Recombinant Saccharomyces cerevisiae Uncharacterized protein YKL065W-A (YKL065W-A)

What are the fundamental characteristics of the uncharacterized protein YKL065W-A in Saccharomyces cerevisiae?

YKL065W-A represents one of several uncharacterized open reading frames (ORFs) in the S. cerevisiae genome. While specific information about YKL065W-A is limited in current literature, researchers typically approach uncharacterized yeast proteins by examining their sequence conservation, genomic context, and expression patterns. Phylogenetic analysis often reveals that uncharacterized proteins may be specific to certain yeast lineages or broadly conserved. S. cerevisiae strains show significant variability in their genetic content, with industrial strains divided into 5 sub-lineages that differ distinctly from wild isolates . This genetic diversity extends to the expression and function of uncharacterized proteins like YKL065W-A. Initial characterization typically involves bioinformatic analysis to identify potential functional domains, followed by experimental verification through techniques such as GFP tagging for localization or deletion studies to examine phenotypic effects.

What expression systems are most effective for recombinant production of YKL065W-A?

For recombinant expression of yeast proteins like YKL065W-A, S. cerevisiae itself often serves as an ideal expression host due to its native post-translational modification capabilities. When designing an expression system for YKL065W-A, researchers should consider several factors: promoter strength, induction conditions, and fusion tags for detection and purification. The choice between constitutive promoters (like GPD) versus inducible systems (like GAL1) depends on whether the protein might be toxic when overexpressed. Expression can be optimized using approaches similar to those employed in other S. cerevisiae recombinant protein studies, where whole heat-killed recombinant yeast have been engineered to encode target proteins for immunotherapy applications . For purification and detection, epitope tags (HA, FLAG, or His6) can be added without significantly altering protein function in most cases. Expression verification should employ multiple methods including Western blotting, mass spectrometry, and activity assays when possible.

How do I design primers for PCR amplification and cloning of the YKL065W-A gene?

Designing primers for amplification of the YKL065W-A gene requires careful consideration of several factors:

• Sequence verification: First, obtain the most up-to-date genomic sequence from databases like SGD (Saccharomyces Genome Database).

• Primer design parameters:

  • Primers should be 18-30 nucleotides long

  • GC content between 40-60%

  • Melting temperature (Tm) between 55-65°C

  • Avoid secondary structures and primer-dimer formation

• Restriction sites: Include appropriate restriction sites for subsequent cloning, with 3-6 additional nucleotides at the 5' end to ensure efficient enzyme digestion.

• Expression considerations: For protein expression, ensure the forward primer contains the start codon in the correct reading frame, and consider codon optimization if expressing in a different host.

The chimeric structure of some yeast genes, as seen with STA1 (composed of fragments from FLO11 and SGA1), highlights the importance of careful primer design . When working with uncharacterized genes like YKL065W-A, it's advisable to sequence the amplified product to confirm correct amplification, especially since genomes assembled from short reads may have difficulty capturing the full gene structure of complex genomic regions .

How should I design experiments to determine the cellular localization of YKL065W-A?

When designing experiments to determine the cellular localization of YKL065W-A, apply a systematic approach following established experimental design principles :

• Define your variables clearly:

  • Independent variable: The fusion construct (e.g., YKL065W-A-GFP vs. control GFP)

  • Dependent variable: Cellular localization pattern

  • Extraneous variables to control: Expression level, cell growth phase, imaging conditions

• Choose appropriate fusion strategies:

  • C-terminal vs. N-terminal tagging: Based on bioinformatic prediction of signal sequences

  • Tag selection: GFP for live cell imaging, epitope tags (HA, Myc) for immunofluorescence

• Experimental controls:

  • Positive controls: Proteins with known localization patterns (e.g., nuclear, mitochondrial, ER)

  • Negative controls: Unfused fluorescent protein

• Validation through multiple approaches:

  • Fluorescence microscopy for GFP fusion proteins

  • Subcellular fractionation followed by Western blotting

  • Immunofluorescence with antibodies against epitope tags

The experimental design should include both between-subjects (comparing different strains) and within-subjects (observing the same cells under different conditions) components . For quantitative assessment, measure colocalization with known organelle markers using appropriate statistical methods.

What are the key considerations for designing knockout/knockdown experiments to study YKL065W-A function?

• Define your research question and hypothesis:

  • Example hypothesis: "Deletion of YKL065W-A affects growth under stress conditions"

• Method selection based on research goals:

  • Complete knockout: CRISPR-Cas9 or homologous recombination

  • Conditional expression: Place under regulatable promoter (tetO, GAL1)

  • Partial knockdown: RNAi (if in appropriate strain background)

• Experimental design considerations:

  • Control for genetic background effects by using isogenic strains

  • Include complementation tests to verify phenotypes are due to target gene

  • Design experiments to test phenotypes under multiple conditions

• Phenotypic analysis matrix:

Remember to assign subjects to groups using appropriate randomization methods, and plan how you will measure your dependent variables with precise metrics . This might include growth rate measurements, metabolite production, gene expression changes, or protein interaction profiles.

