The recombinant YGR226C protein is produced via heterologous expression in E. coli and purified to >90% purity using standard chromatography techniques:
While YGR226C remains functionally uncharacterized, its recombinant form is utilized in:
Protein Biochemistry Studies: Structural analysis via SDS-PAGE and Western blotting .
Functional Screening: Hypothetical roles in yeast cellular processes (e.g., protein-protein interactions, metabolic pathways) .
No direct evidence links YGR226C to specific biological processes, enzymatic reactions, or disease mechanisms .
Critical limitations include:
Functional Annotation: No Gene Ontology (GO) terms or enzymatic activities are assigned .
Interaction Data: No validated protein interactors or pathways identified .
Experimental Validation: Structural studies (e.g., X-ray crystallography, NMR) are absent.
Future research should prioritize functional assays (e.g., yeast two-hybrid screens, knockout phenotyping) to elucidate its biological role .
STRING: 4932.YGR226C
YGR226C is a gene in the yeast Saccharomyces cerevisiae with the systematic name reflecting its chromosomal location (chromosome VII, right arm). The gene is part of the reference genome sequence derived from laboratory strain S288C. Researchers can access the genomic context, coordinates, and sequence information through the Saccharomyces Genome Database (SGD) . When investigating YGR226C, it's essential to consider both the reference strain sequence and potential variations in other laboratory strains, which can be accessed through the "Sequence Details" section of the SGD database.
The YGR226C gene encodes a putative uncharacterized protein with specific sequence-derived properties including length, molecular weight, and isoelectric point. These basic physical characteristics can be found in the protein information section of the Saccharomyces Genome Database. For experimental characterization, researchers should consider analyzing protein abundance (median abundance and median absolute deviation), half-life, domains, and potential modification sites . These parameters are crucial for designing purification protocols and functional assays.
GO annotations for YGR226C provide insights into its potential molecular functions, biological processes, and cellular components. These annotations in the SGD consist of four mandatory components: the gene product (YGR226C), terms from GO controlled vocabularies, references, and evidence codes . Researchers should distinguish between manually curated annotations (higher confidence) and computational predictions (requiring experimental validation). When designing experiments to characterize YGR226C, prioritize testing hypotheses derived from high-confidence GO annotations with strong evidence codes.
Functional characterization of YGR226C should begin with a comprehensive bioinformatic analysis to identify potential domains, motifs, and structural features that might suggest function. Following this initial analysis, researchers should employ a multi-omics approach:
Transcriptomics: Analyze expression patterns across different growth conditions and stress responses
Proteomics: Identify interaction partners through techniques like affinity purification-mass spectrometry
Metabolomics: Assess metabolic changes in deletion or overexpression strains
For experimental validation, create gene knockout (Δygr226c) and overexpression strains, then perform phenotypic assays under various conditions. If YGR226C shares characteristics with known protein kinases (as seen with YGR262c ), test for enzymatic activity using appropriate substrates and cofactors. Remember that protein function may be condition-dependent, so design experiments that explore multiple environmental contexts including anaerobic/aerobic conditions .
When investigating post-translational modifications (PTMs) of YGR226C, consider the following experimental design factors:
Experimental Approach | Key Considerations | Expected Outcomes |
---|---|---|
Phosphorylation Analysis | Test multiple metal cofactors (Mn²⁺, Co²⁺, Mg²⁺) | Identification of specific cofactor requirements |
PTM Mass Spectrometry | Include phosphatase/deubiquitinase inhibitors | Map of modification sites with confidence scores |
Mutagenesis Studies | Create site-specific mutants at predicted PTM sites | Functional impact of modifications |
Kinase/Modifying Enzyme Assays | Test physiologically relevant conditions | Identification of enzymes responsible for modifications |
The methodological approach should be informed by findings from related proteins such as YGR262c, which requires specific cofactors (Mn²⁺ or Co²⁺) for activity, with a unique inability to utilize Mg²⁺ . When presenting PTM data, follow standard scientific reporting guidelines by including both the raw mass spectrometry data and the interpreted results with confidence scores.
When conflicting data emerge regarding YGR226C protein-protein interactions, implement the following systematic approach:
Employ multiple independent techniques (e.g., yeast two-hybrid, co-immunoprecipitation, proximity labeling) to verify interactions
Test interactions under different physiological conditions (carbon sources, growth phases, stress conditions)
Use proper controls including known interactors and non-interacting proteins
Perform reciprocal tagging (tag both YGR226C and the putative interactor in separate experiments)
Develop quantitative interaction scores rather than binary (yes/no) classifications
When presenting contradictory interaction data, use a clear tabular format showing results from different methods and conditions. Include statistical measures of confidence and explicitly address discrepancies in your analysis, proposing testable hypotheses to resolve contradictions . Remember that transient or condition-specific interactions are often biologically significant despite being difficult to detect consistently.
For accurate detection and quantification of YGR226C expression, researchers should consider multiple complementary approaches:
Technique | Advantages | Limitations | Best Application |
---|---|---|---|
RT-qPCR | High sensitivity, quantitative | Measures mRNA not protein | Transcriptional regulation studies |
Western Blot | Protein-specific detection | Semi-quantitative, antibody dependent | Protein abundance changes |
Mass Spectrometry | Direct protein quantification | Complex sample preparation | Absolute quantification |
GFP/Fluorescent Tagging | Live-cell visualization | Tag might affect function | Localization studies |
When designing experiments, consider that YGR226C expression may be condition-dependent. Two-dimensional transcriptome analysis in chemostat cultures can reveal expression patterns across different growth conditions . For optimal results, normalize expression data to appropriate reference genes or proteins that remain stable under your experimental conditions. Present quantitative expression data with appropriate statistical analysis and biological replicates.
