RESEARCH ARTICLES
Transcription, Translation, and the Evolution of Specialists and Generalists
Shaobin Zhong,* Stephen P. Miller, Daniel E. Dykhuizen,à and Antony M. Dean *Department of Plant Pathology, North Dakota State University; Department of Ecology, Evolution, and Behavior, Biotechnology
Institute, University of Minnesota; and àDepartment of Ecology and Evolution, State University of New York at Stony Brook
We used DNA microarrays to measure transcription and iTRAQ 2D liquid chromatography-mass spectrometry/mass
spectrometry (a mass-tag labeling proteomic technique) to measure protein expression in 14 strains of Escherichia coli
adapted for hundreds of generations to growth-limiting concentrations of either lactulose, methylgalactoside, or a 72:28
mixture of the two. The two ancestors, TD2 and TD10, differ only in their lac operons and have similar transcription and
protein expression profiles. Changes in transcription and protein expression are observed at 30–250 genes depending on
the evolved strain. Lactulose specialists carry duplications of the lac operon and show increased transcription and
translation at lac. Methylgalactoside specialists are galS– and so constitutively transcribe and translate mgl, which
encodes a transporter of methylgalactoside. However, there are two strains that carry lac duplications, are galS–, and
show increased transcription and translation at both operons. One is a generalist, the other a lactulose specialist. The
generalist fails to sweep to fixation because its lacþ, galSþ competitor expresses the csg adhesin and sticks to the
chemostat wall, thereby preventing complete washout. Transcription and translation are sometimes decoupled. Lactuloseadapted strains show increased protein expression at fru, a fructose transporter, without evidence of increased
transcription. This suggests that fructose, produced by the action of b-galactosidase on lactulose, may leach from cells
before being recouped. Reduced expression, at ‘‘late’’ flagella genes and the constitutive gat operon, is an adaptation to
starvation. A comparison with two other long-term evolution experiments suggests that certain aspects of adaptation are
predictable, some are characteristic of an experimental system, whereas others seem erratic.
Introduction
One obvious product of evolution is life’s diversity—
trees, birds, fish, germs, etc. Much of this diversity reflects
ecological specialization. Trade-offs are commonly assumed to cause ecological specialization, yet rarely are
demonstrated in rigorous controlled experiments. Other
mechanisms can also produce specialists. Adaptation in different environments leads to independent specializations.
Escherichia coli adapting to high temperatures do not lose
fitness at low temperatures; E. coli adapting to low temperatures do not lose fitness at high temperatures (Bennett and
Lenski 1993, 2007). Accumulation of mutations that are
neutral in the current environment, but deleterious in other
environments, such as temperature-sensitive mutations
(Bennett and Lenski 1993) can produce specialists (Cooper
and Lenski 2000). Independent specialization and mutation
accumulation produce specialists without trade-offs.
Trade-offs, independent specializations, and mutation
accumulation are not mutually exclusive mechanisms. Efforts to quantify their relative contributions in long-term adaptation experiments remain inconclusive (Elena and
Lenski 2003; MacLean and Bell 2003). The role of
trade-offs in ecological specialization remains elusive.
We are exploring the evolution of specialists and generalists using laboratory populations of E. coli competing for
two limiting sugars, lactulose (galactosylfructose) and methylgalactoside, either singly or as a 72:28 mixture (Dykhuizen
and Dean 2004; Zhong et al. 2004). Theory predicts, and experiments demonstrate, that two specialists may coexist
whenever differential resource consumption generates stabi-
Key words: evolution, specialists, generalists, protonomics, E. coli,
lac, chemostats.
E-mail: [email protected].
Mol. Biol. Evol. 26(12):2661–2678. 2009
doi:10.1093/molbev/msp187
Advance Access publication August 25, 2009
Ó The Author 2009. Published by Oxford University Press on behalf of
the Society for Molecular Biology and Evolution. All rights reserved.
For permissions, please e-mail: [email protected]
lizing frequency-dependent selection (Lunzer et al. 2002).
Small changes in fitness are predicted to destabilize the polymorphism, resulting in a selective sweep. Nevertheless, both
initial specialists are usually retained for extended periods. In
those cultures where one is lost, two newly evolved specialists derived from the remaining strain can be isolated. Not
only do polymorphisms of specialists routinely evolve in
long-term chemostat cultures but, even more remarkably,
strains commonly switch resource specializations. Generalists are rare. The repeated independent evolution of resource
specialists and resource switching strongly suggests, but is
not definitive proof of, a role for trade-offs.
Genomic analysis of evolved strains (Zhong et al. 2004)
reveals a major role for insertion sequences (ISs) in adaptation. IS-induced duplications of the lac operon are associated
with specialization toward lactulose. IS insertions in galS
disrupt the repressor of the mgl transport system and are
associated with specialization toward methylgalactoside.
Only one of 42 strains analyzed carries both a lac duplication
and a galS disruption and appears to be a generalist. Other IS
mutations, deletions extending into the fli operon (part of the
flagellar regulon), and two deletions of the constitutively
expressed gat operon, presumably confer their benefits by
eliminating unnecessary gene expression. Regulation and
gene expression are evidently major targets of adaptation.
RNA transcription profiling has been used to quantify
gene expression changes during experimental evolution.
Ferea et al. (1999) found parallel changes in transcript levels at genes responsible for glucose uptake and utilization in
three lines of yeast evolved in glucose-limited cultures.
Cooper et al. (2003) compared transcript profiles of two
strains evolved for 20,000 generations by serial transfer
in glucose minimal medium and showed parallel changes
in 59 genes. Riehle et al. (2005) identified gene expression
changes, some of which might be related to the evolution
of the temperature niche. With the exception of the increased expression of transport systems during prolonged
starvation (Novick and Horiuchi 1961; Horiuchi et al.
2662 Zhong et al.
Table 1
Evolved Strains (Dykhuizen and Dean 2004)
Chemostat
1
1
10
10
19
19
20
20
21
21
22
22
23
23
Sugars
Ancestor
Strain
TD2
TD2
TD2
TD2
TD10
TD10R
TD2R
TD10
TD10R
TD2
TD10R
TD2
TD10
TD10R
TD2
TD10R
DD2459
DD2460
DD2557
DD2558
DD2268
DD2269R
DD2253R
DD2255
DD2302R
DD2304
DD2261R
DD2262
DD2266
DD2267R
LU
LU
MG
MG
Mix
Mix
Mix
Mix
Mix
Mix
Mix
Mix
Mix
Mix
Specializationa
Generation Isolated
LU (ancestor)
MG (ancestor)
Both
MG
LU
LU
MG
LU
MG
MG
LU
598
598
332
332
471
471
260
260
610
610
335
335
471
471
Fitnessb on LU
Fitnessb on MG
0.91 ± 0.004
1.31 ± 0.004
1.23 ± 0.02
1.22 ± 0.01
1.11 ± 0.01
1.26 ± 0.01
0.78 ± 0.01
0.90 ± 0.01
1.36 ± 0.02
0.48 ± 0.01
1.67 ± 0.001
0.39 ± 0.01
LU, lactulose; MG, methylgalactoside; Mix, 72:28 mixture of laculose:methylgalactoside; and R, resistance to phage T5.
a
Specialization as determined from relative fitnesses on pure sugars.
b
Fitness of evolved strain relative to its evolved partner.
1962, 1963; Dean 1989; Sonti and Roth 1989; Ferea et al.
1999; Zhong et al. 2004), the deletion of unneeded constitutively expressed genes (Zhong et al. 2004), and the increased expression of enzymes involved in cross-feeding
in an evolved commensalistic community derived from
a single clone of E. coli (Rosenzweig et al. 1994), the physiological mechanisms underpinning adaptation to novel environments are not well understood, even when parallel
beneficial mutations have been identified.
Several studies have shown that changes in transcription
need not be matched by similar changes in protein expression
(Griffin et al. 2002; Corbin et al. 2003; Greenbaum et al.
2003). This decoupling of transcription from translation
means that microarray data alone are insufficient to make
strong inferences with respect to organismal phenotypes,
fitnesses, and adaptation. Although essential to many studies,
microarray data should form only part of a more comprehensive approach to the study of adaptive evolution.
Here, we explore the evolution of gene regulation using microarray RNA transcript profiling and iTRAQ twodimensional liquid chromatography mass spectrometry/
mass spectrometry (2D LC–MS/MS) (Ross et al. 2004) protein expression profiling. Our goal is to identify changes in
transcript levels and protein expression associated with
specialization toward lactulose and methylgalactoside, to
identify other changes in expression associated with adaptation to slow growth chemostats in general, and to determine whether these changes are always associated with IS
mobilization or whether other mutations (e.g., base substitutions) also contribute.
Materials and Methods
Bacterial Strains
Escherichia coli strains TD2 and TD10 are the ancestral strains used to initiate all long-term experiments (table 1).
They carry different lac operons but are otherwise genetically identical. Strains designated R (e.g., TD10R) carry
a selectively neutral genetic marker, fhuA, that confers re-
sistance to the bacteriophage, T5. TD10(R) and TD2(R)
form a frequency-dependent balanced polymorphism on
a 72:28 lactulose:methylgalactoside mixture (Lunzer et al.
2002). Long-term chemostat cultures were initiated with
a pair of strains, one T5 sensitive and one T5 resistant
(e.g., TD10 with TD10R or TD2R and TD10, etc.). Strains
designated DD (e.g., DD2261) are isolates from long-term
chemostat cultures with lactulose, methylgalactoside, or
a 72:28 mixture of both as limiting resources (Dykhuizen
and Dean 2004). After hundreds of generations of evolution, one sensitive and one resistant isolate were randomly
chosen from each chemostat. Isolates from chemostats fed
mixed sugars were shown capable of reestablishing the
equilibrium T5R frequency seen in each evolved population at the time of sampling (and which had evolved away
from the initial equilibrium). This demonstrates that the isolates are representative of the dominant lineages in each
chemostat (Dykhuizen and Dean 2004).
