Correcting direct effects of ethanol on translation and
transcription machinery confers ethanol tolerance
in bacteria
Rembrandt J. F. Hafta, David H. Keatinga, Tyler Schwaeglera, Michael S. Schwalbacha, Jeffrey Vinokura,
Mary Tremainea, Jason M. Petersb,c,1, Matthew V. Kotlajichb, Edward L. Pohlmanna, Irene M. Onga, Jeffrey A. Grassa,
Patricia J. Kileya,d, and Robert Landicka,b,e,2
a
Great Lakes Bioenergy Research Center and Departments of bBiochemistry, cGenetics, dBiomolecular Chemistry, and eBacteriology, University
of Wisconsin-Madison, Madison, WI 53706
Edited by Tina M. Henkin, Ohio State University, Columbus, OH, and approved May 13, 2014 (received for review January 29, 2014)
The molecular mechanisms of ethanol toxicity and tolerance in
bacteria, although important for biotechnology and bioenergy
applications, remain incompletely understood. Genetic studies
have identified potential cellular targets for ethanol and have
revealed multiple mechanisms of tolerance, but it remains difficult
to separate the direct and indirect effects of ethanol. We used
adaptive evolution to generate spontaneous ethanol-tolerant
strains of Escherichia coli, and then characterized mechanisms of
toxicity and resistance using genome-scale DNAseq, RNAseq, and
ribosome profiling coupled with specific assays of ribosome and
RNA polymerase function. Evolved alleles of metJ, rho, and rpsQ
recapitulated most of the observed ethanol tolerance, implicating
translation and transcription as key processes affected by ethanol.
Ethanol induced miscoding errors during protein synthesis, from
which the evolved rpsQ allele protected cells by increasing ribosome accuracy. Ribosome profiling and RNAseq analyses established
that ethanol negatively affects transcriptional and translational
processivity. Ethanol-stressed cells exhibited ribosomal stalling at
internal AUG codons, which may be ameliorated by the adaptive
inactivation of the MetJ repressor of methionine biosynthesis
genes. Ethanol also caused aberrant intragenic transcription termination for mRNAs with low ribosome density, which was reduced
in a strain with the adaptive rho mutation. Furthermore, ethanol
inhibited transcript elongation by RNA polymerase in vitro. We
propose that ethanol-induced inhibition and uncoupling of mRNA
and protein synthesis through direct effects on ribosomes and
RNA polymerase conformations are major contributors to ethanol
toxicity in E. coli, and that adaptive mutations in metJ, rho, and
rpsQ help protect these central dogma processes in the presence
of ethanol.
(14, 15), overexpression of native gene libraries (16–18), overexpression of protein chaperones (19), engineered oxidation
of ethanol (20), transcriptional rewiring through the catabolite
activator protein (21) or the transcription factor σ70 (22), and
modulation of cellular fatty-acid composition (23). The mechanisms by which these genetic changes confer tolerance remain
unclear. Additional mechanistic studies are needed to determine
the most physiologically relevant impacts of ethanol and to distinguish primary effects from downstream consequences.
An intriguing possibility raised by previous results is that the
fundamental processes of transcription and translation may be
affected by ethanol. For instance, biochemical studies have
shown that purified E. coli ribosomes are prone to misreading
errors when treated with ethanol (24–26) although in vivo effects
of ethanol on translational error have not been reported. Also,
ethanol-treated E. coli produce the alarmone (p)ppGpp (27),
a signal of translation inhibition (28). Detrimental effects of
Significance
Microbially produced aliphatic alcohols are important biocommodities but exert toxic effects on cells. Understanding the
mechanisms by which these alcohols inhibit microbial growth
and generate resistant microbes will provide insight into microbial physiology and improve prospects for microbial biotechnology and biofuel production. We find that Escherichia
coli ribosomes and RNA polymerase are mechanistically affected by ethanol, identifying the ribosome decoding center as
a likely target of ethanol-mediated conformational disruption
and showing that ethanol inhibits transcript elongation
via direct effects on RNA polymerase. Our findings provide
conceptual frameworks for the study of ethanol toxicity in
microbes and for the engineering of ethanol tolerance that
may be extensible to other microbes and to other shortchain alcohols.
A
liphatic alcohols such as ethanol are important microbial
bioproducts whose toxic effects are known to limit their
production in both bacteria and yeast (1–4). Thus, elucidating
the mechanisms by which alcohols exert toxic effects and understanding modes of microbial tolerance are important both to
understand basic microbial physiology and to engineer microbes
with more efficient fermentative capacities (5, 6). Escherichia coli
is a model of choice for these studies. Its well-defined physiology
and powerful genetic tools have allowed the identification of
multiple effects of ethanol on biomolecules and cellular processes.
One well-established toxic effect of ethanol on E. coli is an
increase in cell-envelope permeability (7–9). E. coli exposed to
ethanol exhibit reduced peptidoglycan cross-linking, which is
detrimental to viability, and altered membrane-lipid composition, which may represent an attempt to cope with ethanol stress
(10, 11). Ethanol induces broad transcriptional changes in E. coli
that extend beyond membrane-stress responses (12, 13), however, suggesting that membrane effects explain only a part of
the toxicity of ethanol. Consistent with this idea, widely varied
approaches have successfully been used to achieve modest ethanol tolerance in E. coli, including random transposon insertion
E2576–E2585 | PNAS | Published online June 9, 2014
Author contributions: R.J.F.H., D.H.K., M.S.S., M.T., J.M.P., M.V.K., P.J.K., and R.L. designed
research; R.J.F.H., D.H.K., T.S., M.S.S., J.V., M.T., J.M.P., M.V.K., E.L.P., and J.A.G. performed
research; R.J.F.H., D.H.K., T.S., M.S.S., M.T., J.M.P., M.V.K., E.L.P., I.M.O., and J.A.G. analyzed data; and R.J.F.H., D.H.K., J.M.P., M.V.K., and R.L. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
Data deposition: The data reported in this paper have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE56408).
Lists of noncoding RNA regions, pseudogenes, and gene sets used in ribosome profiling
analysis are available from GEO under accession no. GSE56372.
1
Present address: Department of Microbiology and Immunology, University of California,
San Francisco, CA 94143.
2
To whom correspondence should be addressed. E-mail:[email protected].
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1401853111/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1401853111
Mutations in metJ, rho, and rpsQ Confer Ethanol Tolerance and
Improve Fermentative Yields. To reveal physiologically important
targets of ethanol, we performed serial-passage evolution experiments to select for spontaneous mutations that conferred a growth
advantage to E. coli in minimal glucose medium containing increasing concentrations of ethanol up to 65 g/L (Fig. 1A). We
isolated clonal strains from the evolved cultures by growing culture
aliquots on nonselective plates and selecting single colonies for
further study. We tested ethanol tolerance of clonal strains by
following growth in media containing 40–65 g of EtOH/L and selected the three highly tolerant isolates MTA156, MTA157, and
MTA160 for genomic sequencing (Table 1). MTA156 and
MTA160 were independently isolated from a single evolved
culture whereas MTA157 was isolated from a separate culture
evolved in parallel. Mutations present in these strains (Table S1)
affected genes involved in various pathways, but six of the eleven
mutant alleles encoded variant proteins with clear ties to transcription and translation, including two variants of transcription
termination factor Rho, two variants of the master repressor of
methionine synthesis (MetJ), a variant ribosomal protein S17
(RpsQ), and a variant of the conserved transcription elongation/
termination factor NusA.
