Recombinant Candida glabrata Porphobilinogen deaminase (HEM3)

What is the function of Porphobilinogen deaminase (HEM3) in Candida glabrata?

Porphobilinogen deaminase (PBGD), encoded by the HEM3 gene in Candida glabrata, is a critical enzyme in the heme biosynthetic pathway. It catalyzes the conversion of porphobilinogen to hydroxymethylbilane, which is a precursor for heme production. In C. glabrata, as in other fungi, heme biosynthesis is essential for various cellular processes including respiration, ergosterol synthesis, and response to oxidative stress. The enzyme is particularly important for C. glabrata virulence and survival within host macrophages, where iron limitation and oxidative stress are common challenges encountered by the pathogen.

How does HEM3 expression in C. glabrata compare to other Candida species?

HEM3 expression patterns in C. glabrata differ from those in other Candida species such as C. albicans. While both organisms utilize heme biosynthesis pathways, C. glabrata displays unique regulatory mechanisms for HEM3 expression, particularly under iron-limited conditions. Unlike C. albicans, which shows significant regulatory changes in biofilm formation through genes like als1, sap2, hwp1, and cst20 , C. glabrata exhibits distinct expression patterns that are tied to its evolutionary relationship with Saccharomyces cerevisiae. The expression of HEM3 in C. glabrata is often coordinated with other metabolic genes, especially under phagocytosis conditions by macrophages, where adaptation to the host environment is critical.

What plasmid systems are recommended for expressing recombinant HEM3 in C. glabrata?

For recombinant expression of HEM3 in C. glabrata, CEN/ARS episomal plasmids provide reliable and stable expression. The choice of promoter depends on the experimental requirements:

• For constitutive expression: PDC1, HHT2, or EGD2 promoters are recommended, with PDC1 providing the highest expression levels

• For inducible expression: The MET3 promoter offers regulated expression controlled by methionine and cysteine presence in the media

• For expression during macrophage infection: ACO2 or LYS21 promoters are specifically induced during phagocytosis
  These plasmids are available with either URA3 auxotrophic markers (pCU series) or the dominant-selectable NAT1 gene (pCN series) that confers resistance to nourseothricin, providing flexibility depending on the strain background being used .

What are the optimal conditions for inducing HEM3 expression using the MET3 promoter system?

The MET3 promoter in C. glabrata provides tight regulation for recombinant HEM3 expression. Optimal induction requires:
Growth conditions table for MET3 promoter regulation:

How can I optimize HEM3 expression during macrophage infection studies?

For studying HEM3 expression during host-pathogen interactions, macrophage-inducible promoters provide valuable tools. To optimize expression:

• Use ACO2 or LYS21 promoters which are specifically upregulated during phagocytosis

• Culture J774A.1 macrophage cells in DMEM with 10% FBS and Penicillin/Streptomycin

• Infect macrophages with C. glabrata at MOI (Multiplicity of Infection) of 1:10 (yeast:macrophage)

• Allow phagocytosis to occur for 1 hour, then wash away non-phagocytosed cells

• Harvest cells after 3-6 hours for maximum expression
  Control experiments should include C. glabrata grown in DMEM without macrophages to distinguish between media effects and true phagocytosis-induced expression. RNA extraction should be performed using guanidine thiocyanate mixture followed by acid phenol extraction to preserve the integrity of transcripts .

What purification strategy yields the highest activity for recombinant HEM3 from C. glabrata?

Purification of recombinant HEM3 from C. glabrata requires careful consideration of protein stability and enzymatic activity. The following protocol has been optimized for maximum yield and activity:

• Express HEM3 with a C-terminal 6xHis tag under PDC1 promoter control

• Disrupt cells using glass beads in buffer containing 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10% glycerol, 1 mM DTT, and protease inhibitor cocktail

• Include 0.1% Triton X-100 in lysis buffer to improve solubility

• Perform purification at 4°C to maintain enzyme stability

• Use two-step purification: Ni-NTA affinity chromatography followed by size exclusion chromatography

• Add 5-10 μM hemin during purification to stabilize the enzyme structure
  This approach typically yields 2-5 mg of purified protein per liter of culture with >90% purity and specific activity of approximately 10-15 μmol/h/mg protein.

How should I design experiments to study HEM3 function in biofilm formation?

HEM3's role in biofilm formation can be studied through carefully designed experiments:

• Generate HEM3 knockout and overexpression strains using CRISPR-Cas9 or traditional homologous recombination

• Use both constitutive (PDC1) and inducible (MET3) promoters to control expression levels

• Quantify biofilm formation using crystal violet staining and confocal microscopy

• Analyze EPS (Extracellular Polymeric Substances) composition, focusing on:

  • Polysaccharide content using phenol-sulfuric acid method

  • Protein content using Bradford assays

  • eDNA content using PicoGreen assays

  • Lipid content using Nile Red staining

• Perform comparative transcriptomic analysis of biofilm vs. planktonic cells

• Examine the impact of iron availability on HEM3 expression and biofilm formation
  When analyzing results, focus on changes in biofilm matrix composition, as C. albicans studies have shown that oleic acid treatment can reduce polysaccharides and lipids in EPS by 35-41% and 26-47% respectively . Similar effects might be observed in HEM3 mutants due to altered heme metabolism.

What controls are essential when studying HEM3 regulation under different growth conditions?

Proper experimental controls are critical when studying HEM3 regulation:
Essential controls table:

How can I assess the impact of HEM3 expression on C. glabrata virulence?

To evaluate the relationship between HEM3 expression and virulence:

• Generate strains with varied HEM3 expression using different promoters (constitutive and inducible)

• Assess virulence factors in vitro:

  • Adhesion to epithelial cells

  • Growth under iron limitation

  • Resistance to oxidative stress (H₂O₂ challenge)

  • Biofilm formation capacity

• Evaluate gene expression changes in key virulence pathways:

  • Cell wall-related genes (CHS3, CHT4)

  • Adhesion genes (ALS1, RAS1)

  • Hyphal elongation factors (HWP1, CPH1)

• Use in vivo models:

  • Murine systemic infection model (tail vein injection)

  • Organ burden quantification (CFU determination)

  • Survival analysis

  • Histopathological examination
    Compare results with known virulence factors and control strains to establish the specific contribution of HEM3 to pathogenicity.

How should qRT-PCR data for HEM3 expression be normalized and analyzed?

For accurate qRT-PCR analysis of HEM3 expression:

• Select appropriate reference genes:

  • TUB1 (α-tubulin) is a stable reference gene for C. glabrata under various conditions

  • Use multiple reference genes (ACT1, EFB1) for more robust normalization

  • Validate reference gene stability under your specific conditions

• Data normalization workflow:

  • Calculate ΔCt = Ct(HEM3) - Ct(reference gene)

  • For multiple reference genes, use geometric mean for normalization

  • Calculate relative expression using 2^(-ΔΔCt) method

  • Present data as fold-change relative to control condition

• Statistical analysis:

  • Perform experiments with at least three biological replicates

  • Use technical triplicates for each qPCR reaction

  • Apply appropriate statistical tests (t-test for two conditions, ANOVA for multiple conditions)

  • Consider non-parametric tests if data doesn't follow normal distribution
    Relative GFP expression can be calculated by normalizing GFP signal to TUB1 values, as demonstrated in previous C. glabrata studies using promoter-GFP constructs .

How can I interpret discrepancies between protein levels and gene expression data for HEM3?

Discrepancies between HEM3 mRNA and protein levels are common and can be analyzed systematically:

• Potential causes of discrepancies:

  • Post-transcriptional regulation (mRNA stability, miRNA)

  • Translational efficiency differences

  • Post-translational modifications affecting protein stability

  • Technical limitations in detection methods

• Investigation approach:

  • Measure mRNA half-life using transcription inhibition (thiolutin treatment)

  • Analyze polysome profiles to assess translational efficiency

  • Examine protein degradation rates using cycloheximide chase

  • Investigate post-translational modifications using mass spectrometry

• Integration strategies:

  • Normalize protein data to housekeeping proteins

  • Compare trends rather than absolute values

  • Consider time-course experiments to capture delayed effects

  • Use mathematical modeling to account for synthesis and degradation rates
    When analyzing such discrepancies, consider that C. glabrata gene expression often shows unique patterns compared to other Candida species, potentially due to its closer evolutionary relationship to S. cerevisiae.

What statistical approaches are recommended for analyzing growth phenotypes of HEM3 mutants?

For robust statistical analysis of HEM3 mutant phenotypes:

Why is my recombinant HEM3 expression level low despite using a strong constitutive promoter?

Low expression despite using strong promoters like PDC1 can have several causes:

• Plasmid stability issues:

  • Confirm plasmid maintenance by plating on selective media

  • Check copy number stability over time

  • Verify CEN/ARS function by testing plasmid loss rate in non-selective media

• Protein toxicity:

  • Try inducible MET3 promoter system to control expression

  • Use lower-strength constitutive promoters (HHT2 or EGD2)

  • Check for growth defects indicating toxicity

• Codon optimization issues:

  • Analyze codon usage bias in your HEM3 construct

  • Consider synthesizing a codon-optimized version for C. glabrata

  • Check for rare codons that might cause translational pausing

• Media and growth conditions:

  • Optimize growth conditions (temperature, pH, media composition)

  • Test expression in different growth phases

  • Ensure selection pressure is maintained with correct antibiotic concentrations (NAT at 50 μg/ml in liquid, 100 μg/ml in plates)
    If using NAT-marked plasmids, remember that nourseothricin is not inhibitory in the presence of ammonium sulfate, so use SED media (with monosodium glutamate) instead of standard SD media .

How can I address aggregation issues when purifying recombinant HEM3?

Aggregation of recombinant HEM3 during purification can be addressed through these strategies:

• Buffer optimization:

  • Increase salt concentration to 300-500 mM NaCl

  • Add 5-10% glycerol as stabilizer

  • Include mild detergents (0.05% Triton X-100 or 0.1% Tween-20)

  • Test different pH ranges (pH 7.0-8.5) for optimal solubility

• Fusion tags approach:

  • Test solubility-enhancing tags (MBP, SUMO, Thioredoxin)

  • Position tags at N-terminus if C-terminal aggregation domains are suspected

  • Include TEV or PreScission protease sites for tag removal

• Co-expression strategies:

  • Co-express with chaperones (Hsp70, Hsp90)

  • Express with heme biosynthesis enzymes in the same pathway

  • Consider low-temperature expression (20-25°C)

• Refolding approaches:

  • Use pulse refolding with decreasing denaturant concentration

  • Try on-column refolding during affinity purification

  • Include additives like arginine or non-detergent sulfobetaines
    Each approach should be systematically tested and optimized to maintain HEM3 enzymatic activity while preventing aggregation.

What strategies can resolve contradictory results between in vitro and in vivo HEM3 studies?

Addressing discrepancies between in vitro and in vivo findings requires systematic investigation:

• Environmental factors analysis:

  • Compare media composition to host environment (iron availability, pH, nutrients)

  • Test conditions that mimic specific host niches (vaginal pH, blood glucose levels)

  • Evaluate temperature effects (30°C vs. 37°C)

• Host factor considerations:

  • Analyze impact of host-derived factors (cytokines, antimicrobial peptides)

  • Test growth in serum or macrophage-conditioned media

  • Examine gene expression in ex vivo samples vs. in vitro cultures

• Genetic background effects:

  • Validate findings in multiple C. glabrata clinical isolates

  • Consider strain-specific regulatory differences

  • Test mutants in different parental backgrounds

• Experimental design reconciliation:

  • Match timepoints between in vitro and in vivo experiments

  • Consider using mouse-adapted strains for animal studies

  • Develop ex vivo models that bridge in vitro and in vivo conditions Integration of results from different experimental systems requires acknowledging the limitations of each approach and identifying common mechanisms that explain the observed discrepancies.