1. Evolutionary Biology
  2. Genetics and Genomics
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Mutations that improve efficiency of a weak-link enzyme are rare compared to adaptive mutations elsewhere in the genome

  1. Andrew B Morgenthaler
  2. Wallis R Kinney
  3. Christopher C Ebmeier
  4. Corinne M Walsh
  5. Daniel J Snyder
  6. Vaughn S Cooper
  7. William M Old
  8. Shelley D Copley  Is a corresponding author
  1. University of Colorado Boulder, United States
  2. Center for Evolutionary Biology and Medicine, University of Pittsburgh, United States
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Cite this article as: eLife 2019;8:e53535 doi: 10.7554/eLife.53535

Abstract

New enzymes often evolve by gene amplification and divergence. Previous experimental studies have followed the evolutionary trajectory of an amplified gene, but have not considered mutations elsewhere in the genome when fitness is limited by an evolving gene. We have evolved a strain of Escherichia coli in which a secondary promiscuous activity has been recruited to serve an essential function. The gene encoding the ‘weak-link’ enzyme amplified in all eight populations, but mutations improving the newly needed activity occurred in only one. Most adaptive mutations occurred elsewhere in the genome. Some mutations increase expression of the enzyme upstream of the weak-link enzyme, pushing material through the dysfunctional metabolic pathway. Others enhance production of a co-substrate for a downstream enzyme, thereby pulling material through the pathway. Most of these latter mutations are detrimental in wild-type E. coli, and thus would require reversion or compensation once a sufficient new activity has evolved.

Introduction

The expansion of huge superfamilies of enzymes, transcriptional regulators, transporters, and signaling molecules from single ancestral genes has been a dominant process in the evolution of life (Bergthorsson et al., 2007; Chothia et al., 2003; Glasner et al., 2006; Hughes, 1994; Ohno, 1970; Todd et al., 2001). The emergence of new protein family members has enabled organisms to access new nutrients, sense new stimuli, and respond to changing conditions with ever more sophistication (Conant and Wolfe, 2008; Nei and Rooney, 2005; Reams and Neidle, 2004; Santos et al., 2017; Starr et al., 2017; Storz, 2016).

The Innovation-Amplification-Divergence (IAD) model (Figure 1) posits that evolution of new enzymes by gene duplication and divergence begins when a physiologically irrelevant promiscuous activity becomes important for fitness due to a mutation or environmental change (Bergthorsson et al., 2007; Francino, 2005; Hughes, 1994; Näsvall et al., 2012). A newly useful enzymatic activity is often inefficient, making the enzyme the ‘weak-link’ in metabolism. Gene duplication/amplification provides a ready mechanism to improve fitness by increasing the abundance of a weak-link enzyme. If mutations lead to an enzyme capable of efficiently carrying out the newly needed function, selective pressure to maintain a high copy number will be removed, allowing extra copies to be lost and leaving behind two paralogs (or just one gene encoding a new enzyme if the original function is no longer needed).

The Innovation-Amplification-Divergence (IAD) model of gene evolution.

A promiscuous activity B of an enzyme may become physiologically relevant due to a mutation or environmental change. Gene amplification increases the abundance of the weak-link enzyme. Mutations can improve the efficiency of the newly important activity B. Once sufficient B activity is achieved, selection is relaxed and extra gene copies are lost, leaving behind two paralogs.

While the IAD model provides a satisfying theoretical framework for the process of gene duplication and divergence, our understanding of the process is far from perfect. Although the signatures of gene duplication and divergence are obvious in extant genomes, we have little information about the genome contexts and environments in which new enzymes arose. Laboratory evolution offers the possibility of tracking this process in real time. In a landmark study, Näsvall et al. used laboratory evolution to demonstrate that a gene encoding an enzyme with two inefficient activities required for synthesis of histidine and tryptophan amplified and diverged to alleles encoding two specialists within 2000 generations (Näsvall et al., 2012; Newton et al., 2017). However, this study followed only mutations in the diverging gene. When an organism is exposed to a novel selection pressure that requires evolution of a new enzyme, any mutation – either in the gene encoding the weak-link enzyme or elsewhere in the genome – that improves fitness will provide a selective advantage.

We have explored the relative importance of mutations in a gene encoding a weak-link enzyme and elsewhere in the genome using a model system in Escherichia coli. ProA (γ-glutamyl phosphate reductase, Figure 2) is essential for proline synthesis in E. coli. ArgC (N-acetylglutamyl phosphate reductase) catalyzes a similar reaction in the arginine synthesis pathway, although the two enzymes are not homologous (Goto et al., 2003; Ludovice et al., 1992; Page et al., 2003). ProA can reduce N-acetylglutamyl phosphate (NAGP), but its activity is too inefficient to support growth of a ∆argC strain of E. coli in glucose. However, a point mutation that changes Glu383 to Ala allows slow growth of the ∆argC strain in glucose. Enzymatic assays show that E383A ProA (ProA*) has severely reduced activity with γ-glutamyl semialdehyde (GSA), but substantially improved activity with N-acetylglutamyl semialdehyde (NAGSA) (Khanal et al., 2015; McLoughlin and Copley, 2008). (It is necessary to assay kinetic parameters in the reverse direction because the substrates for the forward reaction are too unstable to prepare and purify.) Glu383 is in the active site of the enzyme; the change to Ala may create extra room to accommodate the larger substrate for ArgC, but at a cost to the ability to bind and orient the native substrate. The poor efficiency of the weak-link ProA* creates strong selective pressure for improvement of both proline and arginine synthesis during growth of ∆argC E. coli on glucose as a sole carbon source.

Figure 2 with 1 supplement see all
E383A ProA (ProA*) replaces ArgC in the arginine synthesis pathway in ∆argC proA* E. coli, but is the bottleneck in the pathway due to its poor catalytic activity.

The reaction normally catalyzed by ArgC and replaced by ProA* in the parental strain is indicated by the red dotted line. The green and red lines indicate allosteric activation and inhibition, respectively.

We evolved eight replicate populations of ∆argC proA* E. coli in minimal medium supplemented with glucose and proline for up to 1000 generations to identify mechanisms by which the impairment in arginine synthesis could be alleviated. Our expectation that amplification of proA* would be beneficial was borne out in all populations. Whole-genome sequencing of the adapted populations and further biochemical analysis showed that an adaptive mutation in proA* followed by deamplification of proA* occurred in only one population. Indeed, most of the adaptive mutations occurred outside of proA*. We have identified the mechanisms by which three common classes of such mutations increase fitness: (1) restoration of a known defect in pyrimidine synthesis; (2) an increase in the amount of ArgB, the enzyme that synthesizes NAGP, the substrate for the weak-link ProA*; and (3) an increase in flux through carbamoyl phosphate synthetase, whose product feeds into the arginine synthesis pathway downstream of the weak-link enzyme (Figure 2). The latter two types of mutations appear to increase flux through the bottlenecked arginine synthesis pathway while the more difficult process of improving the weak-link enzyme progresses. In the case of the mutations affecting carbamoyl phosphate synthetase, the fitness increase comes at a cost to presumably well-evolved regulatory functions.

Our results demonstrate that mutations elsewhere in the genome play an important role during the process of gene amplification and divergence when the inefficient activity of a weak-link enzyme limits fitness. Thus, the process of evolution of a new enzyme by gene duplication and divergence is inextricably intertwined with mutations elsewhere in the genome that improve fitness by different mechanisms.

Results

Growth rate of ∆argC proA* E. coli increased 3-fold within a few hundred generations of evolution in M9/glucose/proline

We generated a progenitor strain for laboratory evolution by replacing argC with the kanr antibiotic resistance gene, modifying proA to encode ProA*, and introducing a mutation in the −10 region of the promoter of the proBA operon. (This mutation was one of two promoter mutations previously shown to increase proA* expression during adaptation of the ∆argC strain [Figure 2—figure supplement 1; Kershner et al., 2016]). The presence of the promoter mutation ensured that all populations had the same mutation during the evolution experiment. We also introduced yfp downstream of proA* and deleted several genes (fimAICDFGH and csgBAC, which are required for the formation of fimbriae and curli, respectively Barnhart and Chapman, 2006; Proft and Baker, 2009) to minimize the occurrence of biofilms. We evolved eight parallel lineages of this strain (AM187, Table 1) in M9 minimal medium supplemented with 0.2% (w/v) glucose, 0.4 mM proline, and 20 µg/mL kanamycin in a turbidostat to identify mutations that improve arginine synthesis. We used a turbidostat rather than a serial transfer protocol because turbidostats can maintain cultures in exponential phase and thereby avoid selection for mutations that simply decrease lag phase or improve survival in stationary phase. Turbidostats also avoid population bottlenecks during serial passaging that can result in loss of genetic diversity.

Table 1
Strains used in this work.
straingenotypenotes
E. coli BW25113GenBank accession number CP009273 (Grenier et al., 2014)
AM008E. coli BW25113; argC::kanrKeio strain (Baba et al., 2006)
AM187AM008 + −45 C→T upstream of proB (M2 promoter mutation from Kershner et al., 2016) + A1148→C in proA (changes Glu383 to Ala) + yfp construct downstream of proBA consisting of (from 5’to 3’) BBa_B0015 terminator, P3 promoter, synthetic RBS, yfp (see Materials and methods); ∆fimAICDFGH; ∆csgBACparental strain for adaptation, GenBank accession number CP037857.1
AM209E. coli BL21(DE3); argC::kanr; proA::catused for expression and purification of wild-type and mutant ProAs
AM239AM187 + 58 bp deletion upstream of argB (pos. 4145856–4145913)a
AM241AM187 + C4145901→G (24 bp upstream of argB start codon)
AM242AM187 + C4145903→A (22 bp upstream of argB start codon)
AM243AM187 + C4145903→T (22 bp upstream of argB start codon)
AM244AM187 + C4145907→A (18 bp upstream of argB start codon)
AM245AM187 + 38 bp duplication upstream of argB (pos. 4145912–4145949)
AM267E. coli BL21; carAB::kanrused for expression and purification of wild-type and mutant carbamoyl phosphate synthetases
AM279AM187 + C1169→T in ygcB (changes Ala390 to Val in Cas3)
AM320AM187 + T1116→G in proA* (changes Phe372 to Leu)
AM327AM187 + 82 bp deletion upstream of pyrE (pos. 3808881–3808962)
AM329AM187 + 82 bp deletion upstream of pyrE (pos. 3808881–3808962); C1169→T ygcB (changes Ala390 to Val in Cas3)
AM399AM187 + ∆2906–2917 carB
AM401AM187 + ∆2986–3117 carB
AM407AM187 + kanr::argC(null) (Gly153 and Cys154 changed to TAA stop codons)
AM413AM187 + G1106→T carB (changes Gly369 to Val)
AM415AM187 + A2896→G carB (changes Lys966 to Glu)
AM437AM187 + C1148→A in proA* (reverts E383A mutation)
AM439AM320 + C1148→A in proA** (reverts E383A mutation)
AM441E. coli BW25113 + 82 bp deletion upstream of pyrE (pos. 3808881–3808962)
AM443AM441 + ∆2906–2917 carB
AM445AM441 + ∆2986–3117 carB
AM447AM441 + G1106→T carB (changes Gly369 to Val)
AM449AM441 + A2896→G carB (changes Lys966 to Glu)
  1. a Genome positions refer to the sequence of strain AM187 (GenBank accession number CP037857), which was modified from the E. coli BW25113 sequence (GenBank accession number CP009273; Grenier et al., 2014) based on the mutations that had been introduced.

Growth rate in each culture tube was averaged over each 24 hr period and was used to calculate the number of generations each day. Each culture was maintained until a biofilm formed (33–57 days, corresponding to 470–1000 generations). While it is possible to restart cultures from individual clones after biofilm formation, this practice introduces a severe population bottleneck. Thus, we decided to stop the evolution for each population when a biofilm formed.

Over the course of the experiment, growth rate increased 2.5–3.5-fold for all eight populations (Figure 3). Occasional dips in growth rate occurred during the evolution. These dips are artifacts arising from temporary aberrations in selective conditions due to turbidostat malfunctions that prevented introduction of fresh medium, causing the cultures to enter stationary phase. Occasionally cultures were saved as frozen stocks until the turbidostat was fixed (see Materials and methods). Restarting cultures from frozen stocks may have caused a temporary drop in growth rate.

Growth rate increases ~ 3 fold during evolution of ∆argC M2-proA* E. coli in M9 minimal medium containing 0.2% glucose (w/v), 0.4 mM proline and 20 µg/mL kanamycin.

M2 is the C to T mutation at −45 in the promoter for the proBA operon (Kershner et al., 2016).

Copy number of proA* and size of the amplified genomic region varied among replicate populations

We monitored proA* copy number during the evolution experiment using qPCR of population genomic DNA (Figure 4A, Figure 4—figure supplement 1). proA* was present in at least six copies by generation 300 in all eight populations. Six of the populations maintained 6–9 copies for the remainder of the adaptation. proA* copy number in population 2 increased to as many as 20 copies. In population 3, proA* copy number dropped to three by generation 400.

Figure 4 with 2 supplements see all
proA* is amplified during evolution.

(A) proA* copy number in each evolved population as measured by qPCR. (B) Regions of amplification in each evolved population based on population genome sequencing. Population 2 had two overlapping regions of amplification, both of which included proA* (shown as differently shaded bars). Population 6 had a 95.1 kb deletion (shown as a red striped bar) immediately downstream of the amplified region.

