Recombinant Escherichia coli Uncharacterized protein yfdY (yfdY)

What is yfdY protein in Escherichia coli?

yfdY is an uncharacterized protein in Escherichia coli K-12 strain with 80 amino acids. It is encoded by the yfdY gene (also known as b2377 or JW2374) and has been identified as a membrane component with potential roles in transport functions and stress response mechanisms . Recent evidence suggests that yfdY participates in biofilm formation as a defense mechanism against oxidative stress, particularly hypochlorite (HOCl) . The protein is classified in protein interaction networks with moderate confidence connections to several other proteins, including membrane transporters and stress-response elements .

How is yfdY involved in stress response mechanisms?

Research has identified yfdY as part of the oxidizing agent resistance network in E. coli. Specifically, it appears among genes whose expression can make E. coli cells resistant to oxidizing agents such as hypochlorite (HOCl) . Genome-wide screening studies have categorized yfdY as a membrane component involved in stress responses, particularly against oxidative stress. The protein's participation in biofilm formation represents a significant stress defense mechanism, as biofilms protect bacterial populations from environmental stressors through matrix formation and altered metabolic states . This function appears consistent with the broader pattern of membrane transporters playing crucial roles in stress adaptation by modifying membrane permeability or facilitating the export of toxic compounds.

What protein interactions has yfdY been associated with?

According to the STRING interaction database, yfdY has been associated with several other E. coli proteins with varying confidence scores :

These interaction partners suggest potential involvement in membrane transport, stress response, and antimicrobial resistance mechanisms. The highest confidence interaction with ydcZ (another membrane protein) supports the hypothesis that yfdY functions within membrane-associated protein complexes .

Why is yfdY considered an "uncharacterized" protein?

yfdY falls into the category of "uncharacterized proteins" because its precise biochemical function, substrate specificity, and regulatory mechanisms remain experimentally unverified . This classification applies to genes that have been identified through genome sequencing but lack experimental validation of their function. According to BioCyc database criteria, proteins are considered "uncharacterized" when they have "no sequence similarity to known proteins" or only "extremely limited information about their function has been obtained" .

The E. coli K-12 genome contains numerous such uncharacterized genes despite decades of intensive study, highlighting the challenges in functional genomics. These genes often represent overlooked aspects of bacterial physiology that may be critical under specific environmental conditions not routinely tested in laboratory settings . Recent transcriptomic studies have revealed that many uncharacterized genes, including yfdY, are differentially expressed under stress conditions, suggesting important but previously unrecognized roles in bacterial survival mechanisms .

What expression systems are optimal for producing recombinant yfdY protein?

For recombinant expression of membrane proteins like yfdY, specialized expression systems that address the challenges of membrane protein production are recommended:

How can researchers optimize solubility when expressing membrane proteins like yfdY?

Optimizing solubility for membrane proteins like yfdY requires specialized approaches:

• Controlled expression rate: Reduce expression rate by lowering temperature (16-20°C), using lower inducer concentrations, or employing weaker promoters to prevent overwhelming the membrane insertion machinery .

• Oxidizing environment manipulation: For membrane proteins that may contain disulfide bonds, consider expression in oxidizing cytoplasmic environments using specialized strains like Origami or SHuffle, or co-expression with sulfhydryl oxidase and isomerase .

• Detergent screening: Systematic screening of detergents is crucial for membrane protein solubilization. Begin with mild detergents like n-dodecyl-β-D-maltoside (DDM), CHAPS, or digitonin in initial extraction trials .

• Fusion strategies: Fusion with solubility-enhancing partners can improve membrane protein handling. For yfdY specifically, consider:

  • N-terminal fusions that do not interfere with membrane insertion

  • GFP fusions that allow monitoring of proper folding

  • MBP or SUMO tags that can enhance solubility while being removable

• Liposome reconstitution: For functional studies, consider direct incorporation into artificial liposomes or nanodiscs after extraction, which can maintain the native-like lipid environment required for proper folding and function .

