Understanding Functional Redundancy and Promiscuity of Multidrug Transporters in E. coli under Lipophilic Cation Stress

Abstract

Multidrug transporters (MDTs) are major contributors to microbial drug resistance and are further utilized for improving host phenotypes in biotechnological applications. Therefore, the identification of these MDTs and the understanding of their mechanisms of action in vivo are of great importance. However, their promiscuity and functional redundancy represent a major challenge towards their identification. Here, a multistep tolerance adaptive laboratory evolution (TALE) approach was leveraged to achieve this goal. Specifically, a wild-type E. coli K-12-MG1655 and its cognate knockout individual mutants ΔemrE, ΔtolC, and ΔacrB were evolved separately under increasing concentrations of two lipophilic cations, tetraphenylphosphonium (TPP⁺), and methyltriphenylphosphonium (MTPP⁺). The evolved strains showed a significant increase in MIC values of both cations and an apparent cross-cation resistance. Sequencing of all evolved mutants highlighted diverse mutational mechanisms that affect the activity of nine MDTs including acrB, mdtK, mdfA, acrE, emrD, tolC, acrA, mdtL, and mdtP. Besides regulatory mutations, several structural mutations were recognized in the proximal binding domain of acrB and the permeation pathways of both mdtK and mdfA. These details can aid in the rational design of MDT inhibitors to efficiently combat efflux-based drug resistance. Additionally, the TALE approach can be scaled to different microbes and molecules of medical and biotechnological relevance.

1. Introduction

Multidrug transporters (MDTs) provide microbes and other organisms the ability to defend themselves against external toxic compounds as well as toxic metabolites. A large arsenal of these MDTs is now known in prokaryotes, and mutations in these MDTs provide resistance to a wide range of xenobiotics including drugs [1,2]. Therefore, these transporters were considered as major contributors to microbial drug resistance, a huge worldwide health problem [3]. In addition, their efflux activities have been harnessed for improving product titers as well as enhancing host’s tolerance in biotechnological applications [4,5]. The significant role MDTs play in drug resistance relies on two main features: promiscuity, and functional redundancy. From their name, MDTs catalyze the efflux of structurally similar and dissimilar classes of drugs and other xenobiotics [6,7]. Additionally, activities of redundant MDTs are typically undetectable in wild-type microbes. However, they become important (i.e., observable) under increasing drug doses and/or after deletion of a potential transport system [8,9,10]. Thus, the functional redundancy of MDTs and their promiscuous nature can further serve as a starting point for evolving new functions, allowing for novel adaptative mechanisms. Although the advances in our genetic and structural understanding of different MDTs have been significant [6], revealing redundant MDTs and elucidating molecular details on multidrug recognition and transport in vivo remains challenging.

Tolerance Adaptive Laboratory Evolution (TALE) experiments have revealed precise information about the genetic basis underlying multiple phenotypes, including microbial drug resistance [11,12,13,14]. Previous TALE-based investigations showed how the rate and genotypic path to resistance varies across different controlled drug treatment regimens [14]. In conventional selection experiments, bacterial populations are subjected to fixed drug doses aiming at applying selective advantage for resistant mutants, but this typically identifies a single adaptive step [15,16,17,18]. However, after the generation of resistant mutants, higher drug doses are needed to maintain the selection pressure on the population’s current-higher resistance level and to reveal how multiple mutations can accumulate/enrich to confer stronger resistance. Obviously, the rate at which the inhibitory drug doses increase, which reflects the rate of evolution of resistance, can vary across the evolutionary trajectory and with the use of different drugs or strains with different starting genetic makeup [9,19,20,21]. Therefore, there is a need for a multistep long-term evolution experimental methodology in which the effective drug concentration is continuously tuned according to the actual rate of evolutionary adaptation. Technological innovations in liquid handling now facilitate rapid multistep TALE experiments, revealing long-term evolutionary paths of many organisms under various controlled lab conditions [11]. Additionally, the TALE approach has been introduced for the in vivo identification and modulation of membrane transporters, and the efficient generation of tolerance phenotypes of several microorganisms [22,23,24,25,26,27,28].

The knowledge base of multidrug transporter families in E. coli includes crystallographic data [6,29] of at least some of the major efflux pumps bound to drug substrate(s), which provide a foundation to explore similarities in drug recognition and drug export mechanisms. An example is emrE and the tripartite acrAB/tolC which are notable for their versatility in the efflux of several drugs in gram-negatives. Both efflux systems also share the binding and export of the two lipophilic cations tetraphenylphosphonium (TPP⁺) and methyltriphenylphosphonium (MTPP⁺) [30,31,32,33]. Thus, the promiscuous nature of the emrE and acrAB/tolC complex, and the two lipophilic cations TPP⁺ and MTPP⁺ are fundamental components in the experimental design used here. Accordingly, a wild-type E. coli K-12-MG1655 and its cognate knockout individual mutants ΔemrE, ΔtolC, and ΔacrB were evolved separately under increasing concentrations of TPP⁺ and MTPP⁺. The use of the wild type is hypothesized to signify the most reachable transport systems of both cations, while knockout strains would allow the discovery of redundant backup mechanisms that cover the loss of deleted transporters. The results showed that the multistep TALE approach allowed the generation of strains with high tolerance and that facilitated the discovery of several redundant MDTs and the mutational mechanisms underlying their modulation in vivo.

2. Materials and Methods

2.1. Strains, Chemicals, and Culture Media

The wild-type strain E. coli K-12 MG1655 (ATCC 47076) was initially spread on LB agar plates overnight, and unique single colonies were picked for further generation of single gene knock-out mutants based on using λ red recombineering and CRISPR-CAS9 as a counter selection. Adaptive laboratory evolution experiments were performed in M9 medium with 0.2% glucose, with added trace elements and vitamins, as described previously [22,27]. Tetraphenylphosphonium (TPP⁺) and methyltriphenylphosphonium bromide (MTPP⁺) were supplemented separately on an incremental basis. The manufacturer of the chemicals used was Sigma-Aldrich (St. Louis, MO, USA) unless otherwise stated.

To generate knockout strains, each chromosomal emrE, tolC, and acrB deletion was introduced using a temperature-sensitive pMP11 carrying λ-recombinases and Streptococcus pyogenes Cas9 [34,35]. Briefly, E. coli MG1655 was transformed with a temperature-sensitive pMP11. Lambda recombinases were induced with 0.2% arabinose at 37 °C for 45 min. Induced cells were transformed with a 200 ng gRNA plasmid for emrE, together with 100 pmol synthetic oligo, and each 200 ng FRT cassette containing each flanking 50 bp homologous sequence for tolC and acrB. Upon transformation, cells were recovered at 30 °C for 2 h. Cells were then transferred to 2 mL of LB containing ampicillin and chloramphenicol for the emrE knockout strain, and grown overnight at 30 °C. Each tolC and acrB knockout strain was selected on an LB agar plate containing kanamycin at 30 °C. EmrE knockout transformants were isolated by plating and validated by colony PCR and Sanger sequencing. The loss of guide RNA plasmid was achieved by growing confirmed isolates in LB containing ampicillin and 200 μg/L of anhydrotetracycline at 30 °C for at least 6 h. Loss of pMP11 was achieved by propagation at 37 °C. The loss of both plasmids was validated by checking the cell’s sensitivity to antibiotics. Plasmids and oligos used for strain reconstruction and PCR confirmation are listed in Supplementary Table S4.

