, 1263 (2009);
et al.Eva Yus,
Metabolism and Its Regulation
Impact of Genome Reduction on Bacterial
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Impact of Genome Reduction
on Bacterial Metabolism
and Its Regulation
Eva Yus,1Tobias Maier,1Konstantinos Michalodimitrakis,1Vera van Noort,2Takuji Yamada,2
Wei-Hua Chen,2Judith A. H. Wodke,1Marc Güell,1Sira Martínez,1Ronan Bourgeois,1
Sebastian Kühner,2Emanuele Raineri,1Ivica Letunic,2Olga V. Kalinina,2,3Michaela Rode,2
Richard Herrmann,3Ricardo Gutiérrez-Gallego,4Robert B. Russell,2Anne-Claude Gavin,2
Peer Bork,2* Luis Serrano1,6
To understand basic principles of bacterial metabolism organization and regulation, but also
the impact of genome size, we systematically studied one of the smallest bacteria, Mycoplasma
pneumoniae. A manually curated metabolic network of 189 reactions catalyzed by 129 enzymes
allowed the design of a defined, minimal medium with 19 essential nutrients. More than 1300
growth curves were recorded in the presence of various nutrient concentrations. Measurements of
biomass indicators, metabolites, and13C-glucose experiments provided information on
directionality, fluxes, and energetics; integration with transcription profiling enabled the global
analysis of metabolic regulation. Compared with more complex bacteria, the M. pneumoniae
metabolic network has a more linear topology and contains a higher fraction of multifunctional
enzymes; general features such as metabolite concentrations, cellular energetics, adaptability, and
global gene expression responses are similar, however.
ogy. For this purpose, all components and reac-
tions of a target system should be listed and
validated, and their quantitative relations should
be determined and analyzed in the context of
the physiology of the organism (1). We have se-
lected Mycoplasma pneumoniae, a human path-
ogen that causes atypical pneumonia (2), as a
model organism for bacterial and archaeal sys-
tems biology. Similar to other Mollicutes, M.
pneumoniae has undergone a massive genome
reduction to include only 689 protein coding
genes, 231 of which have unknown function (table
S1) (3), yet it can be cultivated in vitro without
helper cells (4). The genome reduction of M.
pneumoniae favors its suitability as a systems
biology model because it largely follows genome
size–scaling principles (fig. S1) (5). We manually
reconstructed and validated the metabolic network
of M. pneumoniae and studied its regulation,
complementing analyses of the transcriptome
(6) and the proteome organization (7).
The metabolism of M. pneumoniae has been
studied biochemically (8) and computationally
ccurate representation of cellular net-
works through mathematical models is a
central goal of integrative systems biol-
(9). We integrated these approaches in a frame-
work that maximized coverage and accuracy (10).
To build a comprehensive metabolic network,
we complemented the reactions from the Kyoto
Encyclopedia of Genes and Genomes (KEGG;
www.genome.jp/kegg) with activities obtained
manually from the literature and new annota-
tions (fig. S2 and tables S1 to S5) (11). We also
considered other genomic (co-occurrence in one
operon), sequence (homology to known enzymes),
and structural information (identification of cat-
alytic residues to ensure enzyme functionality)
(Fig. 1A and figs. S2 and S3). For example, we
identified an incomplete ascorbate pathway
through sequence analyses and filled the gap by
assigning a critical enzyme [L-ascorbate-6-phosphate
lactonase (mpn497)] on the basis of sequence ho-
mology, predicted activity (metal-dependent hy-
drolase), and its position in the ascorbate operon
(mpn492 to mpn497). For pathways in which
only one enzyme was missing, we closed the gap
by adding an unassigned reaction (for example,
transketolase activity in the pentose phosphate
pathway). Putative enzymes missing conserved
catalytic residues were discarded (for example,
Mpn255 and Mpn673 enzymes of the terpenoid
pathway). Lastly, for enzymes that could carry out
more than one reaction, we removed the reactions
that were decoupled from pathways and those for
which the substrate was unavailable. The final
result was a map without gaps, isolated reactions,
or open metabolic loops (Fig. 2).
A number of alternative pathways, interac-
tions between pathways, as well as missing en-
zymes still needed to be validated, and reaction
directionalities had to be inferred. For this, we
used two different experimental strategies. We
first used the rich medium (fig. S4) to validate
the pathway functionality in various carbon
sources. As expected from the map (Fig. 2), all
known carbon sources except mannitol supported
growth to various extents (figs. S5 and S6) (12).