PNAS PLUS
Directed evolution for ethanol tolerance
g EtOH/L
50
55
60
65
Wild-type
(MG1655)
MTA156
Passage 0
B
10
4
18
24
Response of growing cultures to ethanol stress
0.750
0.500
Wild-type
metJ rho rpsQ
MTA156
40 g EtOH/L
0.250
T0
0.125
0.080
-1
T2
T1
Growth rates (hr-1)
T0
T2
Wild-type 0.71 ± 0.03 0.11 ± 0.01
MTA156 0.48 ± 0.04 0.16 ± 0.01
metJ rho rpsQ 0.49 ± 0.03 0.17 ± 0.02
0
1
2
Time (hr relative to ethanol addition)
3
Fig. 1. Selection of mutations conferring ethanol tolerance. (A) Summary of
directed evolution experiment, with number of serial passages at each step
of increasing ethanol concentration indicated. (B) Representative growth
curves showing response of MG1655, MTA156, and ethanol-tolerant triple
mutant (EP61) to ethanol addition. Mean growth rates for pre- and poststress cultures are shown ± SEM for four biological replicates. Time points
noted were used for sampling in ribosome-profiling experiments. Wild-type
MG1655 and the ethanol-tolerant triple mutant EP61 were sampled before
ethanol addition (T0), during acute stress (T1), and during chronic stress (T2).
We selected for detailed study a single clonal strain, MTA156,
which displayed improved growth in the presence of ethanol (Fig.
1B and Fig. S1) comparable with that reported for highly ethanoltolerant E. coli strains described by various groups (1, 17, 18, 20,
22). Genomic resequencing of MTA156 identified mutations affecting ispB, lptF, metJ, rho, rpsQ, and topA (Table 1 and Table
S1). Replacement of these loci with wild-type alleles reduced
ethanol tolerance in each case except that of topA (Fig. S1A).
Tolerance conferred by mutations in genes involved in LPS
transport (lptF) and quinone biosynthesis (ispB) likely act by
ameliorating effects of ethanol on processes occurring in the
E. coli cell envelope. The other three tolerance-conferring alleles
had clear ties to transcription and translation: metJ[ΔE91], rho
[L270M], and rpsQ[H31P] (Table 2). Combining these evolved
alleles in an otherwise wild-type background improved aerobic
Table 1. Strains used in the current study
Strain
MG1655
MTA156
MTA157
MTA160
EP23
EP49
EP61
MTA376
MTA722
RL2325
RL2739
Haft et al.
Relevant genotype
Source
E. coli K-12 F λ ilvG rfb-50 rph-1
MG1655 ispB[I32L] lptF[ΔE265] metJ[ΔE91] rho[L270M] rpsQ[H31P] topA[H122L]
MG1655 metJ[V33E] nusA[R258G] setB[ΔL33-V393] tqsA[N234Y]
MG1655 ispB[I32L] lptF[ΔE265] metJ[ΔE91] rho[L207Q] rpsQ[H31P] topA[H122L]
MG1655 metJ::kan
MG1655 rpsQ[H31P]
MG1655 metJ[ΔE91] rho[L270M] rpsQ[H31P]
MG1655 ΔldhA::FRT pJGG2 (Z. mobilis pdc adhB)
MTA376 metJ[ΔE91] rho[L270M] rpsQ[H31P]
MG1655 rho[L270M]
MG1655 rpsL150
(83)
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
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–
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PNAS | Published online June 9, 2014 | E2577
MICROBIOLOGY
Results
A
A600
ethanol on transcription are suggested by two independent
reports linking decreased Rho activity to ethanol tolerance in
E. coli by a point mutation in rho (14, 15) or by inhibition of Rho
activity by overexpression of YaeO/Rof (18). Rho is a hexameric
RNA helicase that can terminate transcription when it is not
coupled with translation (29). Reduced Rho activity leads to
numerous changes in gene expression that might confer ethanol
tolerance (14, 30). The reported effects of ethanol on ribosomes
and (p)ppGpp induction suggest that uncoupling of translation
from transcription could additionally cause maladaptively high
Rho termination within genes in ethanol-stressed cells.
We sought to identify the primary effects of ethanol on E. coli
and to understand mechanisms of tolerance using a two-stage
strategy. First, we selected for tolerance-conferring mutations by
directed evolution, allowing cultures to accumulate spontaneous
mutations while minimizing bias against mutations in essential
genes that can accompany transposon mutagenesis or overexpression. Second, we tested for physiological effects of ethanol
suggested by the evolved alleles and investigated the mechanisms
by which evolved alleles counteracted ethanol toxicity. Our
results demonstrate that ethanol has detrimental effects on
central dogma processes in vivo, including increases in translational error, ribosome stalling, and intragenic Rho-dependent
transcription termination, as well as on transcript elongation in
vitro. Mutant alleles isolated in this study help to ameliorate in
vivo effects of ethanol, underscoring the physiological relevance
of transcription and translation in ethanol toxicity and resistance.
A
GlyAsnLeu
ggaAAUcuc
Correct
translation
Mutant luc
mRNA
GlyLysLeu
Mistranslation
0.02
8.8x
Inactive
luciferase
Active
luciferase
Wild-type
metJ rho rpsQ
rpsQ
3.1x
0.01
1.8x
5.1x
1.2x 1.4x
6.1x
1.6x 2.2x
0.00
0
10
20
40
Ethanol concentration (g/L)
B
Synergy between ethanol and translation inhibitors
Relative fitness
0.8
Log-phase growth rates
untreated
µ2
µ1
Fitness =
µ1
treated
µ2
Time
log A595
1.0
Ethanol Induces Toxic Translational Misreading. The evolved rpsQ
[H31P] allele is identical to the nea301 rpsQ allele previously
selected as a neamine-resistance mutation (33). The nea301 allele encodes a variant of protein S17 that increases translational
accuracy in vitro in the presence of ethanol and other misreading
agents (25). The in vivo effects of ethanol on translational accuracy have not been reported, but isolation of a hyperaccurate
ribosomal mutation led us to hypothesize that ethanol would
stimulate translational misreading, which would in turn be reduced by the evolved rpsQ allele.
We tested this hypothesis by measuring misreading in the
presence of varying concentrations of ethanol using a sensitive
assay developed by Kramer and Farabaugh (34). In this assay, an
active-site codon of firefly luciferase (F-luc) is mutated (K529N)
such that it produces an inactive enzyme if correctly translated;
if ribosomal error leads to insertion of a lysine at this position,
an active enzyme is produced instead. The firefly luciferase is
translationally fused to a wild-type jellyfish luciferase active under different chemical conditions, which was used to control for
changes in transcription/translation rate, protein stability, and
other factors. All measurements of mutant F-luc activity were
normalized to an isogenic strain expressing wild-type F-luc fused
to jellyfish luciferase to control for effects of ethanol on the two
enzymes. Ethanol caused a dose-dependent increase in normalized F-luc activity expressed by wild-type E. coli, up to ninefold
higher than untreated controls at 40 g of EtOH/L (Fig. 2A),
representing an increase in the miscoding error rate similar to
that previously reported for inhibitory concentrations of streptomycin (34). Strikingly, the ethanol-tolerant triple mutant and
an otherwise wild-type strain bearing the rpsQ[H31P] allele
showed dramatically reduced levels of F-luc activity at all concentrations of ethanol, such that F-luc activity in the mutant
strains grown at 40 g of EtOH/L was similar to wild-type cultures
without ethanol. These results demonstrate that ethanol increases
translational misreading in vivo and that the evolved rpsQ allele
strongly reduces translational misreading in the presence or absence
of ethanol.
