
Figure 4
Expression map of M.pneumoniae. Color scale represents the normalized gene expression. Black boxes represent non-coding RNA genes (tRNAs, ribosomal RNAs, etc.). Genes that were not subject to the microarray analysis are white. White squares above the ...
Table 2.
Absolute expression of genes in different expression profiles

Control experiments

In all experiments, the transcription profiles proved to be reproducible, independent of total RNA or filter preparations. However, to minimize artifacts or misinterpretations, a number of control experiments had been done. One of the most critical parameters in a transcriptome analysis concerns synthesis of cDNA. A poor primer selection of sequence-specific primers may be the cause for meager cDNA synthesis due to low hybridization to an mRNA template. For this reason, cDNA synthesis was alternatively done with random hexamers. Although the specific activity of the random hexamer-primed cDNA was higher (107 c.p.m./10 µg of total RNA) than the activity of 18–20mer gene-specific primed cDNA (105–106 c.p.m./10 µg of total RNA), fewer positive signals were counted after filter hybridization with random hexamer-primed cDNA, but the background was consistently higher. The reason for the high activity is probably the reverse transcription of the very redundant tRNA and rRNA species with random hexamer primers (see Discussion). Except MPN123 [topoisomerase IV subunit A (parC)], MPN356 [cysteinyl-tRNA synthetase (cysS)], MPN349 (conserved hypothetical), MPN345 [type 1 restriction enzyme (hsdR) homolog] and MPN448 (conserved hypothetical), which showed a higher signal intensity when cDNA was synthesized with random hexamers, all probes which gave positive signals with hexamer-primed cDNA were also detected in profiles derived with gene-specific primed cDNA. The array signals of the cDNA, which was synthesized with the gene-specific primer mix, were much stronger than that of hexamer-primed cDNA. Evidently, not all mRNA species are reverse transcribed with a random hexamer primer mix as efficiently as with sequence-specific primers. For this reason, the signal pattern of the array profiles is different from that derived with gene-specific primers. The more precise way for transcribing the majority of each mRNA species is by using gene-specific primers in an excess concentration. This is the approach that we took in our experiments.

The even spotting pattern of the probes on the membranes was tested by hybridization with nick translated DNA. All M.pneumoniae-specific probes on the filter reacted positively under the selected conditions, although the signal strength was not homogenous. When comparing this control profile with the standard transcription profile using the same filter, we found them to have no correlation, as expected. From this comparison, we concluded that the loading of the probes onto the filter worked well and that the distribution of the signal intensities in the transcription profiles reflects a true difference in the number of transcripts and labeled genomic DNA, respectively, in the hybridization mixture. To confirm the signals and signal differences obtained with 33P-labeled cDNA and to eliminate any bias introduced by the labeling procedure, RT–PCRs were done with selected genes and gene (mRNA)-specific primers. The selected candidates fell in the three categories of up-, down- and non-regulated genes, based on the evaluated transcription profiles. The intention was to compare transcription of a given gene from cells grown at different temperatures rather than comparing the expression differences among different genes. The results confirm the temperature-dependent signal differences obtained in the transcriptome analysis for many of the genes (Fig. (Fig.5).5). In the RT–PCR, however, more genes showed regulation. Numerous genes, which showed no statistically significant regulation in the array experiment, showed an up- or down-regulation in the RT–PCR, and some genes, which proved to be regulated in the array experiment, showed no regulation in the RT–PCR. An interesting result from the RT–PCR experiment was, for instance, the heat shock-induced up-regulation of several genes involved in the purine and pyrimidine salvage pathway. Five of these genes, MPN065 cytidine deaminase (cdd), MPN064 thymidine phosphorylase (deoA), MPN063 deoxyribose-phosphate aldolase (deoC), MPN062 purine-nucleoside phosphorylase (deoD) and MPN061 signal recognition particle protein (ffh), are clustered in the same orientation on the genome. Of these, only MPN065 was shown to be significantly up-regulated on the microarray. Of course, one has to keep in mind that the array results are a statistical set of data derived from an array of experimental results. RT–PCR, on the other hand, was done only once and the results were, therefore, not averaged. The RT–PCR results, however, are not contradictory to the results obtained from the microarray experiments, as we only observed a discrepancy between significantly expressed to not significantly expressed signals. A larger discrepancy, where ORFs showed regulation in one direction (either up- or down-regulated) in the microarray experiment and regulation in the opposite direction in the RT–PCR experiment, were not observed.