How can I determine protein-protein interactions of YKL065W-A in vivo?

To investigate protein-protein interactions of YKL065W-A in vivo, multiple complementary approaches should be employed:

• Affinity Purification-Mass Spectrometry (AP-MS):

  • Express YKL065W-A with an affinity tag (TAP, FLAG, HA)

  • Purify the protein complex under native conditions

  • Identify interacting partners via mass spectrometry

  • Include appropriate controls: untagged strain, non-specific bait protein

  • Validate interactions using reciprocal tagging of identified partners

• Proximity-Based Labeling:

  • Fusion of YKL065W-A with BioID or APEX2

  • Enables identification of transient or weak interactions

  • Analyze proximity partners by streptavidin pulldown and mass spectrometry

• Yeast Two-Hybrid (Y2H) Screening:

  • Construct bait plasmid with YKL065W-A fused to DNA-binding domain

  • Screen against prey library or targeted preys

  • Verify positive interactions through multiple reporter systems

  • Validate with co-immunoprecipitation or split-fluorescent protein assays

• Integrated Data Analysis:

  • Compare interaction datasets from multiple methods

  • Prioritize interactions found in multiple approaches

  • Correlate with co-expression data and genetic interaction networks

When analyzing results, focus on interaction reproducibility and biological significance. Similar to immunological studies with recombinant S. cerevisiae , careful design of controls is essential for distinguishing specific from non-specific interactions. This might include performing parallel experiments with mutated versions of YKL065W-A or in different growth conditions to identify condition-specific interactions.

What approaches can resolve contradictory data about YKL065W-A function?

When facing contradictory data about YKL065W-A function, a systematic troubleshooting approach can help resolve discrepancies:

• Strain Background Analysis:

  • S. cerevisiae strains show considerable genetic diversity

  • Sequence the YKL065W-A locus in your strains to identify polymorphisms

  • Test function in multiple strain backgrounds to determine if effects are strain-specific

  • Consider ploidy differences, as S. cerevisiae strains can range from haploid to tetraploid

• Methodological Consistency Assessment:

  • Standardize growth conditions, media composition, and cell harvesting procedures

  • Control for expression levels when using tagged or overexpressed constructs

  • Ensure consistent timepoints for analyses of dynamic processes

• Multi-method Validation Strategy:

  • Confirm results using orthogonal techniques

  • For example, validate transcriptomic findings with RT-qPCR

  • Verify protein-level changes with both Western blotting and mass spectrometry

• Systematic Variable Control:

  • Create a matrix of all experimental variables that might affect results

  • Test variables systematically to identify sources of variation

  • Document all methodological details to enable perfect replication

• Data Integration Approach:

  • Develop a working model that accounts for seemingly contradictory observations

  • Consider condition-specific functions or genetic interactions

  • Use Bayesian approaches to weigh evidence from different experimental systems

When designing resolution experiments, follow established protocols for experimental design , ensuring proper control of variables and appropriate statistical analysis of results.

What are the optimal conditions for expressing recombinant YKL065W-A in different S. cerevisiae strains?

Optimizing expression of recombinant YKL065W-A requires consideration of strain characteristics and expression system components:

• Strain Selection Considerations:

  • Laboratory strains (S288C, W303): Well-characterized but may not express efficiently

  • Industrial strains: Higher stress tolerance but genetic manipulation can be challenging

  • Selection based on intended application and genetic background requirements

• Expression Vector Components:

  • Promoter selection:

    • Constitutive (TEF1, GPD): For stable expression

    • Inducible (GAL1, CUP1): For controlled expression timing

  • Selection markers: URA3, LEU2, HIS3 based on strain auxotrophies

  • Copy number: CEN/ARS (low-copy) vs. 2μ (high-copy) vectors

• Optimal Growth Conditions:

  • Temperature: Typically 30°C, lower (24-28°C) for difficult-to-fold proteins

  • Media composition: Rich (YPD) vs. selective (SC) depending on experimental needs

  • Induction timing: Mid-log phase typically optimal (OD600 ≈ 0.8-1.0)

• Expression Optimization Matrix:

• Verification Methods:

  • Western blotting with tag-specific antibodies

  • mRNA quantification via RT-qPCR

  • Activity assays if function is known

When designing expression experiments, consider that industrial S. cerevisiae strains often show higher variation in ploidy and genome content , which may affect expression levels. Similar to the approach used in immunotherapy applications , verify expression using multiple independent methods to ensure consistent and reliable protein production.

How can I optimize protein extraction and purification protocols for YKL065W-A?

Optimizing extraction and purification of YKL065W-A requires adapting general protocols to the specific characteristics of this uncharacterized protein:

• Preliminary Analysis:

  • Conduct bioinformatic analysis for predicted:

    • Molecular weight and isoelectric point

    • Hydrophobicity and transmembrane domains

    • Post-translational modifications

    • Structural features (disulfide bonds, glycosylation sites)

• Cell Lysis Optimization:

  • Mechanical methods: Glass bead disruption, French press, sonication

  • Chemical methods: Detergent-based lysis buffers (Triton X-100, CHAPS, DDM)

  • Enzymatic methods: Zymolyase treatment followed by gentle lysis

  • Buffer composition: pH, salt concentration, reducing agents, protease inhibitors

• Purification Strategy Selection:

  • Affinity chromatography: Based on fusion tags (His6, GST, MBP)

  • Ion exchange: Based on predicted protein charge

  • Size exclusion: For final polishing and buffer exchange

  • Consider multi-step purification for higher purity

• Protocol Optimization Matrix:

• Validation Methods:

  • SDS-PAGE with Coomassie/silver staining

  • Western blot analysis

  • Mass spectrometry for identity confirmation

  • Activity assays (if applicable)

  • Thermal shift assays for stability assessment

What bioinformatic approaches are most effective for predicting the function of YKL065W-A?

For predicting the function of uncharacterized proteins like YKL065W-A, a multi-layered bioinformatic approach yields the most comprehensive results:

• Sequence-Based Analysis:

  • Homology search: BLAST, HHpred for distant homology detection

  • Conserved domain analysis: InterPro, PFAM, CDD

  • Motif identification: MEME, GLAM2

  • Phylogenetic profiling: Identify co-evolution patterns across species

• Structural Prediction:

  • Secondary structure: PSIPRED, JPred

  • Tertiary structure: AlphaFold2, RoseTTAFold

  • Binding site prediction: CASTp, 3DLigandSite

  • Transmembrane topology: TMHMM, Phobius

• Genomic Context Analysis:

  • Synteny analysis: Compare gene neighborhood across related species

  • Co-expression networks: Identify genes with similar expression patterns

  • Genetic interaction networks: Analyze synthetic lethality/sickness patterns

• Integrated Function Prediction:

  • Gene Ontology term prediction: PANNZER2, DeepGOPlus

  • Pathway association: KEGG, Reactome

  • Protein-protein interaction prediction: STRING, PrePPI

• Experimental Data Integration:

  • Incorporate available proteomics, transcriptomics data

  • Analyze condition-specific expression patterns

  • Examine protein localization data if available

When interpreting results, prioritize predictions supported by multiple methods and consider the evolutionary context. S. cerevisiae's genomic diversity, with 26 well-defined clades and various mosaic clusters , means that function predictions may be strain-dependent. Similar to the analysis of chimeric genes like STA1 , consider that YKL065W-A might have domain compositions that suggest multiple functional roles.

How should I analyze and interpret high-throughput data to understand YKL065W-A function in different cellular contexts?

Analyzing high-throughput data for understanding YKL065W-A requires robust statistical approaches and careful interpretation:

• RNA-Seq Data Analysis Workflow:

  • Quality control: FastQC, MultiQC

  • Alignment: STAR, HISAT2 to S. cerevisiae reference genome

  • Quantification: featureCounts, Salmon

  • Differential expression: DESeq2, edgeR

  • Pathway analysis: GSEA, GO enrichment

• Proteomics Data Analysis:

  • MS data processing: MaxQuant, PEAKS

  • Quantification: Label-free, SILAC, or TMT approaches

  • Statistical analysis: Perseus, MSstats

  • Pathway mapping: similar to transcriptomics

  • Cross-reference with transcriptomics for integrated analysis

• Genetic Interaction Screens:

  • Analysis of growth phenotypes under various conditions

  • Calculation of genetic interaction scores

  • Network visualization using Cytoscape

  • Module identification using MCODE or similar algorithms

• Data Integration Framework:

  • Multi-omics integration: MOFA, DIABLO

  • Network analysis: Weighted correlation network analysis (WGCNA)

  • Machine learning approaches: Random forest, SVM for feature importance

• Interpretation Guidelines:

  • Consider strain background effects on gene function

  • Control for batch effects in high-throughput data

  • Validate key findings with targeted experiments

  • Compare with known genes/proteins of similar expression patterns

When analyzing expression data, use methods similar to those employed in LPA (Lymphocyte Proliferation Assay) studies , applying the 2-∆∆CT method for relative quantification of gene expression. Ensure proper normalization using stable reference genes validated for your specific experimental conditions.