How can researchers design experiments to investigate yfdY's role in oxidative stress resistance?

To investigate yfdY's role in oxidizing agent resistance, a comprehensive experimental approach should include:

• Gene expression analysis:

  • qRT-PCR to quantify yfdY expression under various oxidative stressors (H₂O₂, HOCl) at different concentrations and time points

  • Promoter-reporter fusions (e.g., yfdY promoter-GFP) to visualize expression patterns in single cells

  • RNA-seq analysis comparing wild-type and yfdY mutant strains under oxidative stress conditions

• Phenotypic characterization:

  • Growth curves of wild-type vs. yfdY deletion mutants in the presence of oxidizing agents

  • Minimum inhibitory concentration (MIC) determination for various oxidizing agents

  • Survival assays following acute oxidative stress exposure

  • Competition assays between wild-type and mutant strains under stress conditions

• Stress-response pathway analysis:

  • Epistasis studies with known oxidative stress response genes (e.g., katG, katE, oxyR)

  • Transcriptome analysis of genes affected by yfdY deletion under oxidative stress

  • Protein-protein interaction studies to identify functional partners in stress response

• Biofilm formation analysis:

  • Quantitative biofilm assays comparing wild-type and yfdY mutants under oxidative stress

  • Microscopic analysis of biofilm architecture using fluorescent reporters

  • Matrix composition analysis to determine if yfdY affects specific biofilm components

• Complementation studies:

  • Plasmid-based expression of yfdY in deletion mutants to confirm phenotypes

  • Domain-specific mutants to identify functional regions of the protein

  • Cross-species complementation to evaluate functional conservation

What gene knockout and complementation strategies are appropriate for studying yfdY?

For rigorous functional analysis of yfdY, implement the following knockout and complementation strategies:

• Precise gene deletion methods:

  • Lambda Red recombination system for scarless deletion of yfdY

  • CRISPR-Cas9 based genome editing for clean deletions without antibiotic markers

  • Transposon mutagenesis screening to identify conditions where yfdY is essential

• Control strains creation:

  • Generate multiple independent knockout clones to confirm phenotypes

  • Create marker-free deletions to avoid polar effects on neighboring genes

  • Develop controls with deletions in related but functionally distinct genes

• Complementation approaches:

  • Plasmid-based expression with native promoter for physiological expression levels

  • Inducible expression systems for controlled complementation studies

  • Chromosomal integration of yfdY at neutral sites for stable complementation

• Functional domain analysis:

  • Generate point mutations in conserved residues to identify critical amino acids

  • Create truncation mutants to define functional domains

  • Design chimeric proteins with related membrane proteins to identify specificity determinants

• Conditional knockouts:

  • Implement degradation tag systems (e.g., SsrA tags) for temporal control of YfdY levels

  • Design riboswitch-controlled expression for conditional gene activation/repression

  • Use CRISPRi for titratable repression of yfdY expression

Successful complementation should restore wild-type phenotypes related to oxidative stress resistance and biofilm formation, confirming the direct involvement of yfdY in these processes .

What methods can help determine if yfdY directly contributes to oxidizing agent resistance?

To establish whether yfdY directly contributes to oxidizing agent resistance, employ the following methodological approaches:

• Direct resistance assays:

  • Perform survival curve analysis in wild-type, yfdY deletion, and complemented strains exposed to increasing concentrations of oxidizing agents (H₂O₂, HOCl)

  • Conduct disk diffusion assays with oxidizing agents to quantify zones of inhibition

  • Implement gradient plate techniques to visualize resistance patterns

• Localization and interaction studies:

  • Fluorescently tag YfdY to confirm membrane localization during oxidative stress

  • Perform co-immunoprecipitation with known oxidative stress response proteins

  • Use bacterial two-hybrid or split-GFP assays to identify direct interaction partners

• Biochemical activity determination:

  • Assess if YfdY has detoxifying activity against oxidizing agents in vitro

  • Measure intracellular ROS levels in wild-type versus yfdY mutants using fluorescent probes

