Abstract
Despite mounting evidence that in clonal bacterial populations,
phenotypic variability originates from stochasticity in gene
expression, little is known about noise-shaping evolutionary forces and
how expression noise translates to phenotypic differences. Here we
developed a high-throughput assay that uses a redox-sensitive dye to
couple growth of thousands of bacterial colonies to their respiratory
activity and show that in Escherichia coli, noisy regulation of lower
glycolysis and citric acid cycle is responsible for large variations in
respiratory metabolism. We found that these variations are Pareto
optimal to maximization of growth rate and minimization of lag time,
two objectives competing between fermentative and respiratory
metabolism. Metabolome-based analysis revealed the role of respiratory
metabolism in preventing the accumulation of toxic intermediates of
branched chain amino acid biosynthesis, thereby supporting early onset
of cell growth after carbon starvation. We propose that optimal
metabolic tradeoffs play a key role in shaping and preserving
phenotypic heterogeneity and adaptation to fluctuating environments.
Subject terms: Metabolomics, Microbiology, Cellular noise
__________________________________________________________________
It is unclear how noise in gene expression propagates to phenotypic
heterogeneity in clonal bacterial populations. Here, the authors
explore how variability in central sugar metabolism in E. coli can
mediate and promote population diversification.
Introduction
Heterogeneity between individual bacterial cells growing under
macroscopically identical conditions can have important functional
consequences^[30]1. While not always beneficial^[31]2,[32]3, population
diversification is a key mechanism to adapt to fluctuating
environments^[33]4, in that it can allow genotypes to persist in the
face of adverse conditions. For example, growth rate heterogeneity in
bacterial populations contributes to survival upon exposure to
antibiotics^[34]5,[35]6, and differences in the onset of cell division
can favor tolerance and evolution of antibiotic resistance^[36]7–[37]9.
A number of different molecular mechanisms can give rise to phenotypic
heterogeneity^[38]10–[39]12. Typically, these are cellular processes
such as stochastic gene expression^[40]13,[41]14 and stochastic
partitioning of molecules at cell division^[42]15, which ultimately
manifest in the variation of protein copy numbers per cell. Recent
experimental evidence showed that fluctuations in the expression of
growth-limiting enzymes can be responsible for growth fluctuations and
the transmission of noise to other genes^[43]10. While mounting
single-cell studies have shown gene expression to vary within a
population of genetically identical cells under constant
conditions^[44]13,[45]16,[46]17, which cellular processes are
predominantly affected by noisy gene expression and whether noise
propagation is shaped by optimality-based principles are still open
questions.
Despite the expectations that stochasticity of biochemical reactions
should have negligible effect, theoretical models demonstrate how noise
in transcription and translation of metabolic enzymes can affect levels
of metabolic reactants^[47]18,[48]19. However, whether and how
fluctuations in metabolite levels can permeate the metabolic state of a
cell and affect the ability to adapt to changing environments is
unclear. While technological advances enabled monitoring levels of
metabolites at single cell^[49]20,[50]21, directly probing cell-to-cell
variability in the turnover of metabolites remains challenging^[51]22,
ultimately hampering the study of metabolic heterogeneity and its
functional implications. By developing an assay to directly measure
variability in the respiratory rates among colonies of Escherichia coli
growing on solid media, we show that noisy transcriptional regulation
of lower glycolytic and citric acid (TCA) cycle enzymes can be readily
transmitted to metabolic heterogeneity. Surprisingly, we found that
individual colonies within an isogenic population exhibit largely
different respiratory activities, and that while costly^[52]23,
increased respiratory activity facilitates the early onset of cell
growth after starvation by preventing the accumulation of toxic
intermediates. We propose that variability in respiro-/fermentative
metabolism can be fitness invariant, allowing cells to maintain a
greater variation in enzyme expression and potentially employ diverse
adaptive strategies to environmental changes.
Results
Enzymes involved in oxidative-reductive reactions exhibit large cell-to-cell
variability compared to proteins with a similar abundance
To find cellular processes that exhibit large cell-to-cell variability,
we analyzed previously published single-cell proteome data of E.
coli^[53]13 (Fig. [54]1A). In prokaryotes, as in unicellular
eukaryotes, variability in gene expression and protein levels among
cells (i.e., noise) is inversely proportional to the mean expression
level of the population^[55]13,[56]24,[57]25. However, because on
average, in E. coli essential genes encode for highly expressed
proteins (p-value = 2.64e−10) (Fig. [58]1B, C)^[59]13, we hypothesized
that noise in abundant proteins, even if modest, may have important
consequences for phenotypic heterogeneity. To systematically compare
noise among diversely expressed proteins, we estimated the deviating
noise, here defined as the deviation of each protein from the average
noise levels of proteins with similar expression levels (Figs. [60]1A
and S[61]1, Supplementary Data [62]1). Next, we performed a gene set
enrichment analysis and compared biological processes that are enriched
for proteins with a large standard (i.e., squared coefficient of
variation) or deviating noise (Fig. [63]1D, Supplementary Data [64]1).
