Staphylococcus aureus is a significant human pathogen known for causing a variety of infections, particularly those associated with implanted medical devices. Biofilm formation, where bacteria attach to surfaces and create multilayered cell clusters, is a critical factor in these infections . Protein factors, including surface proteins, play a crucial role in biofilm accumulation .
SAS0302, also known as Staphylococcus aureus surface protein C (SasC), is a protein that was, at the time of its discovery, an uncharacterized surface protein found in S. aureus . Research has shown that SasC is involved in cell aggregation and biofilm formation, which are important for the colonization during infection .
sasC was heterologously expressed in Staphylococcus carnosus, resulting in the formation of significant cell aggregates, which indicates intercellular adhesion and biofilm accumulation . Further experiments revealed that:
Expression of sasC in S. carnosus and S. aureus led to enhanced biofilm formation .
The N-terminal domain of SasC was localized as the region conferring cell aggregation and biofilm formation .
SasC does not mediate binding to fibrinogen, thrombospondin-1, von Willebrand factor, or platelets .
SasC contributes to the process of biofilm formation in S. aureus. Biofilms are complex communities of microorganisms that adhere to surfaces, making them resistant to antibiotics and host immune responses . SasC's involvement in cell aggregation and biofilm accumulation suggests it plays a crucial role in the early stages of biofilm development, facilitating bacterial adhesion and the formation of multilayered cell clusters .
S. aureus is a major cause of implant-associated infections, where bacteria colonize implanted medical devices, leading to persistent and difficult-to-treat infections . Given SasC's role in biofilm formation, it may be a potential target for therapeutic interventions aimed at preventing or disrupting biofilm development on medical implants .
Understanding the function and mechanisms of proteins like SasC may lead to the development of new strategies to combat S. aureus infections. Potential therapeutic applications could include:
Developing inhibitors that target SasC to prevent cell aggregation and biofilm formation.
Designing novel surface coatings for medical implants that interfere with SasC-mediated adhesion.
Creating immunotherapeutic approaches that target SasC to enhance the host immune response against S. aureus biofilms.
| Feature | Description |
|---|---|
| Gene Size | 6558 nucleotides |
| Protein Size | 2186 amino acids |
| Molecular Mass | 237.9 kDa |
| Key Domains | N-terminal signal peptide, C-terminal LPXTG cell wall anchorage motif, repeat region with 17 repeats similar to DUF1542 |
| Theoretical pI Value | 5.08 |
| Amino Acid Composition | 25.1% hydrophobic, 12.1% basic, 13.2% acidic |
| Function | Involved in cell aggregation and biofilm formation |
| Binding Properties | Does not mediate binding to fibrinogen, thrombospondin-1, von Willebrand factor, or platelets |
| Expression in | Staphylococcus carnosus, Staphylococcus aureus |
| Effect of Heterologous Expression | Formation of cell aggregates, enhanced biofilm formation |
| Strain | % Identity with SasC |
|---|---|
| S. aureus MW2 | 97% |
| S. aureus MSSA476 | 97% |
| S. aureus USA300 | 96% |
| S. aureus COL | 96% |
| S. aureus Newman | 96% |
| S. aureus NCTC8325 | 96% |
| S. aureus N315 | 96% |
| S. aureus MRSA252 | 89% |
KEGG: sas:SAS0302
When expressing recombinant S. aureus proteins, the experimental design should account for potential toxic effects on the host system and codon optimization requirements. Based on established protocols for similar S. aureus surface proteins, consider the following approach:
Start with E. coli BL21(DE3) as your expression system for initial characterization studies
Compare several expression vectors (pET, pGEX, pMAL) with different fusion tags (His, GST, MBP)
Test multiple induction conditions using a factorial design
The experimental design for expression optimization should be structured as follows:
| Expression Parameter | Tested Conditions | Number of Levels |
|---|---|---|
| Temperature | 16°C, 25°C, 37°C | 3 |
| IPTG Concentration | 0.1 mM, 0.5 mM, 1.0 mM | 3 |
| Induction Time | 4h, 8h, overnight | 3 |
| Host Strain | BL21(DE3), Rosetta, Origami | 3 |
Similar to the simulation project methodology for experimental design, data collection would involve one observation for each treatment group, with variables specifying the levels of factor variables . Protein yield and solubility should be assessed for each condition to determine optimal expression parameters.
Identity confirmation for recombinant SAS0302 should include multiple complementary approaches:
SDS-PAGE analysis coupled with western blotting using anti-tag antibodies
Mass spectrometry analysis (MALDI-TOF or LC-MS/MS)
N-terminal sequencing for the first 5-10 amino acids
Peptide mass fingerprinting after tryptic digestion
For protein sequence verification, compare experimental and theoretical data:
| Verification Method | Expected Result | Confirmation Criteria |
|---|---|---|
| Intact Mass Analysis | Theoretical MW ±0.1% | Mass within expected range for full-length protein with modifications |
| Peptide Coverage | ≥80% sequence coverage | Identification of unique peptides from different regions |
| N-terminal Sequencing | Match to predicted sequence | Confirmation of proper processing and tag cleavage |
This multi-method approach ensures reliable identification before proceeding with functional studies, similar to verification protocols used for other S. aureus surface proteins.
For uncharacterized proteins like SAS0302, a hierarchical approach to structural prediction is recommended:
Begin with primary sequence analysis using multiple bioinformatics tools
Perform secondary structure prediction using consensus methods
Generate tertiary structure models using homology modeling or ab initio methods
Validate predicted structures using molecular dynamics simulations
Based on approaches used for other S. aureus surface proteins like SasG, consider looking for specific domain architectures:
| Prediction Tool | Target Feature | Interpretation Guidelines |
|---|---|---|
| Pfam/SMART | Conserved domains | Identify domain boundaries and functional regions |
| TMHMM/SignalP | Transmembrane/signal regions | Predict cellular localization |
| PSIPRED/JPred | Secondary structure elements | Map β-sheets and α-helices |
| I-TASSER/AlphaFold | 3D structure model | Assess confidence scores for different regions |
Recent structural studies of the SasG protein have revealed distinct lectin domains with varying binding specificities (SasG-I and SasG-II) . Similar approaches could be applied to SAS0302 to identify potential binding pockets or functional domains.
When investigating uncharacterized proteins like SAS0302, comprehensive comparative genomics can provide functional insights:
Perform sequence similarity searches across multiple bacterial species
Analyze genomic context and gene neighborhood conservation
Examine phylogenetic distribution across S. aureus strains
Search for co-evolution patterns with known functional partners
Similar to the phylogenetic analyses performed for SasG variants, where two major divergent alleles (SasG-I and SasG-II) were identified , you should:
| Analytical Approach | Implementation Method | Expected Outcome |
|---|---|---|
| Ortholog Identification | BLAST/OrthoMCL | Identification of related proteins across species |
| Synteny Analysis | MicrobesOnline/SyntTax | Conservation of genomic context |
| Phylogenetic Profiling | Constructed from whole genome data | Co-occurrence patterns |
| Structural Homology | HHpred/Phyre2 | Distant functional relationships |
The clonal complex distribution pattern observed for SasG variants could serve as a model for investigating SAS0302 distribution. This approach may reveal whether SAS0302, like SasG, shows strain-level diversity in gene presence, expression level, and function.
To investigate potential adhesion functions of SAS0302, design a comprehensive experimental approach:
Generate clean deletion and complementation mutants of SAS0302
Perform adhesion assays with multiple cell types (epithelial, endothelial, immune cells)
Compare adhesion between wild-type, deletion mutant, and complemented strains
Test specific inhibitors or blocking antibodies against recombinant SAS0302
Drawing from studies on SasG-mediated adhesion to corneocytes , design your adhesion experiments as follows:
| Experimental Approach | Cell Types/Substrates | Measurements |
|---|---|---|
| Static Adhesion Assay | N/TERT keratinocytes, corneocytes | CFU counts, fluorescence |
| Flow Cell Analysis | Various cell monolayers under shear stress | Real-time adhesion dynamics |
| AFM Force Spectroscopy | Purified cell surface components | Single-molecule binding forces |
| Glycosidase Treatment | Modified cell surfaces | Dependency on specific glycans |
The recent finding that SasG-mediated adhesion can be recapitulated using differentiated N/TERT keratinocytes suggests this could be a valuable model system for testing SAS0302 function as well.
To investigate protein-protein interactions involving SAS0302:
Perform co-immunoprecipitation experiments with epitope-tagged SAS0302
Use bacterial two-hybrid or split-GFP systems for interaction screening
Implement surface plasmon resonance for quantitative binding analysis
Conduct genetic epistasis studies with double mutants
Design a systematic interaction screening approach:
| Interaction Method | Advantages | Limitations | Data Analysis |
|---|---|---|---|
| Bacterial Two-Hybrid | In vivo detection | Limited to cytoplasmic interactions | Colony selection and reporter quantification |
| Pull-down Assays | Direct physical interaction | May miss transient interactions | MS identification of binding partners |
| Crosslinking MS | Captures in vivo interactions | Complex data analysis | XL-MS software algorithms |
| Functional Epistasis | Reveals genetic relationships | Indirect evidence of interaction | Statistical analysis of phenotypic data |
Similar to the analysis of SasG lectin domain interactions with host factors , you could investigate whether SAS0302 shows specific binding to host glycans or other surface components.
For in vivo virulence studies of SAS0302, consider a multi-model approach:
Start with invertebrate infection models (G. mellonella, C. elegans)
Progress to murine infection models (skin, systemic, device-associated)
Compare wild-type, knockout, and complemented strains
Include relevant clinical isolates with variation in SAS0302 expression
Design your animal experiments with statistical power in mind:
| Model System | Infection Route | Readouts | Statistical Considerations |
|---|---|---|---|
| G. mellonella Larvae | Direct injection | Survival, bacterial burden | Kaplan-Meier analysis, n≥20 per group |
| Murine Skin Infection | Subcutaneous | Lesion size, histopathology | ANOVA with repeated measures, n≥8 per group |
| Murine Bacteremia | Tail vein injection | Survival, organ burden | Log-rank test, n≥10 per group |
| Ex vivo Human Skin | Surface application | Penetration, inflammatory response | Paired analysis, n≥6 donors |
When designing these experiments, apply principles similar to those discussed in experimental design simulation projects, where you would specify factor variables and observation numbers for each treatment group .
For RNA-seq studies focusing on SAS0302 regulation:
Include biological triplicates for each condition
Implement spike-in controls for normalization
Validate key findings with RT-qPCR
Confirm protein-level changes for major findings
Experimental design considerations:
| Experimental Component | Recommendation | Validation Method |
|---|---|---|
| RNA Extraction | RIN score >8.0 for all samples | Bioanalyzer quality control |
| Library Preparation | Stranded library preparation | qPCR validation |
| Sequencing Depth | >20 million reads per sample | Saturation analysis |
| Differential Expression | Fold change >2, FDR <0.05 | RT-qPCR of 10-15 selected genes |
For data analysis, implement a factorial design approach similar to the experimental design methodology , treating different growth conditions, genetic backgrounds, or stimuli as factor variables with specified levels.
For multi-omics integration to understand SAS0302:
Combine transcriptomics, proteomics, and metabolomics data
Apply network analysis to identify functional modules
Use dimensionality reduction to visualize relationships
Implement machine learning for pattern recognition
Integration methodology:
| Omics Layer | Analytical Approach | Integration Strategy |
|---|---|---|
| Transcriptomics | Differential expression analysis | Correlation with protein abundance |
| Proteomics | Protein interaction mapping | Network construction with SAS0302 as focal point |
| Metabolomics | Pathway enrichment | Linking metabolic changes to SAS0302 expression |
| Phenomics | Growth/virulence phenotyping | Multi-parameter correlation analysis |
Similar to how researchers analyzed SasG variants across multiple S. aureus strains belonging to 39 clonal complexes , a comprehensive analysis of SAS0302 would benefit from integrating genomic, expression, and functional data across multiple strain backgrounds.
When confronting contradictory results between different experimental systems:
Assess differences in experimental conditions that might explain discrepancies
Consider temporal dynamics of expression and regulation
Evaluate strain-specific variations in SAS0302 sequence or expression
Examine potential compensatory mechanisms in complex systems
Recommended approach for reconciling contradictory data:
| Contradiction Type | Investigation Approach | Resolution Strategy |
|---|---|---|
| Expression Level Differences | Confirm with multiple methods | Time-course analysis in different conditions |
| Phenotypic Impact Variations | Control for genetic background | Generate mutations in multiple strain backgrounds |
| Binding Partner Discrepancies | Validate with multiple interaction assays | Context-dependent interaction analysis |
| Structural Function Conflicts | Domain-specific mutational analysis | Structure-function correlation studies |
This approach parallels the discovery that SasG has evolved multiple variants (SasG-I and SasG-II) with different binding specificities , suggesting that SAS0302 might similarly have context-dependent functions that explain apparently contradictory results.