Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YNL114C (YNL114C)

What is known about the YNL114C gene and its protein product?

YNL114C is an open reading frame located on chromosome XIV in Saccharomyces cerevisiae that encodes a putative uncharacterized protein. While the S. cerevisiae genome has been extensively studied and annotated, this particular gene remains poorly characterized in terms of its specific biological function. Current evidence suggests it may be involved in stress response pathways, particularly oxidative stress tolerance, given that S. cerevisiae employs multiple molecular mechanisms to respond to oxidative environments.

The protein has been identified through genomic sequencing and proteomics approaches, but its precise biochemical activities, cellular localization, and interactions with other proteins remain largely undefined. Like many proteins involved in stress response, YNL114C expression may be regulated by transcription factors such as Yap1p, which is known to be a central regulator in oxidative stress response in yeast .

How can I express recombinant YNL114C protein for biochemical characterization?

For recombinant expression of YNL114C, consider the following methodological approach:

• Vector selection: Choose an appropriate expression vector containing a strong inducible promoter (GAL1, CUP1, or TEF) and suitable selection markers (URA3, LEU2, or HIS3).

• Tag incorporation: Include an affinity tag (6xHis, FLAG, or GST) at either the N- or C-terminus for purification, ensuring the tag doesn't interfere with protein folding or function.

• Expression system options:

  • Homologous expression in S. cerevisiae (preferred for native post-translational modifications)

  • Heterologous expression in E. coli (higher yields but may lack proper modifications)

  • Pichia pastoris (for scaled-up expression)

• Purification protocol:

  • Cell lysis using glass beads or enzymatic methods

  • Affinity chromatography based on the chosen tag

  • Size exclusion chromatography for higher purity

  • Consider adding protease inhibitors throughout purification

• Optimization considerations: Buffer composition, temperature, and induction conditions significantly affect yield and solubility. A methodical approach using experimental design principles with these variables as independent factors and protein yield/quality as dependent variables will identify optimal conditions .

What computational approaches can predict YNL114C function?

Multiple computational approaches can provide insights into YNL114C function:

• Sequence analysis and homology modeling:

  • BLAST and PSI-BLAST searches against characterized proteins

  • Multiple sequence alignment with potential homologs

  • Identification of conserved domains using Pfam, SMART, or CDD

  • Homology modeling using templates with similar sequence features

• Structural prediction and analysis:

  • Ab initio modeling using AlphaFold or Rosetta

  • Secondary structure prediction (PSIPRED, JPred)

  • Fold recognition (threading) approaches

  • Molecular dynamics simulations to identify stable conformations

• Systems biology approaches:

  • Gene co-expression network analysis

  • Functional gene clustering

  • Integrating transcriptomic, proteomic, and metabolomic datasets

  • Phylogenetic profiling across species

• Machine learning models:

  • Support Vector Machines or Neural Networks trained on functional annotation datasets

  • Feature extraction from sequence and structural data

  • Integration of experimental data from high-throughput studies

These computational predictions should generate testable hypotheses that guide wet-lab experimental design, following the systematic experimental approach outlined in research design principles .

How does YNL114C expression change under various stress conditions?

Expression analysis of YNL114C under diverse stress conditions reveals complex transcriptional regulation patterns. Studies examining S. cerevisiae stress responses show that many genes, particularly those involved in oxidative stress response, undergo significant expression changes when exposed to stress conditions.

Based on known patterns of stress-responsive genes in S. cerevisiae, YNL114C likely exhibits expression profiles similar to other genes regulated by stress-response transcription factors like Yap1p. The Yap1p transcription factor plays a central role in oxidative stress response and regulates numerous genes involved in cellular protection mechanisms .

Table 1: Hypothetical YNL114C Expression Changes Under Various Stress Conditions

Methodologically, researchers should employ both genome-wide approaches (RNA-seq, microarray) and targeted methods (RT-qPCR) to validate expression changes. Time-course experiments are particularly valuable to distinguish between primary and secondary transcriptional responses. The experimental design should include appropriate controls and biological replicates to account for strain-specific variations, as S. cerevisiae strains show significant heterogeneity in their stress responses .

What are the genetic interactions between YNL114C and known stress response pathways?

Investigation of genetic interactions between YNL114C and established stress response pathways requires systematic interaction mapping using both classical genetics and modern high-throughput approaches.

Methodological approach:

• Synthetic genetic array (SGA) analysis:

  • Cross YNL114C deletion strain with deletion collection

  • Score growth phenotypes to identify synthetic lethal/sick interactions

  • Focus on interactions with known stress response genes

• Double knockout studies:

  • Create double mutants with key stress response genes (e.g., YAP1, TSA2, GPX2)

  • Assess fitness under normal and stress conditions

  • Quantify epistatic effects

• Overexpression studies:

  • Overexpress YNL114C in strains lacking key stress response genes

  • Test for complementation or synthetic effects

  • Assess changes in oxidative stress tolerance

Genetic interaction mapping often reveals functional relationships that aren't apparent from sequence analysis alone. The glyoxylate cycle and oxidative stress response pathways are particularly relevant for investigation, as these systems are crucial for S. cerevisiae survival under stress conditions . Additionally, genes involved in amino acid biosynthesis should be examined, as these pathways are typically upregulated during stress responses in yeast .

When designing these experiments, it's essential to control for strain-specific effects, as S. cerevisiae contains genetically diverse subpopulations with different stress response capabilities .

How do post-translational modifications affect YNL114C function and localization?

Post-translational modifications (PTMs) often critically influence protein function, localization, and interactions. For YNL114C, a methodical investigation of PTMs requires combining predictive tools with experimental validation.

Research approach:

• Computational prediction:

  • Analyze sequence for common PTM motifs (phosphorylation, ubiquitination, SUMOylation)

  • Predict subcellular localization signals that might be masked or exposed by PTMs

  • Model structural changes induced by potential modifications

• Experimental identification:

  • Mass spectrometry analysis of purified YNL114C under various conditions

  • Site-directed mutagenesis of predicted modification sites

  • Western blot with modification-specific antibodies

• Functional impact assessment:

  • Create phosphomimetic mutants (S/T to D/E) and phospho-null mutants (S/T to A)

  • Examine localization changes using fluorescent protein fusions

  • Assess stress resistance phenotypes of PTM-site mutants

Table 2: Predicted Post-Translational Modification Sites in YNL114C

When designing these experiments, it's crucial to consider that PTMs often occur in response to specific cellular conditions. Therefore, testing multiple stress conditions is essential to capture the dynamic nature of these modifications, particularly those related to oxidative stress response pathways .

What are the optimal experimental designs for characterizing YNL114C function?

Characterizing an uncharacterized protein like YNL114C requires a comprehensive experimental strategy combining multiple approaches. Following experimental design principles, researchers should:

• Define clear research questions and hypotheses:

  • Formulate testable null and alternative hypotheses about YNL114C function

  • Identify independent variables (e.g., growth conditions, genetic background) and dependent variables (e.g., growth rate, stress resistance)

• Control for extraneous variables:

  • Use isogenic strains differing only in YNL114C status

  • Implement appropriate controls (wild-type, empty vector, unrelated gene)

  • Account for strain-specific variations in S. cerevisiae

• Design factorial experiments:

  • Test multiple factors simultaneously (temperature, media composition, stress conditions)

  • Analyze interactions between factors

  • Use statistical design of experiments (DoE) to optimize experimental conditions

• Implement phenotypic profiling:

  • Growth curve analysis under various conditions

  • Stress resistance assays (oxidative, heat, osmotic)

  • Metabolic profiling using various carbon and nitrogen sources

• Molecular characterization:

  • Localization studies using fluorescent protein fusions

  • Protein-protein interaction studies (Y2H, Co-IP, BioID)

  • Transcriptional profiling of deletion and overexpression strains

How can I design experiments to resolve contradictory data about YNL114C?

When facing contradictory data about YNL114C function or characteristics, a systematic approach to experimental design can help resolve discrepancies:

• Root cause analysis:

  • Examine methodological differences between conflicting studies

  • Assess strain-specific variations (S. cerevisiae strains can vary significantly)

  • Evaluate experimental conditions and their potential impact

• Harmonized experimental approach:

  • Standardize protocols across experiments

  • Use multiple strains from different genetic backgrounds

  • Implement consistent growth and induction conditions

• Multi-method validation:

  • Apply orthogonal techniques to verify observations

  • Combine genetic, biochemical, and computational approaches

  • Use both in vivo and in vitro systems

• Controlled variable manipulation:

  • Systematically vary one factor at a time while controlling others

  • Create a matrix of experimental conditions

  • Identify factors that cause divergent results

• Statistical design and analysis:

  • Implement factorial experimental designs

  • Use appropriate statistical tests for hypothesis validation

  • Conduct power analysis to ensure adequate sample sizes

When resolving contradictions, it's particularly important to consider genetic linkage effects, as they can strongly influence how loci are detected and interpreted . Additionally, the mosaic nature of many S. cerevisiae strains means that genetic background can significantly impact protein function and regulation .

What controls are essential when studying YNL114C's role in oxidative stress response?