We assessed the physiological importance of ethanol-induced
errors in protein synthesis by measuring synergistic toxicity between
Translational misreading
0.03
Normalized luciferase activity
growth in the presence of ethanol to a level near that of MTA156
(Fig. 1B and Fig. S1B). The triple mutant (strain EP61) also
showed increased ethanol tolerance under anaerobic conditions
(Fig. S1 C and D). An ethanologenic strain bearing the evolved
metJ, rho, and rpsQ alleles produced 30% more ethanol than the
base strain on a per-cell basis in a synthetic mimic of lignocellulose hydrolysate, indicating that the ethanol-tolerant phenotype can promote biofuel production under industrially relevant
conditions (Fig. S1E).
The tolerance associated with mutations in metJ, rho, and rpsQ
implicated translation and transcription as targets of ethanol.
MetJ represses expression of genes involved in methionine biosynthesis (31). Rho terminates transcription uncoupled from
translation, halting mRNA synthesis when translation terminates
prematurely (29). RpsQ is ribosomal protein S17, an essential
component of the 30S subunit that plays a role in maintaining
translational accuracy (32). We focused on phenotypes related
to these three targets to study the effects of ethanol on transcription
and translation within the cell.
0.6
0.4
0.2
0.0
EtOH
Str
Str +
EtOH
predict
Str +
EtOH
actual
Cm
Cm +
EtOH
predict
Cm +
EtOH
actual
Fig. 2. Ethanol induces toxic translational error. All panels show mean
values and SEM for at least three biological replicates. (A) Normalized K529N
firefly luciferase activity for wild-type (MG1655), ethanol-tolerant triple
mutant (EP61), and rpsQ[H31P] (EP49) in the presence of indicated ethanol
concentrations. Activity for the K529N mutant requires misincorporation of
lysine at an AAU codon. Numbers above bars show fold increase in error
relative to no-ethanol condition for the same strain. (B) Effects of treatment
of wild-type MG1655 with ethanol (40 g/L) and translational inhibitors (1.6
μg Str/mL, 2 μg Cm/mL) singly and in combination. For combinatorial stresses,
predicted fitness values based on a no-synergy model are shown.
ethanol and antibiotics that affect different steps in translation.
Unrelated stressors at sublethal concentrations generally have
multiplicative effects on culture growth when combined whereas
stressors that are functionally related vary from this pattern (35,
36). When combined with streptomycin, an antibiotic that
induces translational misreading, ethanol caused approximately
fourfold greater toxicity than predicted by the multiplicative
model for unrelated stressors (Fig. 2B). In contrast, ethanol did
not show cooperative synergy with chloramphenicol, which
inhibits peptidyl transfer in the ribosome but does not decrease
translational accuracy (37), suggesting that ethanol does not
Table 2. Selected mutations conferring ethanol tolerance
Gene
Nucleotide change
Amino acid change
Measured effect of mutation
metJ
rho
rpsQ
AAAGAGATC→AAGATC
CTG→ATG
CAC→CCC
ΔE91
L270M
H31P
Up-regulation of methionine biosynthesis gene expression (Table S2)
Reduced transcription termination by Rho (Fig. S3)
Increased accuracy during protein synthesis (Fig. 2A)
E2578 | www.pnas.org/cgi/doi/10.1073/pnas.1401853111
Haft et al.
Ribosome Profiling Reveals Ethanol Induction of Ribosomal Termination.
Translational misreading has been shown to cause ribosome stalling and termination (43), and (p)ppGpp production in ethanoltreated E. coli may be a consequence of stalled ribosomes (27). To
investigate whether ethanol stress might cause aberrant termination of polypeptide synthesis, we assessed the genome-wide effects
of ethanol on mRNA levels and ribosomal distribution across
cellular mRNAs using ribosome profiling coupled with RNAseq
(44). These techniques measure mRNA abundance and ribosomal
occupancy with high resolution, revealing the density of ribosomes
on various mRNAs and ribosome abundance along the lengths of
mRNA transcripts.
We designed the ribosome-profiling experiment to compare
three physiological conditions apparent in ethanol-stress experiments (Fig. 1B): logarithmic growth before ethanol stress (T0), the
period of growth cessation due to acute stress (T1), and the phase
in which cells had resumed growth under chronic ethanol stress
(T2). Practical and cost limitations on the number of samples
processed precluded examining the single and double rho, metJ,
and rpsQ mutants individually; thus, we limited the experiment to
comparing the triple mutant to wild type at the three indicated
time points.
To assess ribosome processivity, we examined the change in
relative ribosome occupancy from the 5′ end to the 3′ end of
ORFs, dividing each ORF into eight even segments and averaging
signals across sets of genes. Decreases in relative occupancy at the
3′ end of messages would indicate aberrant translational termination between the 5′ and the 3′ end of the ORF. Because ribosome occupancy is the ratio of site-specific ribosome abundance
over site-specific mRNA abundance, we first quantified mRNA
signals from RNAseq across ORFs (Fig. 3 A–C) and then used the
ratio of raw ribosome footprint signal to mRNA signal to generate
plots of ribosome occupancy (Fig. 3 D–F). To facilitate genomewide comparisons, we identified a set of 3,048 protein-coding
genes with unambiguously mapped reads in all RNAseq and
ribosome footprinting datasets. Initially, we looked for evidence
of translation termination by averaging relative occupancy values
mRNA abundance
Ribosome profiling
Relative
mRNA signal
C
0.9
0.8
1
WT T0
ORF
WT T1 *
WT T2
segments
2 3 4 5 6 7
8
0.8
1
Mut T0
Mut T1 *†
Mut T2
2 3 4
5
6
7
8
High ribosome density: mRNA
1.0
0.9
0.9
0.8
0.7
E
1.0
1
T0
T1
T2
2 3
0.8
4
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8
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1
T0
T1
T2
2 3
4
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8
Low ribosome density: mRNA
0.9
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0.8
0.7
F
1.0
1.0
1
T0
T1 *
T2 *
2 3
0.8
4
5
6
7
8
0.7
1
T0 †
T1 *†
T2 *†
2 3
4
* = Significantly different from same strain at T0 (P < 0.01)
5
6
7
8
All genes: ribosomes
1.0
1.0
0.9
0.9
0.8
0.7
ORF
1
WT T0
ORF
WT T1 *
WT T2
segments
2 3 4 5 6 7
0.8
8
0.7
1
Mut T0
Mut T1 *
Mut T2
2 3 4
5
6
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8
4
5
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5
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8
High ribosome density: ribosomes
1.0
1.0
0.9
0.9
0.8
0.8
0.7
0.6
1
T0
T1 *
T2
2 3
0.7
4
5
6
7
8
0.6
1
T0
T1 *
T2
2 3
Low ribosome density: ribosomes
1.0
1.0
0.9
0.9
0.8
0.8
0.7
0.6
1
T0
T1
T2
2 3
0.7
4
5
6
7
8
0.6
1
T0
T1
T2
2 3
† = Significantly different than wild-type at same time point (P < 0.01)
Fig. 3. Ribosome profiling reveals ethanol-induced ribosome halting and Rho activity. (Top) A summary of ribosome profiling sample treatment for sideby-side RNAseq and ribosome footprinting, adapted from Ingolia et al. (44). (A–C) Relative mRNA signal levels from 5′ to 3′ for groups of ORFs: “all genes”
(the 3,048 genes represented in mRNA and ribosome footprint datasets), high ribosome density quintile (610 genes), and low ribosome density quintile (610
genes). (D–F) Relative ribosome occupancy signal levels (ribosome footprint signal divided by mRNA signal) from 5′ to 3′ for groups of ORFs as in A–C. Data
from wild-type (MG1655) cultures are shown in gray squares, and data from the ethanol-tolerant triple mutant (EP61) cultures are shown in colored triangles.
Error bars show SEM. T0, T1, and T2 represent prestress, acute stress, and chronic stress conditions (Fig. 1B).
Haft et al.
PNAS | Published online June 9, 2014 | E2579
MICROBIOLOGY
0.9
D
Relative
ribosomes/mRNA
1.0
Relative
ribosomes/mRNA
Relative
mRNA signal
B
All genes: mRNA
1.0
ribosome density
footprint
sequencing
nuclease digestion
ORF
Relative
ribosomes/mRNA
Relative
mRNA signal
A
# reads
RNAseq
ribosome
# reads
mRNA density
mRNA
PNAS PLUS
impede the peptidyl transfer reaction. These results strongly indicate that sublethal ethanol stress induces physiologically damaging levels of translational misreading, perhaps compromising
the conformation of the ribosomal decoding site known to be
targeted by streptomycin (38, 39).
The observed synergy between ethanol and streptomycin suggested that ribosomal proteins other than RpsQ classically implicated in translational accuracy, like RpsL (protein S12), might also
play a role in ethanol tolerance. Consistent with this hypothesis,
we observed that the rpsL150 allele (40), which confers streptomycin resistance, increased fitness during growth at a modest
concentration (20 g/L) of ethanol (Fig. S2). Ethanol has also been
reported to rescue streptomycin-dependent mutants with lesions
mapping at or near rpsL (41), which generally display a hyperaccurate translation phenotype (42), implying a potent effect of
ethanol on ribosome decoding activity.
across this set of “all genes” (Fig. 3D). Ethanol-stimulated
translational termination was evident and followed similar patterns in both wild-type and mutant cultures. Before stress,
ribosomes were widely distributed across mRNAs. During acute
stress (T1), relative ribosomal occupancy near the 3′ ends of
genes dropped from ∼0.95 to ∼0.75. After cells resumed growth
under chronic stress (T2), genome-wide ribosome occupancy
shifted back to near prestress patterns. These observations indicate that the acute stress phase, which is coupled to growth
cessation, was characterized by widespread aberrant termination
of translation within mRNAs. By the time growth resumed, both
strains had largely corrected this defect.
In analyzing the ribosome profiling data, we observed that
mRNAs with higher prestress ribosome density exhibited greater
decreases in 3′ ribosome occupancy. To assess this pattern further, we separated the “all gene” set into quintiles based on ribosome density in unstressed wild-type cells and examined the
upper and lower quintiles. The upper ribosome density quintile
exhibited a decrease in occupancy at the 3′ end of genes that was,
on average, greater in magnitude than the analogous decrease in
the set of all genes (Fig. 3E). This result indicates that a high
density of ribosomes on the message could not prevent ethanolinduced ribosomal halting. The low ribosome density quintile, in
contrast, did not exhibit such a decrease in ribosome occupancy
(Fig. 3F). Although this flatter ribosome occupancy curve could
indicate a smaller effect of ethanol on translation of low ribosome
density mRNAs, a likely alternative explanation is that translation
termination on these messages might be coupled to termination of
transcription. Because ribosome occupancy is ribosome abundance corrected for mRNA abundance, parallel decreases in both
would appear as relatively flat ribosome occupancy across a message, as observed for the low-density quintile.
Coupling of ethanol-induced translation termination to transcription termination could explain why we and others found that
mutations reducing Rho activity confer ethanol tolerance. Rho
terminates transcription of untranslated mRNA molecules; thus,
poorly translated mRNAs might be particularly susceptible to
ethanol-induced Rho-dependent termination because an ethanol-induced increase in ribosomal termination could not be
compensated by other ribosomes on the mRNA. We therefore
used the RNAseq data to assess changes in intragenic transcription termination after ethanol treatment.
Rho-Dependent Transcription Termination Is Increased During Ethanol
Stress. Intragenic transcription termination results in decreased
mRNA levels from the 5′ end to the 3′ end of ORFs. We tested
for this pattern by dividing the RNAseq reads into the same gene
sets used to analyze ribosome occupancy (Fig. 3 A–C). In the “all
gene” gene set, we observed statistically significant decreases in 3′
abundance relative to 5′ abundance, indicative of transcriptional
termination, during acute stress for both wild type and the mutant
(T1) (Fig. 3A). This change was markedly greater in magnitude for
wild-type cells, suggesting that the variant Rho expressed by the
mutant may reduce intragenic transcription termination. However,
mRNAs with high ribosome density did not exhibit obvious ethanol-induced decreases in 3′-proximal signal (Fig. 3B), suggesting
that high ribosome density may protect transcripts from termination.
The low ribosome-density gene set exhibited a similar pattern
to the set of all genes, but with stronger and more persistent
effects (Fig. 3C). For these genes, mRNA levels were maximal at
the 5′ end and dropped as transcription proceeded toward the 3′
end in both strains. The decrease in mRNA level after the 5′ end
of the gene was greatest during acute ethanol stress and only partially recovered during chronic stress, indicating that ethanol
inhibited the ability of cells to produce full-length transcripts of these
genes even after cells had resumed active growth. Although both
strains exhibited this general pattern of transcription termination,
the tolerant mutant had a clear advantage in producing fullE2580 | www.pnas.org/cgi/doi/10.1073/pnas.1401853111
length transcripts, maintaining significantly higher mRNA levels
across the length of genes than wild type at all time points.
These results suggested that Rho[L270M] reduced transcription
termination. To test this possibility, we performed an RNAseq
analysis of a strain containing only the rho(L270M) mutation. The
experiment confirmed the reduced rho activity phenotype, revealing increased readthrough of a set of previously defined Rhodependent terminators (45) in the rho[L270M] strain compared
with wild type (Fig. S3). Of 114 Rho-dependent terminators for
which fold change could be reliably calculated using our RNAseq
dataset (Dataset S1), 84% exhibited increased terminator readthrough in the mutant, and nearly half (45%) exhibited a twofold
or greater increase (Mann–Whitney U test; P < 0.001). Thus, the
rho[L270M] mutation confers a global defect in Rho-dependent
transcription termination.
We concluded that, during ethanol stress, Rho aberrantly
terminates transcription of hundreds of genes. RNA polymerases
(RNAPs) synthesizing mRNAs with fewer ribosomes than average were more prone to Rho-dependent termination. Especially
for these poorly translated mRNAs, the reduced activity of Rho
[L270M] favored production of full-length transcripts. Thus, our
results are consistent with the hypothesis that ethanol-induced
translational termination uncouples translation from transcription, stimulating Rho to terminate mRNA elongation.
Ethanol Inhibits mRNA Synthesis by RNA Polymerase in Vitro. Another possible way ethanol could increase Rho-dependent transcription termination is to slow transcript elongation by RNA
polymerase, which would increase the time available for Rho
action. To investigate this possibility, we tested whether modest
concentrations of ethanol could affect transcript elongation by
RNAP in vitro in the absence of translation. In the presence of
ethanol at concentrations of 30 g/L or 60 g/L, the average rate of
transcript elongation by E. coli RNAP was reduced by 10–30%,
and transcriptional pausing at a subset of sites was exacerbated
(Fig. 4). Thus, ethanol not only may increase chances for Rho
loading by causing translational misreading and termination, but
also may increase Rho action by slowing RNAP and thus increasing
the kinetic window within which Rho can effect termination.
Ethanol Inhibits Translation of Nonstart AUG Codons. The results
described above (see Ribosome Profiling Reveals Ethanol Induction
of Ribosomal Termination) strongly implicated ribosome stalling in
the toxic effect of ethanol on E. coli. Such stalling could be random or could be biased toward specific sites in the genome. We
hypothesized that ribosomes in ethanol-treated cells might be
prone to stop at AUG codons due to methionine limitation because
a mutation in the master repressor of methionine biosynthesis,
metJ, contributed to ethanol tolerance. We therefore used the ribosome profiling data to assess ethanol-induced changes in ribosome occupancy at start and nonstart AUG codons compared with
codons for other amino acids and stop codons (Fig. 5). Increased
codon occupancy in the ribosome profiles reflects increased dwell
time of ribosomes at those codons relative to others, thus revealing
codons whose translation was inhibited by ethanol.
