Recombinant Saccharomyces cerevisiae Uncharacterized membrane protein YOR034C-A (YOR034C-A)

What is YOR034C-A and where is it found in the yeast genome?

YOR034C-A is an uncharacterized membrane protein encoded in the Saccharomyces cerevisiae genome. It is also referenced as NOR002C in some databases. The protein is 80 amino acids long with a full amino acid sequence of MNTQELCKIFVAREYPLVVVPFIYFVLFLHQKYHTTLNYVWYPTCSKRIWVREKGRKCSFFFFSKVPRSDGFANNRCQRK . The protein has been identified in multiple strains of S. cerevisiae including the widely used laboratory strain ATCC 204508 / S288c.

The protein is classified as "uncharacterized" because its precise biological function has not been fully elucidated, though research suggests it plays a role in biofilm formation through FLO11 induction pathways . Researchers investigating this protein should begin with genomic database searches to confirm its presence in their specific yeast strain of interest.

What experimental approaches are most effective for expressing and purifying recombinant YOR034C-A?

For successful expression and purification of recombinant YOR034C-A, researchers should implement a multi-step methodology:

• Gene cloning: The YOR034C-A coding region (expression region 1-80) should be PCR-amplified and cloned into an appropriate expression vector with a purification tag.

• Expression system selection: While E. coli systems can be used, expression in the native organism (S. cerevisiae) often provides advantages for proper folding and post-translational modifications of membrane proteins.

• Membrane protein solubilization: Since YOR034C-A is a membrane protein, effective detergent selection is critical. A screening approach testing multiple detergents (DDM, OG, LDAO) at varying concentrations is recommended.

• Purification strategy: Affinity chromatography followed by size-exclusion chromatography typically yields the purest preparations. For long-term storage, a Tris-based buffer with 50% glycerol is recommended, with storage at -20°C for short-term use and -80°C for extended storage .

Researchers should validate protein purity via SDS-PAGE and confirm identity through either Western blotting or mass spectrometry before proceeding to functional studies.

How can researchers accurately design data tables for YOR034C-A expression experiments?

Creating effective data tables for YOR034C-A expression experiments requires careful consideration of variables and formatting. Follow these methodological steps:

• Identify independent and dependent variables clearly:

  • Independent variables might include: strain background, growth conditions, deletion/mutation type

  • Dependent variables typically include: expression levels, biofilm formation measurements, phenotypic characteristics

• Structure tables with appropriate columns and rows:

  • Include all experimental conditions and biological replicates

  • Create separate columns for each measured parameter

  • Include statistical analysis columns (p-values, standard deviation)

• Format data tables professionally:

  • Use consistent units throughout (specify units in column headers)

  • Apply appropriate decimal places for precision

  • Include borders for visual clarity4

Example experimental data table structure:

This structured approach ensures data is presented clearly and facilitates comparison across different experimental conditions4.

What are the basic techniques for studying the role of YOR034C-A in yeast cells?

Several foundational techniques are essential for characterizing YOR034C-A function:

• Gene deletion analysis: Create precise yor034c-a deletion mutants using homologous recombination techniques. This provides the basis for understanding phenotypic consequences of YOR034C-A absence.

• Fluorescence microscopy: Determine subcellular localization by tagging YOR034C-A with fluorescent proteins. This confirms its membrane localization and potential co-localization with other proteins.

• Growth phenotype assays: Compare growth rates between wild-type and yor034c-a mutant strains under various conditions (different carbon sources, temperatures, stressors) using plate-based or liquid culture methods.

• RNA expression analysis: Quantify YOR034C-A expression levels using RT-PCR or RNA-Seq across different growth conditions to identify regulatory patterns.

• Biofilm formation assays: Assess biofilm-forming capacity using crystal violet staining, normalizing to total biomass (OD595nm/OD600nm), and analyzing at multiple time points (e.g., 46 hours and 96 hours) .

These techniques establish the fundamental properties of YOR034C-A before advancing to more complex functional studies.

How does YOR034C-A contribute to biofilm formation in Saccharomyces cerevisiae?

YOR034C-A has been identified among several genes that control biofilm development in S. cerevisiae through FLO11 induction . The molecular mechanism involves a complex regulatory network:

• Genetic evidence: Deletion mutants lacking YOR034C-A lose the ability to form biofilms in liquid medium. These mutants also typically lose the ability to form surface-spreading biofilm colonies (mats) on agar and approximately 69% also lose invasive growth capability .

• Regulatory pathway: YOR034C-A appears to function within a regulatory network that includes protein kinase A isoforms and transcription factors such as Sfl1p and Flo8p, which compete to regulate FLO11 expression. The toggle switch between Sfl1p and Flo8p leads to variegated FLO11 expression, where only a subpopulation of cells expresses FLO11 and contributes to cell-cell adhesion .

• Experimental approach for investigation:

  • Create precise yor034c-a deletion mutants

  • Quantify biofilm formation using standardized crystal violet assays

  • Normalize biofilm measurements to total biomass (OD595nm/OD600nm)

  • Measure FLO11 expression using fluorescent in situ hybridization with Stellaris probes

  • Record the percentage of cells expressing FLO11 mRNA by counting cells with one or more fluorescent foci

This methodological approach reveals that YOR034C-A functions as a positive regulator in the biofilm formation pathway, likely through indirect activation of FLO11 expression .

What methodologies can effectively measure the impact of YOR034C-A mutations on biofilm formation?

To rigorously measure the impact of YOR034C-A mutations on biofilm formation, researchers should implement a comprehensive methodology:

• Standardized biofilm quantification protocol:

  • Grow cultures under consistent conditions (media, temperature, aeration)

  • Measure biofilm formation using crystal violet staining

  • Normalize measurements to total biomass (OD595nm/OD600nm)

  • Log-transform data to achieve normal distribution

  • Calculate median values from multiple biological replicates

  • Define significance thresholds (e.g., ±2σ from wild-type mean)

• Time-course analysis:

  • Evaluate biofilm development at multiple time points (e.g., 46h and 96h)

  • Calculate formation rates rather than single time point measurements

  • Use regression analysis to model development curves

• Comprehensive data analysis approach:

  • Compare normalized biofilm scores between mutant and wild-type strains

  • Apply appropriate statistical tests (ANOVA with post-hoc tests)

  • Exclude samples with insufficient biomass (OD600nm <0.01)

Example data table for biofilm quantification:

This methodological approach allows precise quantification of biofilm phenotypes associated with YOR034C-A mutations and facilitates comparison across different experimental conditions .

How can researchers design experiments to investigate the interaction between YOR034C-A and FLO11?

Investigating the interaction between YOR034C-A and FLO11 requires a multi-faceted experimental approach:

• Gene expression analysis:

  • Quantify FLO11 mRNA levels in wild-type vs. yor034c-a deletion mutants using RT-PCR

  • Use fluorescent in situ hybridization (FISH) with Stellaris probes covering the stretch +9 to +708 of FLO11

  • Include ACT1 probes as positive hybridization control

  • Count cells as FLO11-positive if they display one or more red foci

  • Compare the percentage of FLO11-expressing cells between wild-type and mutant populations

• Genetic interaction analysis:

  • Create double mutants (yor034c-a/flo11) to assess epistatic relationships

  • Test whether YOR034C-A overexpression can rescue flo11 mutant phenotypes

  • Create domain-specific mutations in YOR034C-A to identify regions important for FLO11 regulation

• Phenotypic assays:

  • Measure invasive growth using the plate-washing assay (patch cells on 2.0% YPD agar for 3 days at 30°C)

  • Assess mat formation on low-agar plates

  • Quantify cell-cell adhesion in liquid culture

• Biochemical interaction studies:

  • Perform chromatin immunoprecipitation (if YOR034C-A has potential DNA-binding properties)

  • Use co-immunoprecipitation to test for protein-protein interactions

  • Apply tandem affinity purification to identify protein complexes involving YOR034C-A

These methodological approaches collectively provide a comprehensive understanding of how YOR034C-A influences FLO11 expression and function in biofilm formation .

What factors influence the variability in YOR034C-A expression across different yeast strains?

The variability in YOR034C-A expression and function across different yeast strains presents a significant research challenge that requires systematic investigation:

• Strain background considerations:

  • Laboratory strains (e.g., S288C) versus wild isolates show substantial differences

  • Σ1278b strain background demonstrates more robust biofilm and invasive growth phenotypes than S288C

  • Industrial and clinical isolates may contain unique YOR034C-A sequence variants

• Genetic context factors:

  • Promoter sequence variations between strains affect expression levels

  • Trans-acting factors (transcription factors, chromatin modifiers) differ between strains

  • Background mutations in biofilm-related genes create strain-specific dependencies

• Methodological approach for strain comparison:

  • Sequence YOR034C-A locus across multiple strains to identify polymorphisms

  • Measure baseline expression levels using qRT-PCR with strain-specific normalization

  • Create isogenic strains by introducing YOR034C-A variants into a common background

  • Test complementation by introducing YOR034C-A from one strain into deletion mutants of another

• Environmental response variations:

  • Test strain-specific responses to glucose limitation

  • Examine differential regulation under various stress conditions

  • Assess growth phase-dependent expression patterns

Cross-strain comparative analysis data table:

This systematic approach helps identify strain-specific factors influencing YOR034C-A function and explains seemingly contradictory results across different studies .

How should researchers analyze data from YOR034C-A deletion experiments in the context of biofilm regulatory networks?

Analyzing data from YOR034C-A deletion experiments requires integrating multiple data types within the broader context of biofilm regulatory networks:

• Normalization and statistical analysis:

  • Normalize biofilm measurements to account for growth differences (OD595nm/OD600nm)

  • Log-transform data to achieve normal distribution for statistical testing

  • Calculate median values from replicate experiments

  • Establish significance thresholds (e.g., scores less than 0.584 indicate significantly reduced biofilm)

• Network-based analysis approach:

  • Position YOR034C-A within known regulatory pathways (PKA, MAPK, TOR)

  • Compare yor034c-a phenotypes with other biofilm regulators (flo8, sfl1, tpk3)

  • Create genetic interaction profiles by analyzing double mutants

  • Construct regulatory network models using systems biology approaches

• Transcriptomic data integration:

  • Identify genes differentially expressed in yor034c-a mutants

  • Focus on changes in FLO11 and other adhesion-related genes

  • Compare expression profiles with other biofilm-defective mutants

  • Use clustering analysis to identify functional gene groups

• Phenotypic correlation analysis:

  • Correlate biofilm measurements with invasive growth and mat formation

  • Assess FLO11 expression levels using FISH in single cells

  • Calculate the percentage of cells expressing FLO11 in different genetic backgrounds

  • Compare with known regulators like tpk3 mutants, which show increased FLO11 transcription

This integrated analytical approach positions YOR034C-A within the complex regulatory network controlling biofilm formation and provides mechanistic insights into its function.