Recombinant Bacillus subtilis Uncharacterized protein ylaF (ylaF)

What are the primary structural characteristics of the YlaF protein in Bacillus subtilis?

While YlaF remains largely uncharacterized, structural analysis approaches would follow similar methodologies to those used for other B. subtilis proteins. Researchers should employ a combination of computational prediction tools and experimental techniques. Begin with sequence alignment against characterized proteins using tools like BLAST and Pfam to identify potential conserved domains. For experimental structure determination, express the recombinant protein with appropriate tags (His6 is commonly used) and purify using affinity chromatography followed by size exclusion chromatography to obtain homogeneous protein samples. X-ray crystallography or NMR spectroscopy can then be used to determine the three-dimensional structure, while circular dichroism provides information on secondary structure elements. Computational approaches using homology modeling based on proteins with similar sequences, like those used for the Tm1631 protein from Thermotoga maritima, can predict structural features when experimental data is limited .

What approaches can be used to express and purify recombinant YlaF protein?

For optimal expression of recombinant YlaF, consider using E. coli BL21(DE3) or similar expression systems optimized for Bacillus subtilis proteins. Construct an expression vector containing the ylaF gene with an N-terminal His6-tag and a TEV protease cleavage site. Culture transformants in LB medium supplemented with appropriate antibiotics at 37°C until OD600 reaches 0.6-0.8, then induce with 0.5-1 mM IPTG. After induction, grow cells at 18°C for 16-18 hours to enhance soluble protein production.

For purification, harvest cells by centrifugation and lyse using sonication in a buffer containing 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10 mM imidazole, and protease inhibitors. Clarify the lysate by centrifugation and load the supernatant onto a Ni-NTA affinity column. Elute the protein using an imidazole gradient (50-300 mM). For higher purity, perform additional purification steps including ion exchange chromatography and size exclusion chromatography. Verify protein purity using SDS-PAGE and Western blotting. This approach parallels successful purification protocols used for other Bacillus subtilis proteins such as YngB .

How can I assess the cellular localization of YlaF in Bacillus subtilis?

To determine YlaF localization, employ a multi-method approach. Start by creating a YlaF-GFP fusion construct and transform it into B. subtilis. Visualize the localization pattern using fluorescence microscopy with appropriate controls. For more detailed localization, perform cellular fractionation to separate cytoplasmic, membrane, cell wall, and extracellular fractions. Extract proteins from each fraction and analyze by Western blotting using anti-YlaF antibodies or anti-tag antibodies if using a tagged version.

Additionally, perform immunofluorescence microscopy using fixed B. subtilis cells and specific antibodies against YlaF to confirm localization patterns observed with GFP fusions. For higher resolution analysis, consider immuno-electron microscopy. Compare localization under different growth conditions, including aerobic and anaerobic environments, as protein expression and localization may vary based on oxygen availability, similar to what has been observed with YngB in B. subtilis . Document any changes in localization during different growth phases or in response to environmental stresses.

What computational approaches can predict the function of YlaF based on structural features?

For functional prediction of uncharacterized proteins like YlaF, employ a comprehensive computational pipeline. Begin with advanced homology searches using PSI-BLAST and HHpred to detect remote homologs. Structural prediction using AlphaFold2 or RoseTTAFold can generate high-confidence 3D models even with limited sequence similarity to known proteins.

Next, implement binding site analysis using tools like ProBiS, which has proven effective for functional annotation of uncharacterized proteins by comparing predicted binding sites to a library of candidate structures . Apply this approach to YlaF:

• Generate the YlaF 3D structure prediction

• Identify potential binding pockets using CASTp or POCASA

• Compare these pockets against a binding site library containing thousands of characterized protein structures

• Analyze structural similarities with specific focus on conserved residues at binding interfaces

For YlaF functional analysis, integrate secondary predictions including Gene Ontology terms using tools like DeepGOPlus, analysis of genomic context examining neighboring genes in the B. subtilis genome, and identification of conserved sequence motifs that correlate with specific biochemical functions. This multi-faceted approach increases prediction confidence, especially when multiple methods converge on similar functional hypotheses.

