Recombinant Candida glabrata Protein SEY1 (SEY1)

What is the basic structural composition of recombinant C. glabrata SEY1 protein?

Recombinant C. glabrata SEY1 protein (Q6FLC5) is a full-length protein consisting of 783 amino acids. The commercially available form is typically produced with an N-terminal His-tag to facilitate purification and experimental applications. The protein is expressed in E. coli expression systems and is typically supplied as a lyophilized powder for research use . The complete amino acid sequence begins with MTSQAIQLIDVNKEYNKE and continues through the entire 783-residue sequence as documented in protein databases. Analysis of the primary structure reveals characteristics consistent with its putative function, though detailed structural studies would be necessary for confirmation of specific domains and their functional roles.

How should researchers optimize the purification protocol for His-tagged SEY1 protein?

Purification of His-tagged SEY1 should follow standard immobilized metal affinity chromatography (IMAC) protocols, with specific optimization considerations. Begin with cell lysis in a buffer containing 50 mM Tris-HCl (pH 8.0), 300 mM NaCl, 10 mM imidazole, and protease inhibitors. For fungal proteins like SEY1, consider adding 0.1% Triton X-100 to improve solubility if aggregation occurs. Bind the lysate to Ni-NTA resin, wash with increasing imidazole concentrations (20-50 mM), and elute with 250-300 mM imidazole. Critical steps include maintaining protein stability during purification by working at 4°C and considering the addition of reducing agents like 1-5 mM DTT if the protein contains numerous cysteine residues. Protein purity should be assessed via SDS-PAGE, aiming for >90% homogeneity for most applications. For particularly challenging purifications, a secondary purification step using size-exclusion chromatography may be necessary to remove aggregates and achieve higher purity.

What methodological approaches should be employed to investigate SEY1 protein interactions with other Candida species?

To investigate potential inter-species interactions involving SEY1, researchers should adopt methodologies similar to those used for studying Yhi1-mediated interactions between C. glabrata and C. albicans . Begin with co-culture experiments where C. glabrata expressing SEY1 (wild-type or recombinant) is grown alongside other Candida species under various conditions. Culture filtrates can be collected and their effects on morphology or growth of other species can be monitored microscopically. For more direct evidence of protein-mediated interactions, create SEY1 knockout strains (sey1Δ) using CRISPR-Cas9 or homologous recombination techniques, and compare their inter-species interaction phenotypes with wild-type strains. Additionally, fluorescently labeled SEY1 (e.g., with yeGFP) can be used to visualize protein localization during interactions . Protein-protein interaction studies using techniques such as co-immunoprecipitation or proximity labeling would provide direct evidence of SEY1's binding partners across species. Throughout these experiments, appropriate controls including empty vector transformants and unrelated protein expressions should be maintained.

How can researchers determine if SEY1 contains functional motifs similar to the AXVXH pentapeptide identified in Yhi1?

To identify potential functional motifs in SEY1, implement a systematic structure-function analysis pipeline. First, conduct comprehensive in silico analysis using multiple tools: InterProScan and MOTIF Search for known domains, and custom pattern recognition algorithms to identify novel recurring sequences. For SEY1 specifically, examine the 783-amino acid sequence for pentapeptide patterns that might be functionally significant. After identifying candidate motifs, create truncated versions of SEY1 expressing discrete regions of the protein, similar to the approach used with Yhi1 (e.g., N-terminal and C-terminal halves) . Express these constructs in a sey1Δ background and assess functional complementation. For motifs with promising activity, create site-directed mutants altering key residues within the motif. The functional significance of these mutations should be validated through appropriate biological assays specific to SEY1's known or hypothesized functions. Finally, incorporate identified motifs into unrelated proteins (such as yeGFP) to determine if they confer SEY1-like properties, as was demonstrated with the ADVWH motif from Yhi1 .

What experimental design would best elucidate the potential role of SEY1 in pathogenesis?

A comprehensive experimental design to investigate SEY1's role in pathogenesis should involve a multi-tiered approach. First, generate sey1Δ knockout mutants in C. glabrata using homologous recombination or CRISPR-Cas9 technology. Conduct comparative phenotypic analyses between wild-type and sey1Δ strains, examining growth rates, stress responses, morphology, and biofilm formation. For host interaction studies, implement both in vitro and in vivo models. In vitro, assess adhesion and invasion of human epithelial cell lines, phagocytosis by macrophages, and survival within these phagocytes. Measure cytokine responses from host cells to determine immunomodulatory effects. For in vivo pathogenesis, utilize murine models of disseminated candidiasis, monitoring organ fungal burden, histopathology, and survival rates. Additionally, conduct transcriptome analysis of both the pathogen and host during infection to identify SEY1-dependent gene expression changes. Complement these studies with SEY1 localization during infection using fluorescently tagged proteins. For mechanistic insights, identify SEY1 interaction partners through pull-down assays coupled with mass spectrometry. This comprehensive approach would provide multiple lines of evidence regarding SEY1's role in pathogenesis.

How might the MAPK signaling pathway regulate SEY1 expression and function, similar to its role in Yhi1 regulation?

To investigate potential MAPK pathway regulation of SEY1, researchers should implement a systematic approach examining both transcriptional and post-transcriptional regulation mechanisms. Based on findings with Yhi1, where MAPK components (particularly Fus3) post-transcriptionally regulate protein expression , begin by measuring SEY1 transcript levels in wild-type C. glabrata and mutants lacking key MAPK components (Gpr1, Gpa1, Kss1, Fus3, and Ste6) using RT-qPCR. Simultaneously, assess SEY1 protein levels in these strains using western blot analysis with anti-SEY1 antibodies, examining both intracellular and extracellular fractions. If discrepancies between transcript and protein levels occur (as seen with Yhi1), investigate post-transcriptional regulation mechanisms including protein stability assays with cycloheximide chase experiments, or translational control using polysome profiling. To determine if SEY1 is secreted via transporters like Ste6, create a tagged version of SEY1 and monitor its localization and secretion in wild-type and transporter mutant backgrounds. Finally, perform phosphorylation site analysis to identify potential MAPK target residues within SEY1, followed by site-directed mutagenesis of these residues to determine their functional significance. This comprehensive approach would elucidate whether SEY1, like Yhi1, is regulated through MAPK signaling despite C. glabrata's predominantly asexual reproduction.

What comparative genomics approaches would best identify SEY1 variants across clinical isolates of C. glabrata?

To comprehensively characterize SEY1 variants across clinical isolates, implement a multi-faceted genomic analysis pipeline. First, collect a diverse set of C. glabrata clinical isolates from various geographical regions and patient populations, ensuring representation of different antifungal susceptibility profiles. Extract genomic DNA and either sequence the SEY1 locus specifically through targeted PCR and Sanger sequencing, or perform whole-genome sequencing for broader genomic context. Align SEY1 sequences using tools like MUSCLE or CLUSTALW to identify polymorphisms, with particular attention to non-synonymous substitutions, insertions/deletions, and variations in putative functional domains. Similar to the approach used with Yhi1, where European isolates contained a variant missing two N-terminal amino acids , analyze if specific regional patterns of SEY1 variants emerge. Conduct population genetics analyses including calculating nucleotide diversity (π), Tajima's D, and FST values to determine if SEY1 is under selective pressure. Correlate identified variants with clinical metadata (infection site, treatment outcomes, antifungal resistance) to identify potential associations with virulence or drug resistance. Finally, express representative variants in a sey1Δ background to assess functional differences. This systematic approach would provide comprehensive insights into SEY1 evolution and functional diversity across clinical settings.

How can structural biology approaches be utilized to understand SEY1 function at the molecular level?

A comprehensive structural biology approach to SEY1 would begin with protein production optimization. Express and purify multiple constructs including full-length SEY1 and domain-specific fragments in E. coli systems with various solubility tags (His, GST, MBP). For crystallization trials, employ both vapor diffusion and lipidic cubic phase methods, screening various buffer conditions, precipitants, and additives. If crystallization proves challenging, pursue alternative structural determination methods including cryo-electron microscopy for larger assemblies or NMR spectroscopy for smaller domains. Complement these with integrative structural approaches like small-angle X-ray scattering (SAXS) to determine solution shape and hydrogen-deuterium exchange mass spectrometry (HDX-MS) to identify flexible regions and ligand-binding interfaces. For functional insights, conduct molecular dynamics simulations using obtained structures to assess conformational changes and identify potential binding pockets. Additionally, perform in silico docking studies with candidate interactors identified through proteomic approaches. Site-directed mutagenesis of key residues identified in structural studies, followed by functional assays, would validate structural predictions. This multi-technique approach would provide detailed structural insights into SEY1 function, potentially revealing novel therapeutic targets in this pathogenically relevant protein.