Figure 5
RT–PCR results. Total RNA from M.pneumoniae grown at 32°C and from heat shock-treated cells was reverse transcribed with a mix of 688 gene-specific primers. The cDNA was used in a 24 cycle PCR. The synthesized DNA was loaded onto an agarose ...

Determination of cell numbers; Q-PCR

To interpret the microarray signals in terms of copy number of specific mRNA molecules in a total RNA preparation, or more precisely in the bacterium, at least three variables must be known, i.e. the number of M.pneumoniae cells which was used for a total RNA preparation, the total amount of RNA isolated from a given cell number and the concentration of individual mRNA species within the total RNA preparation. Counting M.pneumoniae cells in a preparation is not trivial, since the bacteria are too small to be seen in a light microscope. In addition, the cells are sticky and have the tendency to form clumps, which excludes a counting of the colonies on agar plates as a reliable method for quantification. Therefore, we decided to define the number of genomes in a bacterial suspension by Q-PCR and to use the data as a reproducible measure (genome equivalent) for cell density. The mean number of genome equivalents in a M.pneumoniae suspension grown at standard conditions in one cell culture flask (150 cm2) (see Materials and Methods) was 2.4 ± 0.8 × 1011. The determination of numbers with the color-changing test provided evidence that the highest 1:10 dilution of a M.pneumoniae suspension, which still showed growth, indicated 1010–1012 cells per flask.

From such a standard bacterial suspension, we routinely isolated 200 µg of total RNA. This means that 200 µg of total RNA corresponds to 2.4 × 1011 genome equivalents or to 1.2 × 1011 bacteria, assuming that one cell, growing under standard conditions, contains two genomes.

To determine the actual copy numbers of individual mRNA species in total RNA preparations, 0.15 µg of RNA was used for cDNA synthesis, priming with a random hexamer primer mix. Table Table33 shows the results of copy numbers of individual mRNA species and of the 16SrRNA. We had chosen these particular mRNA species because they gave different—from low to high—signals in the microarray analysis. Approximately 4 × 1010 16SrRNA copies were measured per 1 µg of RNA or per 1.2 × 109 genome equivalents. The number of molecules of the mRNA, which gave the strongest signals, were a factor of approximately 100 lower (Tables (Tables22 and and33).

Table 3.
Copy numbers of mRNA and rRNA in a 1 µg total RNA preparation (thousands) as acquired by the Q-PCR analysis

The mRNA copy numbers were scaled accordingly and the linear regression coefficients with copy numbers as the independent variable and percentage mRNA microarray signal as dependent variable were calculated (Fig. (Fig.66).

Figure 6
Results of Q-PCR analysis of nine different M.pneumoniae genes. (a) Correlation between the absolute copy number per 1 µg of a total RNA isolation, and the microarray signal (percentage). Different symbols correspond to the three experimental ...


Regulation in heat shock conditions

Mycoplasma pneumoniae lives in a relatively constant environment, the human respiratory tract, where growth conditions do not seem to change dramatically. For instance, the window for temperature change is very small and should not exceed 42°C as the upper limit.

Nevertheless, with the applied microarray analysis we found 47 genes to be significantly up-regulated in heat shock (43°C) conditions at P < 0.001 (Table (Table1).1). These results show that gene regulation takes place in M.pneumoniae, as in other organisms, in response to environmental changes. The up-regulated genes can be classified according to function. The classical heat shock proteins with chaperone or protease activity are represented by DnaK, LonA, GroES, a protein with the j-domain of DnaJ (MPN002) and ClpB. We assume that, as shown for Escherichia coli and other bacteria, the DnaK system prevents aggregation of thermo-labile proteins (26) and together with ClpB solubilizes heat-induced protein aggregates (27). An additional attractive possibility and useful function for a cell could be the observed cooperative action of ClpB and the DnaK system in the activation of the initiation of replication of the plasmid RK2 (28) by converting an inactive protein dimer into an active monomer form. The fact that clpB was the most significantly up-regulated gene (MPN531) and the only conserved member of the clp family in M.pneumoniae stresses the importance of these functions, even for a cell living in a constant environment.