The fitness of TD10R with respect to TD2 is 0.91 on
lactulose and 1.31 on methygalactoside (table 1). Similarly,
for example, the fitness of DD2255 with respect to DD2253
is 1.11 on lactulose and 0.78 on methylgalactoside. Specialists are defined by their fitnesses: DD2255 is a lactulose
specialist and DD2253 is a methylgalactoside specialist.
Fitnesses are not given for strains isolated from chemostats
limited by single sugars. Partitioning the contribution to
fitness made by specialization toward the limiting sugar
from the contribution made by adaptation to the chemostat
environment in general will require extensive genetic manipulations that, though planned, are beyond the scope of
this study.
Transcript Profiling
RNA Extraction
Strains were grown at 37 °C in 30-ml chemostats at a dilution rate of 0.3 h1 in minimal medium (Davis Salts; 40 mM
K2HPO4, 15 mM KH2PO4, 7.6 mM (NH4)2SO4, 1.7 mM Na3Citrate, 1 mM MgCl2 at pH 7.3) (Miller 1972) with 0.2 g/l of
Specialists and Generalists 2663
either lactulose or methylgalactoside as the sole limiting resource and 10 lM isopropyl-b-D-thiogalactopyranoside
(IPTG) (Dykhuizen and Dean 2004). After the population
density (OD600) had stabilized (18–20 h after inoculation),
the entire culture from each chemostat was poured into
a 50-ml conical tube containing 3 ml of ice-cold ethanol/phenol stop solution (5% water-saturated phenol, pH , 7.0, in
ethanol), centrifuged (4,000 rpm for 10 min at 4 °C), and the
cell pellets stored at 80 °C. Total RNA was extracted using
a Master RNA purification kit (Epicentre, Madison, WI) according to the manufacturer’s instructions.
Microarrays and Data Analysis
Gene expression changes were identified by using parallel 2-color cDNA hybridizations to whole-genome E. coli
MG1655 spotted DNA microarrays, which were designed
and printed as described and contained 98.8% of all annotated open reading frames (Khodursky et al. 2000, 2003).
Cy3 dUTP or Cy5 dUTP (Amersham Pharmacia) was incorporated into cDNA made from 15 to 20 g of total RNA
using Scribe First-strand cDNA labeling Kit (Amersham
Pharmacia). The labeled cDNA was purified using a Microcon-30 (Millipore). Replicate experiments were performed
with a dye swap using two RNA samples from independent
replicate populations (the number of RNA preparations was
higher for strains TD2 and TD10 because 1) they were used
in every hybridization and so more RNA needed to be extracted and 2) some preparations were thrown out because
they had already increased levels of expression at lac or
galS, both of which are strongly favored). Following data
normalization, outliers and missing data were replaced by
mean log2 ratio values (evolved strain:ancestor) from the
other replicates. A fixed model analysis of variance (ANOVA) was performed for transcript level ratios of each
evolved strain with respect to its ancestor. No significant
Genes Dyes interaction effects were detected and dyes
swaps were treated as four independent replicates. Each
analysis therefore had Genes and Sugars as main effects
and a Genes Sugars interaction effect. The Sugars main
effect is zero because the data were normalized. Significant
changes in transcript levels were identified using a 1% false
discovery rate (Benjamini and Hochberg 1995).
iTRAQ Proteomics
Growth of Strains
Evolved strains were grown at 37 °C in 100-ml
chemostats at a dilution rate of 0.3 h1 using the same medium to which they had been adapted (minimal medium
with 0.2 g/l of either lactulose, methylgalactoside, or
a 72:28 mixture of both as the sole limiting resource and
10 lM IPTG). The ancestral strains, TD2 and TD10, were
grown with lactose as the sole carbon/energy source to
avoid selected changes in expression at lac and galS that
arise rapidly. After reaching steady state (22–24 h after inoculation), the entire culture from each chemostat was harvested by centrifugation (4,000 rpm for 10 min at 4 °C),
quick frozen, and stored at 80 °C. Each pair of evolved
strains and their ancestors were grown in sets of four parallel chemostats.
Protein Extraction and iTRAQ Labeling
For the proteomic studies, iTRAQ Reagents (Applied
Biosystems, Foster City, CA) were used to label protein
samples, which enabled the simultaneous identification
and quantification of peptides/proteins from four different
samples in a single experiment. For each experiment, the
growth, protein extraction, and iTRAQ labeling of the
evolved strains and their ancestral strain were done in parallel sets of four.
Samples from four chemostats, including the ancestral
strain, were prepared and labeled in parallel for analysis by
2D LC–MS/MS. The cell pellet from each chemostat was
first resuspended in 500 ll of 0.5 M triethylammonium bicarbonate (pH 8.5) containing 0.1% CHAPS and 0.05% sodium dodecylsulfate as denaturants. The suspensions were
sonicated three times for 20 s each on ice with a Branson
Digital Sonifier (Model 250) equipped with a microtip at
25% amplitude. The suspensions were allowed to cool
on ice between sonications. The samples were then centrifuged at 16,000 g for 10 min at 4 °C. The supernatants
(cell extracts) were divided into aliquots and stored at
80 °C. Protein concentrations were determined using
the Bio-Rad Protein Assay Reagent with IgG as a standard.
The four samples were labeled in parallel, each with
a separate iTRAQ Reagent, as recommended by the manufacturer (Applied Biosystems). Briefly, each extract was
diluted in sonication buffer to 5 mg/ml protein, a 100 lg
of protein reduced, and all cysteines blocked with the reagents supplied by the manufacturer. Proteins were digested
overnight at 37 °C with 4 lg trypsin and the resulting peptides covalently labeled with an isobaric iTRAQ reagent at
lysine side chains, N-terminal groups, and tyrosines. It is
important to note that a labeled peptide displays the same
mass and chromatographic properties whether labeled with
iTRAQ Reagent 114, 115, 116, or 117.
Strong Cation Exchange Chromatography of
Differentially iTRAQ-Labeled Sample Mixture
The strong cation exchange (SCX) chromatography of
the labeled peptide mixture was conducted by the Center for
Mass Spectrometry and Proteomics (University of Minnesota, Minneapolis, MN). The four differentially labeled
sample digests were combined, the mixture dried in vacuo,
resuspended in 0.1% trifluroacetic acid (TFA), and applied
to a Sep-Pak C18 cartridge (Waters Corp., Milford, MA).
After washing with 0.1% TFA, the peptide mixture was
eluted with 80% acetonitrile (ACN) in 0.1% TFA. The
eluted peptide mixture was dried in vacuo, reconstituted
in 350 ll of Solution A (20% ACN, 5 mM KH2PO4 at
pH 3.2), and subjected to SCX chromatography. The chromatography was performed on a Magic 2002 highperformance liquid chromatography system (Michrom
Bioresources, Auburn, CA) using a polysulfoethyl A column (1.0 mm ID 150 mm; 5-lm particles with 300 Å
pores; Poly LC, Columbia, MD). Peptides were eluted at
2664 Zhong et al.
a flow rate of approximately 33 ll/min with Solution A and
Solution B (Solution A containing 500 mM KCl) over a gradient of 0–20% B in 40 min, 20–100% B in 20 min, and
constant 100% for 10 min. The absorbance of the eluent
was monitored at 214 nm (peptide bonds) and 280 nm (aromatic residues) with fractions collected at 3-min intervals.
Typically, the peptides eluted in 17 fractions, which
showed mAU280 values .2. These fractions were dried
in vacuo and then analyzed by reversed-phase LC–MS/MS.
Reversed-Phase LC–MS/MS Analysis
LC–MS/MS analysis of SCX fractionated peptides was
performed by the Center for Mass Spectrometry and Proteomics (University of Minnesota, Minneapolis, MN) on
a QSTAR Pulsar i mass spectrometer (Applied Biosystems)
with an online Dionex/LC Packings (LCP, Sunnyvale, CA)
C18 capillary liquid chromatography system as described
previously (Nelsestuen et al. 2005). Each of the dried SCX
fractions was reconstituted in 30 ll of the reversed-phase
loading solution (2% ACN, 0.1% formic acid). The entire
volume of fractions having an mAU280 value , 10 was
loaded onto the LCP C18 precolumn (0.3 mm ID 5 mm). Fractions having an mAU280 value . 10 (typically
12 fractions) were loaded and run twice. Half of the sample
was loaded and the tandem mass spectrometry (MS/MS) data
were collected as described below. The remaining portion of
the fraction was then loaded and data collected using an
exclusion list containing the acquired precursor mass/charge
ratios (m/z) values from the first run.
After loading onto the C18 precolumn, each sample
was washed with the loading solution for 17 min at a flow
rate of 35 ll/min. Peptides were then eluted at the same flow
rate onto an analytical capillary C18 column (75 lm ID)
with solvents A (5% ACN, 0.1% formic acid) and solvent
B (95% ACN, 0.1% formic acid) over a gradient of 0–35%
B in 40 min, 35–80% B in 5 min, and 80–100% B in 2 min.
Product ion spectra were collected in an information-dependent acquisition mode with continuous cycles of one full
scan from 400 to 1100 m/z per 1.5 s followed by four product ion scans from 50 to 2000 m/z at 3 s each. The four
precursor m/z values with the highest intensities were automatically selected for MS/MS fragmentation by the
Analyst QS software (ABI) from the MS scan during acquisition. Collision energy was increased 20% for fragmentation of iTRAQ peptides. Neutral loss of the iTRAQ Reagent
balancer groups during MS/MS fragmentation produces
four reporter group ions in the 113–119 m/z region that can
be used to quantify peptide expression. Unlabeled b and
y ions are also generated and used for peptide identification.
Data Processing
The identification and quantification of the relative
abundance of proteins was determined from the MS/MS
data using the ProteinPilot 2.0 software (Applied Biosystems). All of the MS/MS data files obtained from the peptide fractions of a single SCX chromatography were
searched together against the NCBI E. coli K12 protein database (RefSeq NC000913 and AC000091, Riley et al.