How can high-throughput transcriptomic approaches help characterize YlaF function in B. subtilis?

To characterize YlaF function using transcriptomics, implement a systematic differential expression analysis comparing wild-type B. subtilis with ΔylaF mutants. Culture both strains under various conditions, including standard laboratory conditions, oxygen limitation, nutrient stress, and cell envelope stress (which often affects expression of cell wall-associated proteins in B. subtilis) .

Extract total RNA using hot phenol extraction followed by DNase treatment to eliminate genomic DNA contamination. Assess RNA quality using an Agilent Bioanalyzer (RIN > 9) before preparing sequencing libraries. Perform RNA-seq using a platform like Illumina NovaSeq with at least 20 million reads per sample and biological triplicates for statistical robustness.

For data analysis, align reads to the B. subtilis reference genome using STAR or Bowtie2, then quantify expression using featureCounts or HTSeq. Perform differential expression analysis using DESeq2 or edgeR with a false discovery rate (FDR) < 0.05 and |log2FC| > 1 as significance thresholds. Conduct Gene Ontology and pathway enrichment analysis on differentially expressed genes to identify biological processes potentially associated with YlaF function.

Additionally, analyze transcriptomic data specifically for genes involved in cell wall biosynthesis, teichoic acid decoration, and stress responses, as these pathways are frequently interconnected in B. subtilis . Look for co-expression patterns between ylaF and other genes to identify potential functional relationships. Validate key findings using quantitative RT-PCR.

What is the most effective experimental approach to determine if YlaF has UTP-glucose-1-phosphate uridylyltransferase activity similar to YngB in B. subtilis?

To determine if YlaF possesses UTP-glucose-1-phosphate uridylyltransferase activity similar to YngB, implement a comprehensive biochemical characterization workflow:

• Enzyme activity assay: Develop an in vitro assay using purified recombinant YlaF protein with UTP and glucose-1-phosphate as substrates. Monitor UDP-glucose formation using either a coupled enzymatic assay that measures NADH oxidation spectrophotometrically or by direct detection of UDP-glucose using HPLC or LC-MS/MS. Include GtaB (a known UGPase) as positive control and denatured YlaF as negative control .

• Kinetic characterization: If activity is detected, determine kinetic parameters (Km, Vmax, kcat) for both substrates by varying substrate concentrations while keeping enzyme concentration constant. Calculate catalytic efficiency (kcat/Km) and compare to known UGPases like GtaB and YngB.

• Complementation studies: Construct a B. subtilis gtaB/yngB double mutant strain and transform it with a plasmid expressing ylaF from an inducible promoter. Assess whether YlaF expression restores phenotypes associated with UGPase deficiency, such as:

  • Wall teichoic acid glucose decoration (test using concanavalin A fluorescent staining)

  • Glycolipid production (analyze using thin-layer chromatography)

  • Cell morphology restoration (examine using phase-contrast microscopy)

  • Phage resistance/susceptibility

• Structural analysis: Compare the crystal structure of YlaF (if available) or a computational model with YngB and other UGPases, focusing on the active site architecture and substrate binding residues. Key features of functional UGPases include a nucleotide-binding domain with a characteristic Rossmann fold and conserved residues for binding UTP and glucose-1-phosphate .

How can we resolve contradictory findings about YlaF function in the research literature?

To systematically address contradictory findings about YlaF function, implement a structured analysis framework that combines literature evaluation with targeted experiments:

For experimental contradiction resolution, design controlled experiments that directly address the discrepancies:

• Standardize experimental conditions: Recreate experiments using identical B. subtilis strains, growth media, and environmental conditions to determine if contradictions arise from methodological differences.

• Cross-validate using orthogonal techniques: If contradictory results were obtained using different experimental approaches, apply multiple complementary techniques to the same biological question. For example, if protein-protein interactions show discrepancies, combine bacterial two-hybrid assays, co-immunoprecipitation, and proximity labeling approaches.

• Genetic background effects: Test YlaF function in different B. subtilis genetic backgrounds and related Bacillus species to determine if contradictions stem from strain-specific effects or genetic modifiers.

• Conditional functionality: Systematically test YlaF under varying conditions including oxygen availability, nutrient limitation, and cell envelope stress, as protein function may be context-dependent similar to YngB, which shows condition-specific expression and activity .

What is the optimal strategy for creating clean ylaF deletion and complementation strains in B. subtilis?

For generating precise ylaF deletion and complementation strains in B. subtilis, implement a seamless deletion strategy followed by controlled complementation:

Deletion strain construction:

• Design PCR primers to amplify approximately 1000 bp upstream and downstream of the ylaF coding sequence.

• Clone these fragments into a suicide vector containing a selectable marker (e.g., spectinomycin resistance) flanked by loxP sites.

• Transform the construct into naturally competent B. subtilis 168 and select for single-crossover integrants.

• Counter-select for double-crossover events resulting in marker insertion.

• Remove the marker by expressing Cre recombinase, leaving a clean deletion with only a 34 bp scar sequence.

• Verify deletion by PCR, Sanger sequencing, and RT-PCR to confirm absence of ylaF transcription.

Complementation system development:

• For controlled expression, clone the ylaF gene into vectors with different promoters:

  • Native promoter (for physiological expression)

  • IPTG-inducible Pspac promoter (for titratable expression)

  • Xylose-inducible PxylA promoter (for alternative induction)

• Integrate these constructs at neutral loci (e.g., amyE or lacA) to avoid positional effects.

• Include C-terminal epitope tags (FLAG, HA) for a subset of constructs to enable protein detection.

• Verify successful integration by PCR and confirm expression by Western blot.

For robust phenotypic analysis, compare the wild-type, deletion, and complemented strains using multiple phenotypic assays including growth curves under various conditions, cell morphology analysis, stress resistance, and specific functional assays based on predicted YlaF function. Include relevant control strains, such as deletions of genes with known functions similar to the predicted function of YlaF .

How can I identify potential protein-protein interaction partners of YlaF in B. subtilis?

To comprehensively identify YlaF protein interaction partners, implement a multi-faceted approach combining in vivo and in vitro techniques:

In vivo approaches:

• Affinity purification-mass spectrometry (AP-MS):

  • Express YlaF with an affinity tag (e.g., His6-FLAG tandem tag) in B. subtilis

  • Perform crosslinking with formaldehyde (0.1%, 10 min) to capture transient interactions

  • Lyse cells under native conditions and perform tandem affinity purification

  • Identify co-purified proteins using LC-MS/MS

  • Analyze data using SAINT algorithm to distinguish true interactors from background

• Proximity-dependent biotin labeling (BioID):

  • Generate YlaF-BioID2 fusion protein and express in B. subtilis

  • Induce biotinylation with biotin (50 μM) for 16 hours

  • Purify biotinylated proteins using streptavidin beads

  • Identify labeled proteins by mass spectrometry

In vitro approaches:

• Protein microarrays:

  • Probe a B. subtilis proteome chip with purified recombinant YlaF

  • Detect binding using fluorescently labeled anti-YlaF antibodies

  • Quantify interactions using signal intensity above background

• Surface plasmon resonance (SPR):

  • Immobilize purified YlaF on a sensor chip

  • Flow candidate interactors identified from other screens

  • Determine binding kinetics (kon, koff) and affinity (KD)

Computational prediction:
Predict interaction partners using STRING database, focusing on genomic context, co-expression data, and functional associations. For proteins like YlaF, examine interactions of paralogous proteins with known functions (e.g., YngB) .

For validation, confirm key interactions using independent methods:

• Co-immunoprecipitation with reciprocal pulldowns

• Bacterial two-hybrid assays

• Fluorescence resonance energy transfer (FRET)

• Split-GFP complementation assays

How do oxygen levels affect YlaF expression and function in B. subtilis?

To comprehensively characterize how oxygen levels affect YlaF expression and function, implement a systematic experimental approach that parallels studies of YngB, which demonstrated oxygen-dependent expression patterns :

Expression analysis:

• Culture B. subtilis under precisely controlled oxygen conditions:

  • Aerobic (21% O2, vigorous shaking)

  • Microaerobic (5% O2, limited shaking)

  • Anaerobic (0% O2, anaerobic chamber with N2/H2/CO2 atmosphere)

• Construct a transcriptional fusion of the ylaF promoter region to a reporter gene (lacZ or luciferase) and measure activity under each oxygen condition at multiple time points during growth.

• Perform quantitative RT-PCR to measure ylaF mRNA levels under each condition, normalizing to stable reference genes.

• Develop a YlaF-specific antibody or use epitope-tagged YlaF to quantify protein levels by Western blot under each oxygen condition.

Functional characterization:

• Generate a ΔylaF mutant and analyze its phenotype under each oxygen condition, comparing:

  • Growth rates and biomass accumulation

  • Cell morphology and division patterns

  • Metabolic profiles using LC-MS metabolomics

  • Transcriptomic responses using RNA-seq

  • Sensitivity to various stressors (oxidative, cell wall targeting antibiotics)

• Identify potential regulatory elements in the ylaF promoter region that respond to oxygen levels:

  • Bioinformatic analysis for binding sites of oxygen-responsive regulators (ResD, Fnr)

  • DNA affinity purification to identify proteins binding to the ylaF promoter under different oxygen conditions

  • ChIP-seq for known oxygen-responsive transcription factors to confirm direct regulation

• If YlaF is involved in cell wall modification like YngB, analyze cell wall composition under different oxygen conditions:

  • Characterize teichoic acid modifications using specific lectins like concanavalin A

  • Analyze glycolipid content using thin-layer chromatography or LC-MS

  • Examine peptidoglycan crosslinking and modifications

What bioinformatic approaches can reveal the evolutionary conservation and distribution of YlaF among bacterial species?

To comprehensively analyze YlaF evolutionary conservation and distribution, implement a multi-level bioinformatic approach:

Sequence-based phylogenetic analysis:

• Use the YlaF amino acid sequence from B. subtilis as a query for PSI-BLAST searches against the NCBI non-redundant protein database, adjusting E-value thresholds to capture remote homologs.

• Identify orthologs across diverse bacterial phyla using reciprocal best hit methodology combined with synteny analysis to confirm orthologous relationships.

• Perform multiple sequence alignment of identified orthologs using MAFFT with G-INS-i strategy for maximum accuracy.

• Generate a maximum likelihood phylogenetic tree using IQ-TREE with model testing and ultrafast bootstrap approximation (1000 replicates).

Genomic context analysis:

• Examine the conservation of genes neighboring ylaF across species, as conserved genomic neighborhoods often suggest functional relationships.

• Map ylaF presence/absence onto a bacterial species tree to identify patterns of conservation or loss across evolutionary lineages.

• Compare these patterns with known B. subtilis gene regulatory systems like the LiaFRS module that integrates both positive and negative feedback loops in cell envelope stress response .

Structural conservation analysis:

• Use the ProBiS approach to compare predicted binding sites of YlaF homologs across species, identifying structurally conserved functional sites even when sequence conservation is limited .

• Calculate site-specific evolutionary rates using Rate4Site to identify functionally constrained residues.

• Map conservation scores onto the predicted three-dimensional structure to visualize conserved structural elements.

These data suggest YlaF likely originated in Firmicutes with subsequent horizontal gene transfer to specific lineages in other phyla. The decreasing conservation of genomic context correlates with evolutionary distance from Bacillus, suggesting potential functional divergence across bacterial phyla.

How can integrated multi-omics data help resolve the function of YlaF in B. subtilis?

To elucidate YlaF function through multi-omics integration, implement a comprehensive systems biology workflow combining multiple data types with advanced computational analysis:

Data generation and integration:

• Transcriptomics: Perform RNA-seq comparing wild-type and ΔylaF strains under multiple conditions, focusing on conditions where YlaF is expressed, such as anaerobic growth (based on patterns observed with similar proteins like YngB) .

• Proteomics: Conduct quantitative proteomics using both global approaches (TMT-based quantification) and targeted approaches (parallel reaction monitoring) to identify proteins with altered abundance in ΔylaF strains.

• Metabolomics: Employ untargeted LC-MS metabolomics to identify metabolic pathways affected by ylaF deletion, with particular attention to nucleotide-sugar metabolites like UDP-glucose if YlaF functions similarly to YngB .

• Phenomics: Use Biolog phenotype microarrays to assess growth of ΔylaF mutants across hundreds of conditions, identifying specific phenotypes associated with ylaF deletion.

Integrative analysis approaches:

• Network analysis: Construct condition-specific gene regulatory networks using ARACNE or PANDA algorithms, identifying YlaF-associated modules.

• Bayesian integration: Implement a Bayesian network approach to integrate multi-omics data and identify causal relationships between YlaF and downstream effects.

• Machine learning: Apply supervised machine learning to classify YlaF-dependent molecular signatures and identify the most predictive features across omics datasets.

• Enrichment analysis: Perform gene set enrichment analysis across all omics layers to identify consistently altered pathways, using both pre-defined pathways and data-driven modules.

For final functional prediction, implement a data triangulation approach, where multiple independent lines of evidence pointing to the same function provide higher confidence than any single data type. Visualize the integrated multi-omics data using dimensionality reduction techniques like t-SNE or UMAP to identify condition-specific clusters and potential functional relationships.

What are the most promising research directions for fully characterizing the role of YlaF in B. subtilis physiology?

Based on current understanding of B. subtilis uncharacterized proteins and methodologies for functional characterization, several high-priority research directions emerge for elucidating YlaF's biological role:

• Condition-specific expression profiling: Given that YngB shows oxygen-dependent expression patterns , a systematic analysis of YlaF expression under diverse environmental conditions (oxygen levels, nutrient availability, stress responses) would identify specific conditions where YlaF plays critical roles. This should employ both transcriptional reporter fusions and quantitative proteomics.

• High-resolution structural characterization: Determining the three-dimensional structure of YlaF through X-ray crystallography or cryo-EM would provide crucial insights into potential biochemical functions. Structure-based approaches have proven effective for uncharacterized proteins like Tm1631 from Thermotoga maritima and could similarly resolve YlaF function.

• Synthetic genetic array analysis: Constructing a comprehensive genetic interaction map by combining ylaF deletion with deletions of all non-essential B. subtilis genes would reveal functional connections to known pathways. Particular attention should be paid to interactions with genes involved in cell envelope biogenesis, given the importance of this process in B. subtilis and the role of other uncharacterized proteins in this area .

• In situ localization and dynamics: Implementing advanced imaging techniques including single-molecule tracking and super-resolution microscopy to visualize YlaF localization, dynamics, and interactions within living B. subtilis cells would connect molecular function to cellular processes.

• Systematic biochemical activity screening: Developing a high-throughput biochemical screening platform to test YlaF against diverse substrates would identify potential enzymatic activities, with particular focus on reactions related to cell wall modification and nucleotide-sugar metabolism based on findings with similar uncharacterized proteins .

These complementary approaches, when integrated using systems biology frameworks, offer the most promising path to fully characterizing YlaF function and its role in B. subtilis physiology.

How can contradictions in experimental data about YlaF be reconciled to build a consistent functional model?

To reconcile contradictory experimental data about YlaF and develop a consistent functional model, implement a structured data assessment and integration framework: