10.5061/DRYAD.7WM37PVPP
Blount, Zachary
0000-0001-5153-0034
Michigan State University
Maddamsetti, Rohan
Duke University
Grant, Nkrumah
0000-0002-4555-5283
Michigan State University
Ahmed, Sumaya
Kenyon College
Jagdish, Tanush
Harvard University
Society for the Study of Evolution
Sommerfeld, Brooke
Michigan State University
Tillman, Alice
Kenyon College
Moore, Jeremy
Kenyon College
Baxter, Jessica
Michigan State University
Slonczewski, Joan L
0000-0003-3484-1564
Kenyon College
Barrick, Jeffrey E
0000-0003-0888-7358
Society for the Study of Evolution
Lenski, Richard E
0000-0002-1064-8375
Michigan Molecular Institute (United States)
Genomic and phenotypic evolution of Escherichia coli in a novel
citrate-only resource environment
Dryad
dataset
2020
niche invasion
Gene amplification
evolutionary innovation
gene expression
Bacterial growth curves
evolutionary novelty
National Science Foundation
https://ror.org/021nxhr62
DEB-1451740
National Science Foundation
https://ror.org/021nxhr62
MCB-1923077
National Institute of Food and Agriculture
https://ror.org/05qx3fv49
MICL02253
National Science Foundation
https://ror.org/021nxhr62
Cooperative Agreement DBI-09394541
Michigan State University
https://ror.org/05hs6h993
Rudolph Hugh Award
Michigan State University
https://ror.org/05hs6h993
Ralph Evans Award
Kenyon College
https://ror.org/04ckqgs57
Individual Faculty Development Award
National Science Foundation
https://ror.org/021nxhr62
DBI-0939454
National Institute of Food and Agriculture
https://ror.org/05qx3fv49
MICL02253
2020-08-07T00:00:00Z
2020-08-07T00:00:00Z
en
https://doi.org/10.1101/2020.01.22.915975
https://doi.org/10.7554/eLife.55414
https://doi.org/10.7554/elife.55414
2529646975 bytes
10
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Evolutionary innovations allow populations to colonize new ecological
niches. We previously reported that aerobic growth on citrate (Cit+)
evolved in an Escherichia coli population during adaptation to a minimal
glucose medium containing citrate (DM25). Cit+ variants can also grow in
citrate-only medium (DM0), a novel environment for E. coli. To study
adaptation to this niche, we founded two sets of Cit+ populations and
evolved them for 2500 generations in DM0 or DM25. The evolved lineages
acquired numerous parallel mutations, many mediated by transposable
elements. Several also evolved amplifications of regions containing the
maeA gene. Unexpectedly, some evolved populations and clones show apparent
declines in fitness. We also found evidence of substantial cell death in
Cit+ clones. Our results thus demonstrate rapid trait refinement and
adaptation to the new citrate niche, while also suggesting a recalcitrant
mismatch between E. coli physiology and growth on citrate.
Evolution experiment We previously isolated three random Cit+ clones,
designated as CZB151, CZB152, and CZB154, from the 33,000-generation
sample of LTEE population Ara−3 (Blount et al. 2008). We also isolated
spontaneous Ara+ revertants for each clone, designated as ZDB67, ZDB68,
and ZDB69, respectively. For long-term preservation, we inoculated Luria
Bertani (LB) broth with isolated colonies of each clone and its revertant,
grew them overnight at 37°C with orbital shaking at 120 rpm, and froze
samples of each at −80°C with glycerol as cryoprotectant. We revived the
clones and revertants from the frozen stocks and grew them in LB
overnight. We then diluted the LB cultures 10,000-fold into 9.9 mL of
Davis Mingioli (DM) minimal medium supplemented with 25 mg/L glucose
(DM25), and grew them at 37°C with orbital shaking at 120 rpm. After 24 h,
we diluted these cultures 100-fold in 9.9 mL of fresh DM25 and grown for
another 24 h. This preconditioning acclimated the bacteria to growing on
citrate. The preconditioned cultures were then diluted 100-fold into 9.9
mL of base DM medium (DM0), which lacks any glucose but contains 1 g/L
(1,700 mM) of citrate for carbon and energy. We started two replicate
populations from each LTEE-derived clone and each revertant, for a total
of 12 DM0 populations. At the same time, we inoculated 12 populations into
DM25 (Fig. 1). We maintained these DM25 populations at 37°C with orbital
shaking, and transferred them by 100-fold dilution into fresh DM25 every
24 h (i.e., the same conditions as in the LTEE) for 375 transfers and
2,500 generations in total. The founding Cit+ clones grow poorly in the
citrate-only resource environment. They were unable to reach stationary
phase or, in some cases, exponential phase within 24 or even 48 h. We
therefore incubated the DM0 populations for 72 h after their initial
inoculation so they could reach stationary phase before transfer to fresh
medium. We then diluted them diluted 100-fold into 9.9 mL of DM0 every 48
h for seven cycles (two weeks), and then subsequently every 24 h for a
total of 375 transfers and 2,500 generations. Every 37 days (~250
generations) samples of each population were frozen with glycerol at
−80°C. Isolation of evolved clones We revived each evolved population
sample by inoculating 100 mL of the stock frozen at generation 2,500 into
9.9 mL of LB broth and incubating overnight at 37°C with orbital shaking.
We then diluted the revived DM0- and DM25-evolved populations 10,000-fold
in 9.9 mL of DM0 or DM25, respectively, grew them for 24 h at 37°C with
orbital shaking, followed by 100-fold dilution into fresh DM0 or DM25 and
another 24 h period of growth at 37°C with orbital shaking. We then
diluted each population 100,000-fold in 0.85% saline and spread 100 mL on
an LB agar plate marked with 3 dots on the bottom. We streaked the colony
closest to each dot on an LB plate after 48 h of incubation at 37°C,
thereby providing three randomly chosen clones from each population. We
then inoculated an isolated colony of each clone into LB broth, grew it
overnight, and froze it as before. Fitness assays We
measured fitness by performing competition experiments modified from those
described by Lenski et al. (1991). We revived samples by inoculating 15 mL
(for clones) or 100 mL (for whole populations) from a slightly thawed
frozen stock into 10 mL of LB. These cultures then grew overnight at 37°C
with 120 rpm orbital shaking, after which we diluted each 10,000-fold into
either DM25 or DM0 and preconditioned as described above. We inoculated 50
mL of each competitor’s preconditioned culture into 9.9 mL of the
corresponding medium, vortexed to mix, and then we spread 100 mL of 10−2
and 10−3 dilutions on Tetrazolium Arabinose (TA) indicator agar plates to
estimate the competitors’ initial densities. We estimated their densities
again at the end of the assay by spreading 100 mL of 10−4 and 10−5
dilutions on TA plates. For whole populations, we assayed fitness with
3-fold replication in 1-day competitions, in which final densities were
estimated after 24 h. For the evolved clones, we assayed fitness with
5-fold replication, and measured final densities after 3 days, with
100-fold serial transfers to fresh medium after 24 and 48 h. The realized
growth rates of the two competitors were determined from their starting
and ending densities, accounting for the dilutions. We calculated the
fitness of an evolved clone or population as its realized growth rate
divided by that of the ancestral competitor. In the population fitness
assays, ZDB67 was the common competitor for all Ara− population samples,
and CZB151 was the common competitor for all Ara+ population samples.
Growth curves We chose one of the three evolved clones from generation
2,500 from each DM0 or DM25 population, then revived and preconditioned it
in DM0 or DM25 as described above. We diluted the cultures 100-fold into
9.9 mL of DM0 or DM25, vortexed, and dispensed six 200 mL aliquots of each
culture into wells in a 96-well plate. We randomized well assignments for
the cultures to minimize position effects. We measured optical density
(OD) at 420-nm wavelength every 10 min for 48 h using a Molecular Devices
SpectraMax 384 automated plate reader. We discarded the measurements taken
before 30 min from our analysis. Microscopy and cell viability analyses We
performed microscopy and viability analyses on cells derived from five
clones: the LTEE ancestor (REL606); one of the three Cit+ ancestors in our
evolution experiment (CZB151); two of its descendants that evolved in DM0
and DM25 for 2,500 generations (ZDBp871 and ZDBp910, respectively); and a
Cit+ clone isolated at generation 50,000 of the LTEE (REL11364). We
revived clones from the frozen stocks and preconditioned them as described
above, except that the preconditioning steps in DM0 or DM25 were extended
to 4 daily passages to ensure acclimation to these environments. We
performed preparations for live/dead cell staining and microscopic
analyses on the fifth day. In these preparations, we concentrated the
cells in each culture by centrifugation at 7,745 g for 8 min and decanted
the supernatant. We then resuspended the cell pellets in Corning tubes
containing 10 mL of 0.85% saline, and incubated them at room temperature
for 1 h; we inverted the tubes every 15 min. We then centrifuged these
cultures for an additional 8 min, decanted the supernatant, and
resuspended the cell pellets in 0.85% saline. We adjusted the volume of
saline based on variation in turbidity to ensure that we had sufficient
cells in a typical field of view for microscopy. We examined 14-55 fields
per replicate for each combination of strain and media treatment. Total
cell counts ranged from approximately 15,000 to 60,000 for the various
combinations of clones and culture media. We used the LIVE/DEAD BacLight
Viability Kit for microscopy (ThermoFisher #L7007), following the
manufacturer’s directions for fluorescently labeling cells. In short, we
mixed components A and B in equal amounts, added 1 µl to each culture
containing resuspended cells, and incubated them for 20 min in the dark to
prevent photobleaching. After labeling, we fixed 3 µL of each sample onto
a 1% agarose pad and performed fluorescent microscopy using a Nikon
Eclipse Ti inverted microscope. Phase-contrast images were taken using
diascopic illumination with an exposure time of 100 ms. Fluorescence was
measured with an exposure time of 200 ms at 25% power of the fluorescent
light source using two filter sets, 49003-ET-EYFP and 49008-ET-mCherry
Texas Red (Chroma), which correspond to the fluorescence spectra of “live”
and “dead” cells, respectively. All images were taken at 100×
magnification. We analyzed micrographs using SuperSegger, an
image-processing package (Stylianidou et al. 2016). We first filtered the
data, keeping only those values for segmented regions in the micrograph
that were scored by the neural-network classifier as having P(Cell = True)
> 75%. (Region scores range between −50 and 50, so we used data
only from regions with values between 25 and 50). We then used the
fluorescence values from the SuperSegger output and scored individual
cells as “live” or “dead” depending on whether the fluorescence signal on
the green (YFP) channel was greater or lesser, respectively, than the
signal on the red (RFP) channel. We calculated the proportion of dead
cells across the many fields examined for each of the 5 replicate cultures
that we analyzed for each combination of clone and growth medium, and we
used these values in the statistical analyses. Genomic analysis and
copy-number variation We thawed the 3 Cit+ founder strains (CZB151,
CZB152, CZB154), their respective Ara− derivatives (ZDB67, ZDB68, ZDB69),
and 25 evolved clones (one Cit+ clone from each DM0 and DM25 evolved
population, plus the anomalous Cit− clone ZDBp874) and grew them overnight
in LB broth. We isolated genomic DNA from each sample using the Qiagen
Genomic-tip 100/G DNA extraction kit. The genomic DNA was then sequenced
by the facilities and using the platforms shown in Supplementary File 4.
For genomes sequenced at UT Austin, we purified DNA from E. coli cultures
using the PureLink Genomic DNA Mini Kit (Invitrogen). For each sample, we
fragmented 1 µg of purified DNA using dsDNA Fragmentase (New England
Biolabs). We then used the KAPA Low Throughput Library Preparation kit
(Roche) to construct Illumina sequencing libraries according to the
manufacturer's instructions with two exceptions. First, we reduced
reaction volumes by half. Second, we designed DNA adapters that
incorporate additional 6-base sample-specific barcodes such that the
barcodes are sequenced as the first bases of both read 1 and read 2. We
performed paired-end sequencing with 300-base reads on an Illumina MiSeq
at the University of Texas at Austin Genome Sequencing and Analysis
Facility. Reads were demultiplexed using a custom python script. We
trimmed barcodes and adapter sequences using Trimmomatic version 0.38
(Bolger et al., 2014). When available, we combined short-read data from
different platforms before mutation identification. We identified
mutations using breseq version 0.33.2 (Deatherage and Barrick 2014). We
used a bash script called “generate-LCA.sh” to infer the last common
ancestor (LCA) of all evolved strains by taking the intersection of
mutations found in previously curated genomes for CZB152 and CZB154; those
curated founder genomes (and others) are available at:
https://github.com/barricklab/LTEE-Ecoli. We further analyzed the
mutations called by breseq relative to the LCA using custom python and R
scripts available and described at:
https://github.com/rohanmaddamsetti/DM0-evolution. We used the following
algorithm to find copy-number variation in the genomes. The breseq
pipeline models 1× copy number using a negative binomial distribution fit
to coverage, truncating high and low coverage that might be caused by
amplifications and deletions, respectively. We then identified all
positions in the genome that rejected that negative binomial at an
uncorrected p = 0.05. Finally, we calculated a Bonferroni-corrected
p-value for contiguous stretches of the genome in which the 1× null model
was rejected at each site. We examined coverage at sites separated by the
maximum read length to ensure they were not spanned by a single read. For
example, in the case of a region of elevated coverage that was 1000 bp in
length, covered by 150-base Illumina sequencing reads, the value of
P(coverage=min)6 would be calculated, where min is the minimum coverage in
that region, P(coverage=min) is the probability of that minimum coverage
under the negative binomial null model, and 6 represents the (integer)
number of sites that are 150 bp apart in the 1000-bp stretch. The output
was then filtered for regions longer than 2 × 150 = 300 bp to remove
potential false positives. The Bonferroni calculation included corrections
for checking every site in the genome in addition to the number of sites
that passed the initial 0.05 cutoff for deviations from the negative
binomial expectation. All gene amplifications detected in the DM0- and
DM25-evolved genomes are reported in Supplementary File 2. Statistical
test for selection on parallel IS150 insertions To test for positive
selection on parallel IS150 insertions, we simulated a null model of
insertion-site preferences based on the observed data. We conservatively
assumed that IS150 elements can only insert into the positions where we
observed insertions in one or more sequenced genomes from either this
experiment or the LTEE (Tenaillon et al. 2016). We also assumed that the
probability of IS150 transposing into a given site is proportional to the
observed number of IS150 insertions at that site across the sequenced
genomes, as would be the case if mutational biases alone accounted for the
parallel IS150 insertions. We then used the non-parametric bootstrap
method (100,000 replicates) to calculate the probability that any
particular site would be hit by so many IS150 elements among the
DM0-evolved genomes, holding the number of IS insertions over that group
fixed. RNA-Seq and transcriptome analysis We performed RNA-Seq on six
clones: the three Cit+ clones from the LTEE used as ancestors in our
evolution experiment (CZB151, CZB152, and CZB154) and three evolved
descendants isolated after 2,500 generations of adaptation to DM0
(ZDBp877, ZDBp883, and ZDBp889). We revived each clone from a frozen stock
in LB as described above. We diluted each culture 10,000-fold into DM25
with four-fold replication and allowed them to grow for 24 h at 37°C with
120 rpm orbital shaking for preconditioning to minimal medium. We then
diluted the 16 resulting cultures 100-fold in DM0 and grew them for 48 h
at 37°C with shaking to for preconditioning to the citrate-only medium. We
diluted the mature cultures 100-fold again into fresh DM0, and grown to
OD600 0.2 – 0.3, corresponding to mid-log phase, at which point we
extracted their RNA using the cold phenol-ethanol method (Bhagwat et al.
2003). We recovered RNA using a Qiagen RNeasy MiniKit (#74104), and
removed DNA with a Qiagen RNase-free DNase set (#79254). RNA was diluted
to 50 ng/mL with nuclease-free water and cDNA amplified by RT-PCR.
Purified cDNA was then sequenced by Admera Health (South Plainfield, NJ).
We used kallisto version 0.44 (Bray et al. 2016) to quantify RNA
transcripts and sleuth (Pimentel et al. 2017) to conduct
differential-expression analysis and visualization. These results are
presented in Supplementary File 3. Construction of maeA plasmid We
constructed a medium-copy-number plasmid based on the kanamycin resistance
cassette-containing plasmid, pSB3K3, in which the maeA gene was placed
under the control of a strong constitutive synthetic promoter and ribosome
binding site, P089-R052, described by Kosuri et al. (2013). We used PCR to
amplify the maeA gene from REL606 and the pSB3K3 plasmid. We ordered the
P089-R052 promoter as an oligonucleotide. We assembled these components
using circular polymerase cloning (Quan and Tian 2009) and Gibson assembly
(Gibson 2011). We performed drop dialysis using Millipore membrane filters
(VSWP01300) for 15 min to desalt the assembly reactions before
electroporation. We isolated transformants on LB-Kanamycin plates and used
PCR to find colonies that contained the P089-R052–maeA insert. We used
Sanger-sequencing of plasmid inserts to verify that no unintended point
mutations had occurred during construction. We designated the final
plasmid containing the P089-R052-maeA insert in the pSB3K3 backbone
RM4.6.2. Competition experiments to assess fitness effects of maeA We
transformed the Cit+ ancestral clones CZB151 and CZB152 and their Ara+
revertants, ZDB67 and ZDB68, respectively, with the plasmid RM4.6.2. We
also transformed the same clones with the empty pSB3K3 vector. We froze
stock cultures of each transformant at −80°C with glycerol as a
cryoprotectant. We competed each RM4.6.2 transformant against its cognate
pSB3K3 transformant in the clone with the opposite Ara marker state.
Briefly, we revived all 8 transformants in LB supplemented with 50 mg/mL
kanamycin and grown overnight at 37°C with 120 rpm orbital shaking. We
then diluted each overnight culture 10,000-fold in 9.9 mL DM0 and
incubated for 48 h at 37°C with orbital shaking, after which it was
diluted 100-fold in fresh DM0 every 48 h three times to acclimate cells to
the citrate-only resource environment. We commenced the competition assays
the next day by inoculating 9.9 mL DM0 with 50 mL each of an RM4.6.2
transformant and the oppositely marked pSB3K3 transformant, with 4-fold
replication for a total of 16 competitions. We ran three-day competitions
to estimate fitness as described above.
## Dryad Data repository for "Genomic and phenotypic evolution of
Escherichia coli in a novel citrate-only resource environment" by
Blount ZD, Maddamsetti R, Grant NG, Ahmed ST, Jagdish T, Sommerfeld BA,
Tillman A, Moore J, Slonczewski JL, Barrick JE, Lenski RE. Correspondence
to: zachary.david.blount [AT] gmail [DOT] com and rohan.maddamsetti [AT]
gmail [DOT] com directory contents: - src/ contains python and R code used
for data analysis. - results/ contains figures and tables in the final
manuscript, as well intermediate files generated in the course of the data
analysis, as well as some exploratory analyses that may not be referenced
directly in the manuscript. - data/ contains files for the growth curves
and other analyses. Formatted data used by data analysis scripts are found
in the data/rohan-formatted directory. - plasmid-design/ contains
documentation for maeA plasmid construction, as well as for a couple other
plasmids of interest for other work: pCitT, and pCitA, and some protocols.
- genomes/ contains breseq output for the genomes studied here, as well as
Long-term evolution experiment (LTEE) and mutation accumulation experiment
(MAE) genomes in genome diff format, as published by other studies for
comparison with the genomes studied here. genomes/curated-diffs/ contains
some important notes for how the Last Common Ancestor (LCA) reference
genome was inferred and constructed for mutation calling using breseq. -
Nkrumah_MicroscopyData_DM0_project contains images and output from
SuperSegger software for the analysis of cell death that Nkrumah
conducted. - Illumina sequencing reads for genomic and transcriptomic
analyses have been deposited in the SRA: see data availability statement
in the manuscript for those data. Beyond those bulky 'raw' data
files, all other data analyzed for this project is available in this
repository. For more information or answers to any questions on these data
and analyses, please contact the corresponding authors by email.