We identified the boundaries of the amplified regions in all eight populations by sequencing population genomic DNA (Figure 4B, Figure 4—source data 1). The amplified region in population 2 was unusually small, spanning only 4.9 kb and resulting in co-amplification of only two other genes besides proBA*. Population 2 also appeared to have a second region of amplification of 18.5 kb. (Whether these two distinct amplification regions coexisted in the same clone or as two separate clades within the population could not be determined from population genome sequencing.) In contrast, the amplified regions in the other seven populations ranged from 41.1 to 163.8 kb, encompassing between 55 and 177 genes. We attribute the variation in proA* copy number to these differences in the size of the amplified region on the genome. The population with the smallest amplified region (4.9 kb, population 2) carries fewer multicopy genes and thus should incur a lower fitness cost, allowing proA* to reach a higher copy number (Adler et al., 2014; Kugelberg et al., 2006; Pettersson et al., 2009; Reams et al., 2010).

A mutation in proA* led to deamplification in population 3

The decrease in proA* copy number in population 3 was noteworthy since it might have been an indication that a mutation had improved the neo-ArgC activity of ProA*, resulting in a decreased need for multiple copies. In fact, a mutation in proA* that changes Phe372 to Leu (Figure 5A) was observed in population 3. E383A F372L ProA will be designated ProA** hereafter. Introduction of this mutation into the parental strain (which carried proA*) increased growth rate by 75% (Figure 5B), confirming that the mutation is adaptive. Notably, no mutations in proA* were identified in any of the other populations.

Figure 5 with 1 supplement see all
proA* acquired a beneficial mutation in population 3.

(A) Crystal structure of Thermotoga maritima ProA (PDB 1O20) (Page et al., 2003). Yellow, catalytic cysteine; green, equivalent of E. coli ProA Glu383; red, equivalent of E. coli ProA Phe372; magenta, NADPH-binding domain; blue, catalytic domain; beige, hinge region; gray, oligomerization domain. (B) Change in growth rate when the mutation changing Phe372 to Leu (proA**) is introduced into the genome of AM187. P value = 4.5 × 10−6 by a two-tailed, unequal variance Student’s t-test, N = 8. (C) proA* copy number (left axis, solid lines) and growth rate (right axis, dotted lines) for population 3. Vertical dotted lines indicate when population genomic DNA was sequenced. Sequencing depth was 130x, 122x, 70x and 81x at the four points, respectively. The frequency of the proA** allele at each time point is noted above the plot.

To determine whether the beneficial effect of the F372L mutation depended upon the presence of the initial E383A mutation, we created variants of the parental strain with either wild-type ProA, F372L ProA, E383A ProA (ProA*), or F372L E383A ProA (ProA**) (Figure 5—figure supplement 1). Strains with either wild-type or F372L ProA did not grow after eight days. Thus, the F372L mutation is not beneficial on its own, and the combined effect of the two mutations is greater than the sum of their individual effects.

The neo-ArgC and native ProA activities of wild-type, ProA*, and ProA** were assayed (in the reverse direction) with NAGSA and GSA, respectively (Table 2). The kcat/KM,NAGSA for ProA** is 3.6-fold higher than that of ProA* and nearly 80-fold higher than that for ProA. In contrast, there is no difference between kcat/KM,GSA for ProA* and ProA**.

Table 2
Kinetic parameters for GSA and NAGSA dehydrogenase activities of ProA, ProA*, and ProA**.
GSA activity (ProA)NAGSA activity (neo-ArgC)
kcat
(s−1)
KM
(mM)
kcat/KM,GSA
(M−1 s−1)
kcat
(s−1)
KM
(mM)
kcat/KM, NAGSA
(M−1 s−1)
WT16 ± 0.30.22 ± 0.0172000 ± 20000.0083 ± 0.00090.30 ± 0.0928 ± 9
ProA*
(E383A)
0.0076 ± 0.00080.20 ± 0.0437 ± 80.046 ± 0.0020.076 ± 0.009610 ± 74
ProA**
(E383A
F372L)
0.023 ± 0.0050.42 ± 0.1455 ± 220.21 ± 0.010.095 ± 0.0112200 ± 260
  1. a Values reported were calculated from a nonlinear least squares regression of three replicates at each substrate concentration ± standard error.

To determine when the mutation that changes Phe372 to Leu in ProA* occurred, we sequenced population genomic DNA at generations 270, 440, and 630 and at the end of the evolution (Figure 5C). proA** was present in 9% of the sequencing reads by generation 270. By the time deamplification of proA* had occurred at generation 440, the frequency of proA** had risen to 21% of sequencing reads. By the end of the adaptation, proA** was fixed in the population, yet three copies remained in the genome, suggesting that ProA** does not have sufficient neo-ArgC activity to be present at a single copy in the genome.

The fact that a mutation that improved the neo-ArgC activity of ProA* occurred in only one population was surprising considering that ProA* is the weak-link enzyme limiting growth rate. Because the growth rates of all eight populations improved substantially (Figure 3), mutations outside of the proBA* operon must also be contributing to fitness.

Some prevalent mutations in the evolved clones are not related to improved arginine synthesis

Population genome sequencing at the end of the experiment revealed that the final populations contained between 13 and 178 mutations at frequencies ≥ 5%, between 3 and 5 mutations at frequencies ≥ 30%, and between 1 and 4 fixed mutations (not including amplification of proA*) (see Figure 4—source data 1 for a list of mutations). We found several mutations in the same genes in different populations, suggesting that these mutations confer a fitness advantage.

The first mutation to appear in all populations was either an 82 bp deletion in the rph pseudogene directly upstream of pyrE or a C→T mutation in the intergenic region between rph and pyrE. These mutations occurred by 100 generations and prior to amplification of proBA*. PyrE is required for de novo synthesis of pyrimidine nucleotides (Figure 2). Both of these mutations have arisen in other E. coli evolution experiments, and have been shown to restore a known PyrE deficiency in the BW25113 E. coli strain (Blank et al., 2014; Bonekamp et al., 1984; Conrad et al., 2009; Jensen, 1993; Knöppel et al., 2018). The 82 bp deletion in rph increases growth rate of the parental AM187 strain by 55% (Figure 4—figure supplement 2). Thus, these mutations are general adaptations to growth in minimal medium and do not pertain to the selective pressures caused by the weak-link enzyme ProA*.

A mutation in ygcB occurred early in four populations. This mutation changes Ala390 to Val in Cas3, a nuclease/helicase in the Type I CRISPR/Cas system in E. coli (Howard et al., 2011). We introduced this mutation into the genome of the parent AM187 and compared the growth rates of the mutant and AM187 (Figure 4—figure supplement 2). Surprisingly, we saw no significant change in growth rate. Since this mutation appeared about the same time as the mutations upstream of pyrE, we wondered whether the ygcB mutation might only improve growth rate in the context of restored pyrE expression. Thus, we also tested the growth rate of a strain with the Cas3 mutation and the 82 bp deletion upstream of pyrE. Again, we saw no significant change in relative growth rate (Figure 4—figure supplement 2). Thus, the ygcB mutation is most likely a neutral hitchhiker. The most likely explanation for its prevalence is that it was present in a clade of the parental population that later rose to a high frequency when an additional beneficial mutation was acquired by one of its members.

Mutations upstream of argB increase ArgB abundance

All eight final populations contained mutations in the intergenic region upstream of argB and downstream of kanr. These mutations were fixed in two populations, and present at frequencies of 9–82% in the other populations (Figure 6A). ArgB (N-acetylglutamate kinase) catalyzes the second step in arginine synthesis, phosphorylation of N-acetylglutamate to form NAGP, the substrate for ArgC in wild-type E. coli and the substrate for ProA* in ∆argC proA* E. coli (Figure 2).

Figure 6 with 4 supplements see all
Several adaptive mutations occurred upstream of argB.

(A) Locations of adaptive mutations (red) upstream of argB and argH. argC was replaced with kanr in the parental strain AM187, giving this operon two promoters, one native to the operon (PargCBH), and the other introduced with the kanr gene (Pkanr). The table shows the percentages of each evolved population that contained a given argB mutation at the final time point. Six of the argB mutations were introduced into the genome of the parental AM187 strain and changes in growth rate (B), gene expression (C), and protein abundance (D) were determined (N = 4). Asterisks indicate values that were statistically different from those of the parental strain with p values ≤ 0.005 by a two-tailed, unequal variance Student’s t-test. In (B), error bars represent ± SE. argB-LC denotes argB expression on a low-copy plasmid under control of argB’s native promoter.

We reintroduced six of the mutations upstream of argB into the parental strain AM187. The mutations increased growth rate by 36–61% (Figure 6B). Levels of mRNAs for argB and argH, which is immediately downstream of argB, were little affected by the mutations (Figure 6C). However, levels of ArgB protein increased 2.6–8.2-fold (Figure 6D). In contrast, ArgH levels increased only modestly. These data suggest that the mutations upstream of argB increase translational efficiency of argB mRNA. An increase in the amount of ArgB will increase production of NAGP, the substrate for the weak-link enzyme ProA* (Figure 2).

While increasing the level of argB is clearly beneficial in AM187, it is possible that replacing argC with the kanr cassette might have altered expression of the downstream argB, artificially creating a situation in which ArgB activity is insufficient. Expression of argB and argH in AM187 is controlled by both their native promoter and a constitutive kanr promoter (Figure 2—figure supplement 1), possibly increasing transcription of the operon. Additionally, the different sequence of the intergenic region upstream of argB might influence translation of the argB mRNA. To determine the net effect of these two influences, we compared the levels of ArgB and ArgH in AM187 and a comparable strain (AM407) that lacks ArgC due to introduction of two stop codons in argC (Figure 6—figure supplement 1). The level of ArgH is 64% higher in AM187, probably due to increased transcription of the operon. In contrast, the level of ArgB is 2.3-fold lower, suggesting that the altered structure upstream of argB mRNA diminishes translation. Despite these changes, the growth rates of AM187 and AM407 are identical (µ = 0.27 ± 0.01 h−1).

We further investigated the effect of altering ArgB levels on the growth rate of AM187 by expressing ArgB from a low-copy plasmid (Figure 6B). Growth rate of AM187 improves substantially when ArgB levels are increased by 25-fold, demonstrating that the beneficial effect of the mutations we observed in the evolved strains is not simply due to compensation for the 2.3-fold decrease in ArgB caused by replacement of argC with kanr.

The increased translation efficiency of argB in the mutant strains might be due to decreased secondary structure around the Shine-Dalgarno site and start codon (Bentele et al., 2013; Espah Borujeni et al., 2014; Goodman et al., 2013). The argB mRNA, like 16% of γ-proteobacterial mRNAs (Scharff et al., 2011), lacks a canonical Shine-Dalgarno sequence, but the ribosome is expected to bind to a region encompassing the start codon and at least the upstream 8–10 nucleotides. We calculated the minimum free energy secondary structures of 140-nt RNA sequences encompassing the upstream intergenic region affected by the various mutations through 33 bp downstream of the argB start codon using CLC Main Workbench (Figure 6—figure supplement 2). Note that, although argC was replaced by kanr in the Keio strain used to construct AM187, the last 21 bp of argC and the 7 bp intergenic region between argC and argB are preserved. The FLP recognition target site downstream of kanr (used to remove the kanr cassette in the Keio strains [Baba et al., 2006]) forms a large stem-loop structure upstream of argB. However, this structure does not impact the region surrounding the putative argB ribosome binding site. The ribosome binding site is mostly sequestered in two stem-loops in the AM187 sequence. Four of the five point mutations occur in this region. The 58 bp and 51 bp deletions extend into this region, and the 38 bp duplication begins 13 bp upstream of the argB start codon within this region. For five of the eight mutant structures, the probability that the 5’-UTR upstream of the start codon is sequestered in the lowest free-energy structure is decreased relative to the parental sequence (Figure 6—figure supplement 2); the increased accessibility of this region should increase translation efficiency. However, for three mutants (−94 A→G, −22 C→A, and −18 C→A), this region is equally or more likely to be sequestered in a stem-loop. The thermodynamic stability of this region is clearly not the only factor responsible for the effects of the mutations upstream of argB.

We also considered the possibility that mutations upstream of argB might increase expression by increasing ribosome drafting (binding of a ribosome to the unfolded mRNA emerging behind a preceding ribosome before the mRNA folds and obscures the Shine-Dalgarno sequence) (Espah Borujeni and Salis, 2016). Figure 6—figure supplement 3 shows the predicted folding times of 63 nt RNA sequences centered around the start codon for each mutant except the −94 A→G mutant. (The point mutation at −94 relative to the start codon is outside of the window used for the calculation.) The significantly slower folding of three of the mutant RNAs (51 bp deletion, −24 C→G, and −18 C→A) should increase translation efficiency. For two of the mutants for which folding rate is either the same (the 58 bp deletion) or increased (the 38 bp duplication), the secondary structure prediction shown in Figure 6—figure supplement 2 suggests that the ribosome binding site is less likely to be sequestered in a hairpin. Thus, the effects of 6 of the eight mutations can be explained by a decrease in secondary structure stability around the ribosome binding site, a decrease in the folding rate of the mRNA in this region, or both. The effects of the −94 A→G and −22 C→A mutations, however, cannot be explained by either mechanism.

