Recombinant Saccharomyces cerevisiae Increased recombination centers protein 18 (IRC18)

What is IRC18 and what is its basic function in Saccharomyces cerevisiae?

IRC18 (Increased Recombination Centers protein 18) is a 224-amino acid protein in Saccharomyces cerevisiae that appears to be involved in DNA recombination processes. Studies indicate that IRC18 plays a role in regulating the yeast endogenous 2-μm plasmid levels . The protein contains specific domains that contribute to its function in DNA metabolism and recombination center formation.

Methodologically, IRC18's function can be studied through systematic genome-wide approaches combined with high-content screening. Research has shown that when analyzing DNA damage foci in yeast using Rad52-GFP markers, IRC18 appears among genes that influence DNA damage response pathways . To properly characterize its function, researchers should employ both loss-of-function and gain-of-function studies under various genetic backgrounds.

What are the optimal expression systems for producing recombinant IRC18 protein?

When using S. cerevisiae as an expression system, the following methodological approach is recommended:

For maximum protein yield, culture conditions should be optimized at 30°C with shaking at 180 rpm for approximately 24 hours, which typically yields around 10^9 CFU per mL of yeast culture .

How can high-throughput screening be designed to study IRC18's role in DNA recombination?

To establish a robust high-throughput screening system for IRC18 function in DNA recombination, researchers should implement a multitiered approach combining genetic perturbation with high-content screening:

• Reporter System Development:

  • Utilize a Rad52-GFP marker system to visualize DNA damage foci in response to various genetic or chemical perturbations

  • Extract approximately 470 cellular features from both red (cell boundaries) and green (damage foci) channels using CellProfiler software

• Machine Learning Classification:

  • Implement support vector machine (SVM) algorithms to distinguish nuclei with foci from those without

  • Train the classifier using positive and negative controls with known phenotypes

• Quantification Parameters:

  • Calculate the percentage of cells with foci

  • Determine Z-scores to identify significant deviations from wild-type

  • Establish a threshold cutoff for biologically relevant hits

• Experimental Scale:

  • Observe approximately 1,000 yeast cells per mutant background

  • Test approximately 5,000 different mutant backgrounds

  • Include sensitizing conditions such as SGS1 or YKU80 deletions or phleomycin treatment

This approach has successfully identified 345 genes involved in DNA damage response, demonstrating its effectiveness for studying proteins like IRC18 in recombination processes.

How does IRC18 regulate the yeast endogenous 2-μm plasmid levels?

IRC18 has been identified as a novel regulator of the 2-μm plasmid, which is an endogenous selfish DNA element in yeast. The 2-μm plasmid copy number control system is primarily under the control of the plasmid-encoded recombinase Flp1 .

To properly study IRC18's impact on 2-μm plasmid levels, researchers should implement the following methodological approach:

• Quantitative PCR Analysis:

  • Extract genomic DNA from wild-type and IRC18 mutant strains

  • Perform qPCR using SYBR Green PCR master mix (10 μl of SYBR Green Master Mix, 0.1 mM forward and reverse primers, 10 ng genomic DNA in a 20 μl final volume)

  • Run PCR with the following conditions: 1 cycle at 50°C for 2 min followed by 95°C for 10 min; and 40 cycles of 95°C for 15 s, 60°C for 1 min

  • Calculate fold change of 2-μm number compared to wild-type using the 2^(-ΔΔCT) method

  • Analyze 5-9 independent colonies with qPCR in duplicates or triplicates

• Statistical Analysis:

  • Compare values using Student's t-test to determine statistical significance

  • Establish p-value thresholds (p<0.05) for significance determination

Research suggests that IRC18 may function by regulating Flp1 protein levels, which is critical for maintaining appropriate 2-μm plasmid copy number and segregation during cell division.

How do post-translational modifications affect IRC18 function in DNA repair?

