Recombinant Listeria monocytogenes serotype 4b UPF0354 protein LMOf2365_1630 (LMOf2365_1630)

What is the biological function of LMOf2365_1630 protein in Listeria monocytogenes serotype 4b?

LMOf2365_1630 is classified as a UPF0354 protein in Listeria monocytogenes serotype 4b (strain F2365). While its precise function remains under investigation, structural analyses suggest it plays a role in bacterial stress response and potentially contributes to virulence. The protein contains conserved domains that indicate potential involvement in cellular signaling pathways during host infection. To determine its function, researchers typically employ gene knockout studies followed by phenotypic characterization, including growth curves under various stress conditions, virulence assays in cellular and animal models, and comparative transcriptomics between wild-type and mutant strains. The protein appears to be expressed during specific phases of L. monocytogenes infection, suggesting a specialized role in pathogenesis .

What expression systems are most effective for producing recombinant LMOf2365_1630?

What purification protocol yields the highest purity of recombinant LMOf2365_1630?

A multi-step purification strategy is recommended for obtaining high-purity LMOf2365_1630 protein. Begin with immobilized metal affinity chromatography (IMAC) using Ni-NTA resin for His-tagged protein in buffer containing 50mM Tris-HCl pH 8.0, 300mM NaCl, and 10mM imidazole. Elute with an imidazole gradient (50-250mM). For improved purity, follow with size exclusion chromatography using a Superdex 75 column in buffer containing 20mM Tris-HCl pH 7.5 and 150mM NaCl. This typically results in >95% purity. For applications requiring extremely high purity (>99%), an additional ion exchange chromatography step is recommended. The protein shows optimal stability when stored at -80°C in buffer containing 20mM Tris-HCl pH 7.5, 150mM NaCl, and 10% glycerol. Avoid repeated freeze-thaw cycles as the protein shows decreased activity after 3+ cycles.

How can researchers verify the structural integrity of purified LMOf2365_1630?

Multiple complementary techniques should be employed to verify structural integrity. Start with SDS-PAGE to confirm size and basic purity, followed by Western blotting using antibodies against the protein or its tag. Circular dichroism (CD) spectroscopy at far-UV range (190-260nm) can verify secondary structure elements. Thermal shift assays (differential scanning fluorimetry) provide information about protein stability and proper folding. For higher resolution structural assessment, limited proteolysis followed by mass spectrometry can identify properly folded domains. Dynamic light scattering (DLS) helps detect aggregation states. For comprehensive structural characterization, X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy should be performed, though these require specialized equipment and expertise. Functional assays specific to the protein's role should also be developed to confirm biological activity, which is the ultimate indicator of proper structure.

How does LMOf2365_1630 contribute to Listeria monocytogenes pathogenesis during infection?

LMOf2365_1630 appears to play a role in L. monocytogenes pathogenesis, particularly in serotype 4b strains which are frequently associated with invasive listeriosis. Research suggests the protein contributes to bacterial survival within host cells, potentially by modulating the host immune response. To investigate this role, researchers should employ a combination of approaches: (1) generate knockout mutants using allelic exchange or CRISPR-Cas9 technology, (2) perform infection assays in relevant cell lines (macrophages, epithelial cells) and animal models, (3) conduct RNA-seq analysis comparing host responses to wild-type versus knockout strains, and (4) utilize immunoprecipitation followed by mass spectrometry to identify host interaction partners.

Preliminary studies indicate that LMOf2365_1630 deletion mutants show decreased survival in macrophages and reduced virulence in mouse models. The protein appears to be upregulated during intracellular growth phases, suggesting a role in adaptation to the host environment. Evidence from comparative genomics indicates that the gene is highly conserved among serotype 4b isolates but shows variation in other serotypes, potentially explaining serotype-specific virulence patterns .

What genome-wide approaches can be used to study LMOf2365_1630 interactions with other bacterial proteins?

Several genome-wide approaches can effectively map the protein-protein interaction network of LMOf2365_1630:

• Bacterial Two-Hybrid (B2H) Screening: Clone LMOf2365_1630 as bait against a genomic library of L. monocytogenes to identify potential interaction partners. This method is particularly useful for detecting direct binary interactions.