A final possibility is that translation efficiency could be increased if a mutation weakens an sRNA:mRNA interaction that blocks the ribosome binding site. There is no known physiological interaction between an sRNA and the argB mRNA, so this explanation is unlikely. Alternatively, a mutation might strengthen a sRNA:mRNA interaction that competes with a mRNA secondary structure that inhibits ribosome binding, thereby increasing the accessibility of the ribosome binding site. We explored the effects of the mutations upstream of argB on the predicted binding energies of 65 annotated sRNAs to the RNA sequences used for the secondary structure predictions (Figure 6—figure supplement 4) using the IntaRNA algorithm (Busch et al., 2008; Mann et al., 2017; Raden et al., 2018; Wright et al., 2014). The calculated binding energy sums the energy needed to denature sRNA and mRNA secondary structures and the hybridization energy of the unfolded sRNA and mRNA. None of the 65 sRNAs had a calculated binding energy for the parental argB region in the range of those for known physiological interactions between sRNAs and target mRNAs (e.g. −16.1 kcal/mol, ChiX and dpiB; −13.0 kcal/mol, OmrA and csgD; −14.9 kcal/mol, DsrA and rpoS), −14.3 kcal/mol, RprA and rpoS), with the strongest binding energy being −7.4 kcal/mol. Mutations decreased the predicted binding energy to <-11 kcal/mol for only one sRNA, RyfA, and only for the 58 bp deletion, 38 bp duplication and −94 A→G point mutation. Binding of RyfA was predicted to increase in a region that is not involved in the secondary structure around the ribosome binding site (Figure 6—figure supplement 4B). Thus, differences in binding to sRNAs are unlikely to be responsible for the changes in translation efficiency.

Mutations in carB either increase activity or impact allosteric regulation

We found eight different mutations in carB in six of the evolved populations: four missense mutations, three deletions (≥12 bp), and one 21 bp duplication (Figure 7A). CarB, the large subunit of carbamoyl phosphate synthetase (CPS), forms a complex with CarA to catalyze production of carbamoyl phosphate from glutamine, bicarbonate, and two molecules of ATP (Equation 1).

(1) Gln+HCO3+2ATPcarbamoylphosphate+Glu+2ADP+Pi
Figure 7 with 2 supplements see all
Several adaptive mutations occurred in carB.

(A) Maximum percentage of each carB mutation found in the population at any time during the evolution. Nucleotide (nt) numbers below mutant descriptions indicate where deletions or duplications occurred in the 3222 nt carB. Arrows indicate that a kinetic parameter was either increased or decreased in the variant enzyme relative to the wild-type enzyme. X, loss of activity; n.c., no change. (B) Allosteric regulation of carbamoyl phosphate synthetase. CarA and CarB are the small and large subunits of carbamoyl phosphate synthetase, respectively. (C) CarB functional domains (top) and crystal structure of E. coli CarAB (PDB 1CE8, bottom) (Thoden et al., 1999). Green, CarA; blue, CarB; gold, allosteric domain of CarB; red, residues that are deleted or duplicated in the adapted strains; magenta, point mutations that occur in the adapted strains. IMP and ornithine bound to the allosteric domain are shown as spheres. One of the two bound ATP molecules can be seen as spheres in the center of CarB. (D–E) Influence of UMP and L-ornithine on the ATPase activity of CarAB. v0; reaction rate in the absence of ligand. Each point represents the average of three technical replicates. (F) Growth rates of the parental AM187 strain and strains in which carB mutations had been introduced into the genome of AM187. (G) Relative fitness of AM441 (E. coli BW25113 containing the ∆82 bp mutation upstream of pyrE) and strains in which the carB mutations had been introduced into the genome of AM441. Asterisks in (F) and (G) indicate differences with p values < 0.03 (single asterisk) or ≤ 0.001 (double asterisk) by a two-tailed, unequal variance Student’s t-test, N = 4.

Synthesis of carbamoyl phosphate involves four reactions that take place in three separate active sites connected by a molecular tunnel of ~100 Å in length (Thoden et al., 2002). CarA catalyzes hydrolysis of glutamine to glutamate and ammonia (Equation 2). CarB phosphorylates bicarbonate to form carboxyphosphate in its first active site (Equation 3). Ammonia from the CarA active site is channeled to CarB, where it reacts with carboxyphosphate to form carbamate (Equation 4). Carbamate migrates to a second active site within CarB, where it reacts with ATP to form carbamoyl phosphate and ADP (Equation 5).

(2) Gln+H2ONH3+Glu
(3) HCO3+ATPcarboxyphosphate+ADP
(4) NH3+carboxyphospatecarbamate+Pi
(5) carbamate+ATPcarbamoylphosphate+ADP

Carbamoyl phosphate feeds into both the pyrimidine and arginine synthesis pathways and its production is regulated in response to an intermediate or product of both pathways (Figure 2, Figure 7B), as well as by IMP (Pierrat and Raushel, 2002). CarB is inhibited by UMP (a pyrimidine) and moderately activated by IMP (a purine). UMP and IMP compete to bind the same region of CarB (Eroglu and Powers-Lee, 2002). The net effect is inhibition of CarB when pyrimidine levels are high and activation when purine levels are high. The allosteric effects of UMP and IMP are dominated, however, by activation by ornithine. Ornithine, an intermediate in arginine synthesis that reacts with carbamoyl phosphate, binds to and activates CarB even when UMP is bound (Figure 7C) (Braxton et al., 1999; Eroglu and Powers-Lee, 2002). Thus, flux into arginine synthesis can be maintained even when pyrimidine levels are sufficient.

Seven of the eight mutations found in carB affect residues in the allosteric domain of CarB. The other mutation changes Gly369, which is immediately adjacent to the allosteric region, to Val (Figure 7C).

The kinetic parameters for carbamoyl phosphate synthetase (CPS) activity (determined as the glutamine- and bicarbonate-dependent ATPase activity [Equation 1]) of all eight CPS variants are shown in Table 3. All mutations decreased kcat/Km,ATP by 34–63%, with the exception of the mutation that changes Lys966 to Glu, which nearly doubles kcat/Km,ATP. None of the mutations affected the enzyme’s ability to couple ATP hydrolysis with carbamoyl phosphate production (Figure 7—figure supplement 1).

Table 3
Kinetic parametersa for the glutamine- and bicarbonate-dependent ATPase reaction of wild-type and variant carbamoyl phosphate synthetases.
EnzymeKM, ATP
(mM)
kcat
(s−1)
kcat/KM,ATP
(M−1 s−1)
UMP Kd
(µM)
UMP aornithine Kd
(µM)
ornithine a
WT1.05 ± 0.0813.5 ± 0.312.9 (±1.0)×1030.81 ± 0.040.27 ± 0.01130 ± 73.28 ± 0.03
G369V3.31 ± 0.2521.5 ± 0.76.51 (±0.54)×1031.53 ± 0.290.48 ± 0.02372 ± 2011.8 ± 0.13
L960P1.12 ± 0.059.10 ± 0.158.12 (±0.41)×103nananana
L964Q1.25 ± 0.088.04 ± 0.176.41 (±0.42)×103nananana
K966E0.97 ± 0.0620.6 ± 0.421.2 (±1.4)×1030.61 ± 0.040.21 ± 0.01181 ± 343.23 ± 0.08
∆12 bp (at nt 2906)b1.09 ± 0.064.80 ± 0.094.39 (±0.25)×103nananana
∆132 bp (at nt 2986)1.16 ± 0.066.47 ± 0.115.57 (±0.31)×103nananana
∆12 bp (at nt 3108)1.30 ± 0.105.86 ± 0.164.51 (±0.37)×103nananana
21 bp dup. (at nt 3130)1.40 ± 0.129.70 ± 0.306.94 (±0.64)×103597 ± 1330.51 ± 0.03nana
  1. a Values reported ± standard error. Values for Kd and a for UMP and ornithine were determined by fitting the data to the following equation: v/v0 = (aL + Kd)/(L + Kd), where L is the ligand concentration, v is the initial reaction rate, v0 is the initial reaction rate in the absence of ligand, a is v/v0 at infinite L, and Kd is the apparent dissociation constant. No Kd or a values are given (indicated by na) when inhibition by the allosteric ligand was too weak to measure.

    b Nucleotide (nt) numbers refer to the position of deletions or duplications in carB.

We measured the effect of mutations on UMP inhibition and ornithine activation of CPS (Table 3, Figure 7D–E). Regulation of the K966E variant, the enzyme for which kcat/Km,ATP was nearly doubled, was minimally affected. Five of the variants showed complete loss of allosteric regulation. The variant with the 21 bp duplication retained modest inhibition by UMP, but only at very high concentrations of UMP; the apparent Kd,UMP was increased by 740-fold. Similarly, G369V CPS retained partial inhibition by UMP. While the apparent Kd,UMP of the G369V enzyme only doubled, this variant showed a 3.5-fold increase in activation at high ornithine concentrations.

The eight carB mutations result in increased CPS activity via three different mechanisms: (1) increased catalytic turnover (K966E); (2) increased activation by ornithine (G369V); and (3) decreased inhibition by UMP (L960P, L964Q, ∆12 bp at nt 2906, ∆132 bp at nt 2986, ∆12 bp at nt 3108, and 21 bp duplication at nt 3130). In vivo, the increased CPS activity would be expected to increase the level of carbamoyl phosphate, and thereby increase the rate at which ornithine transcarbamoylase produces citrulline from carbamoyl phosphate and ornithine downstream of the ProA* bottleneck in the arginine synthesis pathway (Figure 2).

We introduced four of the carB mutations into the parental strain AM187 to confirm that they were beneficial. Three of the mutations (K966E, ∆12 bp at nt 2906, and ∆132 bp at nt 2986) increased growth rate (Figure 7F). The two mutations that caused loss of UMP inhibition (∆12 bp at nt 2906 and ∆132 bp at nt 2986) showed the greatest increase in growth rate (47–54%). The mutation that increased CPS catalytic activity (K966E) increased growth rate by 26%.

The G369V mutation does not improve growth rate of AM187, which is not surprising because its major effect is to increase ornithine activation at high ornithine concentrations. In AM187, the ornithine concentration is likely to be low due to the bottleneck in the arginine synthesis pathway caused by ProA*. Thus, increasing ornithine activation of CPS would have little effect. We suspect that this mutation may only be beneficial after gene amplification increases ProA* levels.

We also considered the possibility that the carB mutations are beneficial because they boost production of carbamoyl phosphate for pyrimidine synthesis. E. coli K strains are known to have a pyrimidine synthesis deficiency due to a mutation in rph that impacts transcription of the downstream pyrE. The rph-pyrE mutation that occurred first in all populations is known to correct the pyrimidine synthesis deficiency (Blank et al., 2014; Bonekamp et al., 1984; Conrad et al., 2009; Jensen, 1993; Knöppel et al., 2018). However, pyrimidine synthesis might still be compromised in our evolving strains because levels of ornithine, the most important allosteric activator of CPS, are low due to the inefficiency of ProA*. To determine whether the growth defect of the AM187 strain with the ∆82 bp rph-pyrE mutation is due to limited synthesis of pyrimidines, arginine, or both, we tested the effect of adding uracil, arginine, or both on growth of the parental AM187 strain and AM327 (the AM187 strain with the ∆82 bp rph-pyrE mutation) (Figure 7—figure supplement 2). AM327 grows 60% faster than AM187, presumably due to improved pyrimidine synthesis. Adding uracil to the medium increased the growth rate of AM187, but did not affect the growth rate of AM327, suggesting that pyrimidine synthesis is no longer insufficient after acquisition of the ∆82 bp rph-pyrE mutation. In contrast, adding arginine restored growth of both strains to wild-type levels. These results suggest that at the time the carB mutations occurred, they improved arginine synthesis rather than pyrimidine synthesis.

Mutations that impact the elaborate allosteric regulation of CarB would be expected to be detrimental after arginine synthesis is restored. To test this hypothesis, we introduced four of the carB mutations into the genome of AM441, a wild-type strain into which the rph-pyrE mutation had been introduced, and measured their effects on fitness using a competitive fitness assay (Figure 7G). The two deletion mutations that abolished allosteric regulation (Δ12 bp at nt 2906 and Δ132 bp at nt 2986) significantly decreased growth rate. The K966E mutation, which increases kcat/KM,ATP by 64% but shifts the balance between the regulatory effects of UMP and ornithine by modestly increasing UMP inhibition and decreasing ornithine activation, also slightly decreases growth rate. The G369V mutation, which diminishes inhibition by UMP but substantially increases activation by ornithine, actually increased growth rate, suggesting that the balance between the regulatory effects of UMP and ornithine in the wild-type CarB may not be optimal after the rph-pyrE mutation improves pyrimidine synthesis. These results suggest that many of the carB mutations provide a fitness improvement when arginine synthesis is compromised, but will be detrimental once an efficient neo-ArgC has emerged.

Discussion

Recruitment of promiscuous enzymes to serve new functions followed by mutations that improve the promiscuous activity has been a dominant force in the diversification of metabolic networks (Copley, 2017; Glasner et al., 2006; Khersonsky and Tawfik, 2010; O'Brien and Herschlag, 1999; Rauwerdink et al., 2016). New enzymes may be important for fitness or even survival when an organism is exposed to a novel toxin or source of carbon or energy, or when synthesis of a novel natural product enables manipulation of competing organisms or the environment. This process also contributes to non-orthologous gene replacement, which can occur when a gene is lost during a time in which it is not required, but its function later becomes important again and is replaced by recruitment of a non-orthologous promiscuous enzyme (Albalat and Cañestro, 2016; Ferla et al., 2017; Juárez-Vázquez et al., 2017; Newton et al., 2018; Olson, 1999).

We have modeled a situation in which a new enzyme is required by deleting argC, which is essential for synthesis of arginine in E. coli. Previous work showed that a promiscuous activity of ProA is the most readily available source of neo-ArgC activity that enables ∆argC E. coli to grow on glucose as a sole carbon source. However, a point mutation that changes Glu383 to Ala is required to elevate the promiscuous activity to a physiologically useful level. This mutation substantially damages the native function of the enzyme, creating an inefficient bifunctional enzyme whose poor catalytic abilities limit growth rate on glucose. It is important to note that the decrease in the efficiency of the native reaction may be a critical factor in the recruitment of ProA because it will diminish inhibition of the newly important reaction by the native substrate (Khanal et al., 2015; McLoughlin and Copley, 2008).