Using13C-glucose labeling, we validated (for ex-
ample) the predicted connection between glycol-
ysis, the pentose phosphate pathway, and lipid
synthesis (fig. S3 and table S6), and ruled out the
proposed production of aspartate from pyruvate
(13). For our second strategy, we developed on
the basis of the metabolic map a defined medium
(Fig. 1A and table S7) with which we could val-
idate other pathways (such as vitamin metabo-
lism) (fig. S10) and reaction directionalities that
could not be studied in rich medium (such as the
synthesis of uracyl and thymine nucleotides from
cytosine) (figs. S7 and S8). The low number of
amino acid permeases and transporters and the
existence of a peptide importer (oppB-F cluster)
(table S1) suggested a requirement for peptides
in the medium, which we confirmed experimen-
tally (fig. S9).
We systematically tested the defined medium
in more than 1300 experiments in order to prop-
erly assess all the components necessary for sur-
vival. We replaced these components with simpler
building blocks in order to obtain a defined,
minimal medium that contains only 26 compo-
nents (19 of which are essential) (Fig. 1A). This
medium, as predicted from our metabolic map
and comparative analysis, also supports growth
of M. genitalium (figs. S11 and S12). On the
basis of these experiments, we estimated the
upper flux limits for the use of the various
nutrients (fig. S13). The medium implicitly
validates the reconstructed metabolic map (Fig.
2), which consists of 189 reactions (table S2):
169 are catalyzed by the products of 140 known
genes, and 20 are not yet assigned to any gene
(table S4). The map includes 74 essential
metabolic genes and 34 conditionally essential
ones (depending on medium composition), which
is in agreement with essentiality as determined by
means of transposon mutagenesis analyses (with
a 96% overlap) (fig. S14 and table S8) (14). A
total of 32 enzymes (25%) are multifunctional;
they have more than one activity and together
catalyze 91 reactions (48% of the total) (table
S3). With respect to previous genome annota-
tions (3, 15), we assigned new or refined
functions to 57 metabolic genes (plus 30 non-
metabolic genes; see the new annotations in table
S1). The above strategy could more generally be
used to design media to grow axenically hard-to-
culture bacteria, as was done for the recalcitrant
Tropheryma whipplei (16) and might be appli-
cable in the context of increasing metagenomics
Analysis of the metabolism of M. pneumoniae
reveals that it is more linear than that of larger
bacteria, such as Bacillus subtilis (Fig. 1B). Fur-
thermore, M. pneumoniae has a wider metabolic
network diameter (shortest biochemical path-
way averaged over all pairs of substrates), al-
though the diameter has been reported to increase
with the logarithm of the network size (17). The
1Centre for Genomic Regulation (CRG) and Universitat Pompeu
Fabra, Avenida Dr. Aiguader 88, 08003 Barcelona, Spain.
2European Molecular Biology Laboratory (EMBL),Meyerhofstrasse
1, D-69117 Heidelberg, Germany.
Transmission Problems, Russian Academy of Sciences, Moscow
127994, Russia.4Zentrum für Molekulare Biologie Heidelberg
5Institut Municipal d’Investigació Médica–Hospital del Mar,
Department of Experimental and Health Sciences, Universitat
Pompeu Fabra, Avenida Dr. Aiguader 88, 08003 Barcelona,
Spain.6Institució Catalana de Recerca i Estudis Avançats, Lluis
Companys 23, Barcelona 08010, Spain.
*To whom correspondence should be addressed. E-mail:
3Institute for Information
VOL 32627 NOVEMBER 2009
on November 27, 2009
greater linearity and the wider diameter of the
network suggest that it is less interconnected
and contains fewer parallel paths. Thus, the
M. pneumoniae network is less redundant both
in terms of enzyme paralogy and in network
topology. Yet, the distribution of the number of
metabolites per reaction is similar to other or-
ganisms (fig. S14). This is partly achieved by an
increased fraction of multifunctional enzymes as
compared with that in larger bacteria, as hap-
pens in endosymbionts (17). We did not find
any evidence of M. pneumoniae multifunctional
enzymes being more conserved than others.
