Recombinant Acetohalobium arabaticum Cobalt transport protein CbiM (cbiM)

What is Acetohalobium arabaticum and what ecological niche does it occupy?

Acetohalobium arabaticum strain Z-7288T is a halophilic bacterium belonging to the family Halobacteroidaceae within the order Halanaerobiales of the phylum Firmicutes. It participates with other halophilic bacteria and methanogens in the C1-trophic chain within hypersaline environments . The cells are characterized as Gram-negative, bent rods that are motile by one to two subterminal flagella, though these flagella are not always visible in microscopic studies . The organisms can exist as single cells, in pairs, or forming short chains. A. arabaticum plays a significant role in carbon cycling within extreme saline environments, particularly through its interactions with methanogens in anaerobic settings.

What is the CbiM protein and what is its primary function in Acetohalobium arabaticum?

CbiM (cbiM) in Acetohalobium arabaticum functions as a cobalt transport protein and is classified as an Energy-coupling factor (ECF) transporter substrate-capture protein . This protein serves as the membrane substrate-binding component (equivalent to EcfS in group-II ECF transporters) within the CbiMNQO transporter complex . CbiM plays a crucial role in cobalt acquisition, which is essential for various cellular processes including vitamin B12 (cobalamin) biosynthesis. The protein is positioned horizontally along the lipid membrane with its transmembrane helices arranged roughly parallel to each other . This structural arrangement facilitates the selective binding and subsequent transport of cobalt ions across the bacterial cell membrane, enabling A. arabaticum to survive in environments where cobalt may be limited.

How is recombinant CbiM protein typically produced for research purposes?

Recombinant CbiM protein from Acetohalobium arabaticum is typically produced using E. coli expression systems. The full-length mature protein (amino acids 28-251) can be expressed with an N-terminal His-tag to facilitate purification . The standard production methodology involves:

• Gene synthesis or cloning of the cbiM gene sequence into an appropriate expression vector

• Transformation of the construct into an E. coli expression strain

• Induction of protein expression under optimized conditions

• Cell lysis and protein extraction

• Affinity purification using the His-tag

• Purification verification by SDS-PAGE (achieving >90% purity)

• Lyophilization for storage stability

For experimental use, the lyophilized protein should be reconstituted in deionized sterile water to a concentration of 0.1-1.0 mg/mL, with 5-50% glycerol added as a cryoprotectant for long-term storage at -20°C/-80°C . Researchers should avoid repeated freeze-thaw cycles to maintain protein integrity and functionality.

How does the CbiM protein interact with other components of the CbiMNQO transport complex?

The CbiMNQO complex is a group-I ECF transporter comprising four distinct components that work together in cobalt transport. CbiM interactions within this complex are highly coordinated:

• CbiM-CbiQ Interaction: CbiM (substrate-binding component) interacts directly with CbiQ (scaffold component), and these interactions are critical for the conformational changes required during transport. Structural analyses reveal that CbiM lies horizontally along the lipid membrane and requires rotation or toppling in conjunction with CbiQ during the transport process .

• CbiM-CbiN Interaction: CbiN functions as a coupling component that facilitates conformational changes between CbiQ and CbiM. This suggests that CbiN plays a crucial role in energy transduction during the transport cycle .

• CbiM-CbiO Indirect Interaction: While direct interactions between CbiM and CbiO (the ATP-binding/hydrolysis component) have not been extensively documented, functional studies demonstrate that CbiM stimulates the basal ATPase activity of CbiQO . This stimulation indicates an allosteric communication pathway between the substrate binding site in CbiM and the ATP hydrolysis site in CbiO.

The coordinated interactions between these components enable the coupling of ATP hydrolysis to cobalt transport across the membrane, making the CbiMNQO complex an efficient energy-utilizing transport system.

What methodologies are recommended for studying conformational changes in CbiM during substrate transport?

For researchers investigating the conformational dynamics of CbiM during cobalt transport, several advanced methodologies are recommended:

• X-ray Crystallography: This has been successfully employed to determine the structure of the CbiMQO complex in its inward-open conformation, providing baseline structural information . Researchers should aim to capture different conformational states by varying crystallization conditions or introducing conformational locks.

• Cryo-Electron Microscopy (Cryo-EM): Particularly useful for membrane proteins, this technique can capture the CbiMNQO complex in different functional states without the constraints of crystal packing.

• Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS): This method can reveal dynamic regions of CbiM that undergo conformational changes during substrate binding and transport.

• Site-Directed Spin Labeling coupled with Electron Paramagnetic Resonance (SDSL-EPR): This approach can measure distances between strategic positions in CbiM during different stages of the transport cycle.

• Single-Molecule Förster Resonance Energy Transfer (smFRET): By introducing fluorescent probes at key positions in CbiM, researchers can monitor real-time conformational changes during transport.

• Molecular Dynamics Simulations: Computational approaches can model the behavior of CbiM in a lipid bilayer environment and predict conformational changes induced by substrate binding or interaction with other complex components.

The most comprehensive understanding will come from combining multiple methods to correlate structural changes with functional states of the transport cycle.

What experimental designs are optimal for analyzing the ATPase activity stimulation of CbiQO by CbiM?

Optimal experimental designs for analyzing how CbiM stimulates CbiQO ATPase activity should incorporate the following methodological considerations:

• Component Reconstitution Approach:

  • Express and purify individual components (CbiM, CbiQ, CbiO) separately

  • Reconstitute different combinations (CbiQO alone versus CbiMQO complex)

  • Compare baseline ATPase activity between different reconstituted assemblies

• ATPase Activity Measurement:

  Component Configuration	ATP Concentration (mM)	Reaction Time (min)	Temperature (°C)	Expected Activity Range (nmol Pi/min/mg)
  CbiO only	1.0-5.0	10-30	25-37	Baseline
  CbiQO	1.0-5.0	10-30	25-37	Moderate increase
  CbiMQO	1.0-5.0	10-30	25-37	Significant increase
  CbiMNQO (complete)	1.0-5.0	10-30	25-37	Maximum activity

• Substrate Effect Analysis:

  • Perform assays in the presence and absence of cobalt ions

  • Use non-transportable substrate analogs to differentiate between binding and transport effects

  • Employ varying concentrations of cobalt to determine dose-dependent effects on ATPase stimulation

• Mutation-Based Analysis:

  • Generate point mutations in the L1 loop region of CbiM to assess its role in ATPase stimulation

  • Create mutations at the predicted CbiM-CbiQ interface to identify key interaction residues

  • Employ truncation mutants to map minimal domains required for interaction

• Kinetic Parameter Determination:

  • Measure Km and Vmax values for ATP hydrolysis under different component combinations

  • Analyze the effects of temperature, pH, and ionic strength on the CbiM-mediated stimulation

  • Determine activation energy differences between CbiQO alone and the CbiMQO complex

These experimental approaches should incorporate appropriate controls and be performed in triplicate to ensure statistical validity.

How can researchers design experiments to investigate the substrate-gating function of the L1 loop in CbiM?

The L1 loop of CbiM plays a critical role in substrate gating during cobalt transport . To investigate this function, researchers should consider the following experimental design strategies:

• Structure-Function Analysis of the L1 Loop:

  • Generate a series of point mutations within the L1 loop region

  • Create deletion or substitution variants with altered loop flexibility

  • Assess the impact of these mutations on:
    a) Substrate binding affinity (using isothermal titration calorimetry)
    b) Transport rates (using radioactive 57Co or fluorescent cobalt analogs)
    c) Conformational changes (using techniques described in FAQ 2.2)

• Cross-Linking Studies:

  • Introduce cysteine pairs at strategic positions flanking the L1 loop

  • Perform disulfide cross-linking under different substrate conditions

  • Analyze whether locking the L1 loop in specific conformations affects transport function

• Real-Time Conformational Monitoring:

  • Attach environmentally sensitive fluorophores to the L1 loop

  • Monitor fluorescence changes upon substrate binding and during transport

  • Correlate these changes with transport activity and ATP hydrolysis

• Molecular Dynamics Simulations:

  • Model the behavior of the L1 loop in the presence and absence of cobalt

  • Predict key residues involved in loop movement and substrate coordination

  • Generate testable hypotheses for experimental validation

• Comparative Analysis Across Species:

  • Align L1 loop sequences from CbiM proteins of different organisms

  • Identify conserved residues likely crucial for gating function

  • Test functional complementation using chimeric proteins with L1 loops from different species

This multi-faceted approach will provide comprehensive insights into how the L1 loop facilitates substrate recognition, binding, and translocation during the transport cycle.