While specific post-translational modifications (PTMs) of IRC18 are not directly described in the search results, researching this aspect requires sophisticated methodological approaches:

• Mass Spectrometry Analysis:

  • Express and purify recombinant IRC18 from both bacterial and yeast systems

  • Perform tryptic digestion followed by LC-MS/MS analysis

  • Identify potential phosphorylation, SUMOylation, or ubiquitination sites

  • Compare modification patterns under normal and DNA damage conditions

• Site-Directed Mutagenesis:

  • Generate point mutations at potential modification sites

  • Assess the impact on IRC18 function through complementation studies

  • Measure DNA repair efficiency using reporter systems

• Protein-Protein Interaction Analysis:

  • Implement microscale thermophoresis (MST) titration approaches similar to those used for studying Rev7 interactions

  • Calculate binding affinity (Kd) values for IRC18 interactions with DNA repair proteins

  • Compare wild-type versus mutated versions lacking specific modification sites

• In vivo Dynamics:

  • Create strains expressing IRC18 fused to fluorescent proteins

  • Track protein localization and dynamics before and after DNA damage

  • Correlate with cell cycle phases using established markers

This research direction is particularly important as many DNA repair proteins are regulated through complex PTM networks that control their activity, localization, and stability during the DNA damage response.

What is the relationship between IRC18 and G-quadruplex DNA structures?

Investigation of IRC18's potential relationship with G-quadruplex (G4) DNA structures represents an advanced research direction that builds on observations about other DNA repair proteins:

• G4 Structure Interaction Assays:

  • Express and purify recombinant IRC18

  • Perform electrophoretic mobility shift assays (EMSA) with labeled G4 DNA

  • Conduct circular dichroism spectroscopy to assess structural changes upon binding

  • Measure binding kinetics using surface plasmon resonance (SPR)

• Genomic Analysis:

  • Map IRC18 binding sites genome-wide using ChIP-seq

  • Correlate binding patterns with predicted G4-forming sequences

  • Similar to studies with Vid22 protein , examine IRC18 localization at predicted G-quadruplex regions

• Functional Impact Assessment:

  • Create reporter systems containing G4 structures within transcriptional units

  • Compare expression levels between wild-type and IRC18 mutant strains

  • Measure genetic instability at G4-containing loci

• Co-localization Studies:

  • Perform microscopy studies with fluorescently tagged IRC18 and known G4-binding proteins

  • Induce G4 formation using G4-stabilizing ligands

  • Quantify temporal and spatial relationships during DNA replication and repair

This research direction is particularly relevant as G-quadruplex structures are increasingly recognized as important regulators of genome stability and replication stress, areas where IRC18 may have significant functional roles.

What are the optimal conditions for creating IRC18 knockout strains in different yeast backgrounds?

Creating precise IRC18 knockout strains requires careful methodological considerations:

• Deletion Strategy Design:

  • Use PCR-based gene deletion with 40-60 bp homology arms flanking the IRC18 coding sequence

  • Select appropriate selectable markers (KanMX, HIS3, URA3) based on background strain auxotrophies

  • For studies in different genetic backgrounds, consider the CRISPR-Cas9 system for higher efficiency

• Transformation Protocol:

  • For standard laboratory strains, use lithium acetate transformation

  • For industrial or non-conventional strains, electroporation may be more effective:

    • Parameters: 7.5 kV/cm, capacitance 25 μF, parallel resistor 200 Ω, pulse lengths 5 ms

  • Plate on selective media containing G418 (200 mg/L) for KanMX selection

• Confirmation of Deletion:

  • Verify successful knockouts through PCR with primers external to the integration site

  • Confirm the absence of IRC18 expression using RT-PCR or Western blotting

  • Check for unwanted phenotypes that might indicate off-target effects

• Strain Background Considerations:

  • When working with non-standard backgrounds, adjust integration conditions based on transformation efficiency

  • Consider the genetic interactions that might differ between backgrounds

  • For quantitative studies, ensure isogenic backgrounds by backcrossing as needed

This methodological framework ensures the creation of reliable knockout strains for studying IRC18 function across different genetic backgrounds.

How can researchers effectively study the impact of IRC18 overexpression on recombination rates?

