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.