What strategies can overcome common challenges in expressing recombinant full-length SEY1 protein?

Expressing full-length SEY1 (783 amino acids) may present challenges including poor solubility, incorrect folding, or low yield. To overcome these issues, implement a systematic optimization strategy. First, consider expression construct design: test multiple fusion tags beyond His-tag, including MBP, GST, or SUMO, which can enhance solubility. Design constructs with different N- and C-terminal boundaries based on predicted domain structures. For expression conditions, screen multiple E. coli strains including BL21(DE3), Rosetta, and SHuffle (for disulfide bond formation). Optimize induction parameters by testing various temperatures (16-30°C), IPTG concentrations (0.1-1.0 mM), and induction durations (4-24 hours). For particularly challenging expressions, consider auto-induction media or cold-shock induction systems. If aggregation persists, add solubility enhancers to lysis buffers (0.1-1% Triton X-100, 0.5-2M urea, or 5-10% glycerol). For proteins requiring post-translational modifications, consider eukaryotic expression systems like Pichia pastoris or S. cerevisiae, which have been successful for other C. glabrata proteins . If full-length expression remains problematic, express the protein in segments representing functional domains, similar to the approach used with Yhi1 N-terminal (1-32) and C-terminal (33-66) halves . Document all optimization attempts systematically to identify patterns affecting expression success.

What are the most effective protein-protein interaction assays for determining SEY1 binding partners in host-pathogen contexts?

To identify SEY1 binding partners in host-pathogen contexts, implement a multi-technique approach with complementary strengths. Begin with affinity purification coupled to mass spectrometry (AP-MS) using tagged SEY1 expressed in C. glabrata to pull down interacting proteins from both fungal and host cell lysates. For transient or weak interactions, employ crosslinking mass spectrometry (XL-MS) using reagents like DSS or formaldehyde prior to purification. To validate direct interactions and determine binding affinities, conduct surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC) with purified candidate interactors. For in situ visualization of interactions, implement proximity labeling techniques such as BioID or APEX2 by fusing these enzymes to SEY1 and expressing in C. glabrata during host cell infection. To map interaction interfaces, employ hydrogen-deuterium exchange mass spectrometry (HDX-MS) comparing deuterium uptake of SEY1 alone versus in complex with binding partners. For high-throughput screening, consider yeast two-hybrid assays using SEY1 as bait against cDNA libraries from relevant host cells or fungal species. Throughout these experiments, include proper controls such as unrelated proteins of similar size and charge characteristics. Integration of data from multiple techniques will provide a high-confidence interactome map for SEY1, revealing potential mechanisms in host-pathogen interactions and identifying targets for therapeutic intervention.

What analytical techniques should be employed to study potential post-translational modifications of SEY1?

A comprehensive analysis of SEY1 post-translational modifications (PTMs) requires integration of multiple analytical techniques. Begin with prediction algorithms to identify potential modification sites, including phosphorylation (NetPhos), glycosylation (NetNGlyc, NetOGlyc), and other PTMs. For experimental validation, employ mass spectrometry-based approaches including bottom-up proteomics with enrichment strategies specific to each PTM type: titanium dioxide or IMAC for phosphopeptides, lectin affinity for glycopeptides, and antibody-based enrichment for acetylation or methylation. Implement both collision-induced dissociation (CID) and electron transfer dissociation (ETD) fragmentation methods to improve PTM site localization. For challenging modifications like glycosylation, consider specialized approaches such as hydrophilic interaction liquid chromatography (HILIC) separation prior to MS analysis. To assess PTM stoichiometry, use targeted quantitative proteomics with synthetic peptide standards corresponding to modified and unmodified forms. Additionally, perform top-down proteomics analysis of intact SEY1 to identify combinatorial PTM patterns that might be lost in bottom-up approaches. Complement these MS approaches with specific biochemical assays: Pro-Q Diamond staining for phosphorylation, periodic acid-Schiff staining for glycosylation, and Western blotting with PTM-specific antibodies. Finally, investigate the functional significance of identified PTMs through site-directed mutagenesis of modified residues, followed by functional assays relevant to SEY1's biological role.

How does the amino acid sequence of SEY1 compare with homologous proteins in other pathogenic Candida species?

Comparative sequence analysis of SEY1 across pathogenic Candida species provides insights into evolutionary conservation and functional importance. The full-length SEY1 protein from C. glabrata consists of 783 amino acids with a specific sequence signature beginning with MTSQAIQLIDVNKEYNKE . Sequence alignment using BLASTP against other Candida proteomes reveals varying degrees of conservation. The highest sequence identity typically occurs in predicted functional domains, particularly those associated with GTPase activity. Regions of high conservation (>90% identity) likely represent essential functional elements, while divergent regions may confer species-specific functions. Unlike smaller proteins such as Yhi1, which has species-specific distribution , SEY1 homologs are generally present across Candida species but with notable sequence variations. When comparing the pentapeptide motif identification approach used for Yhi1 , researchers should examine SEY1 for conserved short motifs that might be functionally significant across species. Construction of a phylogenetic tree based on SEY1 sequences often reflects the established evolutionary relationships among Candida species, with potential deviations indicating selective pressures on this protein. These comparative analyses help identify critical regions for functional studies and potential species-specific targeting strategies for antifungal development.

What experimental approaches would determine if SEY1 has potential as a diagnostic biomarker for C. glabrata infections?

To evaluate SEY1's potential as a diagnostic biomarker for C. glabrata infections, implement a systematic validation pipeline. First, develop sensitive and specific detection methods: generate high-affinity monoclonal antibodies against SEY1 for immunoassays (ELISA, lateral flow) and optimize PCR primers targeting sey1 gene for nucleic acid detection. Assess analytical performance including limits of detection, specificity against other Candida species and common bacteria, and reproducibility across different sample types. Using a diverse biorepository of clinical samples (blood, urine, tissue) from culture-confirmed C. glabrata infections and appropriate controls, determine clinical sensitivity and specificity with ROC curve analysis to establish optimal cutoff values. Perform head-to-head comparison with existing diagnostic methods including culture, histopathology, and commercial molecular or serological assays. Investigate SEY1 dynamics during infection progression and treatment response using longitudinal samples to determine its utility for monitoring therapeutic efficacy. Evaluate potential confounding factors including antifungal therapy, host factors, and co-infections. Similar to the approach suggested for Yhi1 , which showed potential as a specific biomarker, assess whether SEY1 detection offers advantages in terms of turnaround time, specificity for C. glabrata, or ability to detect culture-negative infections. Finally, conduct cost-effectiveness analysis comparing SEY1-based diagnostics with standard methods to determine clinical and economic impact.

How might researchers exploit the structure-function relationship of SEY1 to develop novel antifungal strategies?

Developing novel antifungal strategies based on SEY1 structure-function relationships requires a systematic drug discovery approach. Begin with detailed structural characterization using X-ray crystallography, cryo-EM, or homology modeling if experimental structures are unavailable. Identify potential druggable pockets using computational tools like SiteMap or DoGSiteScorer, focusing on regions essential for SEY1 function but distinct from human homologs. Conduct structure-based virtual screening of compound libraries against these pockets, followed by biochemical validation of top hits using recombinant SEY1 protein in functional assays. For peptide-based inhibitors, employ an approach similar to that used with Yhi1, where a synthetic peptide derivative (Yhi1 2-13) demonstrated antifungal activity . Design peptide libraries based on critical SEY1 interfaces, screening for those that disrupt protein function or interactions. Lead compounds should be optimized for antifungal efficacy, selectivity, and drug-like properties through medicinal chemistry efforts. Test optimized candidates against diverse C. glabrata clinical isolates, biofilms, and in combination with existing antifungals to identify synergistic effects. Evaluate mechanism of action through resistance selection, transcriptomics, and biochemical assays. Assess cytotoxicity against human cell lines and preliminary pharmacokinetics before advancing to murine models of candidiasis. This pipeline would leverage SEY1 structure-function insights to develop targeted antifungals addressing the increasing challenge of drug-resistant C. glabrata infections.

What cloning strategies are most effective for generating SEY1 expression constructs with various fusion tags?

For optimal cloning of SEY1 expression constructs with various fusion tags, implement a modular gateway-compatible strategy. Begin with PCR amplification of the SEY1 open reading frame (1-783 amino acids) using high-fidelity polymerase and primers containing appropriate restriction sites. Based on approaches used for other C. glabrata proteins, BamHI and SalI restriction sites have demonstrated effectiveness . For E. coli expression, consider vectors like pET series (His-tag), pGEX (GST), pMAL (MBP), or pSUMO. For expression in C. glabrata or S. cerevisiae, vectors like pBEVY-L or p426GPD have proven successful with other fungal proteins . To facilitate secretion in eukaryotic systems, incorporate a signal peptide sequence in the construct design, as demonstrated with SP-yeGFP constructs . For each construct, confirm proper insertion and sequence integrity through restriction enzyme digestion and sequencing. If traditional restriction-based cloning proves challenging due to internal restriction sites within SEY1, employ seamless cloning methods such as Gibson Assembly or In-Fusion cloning. For constructs expressing specific domains or including site-directed mutations, design appropriate overlapping primers as detailed in the methodology for Yhi1 variants . Document transformation efficiency for each construct in the respective expression system, as this may vary considerably based on construct size and host compatibility.

What are the critical parameters for maintaining stability and activity of purified recombinant SEY1 protein?

Maintaining stability and activity of purified recombinant SEY1 protein requires careful optimization of storage and handling conditions. Following purification, conduct buffer optimization screening to identify conditions that maximize protein stability. Test various buffer components including: pH range (6.0-8.5), salt concentrations (100-500 mM NaCl), and buffer systems (phosphate, Tris, HEPES, MES). For a large protein like SEY1 (783 amino acids), add stabilizing agents such as glycerol (10-20%), reducing agents (1-5 mM DTT or TCEP) if cysteine residues are present, and consider protein-specific stabilizers like specific metal ions if SEY1 contains metal-binding domains. After identifying optimal buffer compositions, evaluate protein stability using thermal shift assays (Thermofluor) and dynamic light scattering to monitor aggregation propensity. For long-term storage, compare stability at various temperatures (-80°C, -20°C, 4°C) and after multiple freeze-thaw cycles. Consider alternative storage methods including flash-freezing small aliquots in liquid nitrogen, lyophilization with appropriate cryoprotectants, or room-temperature storage using technologies like silk fibroin encapsulation. Before each experimental use, verify protein activity using functional assays specific to SEY1's known biochemical activities. Document batch-to-batch variation in stability and activity to establish quality control parameters for experimental reproducibility. These optimized conditions will ensure consistent results in downstream applications using recombinant SEY1.

How can researchers develop and validate antibodies against SEY1 for immunological applications?

Developing and validating high-quality antibodies against SEY1 requires a comprehensive strategy spanning antigen design through validation. Begin with antigen preparation: express full-length recombinant SEY1 or, if challenging, select 2-3 immunogenic peptides (15-20 amino acids) from regions predicted to be surface-exposed using epitope prediction algorithms. For peptide antigens, conjugate to carrier proteins (KLH or BSA) to enhance immunogenicity. Immunize at least two animal species (rabbits and mice/rats) to generate polyclonal antibodies, following established immunization protocols with appropriate adjuvants. For monoclonal antibodies, harvest splenocytes from immunized mice and perform hybridoma fusion, followed by limiting dilution cloning. Screen antibody candidates using multiple techniques: ELISA against immobilized antigen, Western blotting against recombinant SEY1 and C. glabrata lysates, and immunoprecipitation to verify native protein recognition. Crucial validation steps include: testing against sey1Δ mutant lysates as negative controls, immunofluorescence microscopy to confirm expected subcellular localization, and cross-reactivity assessment against lysates from related Candida species. For antibodies intended for diagnostic applications, determine analytical parameters including sensitivity, specificity, and reproducibility across different sample types. Document all validation results according to antibody reporting guidelines to ensure reproducibility. This rigorous development and validation approach will yield reliable antibody reagents for SEY1 detection in research and potential diagnostic applications.

What statistical approaches are most appropriate for analyzing SEY1 expression data across different experimental conditions?

For robust statistical analysis of SEY1 expression data across experimental conditions, implement a comprehensive analytical framework. Begin with exploratory data analysis, generating box plots and histograms to assess data distribution and identify potential outliers. For normally distributed data comparing two conditions, apply Student's t-test with appropriate corrections for multiple testing (Bonferroni or Benjamini-Hochberg). For multiple experimental conditions, employ one-way ANOVA followed by post-hoc tests (Tukey's HSD) to identify significant differences between specific groups. When data violates normality assumptions, use non-parametric alternatives including Mann-Whitney U test (two groups) or Kruskal-Wallis with Dunn's post-hoc test (multiple groups). For time-series expression data, apply repeated measures ANOVA or mixed-effects models to account for within-subject correlations. When analyzing SEY1 expression across multiple strains and conditions simultaneously, implement two-way ANOVA with interaction terms to identify condition-specific effects. For complex experimental designs, consider linear mixed models incorporating both fixed and random effects. Calculate effect sizes (Cohen's d or Hedges' g) to quantify the magnitude of differences beyond statistical significance. All analyses should include appropriate sample sizes based on power calculations (typically n≥3 biological replicates with 2-3 technical replicates each), with significance thresholds set at p<0.05. Present results in graphical format with clear indication of statistical significance, similar to the presentation of data for other C. glabrata proteins in published studies .

How should researchers interpret contradictory results between in vitro and in vivo SEY1 functional studies?

When confronted with contradictory results between in vitro and in vivo SEY1 functional studies, implement a systematic reconciliation approach. First, critically evaluate methodological differences: examine protein preparation methods, assay conditions, and experimental models for potential confounding variables. Consider whether in vitro conditions adequately recapitulate the physiological environment, including pH, temperature, ion concentrations, and the presence of host factors that might modulate SEY1 function. For in vivo studies, assess whether route of infection, fungal burden, host genetic background, or microbiome composition might explain discrepancies. Investigate potential post-translational modifications that may occur in vivo but not in vitro, using mass spectrometry to compare SEY1 protein isolated from both contexts. Examine SEY1 expression levels across experimental systems, as expression differences might explain functional disparities. Design bridging experiments that progressively increase complexity from purified protein to cell culture to animal models, identifying the point where discrepancies emerge. Consider whether SEY1 functions as part of a complex requiring additional factors present only in vivo, similar to how Yhi1 requires specific cellular machinery for secretion . Explore potential compensatory mechanisms in vivo that might mask phenotypes observed in vitro. Finally, develop mathematical models integrating in vitro kinetic parameters with in vivo constraints to predict system behavior across contexts. These systematic approaches will help reconcile contradictory results and provide a more comprehensive understanding of SEY1 biology.

What computational tools and databases are most valuable for SEY1 functional prediction and evolutionary analysis?

For comprehensive computational analysis of SEY1, integrate multiple bioinformatic tools and databases across several analytical domains. For structural prediction, employ AlphaFold2 or RoseTTAFold to generate protein structure models, validated through ProQ3 and MolProbity. Identify functional domains using InterProScan, SMART, and Pfam, supplemented with custom motif searches for pentapeptide patterns similar to the AXVXH motif identified in Yhi1 . For evolutionary analysis, construct multiple sequence alignments of SEY1 homologs using MAFFT or MUSCLE, followed by phylogenetic tree reconstruction with IQ-TREE or MrBayes, implementing appropriate evolutionary models selected via ModelFinder. Calculate site-specific evolutionary rates using PAML to identify conserved functional residues under purifying selection. For potential protein interactions, utilize STRING, BioGRID, and fungal-specific interaction databases, complemented with computational prediction tools like PRINCE or InterPreTS. Identify potential post-translational modification sites using NetPhos (phosphorylation), NetNGlyc (N-glycosylation), and other PTM-specific predictors. For comparative genomics, access the Candida Genome Database and FungiDB to compare SEY1 across fungal species and strains. Employ protein disorder predictors (IUPred, PONDR) to identify flexible regions potentially involved in protein-protein interactions. To facilitate integration of these diverse analyses, implement computational pipelines using Nextflow or Snakemake, with results visualized through R or Python libraries. This comprehensive computational approach will generate testable hypotheses about SEY1 function and evolution that can guide experimental design.