What are the optimal conditions for reconstituting functional CbiMNQO complexes in liposomes for transport assays?

Reconstituting functional CbiMNQO complexes in liposomes requires careful attention to lipid composition, protein-to-lipid ratios, and buffer conditions. The following methodology is recommended:

• Liposome Preparation:

  • Utilize a mixture of E. coli polar lipids and phosphatidylcholine (7:3 ratio) to mimic bacterial membrane composition

  • Prepare lipid films by rotary evaporation and hydrate in reconstitution buffer (20 mM HEPES, pH 7.2, 100 mM KCl)

  • Form unilamellar vesicles by extrusion through 400 nm polycarbonate membranes

• Protein Incorporation:

  Component	Molar Ratio	Protein:Lipid Ratio (w/w)	Detergent
  CbiM	1	1:50 - 1:100	DDM 0.03%
  CbiN	1	1:200 - 1:300	DDM 0.03%
  CbiQ	1	1:50 - 1:100	DDM 0.03%
  CbiO	2	1:25 - 1:50	DDM 0.03%

• Reconstitution Procedure:

  • Mix purified CbiM, CbiN, CbiQ, and CbiO at the stoichiometric ratio of 1:1:1:2

  • Add the protein mixture to detergent-destabilized liposomes

  • Remove detergent using Bio-Beads SM-2 or through dialysis

  • Collect proteoliposomes by ultracentrifugation (150,000 × g, 1 hour)

  • Resuspend in assay buffer (20 mM HEPES, pH 7.2, 100 mM KCl)

• Functional Verification:

  • Assess protein incorporation by SDS-PAGE analysis of reconstituted proteoliposomes

  • Verify orientation using protease protection assays

  • Confirm ATPase activity using standard phosphate release assays

  • Test for ATP-dependent uptake of 57Co2+ to confirm transport functionality

• Optimization Parameters:

  • Test various pH conditions (range 6.5-8.0)

  • Evaluate different ionic strengths (50-200 mM KCl)

  • Assess impact of divalent cations (0-5 mM Mg2+)

  • Determine optimal temperature for reconstitution (4°C vs. room temperature)

This reconstitution protocol should yield proteoliposomes with functionally active CbiMNQO complexes suitable for cobalt transport assays and inhibitor screening studies.

How can researchers differentiate between the roles of CbiM and CbiN in the transport mechanism using mutagenesis approaches?

Differentiating between the roles of CbiM and CbiN requires strategic mutagenesis approaches coupled with functional assays. The following methodology is recommended:

• Targeted Mutagenesis Strategy:

  • For CbiM: Focus on residues in the L1 loop region and predicted cobalt-binding sites

  • For CbiN: Target residues at the interface with CbiM and CbiQ

  • Create single-point mutations, alanine-scanning libraries, and domain swaps between CbiM and CbiN

• Functional Complementation Testing:

  • Generate knockout strains lacking cbiM or cbiN genes

  • Transform with plasmids expressing wild-type or mutant variants

  • Assess growth under cobalt-limiting conditions

  • Measure cellular cobalt content using inductively coupled plasma mass spectrometry (ICP-MS)

• In Vitro Transport Assays:

  • Reconstitute complexes with wild-type and mutant variants

  • Compare ATP-dependent cobalt uptake rates

  • Analyze the coupling efficiency (ratio of cobalt transported per ATP hydrolyzed)

  • Determine substrate specificity alterations using various metal ions

• Interaction Analysis:

  • Perform pull-down assays to assess protein-protein interactions between variants

  • Use microscale thermophoresis (MST) to quantify binding affinities

  • Employ crosslinking mass spectrometry to identify interaction interfaces

  • Analyze complex stability using size-exclusion chromatography

• Structural Impact Assessment:

  • Conduct circular dichroism (CD) spectroscopy to verify proper folding of mutant proteins

  • Perform limited proteolysis to assess conformational changes

  • Use hydrogen-deuterium exchange mass spectrometry to identify regions with altered dynamics

By systematically comparing the effects of mutations in CbiM versus CbiN on transport activity, ATP hydrolysis, protein interactions, and conformational changes, researchers can delineate the distinct roles of these components in the transport mechanism.