The meaning of the other up-regulated genes is not that easy to interpret. They belong to the following functional groups: translation and ribosomal proteins, transport and binding proteins, cell envelope proteins, proteins involved in restriction and modification, purine and pyrimidine metabolism, DNA replication, transcription and energy metabolism. Eleven of the 47 up-regulated genes are not assigned a function at all.

Interestingly, many mRNAs encoding different ribosomal proteins were found to be strongly up- or down-regulated. This might be connected to the role of heat shock proteins in the biosynthesis of ribosomes (29) and should be further investigated. Hansen et al. (30) found that the rRNA concentrations are subject to heat shock regulation. We did not find a significant regulation of the 16SrRNA, but it is likely that a significant change in rRNA concentration will not occur after only 15 min of heat shock.

Obvious is the large number of up-regulated genes coding for ribosomal proteins. Eight of 10 of these genes are part of the S10 operon, which consists of 35 genes (MPN164–MN198), most of them coding for ribosomal proteins. All of the eight genes are located at the beginning of the operon, giving the impression that their transcription is differently regulated from the transcription of the residual genes of this operon. So far, there is no experimental evidence as to whether these ribosomal proteins have additional functions under heat shock conditions beyond ribosome formation, which could explain this observation. Orthologs of the genes up-regulated in M.pneumoniae could also be found in transcriptome analyses from other bacterial species (3135), for instance the RNA polymerase sigma-70 factor (sigA, MPN352) transport systems, individual ribosomal proteins, tRNA synthesases, surface-exposed proteins (lipoproteins) or restriction-modification enzymes. The increase in transcription of genes involved transcription, translation or transport or energy metabolism could be the response to the increasing demands of a heat-stressed bacterium, while the increase in transcription of restriction-modification connected genes has been interpreted as a defense strategy against intruding DNA (34) and the synthesis of lipoproteins as a possibility to modify the cell surface (35). Of course, this is highly speculative and needs experimental evidence by constructing knock-out mutants in the genes of interest or gene modifications, which allow a regulated gene expression.

Since no experiments have been done so far with M.pneumoniae to understand the regulation of gene expression after heat shock, one depends on the experimental results of other bacteria. Several global transcription analyses in response to growth temperature variation from various bacterial species exist, e.g. E.coli (31), group A Streptococcus (32), B.subtilis (33), Campylobacter jejuni (34) or Borrelia burgdorferi (35). Because of the close phylogenetic relationship and the extended studies done, B.subtilis seems to be the best choice for comparison. Heat shock genes in this bacterium are assigned to four different classes depending on their mode of regulation of transcription. Class I genes posses the CIRCE element (1,33), a palindromic sequence located between the start codon of the gene and the promoter sequence, which is regulated by the repressor HrcA. Class II genes depend on the alternative sigma factor σB and class III genes are mainly controlled by the repressor CtsR. All other heat shock genes, which do not belong in either of the described classes were assigned as class IV or class U genes, since their mechanisms of transcriptional regulation are unknown.

The heat shock-regulated genes found in M.pneumoniae only fit into either class I or class IV (U). In M.pneumoniae, the genes MPN021 (dnaJ), MPN332 (lonA), MPN434 (dnaK) and MPN531 (clpB) possess the conserved CIRCE element, and since the repressor HrcA (MPN124) is also present, we can assume that these genes are regulated accordingly.

Based on similarity searches, neither the alternative sigma factor σB nor the repressor CtsR could be found in M.pneumoniae, indicating that the genes are not regulated according to the class II and class III rules. Therefore, we have no explanation for the regulation of transcription for the residual heat shock-induced genes in M.pneumoniae. Analysis of promoters with statistical methods did not reveal any heat shock gene-specific sequences and the only other candidate gene for an alternative sigma factor besides σA, MPN626, which shows some similarity with σD from B.subtilis (24), has not been analyzed experimentally. However, we did not find a transcriptional induction of this gene during the applied heat shock conditions, making its role for heat shock regulation unlikely.