While the most significant (q value ≤ 1e−4) processes affected by
standard noise levels consist of lowly expressed genes involved in DNA
repair, DNA recombination, and chemotaxis (Fig. [65]1B–D), we found
that proteins with the largest deviating noise levels are enriched for
metabolic genes involved in oxidative-reductive reactions
(Fig. [66]1D). More specifically, we found that genes encoding for
enzymes in central metabolism (e.g., aceE) exhibit larger than expected
cell-to-cell variability relative to other proteins with similar
expression levels (Figs. [67]1A and S[68]1). On the other hand,
essential genes on average exhibited low deviating noise levels
(p-value ≤ 0.05), indicating that there may be selective pressures for
noise reduction^[69]26. These results suggest that, despite the key
role in bacterial fitness of proteins involved in central metabolism,
fluctuations in their expression, translation, or degradation may have
been preserved by evolution and can be a major source of phenotypic
heterogeneity. To test if deviations of proteins from mean-noise levels
could be the results of stochasticity in their gene expression
regulation, we searched for transcription factors (TFs) regulating the
expression of proteins with large deviating noise. We found that Cra, a
key regulator of flux in lower glycolysis^[70]27, is significantly
enriched (p value ≤ 1e−4 Bonferroni-corrected threshold) for targets
that exhibit high deviating noise levels (Fig. [71]1E). Altogether,
these analyses suggest that noise in protein abundance can potentially
translate into large metabolic cell-to-cell variability and that such
heterogeneity can be at least partially explained by a few TFs.
Fig. 1. Noise propagation to metabolism.
[72]Fig. 1
[73]Open in a new tab
A Time independent protein (p) abundance and noise from single-cells of
E. coli^[74]13. Average copy number (μ[p]) vs. protein expression noise
(η = σ[p]^2/μ[p]^2, where σ[p] is the standard deviation) of 1018
proteins (gray dots). A moving average smoothing method is used to
estimate average noise levels at different protein abundance (
[MATH: η¯p :MATH]
) (red line). Deviating noise (blue dots) is calculated as the ratio
between the noise of individual proteins and average noise levels of
proteins with similar abundance (
[MATH:
εp=ηp/η¯p :MATH]
) (Supplementary materials and Supplementary Data [75]1). Highlighted
with gene names, proteins that exhibit the highest deviating noise
levels. The black dashed line corresponds to zero deviating noise. B, C
Distributions of average protein abundance and deviating noise levels.
Asterixis indicate significance, two-tailed t-test: *p value ≤ 0.05,
**p value ≤ 0.01, ***p value ≤ 0.001. D Gene ontology enrichment
([76]http://geneontology.org/) reporting biological processes that are
significantly (permutation test with Storey correction q value < 0.001)
enriched for proteins with high deviating or standard noise levels. E
For each transcription factor (TF) we estimated the average protein
deviating noise of target regulated genes and the significance of the
enrichment for target regulated genes with large deviating noise
levels. TFs regulating proteins with a positive or negative median
deviating noise is colored in red and blue, respectively. Size of the
dot scales with the number of target-regulated genes. F For each enzyme
in central metabolism we used ordinary least square regression analysis
to estimate the proportionality and significance between protein copy
number^[77]29 and corresponding flux rates^[78]30 across seven
different conditions. Enzymes are colored by pathway:
orange-glycolysis, blue-pentose phosphate pathway, green-TCA, as
indicated by the small schematic on the upper left corner (see Fig.
S[79]1 for full details).
Noise propagation to respiro-/fermentative metabolism
Our results are consistent with other studies reporting on large
variations in enzyme levels^[80]10,[81]28. However, whether and which
noisy enzymes are able to cause fluctuations in the rates of the
corresponding enzymatic reactions, and hence may affect phenotypic
heterogeneity, is still unclear. To address this question, we
integrated bulk measurements of protein copy-numbers^[82]29 and
metabolic fluxes^[83]30 in E. coli growing in minimal media with seven
different carbon sources. By quantifying the linear dependency between
changes in the copy number of 40 enzymes and 25 rates of the
corresponding metabolic reactions, we found that changes in the
abundance of enzymes in lower glycolysis (e.g., Pdh, GapA, Pgk) and
citric acid (TCA) cycle (e.g., GltA) directly scale with changes in
fluxes (Figs. [84]1F and S[85]2, Supplementary Data [86]1). Hence,
expression noise in these enzymes can directly translate into
cell-to-cell flux variability and overall is likely to cause large
changes in respiro-/fermentative metabolism.
Experimentally validating noise propagation from enzyme expression to
metabolism remains challenging^[87]31,[88]32. Currently, experimental
tools probing the metabolism of single cells are limited to
the monitoring of metabolite abundance^[89]33,[90]34, and so far direct
measurements of metabolic fluxes (i.e., metabolite’s turnover rate) at
the single-cell level are lagging behind. As a result, much less is
known about heterogeneity at the level of metabolism, and whether such
variability can have direct functional consequences. To overcome this
problem, instead of single-cell analytical methods we developed a
multiparametric assay that monitors phenotypic and metabolic
heterogeneity of thousands of colonies-forming E. coli growing on solid
agar media (Fig. S[91]3). Our approach was inspired by studies using a
scanner array coupled with image analysis to monitor
colony-growth^[92]35,[93]36. For each colony, we estimated the maximum
growth rate and lag time by fitting colony area over time with a
Gompertz growth function^[94]37 (Fig. S[95]3). In addition, we added
2,3,5-triphenyl-2H-tetrazolium chloride (TTC) to the agar medium.
Commonly used in viability assays, TCC is a redox-sensitive dye that is
reduced by electron transfer from the respiratory chain with the
formation of 1,3,5-triphenylformazan (TPF), a water-insoluble red
fluorescent intracellular formazan^[96]36,[97]38 (Fig. S[98]3). By
measuring the rate at which each colony turns red, TCC allowed us to
directly measure the overall cellular reduction rate, and hence to
estimate the respiratory rate in single colonies (Fig. S[99]3,
Supplementary Data [100]1). Overall, we found large variability in
growth kinetic parameters among colonies growing on Luria Bertani (LB)
and glucose M9 agar plates (Fig. [101]2A–D) (coefficient of variation
(CV) ~40 and 15% for growth rate and lag time, respectively measured in
three biological replicates). Moreover, consistent with proteome-based
predictions, we found considerable variability also in respiratory
rates (Fig. [102]2E) (CV ~18 and 30% in M9 and LB, respectively).
