Recombinant Escherichia coli Quaternary ammonium compound-resistance protein sugE (sugE)

What is the SugE protein and what is its function in E. coli?

SugE (Suppressor of groEL mutation) is a member of the small multidrug resistance (SMR) family in Escherichia coli. It was first identified as a suppressor of groEL mutations, mimicking the effect of groE overexpression when present on a multicopy plasmid . While initially characterized through its interaction with the groEL chaperone system, subsequent research has demonstrated that SugE functions as a drug efflux pump that confers resistance to a specific subset of quaternary ammonium compounds (QACs) .

The protein represents one of several homooligomeric SMR family members in E. coli that contribute to antimicrobial resistance phenotypes. Unlike some multidrug resistance proteins with broad substrate ranges, SugE demonstrates highly selective resistance capabilities, specifically targeting certain toxic quaternary ammonium compounds while showing no apparent activity against structurally related QACs or cationic dyes .

How does the chromosomal sugE gene differ from the plasmid-encoded sugE(p)?

The chromosomal sugE gene and the plasmid-encoded sugE(p) exhibit distinctive characteristics in terms of distribution, sequence conservation, and potentially function:

• Genetic Context: Chromosomal sugE is located in the 94-minute region of the E. coli chromosome , while sugE(p) is predominantly found on mobile genetic elements, particularly plasmids in Enterobacterales .

• Sequence Conservation: sugE(p) demonstrates remarkable sequence conservation across different bacterial species, with only 1.2% of surveyed sequences showing synonymous nucleotide changes that don't alter amino acid translation . This strong conservation suggests selective pressure maintaining the functional integrity of the protein.

• Species Distribution: While chromosomal sugE is primarily limited to its host species, sugE(p) has been identified across diverse bacterial families. Although originally characterized in Vibrionales and Aeromonadales, sugE(p) predominates within Enterobacterales plasmids, particularly those isolated from E. coli and Salmonella enterica .

• Associated Mobile Elements: sugE(p) is frequently part of a conserved four-gene cluster that includes genes for Tn5 transposase (tnpA), β-lactamase CMY-2 (blaCMY-2), and an outer membrane lipoprotein (blc) , facilitating its horizontal transfer between bacterial species.

What experimental evidence demonstrates that SugE functions as a drug efflux pump?

The functional characterization of SugE as a drug efflux pump has been established through systematic expression studies and resistance phenotype analysis:

• Cloning and Expression Systems: Researchers cloned the E. coli sugE gene into expression vectors (pBAD24 and pCR2.1) and demonstrated that its overexpression in various E. coli strains confers resistance to a specific subset of quaternary ammonium compounds .

• Resistance Phenotype: High-level expression of sugE leads to measurable resistance against toxic quaternary ammonium compounds, confirming its functional role in antimicrobial resistance .

• Substrate Specificity Assessment: Through systematic testing of various potential substrates, including structurally related quaternary ammonium compounds and cationic dyes, researchers determined that SugE has a highly specific substrate profile, functioning against only a narrow range of QACs .

• Biofilm vs. Planktonic Growth: For sugE(p) and the related gdx gene, significant increases in QAC resistance were observed specifically when transformants were grown as biofilms, suggesting context-dependent functionality .

This empirical evidence collectively confirms that SugE functions as a drug efflux pump with highly specific substrate preferences, answering "the long-standing question as to whether or not the product of the sugE gene, like several of its characterized distant homologues, is capable of functioning as a drug efflux pump" .

What mechanisms explain SugE's specificity for certain quaternary ammonium compounds but not others?

The highly selective resistance profile of SugE toward specific quaternary ammonium compounds involves several molecular mechanisms:

• Structural Determinants: The protein's transmembrane domains likely contain specific binding sites that accommodate certain QAC molecular structures while excluding others. The quaternary ammonium moiety provides a positive charge that interacts with negatively charged amino acid residues within the protein's transport channel .

• Recognition Elements: SugE appears to recognize specific structural elements in quaternary ammonium compounds beyond just the charged group. This selectivity suggests the presence of additional binding pockets or recognition sites within the protein that interact with particular chemical groups on the QAC molecules .