2.2. Tolerance Adaptive Laboratory Evolution (TALE)

Laboratory evolution experiments were conducted on an automated platform using a liquid handling robot as previously described [22,26,27]. Briefly, four independent populations of Ref, uΔemrE, uΔtolC, and uΔacrB were evolved separately on each of the cations. Cells were serially propagated (400 μL passage volume) in 15 mL (working volume) flasks of M9 minimal medium supplemented with 2 g/L glucose, kept at 37 °C through placement in a heat block and aerated by magnetic tumble stirrers at 1200 rpm. Cultures were monitored via changes in OD600 nm with a Tecan Sunrise plate reader (Tecan Inc., Männedorf, Switzerland) as a proxy for cell density. Growth rates were calculated for each flask using OD600 nm measurements and a best fit approximation for the growth using a three-phase model for lag, exponential growth, and stationary phase. The starting concentration of each amino acid was slightly above a predetermined minimum inhibitory concentration using the same liquid handler. There was a ‘resting phase’ parameter used to represent the minimum number of flasks at which the chemical concentration was not changed. This resting phase parameter varied between growing TALE replicates. Additionally, there was another parameter, ‘step increase’, which was set to define the increase in chemical concentration over the current amount once a culture met the increase criteria. When increased growth rate was achieved after a defined period of time (nearly equal to time it took for completion of two flasks, although this number varies according to the strain fitness) at a particular concentration, the cation concentration was increased when an observed fitness over a threshold was achieved (for example, a growth rate of 0.35 h⁻¹). Occasionally, when there was not a considerable increase in the growth rate over the threshold, populations were left for the time it took for completion of three flasks. This process was repeated until a significant increase in tolerance was achieved. Each lineage was periodically validated by PCR, including the final flask, using the respective oligos (Supplementary Table S4).

2.3. Validation of Resistant Phenotypes and Cross-Cation Resistance

Evolved isolates were screened in high throughput for tolerance on selected concentrations of TPP⁺ and MTTP⁺ (Supplementary Table S1) using Growth Profiler (EnzyScreen BV, Leiden, The Netherlands). The sequenced clonal isolates derived from the endpoint populations (see Supplementary Table S2) were screened at a range of different concentrations (i.e., 8, 11, 14, 18, 24, 31, and 40 mM) of TPP⁺ and MTTP⁺. Colonies from the parental strains (i.e., E. coli K-12 MG1655 ‘Ref’ and its cognate knockout strains uΔemrE, uΔtolC, and uΔacrB (denoted ‘unevolved’ knockout strains)), were used as a control. Cells were inoculated into 500 µL M9 glucose medium in deep well plates and incubated in a plate shaker at 37 °C and 300 rpm shaking. Later, cells were diluted 10× in M9 glucose medium, from which 30 µL was transferred to clear-bottom 96 half-deep well plates (EnzyScreen BV, Leiden, The Netherlands) containing M9 glucose medium with either of the two cations, such that the final concentration was equal to the range of the tested concentrations. For cross-cation resistance, cells were alternatively tested in the concentration gradient mentioned above in relation toeither cation toward which they were not evolved. The half-deep well plates were incubated at 37 °C with 225 rpm shaking in the Growth Profiler, with scans recorded at 20 min intervals. G-values were obtained from the plate images using the manufacturer’s software. G-values were converted to OD600 values using the formula

OD₆₀₀ = a × (G_Value − G_Blank)^b

with the predetermined values a = 0.0024 and b = 1.69.

Growth rates were determined by using the R package Growthcurver [36]. Minimum inhibitory concentration (MIC) was determined as the lowest concentration at which less than 10% growth was detected relative to the growth in the basic M9 medium without the addition of either cation.

2.4. Whole Genome Sequencing and Mutational Analysis

To initiate the sequencing process, selected samples represent each evolved population at three time points of the evolutionary trajectory: the endpoint and two intermediate points approximately at the middle of the trajectory. Additionally, clonal isolates, derived from each selected population, was also selected. In total, 92 populations and 122 clonal isolates were re-sequenced. Selected cells were grown until reaching the stationary phase in M9 minimal medium supplemented with the highest concentration of the cation toward which the cells evolved. The flask numbers at which genomic DNA samples were taken for whole genome re-sequencing (WGS) are listed in Supplementary Table S2. Cell pellets obtained by centrifugation of 2 mL of culture and genomic DNA were extracted using PureLink^® Genomic DNA Kits (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol. The quality of extracted DNA was assessed with UV absorbance ratios using a nanodrop. The concentration of DNA was quantified using a Qubit ds-DNA high sensitivity assay. Paired-end resequencing libraries were generated using a 300 cycle (150 bp × 2) kit from Illumina (San Diego, CA, USA) with a loading concentration on a Nextseq 1.2 pico-Molar with 1% PhiX spike (Illumina, San Diego, CA, USA) of total input DNA. Raw sequence data reported in this paper have been deposited in the European Nucleotide Archive under Bioproject number PRJNA890058. Mutation identification was performed using a computational pipeline tool, as described in [37], based on breseq version 0.30.1 [38] in order to map sequenced reads to the reference strain (NCBI accession number NC_000913.3, K-12 MG1655). For the sequenced population samples, mutations were reported if they were over 25% frequency unless they were found in a clone isolated from a given population sample. This value was selected to focus on clearly causal mutations. Potential mutations were determined by comparing mutations from intermediate and endpoint samples, and identifying genes or intergenic regions that had multiple unique mutations (i.e., instances of parallel evolution) or were mutated across intermediate and endpoint isolates [37]. Mutations were filtered following a systematic logic (see Supplementary Figure S3) and categorized: (1) mutations targeting a unique ORF or ORF intergenic region identified for MDTs, (2) mutations affecting the upstream regulation of MDTs, and (3) other non-specific mutations which is important for the overall fitness (media adaptive mutations) or regulating osmotic stress. Amino acid substitutions were shown in the respective protein structures using PyMOL version 2.5.3 (https://pymol.org/2/ (accessed on 6 August 2022)).

2.5. Fluorophore Accumulation Transport Assay

Small flakes of bacteria in lysogenic broth (LB) glycerol stocks were spread on to LB agar plates containing antibiotic. Overnight single colonies were cultured in 5 mL of LB. The overnight cultures of bacteria were diluted 1:1000 in 5 mL of LB for slow growing strains (eTRef_A2F45I1, eTRef_A2F45I2, eMRef_A1F76I1, and eMRef_A1F76I2) or 1:2500 for the rest of the strains. After 2 h of incubation at 37 °C and shaking at 200 rpm, 50 μL of diluted cultures were transferred into 384-well plates already seeded with fluorophores at 2 μM final concentration (full list in Supplementary Note S1). After 15 min at 37 °C and shaking at approximately 1300 rpm, the 384-well plates were then sampled and analysed in the Intellicyt iQue (Sartorius, Göttingen, Germany) flow cytometer using all 13 channels. The flow cytometer protocol parameters were as follows: 2 min buffer (Qsol, Sartorius, Göttingen, Germany) priming with 10 s shaking at 2000 rpm, and 1 s sampling with two 0.5 s washes in buffer between each sample. Plates were shaken for 2 s every 12 wells while the probe sampled buffer. The final steps involved 30 s in flushing solution (Sartorius), 30 s in cleaning solution, and 60 s in deionised water. The prevalent cluster of light scattering events were gated in the forward scatter versus side scatter plot (population 1). Inside population one, a nested population was gated using the plot of forward scatter height versus forward scatter area and denoted population two. The gated population two would leave out most doublets. The median of the height distribution of the fluorescence signals (from all 13 channels) of population two for every sample were taken for data analyses, together with the number of counts. In total, 4 biological replicates were carried out for 17 bacteria strains with the reference strain ‘Ref’ present in every 384-well plate. Fluorescence signals were normalised against number of counts per sample. Data parsing, annotation, and visualisation were carried out using R.