Creating reliable YGR226C mutant strains requires careful methodological considerations:
Design deletion cassettes with unique barcode identifiers for tracking in competitive growth assays
Verify gene deletion by PCR from multiple primer pairs and sequencing of junction regions
Complement deletion strains with plasmid-borne wild-type or mutant alleles to confirm phenotype specificity
Use clean genetic backgrounds and include multiple independently constructed mutants in experiments
Create conditional alleles (temperature-sensitive, auxin-inducible degron) if complete deletion causes severe growth defects
After construction, validate strains by confirming:
Complete absence of target gene expression
Expected marker expression
Absence of second-site mutations (through whole-genome sequencing)
Growth characteristics in standard conditions
Strain stability through multiple generations
When reporting mutant phenotypes, present comprehensive growth data across multiple conditions with appropriate statistical analysis to distinguish primary from secondary effects.
When investigating regulatory elements for YGR226C, implement the following experimental approach:
Bioinformatic analysis to identify putative transcription factor binding sites in the promoter region
Promoter truncation and mutation studies using reporter gene assays
ChIP-seq to identify transcription factors binding to the YGR226C promoter
Analysis of expression under different environmental conditions and stress responses
Pay particular attention to anaerobic regulatory elements like the binding sites for Upc2p (CGTTT) and Rox1p (ATTGTTC), which are common regulatory motifs in yeast . Experimental designs should include positive controls (genes with known regulation patterns) and negative controls (non-regulated promoters). When analyzing promoter regions, search for previously uncharacterized motifs like AAGGCAC that might represent novel regulatory elements.
When analyzing functional genomics data for YGR226C, select statistical methods appropriate to your specific data type and experimental question:
Data Type | Recommended Statistical Approach | Key Considerations |
---|---|---|
Differential Expression | DESeq2 or limma with FDR correction | Account for batch effects |
Fitness Assays | Mixed-effects models with biological replicates | Test for genetic background effects |
Protein Interactions | SAINT or CompPASS scoring algorithms | Include abundance normalization |
Multi-omics Integration | Canonical correlation analysis or MOFA | Validate with independent datasets |
Avoid qualitative descriptors like "remarkably" decreased or "extremely" different; instead, provide exact p-values and effect sizes . For complex datasets, consider dimension reduction techniques (PCA, t-SNE) to visualize relationships. Present statistical results in clear tables with appropriate statistical notation and confidence intervals, not just p-values.
For effective presentation of YGR226C phenotypic data, follow these guidelines based on scientific reporting standards:
Select the appropriate presentation format:
Use tables for precise numerical values and statistical comparisons
Use graphics for trends and patterns across conditions
Reserve text for interpretation of the most important findings
For growth phenotypes, present:
Growth curves with error bars representing biological replicates
Area under the curve (AUC) calculations for quantitative comparisons
Statistical analysis comparing mutant to wild-type across conditions
For complex phenotypes, create visual summaries comparing multiple variables across strains and conditions
When writing results, combine data presentation with clear interpretation. Rather than stating "Mean baseline measurement of YGR226C expression before intervention was X and after intervention was Y," write "YGR226C expression decreased from X to Y after intervention" . This approach provides both data and its interpretation, making results more accessible to readers.
To connect YGR226C function to broader cellular processes:
Perform systematic genetic interaction screens (SGA) to identify genetic relationships
Map YGR226C into existing functional networks using tools like STRING and GeneMANIA
Conduct comparative transcriptomics/proteomics between wild-type and Δygr226c strains
Use gene set enrichment analysis (GSEA) to identify cellular pathways affected by YGR226C perturbation
When interpreting these analyses, distinguish between direct and indirect effects by validating key findings with targeted experiments. Present network analyses using visualization tools that highlight the most statistically significant connections while maintaining appropriate complexity. Include tables of enriched pathways with statistical measures (p-values, q-values, enrichment scores) and clearly indicate the methodology used for enrichment calculations.
Researchers studying YGR226C should utilize these specialized resources:
Resource Category | Recommended Tools | Primary Applications |
---|---|---|
Genomic Databases | SGD, YeastMine, NCBI Gene | Sequence and annotation access |
Functional Prediction | BLAST, Pfam, I-TASSER | Domain and structural prediction |
Expression Data | SPELL, Expression Atlas | Condition-specific expression patterns |
Interaction Networks | BioGRID, STRING, MINT | Protein-protein interaction analysis |
Evolutionary Analysis | Homologene, OrthoDB | Identifying orthologs across species |
The Saccharomyces Genome Database (SGD) serves as the primary resource, providing sequence information, functional annotations, and links to literature . For effective analysis, combine multiple prediction tools and validate computational predictions with experimental approaches. When reporting bioinformatic analyses, clearly document the software versions, parameters, and databases used to ensure reproducibility.
For optimal PCR-based studies of YGR226C, follow these primer design guidelines:
Obtain the reference sequence from SGD, noting any strain-specific variations
For standard PCR amplification:
Design primers with 18-25 nucleotides
Maintain GC content between 40-60%
Ensure similar melting temperatures (±2°C) between primer pairs
Check for secondary structures and self-complementarity
For specific applications:
Gene deletion: Include 40bp homology arms for homologous recombination
qPCR: Design amplicons of 80-150bp spanning exon junctions when possible
Tagging: Ensure in-frame fusion without disrupting functional domains
SGD provides tools for primer design specifically optimized for yeast genetics . Always validate primers through in silico PCR against the S. cerevisiae genome to ensure specificity, and test experimentally with appropriate controls before use in critical experiments.