  • Quantify oxidation states of cellular components (lipids, proteins) in the presence/absence of yfdY

• Membrane integrity analysis:

  • Measure membrane permeability changes during oxidative stress using fluorescent dyes

  • Analyze membrane lipid composition alterations in yfdY mutants under stress

  • Perform electrophysiology studies if yfdY functions as an ion channel or transporter

• Direct substrate identification:

  • Implement metabolomic profiling to identify molecules affected by yfdY deletion

  • Conduct transport assays using reconstituted YfdY in liposomes

  • Utilize chemical crosslinking to capture transient interactions with potential substrates

These approaches collectively can establish whether yfdY's contribution to oxidative stress resistance is direct (through substrate transport or enzymatic activity) or indirect (through effects on membrane properties or gene regulation) .

How can researchers interpret protein interaction data for yfdY from the STRING database?

When interpreting STRING database interaction data for yfdY, researchers should apply the following analytical approaches:

• Confidence score evaluation: Critically assess the confidence scores for each interaction. For yfdY, the highest confidence interaction is with ydcZ (0.785), suggesting a reliable functional connection with this DUF606 family inner membrane protein. Interactions with scores below 0.500 (like with rutC, yoaE, and elaA) should be considered tentative and require experimental validation .

• Interaction type analysis: Differentiate between interaction types in STRING (physical binding, genetic interactions, co-expression, etc.). For yfdY, determine which interaction evidence types contribute to each confidence score to better understand the nature of the predicted interactions .

• Functional clustering:

  • Group interaction partners by function to identify patterns

  • For yfdY, note the high proportion of membrane-associated proteins (ydcZ, ybhR, yoaE)

  • Observe the presence of stress-response related proteins (sanA - vancomycin resistance; ydcX - stress-related toxin)

• Network expansion analysis:

  • Extend the network to second-degree interactions to identify functional modules

  • Apply clustering algorithms to identify functional communities

  • Calculate network centrality measures to assess yfdY's position in larger interaction networks

• Validation strategy development:

  • Prioritize high-confidence interactions (ydcZ, ytfI, tfaR) for experimental validation

  • Design co-immunoprecipitation or bacterial two-hybrid experiments for top candidates

  • Plan phenotypic comparisons between yfdY and interaction partner mutants

The strong interaction with membrane proteins supports yfdY's classification as a membrane component, while connections to stress response proteins align with its role in oxidative stress resistance .

What bioinformatic approaches can help predict the function of yfdY?

Multiple bioinformatic approaches can provide insights into yfdY's potential function:

• Sequence-based analysis:

  • Profile Hidden Markov Models to identify distant homologs

  • Protein domain prediction to identify functional motifs

  • Transmembrane topology prediction using tools like TMHMM, Phobius, or TOPCONS

  • Signal peptide prediction to determine subcellular localization

• Structural prediction:

  • Ab initio structure prediction using methods like AlphaFold2 or RoseTTAFold

  • Homology modeling if distant structural homologs exist

  • Molecular dynamics simulations to predict membrane interactions

  • Binding site prediction to identify potential substrate pockets

• Genomic context analysis:

  • Operon structure examination to identify functionally related genes

  • Phylogenetic profiling to find co-evolving genes across species

  • Comparative genomics to identify conserved genomic neighborhoods

  • Regulatory motif analysis to predict transcriptional control mechanisms

• Expression correlation:

  • Analysis of transcriptomic data across diverse conditions

  • Co-expression network construction to identify functionally related genes

  • Condition-specific expression pattern analysis, particularly under stress conditions

  • Integration of expression data with protein interaction networks

• Function prediction algorithms:

  • Implement ensemble methods that combine multiple prediction approaches

  • Apply machine learning algorithms trained on characterized proteins

  • Use gene ontology term prediction based on sequence features

  • Employ guilt-by-association approaches using known interaction partners

These approaches collectively can generate testable hypotheses about yfdY's function, particularly its potential roles in membrane transport and stress response mechanisms .

How should researchers analyze yfdY's involvement in biofilm formation as a defense against HOCl?