3. Results and Discussion

3.1. The TALE Process

The TALE process was performed to generate strains that could tolerate elevated levels of two lipophilic cations, TPP⁺ and MTPP⁺, using a previously described methodology [22,26,27]. In brief, a wild-type E. coli K-12-MG1655 (denoted as ‘Ref’) was used as a parental strain to generate three mutant strains in which a promiscuous efflux pump was knocked out: uΔemrE, uΔtolC, and uΔacrB (denoted ‘unevolved’ knockout strains). A total of 4 independent populations of each of Ref, uΔemrE, uΔtolC, and uΔacrB were evolved separately on both cations for a total of 32 independent evolutions. TPP⁺-evolved strains were designated as follows: eTRef, eTΔemrE, eTΔtolC, and eTΔacrB with ‘T’ denoting TPP⁺, and likewise, MTPP⁺ evolved strains will be designated as eMRef, eMΔemrE, eMΔtolC, and eMΔacrB with ‘M’ denoting MTPP⁺. Cells were serially passaged during the late exponential growth under increasing concentrations of the cations until a significant increase in tolerance was achieved. The evolved strains displayed a divergence in the properties of the TALE experiments (Supplementary Table S1) in response to different treatment conditions. The observed fitness trajectories for representative TALE experiments for each strain, as well as the initial and final cation concentrations, are shown in Figure 1. Similar plots for the remaining lineages are shown in Supplementary Figures S1 and S2. Comparing the median values of the achieved final concentrations of both cations by all evolved lineages showed an enhanced tolerance compared to the ancestral strains. Specifically, the eTRef and eTΔemrE populations showed an increase in TPP⁺ final concentrations equivalent to 9.2 and 7.5-fold, respectively, while both eMRef and eMΔemrE populations showed a similar increase in MTPP⁺ concentration equivalent to 14-fold, relative to the starting concentration. These observations suggest that eTΔemrE and eMΔemrE evolved lineages exhibited a similar progression to the eTRef and eMRef with a marginal or no difference in tolerance towards both cations. Likewise, eTΔtolC and eTΔacrB populations showed an increase in TPP⁺ final concentrations equivalent to 256.8 and 1096.5-fold, respectively, while eMΔtolC and eMΔacrB populations showed an increase in MTPP⁺ concentration equivalent to 23- and 29-fold, respectively. However, this fold increase indicates an enhanced tolerance, the maximal cation concentrations achieved by these lineages were significantly lower (4.6- to 43-fold) compared to those achieved by eT/MRef and eT/MΔemrE. In fact, the high sensitivity of the respective ancestral strains uΔtolC and uΔacrB at the onset of the TALE experiments towards either cation highlight the impactful disruption of acrAB/tolC tripartite, thus allowing selection of versatile backup transport mechanisms. On the real-time evolutionary trajectories, population fitness (i.e., calculated growth rate per each flask) fluctuated in response to the incremental dosage of either cation. However, the final growth rates of the endpoint populations ranged from 0.2 to 0.4 h⁻¹. Consequently, the cumulative cell divisions (CCD) experienced by the evolved endpoint populations ranged between 2.5 × 10¹² to 5.5 × 10¹² regardless of the genotypic origin of the TALE lineages. The use of CCD has previously been shown as a more meaningful scale for the time coordinate of a TALE than generations [39], as mutations occur predominantly due to DNA polymerase errors in genomic replication during cell division [40]. Screening of the evolved populations was subsequently performed to validate the overall tolerance compared to the ancestral strains, and to demonstrate possible cross-cation resistance.

3.2. Validation of Resistant Phenotypes and Cross-Cation Resistance

The enhanced tolerance phenotypes of the evolved lineages were further validated and compared to their respective ancestors. Additionally, a demonstration of possibly acquired cross-cation resistance was performed by testing the growth of the evolved cells on the alternative cation toward which these cells were not explicitly evolved. For these experiments, two randomly selected clones derived from each TALE lineage endpoint population (64 clones in total) were phenotypically screened in a broad range of TPP⁺ and MTPP⁺ concentrations (i.e., 0, 8, 11, 14, 18, 24, 31, and 40 mM). An example of the phenotypic screen of eTRef and eMRef, in comparison to their respective ancestor, is shown in Figure 2. The MIC was used as a metric for the assessment of both cation tolerance and acquired cross-cation resistance. The determined average MIC values of both TPP⁺ and MTPP⁺ towards all evolved clones are summarized and compared to controls as shown in Figure 3. Generally, both TPP⁺ and MTPP⁺ evolved clones which showed a significant tolerance compared to their ancestors, and developed an acquired cross-resistance towards the alternative cations. Specifically, eTRef and eMRef showed a 1.6- and 3.75-fold increase in MIC of TPP⁺ and MTPP⁺, respectively, relative to their ancestors. The screened clones derived from one of the eMRef lineages (i.e., T1) showed an exception with an MIC value equivalent to 40 mM of MTPP⁺ which is 68% above the mean (but consistent), and even 80% higher than the maximum MTTP⁺ concentration achieved during the respective TALE experiment. Similarly, eTΔemrE and eMΔemrE showed an increase in MIC values of TPP⁺ and MTPP⁺ with 5- and 3.37-fold, respectively, compared to their ancestors. Despite the observed susceptibility to both cations in the duration of the TALE experiments, the reported MIC values of TPP⁺ for eTΔtolC and eTΔacrB showed 12.4 and 34.7-fold increase, respectively, while MIC values of MTPP⁺ for eMΔtolC and eMΔacrB showed 50 and 48-fold increase, respectively, all compared to their ancestors.

Cross-cation resistance was apparent among the majority of the evolved isolates with different degrees (Figure 2). The MIC values generated for clones derived from three TPP⁺ evolved lineages (i.e., eTRef, eTΔemrE, and eTΔacrB) suggested a high degree of acquired cross resistance against MTPP⁺ (with similar or marginally different MICs). Exceptionally, eTΔtolC isolates retained 1.6-fold increase in MIC values of MTPP⁺ (a cation toward which these clones were not evolved) compared to MIC values of TPP⁺. Clearly the reported MIC values of TPP⁺ were lower in all tested clones compared to MTPP⁺, and consequently all TPP⁺ clones retained high MICs of MTPP⁺ in cross tolerance trials. These results are consistent with the fact that the level of cross resistance depends on the degree of susceptibility of the host bacterium and possibly the underlying resistance mechanism has no absolute value [41]. However, a reversed pattern of cross resistance has been shown in all MTPP⁺ evolved mutants (i.e., eMRef, eMΔemrE, eMΔtolC, and eMΔacrB). The higher MIC values of MTPP⁺ experienced by these clones accompanied by lower MIC values of TPP⁺ in all cross-resistance trials (although generally retained higher MICs compared to their respective ancestors). The genetic basis underlying the overall enhanced tolerance phenotypes was determined by whole genome resequencing.

3.3. Whole Genome Re-Sequencing and Mutational Analysis

Whole genome resequencing (WGS) was used to determine the genetic basis underlying the increased resistance phenotypes, focusing on mutations related to MDTs. Potential mutations were determined by comparing mutations from intermediate and endpoint samples. and identifying genes or intergenic regions that had multiple unique mutations (i.e., instances of parallel evolution) or were mutated across intermediate and endpoint isolates of the same lineage [37]. Different time points (including all screened clones) are listed in Supplementary Table S3. Mutations were filtered following a systematic logic (see Methods and Supplementary Figure S3) and categorized. (1) Mutations targeting a unique ORF or ORF intergenic region identified for MDTs (

Table 1. Mutations that targeted unique ORFs or ORF intergenic regions identified for potential multidrug transporters (MDTs). Represented are the converged mutations that occurred in parallel evolution experiments (n = 4). The number of occurrences represents the number of parallel replicates at which a given mutation occurred. Exceptions that are represented with number of occurrences = 1 are explained in the table legend.

Strain	Cation	Gene	Mutation Types (Unique Counts)	Product	Number of Occurrences across Replicates (n = 4) a,b,c
eTRef	TPP+	ybjG/mdfA	SNP (2)	undecaprenyl pyrophosphate phosphatase/multidrug efflux system protein	1 *
		acrB	SNP (4)	multidrug efflux system protein	4
		mdtK	SNP (3)	multidrug efflux system transporter	4
eTΔemrE		mdtK	SNP (3)	multidrug efflux system transporter	3
		acrB	SNP (6)	multidrug efflux system protein	4
		ybjG/mdfA	MOB (1)	undecaprenyl pyrophosphate phosphatase/multidrug efflux system protein	2
			SNP (1)		1 a
		ribC/mdtK	SNP (1)	riboflavin synthase, alpha subunit/multidrug efflux system transporter	1 c
		mdfA	SNP (1)	multidrug efflux system protein	1 b
eTΔtolC		mdtK	SNP (2)	multidrug efflux system transporter	3
		ribC/mdtK	DEL (1)	riboflavin synthase, alpha subunit/multidrug efflux system transporter	2
			SNP (1)		2
eTΔacrB		ribC/mdtK	SNP (1)	riboflavin synthase, alpha subunit/multidrug efflux system transporter	1 b
		mdtK	SNP (4)	multidrug efflux system transporter	2
		acrS/acrE	MOB (3)	acrAB operon transcriptional repressor/cytoplasmic membrane lipoprotein	4
eMRef	MTPP+	acrB	SNP (8)	multidrug efflux system protein	4
		acrA	SNP (2)	multidrug efflux system protein	1 c
		ribC/mdtK	SNP (1)	riboflavin synthase, alpha subunit/multidrug efflux system transporter	1 *,b
		mdtK	SNP (2)	multidrug efflux system transporter	3
		mdtL	SNP (1)	multidrug efflux system protein	1 *,b
		mdtP	SNP (1)	outer membrane factor of efflux pump	1 *,b
		ybjG/mdfA	SNP (1)	undecaprenyl pyrophosphate phosphatase/multidrug efflux system protein	1 b
eMΔemrE		nudF/tolC	SNP (1)	ADP-ribose pyrophosphatase/transport channel	1 c
			DEL (1)		3
		acrB	SNP (7)	multidrug efflux system protein	4
		mdtK	SNP (1)	multidrug efflux system transporter	2
		ybjG/mdfA	SNP (2)	undecaprenyl pyrophosphate phosphatase/multidrug efflux system protein	2
eMΔtolC		ybjG/mdfA	SNP (2)	undecaprenyl pyrophosphate phosphatase/multidrug efflux system protein	4
			INS (2)		4
		mdfA	SNP (2)	multidrug efflux system protein	1 a
		tisB/emrD	SNP (1)	toxic membrane persister formation peptide, LexA-regulated/multidrug efflux system protein	1 b
eMΔacrB		ybjG/mdfA	SNP (1)	undecaprenyl pyrophosphate phosphatase/multidrug efflux system protein	3
			INS (2)		2
		acrS/acrE	MOB (3)	acrAB operon transcriptional repressor/cytoplasmic membrane lipoprotein	3
			SNP (2)		4

* Mutations were reported in one hypermutator population and/or a clonal isolate derived from it. ^a Mutations were reported in the intermediate point of only one TALE replicate (considered as an adaptive mutation in the early evolutionary trajectory). ^b Mutations were reported in the end point of only one TALE replicate (considered as an adaptive mutation in the late evolutionary trajectory). ^c Fixed mutation that was reported in at least one intermediate point and the end point samples derived from one TALE replicate. (Abbreviations: SNP: single-base substitution, MOB: mobile element insertion, DLE: deletion, and INS: insertion).

and Figure 4), (2) mutations affecting the upstream regulation of MDTs (Supplementary Table S3), and (3) other non-specific mutations which were important for the overall fitness (media adaptive mutations) or regulating osmotic stress (Supplementary Data S1).

The first category highlighted nine distinct MDTs throughout the evolution experiments, including mdtK, mdfA, acrB, acrE, tolC, acrA, emrD, mdtL, and mdtP, ranked from highest to lowest number of occurrences. As visualized in (Figure 4A), TPP⁺ evolved mutants showed a diverse number of mutations that split into 47% of structural (coding) mutations and 53% of intergenic (non-coding) mutations. Further, all structural mutations were single nucleotide polymorphisms (SNPs), while intergenic mutations diversified into 63% SNPs, 13% small deletions (DEL), and 25% mobile element insertion (MOB). Likewise, intergenic mutations observed in MTPP⁺ evolved mutants were prevalent (63%), as shown in (Figure 4B), and these mutations varied between SNPs (67%), insertions or INS (17%), DEL (8%), and MOB (8%). The remaining share (37%) represented the structural mutations, and all were SNPs. A detailed enumeration of these mutations and their possible effects on MDTs will be further presented in Section 3.3.1 and Section 3.3.2. There was a major overlap (i.e., similarity) in both mutation types and the affected MDTs (either ORF or ORF intergenic region) among different strains evolved in each condition (Figure 4C,D). There was approximately 87% of mutations that commonly shared between different strains evolved in both conditions. This initially hinted at the overlapping specificity/multiplicity of the reported MDTs and explained the observed cross-cation resistance (Section 3.2).

The second category highlighted two frequently mutated transcriptional repressors acrR and marR that were targeted by mutations in their coding region. Mutations in acrR occurred frequently in eTRef, eTΔemrE, eMRef, and eMΔemrE evolved mutants, while mutations in marR occurred only in eMΔemrE evolved mutants. Mutations targeted acrR were diverse including SNPs, DELs, small insertions—INS, and mobile element insertions—MOB). Examining the impact of two SNPs (GAA→TAA and TCG→TAG) and a MOB mutation suggested a loss of acrR function due to the generation of premature stop codons or disruption of the acrR ORF by an IS2 element insertion. The impact of other mutations was assessed computationally, as previously described [42], and these mutations were predicted to either target a highly conserved region or were destabilizing amino acid substitutions. Likewise, mutations in the coding region of marR (SNPs resulted in amino acid substitution) either affected the conserved region of the protein or marR dimer interaction interface. There were two instances of eMΔemrE parallel evolutions where mutations in both marR and acrR co-occurred. It has been reported that mutation in their encoding genes, marR and acrR, can maintain the overactivity of the AcrAB-TolC pump via overexpression of the acrAB operon [43,44]. Therefore, the partial or whole inactivation of both acrR and marR reported here suggests a possible mitigation of transcriptional repression of acrAB. It is also worthy to mention that mutations in acrR and marR co-occurred with structural mutations in acrB ORF (but not in the acrB ORF intergenic region) in the same evolved mutants (i.e., eTRef, eTΔemrE, eMRef, and eMΔemrE).