Ethanol had small effects on ribosome occupancy at most
codons, but strongly affected occupancy at nonstart AUG
codons. Nonstart AUG occupancy dramatically increased during
acute toxicity (T1) and only partially recovered during chronic
toxicity (T2) in both strains. This pattern correlates with the
overall pattern of translation termination observed by ribosome
profiling (Fig. 3D), which was strongly induced at T1 but largely
recovered by T2. The magnitude of the ethanol effect on nonstart
AUG occupancy was less for the mutant than for wild type at both
time points (Wilcoxon matched-pairs rank test; P < 0.001). We
infer that the metJ[ΔE91] allele protects against ethanol stress at
least in part because it leads to increased expression of methionine
biosynthesis genes (Table S2), potentially increasing the methioHaft et al.
Time
No
EtOH
30 g
EtOH/L
60 g
EtOH/L
PNAS PLUS
A
B
In vitro transcription -/+ EtOH
No EtOH
30 g EtOH/L
60 g EtOH/L
RO
Mean transcript length (nt)
622
527
2 Minutes
Relative Intensity
404
mean ±
15% signal
307
Time (min)
g EtOH/L 2
4
8
0 340 410 500
30 270 340 450
60 240 300 430
4 Minutes
8 Minutes
Transcription
201
0.3
0.6
0.9
1.2
Transcript length (kb)
1.5
Fig. 4. Ethanol slows transcription in vitro. (A) Representative gel lanes showing in vitro transcript elongation in the presence of 0 g, 30 g, or 60 g ethanol/L.
Elongation complexes (10 nM) were halted at the end of a 26-nt C-less cassette and then exposed to ethanol. NTPs (30 μM each) were added subsequently,
and products from 2-min, 4-min, and 8-min extensions were resolved by gel electrophoresis. RO indicates template run-off products. Dots to the left of the gel
panels indicate the mean transcript length of RNA products for the experiment shown, and vertical lines show regions containing 15% of the signal upstream
and downstream from the mean. (B) Densitometric traces of gel lanes in A. Height of traces corresponds to relative densitometric signal at a given nucleotide
position. Mean average transcript lengths from two independent experiments are shown.
nine pool available to the cell. Consistent with this hypothesis,
deletion of the metJ repressor or addition of excess methionine
improved ethanol tolerance of wild-type E. coli (Fig. S4).
In contrast to nonstart AUG codons, ribosome occupancy at
AUG start codons increased in the mutant after ethanol addition
but decreased in the wild-type strain (Fig. 5). In principle, relative start codon occupancy reflects the rate of translation initiation relative to elongation (i.e., how much time that ribosomes
spend at start codons relative to other codons). Because rates of
Discussion
Our study of the effects of ethanol on central dogma processes
was motivated by the discovery that significant ethanol tolerance
in E. coli could be recapitulated by mutations in genes encoding
Ethanol-induced changes in ribosome occupancy by residue
codon
T2/T0
Log2
0.8
Non-start AUG codons
2.0
1.8
1.6
1.4
WT T0 Mut T0 WT T2 Mut T2
WT: T1/T0
WT: T2/T0
Mut: T1/T0
0.4
Mut: T2/T0
Stop
Val
Tyr
Trp
Thr
Ser
Pro
Phe
Met (non-start)
Met (start)
Ile
His
Gly
Glu
Gln
Cys
Leu
Lys
-0.4
Asp
Asn
Arg
Ala
0.0
Fig. 5. Ethanol-induced changes in ribosome occupancy by encoded amino acid. Log2 values of mean ethanol-induced ribosome occupancy changes for
codons encoding the indicated amino acid are shown. Changes were measured as the ratio of poststress occupancy to prestress occupancy. Data from wildtype (MG1655) cultures are shown in blue, and data from the ethanol-tolerant triple mutant (EP61) cultures are shown in red. The Inset at upper right shows
genome-wide mean ribosome occupancy values at nonstart AUG codons + SEM for T0 and T2.
Haft et al.
PNAS | Published online June 9, 2014 | E2581
MICROBIOLOGY
Δ codon
occupancy
T1/T0
Log2
T0: no EtOH T1: 10’ EtOH T2: 70’ EtOH
Mean codon occupancy
1.2
Log2 change in mean codon occupancy
translation initiation and growth are positively correlated in
E. coli (46), these different responses likely reflect the lesser
effect of ethanol on mutant versus wild-type growth rates (<3×
versus >6×, respectively) (Fig. 1B).
a ribosomal protein involved in decoding (RpsQ), the master
feedback repressor of methionine biosynthesis (MetJ), and a transcription factor that terminates transcription when it becomes
uncoupled from translation (Rho). These three mutations exhibited specific effects on transcription and translation consistent with the hypothesis that ethanol alters the conformations of
ribosomes and RNAP in ways that increase misreading, ribosome
stalling, and RNAP pausing, leading to increased Rho termination
when transcription and translation become uncoupled. RpsQ[H31P]
increased translational fidelity and suppressed ethanol-induced
misreading (Fig. 2). MetJ[ΔE91] up-regulated expression of methionine biosynthesis genes and may have partially countered an
ethanol-induced stalling of ribosomes at nonstart AUG codons
(Table S2, Fig. 5, and Fig. S4). Rho[L270M] reduced transcript
termination at Rho-dependent terminators and contributed to
amelioration of ethanol-induced premature termination on poorly
translated genes (Fig. S3 and Fig. 3). Finally, we found that ethanol
directly slows transcript elongation by RNA polymerase, which
could increase opportunities for intragenic Rho-dependent termination (Fig. 4).
Taken together, these data indicate that multiple effects on
the transcription and translation machinery are important components of cellular ethanol stress (Fig. 6). In the presence of
ethanol, increased ribosome stalling (particularly at nonstart
AUG codons) and increased ribosome misreading inhibit polypeptide synthesis and increase the levels of misfolded and aberrant proteins in the cell. Ethanol-induced misreading errors
may exacerbate stalling and chain termination due to ribosomal
proofreading after peptide bond formation (43). Slower translation and increased translational termination can decouple
translation from transcription, promoting Rho-dependent termination of transcription within genes. Ethanol-induced slowing
of RNAP further sensitizes transcription to termination by Rho.
The ethanol tolerance conferred by metJ mutations may reflect
the inhibitory effects of ethanol on translation that are affected
by methionyl-tRNAMet levels. Ethanol increases ribosome dwell
Coupled transcription and translation with no ethanol
Correctly translated
polypeptide
5’
Ribosome
RNA polymerase
mRNA
Toxic effects of ethanol on transcription and translation
Increased
stalling
Ethanol
Increased
pausing
Translation
error
Increased
misreading
Rho termination
Fig. 6. Summary of ethanol effects on transcription and translation. In unstressed cells, transcription and translation are coupled by RNAP pausing relieved by ribosome translocation (Upper). Ethanol exerts direct effects on
ribosomes and RNAP that increase RNAP pausing required for Rho termination
and uncouple transcription and translation via effects on the ribosome
decoding center that allow Rho access to RNAP and decrease translational
accuracy (Lower).
E2582 | www.pnas.org/cgi/doi/10.1073/pnas.1401853111
time on internal AUG codons (Fig. 5), suggesting that methionyltRNAMet becomes limiting for protein synthesis; thus, elevated
methionine levels might partially compensate for this effect.
Consistent with this hypothesis, methionine supplementation or
metJ deletion increased ethanol tolerance of wild-type E. coli
grown in minimal medium (Fig. S4). Deletions and point mutations in metJ are known to increase methionine levels in E. coli
(47, 48); the metJ[ΔE91] mutation likely causes a similar effect
by elevating transcription of the metABFIKNR genes (Table S2).
Interestingly, elevating methionine biosynthesis or supplementing
with methionine also compensates for other stresses in E. coli,
including acetate, organic acids, nitrosating agents, and heat shock
(49–51). MetA, which catalyzes the first step in methionine biosynthesis, is known to aggregate during heat stress (51), suggesting
that stress-induced MetA aggregation may reduce functional
MetA and resultant methionine levels. Ethanol is a potent inducer
of the heat shock response (27) and thus could similarly cause
MetA aggregation, resulting in decreased methionine and
methionyl-tRNAMet levels that could be partially corrected by
the metJ[ΔE91] mutation. Although other compensatory effects
on cell growth of elevating methionine biosynthesis remain
possible, effects on translation are consistent with our detection
of ethanol-induced stalling on internal Met codons.
In addition to causing toxic effects related to methionine, our
data indicate that ethanol interferes with the processes by which
ribosomes and RNAP produce proteins and RNA. Ethanol may
alter the activities of the ribosome and RNAP through interactions at specific sites or through less-specific solute effects on
macromolecular conformation. The latter possibility is attractive
because both the ribosome and RNAP are multisubunit complexes whose activities require significant conformational
changes during repeated cycles of chain extension and because
the ethanol levels at which toxic effects occur (1-2 M) are sufficient to alter the activities of water and solutes at protein
surfaces (52). Indeed, solute effects are known to alter RNAP
pausing and elongation (53) in patterns and magnitude similar to
those we observed for ethanol (Fig. 4). Ethanol appears to destabilize proteins by promoting exposure of hydrophobic regions
(54, 55) through direct interactions (56). Ethanol destabilization
of protein structure via global effects as a solute may contribute
to the well-known induction of the unfolded protein response
(heat-shock response) by ethanol in E. coli (57) although ethanol-induced amino acid misincorporation to generate nonnative
proteins also could contribute. However, ethanol is less well
studied than other protein-perturbing solutes, and both mRNA
decoding by the ribosome and pausing and elongation by RNAP
are complex phenomena. Thus, further study will be needed to
understand which parts of these complex molecular machines
may be altered to cause the effects we observed.
Nonetheless, our finding that ethanol causes decoding errors
similarly to inhibitors such as streptomycin, coupled with previous findings, suggests that ethanol may alter the conformation
of the decoding center of the small ribosomal subunit. Ribosomes select the correct aminoacyl-tRNA by coupling correct
codon-anticoding pairing in the decoding center to GTP hydrolysis by elongation factor Tu (EF-Tu), leading to tRNA accommodation into the peptidyl transferase center (58). Ethanol
increases ribosomal misreading in vitro (24–26) and causes
changes to the ribosome footprint in a manner similar to antibiotics such as streptomycin and neomycin (59). A mutation in
rpsL, encoding a ribosomal protein known to be important for
streptomycin binding and accurate translation (S12), was shown
to increase ethanol tolerance in an E. coli rho mutant background (14). Additionally, ethanol tolerance is conferred on E.
coli by overexpression (17) of RlmH, which methylates a 23S
rRNA pseudouridine near the ribosomal decoding center (60),
and of TruB, which catalyzes formation of pseudouridine-55 on
tRNAs and is important for efficient translation of certain
Haft et al.
Haft et al.
PNAS PLUS
Materials and Methods
Bacterial Strains, Media, and Growth Conditions. All strains are derivatives of
E. coli K12 strain MG1655 (Table 1). Strains MTA156, MTA157, and MTA160
were selected after 24 aerobic serial passages of MG1655 in M9 minimal medium (70), supplemented with MgSO4 (1 mM), CaCl2 (0.1 mM), and glucose
(10 g/L), and increasing amounts of ethanol up to 65 g/L (Fig. 1). We isolated
MTA156, MTA157, and MTA160 as clonal strains from the evolved cultures
by single-colony purification. The genomes of MG1655, MTA156, MTA157,
and MTA160 were sequenced at the University of Wisconsin-Madison (UWMadison) Biotechnology Center using the Illumina HiSeq 2000 platform. Unmarked transfer of alleles between MG1655 and MTA156 was achieved by P1
transduction, first transducing in an auxotrophic marker from the Keio collection (71) linked to the desired locus and then selecting for prototrophic
transductants from the appropriate donor, as previously described for rho
mutations (72). EP23 was constructed by P1 transduction of the metJ::kan
mutation from the Keio collection into MG1655. RL2739 was constructed by λ
Red recombination of the rpsL150 allele into MG1655 after amplification from
strain DH10B (40) by PCR using forward primer 5′-GCCTGGTGATGGCGGGATCG
and reverse primer 5′-CGCGACGACGTGGCATGGAA.
Fermentation Experiments. For fermentation data shown in Fig. 1E, strains
MTA376 and MTA722 were constructed by introducing the ldhA::kan allele
from the Keio collection into MG1655 and EP61, respectively, by transduction,
followed by removal of the kan marker by FLP recombinase (71). The ethanologenic PET cassette was expressed in each strain from plasmid pJGG2 (52).
Fermentative cultivations were performed in stirred flasks incubated at 37 °C
in an anaerobic chamber with an atmosphere of 10% CO2 + 10% H2. The
medium for fermentations was synthetic corn stover hydrolysate (73) supplemented with glucose (60 g/L) and xylose (30 g/L) to mimic 9% glucan
loading. End product detection was done as described previously (73).
Rho-Dependent Terminator Readthrough Analysis. MG1655 and RL2325 cultures were grown, RNA was harvested, and RNAseq libraries were prepared as
described (30). Sequencing was performed by the Joint Genome Institute
using an Illumina Genome Analyzer II. Normalized RNAseq reads were summed within regions previously defined by terminator readthrough in Rhoinhibited cells (45). Fold changes in terminator readthrough were calculated
by averaging the read counts for two biological replicates, and then dividing
the mutant read count by the wild-type read count. Fold change could not be
calculated for terminators with an average read count of zero for the wildtype samples; such terminators were removed from the final analysis. Statistical significance was tested by comparing fold changes for Rho-dependent
terminators versus random sites in the genome of the same length (obtained
by rotating the positions of terminator readthrough by 1 Mb).
In Vitro Transcription Assay. Template DNA was made from plasmid pMK110,
which was constructed from pIA267 (74) by inserting a region from the E. coli
bgl operon into the SpeI site using forward primer 5′-GCGAGCACTAGTTGTTCAAGAATACGCCAGGA and reverse primer 5′-GCGAGCACTAGTGGCGATGAGCTGGATAAACT. pMK110 template DNA contains a λPR promoter followed
by a 26-nucleotide C-less cassette. The linear pMK110 template for transcription reactions was generated by PCR amplification using forward primer 5′CGTTAAATCTATCACCGCAAGGG and reverse primer 5′-CAGTTCCCTACTCTCGCATG. PCR products were electroeluted from an agarose gel and phenol
extracted. Core E. coli RNAP and E. coli σ70 were purified as described previously (75, 76). RNAP holoenzyme (core ββ′α2 plus σ70) was prepared by incubating twofold molar excess of σ70 with core for 30 min at 30 °C.