• Efflux Mechanism: As part of the SMR family, SugE likely utilizes proton motive force to drive the export of toxic compounds. The specificity of this mechanism may depend on precise coupling between proton translocation and substrate binding, which only occurs efficiently with certain QAC structures .

• Oligomerization State: Evidence suggests that SMR family members function as homooligomeric complexes. The quaternary structure of SugE may create a specific transport channel geometry that accommodates only certain QAC configurations .

This narrow substrate specificity distinguishes SugE from broader-spectrum multidrug resistance transporters and suggests it evolved to address specific environmental challenges rather than providing general protection against diverse antimicrobials.

How do experimental designs for studying SugE function differ between planktonic and biofilm growth conditions?

Studying SugE function requires distinct experimental approaches for planktonic versus biofilm growth conditions, as significant differences in resistance phenotypes have been observed between these growth modes:

Planktonic Growth Experimental Design:

• Culture Preparation: Standard liquid cultures in nutrient-rich media with appropriate antibiotic selection for plasmid maintenance.

• Expression Induction: Controlled expression using inducible promoters (e.g., BAD promoter with arabinose induction in pBAD24 vector systems) .

• Minimum Inhibitory Concentration (MIC) Determination: Serial dilutions of QACs in liquid media with standardized cell densities to determine the concentration at which growth is inhibited.

• Growth Kinetics: Monitoring growth rates in the presence of various QAC concentrations using spectrophotometric measurements.

Biofilm Growth Experimental Design:

• Biofilm Formation: Static cultures in appropriate vessels (microtiter plates, flow cells, or specialized biofilm reactors) allowing attachment and biofilm development over 24-72 hours.

• Expression Control: Careful regulation of inducer concentrations within the biofilm, potentially requiring different concentrations than planktonic cultures due to diffusion limitations.

• Biofilm Quantification: Crystal violet staining and biomass quantification to assess biofilm development under various QAC concentrations.

• Viability Assessment: Fluorescent viability staining (e.g., LIVE/DEAD staining) and confocal microscopy to visualize spatial patterns of resistance within the biofilm structure.

• Biofilm Resistance Testing: Modified susceptibility testing methods such as Minimum Biofilm Eradication Concentration (MBEC) assays rather than standard MIC tests.

Research has shown that gdx and sugE(p) transformants specifically demonstrate significant increases in QAC resistance when grown as biofilms, suggesting that the protein's function may be enhanced in biofilm settings . This finding highlights the importance of testing resistance phenotypes in both growth modes to fully characterize SugE functionality.

What sequential experimental design approach would optimize the characterization of SugE substrate specificity?

A comprehensive Sequential Design of Experiments (SDOE) approach would significantly enhance the characterization of SugE substrate specificity:

Initial Exploratory Phase:

• Candidate Compound Selection: Establish a diverse library of quaternary ammonium compounds with systematic structural variations in:

  • Alkyl chain length

  • Head group structure

  • Side chain functionalization

  • Degree of symmetry in the molecule

• Space-Filling Design: Implement an initial space-filling design to broadly explore the chemical space of potential substrates, selecting representative compounds that maximize coverage of structural diversity .

• Baseline Resistance Testing: Determine resistance profiles against these candidates using standardized methods in both planktonic and biofilm growth conditions.

Adaptive Learning Phase:

• Model Development: Based on initial results, develop a structure-activity relationship model that correlates molecular features with observed resistance levels .

• Model-Guided Selection: Use the predictive model to identify the next batch of compounds for testing, focusing on:

  • Boundary cases that define the limits of specificity

  • Structural variations that maximize information gain

  • Compounds with high uncertainty in the prediction model

• Iterative Testing: Run successive batches of experiments, updating the model after each round to refine understanding of specificity determinants .

Final Validation Phase:

• Structured Confirmation: Test precise structural analogs that systematically vary single molecular features to confirm specific structural requirements.

• Cross-Validation: Test predictions on novel compounds not included in model development.

• Mechanistic Investigation: Design site-directed mutagenesis experiments targeting residues predicted to be involved in substrate recognition based on the model.

This SDOE approach leverages principles of "putting experimental runs where they are of maximum value" and "the interdependence of the runs to estimate model parameters" , providing a robust methodology for defining SugE substrate specificity with minimal experimental runs.

What cloning strategies are most effective for expressing functional recombinant SugE protein?

Successful expression of functional recombinant SugE requires careful consideration of cloning strategies to maintain proper membrane insertion and folding of this small hydrophobic protein:

Vector Selection:

• Expression Control: Vectors with tunable promoters like pBAD24 (arabinose-inducible) allow precise control of expression levels, which is critical since overly high expression of membrane proteins can be toxic .

• Copy Number Consideration: Both high-copy (pCR2.1) and low-copy (pMS119EH) vectors have been successfully used for sugE expression, with different applications:

  • High-copy vectors for phenotypic screening and overexpression studies

  • Low-copy vectors for physiological expression levels and reduced cellular stress

Cloning Approach:

• PCR Amplification Strategy: For optimal results, design primers that:

  • Include 5-10 base flanking regions before restriction sites

  • Maintain the native Shine-Dalgarno sequence

  • Ensure proper reading frame

  Example primers successfully used for sugE cloning:

  • For pBAD24: 5'-CAGAATTCGATATGTCCTGGATTATCTTAG-3' (sense) and 5'-CACTGCGCCTGGTAGTTAGTGAGTGC-3' (antisense)

  • For pCR2.1: 5'-CAGCGATAGTCACAAAGGTAATAG-3' (sense) and 5'-CACTGCAGCCTGGTAGTTAGTGAG-3' (antisense)

• Restriction Site Selection: Choose restriction sites absent from the gene sequence but compatible with the vector's multiple cloning site.

Host Strain Considerations:

• E. coli Strains: Multiple strains have been successfully used, including:

  • BW25113: Standard laboratory strain for general expression

  • KAM32: Specialized strain with mutations in major multidrug efflux systems, providing a clean background for resistance phenotype studies

• Expression Conditions: Optimal expression typically involves:

  • Induction during early-mid log phase

  • Moderate inducer concentrations to prevent toxicity

  • Growth at 30°C rather than 37°C to allow proper membrane protein folding

These strategies have been demonstrated to produce functional SugE protein capable of conferring resistance to specific quaternary ammonium compounds, confirming the integrity of the expressed transporter .

How can researchers effectively compare the resistance profiles of chromosomal sugE versus plasmid-encoded sugE(p)?

Comprehensive comparison of chromosomal sugE and plasmid-encoded sugE(p) resistance profiles requires systematic methodological approaches to ensure valid comparisons:

Standardized Expression System:

• Vector Normalization: Clone both genes into identical expression vectors with the same promoter, ribosome binding site, and regulatory elements to ensure comparable expression levels .

• Copy Number Control: Use low-copy-number vectors like pMS119EH to better approximate physiological expression conditions and reduce artifacts from extreme overexpression .

• Induction Calibration: Titrate inducer concentrations to achieve equivalent protein expression levels, confirmed by Western blot analysis.

Comprehensive Resistance Testing:

• Growth Mode Comparison: Test resistance in both planktonic and biofilm growth conditions, as significant differences have been observed particularly for sugE(p), which shows enhanced resistance specifically in biofilms .

• Substrate Range Assessment: Test a wide panel of quaternary ammonium compounds with systematic structural variations to fully map specificity differences.

• Quantitative Measures: Employ multiple complementary assays:

  Method	Planktonic Growth	Biofilm Growth
  MIC Determination	Serial dilutions in broth	MBEC assay
  Growth Kinetics	Growth curves at sub-MIC concentrations	Biofilm formation rate
  Viability Assessment	CFU counting after exposure	LIVE/DEAD staining and microscopy
  Transport Assays	Fluorescent substrate accumulation	Penetration through biofilm layers

• Environmental Condition Testing: Evaluate resistance under varying conditions relevant to natural habitats:

  • pH variations (5.5-8.0)

  • Temperature ranges (25°C-42°C)

  • Nutrient limitation stress

  • Competition with other microorganisms

Control Experiments:

• Empty Vector Control: Include the expression vector without insert as a negative control.