The whole bulk of our data can be retrieved at http://www.zmbh.uni.heidelberg.de/M_pneumoniae/transcriptome/.

Statistical evaluation of the data

Two approaches (Student’s t-test and Mann–Whitney U-test) were applied to test for significant differences in gene expression between both profiles (32°C versus heat shock). Student’s t-test is a parametric test assuming both the normality of the data and a constant variance for a given gene for each microarray. Since both of these assumptions are questionable in the case of microarrays (11), we decided to use the non-parametric alternative which does not make such assumptions. A common problem for the non-parametric alternatives is the large number of type I errors, i.e. false negatives. These tests, while not making any assumptions about the distribution of the samples, are weaker than their parametric counterparts. An extremely simple (and also very weak) non-parametric test used in evaluating microarray data is the min/max separation (12), where two sample groups are assumed to be significant if the lowest value in one group is larger than the highest value in the other group. We have applied the stronger Mann–Whitney U-test for unpaired samples, which will give statistical significance in all the genes that will also show a positive min/max separation.

Finally, one has to apply a low significance threshold, otherwise a high rate of type II errors (false positives) will be expected. In spite of the differences in approach and type of statistics used, both tests (Student’s t-test and Mann–Whitney U-test) gave strikingly similar results, which confirms our findings.

Reliability of the data

When evaluating quantitative data from RNA analysis, one has to keep in mind that there is no direct method for specific quantification of the minute amounts of mRNAs present in bacterial cells, and that any method used to achieve this aim is inevitably loaded with different sources of errors (10). Both microarray technology and Q-PCR quantification rely on an exact and homogenous cDNA synthesis. However, cDNA synthesis itself relies on different conditions, which might, or might not, be gene specific.

Radioactively labeled cDNA for microarray analysis was synthesized using gene-specific primers. In theory, this should yield exact data, provided that there is no interaction among the 676 primers added to the reaction, and that the primers show equivalent hybridization kinetics. When these conditions are met, synthesis of similar amounts of cDNA per copy of mRNA will be synthesized independently of the genes. When primers cross-hybridize, however, a series of unspecific signals will be detected in the array experiment. To determine the severity of this problem in our experimental set-up, we have done control microarray experiments using a limited number of gene-specific primers. We have synthesized cDNA with a set of 41 gene-specific primers to detect any trend for significant cross-hybridization (data not shown). Aside from the expected 41 ORFs, at least 87 different ORFs showed significant signals. The effect of unspecifically primed cDNA might be the result of at least two factors. Both the low temperature of 37°C during reverse transcription and the unfractionated primer mix might contribute to this undesired effect. The low reaction temperature supports unspecific hybridization of primers and the n – 1 fragments in the primer mix serve as alternate primers, binding randomly all over the RNA templates. Furthermore, genes with significant sequence similarities, like those which contain repetitive DNA sequences (19) might cause problems due to false positive signals (cross-hybridization). In these cases, a protein analysis by mass spectrometry with partial amino acid sequences will provide more confident data (36).

It is worth mentioning that a number of genes did not give significant transcription signals, but their translation products could be identified by mass spectrometry (36).

The overall trends (i.e. high or low expression) seen in the data derived from microarrays were confirmed by the more variable data derived from the real-time PCR analysis and the regulation seen in the case of microarray-derived data could be confirmed. It is probable that the microarray technology is less amenable to the varying efficiency of the cDNA reaction and the overall amount of cDNA used because only relative, and not absolute, amounts of cDNA are measured.

Microarray signal and mRNA concentration

The results of a microarray analysis are always relative to the total acquired signal. Therefore, they give no information about the absolute number of mRNA molecules found in a cell or per 1 µg of total RNA. Ribosomal RNA is sometimes used for normalizing the data, but it has been shown that rRNA in E.coli is also regulated during heat shock conditions (30).