Halted elongation complexes were formed by incubating 10 nM linear
pMK110 template and 15 nM RNAP holoenzyme in transcription buffer [40
mM Tris·HCl (pH 8.0), 50 mM KCl, 5 mM MgCl2, 0.5 mM DTT, and 5% (vol/vol)
glycerol] with 150 μM ApU, 10 μM ATP and UTP, 2.5 μM GTP, and 0.37 μM (10
μCi) [α-32P]GTP for 10 min at 37 °C to stall complexes 26 nucleotides downstream of the transcription start site. Resumption of transcription was
allowed by adding 30 μM each ATP, UTP, CTP, and GTP, 100 μg rifampicin/
mL, and 0.1 U RNasin/μL (Promega). Ethanol was added where indicated.
Samples were taken at indicated time points by mixing with an equal volume of 2× stop dye (8 M urea, 30 mM Na2EDTA, and 0.05% bromophenol
blue and xylene cyanol). Samples were heated for 2 min at 90 °C and
separated by electrophoresis in a denaturing 6% polyacrylamide gel (19:1
acrylamide:bisacrylamide) in 7 M urea, 1.25 mM Na2EDTA, and 44 mM Tris
borate (pH 8.3). Gels were exposed to a PhosphorImager screen, which was
scanned using a Typhoon PhosphorImager and quantified using ImageQuant software (GE Healthcare).
PNAS | Published online June 9, 2014 | E2583
MICROBIOLOGY
codons (61). Finally, ethanol has been reported to promote the
growth of streptomycin-dependent mutants of E. coli in the absence of streptomycin (41, 62), suggesting that ethanol, like streptomycin, may induce compensatory conformational changes in the
decoding center of the ribosome. We propose that ethanol disrupts
the natural conformation of the ribosomal decoding center and
thus, like streptomycin, allows EF-Tu GTP hydrolysis and accommodation upon binding of noncognate aminoacyl-tRNAs. An ethanol-induced rearrangement of the decoding center might also
affect the propensity of the ribosome to stall and possibly its ability
to recognize methionyl-tRNA although other, distal effects of ethanol on the ribosome also could cause translational halting.
Our proposal that ethanol exerts its toxic effects on E. coli in
part through direct effects on the ribosome and RNAP contrasts
with previous suggestions that ethanol tolerance mutations modifying proteins involved in transcription and translation function
indirectly by “rewiring” gene-expression networks. For example,
increased alcohol tolerance of strains with reduced Rho activity
(14, 20) or mutations in RNA polymerase subunits or the TATAbinding protein (4, 22, 63) have been attributed to altered expression of specific genes controlled by termination or initiation.
Our results do not preclude the possibility that increased expression of some genes contributes to ethanol tolerance; both effects
on the transcription/translation machinery and effects on gene
expression may occur, and both may be important. Rather, we
suggest that the potentially important and simpler explanation that
compensatory mutations may help shield the ribosome and RNAP
from direct effects of ethanol should not be overlooked. Indeed,
some indirect effects of ethanol may also be consequences of
primary effects on the ribosome or RNAP. For example, we
speculate that the ethanol-induced changes in E. coli membrane
lipid composition (10) might be caused in part by ethanol’s effects
on the ribosome. Ethanol stress results in (p)ppGpp production
(27), likely by the RelA synthase bound to stalled ribosomes. (p)
ppGpp binds numerous cellular enzymes (64) and is known to
inhibit the activity of the phospholipid synthesis enzyme PlsB in
vivo and in cell-free extracts (65, 66). Mutation of plsB alters fatty
acid composition similarly to effects of ethanol (increased unsaturated C18:1 fatty acids) (67), providing a potential link between
the effects of ethanol on the ribosome and on membrane composition (10, 11) mediated by (p)ppGpp. Increased (p)ppGpp
levels affect transcription from numerous promoters in the cell
(28, 68), which could lead to other downstream effects as well.
Thus, our finding that ethanol induces ribosome stalling also potentially provides a simple mechanistic explanation for ethanol
stimulation of (p)ppGpp production and consequent effects on
multiple cellular functions as a signal of translational stress.
A key question raised by our work is whether modes of ethanol
tolerance related to its direct effects on the ribosome and RNAP
are broadly applicable to other solvents and other microbes.
Some ethanol-tolerant mutants of Saccharomyces cerevisiae carry
ribosomal mutations that alter antibiotic sensitivities (69), suggesting that the toxic effects of ethanol on the ribosome may
operate in yeast as well as bacteria. Do naturally ethanol-tolerant
species such as S. cerevisiae protect their ribosomes from ethanol-induced translational error and their RNAPs from ethanolinduced transcriptional slowing? Do other small-molecule solvents have ethanol-like toxic effects? The answers to these questions
could have important consequences for biosynthetic engineering. If
translation and transcription are widely prone to inhibition by solvents such as ethanol, then engineering microbes with resilient
ribosomes and RNAPs may enhance biological production of a variety of small molecules. More generally, our results highlight the
importance of considering direct effects on central dogma processes
when evaluating the effects of solutes on microbes.
Measurement of Translational Misreading. Cultures for misreading experiments were diluted from unstressed overnight cultures and grown to midlogarithmic phase in aerobic tubes in Neidhardt rich medium with 2 g glucose/L
(77) supplemented with 100 μg ampicillin/mL (to maintain luciferase-expressing
plasmids), 0–40 g EtOH/L, and 10–100 μM isopropyl β-D-1-thiogalactopyranoside
(IPTG). Equivalent numbers of cells were harvested from each culture. Cells were
lysed, and firefly luciferase (F-luc) and jellyfish luciferase (R-luc) activities were
measured as described (34). Luminescence was measured using a Tecan
Infinite F200 plate reader. Values are expressed as F-luc/R-luc ratios normalized to the F-luc/R-luc ratio of an isogenic strain carrying the wild-type
firefly luciferase to control for any differences between cultures and differential effects of ethanol on the two enzymes.
exhibited growth responses to ethanol. Ribosome profiling and library
generation were performed as described by Oh et al. (80), with cells harvested by rapid vacuum filtration onto 0.2-μm filters and flash-frozen in
liquid N2 to arrest ribosomes. After cryogenic lysis, monosomes were purified
by sucrose gradient fractionation.
Sequencing was performed at the UW-Madison Biotechnology Center
using an Illumina HiSeq 2000 set for 50-bp single-end reads. Raw reads were
trimmed by 2 nt from the 5′ end (to remove any nontemplated nucleotides
added by reverse transcriptase) and were then mapped using the BurrowsWheeler Aligner (81) to the MG1655 genome (National Center for Biotechnology Information accession no. NC_000913). Reads were not trimmed
to individual codons to avoid errors arising from the variation in ribosome
footprint dimensions at different genomic locations (82). Signals mapping to
noncoding RNA regions were removed from the dataset, and each dataset
was normalized to reads per million per position before further analysis.
Data manipulation and analyses were performed with custom Perl scripts.
Statistical analyses were performed using GraphPad Prism (GraphPad Software). For metagene analyses (gene-segment analyses and codon-type
analyses), pseudogenes and genes not represented in one or more datasets
were excluded, leaving 3,048 genes in the “all genes” dataset. High-translation and low-translation quintiles were defined as the gene sets with
highest/lowest ribosome-occupancy-to-RNA signal ratios in MG1655 before
ethanol treatment. For gene-segment analyses, differences between datasets were assessed using area under the curve (rectangular midpoint
approximations) for each gene in the set.