• Genetic controls:

  • YNL114C deletion strain (complete ORF deletion)

  • YNL114C overexpression strain (controlled expression system)

  • Known oxidative stress response mutants as positive controls (yap1Δ, tsa2Δ, gpx2Δ)

  • Unrelated gene deletion as negative control

• Treatment controls:

  • Dose-response curves for oxidative agents (H₂O₂, paraquat, menadione)

  • Time-course experiments to capture dynamic responses

  • Multiple oxidative stressors to distinguish general vs. specific responses

• Strain controls:

  • Multiple S. cerevisiae backgrounds due to strain heterogeneity

  • Complementation studies to confirm phenotype causality

  • Single colony isolates to minimize population heterogeneity

• Technical controls:

  • Verification of gene deletion/modification (PCR, sequencing)

  • Expression confirmation (RT-qPCR, Western blot)

  • Vehicle controls for all treatments

• Recovery controls:

  • Adaptive response experiments (mild stress followed by severe stress)

  • Post-stress recovery monitoring

  • Multigenerational analysis to assess stable adaptations

The oxidative stress response in S. cerevisiae involves multiple pathways and redundant systems, making comprehensive control design essential. Particular attention should be paid to the thioredoxin system, which has been shown to be expressed during stress conditions and is crucial for survival in oxidative environments .

How should I analyze high-throughput data sets to identify YNL114C function?

Analyzing high-throughput datasets to elucidate YNL114C function requires a systematic approach combining statistical rigor with biological context:

• Transcriptomic data analysis:

  • Compare expression profiles between wild-type and YNL114C deletion strains

  • Identify differentially expressed genes using appropriate statistical methods (DESeq2, limma)

  • Perform gene set enrichment analysis (GSEA) to identify affected pathways

  • Construct co-expression networks to identify functionally related genes

• Proteomic data analysis:

  • Quantify protein abundance changes using statistical approaches for mass spectrometry data

  • Identify post-translational modifications

  • Map protein-protein interactions through AP-MS or BioID datasets

  • Integrate with transcriptomic data to identify post-transcriptional regulation

• Phenomic data analysis:

  • Analyze growth curves using parametric models

  • Quantify stress resistance using survival analysis methods

  • Compare metabolic profiles using multivariate statistical approaches

  • Integrate phenotypic data with genetic information

• Multi-omics integration:

  • Perform correlation analysis across different data types

  • Use machine learning approaches to identify patterns across datasets

  • Construct integrated networks incorporating multiple data layers

  • Apply dimension reduction techniques to visualize complex relationships

When analyzing these datasets, it's crucial to implement appropriate statistical controls for multiple testing and to consider biological significance alongside statistical significance. For oxidative stress studies specifically, time-course data is particularly valuable as stress responses evolve dynamically .

What statistical approaches best detect genetic interactions involving YNL114C?

Detecting and quantifying genetic interactions involving YNL114C requires specialized statistical approaches tailored to the experimental design:

• Quantitative interaction scoring:

  • Calculate expected phenotypes based on single mutant effects

  • Measure deviations from expected values to identify interactions

  • Apply appropriate models (multiplicative, additive, or log-based) depending on phenotype characteristics

  • Calculate confidence intervals to assess interaction significance

• High-dimensional interaction analysis:

  • Apply dimension reduction techniques (PCA, t-SNE) to visualize interaction networks

  • Use hierarchical clustering to identify functionally related gene groups

  • Implement Bayesian approaches to estimate interaction probabilities

  • Apply graph theory methods to characterize network properties

• Experimental design considerations:

  • Include sufficient biological replicates (minimum 3-5)

  • Implement robust normalization procedures to account for batch effects

  • Use randomized block designs to control for environmental variations

  • Include positive and negative interaction controls

• Validation approaches:

  • Cross-validate findings using independent datasets

  • Confirm key interactions with targeted experiments

  • Assess consistency across different genetic backgrounds

  • Compare results with existing interaction databases

When analyzing genetic interactions, it's particularly important to consider linkage effects, as research has shown that "linkage among genetic variants strongly influences how loci are detected" . Additionally, strain background can significantly impact genetic interaction patterns due to the mosaic nature of many S. cerevisiae strains .

How can I integrate results from different experimental approaches to build a comprehensive model of YNL114C function?

Building a comprehensive functional model of YNL114C requires thoughtful integration of diverse experimental data:

• Hierarchical evidence evaluation:

  • Assess evidence quality using standardized criteria

  • Assign confidence levels to different data types

  • Prioritize direct experimental evidence over correlative observations

  • Consider replication status across independent studies

• Pathway reconstruction:

  • Map molecular interactions (physical, genetic, regulatory)

  • Identify upstream regulators and downstream effectors

  • Reconstruct signaling or metabolic pathways involving YNL114C

  • Compare with known stress response pathways in S. cerevisiae

• Systems biology modeling:

  • Develop mathematical models of pathways involving YNL114C

  • Simulate system behavior under different conditions

  • Test model predictions with targeted experiments

  • Refine models iteratively as new data becomes available

• Visualization and communication:

  • Create interaction maps and network diagrams

  • Develop process models illustrating functional relationships

  • Use standardized ontologies for functional annotation

  • Present alternative models when evidence is ambiguous

Table 3: Evidence Integration Framework for YNL114C Functional Model

When integrating results, particular attention should be paid to experiments examining oxidative stress response, as multiple search results suggest connections between uncharacterized S. cerevisiae proteins and stress response mechanisms .