Notably, while on average TCC inhibits bacterial growth, it does not
affect colony-to-colony variability (Fig. S[103]3). Our analysis of
protein noise levels in E. coli (Fig. [104]1) predicted that
variability in respiratory activity relates to noise in the regulation
of lower glycolytic enzymes. We hypothesized that using an inducible
promoter to control Cra overexpression would reduce expression noise in
its target genes and overall result in lower colony-to-colony
variations. To verify our hypothesis, we compared the heterogeneity in
growth and respiratory rates among colonies of wild-type, Δcra, and cra
overexpression mutants (cra^+) (Supplementary Data [105]1). Consistent
with our expectations, we observed that Cra overexpression induces a
significant (p value ≤ 0.01) reduction in the variability of
respiratory rates as well as lag times (Figs. [106]2F–H and S[107]4),
indicating that even small variations of the levels of lower glycolytic
enzymes can translate in large phenotypic differences. Somehow
unexpectedly, noise reduction was not observed at the growth rate level
(Fig. [108]2F).
Fig. 2. Colony-to-colony variation.
[109]Fig. 2
[110]Open in a new tab
A, D, E Distribution of maximum growth rates, lag times, and
respiratory rates, estimated for 5485 colonies of wild-type E. coli
growing in LB (red) and glucose M9 (blue) agar plates (Supplementary
Data [111]1). Estimates were derived from colonies grown independently
in three different plates with LB and three plates with M9 glucose
solid media. B, C 2D density plots in which each dot represents one
colony and the red line a locally weighted smoothed average (i.e.,
lowess function). On the upper right corner of each panel, we reported
the Spearman correlation (Sp) and corresponding p value (P, testing the
hypothesis of no correlation against the alternative hypothesis of a
nonzero correlation using the Spearman’s Rho test) between growth rate,
lag time and respiratory activity in LB (B) and glucose M9 (C). F, G, H
Distribution of CVs for growth parameters estimated from 1000 random
selections of 400 colonies from wild-type (blue), Δcra (red), and cra+
mutant (green) (Fig. S[112]4). Reported significance was estimated from
a two-tailed t-test.
Pareto optimal tradeoffs between respiration and fermentation
If noise in gene expression of enzymes in central metabolism is
responsible for fluctuations in respiratory rates, the next question is
whether and how such metabolic fluctuations propagate and relate to
phenotypic heterogeneity. To address this question, colony-to-colony
variability in growth rate, lag time and respiratory activity of E.
coli growing on LB or glucose M9 agar plates are related to each other
by estimating their pair-wise Spearman correlation (Fig. [113]2B, C).
Overall, we found that respiratory activity and growth rates are poorly
correlated, and no correlation was observed between lag time and
maximum growth rate in the LB medium, consistent with previous
studies^[114]39 (Fig. [115]2B, C). Surprisingly, we found a strong
correlation between respiratory activity and lag time (Fig. [116]2B,
C), suggesting that the higher the respiratory activity the shorter is
the time needed for cells to start duplicating. To test whether this
phenomenon generalizes to largely diverse conditions and growth rates,
we monitored growth and metabolic activity in thousands of colonies
(i.e., ~8000) in the presence of 13 different perturbing agents on
independent LB plates. These are mostly antibiotics interfering with
different cellular processes, such as protein/RNA (i.e.,
Chloramphenicol, Rifampicin, Erythromycin, Tetracycline) and cell wall
synthesis (i.e., Bacitracin, Fosfomycin, Ampicillin, Cefaclor), DNA
replication (i.e., Ciprofloxacin, Nalidixic acid, Nitrofurantoin), and
ATP biosynthesis (i.e., Carbonyl cyanide, Sodium Azide). To test
different concentrations of the same antibiotic in one plate, we
spotted the center of the dish with the antibiotic (Supplementary
Data [117]1), and let it diffuse for 3 h before inoculation. This way,
the closer the colony is to the center of the plate, the higher is the
concentration of the antibiotic (Fig. [118]3A). This approach allowed
us to test a continuous range of growth rates, lag times, and
respiratory activities in a single petri dish (Fig. [119]3A–C).
Remarkably, the strong correlation between respiratory rate and lag
time held true in all conditions tested (Fig. S[120]5), suggesting that
the link between heterogeneity in respiratory activity and the onset
time of colony expansion might reflect a fundamental mechanism at the
basis of the decision-making process of cell division.
Fig. 3. Respiratory activity vs. lag time.
[121]Fig. 3
[122]Open in a new tab
A–C Growth rates, lag times, and respiratory activities of individual
colonies are plotted against their distance from the center of the
plate (x-axis). Each dot represents a colony, growing in the same plate
with only LB solid medium (blue), or with the addition of
chloramphenicol spotted in the center of the plate. Blue (control) and
red (Chloramphenicol treatment) thick lines are smoothed values
obtained from a locally weighted smoothing function (i.e., lowess
function). D–F 2D density plots of estimates from all 8718 individual
colonies’ growth rate, lag time, and respiratory activity across all 14
tested conditions (i.e., one plate per condition) (Supplementary Data
1). Red thick lines are smoothed values obtained from a locally
weighted smoothing function. On the upper right corner of each panel,
we reported the Spearman correlation (Sp) and corresponding p value
(P), testing the hypothesis of no correlation against the alternative
hypothesis of a nonzero correlation using the Spearman’s Rho test.
Mounting evidence has shown that respiration, although being a more
efficient way for E. coli to utilize available nutrients for energy
generation, requires larger investments in proteins^[123]23. Hence, for
the same ATP production, fermentation consumes more carbon but requires
smaller investment in catalytic machinery allowing faster
growth^[124]23,[125]40. Coherently, the fastest growing colonies in M9
with glucose exhibited relatively low respiratory activities
(Fig. [126]2C). On the other hand, colonies with the highest
respiratory activities, while generally showing lower growth rates,
featured the shortest lag times (Fig. [127]2B, C). This finding
suggests that maximization of growth rate and minimization of lag time
are competing cellular objectives and that the regulation of
respiro-/fermentative metabolism can favor one objective over the
other—i.e., while fermentation allows for faster growth^[128]23,
respiration fosters quicker initiation of cell growth. Moreover,
instead of a large population of equally fast-growing individuals and a
small unfit sub-population that could ensure survival upon unfavorable
environmental changes (e.g., persisters), we observed a continuum of
colonies exploiting different metabolic strategies. How can this
metabolic diversity be maintained? While in a given constant
environment, only one strategy is optimal, in a naturally fluctuating
environment the advantages of short lag time or fast growth rate might
average out. We hypothesized that variability in respiro-/fermentative
metabolism is fitness-neutral—i.e., cells can be equally optimal in
spite of different metabolic strategies. Colonies that find an optimal
compromise between two competing objectives are called Pareto
optimal^[129]41—i.e., a colony is Pareto optimal if there exists no
other colony that is at least as good in all objectives, but strictly
better in at least one objective. By using a simple exponential model
of bacterial growth, one can derive that in equally fit cells—i.e.,
cells able to reach the same number of divisions in a fixed period of
time—growth rate and lag time are inversely related (Supplementary
text). Hence, according to our hypothesis, the space of feasible growth
rates and lag times should be delimited by a Pareto front^[130]41
approximating an inverse relationship between the two. Moreover,
natural selection of optimal fitness will force colonies to operate
metabolism in the proximity of the optimal tradeoffs^[131]42, and hence
colonies shall not randomly occupy the space of feasible growth rates
and lag times. Moreover, because of the already known direct
proportionality between fermentative metabolism and growth
rate^[132]23, we expect that in Pareto optimal colonies, growth rate
negatively scales with the respiratory rate (Supplementary text: lag
time vs. growth rate). Our theory is consistent with growth kinetics
observed in wild-type E. coli colonies growing on glucose as the sole
carbon source (Fig. [133]4A). We empirically found that the space of
growth rates and lag times occupied by the vast majority of colonies is
delimited by a front which is well described by approximating colony
fitness as growth rate over lag time (Fig. [134]4A). Moreover, as
hypothesized, colonies’ growth rates and lag time are not uncoupled and
hence are not uniformly distributed over the space of feasible growth
parameters (Fig. [135]4B). Rather, most of the colonies operate in the
proximity of a constant ratio between growth rate and lag time
(Fig. [136]4B). Consistent with this front being Pareto optimal, we
found that only in its proximity, growth rate exhibits a significant
monotonic decrease with the increase of respiratory rate, in agreement
with the theory of optimal proteome allocation^[137]23 (Fig. [138]4B,
C). Hence, we propose that fitness in a natural environment is largely
invariant to fluctuations in respiratory activity, and as a
consequence, expression noise in central metabolic enzymes had not been
counter selected by evolution^[139]3.
Fig. 4. Respiro-/fermentative tradeoffs between growth rate and lag time.
[140]Fig. 4
[141]Open in a new tab
A Growth rate and the inverse of the lag time is plotted for each
colony of wild-type E. coli growing in three independent plates with
glucose M9, same as Fig. [142]2C. Values have been normalized between 0
and 1. For each colony, the respiratory activity is color-coded (i.e.,
blue/red). The underlying heatmap reports on the respiratory rate
estimated from averaging respiratory levels in proximal colonies.
Isoclines identify points with a constant ratio between growth rate and
lag time (i.e., approximation of colony fitness). B Histogram of the
distribution of growth rate over lag time reported in panel (A). The
green dashed line is the number of colonies expected by random choice
in each histogram bin. Color-coded dots indicate Spearman correlation
and significance (Spearman’s Rho test) between growth and respiratory
rates in colonies with increasing fitness levels (i.e., isoclines in
panel (A)). Dashed blue lines highlight the 0.7 and 0.8 quantiles of
the distribution. C Each dot reports on the respiratory activity and
growth rate of a colony with a fitness value between blue dashed lines
in panel (B). D Respiratory activity in FSw and SSw mutants is
estimated as the fraction of glucose carbon that is not secreted as
acetate, denoted as respired carbon fraction. Reported data are
average ± standard deviation over three biological replicates. E, F
Distributions of growth rates and respired carbon fractions in FSw and
SSw strains. Red-dashed line reports on the ancestor level.
Significance was calculated by a two-tailed t-test. G Lag time vs.
growth rate in glucose minimal medium after 2 hours of carbon
starvation in wild-type, ΔarcA and arcA^+ mutants of E. coli. Reported
data are average ± standard deviation over three biological replicates.
Higher respiro- vs. fermentative metabolism prevents accumulation of toxic
branched-chain amino acid intermediates
The coexistence of diverse but equally optimal metabolic strategies
that diversify cells towards fast growth or early growth initiation is
consistent with the evolutionary trajectories of E. coli evolved in
batch liquid cultures containing glucose and acetate as sole carbon
sources^[143]43. During adaptive diversification, two coexisting
ecotypes emerge: one exhibiting fast growth in glucose but long lag
time when switching from glucose to acetate, and one exhibiting shorter
switching lag time and slow growth^[144]43. In addition to previous
work demonstrating that fast-switchers (FSw) operate metabolism so as
to require a minimum adjustment between growth on glucose and
acetate^[145]42, we predicted that FSw would favor respiratory
metabolism, while slow-switchers (SSw) would feature higher
fermentative metabolism (i.e., acetate overflow). To test our
predictions we used previously published data^[146]42 measuring growth
rate, glucose-uptake, and acetate secretion for seven FSw and eight SSw
randomly selected clones grown in liquid glucose minimal medium. In
agreement with our predictions, we found that FSw on average have
significantly (p value = 0.028) higher respiratory activity and slower
growth rates than SSw (Fig. [147]4D–F).