To systematically study the effects of IRC18 overexpression on recombination rates, researchers should implement the following comprehensive methodology:

• Overexpression System Construction:

  • Create an expression vector containing IRC18 under control of either an inducible promoter (GAL1) or strong constitutive promoter (PGK1)

  • Include a C-terminal epitope tag (HA or FLAG) for detection without disrupting function

  • Generate both integrative (single-copy) and episomal (multi-copy) constructs for different expression levels

• Recombination Rate Measurement:

  • Implement direct-repeat recombination assays using URA3 or ADE2 reporter systems

  • Calculate recombination rates using fluctuation analysis with the Lea-Coulson median estimator

  • Compare rates between wild-type, IRC18-deleted, and IRC18-overexpressing strains

• Data Analysis Framework:

  Strain	Mean Recombination Rate	Fold Change	Statistical Significance (p-value)
  Wild-type	(baseline rate)	1.0	-
  IRC18Δ	(measured rate)	(calculated)	(from t-test)
  IRC18-OE	(measured rate)	(calculated)	(from t-test)

• Temporal Analysis:

  • For inducible systems, measure recombination rates at different time points after induction

  • Correlate with IRC18 protein levels to establish dose-response relationships

  • Use time-lapse microscopy with recombination reporters to observe events in real-time

• Genetic Background Effects:

  • Test the effect of IRC18 overexpression in strains deficient in key recombination factors

  • Establish epistatic relationships to determine the pathway(s) through which IRC18 functions

This methodological approach provides a comprehensive framework for understanding how altered IRC18 expression impacts recombination processes in yeast.

How does IRC18 integrate into the broader DNA damage response network?

Understanding IRC18's position in the DNA damage response network requires system-level analysis approaches:

• Genetic Interaction Mapping:

  • Perform systematic genetic interaction screens combining IRC18 deletion with mutations in known DNA repair pathways

  • Use synthetic genetic array (SGA) methodology to generate double mutants at genome scale

  • Calculate genetic interaction scores to identify positive and negative genetic interactions

  • Cluster IRC18 with genes showing similar genetic interaction profiles

• Protein-Protein Interaction Network:

  • Perform tandem affinity purification followed by mass spectrometry (TAP-MS)

  • Use yeast two-hybrid screening to identify direct interaction partners

  • Validate key interactions using co-immunoprecipitation or microscale thermophoresis

  • Construct a protein interaction network centered on IRC18

• Transcriptional Response Analysis:

  • Perform RNA-seq comparing wild-type and IRC18Δ strains under normal and DNA damage conditions

  • Identify differentially expressed genes and enriched pathways

  • Use Gene Ontology enrichment analysis to determine biological processes affected

• Integration with Existing Networks:

  • Map IRC18 connections onto established DNA repair pathway maps

  • Use methods similar to those in the "Quantitative Yeast Phenomics" approach described in search result

  • Identify potential regulatory relationships and feedback loops

This systematic approach allows for positioning IRC18 within the complex network of DNA damage response and repair pathways, providing context for its molecular functions.

What are the best statistical approaches for analyzing IRC18 impact on DNA damage foci formation?

Robust statistical analysis of IRC18's impact on DNA damage foci requires specialized approaches:

• Image Analysis Pipeline:

  • Acquire high-resolution images of cells expressing DNA damage markers (e.g., Rad52-GFP)

  • Implement automated cell segmentation using CellProfiler or similar software

  • Extract multiple parameters including focus number, intensity, size, and nuclear position

• Quantification Methods:

  • Calculate the percentage of cells with foci (primary metric)

  • Determine focus persistence time through time-lapse imaging

  • Measure focus intensity as a proxy for DNA damage severity

  • Track focus mobility within the nucleus

• Statistical Framework:

  Analysis Type	Method	Application
  Mean Comparison	Student's t-test	For single timepoint comparisons
  Time Series	Repeated measures ANOVA	For tracking foci over time
  Distribution Analysis	Kolmogorov-Smirnov test	For comparing focus intensity distributions
  Pattern Classification	Support Vector Machine	For automated phenotype detection

• Multiple Testing Correction:

  • Apply Bonferroni or Benjamini-Hochberg FDR correction when testing multiple hypotheses

  • Establish rigorous significance thresholds based on experimental scale

  • Consider using Z-scores to standardize results across experiments

• Visualization Techniques:

  • Box plots for showing focus distribution across populations

  • Cumulative distribution functions for comparing focus persistence

  • Heat maps for showing correlations between different phenotypic parameters

These statistical approaches provide a comprehensive framework for analyzing the complex phenotypic effects of IRC18 mutations on DNA damage response, enabling detection of subtle but biologically significant effects.