2006) which contains 8,772 proteins. The ‘‘thorough search
effort’’ algorithm was used with the threshold for protein
identification at 95% confidence. Common biological modifications and amino acid substitutions were automatically
included in every search. The ProteinPilot software determines the relative abundance of each peptide in the evolved
strain verses its ancestor by calculating the ratio of the peak
areas of their iTRAQ reporter ions. These results are then
compiled into protein groups based on the database search.
Peptides shared between distinct proteins are not used in
quantification. The average of the peptide iTRAQ ratios
is calculated for each protein. This average ratio includes
only those peptides for which all four iTRAQ reporter ions
were detected. Data are normalized assuming that the majority of proteins do not show differential expression and
that the median iTRAQ ratio of all the proteins is 1.
All proteins discussed were identified with a minimum
of three peptides for quantification. The MS and MS/MS
spectra of each was inspected manually and curated based
on the precursor ion spectra.
Standard Polymerase Chain Reaction (PCR) and DNA
Sequencing
Primers, used to amplify galS (the repressor of the mgl
operon) and gat (the galactitol operon was amplified in two
sections), were designed using the genomic sequence of E.
coli K12 strain MG1665. Herculase DNA polymerase
(Stratagene) was used with the following cycling conditions: 95 °C for 15 min, 35 cycles of 94 °C for 30 s,
60 °C for 90 s, 72 °C for 2 min, and a final extension step
at 72 °C for 10 min. Amplicons were sized by agarose electrophoresis with a 1-kb DNA ladder as a standard. GalS
fragments were purified using StrataPCR purification columns and sequenced by the Advanced Genetic Analysis
Center at the University of Minnesota.
Curli Expression
Expression of the extracellular adhesin protein Curli
was confirmed by the intense red staining of colonies
on minimal glucose plates containing 0.1 g/l congo red
(Hammar et al. 1995).
Results
Ancestral Strains TD2 and TD10
Ancestral strains TD2 and TD10 are genetically identical derivatives of E. coli strain K12 that carry different lac
operons (Lunzer et al. 2002). Thus, except for the expression
of the lactose operon, we expect the transcription profile and
the protein expression for these two strains to be the same regardless of the environment. The next two sections test this
assumption. But they also test the ability to use transcription
profiling and proteomics to determine evolutionary change.
If we find many genes that are expressed differently between
these strains, we would question the reliability of these
methods to distinguish evolutionary differences.
Specialists and Generalists 2665
Table 2
ANOVAs of Variance of Lactulose:Methylgalactoside Transcript Ratios in Ancestors TD2 and TD10
Item
Strains
Genes
SG
Error
Total
df
SS
MS
F
P
1
4,141
4,141
10,612
18,895
0.0
563.4
312.2
1,039.6
1,915.1
0.0
0.136
0.754
0.979
0.0
1.39
0.77
1
,0.0001
1
df, degrees of freedom; SS, sum of squares.
Transcription in Ancestral Strains TD2 and TD10
Overall, transcript profiles are similar for each strain
regardless of which sugar, lactulose or methylgalactoside,
was the limiting resource (i.e., all TD2LU:TD2MG transcript
ratios 1 and all TD10LU:TD10MG transcript ratios 1, so
that changing the chemostat sugar does not greatly affect
transcription). An ANOVA of lactulose:methylgalactoside
transcript ratios (table 2) produces no significant SxG
(Strains by Genes) interaction term, indicating transcription
responds similarly in both strains to the change in resource.
Significance of the Genes term indicates transcription
differs during growth on lactulose and on methylgalactoside. Seven genes (table 3) were identified with a 1% false
discovery rate (Benjamini and Hochberg 1995). However,
the changes in transcription are modest (generally less
than 1.5-fold) and, with the possible exception of galP
(a transporter that might be induced to scavenge galactose),
there are no obvious reasons why any should be differentially expressed on the two sugars. We suspect these
changes in transcript levels are either incidental or spurious. Data normalization eliminates any difference in the
mean lactulose:methylgalactoside transcript ratios between
the strains.
When the ancestoral strains are compared directly, an
ANOVA of TD2LU:TD10LU and TD2MG:TD10MG transcript ratios (table 4) on each of the two resources produces
no significant R G (Resources by Genes) interaction
term. This supports our contention that transcription responds similarly to the change in resource in both strains.
Significance of the Genes term indicates transcription at
some genes differs between the strains. Twenty genes with
modest changes in transcription were identified using a 1%
false discovery rate (table 5). Again, there are no obvious
reasons why any should be differentially expressed on the
two resources. Other genes cotranscribed with the four
genes, flgM, flgF, rpsD, and metE, show no evidence of
changed expression. We suspect these changes in transcript
levels are spurious. Data normalization eliminates any difference in the mean TD2:TD10 transcript ratios between the
strains. We conclude that transcription profiles of the two
ancestral strains are very similar—if they differ at all, they
do so in minor ways.
Protein Expression in Ancestral Strains TD2 and TD10
Protein expression was explored using trypsindigested samples labeled with iTRAQ, mixed samples being
fractionated by strong cation high-pressure liquid chromatography and separated by reversed-phase high-pressure
liquid chromatography immediately before entering an
electrospray ion trap mass spectrometer for peptide identification and quantification. The sample preparation used is
biased against hydrophobic (e.g., membrane) proteins.
An example of iTRAQ 2D LC–MS/MS proteomics
data (fig. 1) illustrates how a single peptide can be isolated,
fragmented, sequenced, and expression quantified in four
strains simultaneously. Mass spectrometry is extraordinarily precise in determining mass charge (m/z) ratios so
sequencing errors are rare. Intensities, being subject to vagaries in sample preparation, are less precisely determined.
Often, as in this example, numerous peptides from the same
protein can be identified. Averaging them improves expression estimates. iTRAQ proteomics routinely identifies peptides from more than 450 proteins and can be used to
confirm changes in transcription rates and, where systematic discrepancies arise, point to the possibility of translational regulation of specific mRNAs.
Strains TD2 and TD10 showed very similar patterns of
protein expression. TD2 showed significantly higher expression of LacA than TD10 (log2(TD2/TD10) 5 3.45 ±
0.75). No other significant differences in expression were
detected among the 740 proteins quantified. We conclude
that translation profiles of the two ancestral strains are very
similar.
Table 3
Seven Genes Differentially Expressed on Different Sugars in Ancestors TD2 and TD10
b-Number
b0719
b0879
b1639
b2943
b3287
b3306
b3508
SE, standard error.
log2 LU/MG
SE
Gene
Function
0.45
0.51
0.66
0.52
0.36
0.66
0.48
0.11
0.11
0.16
0.11
0.08
0.16
0.11
ybgD
macB
mliC
galP
def
rpsH
yhiD
Predicted fimbrial-like adhesin
Subunit of MacAB–TolC macrolide efflux
Inhibitor of c-type lysozyme
Galactose transporter
Peptide deformylase
30S ribosomal subunit protein S8
Predicted Mgþþ transport ATPase
2666 Zhong et al.
Table 4
ANOVA of TD2:TD10 Transcript Ratios on Lactulose and Methylgalactoside
Item
Resources
Genes
RG
Error
Total
df
SS
MS
F
P
1
4,141
4,141
29,508
37,791
0.0
484.1
309.5
2,133.0
2,926.6
0.0
0.117
0.075
0.072
0.0
1.62
1.04
1
,0.0001
0.0763
Transcription in Evolved Strains
We analyzed transcript levels in seven pairs of evolved
strains that had evolved together: one pair adapted to 100%
lactulose, one pair adapted to 100% methylgalactoside, and
five pairs adapted to a 72:28 lactulose:methylgalactoside
mixture. Unlike the other five strains, the first two pairs
could have a common ancestor within the experiment. Each
evolved strain was grown in a chemostat on a pure resource,
RNA isolated, reverse transcribed, and competitive hybridization used to assess any changes in transcript levels between the ancestor and the descendent. ANOVAs of
evolved strain:ancestor transcript Log2 ratios are strongly
significant with respect to differences in gene expression
(many are greater than 4-fold). The frequency distribution
of 906 effects determined to be significant (a 5 0.01) by
ANOVAs for the 14 evolved strains analyzed (fig. 2) shows
that transcript levels of certain genes are routinely changed
during evolution. In contrast, R G interactions are rare
and more nearly binomially distributed. One possibility
is that R G interactions are erratic in their evolution. More
probably they are artifacts of sophisticated experimental
procedures. Most are weakly significant (the overwhelming
majority of which are less than 1.5-fold) and the one notable
exception described below is indeed an artifact.
Given the huge number of comparisons made, keeping
a significance level at a 5 0.01 is likely to produce a considerable number of unique false positives. We therefore
used a 1% false discovery rate (Benjamini and Hochberg
1995) to identify significant changes in expression in individual strains. By this criterion, 661 transcript levels (rather
than 906) of 4,141 genes were identified as changed using
a 1% false discovery rate.
Each strain had a unique pattern of transcriptional
change. However, an overall impression of the pattern of
transcriptional evolution can be obtained by summing
the number of significant increases and decreases in transcription for each gene (fig. 3). Increased transcript levels
are common at lac, mgl, and galP. Increased transcription at
the maltose regulon (malEKM, malP, and lamB) is also observed in some strains. Lower transcript levels are evident at
gat. Lower transcript levels are routinely observed at fliE-R,
fliC-T, and other genes involved with motility (flgK-M, trg,
che, aer, yhjH, and tsr). However, some genes involved
with motility often show increased levels of expression
(flgA-J, flhA, and fliAZY). Changes in gene transcription
of outer membrane proteins are common, with increases
at yraJ and fec and reductions at ompA, ompC, fadL,
and fimA.