• Known Efflux Pump Control: Include a well-characterized efflux pump (e.g., EmrE) as a positive control and benchmark .

• Gene Deletion Background: When possible, conduct experiments in strains with the chromosomal sugE deleted to eliminate background activity.

This methodological framework enables rigorous comparison of the functional differences between chromosomal sugE and plasmid-encoded sugE(p), revealing insights into their evolutionary adaptations and specific ecological roles .

What analytical methods can determine the structural basis of SugE substrate specificity?

Elucidating the structural basis of SugE substrate specificity requires integrating multiple analytical approaches:

Structural Characterization Techniques:

• Protein Crystallography: Though challenging with membrane proteins, crystallization of SugE in complex with substrate QACs would provide atomic-level insights into binding interactions. Approaches include:

  • Lipidic cubic phase crystallization

  • Detergent screening for optimal solubilization

  • Co-crystallization with antibody fragments to enhance crystal contacts

• Cryo-Electron Microscopy: Single-particle cryo-EM analysis of purified SugE in nanodiscs or detergent micelles, potentially capturing different conformational states during the transport cycle.

• NMR Spectroscopy: Solution NMR using isotopically labeled protein to investigate:

  • Substrate binding sites through chemical shift perturbations

  • Protein dynamics during substrate binding and transport

  • Conformational changes associated with transport activity

Functional Analysis Approaches:

• Site-Directed Mutagenesis: Systematic mutation of predicted substrate-interacting residues based on:

  • Sequence conservation analysis across SMR family members

  • Computational docking predictions

  • Homology modeling insights

• Chimeric Protein Construction: Creation of chimeric proteins between SugE and related transporters with different specificity profiles to map domains responsible for substrate recognition.

• Accessibility Scanning: Substituted cysteine accessibility method (SCAM) to map residues lining the transport pathway and their interaction with substrates.

Computational Methods:

• Molecular Docking: In silico docking of QAC substrates to predict binding modes and key interaction residues.

• Molecular Dynamics Simulations: Simulating SugE embedded in a lipid bilayer with various substrates to understand:

  • Dynamic interactions during binding and transport

  • Conformational changes associated with substrate recognition

  • Energetics of substrate binding and release

• Quantitative Structure-Activity Relationship (QSAR): Correlating molecular features of QACs with experimental transport or resistance data to identify critical structural determinants of substrate recognition.

Data Integration Framework:

Integrating these complementary approaches would provide a comprehensive understanding of how SugE achieves its remarkable specificity for certain quaternary ammonium compounds while excluding structurally related molecules.

What ecological and evolutionary factors drive the conservation of sugE(p) on bacterial plasmids?

The remarkable conservation of sugE(p) on bacterial plasmids across diverse species suggests strong selective pressures maintaining this gene:

Environmental Selection Pressures:

• QAC Exposure in Food Production: The frequent isolation (78%) of sugE(p)-containing plasmids from bacteria in contaminated foods and retail animal sources, particularly poultry, suggests selection by QAC disinfectants commonly used in food production environments .

• Guanidinium Compound Exposure: The presence of guanidine II riboswitches similar to those upstream of E. coli gdx in plasmids carrying sugE(p) suggests selection related to guanidinium (Gdm+) exposure in certain environments .

• Niche Adaptation: The conservation of sugE(p) may reflect adaptation to specific ecological niches where exposure to particular QACs or guanidinium compounds is common.

Genomic Context Advantages:

• Co-Selection with β-lactamase Genes: The frequent presence (93%) of sugE(p) within a conserved gene cluster containing the β-lactamase gene blaCMY-2 suggests co-selection with antibiotic resistance determinants .

• Mobile Genetic Element Association: The consistent association with transposase genes (tnpA) facilitates horizontal gene transfer, allowing rapid dissemination when selective pressure is applied .

• Functional Synergy: The gene cluster containing sugE(p) often includes an outer membrane lipoprotein (blc), potentially creating functional synergy in resistance mechanisms .

Evolutionary Considerations:

• Synonymous Mutation Pattern: The observation that only 1.2% of surveyed sugE(p) sequences contained synonymous nucleotide changes (which don't alter amino acid translation) suggests strong selection maintaining protein function rather than just gene presence .

• Plasmid Diversity: The finding that sugE(p)-carrying plasmids "were not identical in sequence, had different incompatibility groups, and possessed different antimicrobial resistance genes" indicates that sugE(p) has been maintained across diverse plasmid backgrounds rather than simply being carried along with a successful plasmid lineage .

• Cross-Species Distribution: The presence of identical sugE(p) sequences across diverse bacterial families (Enterobacterales, Vibrionales, Aeromonadales) demonstrates successful horizontal gene transfer and selection in different bacterial hosts .

These observations collectively suggest that sugE(p) provides a specific adaptive advantage related to QAC and guanidinium resistance in environments where these compounds are frequently encountered, particularly food production settings, driving its conservation and dissemination on bacterial plasmids.

How might the sequential design of experiments approach optimize studies of SugE resistance in biofilm versus planktonic states?

The Sequential Design of Experiments (SDOE) methodology provides a powerful framework for systematically investigating the differential resistance conferred by SugE in biofilm versus planktonic growth states:

Experimental Design Framework:

• Initial Space-Filling Design Phase:

  • Identify key input factors: QAC structural variations, concentration ranges, growth conditions, expression levels

  • Define the experimental constraints: biofilm growth limitations, detection thresholds, equipment availability

  • Construct a space-filling design to broadly sample the experimental space with minimal runs

  • Include both biofilm and planktonic conditions in parallel for direct comparison

• Response Variable Selection:

  • Primary measures: Survival rates, MIC/MBEC values, growth inhibition

  • Secondary measures: Gene expression levels, protein localization, membrane integrity

  • Tertiary measures: Metabolic activity, stress response markers, biofilm structure

• Adaptive Learning Implementation:

  • Update models after each experimental batch

  • Select new experimental points that maximize information gain about differences between growth modes

  • Focus subsequent experiments on regions showing divergent responses

Specific SDOE Application Strategy:

Utility Function Optimization:

• Multi-Objective Design Criteria:

  • Maximize information about differential resistance mechanisms

  • Reduce uncertainty in predictive models for both growth states

  • Identify boundary conditions where growth state impacts resistance

  • Optimize experimental efficiency through strategic run selection

• Experimental Run Selection Strategy:

  • Select runs that target the highest uncertainty regions in comparative models

  • Prioritize experimental conditions near transition points between resistance and susceptibility

  • Focus on QAC structural variants showing the greatest differential effects between growth states

This SDOE approach "allows for adaptive learning based on incoming results as the experiment is being run" , making it ideally suited for investigating the complex, state-dependent resistance mechanisms of SugE. By systematically exploring the experimental space and strategically selecting new conditions based on ongoing results, researchers can efficiently characterize the biofilm-specific resistance enhancement observed for sugE(p) while minimizing experimental resources required.

What experimental approaches could identify potential synergies between SugE and other resistance mechanisms?

Investigating synergistic interactions between SugE and other resistance mechanisms requires integrated experimental approaches:

Genetic Interaction Studies:

• Combinatorial Expression Systems: Develop dual or multiple expression systems to simultaneously control expression levels of sugE and other resistance genes. This allows systematic exploration of phenotypic consequences across varying expression ratios.

• CRISPR-Based Screening: Implement CRISPR interference (CRISPRi) or activation (CRISPRa) screens to systematically modulate expression of potential interacting genes in a sugE-overexpressing background.

• Synthetic Genetic Array Analysis: Adapt yeast synthetic genetic array methodology for bacterial systems to systematically create double mutants combining sugE modifications with genome-wide mutations.