This suggests the larger number could be due to
function acquisition that is not present (or de-
tected) in their homologs. This might represent a
more general mechanism expected to facilitate
further genome reduction (Fig. 1, B and C). The
increased linearity and limited redundancy in the
metabolic network suggest limited robustness
and adaptability to external factors (18): Of the
metabolic enzymes, 60% are essential (19), in
contrast to only 15% in Escherichia coli (www.
M. pneumoniae has a relatively long dupli-
cation time (at least 8 hours) in comparison with
E. coli or L. lactis (20 min), both in culture (20)
and in the presence of host cells (21). Slow growth
in genome-reduced, pathogenic bacteria has been
proposed to be the result of (i) less efficient en-
zymatic activity that is explained by the accu-
mulation of mutations resulting from genetic
drift (22), (ii) a reduced number of ribosomal
RNA (rRNA) operons, and/or (iii) other mech-
anisms related to the adaptation to a pathogenic
lifestyle. To understand the causes of slow growth,
it is necessary to measure the overall energetics of
the metabolic network (Fig. 2) as well as the
changes in macromolecules (Fig. 3A) and metab-
olites along the growth curve (Fig. 3B).
We used the metabolic map, the measured
protein concentration (10 fg of protein per cell),
and the estimated turnover rates of macromol-
ecules (~20 hours for proteins and ~7 min for
mRNA) (table S9 and fig. S15) to estimate the
rate of glucose uptake required to duplicate a cell
every 8 hours at 18,000 to 24,000 glucose mol-
ecules per second [assuming that the majority
of adenosine triphosphate (ATP) is used for bio-
mass production] (10). This figure closely matched
the experimentally determined value under ex-
ponential growth: ~19,000 glucose molecules
per cell per second (Fig. 3C) (10). When cultures
approached stationary phase (Fig. 3A), the rate
increased to ~45,000 glucose molecules per cell
per second (Fig. 3C), concomitantly with the
increased transcription of many glycolytic and
fermentation genes (Fig. 3D and tables S10 and
S11). In both cases, at least 95% of the glu-
cose carbon was found in lactate and acetate
(Fig. 3B and fig. S16), implying that the glu-
cose is used primarily for energy production.
At the fastest glucose consumption rate, assum-
ing all ATP were devoted to biomass produc-
tion, M. pneumoniae could divide about every
Fig. 1. Metabolic network development and properties and minimal medium design. (A) Schematic
diagram of the process leading to M. pneumoniae metabolic network reconstruction and the design of a
minimal medium. (B) Comparison of M. pneumoniae metabolic network properties with those of other model
bacteria. (C) Quantification of enzyme multifunctionality among prokaryotic genomes. M. pneumoniae,
red; L. lactis, yellow; B. subtilis, green; E. coli, blue; and other bacterial species, gray.
27 NOVEMBER 2009 VOL 326
on November 27, 2009
Fig. 2. Metabolic map of M. pneumoniae. Main metabolites are shown as boxes, and enzymes
and transporters are shown as pentagons. Input metabolites are indicated in blue, and output
products are indicated in red. New enzymatic activities determined in this study are displayed in
yellow, and enzymes catalyzing multiple reactions are bold. Essential enzymes (according to the
mutagenesis study in M. genitalium) are indicated with a black triangle. Minimal medium
components have been shadowed in blue. See the bottom-right legend for details, fig. S12 and
table S2 for description of the enzymatic reactions and enzymes, and table S25 for metabolite
abbreviations. aaRS, aminoacyl-tRNA synthase.
VOL 326 27 NOVEMBER 2009
on November 27, 2009
3 hours. However, most of the energetic param-
eters (the concentration of glycolytic intermediates
fructose-6-phosphate, ribose-5-phosphate, and
glycerone phosphate, as well as glucose uptake)
that we measured were similar to those of larger
bacteria (table S9) (10), which suggests compa-
rable enzyme efficiencies. This similarity extended
to regulatory processes seen in Lactococcus
lactis (23). For example, as in L. lactis, we ob-
served both a shift from mostly mixed-acid to
homolactic fermentation and an acceleration of
glycolysis when the medium acidifies (Fig. 3, A
and B); the drop in O2concentration relieves
inhibition of lactate dehydrogenase (10, 22, 24).
Also, the ATP yield per fermented glucose (two
to four ATP, depending on lactate or acetate fer-
mentation) is the same as in L. lactis (table S9).
Given all of the above, we cannot explain
the slow growth of M. pneumoniae on the basis
of glycolytic efficiency or ATP yield. One of the
main differences compared with fast dividing
bacteria is the number of rRNA operons per
genome [just one in M. pneumoniae and six in
L. lactis (fig. S17) and five to 10 times propor-
tionally fewer ribosomes as compared with those
of E. coli (table S10)] (7). In many bacteria, the
number of ribosomes correlates with the divi-
sion rate (25). For M. pneumoniae, we see a cor-
relation of changes in biomass duplication speed
with the number of ribosomes but not with the
glycolytic rate (Fig. 3, C and D, and fig. S17).
We thus suggest that the slow division rate of
M. pneumoniae is not due to less efficient en-
ergy production but to the limit in protein bio-
synthesis capacity. This small pathogenic bacterium
does not appear to be optimized for biomass
production. Instead, more complex strategies for
fitness, such as suppression of growth by other
microorganisms (26) or optimization of interac-
tions with host cells, might determine growth
rate in small organisms.
It has been suggested that genome-reduced
organisms have limited adaptability to exter-
nal factors (24). To determine the capacity of
M. pneumoniae to respond to environmental
changes, we performed three types of experi-
ments. First, we followed the changes in gene
expression from the exponential growth phase
to the stationary phase (Fig. 4A). Analysis of
changes in gene expression (validated by means
of tiling arrays and quantitative polymerase chain
reaction) (fig. S18) at different points along the
growth curve showed that a large part of the
transcriptome can be grouped into four time-
dependent expression clusters (Fig. 4A, figs.
S19 and S20, and tables S11 and S12). These
clusters can be regarded as two pairs of anticor-
relating patterns, indicating a complex regu-
lation. Subsequent analysis by means of mass
spectrometry for a subset of enzymes showed
correlation between changes in mRNA and pro-
tein abundance (fig. S18 and table S10). For
example, the production of lactate by lactate de-
hydrogenase (Mpn674|Ldh) revealed the close
temporal coordination of gene and protein ex-
pression and metabolite turnover (Fig. 3, B and
D, and table S10).
Second, we analyzed the response of M.
pneumoniae to specific individual metabolic per-
turbations encountered as the population grows,
such as low pH, accumulation of fermentation
end-products, and sugar and amino acid starva-
tion, as well as to more complex stimuli, such as
entry into the stationary phase (Fig. 4, B to D,
and tables S13 to S16). We found coordinated
changes in gene expression specific to each con-
dition (Fig. 4B and fig. S21). For example, there
was a general inhibition of transcription and
translation upon glucose deprivation and an in-
crease of ATP proton pump genes at pH 6.5
(Fig. 4, B and C). Induction of the stringent re-
sponse (a global response to the absence of
amino acids) results in up-regulation of peptide
and amino acid transporters (Fig. 4D). Also, a
specific repression of the Thr-tRNA synthetase
gene (mpn553) (table S17), which is a core
component of a tRNA synthetase complex (7),
suggests its possible regulatory role in complex
assembly and therefore in regulation of transla-
tion. We found some common responses to mul-
tiple stresses. Some were known, such as the
Fig. 3. Determination of various
metabolic parameters in growing
cultures. Consistently generated het-
erogeneous data, all derived by
using a rich medium, are compared
through time in hours (x axis). (A)
M. pneumoniae growth determined
by monitoring the decrease in extra-
cellular pH and the concomitant
changes in the amount of protein,
DNA, and total RNA. (B) Determina-
tion of glucose consumption and its
fermentation to lactate and acetate.
(C) Changes in the number of glu-
cose molecules imported by a cell
per second and comparison with the
biomass doubling time. (D) Changes
in gene expression of a representa-
tive ribosomal protein (rplX) and two
enzymes [ldh and pdhA (a compo-
nent of the pyruvate dehydrogenase
complex), which are enzymes from
the two fermentation branches] and
the relation with the shift from ace-
tate to lactate production (the ratio
between acetate and lactate is shown
in green, which can be compared with
that of cells grown in the presence of
oxygen, shown in red).
27 NOVEMBER 2009VOL 326
on November 27, 2009
down-regulation of ribosomal proteins or pep-
tide importers, which is common to all stresses.
Others, like the up-regulation of ldh and glycerol-
3-P dehydrogenase (mpn051), were unexpected
and suggest additional functions for these pro-
teins during stress (Fig. 4B).
Third, we adapted the cells by means of serial
passage (15 passages) to efficient growth in other
carbon sources (fructose, mannose, and glycerol)
(tables S18 to S20). Fructose adaptation re-
sulted in overexpression of fruA and fruK (>3
log2), and mannose-adapted cells overexpressed
the mannitol importer (>5 log2) (tables S19 and
S20). Thus, M. pneumoniae shows surprising
adaptation capability similar to that reported
for E. coli (27).
The coordinated changes in gene expression
along the growth curve, the specific responses to
many various metabolic perturbations, and the
adaptability of the cells to various carbon sources
indicate that M. pneumoniae retains some ro-
bustness and adaptability despite its extreme
Compared with more complex bacteria,
M. pneumoniae lacks the majority of transcrip-
tion factors (TFs) regulating metabolic gene fac-
tors [such as the catabolite regulation protein
(CRP)], major sigma factors, and other regula-
tors (28). Gene assignment on the basis of se-
quence analysis (table S1), in some cases validated
through copurification with the RNA polymer-
ase complex (such as mpn266|spxA) (7), re-
vealed four TFs (mpn239|gntR, mpn329|fur, and
mpn124|hrcA), the general sigma 70 factor
(mpn352|sigA), two putative sigma-like factors
(mpn626|sigD and mpn424|ylxM), and a puta-
tive DNA-binding protein (mpn241|whiA) (fig.
S2 and table S1). Despite this apparently re-
duced gene regulatory toolbox, both environ-
mental stresses (6) and metabolic insults induced
complex, specific transcriptional responses; com-
parison with more complex bacteria showed sim-
Fig. 4. Regulation of metabolism. (A) Representative plot of the four main
gene co-expression clusters identified along the growth curve, named after
the main functional classes of the genes involved. (B)(Top) Overlap between
changes in gene expression under various stresses: lactate (80 mM buffered
lactate), low pH (pH 6.5), glucose or amino acid starvation (stringent re-
sponse), and the entry into stationary phase. (Bottom) Heat map of the genes
found to be commonly up- or down-regulated under stress and growth
inhibition. (C) Gene Ontology functional classification of genes significantly
regulated during exponential growth and glucose deprivation in M. pneumo-
niae (Mpn) or L. lactis (Lla). The average of the significant changes within each
category is depicted. C, energy production and conversion; D, cell division and
chromosome partitioning; E, amino acid transport and metabolism; F, nu-
cleotide transport and metabolism; G, carbohydrate transport and metabolism;
H, coenzyme metabolism; I, lipid metabolism; J, translation, ribosomal struc-
ture, and biogenesis; K, transcription; L, DNA replication, recombination, and
repair; M, cell envelope biogenesis, outer membrane; O, posttranslational mod-
ification, protein turnover, and chaperones; P, inorganic ion transport and
metabolism; R, general function; T, signal transduction mechanisms. (D) Strin-
gent response expression pattern was compared with that of L. lactis and B.
subtilis (Bsu). The average of the significant changes upon stringent response
induction (with norvaline) is shown. (E) Venn diagrams showing the overlap in
M. pneumoniae and L. lactis of ortholog genes under various metabolic
conditions. High P value in the case of the stringent response indicates that it is
not statistically significant. (F) (Top) Growth curve of cells growing in minimal
medium plus increasing amounts of glycerol. (Bottom) Glucose titration in
Hayflick is shown for comparison.
VOL 326 27 NOVEMBER 2009
on November 27, 2009
ilarities but also some specific differences in Download full-text
regulation of gene expression (tables S21 to S23).
For example, we observed an increase in mRNA
and protein expression of glycolytic enzymes
concomitant with the increase of glycolytic rate
upon medium acidification (Fig. 3, C and D, fig.
S20, and table S15), very similar to what has
been described in L. lactis cultures (29). Re-
sponse to glucose starvation was also similar to
that of L. lactis (Fig. 4, C and E, and table S21)
(30). Part of the stringent response, such as the
induction of peptide and amino acid transporters
and down-regulation of carbohydrate catabo-
lism (31), was conserved in M. pneumoniae (table
S22); other mechanisms, such as the repression
of ribosomal protein operons or rRNA synthe-
sis, were not observed (Fig. 4D and fig. S22).
This is in agreement with the proposed involve-
ment of the RNA polymerase omega subunit
(missing in M. pneumoniae) in sensing guano-
sine pentaphosphate/tetraphosphate [(p)ppGpp]
and thus arresting rRNA biosynthesis (32).
We believe it unlikely that the conserved re-
sponses, and the specific differences in regula-
tion, can be caused only by combinations of the
few TFs that regulate operons and suboperons,
even if one includes regulation through antisense
RNA (6). The presence of genes for synthesis or
degradation of a number of chemical messengers,
such as (p)ppGpp (mpn397|spoT), AppppA
(mpn273|hit1), or c-di-AMP (mpn244|disA) (fig.
S2 and table S1) (33), implies that signaling
mechanisms have been preserved despite ge-
nome reduction. For example, overexpression of
the spoT gene that regulates (p)ppGpp levels
(31) results in substantial changes in gene ex-
pression, mainly related to the stringent response
(table S24). The presence of genes coding for a
Ser/Thr phosphatase (mpn247|ptc1), two protein
kinases (Ser/Thr/Tyr kinase mpn248|prkC and
mpn223|hrpK, an HPr kinase), and the differen-
tial phosphorylation of key metabolic enzymes
under various growth conditions (33) suggest
posttranslational control. Also, metabolites such
as glycerol regulate gene expression at the base
of the fermentation branches in M. pneumoniae
(34) as well as glucose import (35). This explains
why glycerol is essential in the minimal medium
in a concentration-independent manner (Fig. 4F).
Our results suggest that complex metabolic
regulation can be achieved in a streamlined ge-
nome despite the absence of the respective TFs
probably because of a combination of transcrip-
tional regulators, posttranslational modifications,
and small molecules, including chemical mes-
sengers and metabolites.
Taken together, our newly established M.
pneumoniae resource, containing a manually an-
notated metabolic map, full annotations, reactome,
consistently measured growth curves, and gene
expression profiles corresponding to an exten-
sive list of metabolites, should facilitate integra-
tive systems biology studies at a high resolution.
Comparison with more complex bacteria revealed
systemic features associated with genome stream-
lining, which should be examined in other small
bacteria. Despite its apparent simplicity, we have
shown that M. pneumoniae shows metabolic re-
sponses and adaptation similar to more complex
bacteria, providing hints that other, unknown
regulatory mechanisms might exist.
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hybridization of samples in custom made arrays;
A. Wieslander (Stockholm University, Sweden) for advice
on the lipid requirements for the minimal medium;
J. Marcos del Aguila (Pompeu Fabra University, Barcelona,
Spain) for conceptual and experimental input on the
gas chromatography–mass spectrometry measurements;
T. Doerks (EMBL, Heidelberg, Germany) for operon analysis
and help in annotation; and J. Leigh-Bell for editorial
help. L.S. is an Institució Catalana de Recerca i Estudis
Supporting online Material
Materials and Methods
Figures S1 to S22
Tables S1 to S25
3 June 2009; accepted 2 October 2009
Transcriptome Complexity in a
Marc Güell,1Vera van Noort,2Eva Yus,1Wei-Hua Chen,2Justine Leigh-Bell,1
Konstantinos Michalodimitrakis,1Takuji Yamada,2Manimozhiyan Arumugam,2
Tobias Doerks,2Sebastian Kühner,2Michaela Rode,2Mikita Suyama,2* Sabine Schmidt,2
Anne-Claude Gavin,2Peer Bork,2† Luis Serrano1,3†
To study basic principles of transcriptome organization in bacteria, we analyzed one of the smallest
self-replicating organisms, Mycoplasma pneumoniae. We combined strand-specific tiling arrays,
complemented by transcriptome sequencing, with more than 252 spotted arrays. We detected
117 previously undescribed, mostly noncoding transcripts, 89 of them in antisense configuration to
known genes. We identified 341 operons, of which 139 are polycistronic; almost half of the
latter show decaying expression in a staircase-like manner. Under various conditions, operons
could be divided into 447 smaller transcriptional units, resulting in many alternative transcripts.
Frequent antisense transcripts, alternative transcripts, and multiple regulators per gene imply a
highly dynamic transcriptome, more similar to that of eukaryotes than previously thought.
specific data sets are still missing, limiting our
Similarly, the number of classified noncoding
lthough large-scale gene expression
studies have been reported for various
bacteria (1–7), comprehensive strand-
but a complete and unbiased repertoire is still not
available. To obtain a blueprint of bacterial tran-
scription,we combinedthe robustnessand versa-
and 252 array experiments (9)], the superior res-
27 NOVEMBER 2009 VOL 326
on November 27, 2009