What analytical techniques are most effective for studying the ATP binding and hydrolysis cycle in CbiO within the context of the complete CbiMNQO complex?

Studying ATP binding and hydrolysis cycles in CbiO within the complete CbiMNQO complex requires sophisticated analytical techniques:

• Real-Time ATP Hydrolysis Measurement:

  • Enzyme-coupled assays (pyruvate kinase/lactate dehydrogenase system) for continuous monitoring of ATPase activity

  • Malachite green phosphate detection for endpoint measurements

  • 32P-ATP hydrolysis assays for high sensitivity detection

• Nucleotide Binding Analysis:

  • Isothermal titration calorimetry (ITC) to determine binding constants, stoichiometry, and thermodynamic parameters

  • Fluorescently labeled ATP analogs (TNP-ATP) to monitor binding through fluorescence changes

  • Filter binding assays with radiolabeled nucleotides for high sensitivity

• Conformational Change Detection:

  • Intrinsic tryptophan fluorescence to monitor protein conformational changes upon nucleotide binding

  • FRET-based assays using strategically placed fluorophores to detect domain movements

  • EPR spectroscopy with spin-labeled CbiO to track conformational changes during the ATPase cycle

• Pre-Steady State Kinetics:

  • Rapid mixing techniques (stopped-flow spectroscopy) to detect transient intermediates

  • Quenched-flow experiments to trap catalytic intermediates

  • Single-turnover experiments to determine rate-limiting steps

• Structural Analysis of Different States:

  • X-ray crystallography of CbiO in various nucleotide-bound states (ATP, ADP, AMP-PNP)

  • Cryo-EM of the complete complex in different steps of the catalytic cycle

  • Hydrogen-deuterium exchange mass spectrometry to identify regions undergoing conformational changes

• Mutational Analysis of Key Residues:

  • Walker A and B motif mutations to disrupt ATP binding and hydrolysis

  • Sensor I and II region mutations to interfere with communication between ATP binding and conformational change

  • Analysis of these mutations on both ATP hydrolysis and cobalt transport

These techniques should be applied in combination to establish a complete model of how ATP binding, hydrolysis, and product release drive conformational changes required for cobalt transport.

How should researchers interpret discrepancies between in vitro and in vivo functional data for CbiM mutants?

When researchers encounter discrepancies between in vitro and in vivo functional data for CbiM mutants, a systematic analytical approach is necessary:

• Potential Sources of Discrepancies:

  • Differential protein expression or stability in vivo versus purified systems

  • Presence of compensatory mechanisms or redundant transporters in vivo

  • Differences in membrane composition affecting protein function

  • Post-translational modifications present only in native systems

  • Interaction with cellular factors absent in reconstituted systems

• Analytical Framework:

  Parameter	In Vitro Measurement	In Vivo Measurement	Reconciliation Approach
  Expression level	SDS-PAGE, Western blot	qRT-PCR, Western blot	Normalize functional data to expression levels
  Protein stability	Thermal shift assays	Pulse-chase experiments	Compare half-lives across systems
  Transport activity	Radioisotope uptake	Growth complementation	Correlate transport rates with growth patterns
  Metal specificity	Direct binding assays	Competitive metal growth	Test for altered specificity in different environments
  Complex formation	Size exclusion chromatography	Co-immunoprecipitation	Assess complex integrity in both systems

• Reconciliation Strategies:

  • Perform gradient analysis with varying expression levels to identify threshold effects

  • Test mutants under stress conditions that may reveal phenotypes masked under standard conditions

  • Employ membrane extracts rather than purified components to maintain native lipid environment

  • Use permeabilized cell systems as an intermediate between purified proteins and intact cells

  • Develop complementary in vivo assays with higher resolution (e.g., intracellular metal sensors)

• Interpretation Guidelines:

  • In vitro data typically provides mechanistic insights at molecular resolution

  • In vivo data captures physiological relevance and system-level integration

  • Discrepancies often reveal important biological context or regulatory mechanisms

  • Agreement between systems provides strong validation of mechanistic models

By applying this structured analytical approach, researchers can transform discrepancies from confounding variables into valuable insights about the biological context of CbiM function.

What statistical methods are most appropriate for analyzing structure-function relationships in CbiM based on mutagenesis data?

Analyzing structure-function relationships in CbiM requires robust statistical methods to identify significant correlations and causal relationships:

• Correlation Analysis:

  • Pearson correlation coefficients for relationships between continuous variables (e.g., transport activity vs. binding affinity)

  • Spearman rank correlation for non-linear or non-parametric relationships

  • Multiple correlation analysis to handle multivariate relationships between structural parameters and functional outcomes

• Regression Modeling:

  • Multiple linear regression to assess contributions of different structural parameters to function

  • Logistic regression for binary outcomes (functional vs. non-functional)

  • Partial least squares regression for handling multicollinearity among structural parameters

• Classification Approaches:

  • Discriminant analysis to classify mutations based on functional outcomes

  • Support vector machines to identify structural determinants of function

  • Random forest algorithms to handle complex, non-linear relationships between structure and function

• Sequence-Structure-Function Analysis:

  • Position-specific scoring matrices to identify conservation patterns

  • Statistical coupling analysis to detect co-evolving networks of residues

  • Mutual information analysis to quantify information shared between positions

• Visualization and Dimensionality Reduction:

  • Principal component analysis to identify major modes of variation

  • t-SNE or UMAP for non-linear dimensionality reduction and visualization

  • Hierarchical clustering to group functionally similar mutations

• Statistical Significance Testing:

  • ANOVA with post-hoc tests for comparing multiple mutant groups

  • Bootstrapping methods for robust confidence interval estimation

  • False discovery rate control for multiple hypothesis testing

• Practical Implementation Example:

  • Create a comprehensive database of CbiM mutations with structural parameters (solvent accessibility, secondary structure, conservation) and functional outcomes

  • Apply multiple regression model: Function = β₀ + β₁(Conservation) + β₂(Solvent Accessibility) + β₃(Secondary Structure) + ε

  • Validate model with cross-validation and permutation testing

  • Identify statistically significant coefficients to determine structural features most predictive of function

These statistical approaches should be applied in combination to develop robust models linking structural features of CbiM to its transport function.

How can researchers integrate structural data with functional assays to propose a comprehensive mechanistic model of the CbiMNQO transport cycle?

Developing a comprehensive mechanistic model of the CbiMNQO transport cycle requires thoughtful integration of structural and functional data. The following methodological framework is recommended:

• Data Integration Workflow:

  • Map functional data from mutagenesis studies onto available structural models

  • Correlate conformational states identified in structural studies with discrete steps in the transport cycle

  • Link ATP hydrolysis events with specific conformational transitions

  • Integrate substrate binding data with structural changes in the transport pathway

• State Identification and Characterization:

  • Define discrete conformational states within the transport cycle (e.g., inward-open, occluded, outward-open)

  • Characterize each state using both structural features and functional parameters

  • Identify transitions between states and their energy requirements

• Energy Coupling Analysis:

  • Determine how ATP binding, hydrolysis, and product release events couple to substrate translocation

  • Quantify energetic contributions of specific protein-protein interactions

  • Map the flow of conformational changes from the ATP-binding domains to the substrate-binding site

• Model Validation Approach:

  Prediction	Structural Evidence	Functional Evidence	Validation Method
  State transitions	Structural snapshots	ATPase rate measurements	Cross-linking to trap intermediate states
  Conformational coupling	Interface contacts	Mutational analysis	FRET measurements of domain movements
  Transport directionality	Substrate pathway analysis	Directional uptake assays	Site-directed spin labeling EPR
  Rate-limiting steps	Energy barriers between states	Pre-steady state kinetics	Time-resolved structural methods

• Computational Model Development:

  • Implement molecular dynamics simulations to model transitions between observed states

  • Develop kinetic models incorporating rates determined from functional assays

  • Use Markov state modeling to identify key intermediates and transition probabilities

  • Perform in silico mutagenesis to test hypotheses derived from experimental data

• Iterative Refinement Process:

  • Generate testable predictions from the initial model

  • Design experiments specifically to challenge model assumptions

  • Refine the model based on new experimental data

  • Repeat until the model robustly explains both structural and functional observations

Through this systematic integration of structural and functional data, researchers can develop a mechanistic model that accurately describes how the CbiMNQO complex couples ATP hydrolysis to cobalt transport, with specific roles assigned to each component within the transport cycle.

What emerging technologies hold the most promise for advancing our understanding of CbiM structure and function?

Several cutting-edge technologies are poised to significantly advance our understanding of CbiM structure and function:

• Cryo-Electron Tomography (Cryo-ET):

  • Enables visualization of CbiMNQO complexes in their native membrane environment

  • Allows capturing different conformational states without artificial crystallization

  • Combined with subtomogram averaging, can achieve near-atomic resolution of membrane protein complexes in situ

• Integrative Structural Biology Approaches:

  • Combining multiple data sources (X-ray crystallography, cryo-EM, SAXS, NMR, crosslinking-MS)

  • Computational integration to generate complete structural models even with partial experimental data

  • Particularly valuable for flexible regions of CbiM not well-resolved in traditional structural studies

• Single-Molecule Techniques:

  • Single-molecule FRET to track conformational changes in real-time

  • Optical tweezers to measure forces generated during the transport cycle

  • Nanodiscs combined with single-molecule spectroscopy for native-like membrane environment studies

• Time-Resolved Serial Crystallography:

  • X-ray free electron lasers (XFELs) to capture transient conformational states

  • Mixing-injector technologies to initiate reactions microseconds before measurement

  • Potential to visualize short-lived intermediates in the transport cycle

• Advanced Computational Methods:

  • AI-driven protein structure prediction (AlphaFold, RoseTTAFold) for modeling conformational ensembles

  • Enhanced sampling molecular dynamics to access longer timescales relevant to transport

  • Quantum mechanics/molecular mechanics (QM/MM) for modeling metal coordination and transport

• Genetic Technologies:

  • CRISPR-based approaches for precise genome editing to study CbiM in native contexts

  • Deep mutational scanning to comprehensively map sequence-function relationships

  • In vivo directed evolution to identify optimized or specialized variants

These emerging technologies, especially when used in combination, hold exceptional promise for resolving the remaining questions about CbiM structure, function, and transport mechanism.

What are the most significant unresolved questions regarding CbiM function and cobalt transport mechanisms?

Despite significant advances in understanding CbiM and the CbiMNQO complex, several crucial questions remain unresolved:

Addressing these unresolved questions will require interdisciplinary approaches combining structural biology, biochemistry, biophysics, computational modeling, and cell biology.

How might insights from CbiM structure and function contribute to the development of new antimicrobial strategies?

Understanding CbiM structure and function offers several promising avenues for novel antimicrobial strategy development:

• Targeted Inhibition Rationale:

  • CbiM and the CbiMNQO complex are absent in humans but essential for many pathogenic bacteria

  • Cobalt acquisition is critical for vitamin B12-dependent processes in many pathogens

  • Structure-based inhibitor design targeting CbiM could yield highly selective antimicrobials

• Potential Inhibition Strategies:

  • Competitive inhibitors that bind the cobalt-binding site but cannot be transported

  • Allosteric inhibitors that lock CbiM in non-functional conformations

  • ATP-competitive inhibitors targeting CbiO to disrupt energy coupling

  • Peptide inhibitors disrupting critical protein-protein interactions within the complex

• Advantages as an Antimicrobial Target:

  • Surface accessibility as a membrane protein

  • Essential function in many pathogens, particularly in cobalt-limited environments

  • Structural uniqueness compared to human transporters

  • Potential for narrow-spectrum activity by targeting species-specific features

• Experimental Approaches for Inhibitor Development:

  • High-throughput screening against reconstituted CbiMNQO transport activity

  • Fragment-based drug discovery utilizing structural data

  • Virtual screening against identified binding pockets

  • Peptidomimetic design based on protein-protein interaction interfaces

• Potential Applications in Different Pathogen Classes:

  Pathogen Group	Dependency on Cobalt	Potential Antimicrobial Impact
  Anaerobic bacteria	High (for B12-dependent metabolism)	High priority targets
  Mycobacteria	Moderate	Potential adjuvant therapy
  Enteric pathogens	Variable	Species-specific applications
  Extremophiles	Often essential	Environmental control applications

• Resistance Considerations:

  • Potential resistance mechanisms through mutations in CbiM binding sites

  • Possibility of alternate cobalt acquisition pathways in some organisms

  • Strategies for overcoming potential resistance through multi-target approaches

The detailed structural and mechanistic understanding of CbiM provides a solid foundation for rational design of novel antimicrobials with potentially lower resistance development and minimal impact on beneficial microbiota.