Functional Synergy Assessment:

• Checkerboard Assay Modifications: Adapt traditional antimicrobial synergy testing methods to evaluate interactions between SugE substrates and other antimicrobials in:

  • Wild-type strains

  • sugE-overexpressing strains

  • sugE-deletion strains

• Time-Kill Kinetics: Perform time-course experiments measuring bacterial killing rates when exposed to combinations of SugE substrates and other antimicrobials.

• Biofilm-Specific Synergy Testing: Develop specialized methods to assess synergistic interactions specifically in biofilm growth states, where sugE(p) shows enhanced activity .

Molecular Mechanism Investigation:

• Membrane Proteomics: Implement quantitative proteomics to track changes in membrane protein composition when sugE is overexpressed, identifying potential functional partners.

• Protein-Protein Interaction Studies: Use techniques such as bacterial two-hybrid systems, co-immunoprecipitation, or crosslinking mass spectrometry to identify direct interaction partners of SugE.

• Metabolic Flux Analysis: Apply metabolic flux analysis to determine how SugE expression alters cellular metabolic networks, potentially revealing indirect interactions with other resistance mechanisms.

Resistance Development Dynamics:

• Experimental Evolution: Conduct long-term experimental evolution studies under selective pressure from multiple antimicrobials to observe emergence of synergistic resistance mechanisms involving sugE.

• Single-Cell Analysis: Implement microfluidic systems and time-lapse microscopy to track resistance development at the single-cell level, revealing temporal dynamics of synergistic interactions.

• Mathematical Modeling: Develop predictive models of resistance evolution incorporating multiple mechanisms to identify conditions favoring synergistic development.

These approaches would reveal not only existing synergies between SugE and other resistance mechanisms but also predict potential evolutionary trajectories toward new synergistic combinations, informing strategies to counter resistance development.

How might structural studies of SugE inform the design of inhibitors to overcome quaternary ammonium compound resistance?

Structural elucidation of SugE provides a foundation for rational inhibitor design to counteract quaternary ammonium compound resistance:

Critical Structural Information Requirements:

• Binding Site Characterization: Determination of the precise residues involved in QAC recognition and binding through:

  • High-resolution crystal structures with bound substrates

  • Site-directed mutagenesis of putative binding residues

  • Computational docking validated by experimental data

• Transport Pathway Mapping: Identification of the complete translocation pathway from initial binding to substrate release, capturing multiple conformational states during the transport cycle.

• Oligomerization Interface: Characterization of the interfaces between monomers in the functional oligomeric complex, which may offer additional targets for disruption.

Inhibitor Design Strategies:

• Competitive Inhibitors: Design molecules that:

  • Maintain the quaternary ammonium moiety for recognition

  • Incorporate modifications that increase binding affinity

  • Include structural elements that prevent completion of the transport cycle

• Allosteric Inhibitors: Develop compounds targeting sites distinct from the substrate binding pocket that:

  • Lock the transporter in an inactive conformation

  • Disrupt conformational changes necessary for transport

  • Interfere with oligomerization or membrane insertion

• Dual-Action Inhibitors: Create bifunctional molecules that:

  • Simultaneously bind SugE and another target (e.g., membrane)

  • Combine a SugE inhibitor with an antimicrobial warhead

  • Link SugE binding moieties to molecules that recruit cellular degradation machinery

Structure-Based Design Pipeline:

Advanced Inhibitor Design Considerations:

• Resistance Evolution Prevention: Design inhibitors with:

  • Multiple binding modes to raise the barrier to resistance

  • Targeting of highly conserved regions essential for function

  • Simultaneous inhibition of multiple resistance determinants

• Biofilm Penetration Enhancement: Develop inhibitors with:

  • Physicochemical properties promoting biofilm penetration

  • Dual activity against both SugE and biofilm matrix components

  • Activation in biofilm-specific environments

• Broad-Spectrum Activity: Create inhibitors effective against:

  • Both chromosomal sugE and plasmid-encoded sugE(p)

  • Related SMR family transporters with similar functions

  • Multiple resistance mechanisms through synergistic design

This structural biology-guided approach to inhibitor design represents a promising strategy to overcome quaternary ammonium compound resistance mediated by SugE and related transporters.