The evidence presented so far is mostly correlative. Hence, it is still
unclear whether differences in respiratory activity are causal for
changes in lag times, and whether they are sufficient to explain the
tradeoff between growth and lag time. To test the causality of this
relationship, we used a knockout strain of arcA (ΔarcA) with an
isopropyl β-D-1-thiogalactopyranoside (IPTG) inducible promoter to
modulate the arcA expression (arcA^+). ArcA is a key repressor of genes
involved in the TCA cycle and oxidative phosphorylation. While arcA
deletion causes an increased carbon flux into the TCA cycle^[148]44,
respiration is inhibited and acetate fermentation increased upon arcA
overexpression^[149]45. Hence, we expect lag times to increase with an
increased expression of arcA. To test this hypothesis, we grew
wild-type E. coli, the ΔarcA and arcA^+ strains in batch liquid
cultures of glucose minimal medium up to mid-exponential phase (optical
density at 600 nm (OD[600])~1), washed the cells, and incubated them in
M9 medium without carbon sources together with the IPTG inducer
(0.02 mM) for 2 h. Next, we resuspended cells in M9 medium with glucose
and monitored OD[600] in a plate reader using different levels of IPTG
inducer. Consistent with a previous study^[150]45, mild induction of
arcA (below 0.03 mM IPTG) is able to improve growth rate with respect
to the wild-type (Fig. [151]4G). In addition, while ΔarcA grows slower
than the wild-type, it also exhibits the shortest lag time, despite
similar energy metabolism (Fig. S[152]7). Overall, arcA expression
levels directly correlate with the length of the lag. This phenomenon
is similar when cells are resuspended in an M9 medium with a glycolytic
(i.e., fructose) or gluconeogenic (i.e., acetate) substrate as sole
carbon sources (Fig. S[153]6). Altogether, this additional experimental
evidence supports a key role of respiratory activity in facilitating
rapid growth resumption and reveals the conflicting roles between
respiration and fermentation, in preparing cells for rapid growth
resumption and maximization of growth rates, respectively. Such a
tradeoff might explain why at the population level E. coli cells are
neither optimized for growth rate nor for lag time
exclusively^[154]42,[155]45, but rather tend to adopt optimal
tradeoffs, which may vary within the cell population (Fig. [156]4B, D).
Finally, we investigated how increasing respiro vs. fermentative
metabolism in ΔarcA can affect the length of the lag period. To this
end, we use metabolomics^[157]46 to characterize the differences in the
abundance of ~1000 metabolites between wild-type and ΔarcA in glucose
M9 at exponential growth and during 2 h of carbon starvation
(Supplementary Data [158]1). We found that exponentially growing ΔarcA
mutants exhibit significantly (q value < 1e−2) lower levels of
biosynthetic intermediates in branched-chain amino acids (BCAA) (e.g.,
2-methylmaleate, 2-isopropylmaleate), including the main precursor
pyruvate and the end product valine (Fig. [159]5A, B). On the other
hand, dynamic metabolic changes upon carbon starvation are largely
consistent between wild-type and ΔarcA, resulting in fewer and smaller
metabolic differences, with the notable exception of non-degradable
amino acids, such as valine and (iso-)leucine (Fig. [160]5C).
Non-degradable amino acids accumulate during starvation^[161]47
(Fig. [162]5D), but to a lower extent in ΔarcA than in the wild-type.
Imbalance in the levels of BCAA intermediates can be highly toxic for
E. coli, especially valine and leucine^[163]48,[164]49. Intracellular
accumulation of amino acids inhibits their own biosynthesis together
with the biosynthesis of related amino acids^[165]48 and the uptake of
carbon sources^[166]49–[167]51. Hence, we hypothesized that by
maintaining lower levels of BCAA, ΔarcA is able to more rapidly resume
the biosynthesis of biomass precursors once carbon becomes available.
To test this hypothesis, we measured the lag time of carbon starved E.
coli wild type and ΔarcA after supplementing glucose together with
different amino acids: leucine, isoleucine, leucine+iso-leucine+valine,
tryptophan, methionine, and glutamate (Fig. [168]5E, F). ΔarcA and
wild-type exhibit a radically different response to supplementation of
glucose and leucine after carbon starvation. While leucine sensibly
prolongs lag time in wild-type, in ΔarcA leucine has a mild but
beneficial effect (Fig. [169]5E, F). Moreover, BCAA supplementation in
wild-type is able to reduce the lag-time by nearly 40%, more than
costly amino acids^[170]49, such as methionine (Fig. [171]5E).
Altogether, experimental evidence suggests that the homeostasis of BCAA
metabolism plays a crucial role during the initial growth of bacteria
after starvation. We previously found that intracellular levels of the
BCAA precursor pyruvate decrease with an increase in respiro vs.
fermentative metabolism^[172]49 (Fig. [173]5G). Metabolic changes
induced by arcA deletion fit very well with the previously established
relationship (Fig. [174]5G). We propose that by increasing respiratory
capacity, ΔarcA has lower pyruvate levels (Fig. [175]5A) and
consequently can maintain lower levels of BCAA intermediates
(Fig. [176]5A), thereby reducing the risk of their toxic accumulation.
Fig. 5. Interplay between respiro-/fermentative metabolism and BCAA
biosynthesis.
[177]Fig. 5
[178]Open in a new tab
A Results from the metabolome-based analysis. Each dot in the volcano
plot represents the relative difference in ion abundance for 955
putatively annotated metabolites between wild-type and ΔarcA, averaged
across three biological replicates. Metabolites with significant
differences (q value ≤ 0.05, estimated by Storey correction of p values
from two-tailed t-test) are highlighted in red. B KEGG pathway
enrichment analysis reporting metabolic pathways that are significantly
(q value < 0.01, estimated by Storey correction of p values from
permutation test) enriched for metabolites with a large difference
between wild-type and ΔarcA. C Volcano plot of metabolic differences
between wild-type and ΔarcA after 2 h of carbon starvation (i.e.,
glucose deprivation), averaged across six biological replicates. For
each metabolite, the color of the dot reflects the metabolite fold
change in wild-type between 5 and 120 min of carbon starvation. Dots
marked in black are metabolites that exhibit a significant (q
value ≤ 0.05, estimated by Storey correction of p values from
two-tailed t-test) difference between wild-type and ΔarcA. D Time
course profiles of metabolite levels from 5 to 120 min of carbon
starvation in wild-type (blue) and ΔarcA (red). We report the mean
(thick line) ± standard deviation (shaded region) across six biological
replicates. E Lag time of wild-type and F ΔarcA, 2 h after carbon
starvation. Cells were resuspended in M9 glucose with 1 mM of: leucine
(Leu), isoleucine (iso-Leu), valine+leucine+iso-leucine
(Leu + Iso-Leu+Val), tryptophan (Tryp), methionine (Met), and glutamate
(Glt). We report the lag time of individual replicates relative to the
average lag time in M9. G Difference in the abundance of pyruvate
between ΔarcA (green dot—GLC) and wild-type in M9 glucose (panel (A))
against difference in respiro-/fermentative metabolism calculated as
the ratio between acetate secretion and glucose uptake (mmol/gDW/h)
estimated in^[179]44. Differences in ΔarcA were overlaid on previously
acquired^[180]49 relative changes in pyruvate levels and
respiro-/fermentative metabolism in wild type across different
nutritional environments: minimal medium with either glucose (GLC) or
glucose minimal media supplemented with casamino acids (CAA), synthetic
amino acid mix (SAA), SAA deprived of following amino acids: threonine,
glycine, and serine (STG), threonine, glycine, serine, tryptophan,
cysteine, and alanine (STGTCA), glutamate, glutamine, proline, and
arginine (GGPA), aspartate and asparagine (AA), and glucose minimal
medium with 0.125 g/L of glutamate (GLT). The red line represents the
result from linear least squares regression analysis and 95% confidence
intervals (shaded region). Reported data are average ± standard
deviation over three biological replicates.
Discussion
Here we monitored phenotypic and metabolic heterogeneity arising at a
single colony level. A key advantage of this approach is the ability to
directly monitor the variability in metabolic rates rather than
reporting on the level of individual metabolites. It is worth noting
that bacterial colonies develop in complex 3D structures in which
individual bacteria can experience different metabolic
gradients^[181]52–[182]54. However, in E. coli, the first phase of
radial colony growth is not limited by nutrient gradients^[183]53,
suggesting that fundamental characteristics of colony-forming cells,
such as protein levels, can propagate to daughter cells and generate
spatial correlations^[184]54,[185]55 detectable at the colony level.
Moreover, transcriptional regulation of metabolism (e.g., Cra) often
involves negative feedback loops in which metabolites can directly
regulate transcription factors activity^[186]56. Such type of
regulatory interactions can function as negative integral feedback and
provide quasi-adaptation to small perturbations^[187]57,[188]58,
thereby increasing the number of generations that are necessary for
daughter cells to significantly diverge from the transcription factor
activity of the ancestor. Therefore, while clearly different from
single-cell measurements, by simultaneously monitoring thousands of
colonies and their variation in growth kinetic parameters and
respiratory rates, we could make experimentally testable predictions on
the functional impact of metabolic diversity on phenotypic
heterogeneity.
In this study, we showed that the regulation of key proteins involved
in funneling carbon into the TCA cycle plays a fundamental role in the
phenotypic heterogeneity of colonies growing on solid media. While not
all regulatory solutions can be equally optimal, the existence of
multiple and largely diverse metabolic tradeoffs that are
fitness-invariant can be a major driving force preserving and shaping
phenotypic heterogeneity and plasticity—i.e., the ability to develop
varied phenotypes under fluctuating environmental conditions.
Independent experimental evidence of planktonic cultures that evolve in
coexisting subpopulations exhibiting the same predicted tradeoffs
between lag time, growth rate, and respiratory activity, suggests that
the same concepts can be generalized to largely diverse growth
conditions.
Moreover, explaining colony-to-colony variations in growth rate and lag
time by changes in respiro-/fermentative metabolism, together with the
observation that an arcA deletion significantly suppresses resistance
evolution^[189]59, provides a basis to link independent experimental
evidence relating lag time and energy metabolism^[190]6,[191]60,[192]61
to antibiotic tolerance and resistance^[193]7. Consistent with our
findings, mounting evidence associated evolutionary adaptation of
bacterial persistence to mutations in enzymes of the respiratory
metabolism, such as the proton-pumping NADH:ubiquinone oxidoreductase
(Nuo complex)^[194]61,[195]62. We demonstrated that homeostasis in BCAA
can play a fundamental role during the early growth phases. We propose
that valine/leucine and isoleucine and possibly intermediates in their
biosynthesis can function similarly to toxin–antitoxin systems
mediating bacterial dormancy. Toxin–antitoxin modules, such as
HipAB^[196]63, have well-established roles in regulating the
bistability of clonal populations^[197]64 and in the formation of
growth-arrested persisters^[198]63. In a similar way, the accumulation
of toxic BCAA intermediates in clonal cells with different metabolic
activities can lead to cellular stasis by inhibiting BCAA biosynthesis
and/or carbon uptake^[199]49, ultimately affecting the biosynthesis of
essential components for cell growth and division. The existence of
different Pareto-optimal metabolic strategies within a clonal bacterial
population, on the one hand, could represent a form of metabolic
«division of labor»^[200]65 and, on the other hand, could provide a
bet-hedging strategy to withstand unforeseen challenges, such as
periods of nutrient limitations or antibiotic treatments^[201]6.
Overall, our findings can have important implications in diverse
theoretical and applicative fields. Understanding the origin and
functional impact of metabolic heterogeneity can open new opportunities
in metabolic engineering and synthetic biology, to incorporate noise
transmission in the design of more efficient biosynthetic strategies.
Moreover, fluctuations in respiro-/fermentative metabolism can be of
particular significance in the design and interpretation of
experimental evolution^[202]7,[203]8. The existence of a tradeoff
between shortest lag time and maximum growth rate and the ability to
shift between these cellular objectives by modulating
respiro-/fermentative metabolism can pave the way to alternative
therapeutic strategies fighting the emergence of tolerant cells and
eventually the appearance of drug resistance^[204]66,[205]67,
potentially beyond antimicrobial treatments^[206]68.
Methods
Strains and media
For all growth experiments, E. coli BW25113 or mutants listed in
Table [207]1 were initially grown overnight in LB or M9 minimal medium.
LB medium consists per liter of deionized water of: 10 g Bacto-Tryptone
(Becton Dickinson and Co.), 10 g NaCl, 5 g Yeast extract (DIFCO
laboratories). The M9 medium contains per liter of deionized water:
7.5 g of Na[2]HPO[4] 2H[2]O, 3.0 g KH[2]PO[4], 1.5 g (NH[4])[2]SO[4],
and 0.5 g NaCl and was adjusted to pH 7 before autoclaving. The
following components were filter-sterilized separately and then added
(per liter of final medium): 1 mL of 1 M MgSO[4], 1 mL of 0.1 M
CaCl[2], 1 mL 0.1 M FeCl[3], and 10 mL of a trace element solution
containing (per liter) 180 mg ZnSO[4] 7H[2]O, 120 mg MnSO[4] H[2]O,
180 mg CoCl[2] 6H[2]O, and 120 mg CuCl[2] 2H[2]O. Carbon source
solutions were filter-sterilized and added separately to the medium,
5 g/L glucose, fructose, acetate. Agar plates were made by adding
15 g/L of Agar.
Table 1.
List of E. coli strains.
Bacterial Strains Collection Reference
E. coli BW 25113 KEIO collection ^[208]79
Δcra/ΔarcA KEIO collection ^[209]79
Cra/arcA overexpression plasmid Obtained from the ASKA clone collection
^[210]80
[211]Open in a new tab
Strains used for the experiments discussed in the main text, their
origin, and corresponding reference.
Growth rate measurements
The growth rate and lag time in batch liquid culture were measured by
monitoring OD[600] in 96-well plates with the multiwell reader Infinite
(Tecan, Switzerland) at 37 °C with shaking. The growth rates were
extracted by fitting the exponential part of the growth. An iterative
approach was used to find the time window of at least 100 min with the
maximum exponential growth rate. The intersection between the initial
OD of the inoculum and the extension of the exponential growth curve
yielded the lag time^[212]69.
Statistical analysis
Statistical analyses were performed using Matlab R2018b (MathWorks).
Gene ontology and metabolic pathway enrichment analysis, and enrichment
of noisy enzymes in TF-target genes were based on an iterative
hypergeometric test described in Ref. ^[213]70. When necessary, p
values were corrected for multiple tests by q value estimation^[214]71.
The network of TF-target genes reported in^[215]72 was used to identify
TF regulating proteins with large deviating noise.
Image acquisition and analysis
Overnight cultures were diluted in LB or M9 media 1:1000. Cells were
grown up to an OD of 1 before plating at appropriate dilutions on solid
agar medium. Standard PC scanners (i.e., Epson Perfection V370 Photo)
were used to acquire images at 800 dpi resolution of Petri dishes every
10 min. Acquired images were analyzed with an in-house image analysis
software implemented in ImageJ IJ1 Macro and Python. In order to
improve the accuracy in detection of bacterial colonies the software
corrects for changes in light across plates (i.e., Otsu thresholding
over each plate), performs colony segmentation (i.e., watershed
algorithm over the standard variation of the experiment’s Z-stack
projection of all images) and tracking. Finally, outliers/artifacts are
filtered out. The final data returns for each colony x and y coordinate
on the plate, size at each time point, and RGB intensity. Full details
and computer codes are available on GitHub
[216]https://github.com/Dfernand1795/PetriScanner2.
Estimation of deviating noise
Here we used time-independent protein (p) abundance and noise from
single-cells of E. coli from^[217]13. Consistent with other studies, at
low copy number (μ[p]) the noise (η[p] = σ[p]^2/μ[p]^2, where σ[p] is
the standard deviation) is dominated by intrinsic noise (e.g.,
stochastic effects in gene expression), while at high expression levels
noise is dominated by extrinsic factors, such as fluctuations in
ribosomes, polymerase, transcription factors and partitioning of the
population between the cell-cycle stages^[218]73–[219]75. To estimate
average noise levels at different protein abundance, we used a moving
average smoothing method on the data reported for 1018
proteins^[220]13. Specifically, we used the malowess function in
Matlab, where for each protein, the expected average noise level (
[MATH: η¯p :MATH]
) is estimated from the average of the 5% closest proteins:
[MATH: η¯p=∑Ω=lbp≤μi≤ubpηi∣Ω
∣ :MATH]
1
Deviating noise is calculated as the ratio between the noise of
individual proteins and average noise levels of proteins with similar
abundance:
[MATH:
εp=ηp/η¯p :MATH]
.
Estimation of colony maximum growth rate, lag time, and respiratory rate
For each colony, the sigmoidal changes in the area over time were
fitted by a Gompertz function^[221]76
[MATH: f=Amin
+(
Amax−
Amin)eek(tm−t)
=Amin+AΔeek(tm−t)f′=AΔe−ek(t<
/mi>m−t)⋅kek(<
mi>tm−t)f″=AΔk2
e−ek(t<
mrow>m−t)⋅ek(t
m−t)(ek(tm−t)−1)f(t=tm)′=AΔekTlag=Amin+AΔe(1−ktm)AΔek :MATH]
2
The Gompertz function is able to describe asymmetrical growth curves
assuming that at the time of inflection t[m] the maximum growth rate is
achieved. Parameter k represents the maximum relative growth rate.
A[max] and A[min] the maximum and minimum area respectively. For
simplicity, we assume colony size at inoculation to be equal to zero.
By fitting this function to each colony growth curve we can
analytically estimate maximum growth rate (f^I) and lag-time (T[lag]).
Fitting was performed in Matlab 2018b using the lsqcurvefit function
using the Truest-region algorithm for optimization from 50 multiple
start points (i.e., MultiStart function). To determine the maximum
respiratory rate we use a linear model and found the time interval
(consisting of at least ten time points) with the largest proportional
coefficient between time and average red intensity of a colony.
[MATH: f=αt+β
:MATH]
, where α is the estimate for maximum respiratory rate (AU/h) and β is
an offset value.
ATP reporter assay
The low concentration ATP reporter plasmid pRS-QUE7mu^[222]21 was
transformed into E. coli bacterial strains BW25113 and BW25113-ΔarcA
(Keio:JW4364) expressing a T7 RNA polymerase (araB::T7RNAP-tetA). For
each sample, a single bacterial colony was grown in LB medium for 6 h
at 37 °C under agitation (170 RPM). 30 microliters of culture were
pelleted, resuspended in 3 ml of M9 medium and further diluted 1:10 in
3 ml of M9 medium supplemented with Glucose 0.5%, ampicillin 50 µg/ml
and arabinose 0.01%. Cultures were grown for 16 h at 37 °C under
agitation (170 RPM) and typically reached OD600 values between 0.05 and
0.3. For the starvation procedure, cells were washed twice and
resuspended in 3 mL of M9 medium only supplemented with ampicillin
50 µg/ml. Samples were brought back to the shaker for two hours before
addition of glucose at 0.5%. Cultures were sampled for FACS analysis
right before the starvation phase, after 30 and 120 min of starvation
and after one and 15 min after addition of glucose. 30 µl of sampled
culture was diluted in PBS containing propidium iodide (1 µg/ml,
ThermoFisher:P3566). FACS measurements were performed on a BD FACSaria
III Cell sorter. Fluorescence was measured with the following channels
Ex488_LP495_BP514/30-H, Ex405_LP502_BP530/30-H and
Ex488_LP610_BP616/23-H (for viability; propidium iodide).
Metabolome profiling
Wild-type and ΔarcA E. coli overnight cultures growing on M9 minimal
medium were diluted in fresh M9 glucose minimal media and grown at
37 °C until exponential phase and an OD[600] of 1. Cells were washed
twice with M9 medium without carbon source and 700 μL cell cultures
were distributed in 96 deep well plates and incubated at 37 °C until
shaking at 2 RCF. Samples for metabolomics profiling were taken during
exponential growth prior to carbon deprivation and 5, 10, 15, 30, 60,
90 and 120 min after carbon starvation. In total, 50 μl of whole cell
broth was directly transferred to 150 μl extraction liquid solution
containing 50% (v/v) methanol and 50% (v/v) acetonitrile at −20 °C. The
extraction was carried out by incubating the samples for 1 h at −20 °C.
Samples were centrifuged for 5 min at 1789 RCFand 80 μl of the
supernatant was transferred to 96 well storage plates and stored at
−80 °C (Supplementary Data [223]1). The mass spectrometry analysis was
performed on a platform consisting of an Agilent Series 1100 LC pump
coupled to a Gerstel MPS2 autosampler and an Agilent 6550 Series
Quadrupole Time of Flight mass spectrometer (Agilent, Santa Clara, CA)
following the protocol described in^[224]77. Mass spectra were recorded
from m/z 50 to 1000 using the highest resolving power (4 GHz HiRes).
All steps of mass spectrometry data processing and analysis were
performed with MATLAB (The Mathworks, Natick). Detected ions were
matched to a list of metabolites based on the corresponding molar
mass^[225]78. For the full list of metabolites used for annotation see
Supplementary Data [226]1.
Reporting summary
Further information on research design is available in the [227]Nature
Research Reporting Summary linked to this article.
Supplementary information
[228]Supplementary Information^ (14.2MB, docx)
[229]Peer Review File^ (354.7KB, pdf)
[230]41467_2021_23522_MOESM3_ESM.pdf^ (975.9KB, pdf)
Description of Additional Supplementary Files
[231]Supplementary Data 1^ (2.4MB, xlsx)
[232]Reporting Summary^ (1.2MB, pdf)
Acknowledgements