To rigorously analyze yfdY's role in biofilm formation as a defense mechanism against HOCl, implement the following analytical framework:

• Quantitative biofilm analysis:

  • Compare biofilm formation capacity between wild-type and yfdY mutants under HOCl stress using crystal violet staining

  • Implement flow cell systems with confocal microscopy for dynamic biofilm architecture analysis

  • Measure biofilm parameters (thickness, biomass, roughness) using COMSTAT or similar software

  • Conduct dose-response studies with varying HOCl concentrations

• Gene expression correlation analysis:

  • Perform transcriptomic analysis of biofilm cells with and without yfdY expression

  • Identify co-regulated genes during biofilm formation under oxidative stress

  • Compare yfdY expression patterns with known biofilm regulators (csgD, bssS, ycfJ)

  • Construct gene regulatory networks to position yfdY within the biofilm formation pathway

• Matrix composition analysis:

  • Quantify extracellular polymeric substances (EPS) in wild-type versus yfdY mutant biofilms

  • Determine if yfdY affects specific biofilm matrix components (exopolysaccharides, eDNA, proteins)

  • Implement specific staining techniques to visualize different matrix components

  • Analyze the protective capacity of the matrix against HOCl penetration

• Mechanistic pathway determination:

  • Test epistatic relationships between yfdY and known biofilm regulators

  • Implement phosphoproteomic analysis to identify signaling pathways affected by yfdY

  • Analyze second messenger (c-di-GMP, cAMP) levels in response to yfdY expression

  • Determine if yfdY affects cell surface properties relevant to biofilm formation

• Survival advantage quantification:

  • Compare survival rates of bacteria within biofilms versus planktonic cells under HOCl stress

  • Measure HOCl penetration into biofilms using specific probes

  • Determine if yfdY expression correlates with increased survival within biofilm structures

  • Calculate fitness advantage conferred by yfdY-dependent biofilm formation

This analytical framework will help establish both the correlation and causation between yfdY expression, biofilm formation, and HOCl resistance .

How can researchers correlate yfdY expression with stress response phenotypes?

To establish robust correlations between yfdY expression and stress response phenotypes, implement the following analytical approaches:

• Expression-phenotype correlation:

  • Construct strains with varying levels of yfdY expression (from native promoter, inducible promoters)

  • Measure stress resistance parameters across expression levels

  • Perform regression analysis to quantify the relationship between expression and phenotype

  • Determine expression thresholds required for stress protection

• Time-course analysis:

  • Monitor yfdY expression and stress response parameters simultaneously over time

  • Implement time-lag correlation analysis to determine if expression precedes phenotypic changes

  • Use mathematical modeling to describe the dynamics of the response

  • Integrate data into ordinary differential equation models of stress response

• Single-cell analysis:

  • Employ fluorescent reporters to monitor yfdY expression at the single-cell level

  • Correlate expression heterogeneity with survival heterogeneity under stress

  • Implement microfluidic systems for real-time observation of stress responses

  • Perform flow cytometry to quantify population-level expression distributions

• Multi-stress comparison:

  • Analyze yfdY expression and phenotypic responses across multiple stressors (HOCl, H₂O₂, antibiotics)

  • Construct stress-specific expression profiles

  • Identify common and distinct features of yfdY-dependent responses

  • Develop multivariate models to describe stress-specific contributions

• Pathway integration analysis:

  • Compare transcriptional responses between wild-type and yfdY mutants under stress

  • Identify pathways that are differentially activated

  • Use network analysis to position yfdY within global stress response networks

  • Implement causal inference methods to determine directionality of effects

This analytical framework will help establish whether yfdY is a primary stress response element or a secondary component that modulates specific aspects of the response .

How can researchers analyze data contradictions in studies of uncharacterized proteins like yfdY?