The third category represented non-specific mutations that are not directly associated with multidrug transport (Supplementary Data S1). The ubiquitously occurring mutations of this category are generally important for enhancing fitness and cell survival under treatment with inhibitory chemicals and molecular regulation of stress response in E. coli [45,46,47,48,49,50]. Consequently, these mutations were not considered here due to the lack of potential relationship with multidrug transport.

There were four hypermutator samples derived from flask 59 and 67 of eMRef replicate T1. These isolates had 433 mutations as compared to the average mutations 15 ± 4 for the non-hypermutating samples with standard deviation shown between replicates. However, mutations in MDTs found in these hypermutators were considered for further discussion (Section 3.3.1 and Section 3.3.2) in case they added to the overall mutations directly associated with MDTs. This particular genotype might explain the exceptional MIC values reported for these mutants during tolerance validation experiments (Section 3.2).

3.3.1. Structural Mutations Affecting MDTs

Structural mutations in MDTs represented a sizable proportion of mutations reported in TPP⁺ and MTPP⁺ evolutions, with 47% and 37% of total mutations, respectively (Figure 4A,B). The ORF of a total of nine distinct MDTs was targeted by structural mutations, most of which were conservative missense mutations (

Table 2. Structural mutations targeted unique ORFs identified for potential multidrug transporters (MDTs). Depicted are the amino acid substitutions in MDTs reported for multiple strains evolved in both experimental conditions. Hydropathy indices are reported from [51].

MDTs	Strain	Mutations	Occurrence (n = 4)	Hydropathy Index/Polarity/Acidity Change
acrB	eTRef	D276G PD Asp → Gly	(2)	−3.5/Polar/Acidic → −0.4/Nonpolar/Neutral
		D276N PD Asp → Asn	(2)	−3.5/Polar/Acidic → −3.5/Polar/Neutral
		E273A PD Glu → Ala	(2)	−3.5/Polar/Acidic → 1.8/Nonpolar/Neutral
		N68K PD Asn → Lys	(1)	−3.5/Polar/Neutral → −3.5/Polar/Basic
	eMRef	V612D PD Val → Asp	(1)	4.2/Nonpolar/Neutral → −3.5/Polar/Acidic
		E273A PD Glu → Ala	(2)	−3.5/Polar/Acidic → 1.8/Nonpolar/Neutral
		D276G PD Asp → Gly	(2)	−3.5/Polar/Acidic → −0.4/Nonpolar/Neutral
		N274K PD Asn → Lys	(1)	−3.5/Polar/Neutral → −3.9/Polar/Basic
		V139F PD Val → Phe	(1)	4.2/Nonpolar/Neutral → −2.8/Nonpolar/Neutral
		Q125K PD Gln → Lys	(1)	−3.5/Polar/Neutral → −3.9/Polar/Basic
		S128R PD Ser → Arg	(1)	−0.8/Polar/Neutral → −4.5/Polar/Basic(strongly)
		Q89K PD Gln → Lys	(1)	−3.5/Polar/Neutral → −3.9/Polar/Basic
	eTΔemrE	D276G PD Asp → Gly	(2)	−3.5/Polar/Acidic → −0.4/Nonpolar/Neutral
		N274K PD Asn → Lys	(1)	−3.5/Polar/Neutral → −3.9/Polar/Basic
		E273A PD Glu → Ala	(1)	−3.5/Polar/Acidic → 1.8/Nonpolar/Neutral
		E130V PD Glu → Val	(1)	−3.5/Polar/Acidic → 4.2/Nonpolar/Neutral
		Q125K PD Gln → Lys	(1)	−3.5/Polar/Neutral → −3.9/Polar/Basic
		N70K PD Asn → Lys	(1)	−3.5/Polar/Neutral → −3.9/Polar/Basic
	eMΔemrE	V612D PD Val → Asp	(1)	4.2/Nonpolar/Neutral → −3.5/Polar/Acidic
		L542R TMD Leu → Arg	(1)	3.8/Nonpolar/Neutral → −4.5/Polar/Basic(strongly)
		D276G PD Asp → Gly	(1)	−3.5/Polar/Acidic → −0.4/Nonpolar/Neutral
		E273A PD Glu → Ala	(1)	−3.5/Polar/Acidic → 1.8/Nonpolar/Neutral
		V139F PD Val → Phe	(2)	4.2/Nonpolar/Neutral → −2.8/Nonpolar/Neutral
		Q89K PD Gln → Lys	(1)	−3.5/Polar/Neutral → −3.9/Polar/Basic
		V32A PD Val → Ala	(2)	4.2/Nonpolar/Neutral → 1.8/Nonpolar/Neutral
mdtK	eTRef	R148S Arg → Ser	(2)	−4.5/Polar/Basic(strongly) → −0.8/Polar/Neutral
		M1M Met → Met	(1)	1.9/Nonpolar/neutral → 1.9/Nonpolar/neutral (synonymous)
		R148G Arg → Gly	(4)	−4.5/Polar/Basic(strongly) → −0.4/Nonpolar/neutral
	eMRef	R81C Arg → Cys	(2)	−4.5/Polar/Basic(strongly) → 2.5/Polar/Neutral
		R148G Arg → Gly	(2)	−4.5/Polar/Basic(strongly) → −0.4/Nonpolar/neutral
	eTΔemrE	R81C Arg → Cys	(1)	−4.5/Polar/Basic(strongly) → 2.5/Polar/Neutral
		R81L Arg → Leu	(1)	−4.5/Polar/Basic(strongly) → 3.8/Nonpolar/Neutral
		R148G Arg → Gly	(3)	−4.5/Polar/Basic(strongly) → −0.4/Nonpolar/neutral
	eMΔemrE	R148G Arg → Gly	(2)	−4.5/Polar/Basic(strongly) → −0.4/Nonpolar/neutral
	eTΔtolC	M1M b Met → Met	(1)	1.9/Nonpolar/Neutral → 1.9/Nonpolar/Neutral (No change)
		G428A Gly → Ala	(1)	−0.4/Nonpolar/Neutral → 1.8/Nonpolar/Neutral
	eMΔacrB	W54G Trp → Gly	(1)	−0.9/Nonpolar/Neutral → −0.4/Nonpolar/Neutral
		A294V Ala → Val	(1)	1.8/Nonpolar/Neutral → 4.2/Nonpolar/Neutral
		G424S Gly → Ser	(1)	−0.4/Nonpolar/Neutral → −0.8/polar/Neutral
		G428S Gly → Ser	(1)	−0.4/Nonpolar/Neutral → −0.8/polar/Neutral
mdfA	eTΔemrE	A144V Ala → Val	(1)	1.8/Nonpolar/Neutral → 4.2/Nonpolar/Neutral
	eMΔtolC	F134C Phe → Cys	(1)	−2.8/Nonpolar/Neutral → 2.5/Polar/Neutral
		M146I Met → Ile	(1)	1.9/Nonpolar/neutral → 4.5/Nonpolar/neutral
acrA	eMRef	T177T Thr → Thr	(1)	−0.7/Polar/neutral → −0.7/Polar/neutral (synonymous)
		G6W (1) Gly→ Trp	(1)	−0.4/Nonpolar/Neutral → −0.9/Nonpolar/neutral
mdtL	eMRef	M166I Met → Ile	(1)	1.9/Nonpolar/neutral → 4.5/Nonpolar/neutral
mdtP	eMRef	Q312H Gln → His	(1)	−3.5/Polar/Neutral → −3.2/Polar/Basic (Weakly)

^PD Porter Domain of acrB. ^TMD Trans-Membrane Domain of acrB.

). These MDTs included acrB, mdtK, mdfA, acrA, mdtL, and mdtP and ranked from most frequently mutated to least frequently mutated based on their occurrence among all trials.

The RND (resistance-nodulation-division) family protein, acrB, is the inner membrane component of the acrAB/tolC tripartite, proton dependent, drug efflux pump [52]. The crystal structure of acrB was determined [53,54,55] with bound ligands rhodamine 6G, ethidium, dequalinium, and ciprofloxacin [56,57] and with the secondary bile acid, deoxycholate [58]. As shown in Figure 5, acrB is structurally divided into a funnel domain (FD), important for acrB trimerization and a trans-membrane domain (TMD) responsible for the energy transduction to facilitate drug transport. A further division is the porter domain (PD) that contains the binding pockets (access pockets (APs) and deep binding pockets (DBPs)) and mediates substrate uptake, recognition, and translocation [59]. All mutations that targeted acrB were located the PD (13 amino acid substitutions excluding replicates), except one mutation that was found in the TMD (Figure 4C). Most of the mutations that targeted the PD affect specifically the DBP of acrB. Consistent with previous studies [60,61,62,63], the specific amino acid residues E130, Q89, V139, N274, D276, and V612 were essential for interaction with several antibiotics and inhibitors. In addition, the amino acid substitution V139F, which is shared between eMRef and eMΔemrE evolved mutants, was found in isolates of E. coli BW25113 and subjected to in-lab evolution against chloramphenicol [9]. Interrogating the observed amino acid substitutions generally indicate a drastic shift in hydropathy indices (either from low to high or vice versa in different locations near DBP), a minor preference to polar amino acids, and a moderate shift toward acidic amino acids (Table 2). This trend is in line with the nature of the interaction between the known ligands and the DBP of acrB [59]. The DBP is predominantly lined by weakly hydrophobic and weakly polar residues that are predicted to create a versatile and adaptable environment with numerous so-called multifunctional sites [59,64,65]. Accordingly, the amino acid alterations reported here are suggested to provide either complementarity towards parts of the cations’ structure, or flexibility in interaction to accommodate and facilitate their efflux.

The MATE (Multidrug And Toxic Compound Extrusion) family protein mdtK is an inner membrane, proton (or sodium) dependent, drug efflux pump within the Multidrug/Oligosaccharidyl-lipid/Polysaccharide (MOP) flippase superfamily [52,66]. Structural mutations in mdtK were shared among many strains and conditions including eTRef, eMRef, eTΔemrE, eMΔemrE, eTΔtolC, and eMΔacrB evolutions (Table 2 and Figure 4). In total, six distinct amino acid substitutions were reported with prevalence given to residue R148 that was frequently mutated to glycine and uncommonly to serine. Both alterations indicate a shift in the hydropathy index from −4.5 (for arginine) to −0.4 and −0.8 for glycine and serine, respectively. Additionally, both alterations alleviated the positive charge conferred by the strongly basic arginine residue. Substitutions of arginine in other locations were reported (e.g., R81C and R81L) which showed a similar change in properties as the previous ones. Other substitutions include G428A/S which was found only in eTΔtolC and eMΔacrB mutants, respectively, and three different substitutions (i.e., W54G, A294V, and G224S) that were found only in eMΔacrB evolved mutants. It is not immediately clear how these mutations affect the transport process due to the lack of information about mdtK structure. However, a homolog called norM found in Neisseria gonorrhoeae was crystalized in complexes with different cations including TPP⁺ [67,68]. The structure of mdtK was predicted [69,70] and aligned with norm, as shown in Figure 6A. Both substitutions R148G/S (in the central cavity) and R81L (facing the cytoplasmic side) are likely located in the substrate permeation pathway. The removal of the positive charge conferred by arginine suggests an alleviation of unfavorable interaction during the export of positively charged cations. In general, none of the newly substituted amino acids are aromatic, specifically all new variants around the multidrug binding cavity. This is consistent with the nature of the multidrug binding cavity of norM, as it was found to be festooned with four negatively charged amino acids, unlike other multidrug effluxes that utilizes aromatic amino acids for multidrug recognition [67].

The MFS (Major Facilitator Superfamily) family protein mdfA is an inner membrane protein that transports several drugs in exchange for protons and other cations [71,72]. The recently reported structure of chloramphenicol (CM)-bound mdfA shows that the transmembrane helices allow alternating access to the cytoplasmic and the periplasmic sides following a rocker-switch mechanism [73,74]. The transport of substrates across these helices mainly follows a transition between outward open (O_o) and inward facing (I_f) states. Three amino acid substitutions were observed in mdfA including A144V, F134C, and M146I that occurred with equal frequency in samples derived from two evolved mutants eTΔemrE and eMΔtolC (Table 2 and Figure 6B). Interestingly, all these residues are in the mdfA transmembrane helix (TM5) whose conformation significantly differs between the two states O_o and I_f. With a special reference to residue M146, which was found to be responsible for the largest deviation in TM5 during the transport cycling [73]. Further, the hydrophobic side chain of residue M146 (TM5) was crucial for local twisting and rearrangement of the hydrophobic core of N-terminal domain of mdfA by resting against the phenolic side chain of Y127 (TM4) during drug (CM) transport. The M146I mutation reported here indicates a drastic change only in hydropathy (i.e., from 1.9 to 4.5), with roughly equal polarity and charge, and this may be delineated as an enhancement in mdfA activity based on the mechanism explained earlier. Even though CM transport by mdfA is electrogenic, while the transport of TPP⁺ is electroneutral, none of the protonation adjusting residues were mutated indicating that these mutations particularly modulate cation binding and efflux. It is worth mentioning that the mutations M146I and A144V co-existed with most mutations affecting the ORF intergenic region of mdfA in samples derived from eMΔtolC and eTΔemrE, respectively (see Figure 4C), indicating the high selection pressure due to the lack of the respective drug efflux components.

Three rarely mutated MDTs were reported: (1) acrA, the periplasmic lipoprotein component of the acrAB/TolC and acrAD/TolC multidrug efflux pumps in E. coli, (2) mdtL, an inner membrane multidrug efflux pump that belongs to the MFS, and (3) mdtP, a putative multidrug efflux outer membrane channel [29]. Despite the drug efflux activities of these transporters, their occurrence was rare and was reported in only a few samples of eMRef evolved mutants. This may either reflect their minor contributions to the overall resistance phenotype, or their eradication (wash out) due to enrichment of more efficient modulating mutations affecting other drug effluxes, as described earlier (see also Section 3.3.2). The second possibility is more plausible as these mutations were found only in the sequenced endpoints, and because the highest concentrations of MTPP⁺ were reported in the eMRef lineages (as described in Table S1). This assumption might be explained in light of a general recruitment of such MDTs to collectively alleviate high MTPP⁺ external supplementation.

3.3.2. Intergenic Mutations Affecting MDTs

The overall mutations occurred in the intergenic region ybjG/mdfA, ribC/mdtK, and acrS/acrE (see Table 1 and Figure 4) and suggest a strong selective pressure and evidence of parallel evolution at these loci. Two different mutational types were captured in the intergenic region ybjG/mdfA, which included three SNPs and one mobile element insertion (MOB) (Table 1). Two SNPs were similar (i.e., C > A), but occurred at two different locations, namely 30 and 12 bps upstream of mdfA. The first SNP occurred in samples derived from eTRef evolved mutants, while the later SNP occurred in samples derived from both eTRef and eTΔemrE. Unexpectedly, the effect of these two SNPs on the promotor region of mdfA is not clear despite the closeness of these SNPs to the proposed −10 consensus region of mdfA promotor [75]. However, the later SNP (i.e., −12) clearly affects a putative ribosome-binding site (RBS), GGCG, which is located 14 to 11 bps upstream of mdfA start codon [75]. This RBS is a suboptimal ShineDalgarno sequence [76], but the −12 SNP changed its sequence to GGAG which was found to stimulate the translation of some genes by several folds [77,78]. This is likely suggestive of a similar effect resulting in mdfA overexpression, which is consistent with the experimental context presented here. The remaining SNP (T > G) was 57 bps upstream of mdfA and found in only one sample of the evolved mutant eMRef. The effect of this SNP is unclear as it did not target any essential regions that might affect either mdfA or ybjG. The remaining MOB mutation was associated with the mobile genetic element IS30 and was reported in two parallel replicates of eTΔemrE. The IS30 insertion occurred 119 bps upstream of mdfA. A similar instance was reported when E. coli MG1655 was exposed to long-term low-shear modeled microgravity and background chloramphenicol exposure, but IS30 insertion occurred −118 bp upstream of mdfA [79]. The authors interpreted this mutation as affecting the transcription of the downstream gene, ybjG (undecaprenyl pyrophosphate phosphatase), but this interpretation was not in line with their experimental context. It has been proven experimentally that IS30 insertions were found to generate promoter sequences at the junctions of the IS elements with the target sequences [80,81,82]. There is a −35 control signal of a potential promoter sequence contained within the left-hand terminal inverted repeat of IS30, while the −10 region is contained in an internal symmetrical sequence [83]. The IS30 insertion site reported here is 14 bp upstream of cmrp2, a proposed −10 consensus of a promoter controlling the expression of mdfA [84]. This rearrangement implies that IS30 insertion would enhance mdfA expression, which is a more plausible assumption that is consistent with the experimental conditions used here. However, this requires further study.

In total, two different mutational types were manifested in the intergenic region ribC/mdtK, which included four SNPs and a small DEL (Table 1). Three SNPs were similar (i.e., G > C) and occurred in the same intergenic site, specifically 111 bps upstream of mdtK. This SNP was commonly shared between samples derived from eTΔemrE, eTΔtolC, and eMRef evolved mutants, while the remaining SNP (i.e., A > C) was 25 bps upstream of mdtK and occurred only in samples derived from eTΔacrB evolved mutants. The additional small DEL was 15 bps in length and occurred within the intergenic region 130 and 71 bps upstream of ribC and mdtK, respectively. Interestingly, the prevalent SNP reported here (i.e., G > C) and the small DEL co-occurred in four samples (out of six) derived from eTΔtolC evolved mutants. Such allelic heterogeneity may infer a high selection pressure at these loci and the observed resistance of these mutants might be enhanced when both mutations co-exist. The effect of all these mutations was examined based on previous reports. There are two computationally identified promoters for mdtK, mdtkp9, and mdtkp10 [85]. The 3 prevalent SNPs reported here are all located only 4 bases downstream of the −35 signal of mdtkp9 and the 15 bp DEL is located 3 bp downstream of the −10 signal of the same promoter. Due to the lack of supportive experimental evidence of the precise identities of both mdtkp9 and mdtkp10, it was difficult to infer the possible effect of the reported mutations on either flanking genes. However, intergenic regions are home to many functional elements required for resistance determinants in vivo and are maintained by purifying selection in many bacterial species [86,87,88]. Additionally, increased expression of mdtK in an E. coli strain lacking acrAB resulted in a 32-fold increase in resistance to TPP⁺ [89] and other antibiotics and dyes [90,91,92] which are similar and relevant experimental conditions. Thus, the reported mutations are expected to affect the expression of the multidrug effluxer mdtK rather than riboflavin synthase ribC.

The Intergenic region acrS/acrE was targeted by two different mutation types (i.e., MOB and SNP) which occurred only in eTΔacrB and eMΔacrB evolved mutants. The MOB mutations were more prevalent and associated with three different mobile genetic elements IS1, IS2, and IS5. Specifically, the IS1 insertion occurred 138 bps upstream of the acrE initiation codon and found in samples derived from eTΔacrB mutants only. The IS2 insertion occurred 86 or 90 bps upstream of the acrE initiation codon, accompanied by doubling of the pentamer GTAGG, and this mutation reported in both eTΔacrB and eMΔacrB evolved mutants. In a particular instance, an IS2 insertion occurred 10 bps upstream of acrF, a multidrug efflux pump located 11 bps upstream of acrE and belonged to the same operon (acrEF). This mutation occurred in all intermediate and endpoint samples (i.e., fixed mutation) of only one TALE replicate of eTΔacrB evolution. The IS5 insertion occurred twice: 163 and 94 bps upstream of the initiation codon of acrE. The latter mutation was found in samples derived from eMΔacrB mutants only. In addition, there were two different SNPs, T > G and G > A that occurred 92 and 86 bps upstream of the acrE start codon. The later SNP (i.e., G > A) co-occurred with the IS5 insertion mutation in five samples derived from three parallel TALE replicates of eMΔacrB, while both SNPs co-occurred with the IS5 insertion mutation in two samples derived from two parallel TALEs of the same strain eMΔacrB. Both SNPs are likely to affect the binding of the transcriptional binding regulator Nac, that might inhibit the transcription of acrE. The effluxer acrE shares 65% amino acid identity with the membrane fusion protein acrA of the acrAB efflux pump complex, while the adjacent gene acrF shares 77% amino acid sequence identity with acrB of the acrAB system [93,94]. Both acrEF are not expressed at significant levels in wild-type E. coli K12 [95,96], although there is strong evidence that acrEF is expressed at a higher level upon integration of IS1 and IS2 into the upstream region of the operon [97]. In the later publication, the authors found IS1 and IS2 insertions at similar (−90) and closer (−187) sites, both relative to acrE, which is consistent with the results reported here. The presence of potential promoters and ribosomal binding sites within the IS1 and IS2 elements was proven experimentally as the main reason for increased expression of acrEF [97,98]. In a similar instance, in vivo development of tigecycline resistance emerged in Klebsiella pneumoniae clinical isolates upon integration of the IS5 upstream of a multidrug effluxer, kpgABC [99]. Moreover, an episomal expression of acrEF in strains lacking acrB resulted in an increased resistance to a wide range of antibiotics [92,100,101]. The mutational events occurred here, and the structural similarity of both acrEF with acrAB and the sole occurrence of these mutations in acrB null mutants, hint at the conservation of the acrEF operon as a backup strategy that compensate the loss of acrAB.