Ribosome Profiling. Cultures were grown aerobically in 1-L volumes of M9
minimal medium (70), supplemented with MgSO4 (1 mM), CaCl2 (0.1 mM),
and glucose (10 g/L) in vigorously shaken (225 rpm) Fernbach flasks. Ethanol
was added to 40 g of EtOH/L once A600 of cultures reached 0.3. This ethanol
concentration was chosen because it allowed comparison of wild-type and
mutant phenotypes under conditions in which both could grow and both
ACKNOWLEDGMENTS. We thank our Great Lakes Bioenergy Research
Center collaborators and R.L. laboratory colleagues for critical reading of
the manuscript. We are grateful to Gene-Wei Li and David Burkhardt for
advice and assistance with ribosome profiling experiments, P. Chu for technical assistance, and Gerwald Jogl for helpful discussions of ribosome decoding. This work was funded by the Department of Energy Great Lakes Bioenergy
Research Center (DOE BER Office of Science Grant DE-FC02-07ER64494).
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PNAS | Published online June 9, 2014 | E2585
Supporting Information
Haft et al. 10.1073/pnas.1401853111
A
Ethanol tolerance: wild-type alleles in MTA156
0.4
B
Ethanol tolerance: MTA156 alleles in wild-type
0.25
0.20
0.3
A 600
A 600
0.15
0.2
0.10
0.1
0.05
D
Anaerobic growth without EtOH
56
TA
1
M
rp
sQ
m
et
J
rh
rp
o
sQ
m
et
J
rp
sQ
m
et
J
Q
rp
s
rh
o
m
et
J
rh
o
16 W
55 T
)
(M
G
rh
Al
o
l(
M
G
16
55
)
m
et
J
B
is
p
Q
rp
s
lp
tF
to
pA
on
e
N
C
rh
o
0.00
0.0
Anaerobic growth with 50 g EtOH/L
2.00
0.32
1.00
0.16
A 600
A 600
0.50
0.25
0.08
0.13
0.04
0.06
Wild-type
Wild-type
metJ rho rpsQ
0.03
0
10
20
30
Time (hr)
40
E
metJ rho rpsQ
0.02
50
0
10
20
30
Time (hr)
40
50
Specific ethanol yield
6
g EtOH L
-1
A600
-1
5
4
3
2
1
MTA376
MTA376 metJ rho rpsQ
0
0
10
20
30
40
50
Time (hr)
Fig. S1. Mutations in rho, metJ, and rpsQ cooperatively confer ethanol tolerance and increase ethanol yield. (A and B) Turbidometric densities achieved after
24 h of aerobic growth in minimal glucose medium containing 60 g ethanol/L are shown for MG1655, the evolved MTA156 mutant, and derivatives of MTA156
with wild-type alleles at the indicated loci (A) or derivatives of MG1655 with various combinations of individual mutations found in MTA156 (B). Error bars
depict the values from two biological replicates. (C and D) Representative anaerobic growth curves for MG1655 and the ethanol-tolerant triple mutant (EP61)
grown in minimal glucose medium in the absence (C) or presence (D) of 50 g ethanol/L. (E) Representative graph of specific ethanol yield over time for
fermentation of synthetic corn stover hydrolysate (1) by MTA376 and MTA722 in stirred anaerobic flasks.
1. Schwalbach MS, et al. (2012) Complex physiology and compound stress responses during fermentation of alkali-pretreated corn stover hydrolysate by an Escherichia coli ethanologen.
Appl Environ Microbiol 78(9):3442–3457.
Haft et al. www.pnas.org/cgi/content/short/1401853111
1 of 4
Fitness in 20 g EtOH/L
1.0
Log-phase growth rates
untreated
μ1
treated
μ2
Time
log A595
Relative fitness
0.9
0.8
Fitness =
0.7
μ2
μ1
0.6
WT
rpsL150
Relative RNAseq signal after terminator
12
8
rho[L270M] / WT
mRNA reads
Fig. S2. The rpsL150 allele confers tolerance to ethanol. Strains MG1655 and RL2739 were cultured in the presence or absence of 20 g ethanol/L. Mean relative
fitness is shown with SEM for at least three replicate cultures.
Relative signal = b/a
rho[L270M]
Transcription
a b
Wild-type
Rho-dependent terminator
4
1
0
114 Rho-dependent terminators
Fig. S3. Readthrough of Rho-dependent terminators in a rho[L270M] strain. The ratios of RNAseq signal for a rho[L270M] mutant (RL2325) versus wild-type
(MG1655) are shown for 114 previously described regions downstream of Rho-dependent terminators (1). Bars represent the average of two biological replicates
for each strain. A black horizontal line at y = 1 indicates the ratio observed if readthrough at a given terminator was identical in wild-type and the mutant.
1. Peters JM, et al. (2009) Rho directs widespread termination of intragenic and stable RNA transcription. Proc Natl Acad Sci USA 106(36):15406–15411.
Haft et al. www.pnas.org/cgi/content/short/1401853111
2 of 4
Growth in the presence of 50 g EtOH/L
0.32
MG1655 no Met
MG1655 + Met
metJ::kan no Met
A 600
0.16
0.08
0.04
0.02
0
2
4
6
8
10
12
Time (hr)
Fig. S4. Methionine supplementation or deletion of metJ led to improved growth in the presence of ethanol. Growth in the presence of 50 g EtOH/L of
MG1655 without methionine supplementation, MG1655 supplemented with 2 mM methionine, and EP23 in the absence of methionine supplementation.
Values shown are the means of two biological replicates, which differed by no more than 6% of the mean value at any point.
Table S1. Nonsynonymous mutations found in evolved strains
Strain(s)
MTA156,
MTA156,
MTA156,
MTA156,
MTA156,
MTA156
MTA157
MTA157
MTA157
MTA157
MTA160
MTA160
MTA160
MTA160
MTA160
MTA160
Gene
Nucleotide change
Amino acid changes
ispB
lptF
metJ
rpsQ
topA
rho
metJ
nusA
setB
tqsA
rho
ATC→CTC
GCAGAACTG→GCACTG
AAAGAGATC→AAGATC
CAC→CCC
CAC→CTC
CTG→ATG
GTG→GAG
CGT→GGT
116-bp deletion
AAT→TAT
CTG→CAG
I32L
ΔE265
ΔE91
H31P
H122L
L270M
V33E
R258G
Q34R T35A P36L STOP
N234Y
L207Q
Table S2. mRNA levels of met genes reveal reduced MetJmediated repression in EP61
Gene
metA
metB
metC
metE
metF
metI
metK
metN
metQ
metR
Wild-type*
545
270
321
12,839
1,105
468
2,501
333
1,722
287
±
±
±
±
±
±
±
±
±
±
220
38
72
253
47
183
122
40
315
1
metJ rho rpsQ*
784
833
393
13,848
1,456
954
3,081
778
1,665
474
±
±
±
±
±
±
±
±
±
±
99
4
154
1280
189
158
278
21
42
28
Fold change
(mutant/WT)
1.4
3.1
1.2
1.1
1.3
2.0
1.2
2.3
1.0
1.7
*Mean total RNA signals from RNAseq experiments in RPKM (reads per
kilobase per million) of two biological replicates are shown for the indicated
genes before ethanol addition in MG1655 and EP61 (errors indicate total
range of biological replicates). For this set of genes, which are all negatively
regulated by MetJ (1), the mutant has an overall higher level of expression
than wild-type (P < 0.01 by the Wilcoxon matched-pairs rank test).
1. Marincs F, Manfield IW, Stead JA, McDowall KJ, Stockley PG (2006) Transcript analysis reveals an extended regulon and the importance of protein-protein co-operativity for the
Escherichia coli methionine repressor. Biochem J 396(2):227–234.
Haft et al. www.pnas.org/cgi/content/short/1401853111
3 of 4
Dataset S1. rho[L270M] effects on readthrough of Rho-dependent terminators
Dataset S1
Haft et al. www.pnas.org/cgi/content/short/1401853111
4 of 4