Recombinant Pseudomonas syringae pv. tomato Membrane-bound lytic murein transglycosylase F (mltF)

What is the primary function of membrane-bound lytic murein transglycosylase F (mltF) in Pseudomonas syringae pv. tomato?

Membrane-bound lytic murein transglycosylase F (mltF) is one of several lytic transglycosylases (LTGs) that play critical roles in bacterial cell wall maintenance. The primary function of mltF is to cleave glycosidic bonds in peptidoglycan, participating in the processing of soluble peptidoglycan strands in the periplasm. Research demonstrates that LTGs collectively prevent toxic crowding of the periplasm with synthesis-derived peptidoglycan polymers . Without sufficient LTG activity, bacteria accumulate uncrosslinked peptidoglycan strands that cannot diffuse through outer membrane porins, leading to increased osmolarity and excessive crowding of the periplasmic space that interferes with normal cellular processes.

How does mltF structurally differ from other LTGs in Pseudomonas syringae?

The membrane-bound lytic murein transglycosylase F in Pseudomonas syringae pv. syringae B728a has a distinct structural organization compared to other LTGs. According to structural data, mltF contains regions of varying confidence in its computed structure model . The protein exhibits a specific domain architecture with very high confidence regions (pLDDT > 90) in its core catalytic domain and some regions of lower confidence that may represent more flexible portions of the protein. The unique structural features of mltF likely contribute to its specific function among the LTG family, which includes proteins such as MltA, MltB, MltC, MltD, and Slt70.

What are the optimal experimental designs for studying mltF function in Pseudomonas syringae pv. tomato?

When studying mltF function in Pseudomonas syringae pv. tomato, researchers should employ a multi-faceted experimental approach:

• Genetic manipulation studies: Create clean deletion mutants and conditional depletion strains using systems like the pTOX5 cmR/msqR allelic exchange system .

• Complementation assays: Utilize ectopic chromosomal expression from IPTG-inducible Ptac promoter through suicide vector integration (like pTD101) .

• Phenotypic characterization: Assess growth under various conditions (standard LB, low-salt LB) and sensitivity to osmotic stress, antibiotics, and polymeric sugars.

• Peptidoglycan analysis: Employ methods to quantify soluble peptidoglycan species (M4, M4N) in the periplasm to measure accumulation of uncrosslinked strands.

• Microscopy: Monitor morphological changes associated with mltF deletion or depletion.

The experimental design should follow proper controls and incorporate multiple variables to ensure statistical validity according to established principles .

What methods are recommended for the recombinant expression and purification of mltF from Pseudomonas syringae pv. tomato?

For recombinant expression and purification of mltF from Pseudomonas syringae pv. tomato, researchers should consider:

• Expression system selection: Use either homologous expression in Pseudomonas or heterologous expression in E. coli, with the latter being more common for high-yield protein production.

• Vector design: Engineer constructs containing:

  • Strong, inducible promoters (e.g., T7 or tac)

  • Appropriate fusion tags (His6, MBP, GST) for purification and solubility enhancement

  • Signal sequences if maintaining proper localization is critical

• Purification strategy:

  • Immobilized metal affinity chromatography (IMAC) for His-tagged proteins

  • Size exclusion chromatography to ensure homogeneity

  • Include proper detergents or amphipols if membrane association is to be maintained

• Activity verification: Develop in vitro assays to confirm enzymatic activity of the purified protein against peptidoglycan substrates.

How can the RecTE(Psy) recombineering system be utilized for targeted mutations in mltF?

The RecTE(Psy) recombineering system provides an efficient method for introducing specific mutations into the mltF gene of Pseudomonas syringae. The methodology involves:

• Preparation of recombineering-competent cells: Transform P. syringae with a plasmid expressing the RecT and RecE homologs from P. syringae pv. syringae B728a .

• Design of recombineering substrates: Create PCR products containing the desired mltF mutations flanked by 50-1000 bp homology arms matching the genomic target region.

• Transformation protocol:

  • Grow cells expressing RecTE(Psy) to mid-log phase

  • Induce expression of RecTE(Psy) proteins

  • Prepare electrocompetent cells

  • Electroporate with the PCR product (1-5 μg)

  • Recover cells in rich medium

  • Select for recombinants using appropriate markers

• Verification: Confirm successful recombination by PCR and sequencing of the targeted region.

This system allows for precise genetic manipulations without the limitations of traditional allelic exchange methods, especially when working with genes where selective pressure might be challenging .

What strategies can be employed to create a conditional mltF mutant for functional studies?

Creating conditional mltF mutants is crucial for studying essential or near-essential functions. Recommended strategies include:

• Inducible promoter replacement:

  • Replace the native mltF promoter with an arabinose-inducible (PBAD) or IPTG-inducible (Ptac) promoter

  • This allows controlled expression by adding or withholding the inducer

• Chromosomal integration of complementing copy:

  • Maintain a wild-type copy of mltF under an inducible promoter at a neutral site

  • Delete the native copy

  • Control expression through inducer concentration

• Degradation tag systems:

  • Fuse mltF with conditional degradation tags (e.g., ssrA tags)

  • Control protein levels post-translationally

• Verification protocol:

  • Confirm depletion using Western blotting

  • Monitor growth phenotypes in permissive and non-permissive conditions

  • Assess morphological changes using microscopy

  • Analyze peptidoglycan composition changes

These approaches enable temporal control of mltF expression, facilitating detailed studies of its role in different growth phases and conditions .

How does mltF contribute to Pseudomonas syringae pv. tomato virulence and host specificity?

The contribution of mltF to Pseudomonas syringae pv. tomato virulence and host specificity involves complex interactions with plant immunity and bacterial physiology:

• Cell wall integrity maintenance: MltF activity ensures proper cell envelope structure, which is essential for bacterial survival during plant infection and exposure to host defense compounds .

• Type III secretion system (T3SS) function: Proper peptidoglycan turnover facilitated by LTGs like mltF may be necessary for efficient assembly and operation of the T3SS, a key virulence determinant in P. syringae .

• Adaptation to plant environment: MltF contributes to bacterial adaptation to the plant apoplast environment, which includes osmotic fluctuations and antimicrobial compounds .

• Response to plant signals: P. syringae pv. tomato infection involves recognition of plant-derived compounds through chemoreceptors and adaptation to compounds like GABA and L-Pro in the tomato apoplast .

Research using host range tests with mltF mutants can determine if altered LTG activity affects the pathogen's ability to infect different plant species or cultivars .

What is the relationship between mltF function and β-lactam antibiotic sensitivity in Pseudomonas syringae pv. tomato?

The relationship between mltF function and β-lactam antibiotic sensitivity in Pseudomonas syringae pv. tomato reveals important insights about bacterial cell wall metabolism:

• Hypersensitivity in LTG-deficient mutants: Studies show that mutants lacking multiple LTGs, including mltF, exhibit hypersensitivity to β-lactam antibiotics that induce futile cycling of peptidoglycan synthesis .

• Mechanistic basis:

  • β-lactams inhibit penicillin-binding proteins (PBPs), leading to accumulation of uncrosslinked peptidoglycan strands

  • In wild-type cells, LTGs process these strands to prevent periplasmic crowding

  • In mltF-deficient strains (especially when combined with other LTG mutations), unprocessed strands accumulate to toxic levels

• Differential sensitivity pattern:

  Antibiotic	Target	WT Sensitivity	ΔmltF Sensitivity	Δ6 LTG Sensitivity	Δ7 LTG Sensitivity
  Penicillin G	General PBPs	+	++	+++	++++
  Aztreonam	PBP3	+	++	+++	++++
  Mecillinam	PBP2	+	++	+++	++++
  Cefsulodin	PBP1b	+	++	+++	++++
  Moenomycin	Transglycosylases	+	+	+	+
  Fosfomycin	MurA	+	+	+	+

• Research applications: This relationship can be exploited for enhanced molecular and genetic analyses, as β-lactam sensitivity provides a selectable phenotype for mltF mutants .

What methods can be used to quantitatively assess mltF enzymatic activity in vitro?

Quantitative assessment of mltF enzymatic activity in vitro requires specialized biochemical approaches:

• Substrate preparation:

  • Isolate purified peptidoglycan from bacterial cells

  • Prepare fluorescently labeled peptidoglycan substrates

  • Generate synthetic peptidoglycan fragments of defined structure

• Activity assays:

  • HPLC analysis: Quantify the release of muropeptides following digestion

  • Fluorescence-based assays: Monitor the increase in fluorescence as labeled substrates are cleaved

  • Turbidimetric assays: Measure the decrease in turbidity as insoluble peptidoglycan is solubilized

• Kinetic parameters determination:

  • Vary substrate concentration to determine Km and Vmax

  • Test activity under different pH and ionic strength conditions

  • Assess the effects of potential inhibitors

• Product analysis:

  • Use mass spectrometry to identify the exact bonds cleaved by mltF

  • Compare the product profile with other LTGs to determine specificity

• Data analysis:

  • Apply appropriate enzyme kinetics models

  • Use statistical methods to verify reproducibility and significance

These methods provide quantitative insights into the catalytic properties of mltF and its substrate preferences.

How do various environmental conditions affect mltF expression and activity in Pseudomonas syringae pv. tomato?

The expression and activity of mltF in Pseudomonas syringae pv. tomato is influenced by multiple environmental conditions, which can be systematically studied:

• Growth phase-dependent regulation:

  • Expression typically changes between exponential and stationary phases

  • Soluble peptidoglycan strands decrease in stationary phase, suggesting altered LTG activity

• Osmotic stress response:

  • Low-salt conditions (LB0N) significantly affect LTG-deficient mutants

  • mltF expression may be upregulated under osmotic stress as a compensatory mechanism

• Plant-associated signals:

  • Plant apoplast components like GABA and L-Pro can trigger changes in bacterial gene expression

  • These compounds are recognized by bacterial chemoreceptors and may influence cell wall metabolism

• Temperature effects:

  • Growth temperature affects membrane fluidity and protein activity

  • Optimal temperature for mltF activity may differ from optimal growth temperature

• Experimental approach:

  • Use transcriptomics (RNA-seq) to monitor expression under various conditions

  • Employ reporter gene fusions (mltF promoter-gfp) to track expression in real-time

  • Conduct activity assays with purified enzyme under varying conditions

Understanding these environmental influences provides insights into how P. syringae adapts its cell wall metabolism during host colonization and pathogenesis.

How has mltF evolved within different pathovars of Pseudomonas syringae, and what implications does this have for function?

The evolutionary trajectory of mltF within different pathovars of Pseudomonas syringae provides insights into functional adaptation:

This evolutionary perspective helps explain functional differences observed between mltF from different pathovars.

What are the functional differences between mltF and other lytic transglycosylases in Pseudomonas syringae pv. tomato?

Understanding the functional differences between mltF and other lytic transglycosylases in Pseudomonas syringae pv. tomato is crucial for targeted research:

• Functional redundancy and specialization:

  • While multiple LTGs can partially complement each other's functions, each has distinct specializations

  • MltF has unique contributions that cannot be fully compensated by other LTGs

  • Overexpression of mltF is toxic in Δ6 and Δ7 LTG mutant backgrounds, indicating complex functional relationships

• Comparative contribution table:

  LTG	Contribution to Cell Division	Role in PG Release	Osmotic Stress Response	β-lactam Resistance
  MltF	++	+	+++	++
  MltG	+++	++++	++++	+++
  MltA	++	+	++	+
  MltB	++	+	++	+
  MltC	+	+	+	+
  MltD	+	+	+	+
  Slt70	++	+	++	+
  RlpA	++++	+	++++	+

• Substrate preferences:

  • Different LTGs may preferentially cleave specific bonds within peptidoglycan

  • MltF may have distinct substrate preferences compared to other LTGs

  • These preferences influence their roles in different cellular processes

• Cellular localization:

  • Membrane association patterns differ among LTGs

  • Localization influences access to substrates and interaction with synthetic machinery

  • MltF's membrane association may target it to specific subcellular regions

• Protein interactions:

  • LTGs form different protein complexes with cell wall synthesis and modification enzymes

  • These interaction networks determine their specific functions in cell wall metabolism

This functional differentiation explains why multiple LTGs are maintained in bacterial genomes despite apparent redundancy.

How can tabular foundation models be applied to predict mltF interactions in Pseudomonas syringae pv. tomato?

Recent advances in tabular foundation models offer powerful approaches for predicting mltF interactions in Pseudomonas syringae pv. tomato:

• Data integration and preprocessing:

  • Compile multi-omics datasets (transcriptomics, proteomics, metabolomics) from P. syringae

  • Structure data in tabular format capturing gene expression, protein abundance, and metabolite levels across conditions

  • Include metadata on experimental conditions, growth phases, and host interactions

• Application of TabPFN approach:

  • Apply Tabular Prior-data Fitted Network (TabPFN) methods to predict functional interactions

  • These models outperform traditional methods on datasets with up to 10,000 samples

  • Use generative transformer-based foundation model capabilities for:

    • Learning reusable embeddings of mltF-related data

    • Fine-tuning on specific P. syringae datasets

• Prediction workflow:

  • Train models to predict mltF expression patterns under various conditions

  • Identify potential genetic interactions by predicting phenotypic outcomes of combined mutations

  • Generate synthetic data to augment limited experimental datasets

• Validation strategy:

  • Verify key predictions through targeted experiments

  • Use cross-validation and independent test sets to assess model performance

  • Compare with established methods like gradient-boosted decision trees

This approach enables researchers to systematically explore the complex interaction network of mltF with other cellular components and prioritize hypotheses for experimental validation .

What bioinformatic approaches can identify novel LTG family members in newly sequenced Pseudomonas syringae strains?

Identifying novel LTG family members in newly sequenced Pseudomonas syringae strains requires sophisticated bioinformatic approaches:

• Sequence-based identification:

  • Create position-specific scoring matrices (PSSMs) or hidden Markov models (HMMs) from known LTGs

  • Perform iterative searches (PSI-BLAST, HMMER) against new genome sequences

  • Validate candidates by checking for conserved catalytic residues

• Structure-based prediction:

  • Use AlphaFold or similar tools to predict 3D structures of candidate proteins

  • Compare with known LTG structures such as the computed model of mltF (AF_AFQ4ZX03F1)

  • Identify structural motifs associated with LTG activity

• Genomic context analysis:

  • Examine the genomic neighborhood of candidate genes

  • Look for co-occurrence with cell wall synthesis or modification genes

  • Analyze synteny across related strains

• Evolutionary classification:

  • Construct phylogenetic trees of identified LTGs

  • Classify new members within established families

  • Identify potential horizontal gene transfer events using methods similar to those used for T3SE analysis

• Functional prediction:

  • Use machine learning approaches to predict substrate specificity

  • Infer potential roles based on expression patterns in available transcriptomic data

  • Predict functional interactions with other proteins

This comprehensive approach enables researchers to continuously update the LTG family catalog as new Pseudomonas syringae genomes become available.

How can mltF be exploited for developing novel antimicrobial strategies against Pseudomonas syringae pv. tomato?

Exploiting mltF for novel antimicrobial strategies against Pseudomonas syringae pv. tomato represents an advanced research direction:

• Inhibitor development approach:

  • Design small molecules that specifically inhibit mltF activity

  • Target unique structural features identified in computational models

  • Develop peptidomimetics that compete with natural substrates

• Synergistic strategy:

  • Combine mltF inhibitors with β-lactam antibiotics

  • Target multiple LTGs simultaneously to prevent compensatory mechanisms

  • Exploit the hypersensitivity of LTG-deficient mutants to osmotic stress and polymeric sugars

• Host-induced silencing:

  • Engineer plants to express RNA interference constructs targeting mltF

  • Develop transgenic tomato plants with enhanced resistance

• Structure-based rational design workflow:

  • Use the AlphaFold model of mltF for virtual screening

  • Perform molecular docking studies to identify potential binding sites

  • Optimize lead compounds through iterative design and testing

• Experimental validation pipeline:

  • In vitro enzyme inhibition assays

  • Bacterial growth inhibition tests

  • Plant infection models

  • Resistance development assessment

This research direction could yield novel plant protection strategies with specificity for P. syringae pathogens.