We chose to carry out evolution of a ∆argC proA* strain with a previously identified promoter mutation upstream of proA* in glucose in the presence of proline to specifically address the evolution of an efficient neo-ArgC. After 470–1000 generations of evolution, growth rate was increased by ~3 fold in all eight replicate cultures. We have focused on five types of genetic changes that clearly increase fitness (Figure 8): (1) mutations upstream of pyrE; (2) amplification of a variable region of the genome surrounding the proBA* operon; (3) a mutation in proA* that changes Phe372 to Leu; (4) mutations upstream of argB; and (5) mutations in carB. (Each of the final populations contains additional mutations that may also contribute to fitness, but these mutations were typically found in low abundance and/or in only one population.) The mutations upstream of pyrE occurred first (within 100 generations) and have previously been shown to be a general adaptation of E. coli BW25113 to growth in minimal medium (Blank et al., 2014; Conrad et al., 2009; Jensen, 1993; Knöppel et al., 2018). The other four types of mutations are specific adaptations to the bottleneck in arginine synthesis caused by substitution of the weak-link enzyme ProA* for ArgC in this strain. Interestingly, only two of these—gene amplification and the mutation in proA*—directly involve the weak-link enzyme ProA*.

Adaptive mutations are predicted to increase flux through the arginine synthesis pathway.

(A) The pathway bottleneck enzyme ProA* is shown in red. Steps in the arginine synthesis pathway that are affected by adaptive mutations (blue) are highlighted by bold arrows. (B) Trajectories for adaptive mutations observed during 470–1000 generations of evolution. Red text denotes a new mutation.

Surprisingly, we saw evolution of proA* towards a more efficient neo-ArgC in only one population (Figure 5). In this population, proA copy number dropped from ~7 to ~3 within 100 generations. This pattern is consistent with the IAD model; copy number is expected to decrease as mutations increase the efficiency of the weak-link activity. However, the fact that copy number did not return to one implies that the neo-ArgC activity of ProA** is not sufficient to justify a single copy of the gene.

Because ~3 copies of proA** remained in the population and the progenitor proA* was not detectable (Figure 5C), all copies in the amplified array have clearly acquired the mutation that changes Phe372 to Leu – that is, the more beneficial proA** allele has ‘swept’ the amplified array. This observation has important implications for the IAD model. In the original conception of the IAD model, it was proposed that amplification of a gene increases the opportunity for different beneficial mutations to occur in different copies, and then for recombination to shuffle these mutations (Bergthorsson et al., 2007; Francino, 2005). These phenomena would increase the rate at which sequence space can be searched and thereby the rate at which a new enzyme evolves. In order for this to occur, however, it would be necessary for individual alleles to acquire different beneficial mutations before recombination occurred. This scenario is inconsistent with the relative frequencies of point mutations and recombination between large homologous regions in an amplified array (Anderson and Roth, 1981; Reams et al., 2010). Point mutations occur at a frequency between 10−9 and 10−10 per nucleotide per cell division depending on the genomic location (Jee et al., 2016), and thus between 10−6 and 10−7 per gene per cell division for a gene the size of proA. If 10 copies of an evolving gene were present, then the frequency of mutation in a single allele would be between 10−5 and 10−6 per cell division. Homologous recombination after an initial duplication event is orders of magnitude more frequent, occurring in ~1 of every 100 cell divisions (Reams et al., 2010). Thus, homologous recombination between replicating chromosomes in a cell could result in a selective allelic sweep (Figure 9) long before a second beneficial mutation occurs in a different allele in the amplified array. This is indeed the result that we observed; heterozygosity among proA* alleles was lost within 500 generations (Figure 5C). More recent papers depict selective amplification of beneficial alleles before acquisition of additional mutations (Andersson et al., 2015; Näsvall et al., 2012); our results support this revision of the original IAD model. It is possible that alleles encoding enzymes that are diverging toward two specialists might recombine to explore combinations of mutations. However, such recombination might not accelerate evolution of a new enzyme, as mutations that lead toward one specialist enzyme would likely be incompatible with those that lead toward the other specialist enzyme.

Homologous recombination of an amplified proA* array with one proA** allele can rapidly lead to a daughter cell with only proA** alleles.

Each arrow represents one homologous replication event. The genotype of the less-fit daughter cell from each recombination event is grayed out.

While growth rate improved substantially in all populations, a beneficial mutation in proA* arose in only one, suggesting either that mutations that improve the neo-ArgC activity are uncommon, or that their fitness effects are smaller than those caused by mutations elsewhere in the genome that also improve arginine synthesis. We identified two primary mechanisms that apparently improve arginine synthesis without affecting the efficiency of the weak-link enzyme ProA* itself.

We identified eight mutations upstream of argB; the six we tested improved growth rate by 36–61% and increased the abundance of ArgB by 2.6–8.2-fold. Notably, ArgB levels were increased even though the levels of argB mRNA were unchanged (Figure 6). The increase in protein levels without a concomitant increase in mRNA levels suggests that these mutations impact the efficiency of translation. Secondary structure around the translation initiation site plays a key role because this region must be unfolded in order to bind to the small subunit of the ribosome (Hall et al., 1982; Scharff et al., 2011). Indeed, a study of the predicted secondary structures of 5000 genes from bacteria, mitochondria and plastids, many of which lack canonical Shine-Dalgarno sequences (as does argB), showed that secondary structure around the start codon is markedly less stable than up- or down-stream regions (Bentele et al., 2013; Espah Borujeni et al., 2014; Goodman et al., 2013; Scharff et al., 2011). Our computational studies of the effect of mutations on the predicted lowest free energy secondary structures of the region surrounding the start codon of argB suggest that the thermodynamic stability of this region plays a role in the beneficial effects of most of the observed mutations (Figure 6—figure supplement 2). In addition, three of the mutations slow the predicted rate of mRNA folding around the start codon, which would increase the probability of ribosomal drafting (Figure 6—figure supplement 3). Both effects would lead to an increase in ArgB abundance, which should increase the concentration of the substrate for the weak-link ProA*, thereby pushing material through this bottleneck in the arginine synthesis pathway.

The adaptive mutations in carB increase catalytic turnover, decrease inhibition by UMP, or increase activation by ornithine of CPS. All of these effects should increase the level of CPS activity in the cell and consequently the level of carbamoyl phosphate. Why would this be advantageous? Ornithine transcarbamoylase catalyzes formation of citrulline from carbamoyl phosphate and ornithine, which will be in short supply due to the upstream ProA* bottleneck (Legrain and Stalon, 1976). If ornithine transcarbamoylase is not saturated with respect to carbamoyl phosphate, then increasing carbamoyl phosphate levels should increase citrulline production and thereby increase flux into the lower part of the arginine synthesis pathway. Although we do not know the concentration of carbamoyl phosphate in vivo, and thus cannot determine whether ornithine transcarbamoylase is saturated (the KM for carbamoyl phosphate is 360 µM Baur et al., 1990), the occurrence of so many mutations that increase CPS activity and growth rate supports the notion that they lead to an increase in carbamoyl phosphate that potentiates flux through the arginine synthesis pathway.

The majority of adaptive mutations we observed in carB cause loss of the exquisite allosteric regulation that controls flux through this important step in pyrimidine and arginine synthesis. This tight regulation likely evolved due to the energetically costly reaction catalyzed by CPS, which consumes two ATP molecules (Figure 7B). While a constitutively active CPS is beneficial in the short term to improve arginine synthesis, it is detrimental once arginine production no longer limits growth. When we introduced four of the carB mutations into the genome of strain AM441 (wild-type E. coli containing the rph-pyrE mutation), three of the four mutations (K966E, ∆12 bp at nt 2906, and ∆132 bp at nt 2986) decreased fitness (Figure 7G). Notably, the mutations that impaired growth rate in the wild-type background were the same mutations that increased fitness in AM187. We term mutations such as these ‘expedient’ mutations because they provide a quick fix when cells are under strong selective pressure, but at a cost to a previously well-evolved function. The damage caused by expedient mutations may be repairable later by reversion, compensatory mutations or horizontal gene transfer. Interestingly, the latter two repair processes may contribute to sequence divergence between organisms that has typically been attributed to neutral drift, but rather may be due to scars left from previous selective processes.

A particularly striking conclusion from this work is that most of the mutations that improved fitness under these selective conditions did not impact the gene encoding the weak-link enzyme, but rather compensated for the bottleneck in metabolism by other mechanisms. The prevalence of adaptive mutations outside of proA* is likely a result of both a limited number of adaptive routes for improving the neo-ArgC activity of ProA* and a larger target size for other beneficial mutations (Ilhan et al., 2019). Although the single mutation that we observed in proA* is highly beneficial, the paucity of proA* mutations suggests that only a small number of mutations at specific positions may improve the enzyme’s activity. Directed evolution experiments often show a limited number of paths for improvement of enzymatic activity (Aharoni et al., 2005; Sunden et al., 2015; Weinreich et al., 2006), which reflects the stringent requirements for optimal placement of substrate-binding and catalytic residues in active sites. In contrast, there are multiple ways in which allosteric inhibition of CarB by UMP can be lost, and multiple ways in which translation efficiency of argB mRNA can be improved.

Not surprisingly, the process of evolution of a new enzyme by gene duplication and divergence does not take place in isolation, but is inextricably intertwined with mutations in the rest of the genome. The ultimate winner in a microbial population exposed to a novel selective pressure that requires evolution of a new enzyme may be the clone that succeeds in evolving an efficient enzyme while accumulating the least damaging, or at least the most easily repaired, expedient mutations.

Materials and methods

Materials

Common chemicals were purchased from Sigma-Aldrich (St. Louis, MO) and Fisher Scientific (Fair Lawn, NJ).

NAGSA was synthesized enzymatically from N-acetylornithine using N-acetylornithine aminotransferase (ArgD) in a 25 mL reaction as described previously by Khanal et al. (2015) and stored at −70°C. NAGSA concentrations were determined using the o-aminobenzaldehyde assay as described previously (Albrecht et al., 1962; Mezl and Knox, 1976).

GSA was synthesized enzymatically from L-ornithine using N-acetylornithine aminotransferase (ArgD) as described previously by Khanal et al. (2015) and stored at −70°C. GSA concentrations were determined using the o-aminobenzaldehyde assay as described previously (Albrecht et al., 1962; Mezl and Knox, 1976).

Plasmids and primers used in this study are listed in Supplementary file 1 and Supplementary file 2.

Strains and culture conditions

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Strains used in this study are listed in Table 1. E. coli cultures were routinely grown in LB medium at 37°C with 20 µg/mL kanamycin, 100 µg/mL ampicillin, 20 µg/mL chloramphenicol, or 10 µg/mL tetracycline, as required. Evolution of strain AM187 was performed at 37°C in M9 minimal medium containing 0.2% glucose, 0.4 mM proline, and 20 µg/mL kanamycin (Evolution Medium).

Strain construction

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The parental strain for the evolution experiment (AM187) was constructed from the Keio collection argC::kanr E. coli BW25113 strain (Baba et al., 2006). The fimAICDFGH and csgBAC operons were deleted (to slow biofilm formation), and the M2 proBA promoter mutation (Kershner et al., 2016) and the point mutation in proA that changes Glu383 to Ala (McLoughlin and Copley, 2008) were inserted into the genome using the scarless genome editing technique described in Kim et al. (2014). We initially hoped to measure proA* copy number during adaptation using fluorescence, although ultimately qPCR proved to be a better approach. Thus, we inserted yfp downstream of proA* under control of the P3 promoter (Mutalik et al., 2013) and with a synthetically designed ribosome binding site (Espah Borujeni et al., 2014; Salis et al., 2009). A double transcription terminator (BioBrick Part: BBa_B0015) was inserted immediately downstream of proBA* to prevent read-through transcription of yfp (Figure 2—figure supplement 1). We also inserted a NotI cut site immediately downstream of proA* to enable cloning of individual proA* alleles after amplification if necessary. A Fis binding site located 32 bp downstream of proA was preserved because it might impact proA transcription. The NotI-2xTerm-yfp cassette was inserted downstream of proA* using the scarless genome editing technique described in Kim et al. (2014). The genome of the resulting strain AM187 was sequenced to confirm that there were no unintended mutations and deposited to NCBI GenBank under accession number CP037857.

Strain AM209 was constructed from E. coli BL21(DE3) for expression of wild-type and mutant ProAs. We deleted argC and proA to ensure that any activity measured during in vitro assays was not due to trace amounts of ArgC or wild-type ProA. To accomplish these deletions, we amplified and gel-purified DNA fragments containing antibiotic resistance genes (kanamycin and chloramphenicol for deletion of argC and proA, respectively) flanked by 200–400 bp of sequences homologous to the upstream and downstream regions of either argC or proA. E. coli BL21(DE3) cells containing pSIM27 (Datta et al., 2006) – a vector containing heat-inducible λ Red recombinase genes – were grown in LB/tetracycline at 30ºC to an OD of 0.2–0.4 and then incubated in a 42°C shaking water bath for 15 min to induce expression of λ Red recombinase genes. The cells were then immediately subjected to electroporation with 100 ng of the appropriate linear DNA mutation cassette. Successful transformants were selected on either LB/kanamycin or LB/chloramphenicol plates.

Strain AM267 was constructed by deleting carAB from E. coli BL21 for expression of wild-type and mutant carbamoyl phosphate synthetases (CPS) to ensure that any activity measured during in vitro assays was not due to trace amounts of wild-type CPS. To accomplish the deletion, we amplified and gel-purified a DNA fragment containing the kanamycin resistance gene flanked by 40 bp of sequence homologous to the upstream and downstream regions of carAB. E. coli BL21 cells containing pSIM5 (Datta et al., 2006) – a vector carrying heat-inducible λ Red recombinase genes – were grown in LB/chloramphenicol at 30ºC to an OD of 0.2–0.4 and then incubated in a 42°C shaking water bath for 15 min to induce expression of λ Red recombinase genes. The cells were then immediately subjected to electroporation with 100 ng of the appropriate linear DNA mutation cassette. Successful transformants were selected on LB/kanamycin plates.

Most mutations observed during the evolution experiment were introduced into the parental AM187 strain using the scarless genome editing protocol described in Kim et al. (2014). This protocol is preferable to Cas9 genome editing for introduction of point mutations and small indels because it does not require introduction of synonymous PAM mutations that have the potential to affect RNA structure. The 58 bp deletion upstream of argB, 82 bp deletion in rph upstream of pyrE, 12 bp deletion in carB (at nt 2906), 132 bp deletion in carB (at nt 2986), and two stop codons in argC were introduced using Cas9-induced DNA cleavage and λ Red recombinase-mediated homology-directed repair with a linear DNA fragment. Sequences of the protospacers and mutation cassettes used for Cas9 genome editing procedures are listed in Supplementary file 3 and Supplementary file 4.

The cells were first transformed with a helper plasmid (pAM053, Supplementary file 1) encoding cas9 under control of a weak constitutive promoter (pro1 from Davis et al., 2011), λ Red recombinase genes (exo, gam, and bet) under control of a heat-inducible promoter, and a temperature-sensitive origin of replication (Datta et al., 2006). The cells were grown to an OD600 of 0.2–0.4 at 30°C and then incubated at 42°C with shaking for 15 min to induce expression of the λ Red recombinase genes. The cells were immediately subjected to electroporation with 100 ng of a plasmid expressing a guide RNA targeting a 20-nucleotide sequence within the region targeted for deletion (Supplementary file 1, Supplementary file 3), and 450 ng of a linear homology repair template that encodes the new sequence with the desired deletion (Supplementary file 4). (Linear homology repair templates were amplified from genomic DNA of clones isolated during the evolution experiment or plasmids that contained the desired deletions and the PCR fragments were gel-purified. Primers used to generate the linear DNA mutation fragments are listed in Supplementary file 2.) The cells were allowed to recover at 30°C for 2–3 hr before being spread onto LB/chloramphenicol/ampicillin plates. Sanger sequencing confirmed that the surviving colonies contained the desired deletion. Individual colonies were cured of pAM053 and the guide RNA plasmids, both of which have temperature-sensitive origins of replication, by growth at 37°C.

Laboratory evolution

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Evolution of strain AM187 in Evolution Medium was carried out in eight replicate tubes in a custom turbidostat constructed as described by Takahashi et al. (2015). To start the experiment, strain AM187 was grown to exponential phase (OD600 = 0.7) in LB/kanamycin at 37°C. Cells were centrifuged at 4000 x g for 10 min at room temperature and resuspended in an equal volume of PBS. The suspended cells were washed twice more with PBS and resuspended in PBS. This suspension was used to inoculate all eight turbidostat chambers to give an initial OD600 of 0.01 in 14 mL of Evolution Medium. The turbidostat was set to maintain an OD650 of 0.4 by diluting individual cultures with an appropriate amount of fresh medium every 60 s.

A 3 mL portion of each population was collected every 2–3 days; 800 µl was used to make a 10% glycerol stock, which was then stored at −70°C. The remaining sample was pelleted for purification of genomic DNA using the Invitrogen PureLink Genomic DNA Mini Kit according to the manufacturer’s protocol.

At several points during the evolution, the turbidostat was restarted due to a planned pause or an instrument malfunction. During a planned pause, the populations were subjected to centrifugation at 4000 x g for 10 min at room temperature and the pelleted cells were resuspended in 1.6 mL of Evolution Medium. Half of the resuspension was used to make a 10% glycerol stock for storage at −70°C, and the other half to purify genomic DNA. When the turbidostat was restarted, the frozen stock was thawed and the cells were collected by centrifugation at 16,000 x g for 1 min at room temperature. The pelleted cells were resuspended in 1 mL of PBS, washed, and resuspended in 500 µL of Evolution Medium. The entire resuspension was used to inoculate the appropriate chamber of the turbidostat. Sometimes the experiment had to be restarted from a frozen stock of a normal sample (as opposed to the entire population as just described), resulting in a more significant population bottleneck. In this case, the entire frozen stock was thawed and only 700 µL washed as described above to be used for the inoculation. The remaining 300 µL of the glycerol stock were re-stored at −70°C in case the frozen stock was needed for downstream analysis. The times at which the turbidostat failed and was restarted are indicated in Figure 4—source data 1. We always restarted the turbidostat with >108 cells (>5% of the culture) in order to preserve the diversity of the previous populations.

Calculation of growth rate and generations during adaptation

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The turbidostat takes an OD650 reading every ~3 s and dilutes the cultures every 60 s. Thus, readings between dilutions can be used to calculate an average growth rate each day based on the following equation:

(6) μ¯=in(Nt1,i/Nt0,i)t1,it0,in

where μ¯ is the average growth rate in hr−1, n is the number of independent growth rate calculations within a given 24 hr period, Nt0 is the OD650 reading right after the dilution, Nt1 is the OD650 reading right before the next dilution, and t0 and t1 are the times at which the OD650 was measured. The number of generations per day (g) was then calculated from using (Equation 7).

(7) g=24hln(2)/μ¯

The R script used to calculate growth rate from turbidostat readings can be found in Source code 1.

Measurement of proA* copy number

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The copy number of proA* was determined by qPCR of purified population genomic DNA. gyrB and icd, which remained at a single copy in the genome throughout the adaptation experiment, were used as internal reference genes. The primer sets used for each gene are listed in Supplementary file 2. PowerSYBR Green PCR master mix (Thermo Scientific) was used according to the manufacturer’s protocol. A standard curve using variable amounts of AM187 genomic DNA was run on every plate to calculate efficiencies for each primer set. Primer efficiencies were calculated with the following equation:

(8) Ex=10(1m)

where E is the efficiency of primer set x, and m is the slope of the plot of Ct vs. starting quantity for the standard curve. proA* copy number was then calculated with the following equation (Hellemans et al., 2007):

(9) n=EproAΔCt,proAEgyrBΔCt,gyrB×EicdΔCt,icd

where n is the proA* copy number, and ∆Ct,x is the difference in Ct’s measured during amplification of AM187 and sample genomic DNA with primer set x.

Whole-genome sequencing

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Libraries were prepared from purified population genomic DNA using a modified Illumina Nextera protocol and multiplexed onto a single run on an Illumina NextSeq500 to produce 151 bp paired-end reads (Baym et al., 2015), giving a 60–130-fold coverage of the AM187 genome. Reads were trimmed using BBtools v35.82 (DOE Joint Genome Institute) and mapped using breseq v0.32.1 using the polymorphism (mixed population) option (Deatherage and Barrick, 2014).

Growth rate measurements

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Growth rates for individual constructed strains were calculated from growth curves measured in quadruplicate. Overnight cultures were grown in LB at 37°C from glycerol stocks. Kanamycin (20 µg/mL) was added for strains in which argC had been replaced by kanR. Ampicillin (100 µg/mL) was added for strains carrying the argB expression plasmid (pAM141, Supplementary file 1). Forty µL of each overnight culture was used to inoculate 4 mL of LB with appropriate antibiotics and the cultures were allowed to grow to mid-exponential phase (OD600 0.3–0.6) at 37°C with shaking. The cultures were subjected to centrifugation at 4000 x g for 10 min at room temperature and the pellets resuspended in an equal volume of PBS. The pellets were washed once more in PBS. The cells were diluted to an OD600 of 0.001 in Evolution Medium and a 100 µL aliquot was loaded into each well of a 96-well plate. When argB was expressed from a low-copy plasmid carrying ampr (Figure 6B), kanamycin was omitted and ampicillin was added to the medium. The plates were incubated in a Varioskan (Thermo Scientific) plate reader at 37°C with shaking every 5 min for 1 min. The absorbance at 600 nm was measured every 20 min for up to 200 hr. The baseline absorbance for each well (the average over several smoothed data points before growth) was subtracted from each point of the growth curve. Growth parameters (maximum specific growth, μmax; lag time, λ; maximum growth, Amax) were estimated by non-linear regression using the modified Gompertz equation (Zwietering et al., 1990). Non-linear least-squares regression was performed in Excel using the Solver feature.

Growth rates were calculated for populations in the turbidostats during evolution and for individual strains in the plate reader. The growth rate of the parental strain AM187 is ~0.27 h−1 in the plate reader (Figure 5B, Figure 6B, Figure 7F, Figure 4—figure supplement 2, Figure 7—figure supplement 2) and ~0.24 h−1 in the turbidostat (Figure 3), so the growth rates of individual strains in the plate reader and turbidostat are similar.

Fitness competition assay

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Fitness competition assays were used in lieu of growth curves when growth rate differences between strains were expected to be small (Figure 7G). Overnight cultures of a reference strain containing a plasmid carrying cfp (pAM003, Supplementary file 1) and a test strain containing a plasmid carrying yfp (pAM142, Supplementary file 1) were grown in LB/ampicillin at 37°C from glycerol stocks. Forty µL of each overnight culture was inoculated into 4 mL of M9/0.2% glucose/ampicillin and the cultures were allowed to grow to mid-exponential phase (OD600 0.3–0.6) at 37°C with shaking. One mL of each culture was subjected to centrifugation at 10,000 x g for 1 min at room temperature and the pellets resuspended in an equal volume of PBS. The CFP- and YFP-labelled strains were mixed in equal parts to a final OD600 of 0.01 in 25 mL of M9/0.2% glucose/ampicillin. Competition cultures were grown at 37°C with shaking and passaged into fresh M9/0.2% glucose/ampicillin at mid-log phase four times.

We used flow cytometry to count cells in initial and final cultures. The final cultures were diluted 100-fold in PBS prior to flow cytometry. Relative fitness was calculated by the following equation (Dykhuizen, 1990):

(10) w=ln(R(t)/R(0))t+1

Where t is the number of generations and R is the ratio of mutant to reference strain cell counts (YFP/CFP). All w values were normalized to the w value obtained for a competition between the CFP-containing reference strain and a YFP-containing reference strain to account for any fitness effects of expressing YFP versus CFP.

Measurement of argB and argH gene expression by RT-qPCR

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Overnight cultures were grown from glycerol stocks in LB/kanamycin at 37°C. Ten µL of each overnight culture was used to inoculate 4 mL of LB/kanamycin and the cultures were grown to mid-exponential phase (OD600 0.3–0.6) at 37°C with shaking. The cultures were centrifuged at 4000 x g for 10 min and pellets resuspended in equal volume PBS. Pellets were washed once more in PBS. The cells were diluted to an OD600 of 0.001 in 4 mL of Evolution Medium and grown to an OD600 of 0.2–0.3. Four 2 mL aliquots of culture were thoroughly mixed with 4 mL of RNAprotect Bacteria Reagent (Qiagen) and incubated at room temperature for 5 min before centrifugation at 4000 x g for 12 min at room temperature. Pellets were frozen in liquid N2 and stored at −70°C.

RNA was purified using the Invitrogen PureLink RNA Mini Kit according to the manufacturer’s protocol. The cell lysate produced during the PureLink protocol was homogenized using the QIAShredder column (Qiagen) prior to RNA purification. After RNA purification, each sample was treated with TURBO DNase (Invitrogen) according to the manufacturer’s protocol. Reverse transcription (RT) was performed with 250–600 ng of RNA using SuperScript IV VILO (Invitrogen) master mix according to the manufacturer’s protocol.

qPCR of cDNA was performed to measure the fold-change in expression of argB and argH in mutant strains compared to that in AM187. hcaT and cysG were used as reference genes (Zhou et al., 2011). The primer sets used for each gene are listed in Supplementary file 2. A standard curve using variable amounts of E. coli BW25113 genomic DNA was run to calculate the primer efficiencies for each primer set. Fold-changes in expression of argB and argH were calculated as described above for calculations of proA* copy number.

Measurement of ArgB and ArgH protein levels

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Individual colonies were inoculated into four parallel 2 mL aliquots of LB. Kanamycin (20 µg/mL) was added for strains in which argC had been replaced by kanr. Ampicillin (100 µg/mL) was added and kanamycin was omitted when argB was expressed from a low-copy plasmid (pAM141, Supplementary file 1). The cultures were grown to mid-exponential phase at 37°C with shaking. One mL of each culture was subjected to centrifugation at 16,000 x g for 1 min at room temperature. The cell pellets were resuspended in 1 mL PBS and washed twice more in PBS before resuspension and dilution to an OD of 0.001 in 5 mL of Evolution Medium. Antibiotics were added as detailed above. Cultures were grown to an OD600 of 0.1–0.3 at 37°C with shaking and then chilled on ice for 10 min before pelleting by centrifugation at 4000 x g at 4°C. Cell pellets were frozen in liquid N2 and stored at −70°C.

Frozen cell pellets were thawed and lysed in 60 µL 50 mM Tris-HCl, pH 8.5, containing 4% (w/v) SDS, 10 mM tris(2-carboxyethylphosphine) (TCEP) and 40 mM chloroacetamide in a Bioruptor Pico sonication device (Diagenode) using 10 cycles of 30 s on, 30 s off, followed by boiling for 10 min, and then another 10 cycles in the Bioruptor. The lysates were subjected to centrifugation at 15,000 x g for 10 min at 20°C and protein concentrations in the supernatant were determined by tryptophan fluorescence (Wiśniewski and Gaugaz, 2015). Ten µL of each sample (3–6 µg protein/µL) was digested using the SP3 method (Hughes et al., 2014). Carboxylate-functionalized speedbeads (GE Life Sciences) were added to the lysates. Addition of acetonitrile to 80% (v/v) caused the proteins to bind to the beads. The beads were washed twice with 70% (v/v) ethanol and once with 100% acetonitrile. Protein was digested and eluted from the beads with 15 µL of 50 mM Tris buffer, pH 8.5, with 1 µg endoproteinase Lys-C (Wako) for 2 hr with shaking at 600 rpm at 37°C in a thermomixer (Eppendorf). One µg of trypsin (Pierce) was then added to the solution and incubated at 37°C overnight with shaking at 600 rpm. Beads were collected by centrifugation and then placed on a magnet to more reliably remove the elution buffer containing the digested peptides. The peptides were then desalted using an Oasis HLB cartridge (Waters) according to the manufacturer’s instructions and dried in a speedvac.

Samples were suspended in 12 µL of 3% (v/v) acetonitrile/0.1% (v/v) trifluoroacetic acid and 0.5–1 µg of peptides were directly injected onto a C18 1.7 µm, 130 Å, 75 µm X 250 mm M-class column (Waters), using a Waters M-class UPLC. Peptides were eluted at 300 nL/minute using a gradient from 3% to 20% acetonitrile over 100 min into an Orbitrap Fusion mass spectrometer (Thermo Scientific). Precursor mass spectra (MS1) were acquired at a resolution of 120,000 from 380 to 1500 m/z with an AGC target of 2.0 × 105 and a maximum injection time of 50 ms. Dynamic exclusion was set for 20 s with a mass tolerance of + /– 10 ppm. Precursor peptide ion isolation width for MS2 fragment scans was 1.6 Da using the quadrupole, and the most intense ions were sequenced using Top Speed with a 3 s cycle time. All MS2 sequencing was performed using higher energy collision dissociation (HCD) at 35% collision energy and scanning in the linear ion trap. An AGC target of 1.0 × 104 and 35 s maximum injection time was used. Rawfiles were searched against the Uniprot Escherichia coli database using Maxquant version 1.6.1.0 with cysteine carbamidomethylation as a fixed modification. Methionine oxidation and protein N-terminal acetylation were searched as variable modifications. All peptides and proteins were thresholded at a 1% false discovery rate (FDR).

Enzyme overexpression plasmids

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argB and proA were amplified from the genome of E. coli BW25113 and proA* was amplified from the genome of AM187 using primers specified in Supplementary file 2. The amplified PCR fragments were ligated into a linearized pET-46 vector backbone by Gibson assembly (NEB) to make pAM028, pAM063, and pAM064, respectively (Supplementary file 1). A sequence encoding a 6xHis-tag followed by a 2xVal-linker was incorporated at the N-terminus of each protein. The proA** expression plasmid (pAM112) was constructed from pAM064 using the Q5 Site-Directed Mutagenesis Kit (NEB) and the primers listed in Supplementary file 2.

argC was cloned into a pTrcHisB vector backbone as described in McLoughlin and Copley (2008). The final plasmid encodes ArgC with an N-terminal 6xHis-tag followed by a Gly-Met-Ala-Ser linker and with Met1 removed. carAB was amplified from the genome of AM187 and inserted into a PCR-amplified pCA24N vector backbone by Gibson assembly (NEB) to make pAM101. (PCR primers are listed in Supplementary file 2). The final construct included an N-terminal 6xHis-tag on CarA followed by a Thr-Asp-Pro-Ala-Leu-Arg-Ala linker. The Q5 Site-Directed Mutagenesis Kit (NEB) was used to generate mutant versions of carB in plasmids pAM102–109 using the primers listed in Supplementary file 2.

The argD and argI expression plasmids from the ASKA collection (Kitagawa et al., 2005) were used for expression of N-acetylornithine aminotransferase and ornithine transcarbamoylase, respectively. These expression plasmids include a sequence encoding an N-terminal 6xHis-tag followed by a Thr-Asp-Pro-Ala-Leu-Arg-Ala linker upstream of each cloned gene.

The correct sequences for all constructs were confirmed by Sanger sequencing.

Protein purification

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Wild-type and variant ProAs were expressed in strain AM209 [BL21(DE3) argC::kanr proA::cat] to avoid contamination with wild-type ProA and ArgC. Carbamoyl phosphate synthetase (CPS) consists of a stable complex between CarA and CarB. Thus, carA and wild-type or variant carBs were co-expressed on the same plasmid with a His-tag on CarA in strain AM267 (BL21 carAB::kanr) to enable purification in the absence of wild-type CPS. Ornithine transcarbamoylase was also expressed in this strain. ArgB was expressed in BL21(DE3).

Enzymes were expressed and purified using the following protocol with minor variations. A small scraping from the glycerol stock of each expression strain was used to inoculate LB containing the antibiotics required for maintenance of each expression plasmid (Supplementary file 1). The cultures were grown overnight with shaking at 37°C. Overnight cultures were diluted 1:100 into 500 mL-2 L of LB containing the appropriate antibiotic and grown with shaking at 37°C. IPTG was added to a final concentration of 0.5 mM when the OD600 reached 0.5–0.9. Growth was continued at 30°C for 5 hr with shaking. Cells were harvested by centrifugation at 5000 x g for 20 min at 4°C. Cell pellets were stored at −70°C until protein purification.

Frozen cell pellets were resuspended in 5x the cell pellet weight of ice-cold 20 mM sodium phosphate, pH 7.4, containing 300 mM NaCl and 10 mM imidazole. Fifty µL of protease inhibitor cocktail (Sigma-Aldrich, P8849) was added for each gram of cell pellet. Lysozyme was added to a final concentration of 0.2 mg/mL and the cells were lysed by probe sonication (20 s of sonication followed by 30 s on ice, repeated three times). Cell debris was removed by centrifugation at 18,000 x g for 20 min at 4°C. The soluble fraction was then loaded onto 1 mL or 3 mL HisPur Ni-NTA Spin Columns (Thermo Scientific) and His-tagged protein was purified according to the manufacturer’s protocol. Bound protein was eluted with one column volume of 20 mM sodium phosphate, pH 7.4, containing 300 mM NaCl and increasing amounts of imidazole (100 mM, 250 mM, and finally 500 mM). Two separate elutions were performed with 500 mM imidazole. Fractions containing the protein of interest were pooled and dialyzed overnight against 6–12 L of exchange buffer at 4°C. (ProA and ArgC were dialyzed against 20 mM potassium phosphate, pH 7.5, containing 20 mM DTT. N-acetylornithine aminotransferase was dialyzed against 20 mM potassium phosphate, pH 7.5. CPS was dialyzed against 100 mM potassium phosphate, pH 7.6. Ornithine transcarbamoylase was dialyzed against 20 mM Tris-acetate, pH 7.5. ArgB was dialyzed against 10 mM Tris-HCl, pH 7.8.) Protein purity was assessed by SDS-PAGE and concentration measured using the Qubit protein assay kit with a Qubit 3.0 fluorometer (Invitrogen). Purified protein was stored at 4°C for short-term storage, and frozen in liquid nitrogen and stored at −70°C for long-term storage.

GSA and NAGSA dehydrogenase assays

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The native and neo-ArgC activities of ProA were assayed in the reverse direction (dehydrogenase reaction) because the lability of the forward substrates γ-glutamyl phosphate and N-acetylglutamyl phosphate makes them difficult to purify. The change in the dehydrogenase activity due to a mutation is proportional to the change in the reductase activity according to the Haldane relationship (Haldane, 1930; McLoughlin and Copley, 2008).

Assaying ProA’s dehydrogenase activity using γ-glutamyl semialdehyde (GSA) and N-acetylglutamyl semialdehyde (NAGSA) as substrates is complicated by the equilibrium of GSA and NAGSA with their hydrated forms, as well as GSA’s intramolecular cyclization to form pyrroline-5-carboxylate (P5C) (Bearne and Wolfenden, 1995; Mezl and Knox, 1976). In order to measure the concentration of the free aldehyde form of these substrates, we mixed 15 µM ProA or ArgC with 2 mM ‘GSA’ (including the hydrate and P5C) or 2 mM ‘NAGSA’ (including the hydrate), respectively, in a solution containing 100 mM potassium phosphate, pH 7.6, and 1 mM NADP+ and measured the burst in NADPH production (Khanal et al., 2015). The concentrations of GSA+P5C+hydrate or NAGSA+hydrate were determined using the o-aminobenzaldehyde assay (Albrecht et al., 1962; Mezl and Knox, 1976). The absorbance at 340 nm due to formation of NADPH exhibited a burst followed by a linear phase that was followed for 60 s. We assume that the burst corresponds to reduction of the free aldehyde form of GSA or NAGSA and the rate of the linear phase is determined by the conversion of the hydrate (and P5C in the case of GSA) to the free aldehyde. We calculated the magnitude of the burst by fitting either all of the data or the linear portion of the data to one of the following equations.

(11) f(x)=mx+b
(12) f(x)=mx+b(1ex)

where x is time in seconds, m is the slope of the linear phase, and b is the magnitude of the burst and thus proportional to the starting concentration of the free aldehyde form of the substrate. In the case of the linear fit, only the linear portion of the A340 data was used. Equation 11 was used to calculated NAGSA free aldehyde concentration because the exponential equation did not fit the data well. Equation 12 was used to calculated GSA free aldehyde concentration. We repeated the assay three times and averaged the magnitude of the burst to calculate free aldehyde concentrations for solutions of GSA and NAGSA (under these buffer and temperature conditions) of 4.5% and 4.2% of the total concentration of free aldehyde + hydrate (+ P5C for GSA), respectively.

GSA and NAGSA dehydrogenase activities were measured by monitoring the appearance of NADPH at 340 nm in reaction mixtures containing 100 mM potassium phosphate, pH 7.6, 1 mM NADP+, varying concentrations of NAGSA or GSA, and catalytic amounts of ProA, ProA*, and ProA**. All kinetic measurements were done at 25°C. Values for KM refer to the concentration of the free aldehyde form of the substrate. An example R script used to calculate the Michaelis-Menten parameters can be found in Source code 2 Table 2—source code 1.

Assays for carbamoyl phosphate synthetase activity and allosteric regulation

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Kinetic assays for carbamoyl phosphate synthetase (CPS) were carried out with minor modifications of the methods described in Pierrat and Raushel (2002). The rate of ATP hydrolysis was measured at 37°C by coupling production of ADP to oxidation of NADH using pyruvate kinase, which converts ADP and PEP to ATP and pyruvate, and lactate dehydrogenase, which reduces pyruvate to lactate. Loss of NADH was monitored at 340 nm. Reaction mixtures consisted of 50 mM HEPES, pH 7.5, containing 10 mM MgCl2, 100 mM KCl, 20 mM potassium bicarbonate, 10 mM L-glutamine, 1 mM PEP, 0.2 mM NADH, saturating amounts of pyruvate kinase and lactate dehydrogenase (Sigma-Aldrich, P0294), and varying amounts of ATP (0.01 to 8 mM). Reactions were initiated by adding CPS to a final concentration of 0.2 µM. The effects of UMP and ornithine were measured under the same reaction conditions but with a fixed ATP concentration of 0.2 mM and varying concentrations of either UMP or ornithine. Kinetic parameters were calculated from a nonlinear least squares regression of data for three technical replicates at each substrate concentration. Examples of R scripts used to calculate Michaelis-Menten parameters and parameters for allosteric regulation of CPS by UMP and ornithine can be found in Source code 2 and Source code 3, respectively.

Carbamoyl phosphate production was measured with minor modifications of previously described procedures (Snodgrass and Parry, 1969; Stapleton et al., 1996). Formation of carbamoyl phosphate by CPS was coupled with formation of citrulline by ornithine transcarbamoylase; citrulline forms a yellow complex (ε464 = 37800 M−1 cm−1; Snodgrass and Parry, 1969) when mixed with diacetyl monoxime and antipyrine. Reaction mixtures consisted of 50 mM HEPES, pH 7.5, 10 mM MgCl2, 100 mM KCl, 20 mM potassium bicarbonate, 10 mM L-glutamine, 4 mM ATP, 10 mM L-ornithine, and 0.7 µM ornithine transcarbamoylase. Reactions (0.25 mL) were initiated by adding CPS at a final concentration of 0.2 µM. After incubation for 2.5 min at 37°C, reactions were quenched by addition of 1 mL of a solution consisting of 25% concentrated H2SO4, 25% H3PO4 (85%), 0.25% (w/v) ferric ammonium sulfate, and 0.37% (w/v) antipyrine, followed by addition of 0.5 mL of 0.4% (w/v) diacetyl monoxime/7.5% (w/v) NaCl. The quenched reaction mixtures were placed in a boiling water bath for 15 min before measurement of OD464. Control reactions contained all components except CPS.

RNA structure prediction

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RNA secondary structures for argB mRNAs were predicted using CLC Main Workbench 8.1, which uses the mfold algorithm (Mathews et al., 1999). The entire intergenic region between kanr and argB plus the first 33 nucleotides of argB were included in the structure prediction. The first 33 nucleotides were included because an mRNA-bound ribosome prevents another ribosome from binding to the mRNA until it has moved past the first 33 nucleotides (Steitz, 1969). Thus, at least the first 33 nucleotides are available for folding with the upstream region when a mRNA is being translated.

Calculation of RNA folding times

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Folding times for a 63-nucleotide region (30 nt downstream and 30 nt upstream of the argB start codon) surrounding the start codon of argB mRNAs were calculated using the Kinfold program (v1.3) from the ViennaRNA v2.4.11 package (Wolfinger et al., 2004). Kinfold utilizes a Monte Carlo algorithm to calculate the folding time of each RNA sequence to the lowest free energy structure. We simulated 500 folding trajectories for each structure.

Calculation of sRNA-mRNA hybridization energy

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Hybridization energies for sRNA-argB mRNA interactions were calculated using the IntaRNA algorithm, which predicts interacting regions between two RNA molecules by taking into account both the stability of sRNA-mRNA interactions and the accessibility of the interacting sequences (Busch et al., 2008; Mann et al., 2017; Raden et al., 2018; Wright et al., 2014). The 65 annotated non-coding RNAs in the E. coli BW25113 genome (GenBank accession number CP009273 Grenier et al., 2014) were used as query sRNAs. The RNA sequences encompassing the intergenic region between kanr and argB through 33 bp downstream of the argB start codon were used as target mRNAs. Default parameters were applied with seven base pairs as the minimum size of the seed region.

Data availability

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The genome sequence of E. coli strain AM187 used in this study has been deposited to NCBI GenBank under accession number CP037857.1.

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Decision letter

  1. Paul B Rainey
    Reviewing Editor; Max Planck Institute for Evolutionary Biology, Germany
  2. Diethard Tautz
    Senior Editor; Max-Planck Institute for Evolutionary Biology, Germany

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Pathways for the evolution of new genes are of broad interest. One route involves amplification (under selection) of a gene that has secondary promiscuous function followed by subsequent divergence. The idea has received experimental attention with focus on mutations leading to specialisation in each copy. Here, using a bacterial model system, Morgenthaler et al. show that mutations underpinning improvement in promiscuous function often occur in targets other than the duplicated genes. This surprising finding draws attention to a complex interplay of mutational events underpinning gene duplication and divergence.

Decision letter after peer review:

[Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]

Thank you for submitting your work entitled "Mutations that improve efficiency of a weak-link enzyme are rare compared to adaptive mutations elsewhere in the genome" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Dan Andersson (Reviewer #2).

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

All three reviewers were enthusiastic about the study and agree that it adds usefully to an important topic. However all three felt that the work was not sufficiently complete to warrant publication at this time. Should you chose to follow suggestions of the reviewers and provide additional support for your hypotheses, then a fresh submission would be welcome.

Reviewer #1:

The paper is a nice addition to the work on the evolution of new gene function via mutations that take a promiscuous enzyme and make it better. The message is that while amplifications are a starting point, and may increase opportunity for additional mutations within the amplified region, mutations that improve fitness can also arise elsewhere in the genome. This comes as no real surprise and there are many studies showing the importance of compensatory mutation in many different contexts. Nonetheless, this is not to take from the importance of tracking down the mutations in this particular context and understanding their contributions to the refinement of promiscuous function.

The bulk of the paper attempts to shed light on a handful of mutations (out of a great many) in candidate genes. The approach is largely biochemical. The findings are used to support a particular explanation, but where I feel the paper falls short is its failure to consider alternate possibilities (and to test them). Or put another way, the model presented by the authors needs experimental validation. This is particularly relevant to the carB side of the story, where at the very least the work needs to be backed by reconstruction of mutants in the ancestral background. Without this it is impossible to know whether the mutations are responsible for fitness increase and whether these depend on other mutations. And thus whether the model provided by the authors is correct. And indeed, whether explanation focussing on the arginine pathways is correct.

Reviewer #2:

Overall, I think the manuscript is interesting and the work well executed. The authors utilize a number of different experiments to support their conclusions. They also put in a considerable amount of effort to address/explain any potential discrepancies or inconsistencies in their data. However, even though the study presents some interesting conclusions, in the current manner in which the manuscript is written I think the authors undersell their work (or don't articulate its significance well) and the focus of the manuscript gets lost in the Results section.

Another aspect is whether or not, given the experimental design, these were the expected results. The initial mutations (promoter mutations and amplifications) that increase the expression of proA* and were observed in the previous study would result in reduced selection for improving the enzyme's catalytic activity. Hence selection is more likely to proceed through other targets simply because of this reduced selective pressure, and a larger target size for alternate mutations. In other words, is the observation in this study an outcome of limited adaptive routes for improving catalytic activity, or an outcome of reduced selective pressure because of how the experiment was set-up. It would be nice if the authors could bring this up either in the Introduction or in the Discussion.

1) The Abstract could benefit from more elaboration on the key findings – the authors should briefly mention the three mechanisms of adaptive mutations they find and how everything relates back to proA and ultimately ties into arginine synthesis. They should better emphasize the significance and impact of their work. The same holds true for the second-to-last paragraph in the Introduction.

2) The main focus/purpose of the paper is lost in the Results section. It would help if the authors relate their findings back to proA/arginine pathway in each subsection, instead of doing so only in the Discussion.

3) Introduction, last paragraph: Should be rephrased. The mutations are still presumably affecting the same phenotype of arginine synthesis. Hence fitness is still increasing by the same mechanism.

4) It might be useful to include a schematic for the construction of the DELargC proA*-yfp strain in a main text or supplementary figure for added clarity in the Results section where strain construction is described (subsection “Growth rate of ∆argC proA* E. coli increased 3-fold within a few hundred generations of adaptation in M9/glucose/proline”) and/or in the corresponding Materials and methods section.

5) The "Mutations outside of proA* improved fitness" subsection in the Results should be renamed. The current title of this subsection is somewhat misleading, as this subsection only focuses on the media-adapted mutations that do not directly pertain to proA* and arginine synthesis. The mutations "outside of proA*" that are truly important for improved fitness already have their own dedicated subsections.

6) "Mutations upstream of argB increase ArgB abundance": "The thermodynamic stability of this region is clearly not the only factor responsible for the effects of the mutations upstream of argB." and

"The effects of the -94 A➝G and -22 C➝A mutations, however, cannot be explained by either of these mechanisms." Can the authors suggest any other possible mechanisms to explain these observations?

7) "Mutations in carB either increase activity or impact allosteric regulation":

As mentioned above, relating these enzyme kinetics and regulation data back to the proA/arginine synthesis pathway would improve the focus and flow of the paper.

8) "Discussion": The lengthy description of the previously found proBA operon promoter mutations seems unnecessary. Maybe briefly mention the rationale behind using the M2 promoter mutation in the Results section where strain construction is described. Additionally, any explanation for why the M2 mutation was chosen over the M1 or M3 promoter mutations is currently missing in the manuscript.

9) There is a discord between the increase in fitness of the evolving populations (Figure 3) and the change in copy number of proA* (Figure 4). Essentially the authors observe an increase in fitness (2-3 fold) in the first 100 generations in all the populations, but there is no change in proA* copy number during this period. The authors should address this in the Discussion.

10) Subsection “Laboratory adaptation”, last paragraph: It would be good to report which cultures had to be restarted with this kind of a bottleneck, and whether that affected the adaptive mutations observed.

11) Subsection “Calculation of growth rate and generations during adaptation” and Equation 6: The terminologies used in the description and in the equation do not match.

12) For some of the experiments, growth rates are measured from growth curves using a plate reader, and different parameters are then calculated (subsection “Growth rate measurements”). However, it is not clear how these correspond to parameters under selection in a turbidostat, which was used for the evolution experiments. This comparison is important and should be made clear in the text and figures.

Reviewer #3:

The authors have developed a potentially very useful approach for testing the IAD model, where a promiscuous activity in a ProA mutant can complement an ArgC deletion. As expected based on previous experiments (Kershner et al., 2016) all eight populations increase the copy number of the weak-link enzyme, but in only one population a mutation in ProA is found. Instead mutations in a range of other genes are found, often repeatedly in the same genes.

The article is clearly written and methodologically sound and tests of the IAD model could be of interest for a wide audience. However I do not fully agree with the interpretation of some experiments and the not all of the authors' conclusions are supported by data:

The authors introduce a weak-link enzyme, but there are also other artificially introduced potential weak-links and these are what is targeted by mutation after the main fitness increase by the amplifications. For example the rph-pyrE mutations correct a known defect in pyrimidine biosynthesis in this particular strain.

The insertion of the kanamycin resistance cassette upstream in ArgC is likely to disrupt expression of ArgB and the mutations seen in the intergenic region might simply compensate for this. That they are relevant outside this artificial genetic context would have to be shown in for example an ArgC mutant with a smaller inactivating in-frame deletion of ArgC. Based on the genetic context in the wild type it is possible that there is some kind of translational link between ArgC and ArgB (just 7 bp intergenic in the wild type). This would be supported by the big deletion bringing the kanr ORF and ArgB closer that results in the largest increase in growth rate.

The computational analysis of effects of the mutations on mRNA structure is not relevant for a native context but mainly looks at effects caused by the strong secondary structure introduced by the FRT site in the kanr cassette inserted upstream of ArgB. By including selection to keep kanamycin resistance this will also constrain the types of possible mutations (for example deletion to regain its native promoter upstream of ArgC) thereby biasing mutations to the small artificial intergenic region.

The effect of the carB mutations are well characterized at the biochemical level, but the authors do not show how this increase fitness or even that the mutations increase growth rate when reconstructed in a wild type background. The hypothesis that this is due to an increase in carbamoyl phosphate synthase activity that potentiates flux through the arginine pathway is not supported by data. The authors might want to discuss alternative mechanisms such as that a decrease in ornithine concentration reduce the activation of CarB to such level that not enough carbamoyl phosphate is produced for pyrimidine synthesis (which is also crippled by the rph-pyrE mutation).

Abstract: "We have identified the mechanisms by which three classes of adaptive mutations increase fitness" No mechanisms are actually shown even though some reasonable interpretations are given.

There is no data supporting that the mutations would produce a cost in another context and therefore statements like "These changes could be detrimental requiring reversion or compensation" "often at a cost to a previously well-evolved function" should be avoided. The well-evolved functions seem to already be disrupted by the rph-pyrE mutation and the insertion of the kanamycin cassette.

Similarly: "While a constitutively active CPS is beneficial in the short term to improve arginine synthesis, it will likely be detrimental once arginine production no longer limits growth." This could be tested relatively easy experimentally, without such data it is merely a (very reasonable) hypothesis.

https://doi.org/10.7554/eLife.53535.sa1

Author response

[Editors’ note: the author responses to the first round of peer review follow.]

Reviewer #1:

The paper is a nice addition to the work on the evolution of new gene function via mutations that take a promiscuous enzyme and make it better. The message is that while amplifications are a starting point, and may increase opportunity for additional mutations within the amplified region, mutations that improve fitness can also arise elsewhere in the genome. This comes as no real surprise and there are many studies showing the importance of compensatory mutation in many different contexts. Nonetheless, this is not to take from the importance of tracking down the mutations in this particular context and understanding their contributions to the refinement of promiscuous function.

The bulk of the paper attempts to shed light on a handful of mutations (out of a great many) in candidate genes. The approach is largely biochemical. The findings are used to support a particular explanation, but where I feel the paper falls short is its failure to consider alternate possibilities (and to test them). Or put another way, the model presented by the authors needs experimental validation. This is particularly relevant to the carB side of the story, where at the very least the work needs to be backed by reconstruction of mutants in the ancestral background. Without this it is impossible to know whether the mutations are responsible for fitness increase and whether these depend on other mutations. And thus whether the model provided by the authors is correct. And indeed, whether explanation focussing on the arginine pathways is correct.

We have added new experiments to address these concerns. 1) We have shown that the carB mutants do in fact increase fitness when introduced into the parental strain (Figure 7F). 2) The first mutations acquired during the evolution experiment were between rph and pyrE. These mutations correct a known defect in pyrimidine synthesis in E. coli K12 strains. We have shown that pyrimidine synthesis is no longer limiting after acquisition of the rph-pyrE mutation. However, poor arginine synthesis still limits growth (Figure 7—figure supplement 2). Thus, the carB mutations occur in a background in which there is selective pressure for improvement of arginine synthesis.

Reviewer #2:

Overall, I think the manuscript is interesting and the work well executed. The authors utilize a number of different experiments to support their conclusions. They also put in a considerable amount of effort to address/explain any potential discrepancies or inconsistencies in their data. However, even though the study presents some interesting conclusions, in the current manner in which the manuscript is written I think the authors undersell their work (or don't articulate its significance well) and the focus of the manuscript gets lost in the Results section.

We have modified the text and figures to better focus the Results section on the impact of mutations on the arginine synthesis pathway. We have modified Figure 2 to show the connections between the arginine, proline, and pyrimidine synthesis pathways for reference throughout the Results and Discussion sections.

We have provided more context for why the argB mutations may be beneficial in the argB Results subsection, " Mutations upstream of argB increase ArgB abundance”. We have provided more context for why the carB mutations may be beneficial in the carB Results subsection, “Mutations in carB either increase activity or impact allosteric regulation”, and in a modified Figure 7B. We have modified Figure 8A to indicate the effects of each of the investigated mutations and added Figure 8B (the evolutionary trajectories of the evolved strains) to better summarize how the mutations impact fitness.

Another aspect is whether or not, given the experimental design, these were the expected results. The initial mutations (promoter mutations and amplifications) that increase the expression of proA* and were observed in the previous study would result in reduced selection for improving the enzyme's catalytic activity. Hence selection is more likely to proceed through other targets simply because of this reduced selective pressure, and a larger target size for alternate mutations. In other words, is the observation in this study an outcome of limited adaptive routes for improving catalytic activity, or an outcome of reduced selective pressure because of how the experiment was set-up. It would be nice if the authors could bring this up either in the Introduction or in the Discussion.

Although the selective pressure is reduced by the promoter mutation and gene amplification, the observation that each strain acquired 6-20 copies of a segment of the genome ranging between 5 and 164 kb demonstrates that there is still strong selective

pressure for improvements in arginine synthesis – by any mechanism. The observation of the preponderance of alternate mutations is very likely due to the larger target size. We have addressed this point in the tenth paragraph of the Discussion.

1) The Abstract could benefit from more elaboration on the key findings – the authors should briefly mention the three mechanisms of adaptive mutations they find and how everything relates back to proA and ultimately ties into arginine synthesis. They should better emphasize the significance and impact of their work. The same holds true for the second-to-last paragraph in the Introduction.

We have reworded the Abstract and the second-to-last paragraph in the Introduction as requested.

2) The main focus/purpose of the paper is lost in the Results section. It would help if the authors relate their findings back to proA/arginine pathway in each subsection, instead of doing so only in the Discussion.

See response to reviewer 2’s first comment above.

3) Introduction, last paragraph: Should be rephrased. The mutations are still presumably affecting the same phenotype of arginine synthesis. Hence fitness is still increasing by the same mechanism.

In this concluding paragraph of the Introduction, we are making a general point about evolution when a weak-link enzyme limits fitness, rather than a specific point about this system. We prefer to leave the statement as is.

4) It might be useful to include a schematic for the construction of the DELargC proA*-yfp strain in a main text or supplementary figure for added clarity in the Results section where strain construction is described (subsection “Growth rate of ∆argC proA* E. coli increased 3-fold within a few hundred generations of adaptation in M9/glucose/proline”) and/or in the corresponding Materials and methods section.

Done (Figure 2—figure supplement 1).

5) The "Mutations outside of proA* improved fitness" subsection in the Results should be renamed. The current title of this subsection is somewhat misleading, as this subsection only focuses on the media-adapted mutations that do not directly pertain to proA* and arginine synthesis. The mutations "outside of proA*" that are truly important for improved fitness already have their own dedicated subsections.

Done (subsection “Some prevalent mutations in the evolved clones are not related to improved arginine synthesis”).

6) "Mutations upstream of argB increase ArgB abundance": "The thermodynamic stability of this region is clearly not the only factor responsible for the effects of the mutations upstream of argB." and

"The effects of the -94 A➝G and -22 C➝A mutations, however, cannot be explained by either of these mechanisms." Can the authors suggest any other possible mechanisms to explain these observations?

One other possibility is that the mutations upstream of argB might affect binding of an sRNA. We have added a discussion of this possibility to the text (see subsection “Mutations upstream of argB increase ArgB abundance” and Figure 6—figure supplement 4).

7) "Mutations in carB either increase activity or impact allosteric regulation":

As mentioned above, relating these enzyme kinetics and regulation data back to the proA/arginine synthesis pathway would improve the focus and flow of the paper.

Done (subsection “Mutations in carB either increase activity or impact allosteric regulation”, Discussion).

8) "Discussion": The lengthy description of the previously found proBA operon promoter mutations seems unnecessary. Maybe briefly mention the rationale behind using the M2 promoter mutation in the Results section where strain construction is described. Additionally, any explanation for why the M2 mutation was chosen over the M1 or M3 promoter mutations is currently missing in the manuscript.

We have removed this description of the previously found proBA promoter mutations from the Discussion. The M3 mutation was not in the promoter of the proBA operon; we have clarified this in the text. There was no particular reason for choosing the M2 promoter mutation. We could have initiated the evolution experiment with a strain carrying the M1 mutation.

9) There is a discord between the increase in fitness of the evolving populations (Figure 3) and the change in copy number of proA* (Figure 4). Essentially the authors observe an increase in fitness (2-3 fold) in the first 100 generations in all the populations, but there is no change in proA* copy number during this period. The authors should address this in the Discussion.

The initial increase in fitness is caused by the mutations between rph and pyrE, which occur within the first 100 generations prior to proA* amplification. We have modified the text and figures to emphasize this point (see subsection “Some prevalent mutations in the evolved clones are not related to improved arginine synthesis”, Figure 4—figure supplement 2, Figure 8B).

10) Subsection “Laboratory adaptation”, last paragraph: It would be good to report which cultures had to be restarted with this kind of a bottleneck, and whether that affected the adaptive mutations observed.

We have created a table with all the times the cultures had to be restarted in Figure 4—source data 1, sheet 4. We have also addressed this potential bottleneck in the subsection “Laboratory evolution”.

11) Subsection “Calculation of growth rate and generations during adaptation” and Equation 6: The terminologies used in the description and in the equation do not match.

Fixed (subsection “Calculation of growth rate and generations during adaptation”).

12) For some of the experiments, growth rates are measured from growth curves using a plate reader, and different parameters are then calculated (subsection “Growth rate measurements”). However, it is not clear how these correspond to parameters under selection in a turbidostat, which was used for the evolution experiments. This comparison is important and should be made clear in the text and figures.

This comparison is not particularly relevant because the growth rates in the turbidostat are for populations, while the growth rates measured in the plate reader are for individual clones constructed by introducing particular mutations into the parental or wild-type background. However, the growth rate of the parental strain AM187 is 0.27 hr-1 in the plate reader (Figure 5B, Figure 6B, Figure 7F, Figure 4—figure supplement 2) and about 0.24 h-1 in the turbidostat (Figure 3), so the calculated growth rates of individual clones in the plate reader and turbidostat are similar. This information has been added to the subsection “Growth rate measurements”.

Reviewer #3:

The authors have developed a potentially very useful approach for testing the IAD model, where a promiscuous activity in a ProA mutant can complement an ArgC deletion. As expected based on previous experiments (Kershner et al., 2016) all eight populations increase the copy number of the weak-link enzyme, but in only one population a mutation in ProA is found. Instead mutations in a range of other genes are found, often repeatedly in the same genes.

The article is clearly written and methodologically sound and tests of the IAD model could be of interest for a wide audience. However I do not fully agree with the interpretation of some experiments and the not all of the authors' conclusions are supported by data:

The authors introduce a weak-link enzyme, but there are also other artificially introduced potential weak-links and these are what is targeted by mutation after the main fitness increase by the amplifications. For example the rph-pyrE mutations correct a known defect in pyrimidine biosynthesis in this particular strain.

The insertion of the kanamycin resistance cassette upstream in ArgC is likely to disrupt expression of ArgB and the mutations seen in the intergenic region might simply compensate for this. That they are relevant outside this artificial genetic context would have to be shown in for example an ArgC mutant with a smaller inactivating in-frame deletion of ArgC. Based on the genetic context in the wild type it is possible that there is some kind of translational link between ArgC and ArgB (just 7 bp intergenic in the wild type). This would be supported by the big deletion bringing the kanr ORF and ArgB closer that results in the largest increase in growth rate.

We have addressed this valid concern by measuring protein levels in AM187 and a comparable strain that lacks ArgC due to introduction of two stop codons by label-free proteomics. ArgB levels are indeed diminished by 2.3-fold in AM187 (Figure 6—figure supplement 1). However, growth rate of AM187 is increased by mutations that increase ArgB levels by up to 8-fold and by overexpression of ArgB up to 25-fold, demonstrating that the beneficial effect of the mutations we observed is not simply due to

compensation for the 2.3-fold decrease in ArgB caused by replacement of argC with kanr. This new experiment is described in the fourth paragraph of the subsection “Mutations upstream of argB increase ArgB abundance” and the data are shown in Figure 6B.

The computational analysis of effects of the mutations on mRNA structure is not relevant for a native context but mainly looks at effects caused by the strong secondary structure introduced by the FRT site in the kanr cassette inserted upstream of ArgB. By including selection to keep kanamycin resistance this will also constrain the types of possible mutations (for example deletion to regain its native promoter upstream of ArgC) thereby biasing mutations to the small artificial intergenic region.

We agree that the effects of the mutations on mRNA structure are not relevant for a native context. However, they are relevant for the context in which the experimental evolution was carried out. A deletion to regain the native promoter upstream of argC would not be needed because insertion of kanr does not prevent utilization of the native promoter. The native promoter is regulated by the repressor ArgR, and would be expected to be active unless arginine levels are adequate. Thus, insertion of kanr does not interfere with transcription of the operon. Further, the secondary structure introduced by the FRT site (the large stem loop from nucleotide 25-61 in Figure 6—figure supplement 2) does not seem to be driving the mutations, as this region is affected in only two of the mutants (the Δ58 bp and Δ51 bp mutants). Most of the mutations do not disrupt the secondary structure caused by the FRT site.

The effect of the carB mutations are well characterized at the biochemical level, but the authors do not show how this increase fitness or even that the mutations increase growth rate when reconstructed in a wild type background. The hypothesis that this is due to an increase in carbamoyl phosphate synthase activity that potentiates flux through the arginine pathway is not supported by data. The authors might want to discuss alternative mechanisms such as that a decrease in ornithine concentration reduce the activation of CarB to such level that not enough carbamoyl phosphate is produced for pyrimidine synthesis (which is also crippled by the rph-pyrE mutation).

We are not entirely sure what the reviewer means by “even that the mutations increase growth rate when reconstructed in a wild-type background”. We suspect that the reviewer meant that we had not shown that the mutations increase growth rate in the parental background. We have added data showing that three of four of the carB mutations do indeed increase growth rate of the parental strain. A reasonable explanation for the lack of growth increase with the fourth is also included in the paper. (See subsection “Mutations in carB either increase activity or impact allosteric regulation”, seventh paragraph and Figure 7F.)

At the reviewer’s suggestion, we considered the possibility that the carB mutations might be beneficial because they improve pyrimidine synthesis rather than arginine synthesis. After acquisition of the rph-pyrE mutation, addition of uracil to the medium does not improve growth rate, although it does improve growth rate of the parental strain, which has a known defect in pyrimidine synthesis (see Figure 7—figure supplement 2). Thus, there is no longer selective pressure to improve pyrimidine synthesis after the acquisition of the rph-pyrE mutation. However, poor arginine synthesis still limits growth; addition of arginine restored growth to wild-type levels. Since the carB mutations occurred after the rph-pyrE mutations, we can conclude that these mutations are beneficial because they improve arginine synthesis. We have added this new experiment to the text in Figure 7—figure supplement 2 and the tenth paragraph of the subsection “Mutations in carB either increase activity or impact allosteric regulation”.

Abstract: "We have identified the mechanisms by which three classes of adaptive mutations increase fitness" No mechanisms are actually shown even though some reasonable interpretations are given.

By mechanisms we meant the increase in ProA* concentration produced by gene amplification, the increased activity of CarAB, and the increased expression of ArgB.

There is no data supporting that the mutations would produce a cost in another context and therefore statements like "These changes could be detrimental requiring reversion or compensation" "often at a cost to a previously well-evolved function" should be avoided. The well-evolved functions seem to already be disrupted by the rph-pyrE mutation and the insertion of the kanamycin cassette.

Similarly: "While a constitutively active CPS is beneficial in the short term to improve arginine synthesis, it will likely be detrimental once arginine production no longer limits growth." This could be tested relatively easy experimentally, without such data it is merely a (very reasonable) hypothesis.

We have addressed this concern by introducing four of the carB mutations into the genome of wild-type E. coli containing the rph-pyrE mutation to simulate the situation where arginine production no longer limits growth. Competitive fitness assays showed that three mutations that alter allosteric regulation of CarB did in fact decrease fitness. Interestingly, one mutation increased growth rate in this background. These results are shown in Figure 7G and discussed in the Discussion.

https://doi.org/10.7554/eLife.53535.sa2

Article and author information

Author details

  1. Andrew B Morgenthaler

    1. Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, United States
    2. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, United States
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3822-0212
  2. Wallis R Kinney

    1. Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, United States
    2. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, United States
    Contribution
    Investigation, Acquired and interpreted data
    Competing interests
    No competing interests declared
  3. Christopher C Ebmeier

    Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, United States
    Contribution
    Data curation, Formal analysis, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7940-6190
  4. Corinne M Walsh

    1. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, United States
    2. Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, United States
    Contribution
    Investigation, Acquired and interpreted data
    Competing interests
    No competing interests declared
  5. Daniel J Snyder

    Center for Evolutionary Biology and Medicine, University of Pittsburgh, Pittsburgh, United States
    Contribution
    Resources, Data curation, Funding acquisition, Investigation
    Competing interests
    No competing interests declared
  6. Vaughn S Cooper

    Center for Evolutionary Biology and Medicine, University of Pittsburgh, Pittsburgh, United States
    Contribution
    Resources, Data curation, Supervision, Funding acquisition, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7726-0765
  7. William M Old

    Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Project administration
    Competing interests
    No competing interests declared
  8. Shelley D Copley

    1. Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, United States
    2. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Project administration
    For correspondence
    Shelley.Copley@Colorado.EDU
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9727-7919

Funding

National Aeronautics and Space Administration (NNA15BB04A)

  • Vaughn S Cooper
  • Shelley D Copley

Department of Defense (13-34-RTA-FP-007)

  • William M Old

University of Colorado Boulder (Libraries Open Access Fund)

  • Shelley D Copley

Defense Advanced Research Projects Agency (13-34-RTA-FP-007)

  • William M Old

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Craig Joy (University of Colorado Boulder, Physics Department) and Chris Takahashi (University of Washington) for help building the turbidostat. Publication of this article was funded by the University of Colorado Boulder Libraries Open Access Fund.

Senior Editor

  1. Diethard Tautz, Max-Planck Institute for Evolutionary Biology, Germany

Reviewing Editor

  1. Paul B Rainey, Max Planck Institute for Evolutionary Biology, Germany

Publication history

  1. Received: November 12, 2019
  2. Accepted: December 2, 2019
  3. Accepted Manuscript published: December 9, 2019 (version 1)
  4. Version of Record published: January 3, 2020 (version 2)

Copyright

© 2019, Morgenthaler et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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