Thus, to give the qualitative microarray data absolute values, it is necessary to apply different techniques, such as Q-PCR. Although this method did not yield very precise results, it was possible to find a correlation between the measured copy number of mRNA molecules and microarray signal strength. From these data, we were able to calculate copy numbers of individual mRNA species or 16SrRNA in a total RNA preparation and to correlate these numbers with genome equivalents and cell numbers. The main problems with these quantitative analyses concern the low numbers of individual mRNA or 16SrRNA species per bacterium (Table (Table3).3). Assuming that the 200 µg of total RNA, which we isolate from a standard growth culture, would consist only of the three ribosomal RNA species, then approximately 800 molecules of each rRNA species would be calculated for a single cell. In E.coli, the preponderance (80%) of total RNA is ribosomal RNA, while mRNA amounts only to 4% of the total RNA (37). Adapting these data for M.pneumoniae, it is obvious that the experimentally derived number of approximately 80 16SrRNA molecules per cell is too low by a factor of 10. The experimental result for the clpB mRNA is even lower, as we find only 0.1 copy per cell. These data represent the concentrations of the mRNA species which are present at a given time in the cell population. There are several potential sources of error (10), like calculation of cell number in our preparation, loss of RNA during isolation, inefficient cDNA synthesis and the Q-PCR itself. In addition, the cells in our culture show most probably growth phase differences. Besides not being synchronized, M.pneumoniae grows in clumps attached to the plastic surface, providing different microenvironments for individual cells. The contribution of errors from the different sources is not equal, meaning that one might account for a greater error than another. We think that the estimation of cell numbers is conservative, although the CCU test, which we used, is not precise as M.pneumoniae grows in clumps. For this reason, the number of cells in the last dilution could deviate from the real number by at least a factor of 10.

Loss of RNA during isolation in the range of 90% seems unlikely, since M.pneumoniae is surrounded by a cytoplasma membrane only. This facilitates a quick lysis of the cell and inactivation of cellular RNAse activities. Nevertheless, the loss of RNA during an RNA extraction is inevitable, however, difficult to quantify. Therefore, we conclude that cDNA synthesis is the most crucial step of all, which is difficult to optimize for all mRNA species in a total RNA preparation. Several parameters in cDNA synthesis are difficult to control, like the efficiency of priming, as well as the dissociation of secondary structures within mRNA species. Both these parameters are associated. To standardize the parameters, we applied two primer types in the microarray experiments (random hexamers and ORF-specific primers) and compared the results.

Our finding with the random primers was that, although the yield in transcribed cDNA was higher, the specificity of reverse transcription of gene probes, i.e. mRNA species, was lower compared with gene-specifically primed cDNA. This led to weak signals and a high background on the microarrays. The ORF-specific primers eliminated these technical problems.

The type of primers used in cDNA synthesis and the experience with optimal results varies among laboratories and seems controversial in the literature. Some authors favor hexamers, others longer gene-specific primers. Arfin et al. (13) presented a comparison between hexamer and oligo priming. They identified more mRNA species with hexamer priming and recommended the use of such primers. In our experiments, we tested both types of primers and the results with hexamers were less successful. Since hexamer priming also labels the rRNA, which comprises ~90% of the total RNA, we thought this might be the reason for our high background problem. It was also important for us to use gene-specific oligonucleotides with respect to experiments aimed to study the interaction of M.pneumoniae with human lung epithelial cells. In such an experimental set-up, we would also have to deal with large amounts of RNA from the epithelial cells aside from the ribosomal RNA.

All the ORF-specific primers were used for the synthesis of PCR products from DNA, therefore, we assume that they also prime during cDNA synthesis. However, although all the primers have a similar melting temperature, it is impossible to provide conditions which are optimal for the cDNA synthesis for all mRNA species. Therefore, comparing signals from mRNA of different genes of cells grown in identical conditions could be misleading about the actual concentration of these mRNAs, since a similar signal strength does not necessarily indicate a similar number of mRNA molecules. In contrast, gene-specific signals derived from cells grown under different conditions can be compared with confidence, since the conditions for all the parameters, except the mRNA copy numbers, are kept constant.

In summary, for a precise determination of mRNA copy numbers it would be important to develop methods which allow measuring the mRNA directly without the additional step of cDNA synthesis.


We thank E. Pirkl for excellent technical assistance and J. Hoheisel and N. Hauser for help with the preparation of nylon membranes. This research was supported by grants from the Deutsche Forschungsgemeinschaft, the Graduiertenkolleg ‘Pathogene Mikroorganismen: Molekulare Mechanismen und Genome’ and by the Fonds der Chemischen Industrie.


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