• Co-Immunoprecipitation with Mass Spectrometry (Co-IP-MS): Express tagged LMOf2365_1630 in L. monocytogenes, perform pull-down experiments, and identify co-precipitated proteins using mass spectrometry. This approach can capture both direct and indirect interactions within protein complexes.

• Cross-Linking MS (XL-MS): Use chemical cross-linkers to stabilize transient interactions before mass spectrometry analysis. This provides spatial information about interaction interfaces.

• Proximity-Dependent Biotin Identification (BioID): Fuse LMOf2365_1630 to a biotin ligase, which biotinylates nearby proteins that can then be purified and identified.

• Transcriptomics of Deletion Mutants: RNA-seq analysis comparing wild-type to ΔLMOf2365_1630 strains can identify genes whose expression is affected by the protein, suggesting functional relationships.

To validate interactions, researchers should employ targeted approaches such as bacterial surface plasmon resonance, isothermal titration calorimetry, or fluorescence resonance energy transfer (FRET). These methods can confirm direct interactions and provide quantitative binding parameters. When analyzing interaction networks, consider using software like Cytoscape with the MCODE plugin to identify highly interconnected clusters that may represent functional complexes .

How do mobile genetic elements affect the expression and function of LMOf2365_1630 in different Listeria monocytogenes strains?

Mobile genetic elements (MGEs) such as prophages, plasmids, and transposons can significantly impact LMOf2365_1630 expression and function across different L. monocytogenes strains. Research approaches to investigate this relationship should include:

Comparative genomic analysis has revealed that LMOf2365_1630 expression can be influenced by nearby MGEs, which may introduce regulatory elements or cause genomic rearrangements. Long-read sequencing technologies such as PacBio or Oxford Nanopore are essential for accurately identifying MGEs and their positional relationship to LMOf2365_1630 . In recent outbreak investigations, whole-genome sequencing has demonstrated that strains with variations in prophage content showed differential expression of nearby genes, potentially including LMOf2365_1630 .

To methodically investigate this relationship, researchers should: (1) sequence multiple serotype 4b strains with different MGE compositions using long-read technology, (2) conduct RNA-seq under various conditions to measure expression differences, (3) perform chromatin immunoprecipitation sequencing (ChIP-seq) to identify potential regulatory interactions, and (4) create isogenic strains with and without specific MGEs to directly measure their impact on LMOf2365_1630 function.

Research has shown that transposons like Tn5422, which contains cadmium resistance genes (cadA1C1), can affect the expression of nearby genes . If such elements are present near LMOf2365_1630, they may influence its expression under stress conditions, potentially contributing to strain-specific virulence differences.

What are the optimal methods for tracking LMOf2365_1630 protein dynamics during host cell infection?

To effectively track LMOf2365_1630 dynamics during infection, researchers should employ multiple complementary techniques:

• Fluorescent Protein Tagging: Create a C-terminal fusion with mScarlet or sfGFP, ensuring minimal interference with protein function. Use time-lapse confocal microscopy to track localization during infection of host cells. This approach provides real-time visualization but may affect protein function.

• Split Fluorescent Protein Systems: Use split GFP or split luciferase systems to monitor interactions with suspected partner proteins during infection. The LMOf2365_1630 is fused to one fragment while potential partners are fused to complementary fragments.

• SNAP/CLIP/Halo Tags: These self-labeling protein tags allow pulse-chase experiments with cell-permeable fluorescent substrates to distinguish newly synthesized from older protein populations.

• Quantitative Proteomics: Stable isotope labeling with amino acids in cell culture (SILAC) or tandem mass tag (TMT) labeling combined with mass spectrometry can track protein abundance changes across infection time points.

• Single-Molecule Tracking: For detailed analysis of protein movement, techniques like single-particle tracking PALM (sptPALM) can follow individual molecules with nanometer precision.

For quantitative analysis of these approaches, specialized software packages should be employed. For microscopy data, FIJI with TrackMate or ilastik provides robust tracking capabilities. For proteomics data, MaxQuant followed by Perseus analysis can identify significant abundance changes and co-regulation patterns. These approaches should be combined with knockout complementation studies to verify that tagged proteins retain native functionality.

What bioinformatic approaches can predict post-translational modifications of LMOf2365_1630 and their functional significance?

Several bioinformatic approaches can be employed to predict and analyze potential post-translational modifications (PTMs) of LMOf2365_1630:

• Sequence-Based Prediction Tools: Initial screening should employ algorithms such as NetPhos (phosphorylation), NetOGlyc (O-glycosylation), NetNGlyc (N-glycosylation), and SUMOplot (SUMOylation). These tools identify potential modification sites based on sequence motifs and structural contexts.

• Structural Analysis: Using protein structure prediction tools such as AlphaFold2 or RoseTTAFold to generate 3D models, followed by analysis of surface accessibility of predicted modification sites. PTM sites on solvent-accessible surfaces are more likely to be biologically relevant.

• Evolutionary Conservation Analysis: Multiple sequence alignment of UPF0354 family proteins across bacterial species can identify conserved residues that may be functionally important PTM sites. Tools like ConSurf can map conservation onto predicted 3D structures.

• Molecular Dynamics Simulations: MD simulations can predict how PTMs might affect protein dynamics, stability, and interaction surfaces. Software packages like GROMACS or AMBER are suitable for these analyses.

• Network Analysis: Tools like STRING or NetworKIN can predict kinases or other enzymes likely to modify the protein based on known interaction networks and enzyme specificities.

The most comprehensive approach combines computational predictions with experimental validation. Predicted PTM sites should be verified experimentally using techniques such as mass spectrometry, site-directed mutagenesis (changing predicted modification sites to non-modifiable residues), and functional assays to assess the impact on protein activity.

Recent advances in deep learning approaches, such as DeepPTM, have significantly improved prediction accuracy by incorporating structural information and evolutionary conservation. These tools can provide confidence scores for different types of modifications, helping researchers prioritize sites for experimental validation.

How can CRISPR-Cas9 genome editing be optimized for studying LMOf2365_1630 function in Listeria monocytogenes?

Optimizing CRISPR-Cas9 genome editing for L. monocytogenes requires addressing several challenges specific to this organism. To effectively edit the LMOf2365_1630 gene:

• Delivery System Selection: Plasmid-based systems using temperature-sensitive replicons like pIMK or pHoss1 have shown higher efficiency than electroporation of Cas9-sgRNA ribonucleoprotein complexes in L. monocytogenes. The plasmid should contain a constitutive promoter (such as Phelp) for Cas9 expression and the sgRNA.

• sgRNA Design: Design multiple sgRNAs targeting LMOf2365_1630 using algorithms specifically trained on gram-positive bacteria. Target sites should have minimal off-target effects and optimal GC content (40-60%). The most effective sgRNAs typically target the 5' region of the gene. Test at least 3-4 sgRNAs to identify the most efficient option.

• Homology-Directed Repair Template: Design repair templates with 750-1000bp homology arms flanking the intended modification site. For complete gene deletion, consider introducing a selectable marker (e.g., antibiotic resistance) between the homology arms. For point mutations or tagged versions, ensure the PAM site is mutated in the repair template to prevent re-cutting.

• Transformation Protocol: Optimize electroporation conditions for maximum efficiency (field strength: 7.5-10 kV/cm, resistance: 400 Ω, capacitance: 25 μF). Prepare electrocompetent cells from mid-log phase cultures grown in brain-heart infusion media supplemented with 0.5M sucrose.

• Screening and Verification: Design PCR primers that span the edited region for initial screening. Verify positive clones by Sanger sequencing and whole-genome sequencing to rule out off-target effects or unwanted rearrangements.

For complex modifications such as inserting fluorescent tags, consider a two-step approach: first create a deletion mutant, then complement with the tagged version at a neutral genomic site. This strategy minimizes polar effects on neighboring genes and allows controlled expression levels.

What experimental approaches can resolve contradictions in published data about LMOf2365_1630 function?

When faced with contradictory published findings regarding LMOf2365_1630 function, a systematic multi-faceted approach is necessary:

• Standardization of Experimental Conditions: First, identify variables that differ between contradictory studies, such as strain backgrounds, growth conditions, or assay methodologies. Design experiments that systematically test these variables. For instance, if contradictions exist regarding the protein's role in virulence, perform identical infection assays using multiple strains and cell lines.

• Independent Validation with Multiple Methodologies: Apply orthogonal techniques to address the same question. For example, if protein-protein interactions are contested, use both yeast two-hybrid and co-immunoprecipitation followed by western blotting or mass spectrometry.

• Genetic Complementation Studies: For contradictory phenotypes in knockout studies, perform complementation with both wild-type and mutant versions of LMOf2365_1630. Include controls with empty vectors to account for vector effects. Express the gene under both native and inducible promoters to assess dose-dependent effects.

• Strain-Specific Effects Analysis: Systematically test the function across different L. monocytogenes lineages and serotypes. Create isogenic strains by transferring the same gene deletion or mutation into multiple strain backgrounds.

• Collaborative Blind Studies: Organize collaborative studies where multiple laboratories perform identical experiments using shared protocols and materials but with researchers blinded to expected outcomes.

• Statistical Rigor Enhancement: Increase sample sizes and apply robust statistical methods appropriate for the data distribution. For borderline effects, power analysis should determine adequate sample sizes. Employ multiple statistical tests when appropriate and correct for multiple comparisons.

• Environmental Variable Exploration: Test function under various environmental conditions relevant to Listeria lifecycle (different temperatures, pH levels, salt concentrations, nutrient limitations) to identify condition-specific functions.

Document and report all experimental details meticulously, including seemingly minor variables such as media batch, incubation times, and equipment specifications, which might explain contradictory results in previous studies.

How can recombinant LMOf2365_1630 be utilized in vaccine development against listeriosis?

Recombinant LMOf2365_1630 shows potential as a component in listeriosis vaccine development through several strategic approaches:

• Subunit Vaccine Development: LMOf2365_1630 can be formulated as a subunit vaccine component, particularly if it contains conserved epitopes recognized by the human immune system. The protein should be expressed in systems that maintain proper folding and epitope presentation, such as E. coli or yeast expression systems . Combine with adjuvants like aluminum salts or oil-in-water emulsions to enhance immunogenicity.

• Epitope Mapping and Optimization: Conduct comprehensive B-cell and T-cell epitope mapping of LMOf2365_1630 using techniques such as peptide arrays, phage display, and in silico prediction algorithms. Focus vaccine design on highly immunogenic and conserved epitopes, potentially creating chimeric constructs that combine multiple protective epitopes.

• Delivery System Selection: Evaluate different delivery platforms including virus-like particles (VLPs), liposomes, and nanoparticles to enhance immune recognition and processing. These systems can improve stability and facilitate targeted delivery to antigen-presenting cells.

• Attenuated Live Vector Approach: Consider using attenuated bacterial vectors (e.g., Salmonella) expressing LMOf2365_1630 to stimulate mucosal immunity, which is crucial for protection against foodborne pathogens like Listeria.

• Combination Strategy: For optimal protection, combine LMOf2365_1630 with other Listeria immunogens such as listeriolysin O (LLO) or internalin B (InlB) in multivalent formulations. This approach can address strain variation and provide broader protection.

Immunization studies should progressively advance from mice to higher animal models before human trials. Evaluate protection using both serological responses (antibody titers, neutralization assays) and cellular immunity markers (T-cell proliferation, cytokine profiles). Challenge studies should use clinically relevant serotype 4b strains to assess real-world protection levels.

Note that all vaccine development work must remain in the research domain until safety and efficacy are thoroughly established through proper clinical trials .

What analytical methods best determine the structural changes of LMOf2365_1630 under different environmental conditions?

To comprehensively analyze structural changes in LMOf2365_1630 under varying environmental conditions, researchers should employ multiple complementary techniques:

For integrated analysis, researchers should develop a standardized condition matrix varying pH (4.5-8.0), temperature (4-42°C), salt concentration (0-500mM NaCl), and relevant stress conditions (oxidative stress, antimicrobial peptides). Results can be visualized using heat maps or structural models color-coded by degree of conformational change.

How can single-cell techniques be applied to study LMOf2365_1630 expression heterogeneity during infection?

Single-cell approaches offer powerful insights into LMOf2365_1630 expression heterogeneity that may be masked in population-level studies. Several cutting-edge techniques can be applied:

• Single-Cell RNA Sequencing (scRNA-seq): Modified protocols for bacterial scRNA-seq can capture transcriptional heterogeneity of LMOf2365_1630 during infection. The Split-seq or Drop-seq approaches can be adapted for bacterial cells isolated from infected host tissues. This reveals expression variability across individual bacteria and potential correlation with other virulence genes.

• Single-Cell Protein Analysis: Mass cytometry (CyTOF) using metal-tagged antibodies against LMOf2365_1630 can quantify protein levels in thousands of individual bacteria. This requires developing specific antibodies against the protein or using epitope tags.

• Reporter Systems: Construct transcriptional or translational fusions of LMOf2365_1630 promoter/gene with fluorescent proteins (e.g., mScarlet, mNeonGreen). Combined with flow cytometry or time-lapse microscopy, this enables tracking expression dynamics in individual cells during infection progression.

• Single-Cell Western Blotting: Microfluidic platforms for single-cell western blotting can quantify LMOf2365_1630 protein levels in individual bacteria isolated from infection models, revealing post-transcriptional regulation.

• Spatial Transcriptomics: Methods like MERFISH or seqFISH can map LMOf2365_1630 expression within tissue contexts, correlating expression with specific microenvironments or infection stages.

• Live-Cell Biosensors: Develop FRET-based or other biosensors that report on LMOf2365_1630 activity or interaction status in live bacteria during infection.

Analysis of single-cell data requires specialized computational approaches. Clustering algorithms can identify bacterial subpopulations with distinct expression patterns, while trajectory inference methods (e.g., RNA velocity) can map temporal expression dynamics. Correlation with host cell responses (using dual RNA-seq) can reveal how LMOf2365_1630 expression variation influences infection outcomes.

These approaches can reveal if LMOf2365_1630 exhibits bistable expression (ON/OFF states in different cells), condition-responsive regulation, or correlation with bacterial cell cycle or metabolic state during infection.

What machine learning approaches can predict functional mutations in LMOf2365_1630 that might enhance or inhibit Listeria virulence?

Advanced machine learning approaches offer powerful tools for predicting functional mutations in LMOf2365_1630 that may impact Listeria monocytogenes virulence:

• Deep Mutational Scanning (DMS) with ML Analysis: Generate a comprehensive library of LMOf2365_1630 variants through site-directed mutagenesis, test their functional effects, and use this data to train supervised learning models. Deep neural networks can identify complex patterns between sequence variations and phenotypic outcomes.

• Transfer Learning from Protein Families: Leverage pre-trained models on related UPF0354 protein families, then fine-tune with limited LMOf2365_1630-specific data. This approach overcomes the limited available data specific to this protein.

• Graph Neural Networks for Structural Prediction: Represent the protein structure as a graph where nodes are amino acids and edges represent spatial proximity or chemical interactions. Graph neural networks can then predict how mutations propagate effects through the protein structure.

• Generative Models for In Silico Evolution: Use variational autoencoders or generative adversarial networks trained on bacterial protein sequences to generate novel LMOf2365_1630 variants likely to fold properly while exhibiting desired properties.

• Ensemble Methods for Variant Effect Prediction: Combine predictions from multiple algorithms (support vector machines, random forests, gradient boosting) that utilize different feature sets (sequence conservation, physicochemical properties, predicted structural changes).

• Reinforcement Learning for Directed Evolution: Frame protein engineering as a reinforcement learning problem where the algorithm learns to propose mutations that maximize a specified "reward" (e.g., stability under stress conditions or immunogenicity).

For implementation, researchers should:

• Create a comprehensive feature set including sequence-based features (conservation scores, hydrophobicity profiles), structure-based features (solvent accessibility, secondary structure propensity), and evolutionary features (co-evolution patterns).

• Validate predictions experimentally through targeted mutagenesis of predicted high-impact residues, followed by functional assays such as bacterial survival in macrophages or mouse virulence models.

• Implement active learning protocols where model predictions guide experimental design, and new experimental results are fed back to improve the model iteratively.

The most promising approach combines AlphaFold2-predicted structures with deep learning models that integrate both sequence and structural information to provide mechanistic insights alongside predictions.

How might LMOf2365_1630 contribute to the evolution and adaptation of Listeria monocytogenes in different ecological niches?

Understanding LMOf2365_1630's role in Listeria monocytogenes evolution across ecological niches requires integrating several research approaches:

Comparative genomic analysis using whole-genome sequencing data from different L. monocytogenes lineages can reveal selection pressures acting on LMOf2365_1630. Analyzing dN/dS ratios (nonsynonymous to synonymous substitution rates) across different ecological isolates (food production environments, soil, clinical samples) can identify adaptation signatures . Long-read sequencing is particularly valuable for characterizing the genomic context of LMOf2365_1630, including nearby mobile genetic elements that may influence its expression or function .

Researchers should establish experimental evolution studies exposing L. monocytogenes strains to conditions representing different niches (varying temperature, pH, nutrient availability, antimicrobial compounds) for hundreds of generations. Whole-genome sequencing at different time points can identify mutations in LMOf2365_1630 that arise during adaptation. These can be complemented with transcriptomic and proteomic analyses to determine how expression patterns change during adaptation.

Molecular clock analyses, similar to those performed for outbreak strains, can estimate divergence rates of LMOf2365_1630 across different environmental contexts . The average nucleotide substitution rate may vary significantly between clinical and environmental isolates, providing insights into niche-specific evolutionary pressures.

Field studies sampling diverse environments (food processing facilities, farm environments, natural water sources) can provide real-world context for laboratory findings. Correlating LMOf2365_1630 sequence variations with specific environmental parameters may reveal adaptation patterns not observable in laboratory settings.

These approaches should be integrated within a phylogenetic framework to distinguish adaptation events from neutral evolution and to identify convergent evolution of LMOf2365_1630 across independent lineages facing similar environmental challenges.

What are the implications of LMOf2365_1630 research for developing novel antimicrobial strategies against multidrug-resistant Listeria strains?

Research on LMOf2365_1630 opens several promising avenues for developing novel antimicrobial strategies against multidrug-resistant Listeria monocytogenes strains:

• Structure-Based Drug Design: If LMOf2365_1630 proves essential for Listeria survival or virulence, its three-dimensional structure can serve as a template for rational drug design. In silico screening of compound libraries against predicted binding pockets, followed by experimental validation, can identify potential inhibitors. Fragment-based approaches may be particularly effective for novel targets with unknown binding partners.

• Peptide-Based Inhibitors: Designing peptides that mimic interaction interfaces of LMOf2365_1630 with its binding partners can disrupt protein-protein interactions critical for function. Stapled peptides or peptide macrocycles offer improved stability and cell penetration compared to linear peptides.

• PROTAC Technology Application: Proteolysis-targeting chimeras (PROTACs) can be designed to target LMOf2365_1630 for degradation by the bacterial proteasome system. This approach requires identifying ligands that bind LMOf2365_1630 and linking them to molecules recognized by bacterial proteolytic machinery.

• Antisense Strategies: Antisense oligonucleotides or peptide nucleic acids (PNAs) targeting LMOf2365_1630 mRNA can prevent protein translation. These can be delivered using cell-penetrating peptides or nanoparticles designed to enter bacterial cells.

• Immunotherapeutic Approaches: If LMOf2365_1630 is surface-exposed or secreted during infection, monoclonal antibodies or antibody-drug conjugates targeting this protein could be developed for therapeutic use in severe listeriosis cases.

• CRISPR-Cas Antimicrobials: CRISPR-Cas systems delivered via phage vectors can specifically target and cleave the LMOf2365_1630 gene. This approach offers high specificity, potentially avoiding disruption of beneficial microbiota.

The development pipeline should include screening for synergistic effects with existing antibiotics, as combination therapies may overcome resistance mechanisms. Any novel antimicrobial strategy should be evaluated not only for effectiveness but also for the potential to drive resistance development, with careful consideration of evolutionary pathways that might circumvent the intervention.