Two unshared mutations in the intergenic regions nudF/tolC and tisB/emrD were found in eMΔemrE and eMΔtolC TALE experiments, respectively (Figure 4C,D). First, the mutations found in the intergenic region nudF/tolC included one prevalent DEL (i.e., 1 bp deletion), and a less frequent (but fixed) SNP (G > A) 180 and 141 bps upstream of tolC, respectively. The effect of the DEL mutation is not clear as it did not target any known essential controlling elements for the transcription of either tolC or nudF. However, the G > A SNP altered the first nucleotide of the DNA-binding site for the transcriptional dual regulator PhoP that activated tolC transcription [84]. The increased demand of tolC as a channelling protein under stress conditions may justify a possible increase in tolC expression due to this mutation. Despite the activation of acrB, mdtK, and mdfA in eMΔemrE evolutions, acrB is the only tolC-dependant effluxer in this list and its activation was based on structural mutations in the distal binding pocket and a possible overexpression due to inactivation of acrR. In addition, the co-occurrence of such an SNP with any of the reported acrB variants was rare (i.e., found in one sample out of seven). This observation would indicate that the enhanced activity of acrB is not concurrently associated with an increased expression of tolC, or a tolC–independent efflux activity of acrB as previously hinted [102]. Second, there was only one rare SNP (C > A), 58 bps upstream of emrD, in a well-known inner membrane multidrug effluxer [71,103,104]. However, this SNP is 18 bps downstream of the absolute position of a putative transcription start site [105] and it does not seem to affect any known essential transcription controlling elements of emrD.

3.4. Estimating Changes in Membrane Transport in Terms of Mutational Convergence

The changes in membrane transport based on the acquired converged mutations were evaluated using a flow cytometry-based phenotyping assay, as previously described [106,107]. Briefly, a set of 46 fluorophores known to accumulate intracellularly in E. coli were chosen to estimate the effect of the converged mutations associated with MDTs in both TPP⁺ and MTPP⁺-evolved mutants. To limit the scale of the experiment, 16 endpoint clones derived from eTRef and eMRef evolved lineages were used. These clones were recognized to possess most of the converged mutations associated with MDTs and their regulation (see Section 2.3). Fluorophore signal ratios were used as a metric to contrast the intracellular accumulation of fluorophores in the evolved mutants versus their ancestral strain ‘Ref’. Fluorescence signal ratios below zero indicate lower intracellular accumulation of fluorophores, and vice versa.

The observed fluorophore signal ratios of most fluorophores suggest a differential accumulation of several fluorophores among the evolved mutants. The fluorophore accumulation profiles of the tested clones against the full list of fluorophores are provided in Supplementary Figure S4 (separate file). Among those profiles, DiSC3(5), SYBR Green I, Acridine orange, and H2FDA were the most contrastive among eTRef and eMRef evolved mutants (Figure 7). The fluorescence signals of DiSC3(5) indicate that most of the eTRef evolved mutants showed approximately ten-fold less accumulation of DiSC3(5) than the ancestral strain ‘Ref’. This trend was also observed with SYBR green I. Both clones eTRef_T3F63I1/2 represented an exception by showing higher accumulation of both fluorophores relative to ‘Ref’. On the other hand, most of the eMRef evolved mutants showed a higher accumulation of both fluorophores relative to ‘Ref’. However, both clones derived from the hypermutator strain (eMRef_T1F76I1/2) showed a higher or a similar accumulation of DiSC3(5) and SYBR green I, respectively, relative to ‘Ref’. The intracellular levels of both H2FDA and acridine orange observed in most of clones derived from eTRef and eMRef evolved mutants were consistently below those of ‘Ref’, despite the presence of a few exceptions that showed higher accumulation of these fluorophores (Figure 7C,D). Given the canonical functions of MDTs and their promiscuity [6], those fluorophores that were differentially accumulated intracellularly among different mutants could be considered as potential ligands to estimate the changes in membrane transport of the evolved mutants.

Converged mutations that occur in parallel evolution experiments provide evidence of a common adaptive trajectory between microbes exposed to the same conditions [37]. In addition, convergence may vary by mutational type [108]. Converged mutations associated with MDTs and their regulations among eTRef and eMRef evolved lineages are summarized in (Figure 8). Overall, the convergence is much higher on the level of genes than on mutational types or specific sites. In both eTRef and eTRef evolved mutants, mutations affecting mdtK, acrB, and acrR occurred most frequently among all isolates, but in different associations with other mutational types or genes. Specifically, the acrR(INS) mutation occurred once with mdtK(R148S) and once with mdtK(R148G) in eTRef_T1F63I1 and eTRef_T1F63I2, respectively. These clones showed a notebly low fluorophore accumulation compared to ‘Ref’. However, the co-occurrence of mutations in acrR(DEL), mdtK(R148G), and acrB(D276N) was correlated with the lowest values of fluorescence signals (as observed explecitly in eTRef_T2F45I1/2). A similar combination of mutations in acrR(INS), mdtK(R148G), and acrB(E273A) occurred frequently among mutants derived from both eTRef and eMRef that showed low fluorescence signals, but with some exceptions that showed high fluorescence signals relative to ‘Ref’. Evolved mutants derived from eMRef lineages generally showed contrastive fluorescence signals. These mutants harbor mutations that occurred less frequently and most of them were reported in clones derived from the hypermutator lineage A1 (i.e., eMRef_T1F76I1/2). Overall, these results mirrored the high frequency of adaptive convergence in the eTRef mutants and hinted at the potential of the mdtK(R148G) mutation in combination with acrR(DEL) and acrB(D276N) as an effective association that provided broader transport versatility. This is also in line with the acquired cross-cation resistance observed among eTRef evolved isolates (Section 3.2). Despite the emergence of other associations that showed similar or opposite outcomes, their broader effects or interactions are yet to be explored, possibly in terms of epistasis [109].

4. Conclusions

Functional redundancy and promiscuity of MDTs represent major challenges towards their identification and modulation in vivo. In this work, a multistep TALE approach was introduced to address these challenges. Briefly, the wild-type E. coli K-12-MG1655 and its cognate knockout individual mutants ΔemrE, ΔtolC, and ΔacrB under separate incremental supplementation of two lipophilic cations, TPP⁺ and MTPP⁺. During TALE experiments, the concentrations of both cations were tuned according to the actual rate of evolutionary adaptation. In that way, the evolved strains acquired a high level of cation resistance relative to the ancestral strains, allowing the selection of apparent as well as redundant MDTs and potential mutational mechanisms that enhance their actions.

Overall, three main outcomes of this study are outlined. First, the TALE approach effectively identified mutations which could be linked to nine MDTs. Specifically, three MDTs including acrB, mdtK, and mdfA were frequently mutated and commonly shared among all lineages evolved in both cations. In addition to these three MDTs, acrE was frequently mutated only in ΔacrB evolved mutants. The other MDTs including emrD, tolC, acrA, mdtL, and mdtP were less frequently mutated and explicitly found in MTPP⁺-evolved strains. This further emphasizes the flexibility of the E. coli multidrug effluxome that provides a very robust backup response under higher drug doses. However, the functional role of all these identified MDTs is consistent with previous studies (see Section 2.3), others such as emrD, mdtL, and mdtP are poorly characterized. Nevertheless, the experimental context used here can provide a guiding point to further evaluate their specific contribution to multidrug resistance. Second, diverse mutational mechanisms appear to modulate the activity of MDTs, more than 85% of them were shared among all strains evolved in both conditions (Figure 4). This highlights the overlapping specificity of these MDTs and confirms the observed cross-cation resistance (Figure 2 and Figure 3). This set of mutations serves as a promising pool of edits to both modulate MDTs and for use in identifying adaptive mutations in pathogenic strains. Third, the recognized structural mutations in the proximal binding domain of acrB and the permeation pathways of both mdtK and mdfA are consistent with previous investigations (see Section 3.3.2). Such insightful molecular details of multidrug recognition and transport can enhance our understanding of drug-protein interactions in vivo. Thus, the recognized mutational changes can reliably pair with other approaches such as molecular dynamic simulations to provide guiding principles for identifying drug permeation pathways, and consequently aid in the rational design of MDTs inhibitors.