Protein Expression in Evolved Strains
With between 450 and 600 proteins quantified per
strain, coverage of protein expression is not as extensive
as that for transcription. Changes in protein expression ratios (evolvant:ancestor) correlate with changes in transcript
ratios (fig. 4). A linear regression, with different slopes for
Table 5
Twenty Genes Differentially Expressed in Ancestors TD2 and TD10 on Lactulose and Methylgalactoside
b-Number
b0308
b0473
b0532
b1023
b1071
b1077
b1241
b1272
b1348
b1972
b1973
b2742
b2761
b3296
b3591
b3829
b3890
b4104
b4286
b4347
log2 LU/MG
SE
Gene
Function
0.29
0.33
0.46
0.42.
0.32.
0.31.
0.43
0.20
0.45
0.38
0.63
0.41
0.19.
0.40
0.48
0.46
0.44.
0.32
0.38.
0.38.
0.07
0.07
0.10
0.10
0.07
0.07
0.10
0.05
0.10
0.10
0.10
0.10
0.05
0.10
0.10
0.10
0.10
0.07
0.10
0.10
ykgG
htpG
sfmD
pgaB
flgM
flgF
adhE
sohB
lar
yedZ
yedY
nlpD
ygcB
rpsD
secG
metE
yiiF
phnE
b4286
symE
Predicted transporter
HSP90 chaperone subunit
Predicted outer membrane protein
Predicted esterase
Anti-sigma factor for fliA
Flagellar protein
Alcohol dehydrogenase
Predicted peptidase
Prophage gene
Inner membrane protein
Reductase
Predicted outer membrane lipoprotein
Predicted protein
30S ribosomal subunit protein S4
Protein secretion complex
Methionine biosynthesis
Predicted protein
Organophosphate ester transport
Predicted protein
Predicted toxin
Specialists and Generalists 2667
FIG. 1.—iTRAQ data for a b-galactosidase peptide from ancestors TD10 and TD10R and the evolved specialists DD2266 (methylgalactoside) and
DD2267R (lactulose). (A) The precursor ion spectrum of the peptide DWENPGVTQLNR—intensity plotted against m/z ratio. The three peaks represent
differences in stable isotope composition. The mass of the dominant peak differs from expected by 1 in 105. (B) The MS/MS (tandem mass
spectrometry) spectrum of the DWENPGVTQLNR peptide showing the b and y ions produced by random fragmentation of its peptide bonds. Peptide
sequences are determined from the differences in the m/z (mass/charge) ratios of sequential ions. Although many b and y ions (red and green) are not
observed because the DWENPGVTQLNR is only partially fragmented, a sufficient number (blue) match the expected to allow unambiguous
identification and quantification. (C) The reporter ion spectrum reveals that only the lactulose specialist (DD2267R) has increased expression: 114.1
TD10 (ratio 1:1), 115.1 TD10R (ratio 0.92:1), 116.1 DD2266 (ratio 0.81:1), 117.1 DD2267R (ratio 3.54:1). (D) Peptide coverage of b-galactosidase.
Identification confidence: .95%, green; .50%, yellow; ,50%, red; no match, gray.
different evolved strains, yields an r2 5 0.32 (r2 range from
0.01 to 0.49 for individual strains). By definition, r2 5 0 for
regressions of an ancestor on itself (in the absence of
changes all ratios are unity and all deviations are experimental errors). Consequently, the correlations depend on
the number and magnitude of evolved changes in expression that have arisen. Removing ratios not significantly different from unity for both RNA and protein improves the fit
with an r2 5 0.57 (r2 range from 0.04 to 0.68 for individual
strains). One expects an r2 5 1 if transcription and trans-
lation are tightly coupled. Some of the scatter is undoubtedly caused by experimental error. However, some changes
do not appear coupled. In particular, a number of highly
expressed proteins show no evidence of similar changes
in RNA transcript levels. Even when the analysis is restricted to significant changes in both protein expression
and transcription levels, 24% of observations involve a significant increase in one and a significant decrease in the
other (table 6). These results suggest that a significant
amount of posttranscriptional regulation occurs in E. coli.
2668 Zhong et al.
Increases in lac Expression
FIG. 2.—Plot showing the frequency distribution of effects determined to be significant (a 5 0.01) by ANOVAs for each of 16 strains
analyzed. The line denotes the frequencies expected from a binomial
distribution: (a þ (1 a))n, with significance level a 5 0.01 and n 5 14.
A large excess of Gene effects (dots) indicates that transcript levels of
certain genes routinely change during evolution. The R G interaction
(squares) effects are rare and more nearly binomially distributed. Either
R G interaction effects are erratic in their evolution or they are artifacts
of sophisticated experimental procedures. A 1% false discovery rate
yields a similar plot in which the number of strains with a uniquely
significant effect is reduced.
Though they vary greatly in size and number of repeats, every IS-generated lac duplication spans a small region extending from prpC through the entire operon into
lacI (Zhong et al. 2004). Every lac duplication shows increased transcription (fig. 5). Adjacent genes codAB (cytosine salvage pathway), cynX (putative cyanate transporter),
and mhpR (transcriptional regulator of the 3-hydroxyphenylpropionate degradation pathway) routinely produce increased transcript levels when duplicated, though whether
or not these affect fitness is not known. Not all duplicated
genes show significant changes in transcription, however
(fig. 5). iTRAQ proteomics, with less coverage than microarrays, nevertheless confirms increased protein expression
of CodA, LacA, and LacZ and, in some lac-duplicated
strains, of PrpC and MhpR (table 7).
Strong R G interactions were detected at lac. Transcript levels were consistently higher in cells harvested
from lactulose-limited chemostats than in cells of the same
strain harvested from methylgalactoside-limited chemostats. Duplications at lac have never been observed during
adaptation to methylgalactoside (Dykhuizen and Dean
2004). Not only are tandem duplications highly unstable
(Bergthorsson et al. 2007), but also overexpression of
lac proteins is strongly deleterious during starvation in chemostats (Stoebel et al. 2008). We consider the lower transcript levels seen in strains grown on methylgalactoside an
artifact generated by selection favoring newly arisen lineages with contracted lac duplications.
Notable Changes in Expression
Increases in fruBKA Expression
Below we describe changes in expression, either in
transcription or translation, that either occur repeatedly
across replicate chemostat experiments or unique events
that are supported by additional experimental evidence.
iTRAQ protein expression data shows many evolved
strains have increased protein expression of FruA, FruB,
and FruK, albeit without evidence of increased transcription
at fruBKA (table 8). However, there is a real concern that the
FIG. 3.—Repeatability of experimental evolution across the Escherichia coli chromosome for genes with at least three significant changes. For each
gene, the number of significant increases is summed and the number of significant decreases subtracted. Genes within the dashed lines have only three
or four significant changes. Genes within boxes are genes of a single operon. Red, sugar transport and metabolism; blue, motility; brown, cell wall;
black, known functions; and white circles, unknown functions.
Specialists and Generalists 2669
FIG. 4.—The correlation between changes in protein expression and changes in transcript levels for 14 evolved strains. Correlated changes
involved with resource use (red circles) include duplications at lac and lowered constitutive expression at, or deletion of, gat. Correlated changes
involved with motility (blue circles) are mostly deletions involving the fli and lowered expression at che. Other correlated changes (black circles)
include changes at modA and pps and unique events. Increased protein expression is not always associated with changes in transcript levels. Increases in
protein abundances for fructose metabolism (fru, red dots) and motility (flgA, flgM, and fliA, blue dots) repeatedly occur without change in transcript
levels. The other proteins that increase uniquely in one culture are shown as black dots.
increased protein expression of FruA, FruB, and FruK seen
in the evolved strains grown on the mixed sugars might be
an artifact of having to grow the ancestors on lactose where
there is no fructose (a product of lactulose hydrolysis) available to induce transcription at fruBKA.
Evolved strains grown on methylgalactoside have the
same fruBKA transcript levels as the ancestors grown on
methylgalactoside and evolved strains grown on lactulose
have the same fruBAK transcript levels as the ancestors
grown on lactulose (table 8). Crucially, ancestors grown
on methylgalactoside have the same fruBAK transcript levels as ancestors grown on lactulose (table 9). Therefore,
transcript levels at fruBAK are unchanged in all strains
in all environments. The increased protein expression of
FruA, FruB, and FruK seen in the evolved strains occurs
without change in fruBAK transcript levels, although
whether this is a physiological or an evolved response is
yet to be determined.
Fructose, liberated during the hydrolysis of lactulose
by b-galactosidase, might leach from cells only to be recouped by the PTS transport system’s fructose-specific
components FruA and FruB. The fructose, now phosphorylated and less membrane permeable, is converted by FruK
(1-phosphofructokinase) into fructose 1,6 bisphosphate,
which enters central metabolism. Increased protein expres-
sion of FruA, FruB, and FruK is sometimes accompanied
by increased protein expression of PTS transport system
components (table 7) including Hpr and PtsI and PtsG (glucose specific) and ManX (hexose, including fructose, specific). This supports the idea that hexoses are being
recouped. Although sometimes confirmed by increased
transcript levels at manX, the result is not confirmed by increased cotranscription of manY and manZ. However, the
source of phosphoenolpyruvate used by the PTS system,
the ppsA-encoded phosphoenolpyruvate synthetase, is generally less abundant than in the ancestral strains with no
apparent change in rates of transcription. Thus, the data
are consistent with cross-feeding, although the extent to
Table 6
Contingency Table of Significant Changes in Transcript
Levels and Protein Abundance of Evolved Strains Relative to
Ancestors
Protein
RNA
–
þ
–
þ
148
35
36
80
2670 Zhong et al.
FIG. 5.—The effect of gene dosage on transcription. Duplications (horizontal lines with strain numbers) center on the lac operon. Increased
transcription at linked genes is partly attributable to gene dosage (note the decline as one moves away from lac) and partly to other causes (the
correlation is not perfect), including experimental error.
which the physiological changes are attributable to an evolutionary adaptive response is yet to be determined.
Increases in mgl Expression
IS inserts in galS disrupt the repressor of the mgl operon that encodes a glucose–galactose transport system
with a serendipitous affinity for methylgalactoside. All
strains with IS inserts in galS have greatly increased mgl
transcript levels. Four strains, DD2253, DD2261,
DD2262, and DD2304 do not have IS inserts in galS
yet also display increased mgl transcript levels. Sequencing
reveals each galS allele carries either a nonsense mutation
or a missense mutation. Evidently, increases in expression
at mgl are adaptations to growth on methylgalactoside
whatever their provenance. iTRAQ proteomics confirms increased protein expression at mgl.
DD2261 but iTRAQ proteomics finds no evidence of
changed protein levels. iTRAQ proteomics reveals reduced
protein expression in many strains (DD2459, DD2460,
DD2268, DD2269, DD2255, and DD2302) but without
any evident reductions in transcript levels. Only in
DD2267 are reduced transcript levels matched by reduction
in protein expression.
Reductions in gat Expression
Wild-type E. coli K12 has an IS3 inserted in gatR that
disrupts the repressor of the galactitol operon. Many
evolved strains display lowered transcript levels at gat.
In strains DD2459 and DD2460, the IS3 transposed into
yegW to delete the entire gat operon (Zhong et al. 2004).
Other evolved strains with lower gat transcript levels retain
the operon (confirmed by PCR). iTRAQ proteomics confirms that protein expression at gat is reduced in these
strains.
Increases in gal Expression
Increased transcription at galP is common and suggests that galactose, liberated by the intracellular action
of b-galactosidase, may leach from cells only to be recouped by the high affinity Hþ-coupled galactose symporter. iTRAQ proteomics failed to identify GalP peptides.
Transcription at galETKM is significantly higher in strains
DD2261 and DD2262 and iTRAQ proteomics results, although not significant, confirm the trend. Strains
DD2558, DD2253, and DD2304 show no evidence of increased transcription yet iTRAQ proteomics consistently
shows increased Gal protein expression.
Changes in mal Expression
Increased transcription at the maltose regulon (malEKM, malP, and lamB) is found in strains DD2557 and
Changes in Motility Gene Expression
Transcription of fliE-R, fliC-T, and many other motility
genes (flgK-M, ycgR, trg, che, aer, yhjH, and tsr) is reduced in
many strains. In all but one case, an IS1-d inserted at yedX in
the ancestors TD2 and TD10 transposes into the fliE-R operon
(Zhong et al. 2004). Crucially, the deletions either remove or
disrupt fliR, a component of the flagellar export apparatus
needed, among other things, to export the flgM encoded
anti-r28 factor. Transcription from class III flagellar promoters requires the fliA-encoded r28. Unable to export FlgM,
r28-dependent transcription of the ‘‘late’’ flagellar genes is
suppressed. Deletion or reduced transcription of fliT, an inhibitor of transcription from class II promotors, probably accounts for the increased transcription of the ‘‘middle’’
flagellar genes in the flg, flh, and fliAZY operons. iTRAQ proteomics confirms that protein expression is reduced for FliG,
Specialists and Generalists 2671
Table 7
Changes in Transcription and Translation
DD Strain
2459
2460
2557
2558
2268
2269
2253
2255
2302
2304
2261
2262
2266
2267
Ancestor
Specialty
Medium
TD2
LU
LU
TD2
LU
TD2
MG
MG
TD2
MG
TD10
Both
Mix
TD10
—
TD2
MG
Mix
TD10
LU
TD10
LU
Mix
TD2
MG
TD10
LU
Mix
TD2
MG
TD10
MG
Mix
TD10
LU
dup
[
[
—
—
—
—
—
—
dup
[
[
—
—
—
—
—
—
dup
[
[
—
[
[
—
—
—
dup
[
[
—
—
—
—
—
—
dup
[
[
—
—
—
—
—
—
IS
[
[
IS
[
[
—
—
—
mis
[
[
—
—
—
—
—
—
mis
[
[
mis
[
[
mis
[
[
IS
[
[
—
—
—
—
—
—
—
—
—
—
—
[
—
—
[
—
—
[
—
—
[
—
—
[
—
—
[
—
—
[
—
—
—
—
—
[
—
—
[
—
—
—
—
—
[
—
—
—
—
—
—
—
—
[
—
—
—
—
—
—
—
—
[
—
—
—
—
—
—
—
—
—
—
—
—
—
Y
Y
—
—
—
—
—
—
—
—
[
—
[
[
—
—
[
—
—
—
—
Y
—
—
[
—
—
—
[
—
—
—
—
—
—
—
[
[
—
—
—
—
—
—
—
—
—
—
—
Y
—
—
—
—
—
Y
—
—
Y
—
—
—
—
—
Y
—
—
Y
—
—
Y
—
—
—
—
—
Y
—
—
Y
—
—
Y
—
—
—
—
—
Y
—
[
Y
—
—
—
—
[
—
—
—
—
—
—
Y
—
Y
Y
—
Y
Y
—
—
—
—
—
—
—
—
—
—
—
—
—
Y
Y
—
Y
Y
—
Y
Y
—
Y
Y
—
Y
Y
—
—
—
—
Y
Y
—
—
—
—
—
—
—
Y
Y
—
Y
Y
—
—
—
—
Y
Y
—
Y
Y
—
Y
Y
—
Y
Y
—
—
—
—
Y
Y
—
Y
Y
—
—
—
—
—
—
D
Y
Y
—
Y
Y
—
—
—
D
Y
Y
D
Y
Y
D
Y
Y
D
Y
Y
—
—
—
—
—
—
—
—
—
—
[
—
—
—
—
—
[
[
—
Y
Y
—
[
—
—
[
[
—
[
[
—
[
[
—
[
[
—
—
—
—
—
—
—
[
[
—
—
—
—
—
—
—
—
[
—
—
Y
—
—
—
—
—
[
—
[
[
—
[
[
—
[
[
—
—
—
—
—
—
—
[
[
Specialist Resource Usage
lacZYA
DNA
dup
RNA
[
Protein
[
mglBAC
galS
—
RNA
—
Protein
—
Cross-Feeding or Recouping
fruBKA
DNA
—
—
RNA
—
—
Protein
[
[
gal
DNA
—
—
RNA
—
—
Protein
—
—
manXYZ
DNA
—
—
RNA
—
—
Protein
[
[
aceBKA
DNA
—
—
RNA
—
—
Protein
Y
Y
Chemostat
malGFE/malKL(lamB)M
DNA
—
—
RNA
—
—
Protein
Y
Y
gat
DNA
—
D
RNA
Y
Y
Protein
Y
Y
cheZYBRtar, cheWA,motBA, tsr
DNA
—
—
RNA
Y
Y
Protein
Y
Y
fliF-K, fliL-R
DNA
D
D
RNA
Y
Y
Protein
Y
Y
flgB-L
DNA
—
—
RNA
[
—
Protein
[
—
fliZYA/flgM
DNA
—
—
RNA
[
[
Protein
[
[
LU lactulose, MG methylgalactoside, dup duplication, D deletion, and mis missense mutation, [ increased expression, Y decreased expression.
FliC, CheZ, CheY, Tar, CheW, CheA, Tsr, Aer, Trg, and
YcgR and increased for FlgH, FlgM, and FliA.
These deletions necessarily remove dsrB (unknown
function), dcm, vsr, rcsA, and dsrA, which lie between yedX
and fliR. Low levels of expression make changes in transcription and protein expression difficult to identify (occasionally dsrB and dcm display significantly reduced levels
of transcription). Dcm is a DNA cytosine methylase that
methylates the second C in 5#-CCWGG sequences (Lieb
and Bhagwat 1996). Vsr is the very short patch repair mismatch endonuclease that nicks DNA in 5#-CTWGG sequences following deamination of the 5-methylcytosine
to thymine in the second position. Deleting both genes
eliminates both mutator and repair system and is anticipated
2672 Zhong et al.
Table 8
ANOVA of Transcript Ratios at fruBKA in Evolved Strains
Item
Strain
Resource
Gene
SR
SG
RG
SRG
Error
Total
df
SS
MS
F
P
9
1
2
9
18
2
18
123
182
0.879
0.019
0.106
1.279
1.869
0.494
0.790
11.839
17.361
0.098
0.019
0.053
0.142
0.104
0.247
0.044
0.096
1.01
0.19
0.55
1.48
1.08
2.57
0.45
0.43
0.66
0.58
0.16
0.38
0.08
0.97
to produce a modest reduction in the mutation rate (Lieb
1991). RcsA is a transcriptional regulator of flagella, capsular polysaccharide, and curli synthesis (Vianney et al.
2005). However, we find no evidence that loss of RcsA
has any impact on transcription in the capsular polysaccharide (wza, wzb, wzc, and wcaAB) and curli (csg) operons.
dsrA is a small antisense RNA that stimulates translation of
the alternative sigma factor RpoS (Majdalani et al. 2005).
Changes in Expression of Outer Membrane Proteins
We find significant changes in the transcription of outer membrane proteins; increases at yraJ and fec and reductions at ompA, ompC, fadL, and fimA. iTRAQ proteomics
failed to detect some hydrophobic proteins.
Changes in the Expression of the csg Operon
Strain DD2269 was observed to stick to chemostat
glass walls. There was a significant increase in transcription
of the csg operon in strain DD2269. Proteomic anaylses
failed to identify csg-encoded proteins as upregulated.
However, protein sample preparations bias against hydrophobic proteins. Instead, the hypothesis that expression of
curli adhesins increased was confirmed by staining cells
with congo red (Hammar et al. 1995).
Other Changes in Transcription and Translation
A number of genes show significantly increased transcription in some strains and significantly reduced transcription in others. For example, cspD shows increased
transcription in three strains and decreased transcription
in three others. Other changes are difficult to rationalize
without extensive biochemical and physiological investigations. Reduced transcription of two tricarboxylic acid cycle
enzymes (sdh and suc) and increased transcription of the
dicarboxylate transporter (dctA) are just a few of many examples. Hierarchical clustering (Eisen et al. 1998) failed to
resolve these transcriptional changes into known regulons
(fig. 6). Occasional increases and decreases in the transcription of individual genes may reflect experimental noise,
particularly when other cotranscribed genes evince no
changes.
iTRAQ proteomics confirms changes in transcription
at many genes, for example, with reduced expression of the
molybdate ABC-type transporter (ModA) and increased expression of the ATP-driven copper transporter (CopA). In
other instances, iTRAQ proteomics fails to confirm the
changes anticipated from microarray studies. For example,
lower transcript levels at melA appear as increases in protein
expression of the encoded a-galactosidase. As with FruA,
FruB, and FruK, iTRAQ proteomics sometimes detects
changes in protein expression where no changes in transcript levels are apparent.
Discussion
As FruBKA dramatically demonstrates, reproducible
changes in protein expression can be produced without
changes in transcript levels. There is indirect evidence in
Salmonella that fruBKA protein expression is regulated
at the level of translation (Sittka et al. 2008). Deletion of
hfq, which encodes a global posttranslational small RNA
dependent regulator, results in higher expression of fruB
among many other proteins. Hfq is not implicated in other
discrepancies apparent in table 7. Regardless of the mechanism, our parallel data sets show that changes in transcription and translation need not move in parallel.
A number of experimental evolution experiments and
population genetic surveys have made use of genomewide
transcript profiling (e.g., Gilad et al. 2006; Agudelo-Romero
et al. 2008; Genissel et al. 2008; Le Gac et al. 2008; St-Cyr
Table 9
ANOVA of Lactulose:Methylgalactoside Transcript Ratios at fruBKA in Ancestors TD2 and TD10
Item
df
SS
MS
F
P
Strains
Genes
SG
Error
Total
1
2
2
6
11
0.071
0.192
0.176
0.845
1.284
0.071
0.096
0.088
0.141
0.51
0.68
0.62
0.50
0.54
0.57
Specialists and Generalists 2673
FIG. 6.—Hierarchical clustering (Eisen et al. 1998) identifies changes at the mgl, lac, gat, and motility operons but fails to resolve other
transcriptional changes into known regulons.
et al. 2008; Vijayendran et al. 2008). Patterns of transcription differ among individuals, populations, ancestors, descendents, and closely related species. Correlations with
other patterns of variation can be shown to be statistically
significant. But as to why they differ is rarely explored
further and hardly ever subjected to definitive testing.
Our approach has been to devise a structured experiment to explore a ubiquitous bipartite phenomenon—the
evolution of specialists and generalists. Specialists can arise
through any of three mechanisms (Elena and Lenski 2003):
passively accumulated neutral mutations that prove deleterious in another environment (mutation accumulation), beneficial mutations that prove selectively neutral in another
environment (independent specialization) and, lastly, beneficial mutations that are deleterious in another environment
(antagonistic pleiotropy). Populations growing on pure sugars can specialize by any or all of these three mechanisms.
Those growing on mixed sugars cannot specialize by mutation accumulation because any mutation selectively neutral
for one sugar and deleterious for the second must be purged by
selection. Independent specialization is expected to produce
generalists. Antagonistic pleiotropy forces specialization.
Specialists and Generalists
Duplicating lac is an adaptation to growth on lactulose
(Zhong et al. 2004). Constitutive mgl expression (galS) is
an adaptation to growth on methylgalactoside (Zhong et al.
2004). In both cases, benefits are predicted to derive from
the increased transport of limiting nutrients, although increased rates of hydrolysis by b-galactosidase will make
a small contribution in the case of lac duplications (Dean
1989, 1995). Evolution in a limiting mixture of 72:28 lactulose:methylgalactoside usually produces a balanced polymorphism of two specialists: one ecotype carries a lac
duplication and is fittest on lactulose, the other ecotype
is mgl constitutive and is fittest on methylgalactoside. Only
one of 26 strains isolated from 13 long-term evolution experiments on the mixed sugars is a galS,lacdup generalist
(Dykhuizen and Dean 2004). Although these observations
suggest specialization through antagonistic pleiotropy, the
presence of just one generalist shows that antagonistic pleiotropy is not ubiquitous.
Invading a New Niche
How is the galSþ,lacþ strain DD2269 maintained in
the presence of the galS,lacdup generalist DD2268?
DD2269 manages to persist at low frequency (ca. 1%) even
though transcription and protein expression at mgl and lac
are no different from wild type. This is achieved by increased expression of csg, the curli operons, which produce
and export a fibrous surface protein (the presence of which
was confirmed by staining with congo red, Hammar et al.
2674 Zhong et al.
1995) that enables cells to stick to the glass wall of the chemostat, thereby preventing complete washout. Perhaps in
response to increased curli expression transcription of clpB,
which encodes a protein that resolubilizes aggregated proteins, is also increased. Similar to the adaptive radiation of
Pseudomonas in static microcosms (Rainey and Travisano
1998), the spatial heterogeneity provided by a chemostat
wall represents a new niche to be exploited.
and translation at gat is greatly reduced. How can DD2557
persist in the face of a superior methylgalactoside specialist? Increases in transcription and translation of crp increase
transcription and translation of a number of catabolite
repressed genes. In particular, increased transcription and
translation at aceBAK, which encodes enzymes of the
gyloxylate bypass, suggests DD2557 may utilize the acetate
fermented by DD2558 as a major source of carbon and
energy (see Fischer and Sauer 2003 for an alternative
possibility).
Genotype versus Phenotype
Another galS,lacdup strain, DD2261, is not a generalist but a lactulose specialist; DD2261 is fitter on lactulose
and less fit on methylgalactoside than its galS competitor
DD2262 (Dykhuizen and Dean 2004). The suggestion that
lacdup might reduce galS fitness on methylgalactoside
points to a role for ‘‘antagonistic pleiotropy’’ in the evolution of specialists and generalists. Certainly, antagonistic
pleiotropy between lacdup and galS alleles provides
a handy explanation for why specialists are commonplace,
but it does not explain why generalists exist. Perhaps
rare—and as yet unidentified—background mutations
negate the proposed antagonistic pleiotropy.
Cross-Feeding or Recouping?
Cross-feeding among evolved strains can also maintain diversity (Rosenzweig et al. 1994). Increased protein
expression of fruBKA is evident following adaptation to
pure lactulose (hydrolysis of which releases galactose
and fructose) but not methylgalactoside (hydrolysis of
which releases galactose and methanol). Similar increases
in protein expression are seen in strains adapted to mixed
sugars, including methylgalactoside specialist. Whether extensive cross-feeding between competitors occurs is by no
means certain, for the majority of fructose leached into the
periplasm of either strain might be recouped before it ever
has a chance to diffuse into the environment. Moreover, the
potential impact of cross-feeding in maintaining diversity
might possibly be negated by the fact that all strains increase expression of fruBKA in the presence of lactulose.
Increased protein expression, but not increased transcription, is sometimes evident at galETKM following adaptation to methylgalactoside. Similar increases seen
during adaptation to mixed sugars are found in several
methylgalactoside specialists but not in the six lactulose
specialists. It is not obvious why increases in gal protein
expression should be so restricted—galactose is released
upon the hydrolysis of either sugar. Increased transcription
at galP, the low affinity galactose transporter, is evident in
many strains but iTRAQ proteomics protocol did not detect
this hydrophobic membrane protein. Again, whether or not
extensive cross-feeding between competitors occurs is not
known.
Strains DD2557 and DD2558 were isolated from a culture adapted to pure methylgalactoside. DD2558 was, as
expected, galS with the expected strong increases in transcription and translation at the mgl operon. By contrast,
DD2557 is galSþ and showed no evidence of increases
in transcription and translation at mgl, whereas transcription
Three Long-Term Evolution Experiments
A detailed series of experiments dissecting adaptation
by E. coli strain MC4100 to glucose-limited chemostats
(i.e, Death and Ferenci 1994, reviewed by Ferenci 2008)
reveal that beneficial increased rates of glucose transport
are caused by mutations that increase activities at mgl (a
high affinity glucose/galactose transporter), ptsG (the glucose-specific PTS permease), and increase expression of the
nonspecific OmpC and OmpF porins and the glucose-/maltdextran-specific LamB porin of the outer cell wall.
In our experiments, adaptation to methylgalactoside
also favors high transport rates at mgl. We also find increased expression at ptsG. Unlike Ferenci, we cannot account for this directly because our strains were not grown
on limiting glucose. Instead, we suggest selection favors
increased expression of the cotranscribed ptsH and ptsI
components common to all PTS systems. These might increase transport of both galactose and fructose. Indeed, increased translation at ptsH and ptsI is seen only in strains
displaying increased translation at gal and/or fru, whereas
the converse is not necessarily true (table 7). Nor do Ferenci
and coworkers (Ferenci 2008) report the increased expression we detect in the nonspecific hexose PTS transporter
ManX. The extent to which these are involved with galactose and fructose cross-feeding is yet to be ascertained. It
could very well be minor, with increased expression being
primarily associated with recouping leached monosaccharides. Transcription at ompC is increased in three strains, reduced in seven more, and unchanged in four others. No
changes in OmpC protein levels were detected. No changes
in transcription were observed at ompF although one strain
shows increased protein expression and three others reduced expression. Lastly, increases in transcription at
mal are not matched by increases in translation—most
strains show lower mal protein expression (table 7). This
supports the contention that our strains are unlikely to be
transporting glucose.
Strains, such as MC4100, that express high levels of
the alternative sigma factor RpoS benefit greatly from its
loss during very slow growth in chemostats (Ferenci
2003, 2005). All yedX-fliR deletions necessarily remove
dsrA, a small antisense RNA that stimulates translation
of rpoS (Soper and Woodson 2008). Loss of dsrA may
therefore confer an advantage in addition to reduced motility gene expression. However, RpoS levels vary even
among E. coli K12 strains (e.g., MG1655 has lower levels,
Spira et al. 2008) and so it is not altogether surprising that
we should find attenuated rpoS transcription in only three
strains and no changes in the others.
Specialists and Generalists 2675
Rosenzweig et al. (1994) discovered that adaptation by
E. coli strain JA122 to a glucose-limited chemostat produced a community of three specialists, one fermenting
the primary resource to acetate and glycerol, with each
of these products consumed by one of two other specialists.
The acetate specialist had increased acetyl-CoA synthetase
activity (Acs) and the glycerol specialist had increased
glycerol kinase activity (GlpK). Treves et al. (1998) showed
that similar acetate specialists appeared in 6 of 12 subsequent long-term evolution experiments. Studies of adaptation by E. coli strain MC4100 to a glucose-limited
chemostat provide no evidence of cross-feeding (reviewed
by Ferenci 2008). We find weak evidence for it at best with
the DD320 background. Of the 14 strains studied, only
DD2266 shows increased transcription at acs—yet without
evidence of increased translation. DD2557 overexpresses
aceBAK (needed to assimilate acetate and possibly attributable to overexpression of Crp) instead of acs. However, this
might instead reflect glucose catabolism via the novel
phosphoenolpyruvate–glyoxylate cycle (Fischer and Sauer
2003). If true, the polymorphism is transient (pure scramble
competition for a single limiting resource—glucose—is incapable of maintaining both strains). Our failure to find evidence of glycerol cross-feeding is likely attributable to an
IS30 insertion in glpF, the glycerol channel (Zhong et al.
2004).
How Idiosyncratic Is Evolution?
Improved uptake of a limiting resource is a common
adaptive response to starvation, one frequently achieved
through increased protein expression of specific transporters (Horiuchi et al. 1962, 1963; Dean 1989; Sonti and Roth
1989; Ferea et al. 1999; Zhong et al. 2004; Ferenci 2008).
Another, attaching to the side of the chemostat, establishes
a subpopulation immune to washout (so-called wall
growth). Loss of motility is possibly the most common
adaptive phenotype during routine laboratory culture, repeatedly appearing in otherwise identical E. coli K12
strains (Macnab 1992).
Other changes are strain specific. Transcription at gat
is routinely downregulated in TD2 and TD10 because it just
happens to be constitutive in these strains. MC4100 usually
reduces its high endogenous RpoS expression as it adapts to
starvation. Similar changes were detected in only 3 of 14
cases with TD2 and TD10. During starvation on glucose,
JA122 often evolves into a community, whereas
MC4100 never does. Other changes are environment specific. Lac duplications are specific to limitation by certain
galactosides but not methylgalactoside. Increased protein
expression at fru is dependent on the presence of fructose.
The benefits conferred by rpoS mutants are higher at low
growth rates.
Perspective
Genomics and proteomics produce so much data that
one can easily lose sight of the trees, let alone the forest and
end up concentrating only on a large pile of leaves. In this
section, we will try to provide a framework for these data,
trying to look at the ‘‘forest’’ again, by assessing the results
in terms of the major question that motivated this work. We
want to understand the causes of natural selection; how environment and genetic variation produce natural selection.
The main experimental model used is to grow E. coli in
a chemostat and investigate genetic changes promoted by
natural selection. Enough work has now been done that
we can suggest certain generalizations.
1. There is selection for constitutive expression in
regulated systems involved with the uptake and
dissemination into metabolism of the limiting nutrient,
up to the point that the substrates cannot escape from
the cell. We have selected for constitutive mutations in
all sugars tested (10), which includes maltose, rhamnose, and fructose, used as the limiting resource in
chemostats (unpublished data). The general principle is
that there will be selection for any genetic change that
increases the gradient of the concentration of the
limiting nutrient between the outside and inside of the
cell.
2. Metabolic systems with constitutive mutations not used
by cells in selective environments, such as gat in our
strains, are eliminated. This principle of selection
against the production of unneeded proteins is quite
general (Stoebel et al. 2008). We predict that if our
chemostats had been run for a longer time, gat would
be eliminated in all cultures, just as rbs is eliminated in
all long-term cultures of E. coli B (Cooper et al. 2001).
The selection coefficient on a repressed operon is
sufficiently small that deletions that remove the operon
are effectively neutral (Stoebel et al 2008). However,
the rbs operon was eliminated by deletion even though
it was regulated (Cooper et al. 2001). The basal level of
rbs proteins was evidently high because there was
a significant decrease in expression in the strains where
the operon was deleted (Cooper et al. 2003). Presumably, this was because of regulatory interactions
with a constitutive duplication of most of the rbs
operon unique to E. coli B (Lin 1996).
3. Metabolic systems induced in the environment used,
but not required, are selectively eliminated or downregulated. Two obvious examples are the flagellar
genes (flagella are useless in a homogeneous, rapidly
mixed liquid environment as is the chemostat) and the
RpoS system. Again, unneeded proteins are selected
against. However, the RpoS system includes a large
number of proteins, some of which might be advantageous and others detrimental, so the outcome of
selection can vary from culture to culture. Points 2
and 3 are changes caused by the same mechanism,
selection against unneeded proteins, but are different in
that the constitutives in point 2 are strain specific and
are present either because of chance or history, whereas
the induction in point 3 will be a natural induction
specific to the environment and found across most
strains.
4. When there are multiple replaceable limiting resources, it
is likely that some of the resources are at a concentration
where only part of the population selects for upregulation
of the uptake and initial metabolism, because of the
2676 Zhong et al.
trade-off between selection for uptake and the selection
against overexpression. For example, in mixed lactose–
maltose chemostats not all the cells are selected for
lactose constitutivity when 10% of the sugar is lactose,
but all are when 25% of the sugar is lactose (Dykhuizen
and Davis 1980). The low concentration resources can be
waste products from fermentation as ethanol or acetate or
can be an intermediate compound that can diffuse out of
the cell as fructose seemingly does in the experiment
reported here.
These four generalizations involve only two causes of
selection: Selection to increase the concentration gradient
of the limiting nutrient across the cell membrane and selection against unnecessary protein synthesis. Except for
RpoS, these changes happen regularly and quickly in most
cultures of an experiment. Thus, we can conclude that these
two causes explain the initial adaptation of bacteria to a chemostat environment. Are there other causes for adaptation
in these experiments? If not, then competition for resources
is likely to be a minor part of evolution, becuase there are so
many other genes performing so many other functions.
However, numerous changes are found in only a minority
of replicate chemostat experiments. These are often in
genes that are not obviously related to the functions given
above. We might expect that these represent other causes of
natural selection. The reasoning for this statement is given
in the next paragraph.
The initial temptation is to assume these uncommon
changes are chance events where selectively neutral mutations hitchhike with advantageous mutations. This is unlikely, because the mutation rate of 1010 per nucleotide
per generation for E. coli is so much smaller than the inverse
of the number of nucleotides in the E. coli genome (5 106
nt). This means that only 1 in 2 103 cells has a new mutation in each generation. After 500 generations, only one of
four cells will have a nucleotide change. In a chemostat after 500 generations, if there is no selection, we expect only
25% of cells to differ by a single nucleotide from the ancestral cell. Thus, for most cells, there will be no neutral
mutations present. However, because selection is present,
all the cells will differ from the ancestoral cell by a number
of changes. This implies almost all the phenotypic changes
we see involve a genetic change that was selected. On the
other hand, there are about 3 109 cells in the chemostat so
that nearly every nucleotide change appears multiple times
in the first 10 generations. This could give rise to considerable clonal interference.
The selection coefficients for upregulation of uptake
systems and against the production of unnecessary proteins
will be large and these mutations will be fixed initially. After these are fixed, genetic changes with lower selection coefficients will be selected. There may be many of these with
small, nearly equal, selection coefficients, giving considerable clonal interference. Which one is present in any cell
will be a matter of chance and a diversity of these changes
should be found in a population. For example, some of the
cells will increase metabolic efficiency, others uptake efficiency (Ferenci 2008; MacLean 2008). Thus, we might expect a much richer array of causes at this second level of
selection.
Supplementary Material
Supplementary material is available at Molecular
Biology and Evolution online (http://www.mbe.
oxfordjournals.org/).
Acknowledgments
We thank Arkady Khodursky for use of his microarray
facilities and Kyeong Jeong for conducting the hierarchical
clustering analysis. This work was supported by a Public
Health Service Grant (GM06380) to A.M.D. and D.E.D.
Literature Cited
Agudelo-Romero P, Carbonell P, Perez-Amador MA, Elena SF.
2008. Virus adaptation by manipulation of host’s gene
expression. PLoS ONE. 3:e2397.
Benjamini Y, Hochberg Y. 1995. Controlling the false discovery
rate: a practical and powerful approach to multiple testing. J
Roy Statist Soc B. 57:289–300.
Bennett AF, Lenski RE. 1993. Evolutionary adaptation to
temperature. II. Thermal niches of experimental lines of
Escherichia coli. Evolution. 47:1–12.
Bennett AF, Lenski RE. 2007. An experimental test of
evolutionary trade-offs during temperature adaptation. Proc
Natl Acad Sci USA. 104:8649–8654.
Bergthorsson U, Andersson DI, Roth JR. 2007. Ohno’s dilemma:
evolution of new genes under continuous selection. Proc Natl
Acad Sci USA. 104:17004–17009.
Button DK. 1985. Kinetics of nutrient-limited transport and
microbial growth. Microbiol Rev. 49:270–297.
Cooper TF, Rozen DE, Lenski RE. 2003. Parallel changes in
gene expression after 20,000 generations of evolution in E.
coli. Proc Natl Acad Sci USA. 100:1072–1077.
Cooper VS, Lenski RE. 2000. The population genetics of
ecological specialization in evolving E. coli populations.
Nature. 407:736–739.
Cooper VS, Schneider D, Blot M, Lenski RE. 2001. Mechanisms
causing rapid and parallel losses of ribose catabolism in evolving
populations of Escherichia coli B. J Bacteriol. 183:2834–2841.
Corbin RW, Paliy O, Yang F, et al. (11 co-authors). 2003. Toward
a protein profile of Escherichia coli: comparison to its
transcription profile. Proc Natl Acad Sci USA. 100:9232–9237.
Dean AM. 1989. Selection and neutrality in lactose operons of
Escherichia coli. Genetics. 123:441–454.
Dean AM. 1995. A molecular investigation of genotype by
environment interactions. Genetics. 139:19–33.
Death A, Ferenci T. 1994. Between feast and famine:endogenous
inducer synthesis in the adaptation of Escherichia coli to
growth with limiting carbohydrates. J Bacteriol.
176:5101–5107.
Dykhuizen D, Davies M. 1980. An experimental model: bacterial
specialists and generalists competing in chemostats. Ecology.
61:1213–1227.
Dykhuizen DE, Dean AM. 2004. Evolution of specialists in an
experimental microcosm. Genetics. 167:2015–2026.
Eisen MB, Spellman PT, Brown PO, Botstein D. 1998. Cluster
analysis and display of genome-wide expression patterns.
Proc Natl Acad Sci USA. 95:14863–14868.
Elena SF, Lenski RE. 2003. Evolution experiments with
microorganisms: the dynamics and genetic bases of adaptation. Nat Rev Genet. 4:457–469.
Ferea TL, Botstein D, Brown PO, Rosenzweig RF. 1999.
Systematic changes in gene expression patterns following
Specialists and Generalists 2677
adaptive evolution in yeast. Proc Natl Acad Sci USA.
96:9721–9726.
Ferenci T. 2003. What is driving the acquisition of mutS and
rpoS polymorphisms in Escherichia coli. Trends Microbiol.
11:457–461.
Ferenci T. 2005. Maintaining a healthy SPANC balance through
regulatory and mutational adaptation. Mol. Microbiol. 57:1–8.
Ferenci T. 2008. Bacterial physiology, regulation and mutational
adaptation in a chemostat environment. Adv Microb Physiol.
53:169–229.
Fischer E, Sauer U. 2003. A novel metabolic cycle catalyzes
glucose oxidation and anaplerosis in hungry Escherichia coli.
J Biol Chem. 278:46446–46451.
Genissel A, McIntyre LM, Wayne ML, Nuzhdin SV. 2008. Cis
and trans regulatory effects contribute to natural variation in
transcriptome of Drosophila melanogaster. Mol Biol Evol.
25:101–110.
Gilad Y, Oshlack A, Smyth GK, Speed TP, White KP. 2006.
Expression profiling in primates reveals a rapid evolution of
human transcription factors. Nature. 440:242–245.
Greenbaum D, Colangelo C, Williams K, Gerstein M. 2003.
Comparing protein abundance and mRNA expression levels
on a genomic scale. Genome Biol. 4:117.
Griffin TJ, Gygi SP, Ideker T, Rist B, Eng J, Hood L,
Aebersold R. 2002. Complementary profiling of gene
expression at the transcriptome and proteome levels in
Saccharomyces cerevisiae. Mol Cell Proteomics. 4:323–333.
Hammar M, Arnqvist A, Bian Z, Olsén A, Normark S. 1995.
Expression of two csg operons is required for production of
fibronectin- and congo red-binding curli polymers in
Escherichia coli K-12. Mol Microbiol. 18:661–670.
Horiuchi T, Horiuchi S, Novick A. 1963. The genetic basis of
hyper-synthesis of b-galactosidase. Genetics. 48:157–169.
Horiuchi T, Tomizawa J, Novick A. 1962. Isolation and
properties of bacteria capable of high rates of b-galactosidase
synthesis. Biochem Biophys Acta. 55:152.
Khodursky AB, Bernstein JA, Peter BJ, Rhodius V,
Wendisch VF, Zimmer DP. 2003. Escherichia coli spotted
double-strand DNA microarrays: RNA extraction, labeling,
hybridization, quality control, and data management. Methods
Mol Biol. 224:61–78.
Khodursky AB, Peter BJ, Cozzarelli NR, Botstein D, Brown PO,
Yanofsky C. 2000. DNA microarray analysis of gene
expression in response to physiological and genetic changes
that affect tryptophan metabolism in Escherichia coli. Proc
Natl Acad Sci USA. 97:12170–12175.
Le Gac M, Brazas MD, Bertrand M, Tyerman JG, Spencer CC,
Hancock REW, Doebeli M. 2008. Metabolic changes
associated with adaptive diversification in Escherichia coli.
Genetics. 178:1049–1060.
Lieb M. 1991. Spontaneous mutation at a 5-methylcytosine
hotspot is prevented by very short patch (VSP) mismatch
repair. Genetics. 128:23–27.
Lieb M, Bhagwat AS. 1996. Very short patch repair: reducing the
cost of cytosine methylation. Mol. Microbiol. 20:467–473.
Lin ECC. 1996. Dissimilatory pathways for sugars, polyols, and
carboxylates. In: Neidhardt FC, Curtiss R III, Ingram JL, Lin
ECC, Low KB, Magasanik B, Reznifoff WS, Riley M,
Schaechter M, Umbarger HE, editors. Escherichia coli and
Salmonella: cellular and molecular biology, 2nd ed.
Washington (DC): ASM Press. p. 307–342.
Lunzer M, Natarajan A, Dykhuizen DE, Dean AM. 2002.
Enzyme kinetics, substitutable resources and competition:
from biochemistry to frequency-dependent selection in lac.
Genetics. 162:485–499.
MacLean RC. 2008. The tragedy of the commons in microbial
populations: insights from theoretical, comparative and
experimental studies. Heredity. 100:471–477.
MacLean RC, Bell G. 2003. Divergent evolution during an
experimental adaptive radiation. Proc Biol Sci.
270:1645–1650.
Macnab RM. 1992. Genetics and biogenesis of bacterial flagella.
Annu Rev Genet. 26:131–158.
Majdalani N, Vanderpool CK, Gottesman S. 2005. Bacterial
small RNA regulators. Crit Rev Biochem Mol Biol.
40:93–113.
Miller J. 1972. Experiments in molecular genetics. Cold Spring
Harbor (NY): Cold Spring Harbor Laboratory Press.
Nelsestuen GL, Zhang Y, Martinez MB, Key NS, Jilma B,
Verneris M, Sinaiko A, Kasthuri RS. 2005. Plasma protein
profiling: unique and stable features of individuals. Proteomics. 5:4012–4024.
Notley-McRobb L, Ferenci T. 1999. The generation of multiple
co-existing mal-regulatory mutations through polygenic
evolution in glucose-limited populations of Escherichia coli.
Env Microbiol. 1:45–52.
Novick A, Horiuchi T. 1961. Hyper-production of b-galactosidase
by Escherichia coli bacteria. Cold Spring Harbor Symp Quant
Biol. 26:239–245.
Rainey PB, Travisano M. 1998. Adaptive radiation in a heterogeneous environment. Nature. 394:69–72.
Riehle MM, Bennett AF, Long AD. 2005. Changes in gene
expression following high temperature adaptation in experimentally evolved populations of E. coli. Physiological and
Biochemical Zoology. 78:299–315.
Riley M, Abe T, Arnaud MB, et al. (18 co-authors). 2006.
Escherichia coli K-12: a cooperatively developed annotation
snapshot –2005. Nucleic Acids Res. 34:1–9.
Rosenzweig RF, Sharp RR, Treves DS, Adams J. 1994.
Microbial evolution in a simple unstructured environment:
genetic differentiation in Escherichia coli. Genetics.
137:903–917.
Ross PL, Huang YN, Marchese JN, et al. (16 co-authors). 2004.
Multiplexed protein quantitation in Saccharomyces cerevisiae
using amine-reactive isobaric tagging reagents. Mol Cell
Proteomics. 3:1154–1169.
Sittka A, Lucchini S, Papenfort K, Sharma CM, Rolle K,
Binnewies TT, Hinton JCD, Vogel J. 2008. Deep sequencing
analysis of small noncoding RNA and mRNA targets of the
global post-transcriptional regulator, Hfq. PLOS Genet.
4:1–20.
Sonti RV, Roth JR. 1989. Role of gene duplications in the
adaptation of Salmonella typhimurium to growth on limiting
carbon sources. Genetics. 123:19–28.
Soper TJ, Woodson SA. 2008. The rpoS mRNA leader recruits
Hfq to facilitate annealing with DsrA sRNA. RNA.
14:1907–1917.
Spira B, Hu X, Ferenci T. 2008. Strain variation in ppGpp
concentration and RpoS levels in laboratory strains of
Escherichia coli K-12. Microbiology. 154:2887–2895.
Stoebel DM, Dean AM, Dykhuizen DE. 2008. The cost of
expression of Escherichia coli lac operon proteins is in
the process, not in the products. Genetics. 178:
1653–1660.
St-Cyr J, Derome N, Bernatchez L. 2008. The transcriptomics of
life-history trade-offs in whitefish species pairs (Coregonus
sp.). Mol Ecol. 17:1850–1870.
Treves DS, Manning S, Adams J. 1998. Repeated evolution of an
acetate-crossfeeding polymorphism in long-term populations
of Escherichia coli. Mol Biol Evol. 15:789–797.
Vianney A, Jubelin G, Renault S, Dorel C, Lejeune P,
Lazzaroni JC. 2005. Escherichia coli tol and rcs genes
2678 Zhong et al.
participate in the complex network affecting curli synthesis.
Microbiology. 151:2487–2497.
Vijayendran C, Barsch A, Friehs K, Niehaus K, Becker A,
Flaschel E. 2008. Perceiving molecular evolution processes in
Escherichia coli by comprehensive metabolite and gene
expression profiling. Genome Biol. 9:R72.
Zieske LR. 2006. A perspective on the use of iTRAQ reagent
technology for protein complex and profiling studies. J Exp
Bot. 57:1501–1508.
Zhong S, Khodursky A, Dykhuizen DE, Dean AM. 2004.
Evolutionary genomics of ecological specialization. Proc Natl
Acad Sci USA. 101:11719–11724.
Jennifer Wernegreen, Associate Editor
Accepted August 3, 2009