When encountering contradictory data regarding uncharacterized proteins like yfdY, implement the following analytical strategies:

• Condition-specific analysis:

  • Systematically compare experimental conditions across contradictory studies

  • Identify key variables that differ (strain backgrounds, media composition, stress parameters)

  • Reproduce experiments under standardized conditions to resolve contradictions

  • Develop a matrix of conditions to determine context-dependent functions

• Strain-specific effects evaluation:

  • Compare results across different E. coli strains (K-12 MG1655, BL21, clinical isolates)

  • Sequence yfdY and surrounding genomic regions to identify strain-specific variations

  • Test identical experimental procedures across multiple strain backgrounds

  • Consider the genomic context and potential polar effects of manipulations

• Methodological bias assessment:

  • Evaluate different methodological approaches used across studies

  • Implement orthogonal techniques to verify contradictory findings

  • Consider detection limits and sensitivity of different assays

  • Analyze statistical approaches and sample sizes for robustness

• Integration of seemingly contradictory data:

  • Develop models that accommodate apparently contradictory observations

  • Consider multifunctional roles that may appear contradictory in different contexts

  • Implement network-based approaches to position contradictory findings in a broader context

  • Utilize Bayesian approaches to weight evidence from different sources

• Systematic literature review and meta-analysis:

  • Apply formal meta-analysis techniques to quantitatively assess contradictory findings

  • Implement evidence quality scoring to weight different studies

  • Identify potential publication biases affecting the literature

  • Develop consensus statements based on quality-weighted evidence synthesis

The recombinant protein expression field faces contradictory results due to the complex interplay between expression systems, host metabolism, and target protein properties. As noted in the literature, "the critical question of what really is the metabolic burden and how it affects both host metabolism and recombinant protein production remains elusive because some experimental results are contradictory" .

What structural biology techniques are most appropriate for characterizing small membrane proteins like yfdY?

For structural characterization of small membrane proteins like yfdY (80 amino acids), researchers should consider these specialized approaches:

• Solution NMR spectroscopy:

  • Particularly suitable for small membrane proteins (<150 amino acids)

  • Requires isotopic labeling (¹⁵N, ¹³C) of recombinant yfdY

  • Can be performed in detergent micelles, bicelles, or nanodiscs

  • Enables dynamic studies and ligand binding analyses

  • Can resolve structures in native-like membrane environments

• Cryo-electron microscopy (cryo-EM):

  • Recent advances allow structure determination of smaller proteins

  • Consider embedding yfdY in scaffold proteins or nanobodies to increase size

  • Use of Volta phase plates can improve contrast for small proteins

  • May require oligomerization or complex formation to achieve suitable size

• X-ray crystallography with specialized approaches:

  • Lipidic cubic phase (LCP) crystallization specifically designed for membrane proteins

  • Antibody fragment co-crystallization to increase hydrophilic surface area

  • Fusion with crystallization chaperones (e.g., T4 lysozyme) to aid crystal packing

  • Serial femtosecond crystallography at X-ray free electron lasers for microcrystals

• Integrative structural biology:

  • Combine lower resolution techniques (SAXS, EPR spectroscopy) with computational modeling

  • Implement cross-linking mass spectrometry to establish distance constraints

  • Use hydrogen-deuterium exchange mass spectrometry to map solvent-accessible regions

  • Incorporate evolutionary covariance data for model validation

• Molecular dynamics simulations:

  • Implement advanced sampling techniques for conformational exploration

  • Use coarse-grained simulations to observe membrane embedding and protein-lipid interactions

  • Employ enhanced sampling methods to explore functional states

  • Validate computational models against experimental data

For yfdY specifically, solution NMR may be ideal given its small size, while integrative approaches combining experimental data with computational modeling would provide comprehensive structural insights into its membrane association and potential functional sites .

How might yfdY contribute to antibiotic resistance mechanisms in E. coli?

Several mechanistic pathways could explain yfdY's potential contribution to antibiotic resistance:

• Membrane permeability modulation:

  • As a membrane component, yfdY could alter membrane fluidity or organization

  • This could reduce penetration of antibiotics, particularly hydrophilic compounds

  • Compare membrane fluidity and antibiotic penetration in wild-type versus yfdY mutants

  • Analyze lipid composition changes associated with yfdY expression

• Efflux pump cooperation:

  • yfdY may function as an accessory protein to known efflux systems

  • Its interaction with ybhR (putative ABC transporter permease) suggests possible involvement in transport

  • Test synergistic effects between yfdY and known efflux systems

  • Measure antibiotic accumulation in cells with varying yfdY expression levels

• Biofilm-mediated resistance:

  • yfdY's role in biofilm formation directly connects to a known antibiotic resistance mechanism

  • Biofilms provide physical barriers to antibiotic penetration

  • Altered metabolic states within biofilms reduce antibiotic efficacy

  • Compare antibiotic resistance in planktonic versus biofilm cells with/without yfdY

• Stress response coupling:

  • yfdY's involvement in oxidative stress response may indirectly enhance antibiotic tolerance

  • Many antibiotics induce oxidative stress as part of their killing mechanism

  • Test if yfdY upregulation occurs during antibiotic exposure

  • Determine if oxidative stress pre-adaptation through yfdY increases antibiotic tolerance

• Cell envelope stress response:

  • yfdY may participate in envelope stress responses similar to its interaction partner sanA

  • Connection to sanA (vancomycin high temperature exclusion protein) suggests a role in cell envelope integrity

  • Analyze expression patterns under cell wall-targeting antibiotic exposure

  • Test susceptibility to cell wall antibiotics in yfdY mutants

Experimental validation could involve minimum inhibitory concentration (MIC) determination across multiple antibiotic classes, with particular attention to those targeting the cell envelope or inducing oxidative stress .

What approaches can determine if yfdY is conserved across different bacterial species?

To comprehensively analyze yfdY conservation across bacterial species, implement the following approaches:

This comprehensive approach will not only identify yfdY homologs but also provide insights into their evolutionary history and potential functional conservation .

How can proteomic approaches help identify the function of uncharacterized proteins like yfdY?

Advanced proteomic strategies offer powerful approaches to decipher the function of uncharacterized proteins like yfdY:

• Interaction proteomics:

  • Implement affinity purification-mass spectrometry (AP-MS) with tagged yfdY

  • Perform proximity labeling techniques (BioID, APEX) to identify neighborhood proteins

  • Use chemical crosslinking mass spectrometry (XL-MS) to capture transient interactions

  • Apply co-fractionation mass spectrometry for native complex detection

  • Compare interaction networks under normal and stress conditions

• Quantitative proteomics for phenotypic comparison:

  • Compare proteome-wide changes between wild-type and yfdY knockout strains

  • Implement SILAC, TMT, or label-free quantification for accurate measurements

  • Focus analysis on membrane proteome changes using specialized extraction methods

  • Identify proteins with correlated expression patterns across conditions

• Post-translational modification analysis:

  • Analyze phosphorylation, oxidation, and other modifications affected by yfdY

  • Implement redox proteomics to identify proteins protected from oxidation by yfdY

  • Study lipidation patterns of membrane proteins in the presence/absence of yfdY

  • Map modification sites using high-resolution mass spectrometry

• Protein turnover and dynamics:

  • Measure protein half-lives using pulse-chase SILAC in wild-type versus mutant strains

  • Implement thermal proteome profiling to detect proteome-wide stability changes

  • Study membrane protein dynamics using hydrogen-deuterium exchange MS

  • Analyze protein complex assembly/disassembly kinetics

• Spatial proteomics:

  • Map subcellular localization changes dependent on yfdY using fractionation-based approaches

  • Implement proximity-dependent labeling to create spatial maps of protein neighborhoods

  • Study membrane domain organization using specialized extraction methods

  • Analyze stress-induced relocalization patterns

These approaches provide complementary insights, from direct physical interactions to system-wide effects, helping to position yfdY within cellular pathways and clarify its functional role .

What are the broader implications of studying uncharacterized proteins like yfdY for understanding bacterial stress responses?

Investigating uncharacterized proteins like yfdY has profound implications for advancing our understanding of bacterial stress responses: