Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YGR073C (YGR073C)

What is YGR073C and why is it classified as a dubious open reading frame?

YGR073C is a putative uncharacterized protein in Saccharomyces cerevisiae that has been classified as a "dubious open reading frame" unlikely to encode a functional protein. This classification is based on available experimental and comparative sequence data, particularly the extensive overlapping with other genomic features that has been observed in the Saccharomyces Genome Database . Despite this classification, recombinant forms of this protein have been produced for research purposes, enabling further investigation into its potential functions or structural characteristics.

What genomic context surrounds YGR073C in S. cerevisiae, and how might this affect its study?

YGR073C is located on chromosome VII of S. cerevisiae. Based on the systematic name (YGR073C), it is situated on the right arm of chromosome VII, and the "C" suffix indicates it is encoded on the Crick (complementary) strand. According to the Saccharomyces Genome Database, YGR073C "extensively overlaps" with other genomic features , which complicates its genetic manipulation and functional analysis. When designing experiments, researchers must consider this overlapping context to avoid unintended effects on neighboring genes when deleting or modifying YGR073C.

What expression systems are recommended for producing recombinant YGR073C protein?

For recombinant production of YGR073C, Escherichia coli has been successfully used as an expression host. According to product information, full-length YGR073C (amino acids 1-123) has been expressed in E. coli with an N-terminal His-tag . The E. coli expression system offers several advantages for producing this yeast protein, including:

• Rapid growth and high protein yields

• Established protocols for induction and purification

• Compatibility with affinity tags like the His-tag

• Cost-effectiveness compared to yeast expression systems

For researchers encountering challenges with E. coli expression, alternative systems such as yeast expression (P. pastoris) might be considered, though optimization would be required for each system.

What purification strategies and buffer conditions optimize YGR073C protein stability?

Based on available product information, the recommended purification approach for His-tagged YGR073C involves immobilized metal affinity chromatography (IMAC). The purified protein has been successfully prepared as a lyophilized powder in Tris/PBS-based buffer with 6% trehalose at pH 8.0 . For storage and handling:

• Store lyophilized protein at -20°C/-80°C upon receipt

• Reconstitute in deionized sterile water to 0.1-1.0 mg/mL

• Add glycerol to 5-50% final concentration for long-term storage

• Prepare working aliquots to avoid repeated freeze-thaw cycles

• Working aliquots may be stored at 4°C for up to one week

The addition of trehalose in the buffer formulation likely serves as a stabilizing agent for the lyophilized protein, protecting it during freeze-drying and reconstitution processes.

What analytical methods are most appropriate for verifying recombinant YGR073C identity and purity?

For comprehensive characterization of recombinant YGR073C, researchers should employ multiple complementary methods:

• SDS-PAGE: To assess protein purity and approximate molecular weight

• Western blotting: Using anti-His antibodies to confirm identity of the His-tagged protein

• Mass spectrometry: For accurate molecular weight determination and peptide mapping

• Circular dichroism (CD): To evaluate secondary structure elements

• Size exclusion chromatography (SEC): To assess homogeneity and oligomeric state

Product information indicates that SDS-PAGE analysis has been used to confirm >90% purity for commercially available recombinant YGR073C . When reporting research findings, detailed description of these analytical methods is essential for reproducibility.

What experimental strategies can determine if YGR073C has biological function despite its classification as a dubious ORF?

Despite YGR073C's classification as a dubious ORF, several experimental approaches can be used to investigate potential biological functions:

• Genetic knockout studies: Precise deletion of YGR073C using CRISPR-Cas9, followed by phenotypic characterization, including growth rate analysis under various conditions.

• Overexpression studies: Expressing YGR073C under a strong promoter to observe any gain-of-function phenotypes.

• Transcriptomic profiling: RNA-seq analysis comparing wild-type and YGR073C deletion strains to identify affected pathways.

• Protein localization: Fusion with fluorescent proteins like GFP to determine subcellular localization, which may provide functional clues.

• Metabolomic analysis: Comparison of metabolite profiles between wild-type and YGR073C mutant strains.

Given that YGR073C has been classified as unlikely to encode a functional protein , researchers should design experiments with appropriate controls and be prepared to detect subtle phenotypic changes that might only manifest under specific conditions.

How can genome-wide expression data be leveraged to infer potential functions of YGR073C?

Genome-wide expression data sets provide valuable resources for studying dubious ORFs like YGR073C. Researchers can:

• Mine existing datasets: Analyze public microarray or RNA-seq data to identify conditions where YGR073C shows differential expression. For example, genome-wide analysis of mRNAs as seen in the search results may contain information about YGR073C expression patterns.

• Correlation analysis: Identify genes whose expression patterns correlate with YGR073C across multiple conditions, suggesting potential functional relationships.

• Co-expression networks: Construct networks of co-expressed genes to place YGR073C in a functional context.

• Expression clustering: Apply hierarchical clustering analysis using tools such as those developed by Eisen et al. to group YGR073C with functionally related genes.

• Regulatory network mapping: Map expression data to compiled transcriptional regulatory networks using methods similar to those of Herrgård et al. to infer regulatory relationships.

When analyzing expression data, researchers should be aware that since YGR073C overlaps with other genomic features, expression signals might reflect neighboring genes rather than YGR073C itself.

What comparative genomics approaches help evaluate YGR073C conservation and potential function?

Comparative genomics provides crucial context for dubious ORFs like YGR073C:

• Ortholog identification: Search for potential orthologs in other yeast species and fungi using BLASTN or BLASTP vs. fungi databases .

• Synteny analysis: Examine the conservation of genomic context around YGR073C across related species.

• Evolutionary rate analysis: Calculate the ratio of nonsynonymous to synonymous substitutions (dN/dS) to assess selective pressure.

• Sequence feature conservation: Identify conserved motifs or domains using tools like PFAM, SMART, or InterPro.

• Structure prediction comparison: Use AlphaFold or similar tools to predict protein structures of YGR073C and its potential orthologs, then compare structural features.

The lack of conservation across species would support the classification as a dubious ORF, while conservation might suggest functional relevance despite current classification.

How can protein-protein interaction studies be designed to identify potential binding partners of YGR073C?

Despite YGR073C's classification as a dubious ORF, protein-protein interaction studies can provide insights into potential functions if the protein is expressed. Researchers should consider these approaches:

• Yeast two-hybrid (Y2H) screening: Using YGR073C as bait to screen a yeast genomic library, with appropriate controls to filter false positives.

• Affinity purification-mass spectrometry (AP-MS): Using tagged YGR073C to isolate protein complexes followed by mass spectrometry identification.

• Proximity-dependent biotin identification (BioID): Fusing YGR073C with a biotin ligase to identify proximal proteins in living cells.

• Co-immunoprecipitation (Co-IP): Using antibodies against the tagged YGR073C to pull down interacting proteins.

• Surface plasmon resonance (SPR): Testing direct interactions between purified YGR073C and candidate binding partners.

When interpreting results, consider that:

• Interactions may be condition-specific or transient

• The His-tag might affect interaction capabilities

• As a dubious ORF, expression levels in native conditions may be low

• Controls with unrelated proteins are essential to filter non-specific interactions

What specialized techniques can determine if YGR073C undergoes post-translational modifications despite its dubious ORF status?

Post-translational modifications (PTMs) could provide insights into the regulation and function of YGR073C if it is expressed. Several techniques can be employed:

• Mass spectrometry-based proteomics:

  • Phosphoproteomics to identify phosphorylation sites

  • Glycoproteomics to detect glycosylation

  • Ubiquitylome analysis to identify ubiquitination

• Modification-specific antibodies: Western blotting with antibodies against common PTMs (phospho, acetyl, ubiquitin, SUMO).

• In vitro modification assays: Testing if YGR073C can serve as a substrate for known kinases, acetyltransferases, or other modifying enzymes.

• Mobility shift assays: Using Phos-tag SDS-PAGE or similar techniques to detect mobility shifts indicative of modifications.

• Site-directed mutagenesis: Mutating predicted modification sites and observing effects on function or localization.

How can contradictory experimental results regarding YGR073C be systematically evaluated and reconciled?

When facing contradictory results in YGR073C research, apply this structured approach:

• Methodological comparison:

  • Compare experimental designs, including strain backgrounds, culture conditions, and analytical methods

  • Evaluate the sensitivity and specificity of different detection methods

  • Consider whether differences in tags or expression systems might explain discrepancies

• Data reanalysis:

  • Reanalyze raw data using consistent analytical pipelines

  • Apply statistical methods appropriate for the data type and distribution

  • Consider meta-analysis techniques if multiple datasets are available

• Verification experiments:

  • Design experiments specifically to test conflicting hypotheses

  • Include appropriate positive and negative controls

  • Use orthogonal techniques to address the same question

• Consider contextual factors:

  • Genetic background effects (S288C vs. other strains)

  • Environmental conditions that might affect YGR073C expression or function

  • Potential interactions with overlapping genes

• Reporting guidelines:

  • Document contradictions transparently in publications

  • Discuss possible explanations for discrepancies

  • Highlight conditions under which different results were obtained

For example, if one study suggests YGR073C has a phenotype while another does not, differences in strain background could be investigated, as S. cerevisiae strains like S288C (the reference strain) may behave differently than other laboratory or wild strains .

What innovative CRISPR-Cas9 strategies can precisely manipulate YGR073C despite its overlapping genomic context?

Given YGR073C's overlapping genomic context , standard gene deletion approaches risk disrupting neighboring genes. Advanced CRISPR-Cas9 strategies can address this challenge:

• Base editing: Using CRISPR base editors to introduce early stop codons or disruptive mutations without double-strand breaks.

• Prime editing: Employing prime editors for precise nucleotide changes that specifically affect YGR073C without altering overlapping sequences.

• Inducible disruption systems: Integrating degron tags or riboswitches that allow controlled depletion of YGR073C product if expressed.

• CRISPRi approach: Using catalytically inactive Cas9 (dCas9) fused to repressors to inhibit transcription specifically at the YGR073C locus.

• Scarless editing: Implementing two-step strategies that introduce then remove selection markers to minimize genomic disruption.

When designing guide RNAs, researchers should:

• Target unique sequences within YGR073C that don't affect overlapping genes

• Perform off-target prediction analysis using tools specific for yeast genomes

• Validate genomic modifications by sequencing the entire affected region

• Assess the expression of neighboring genes to confirm specificity

What are the most effective protocols for studying YGR073C expression under various stress conditions?

To comprehensively analyze YGR073C expression under stress conditions:

• Stress condition matrix:

  • Osmotic stress (0.4M-1.0M NaCl, sorbitol)

  • Temperature stress (heat shock at 37°C-42°C, cold shock at 4°C-15°C)

  • Oxidative stress (H₂O₂, menadione)

  • Nutrient limitation (carbon, nitrogen starvation)

  • Stationary phase

  • DNA damage (UV, MMS)

• Quantification methods:

  • RT-qPCR: Use the protocol described in search result , including:

    • RNA extraction following the Holstege et al. protocol

    • cDNA construction using random oligonucleotides

    • SYBR Green PCR with 15 pmol oligonucleotides in 25 μl reactions

    • Normalization to ACT1 (actin) expression

  • RNA-seq: For genome-wide expression context

  • Northern blot: For validation of transcript size

  • GFP reporter fusion: For single-cell analysis

• Controls and normalization:

  • Include known stress-responsive genes as positive controls

  • Use multiple reference genes for normalization (ACT1, TDH3, ALG9)

  • Confirm biological relevance with protein-level analysis when possible

• Data analysis framework:

  • Time-course analysis to capture temporal dynamics

  • Dose-response relationships for chemical stressors

  • Clustering with known stress-responsive genes

  • Integration with existing stress response datasets

This approach enables detection of condition-specific expression patterns that might reveal functional contexts for YGR073C.

What statistical approaches best distinguish true signals from noise when analyzing high-throughput data related to dubious ORFs like YGR073C?

Analyzing high-throughput data for dubious ORFs requires robust statistical approaches:

• Appropriate filtering and normalization:

  • Apply stringent quality filters for sequencing data

  • Use normalization methods that account for technical variation

  • Consider specialized normalization for overlapping genomic regions

• Statistical testing framework:

  • Employ multiple testing correction (Benjamini-Hochberg FDR)

  • Use moderated t-statistics (limma) for improved variance estimation with small sample sizes

  • Consider Bayesian approaches that can incorporate prior knowledge about dubious ORFs

• Estimation of technical and biological noise:

  • Include technical replicates to quantify assay variation

  • Use spike-in controls for calibration

  • Employ simulation-based approaches to establish noise thresholds

• Validation strategies:

  • Cross-platform validation (e.g., RNA-seq findings confirmed by RT-qPCR)

  • Independent biological replicates from separate experiments

  • Orthogonal techniques to confirm key findings

• Specialized approaches for overlapping genes:

  • Transcript deconvolution algorithms

  • Strand-specific sequencing analysis

  • Junction-spanning reads analysis for transcript structure

A notable example is seen in the genome-wide analysis methodology described in search result , where mRNA copy number per cell was calculated using hybridization signals, assuming 15,000 mRNA molecules per yeast cell. This approach provides quantitative context for expression levels.

How might studies of YGR073C contribute to understanding fundamental mechanisms of dubious ORF evolution and regulation?

Research on YGR073C offers unique insights into evolutionary biology and genome organization:

• Evolutionary mechanisms of dubious ORFs:

  • Analysis of selection pressure on YGR073C sequence can reveal whether it experiences purifying selection despite being classified as dubious

  • Comparative analysis across yeast species can track the emergence and potential functionalization of similar dubious ORFs

  • Investigation of codon usage and GC content may reveal signatures of evolutionary constraints

• Regulatory complexity:

  • YGR073C's overlapping genomic context provides a model for studying regulatory interactions in compact genomes

  • Analysis of transcription factor binding and chromatin structure in the YGR073C region can reveal regulatory mechanisms affecting overlapping genes

  • Investigation of antisense transcription and other non-coding RNA interactions may reveal regulatory functions

• Genome annotation refinement:

  • Detailed characterization of YGR073C expression and potential function contributes to improved genome annotation

  • Development of criteria to distinguish between truly non-functional ORFs and those with cryptic or condition-specific functions

  • Creation of methodological frameworks for reassessing dubious ORFs genome-wide

This research has broader implications for understanding genome evolution, regulatory networks, and the definition of genes in compact genomes.

What are the most promising experimental designs to detect potential condition-specific expression of YGR073C?

To identify conditions where YGR073C might be functionally relevant:

• Comprehensive condition screening:

  • Systematic testing of growth media (different carbon sources, minimal vs. rich media)

  • Environmental stress panel (pH, temperature, osmotic pressure, oxidative stress)

  • Chemical compound library screening

  • Life cycle stages (sporulation, mating, stationary phase)

• Advanced expression monitoring:

  • Dual reporter systems (YGR073C promoter driving fluorescent protein expression)

  • Single-cell RNA-seq to capture heterogeneous or rare expression events

  • Live-cell imaging with destabilized fluorescent proteins to detect transient expression

  • Nascent transcription assays to detect short-lived transcripts

• Experimental design considerations:

  • Time-course sampling to capture transient expression

  • Genetic background variations (lab strains vs. wild isolates)

  • Integration of multiple omics approaches (RNA-seq, proteomics, metabolomics)

  • Positive controls using known condition-specific genes

• Data collection and analysis framework:

  • Statistical power calculations to determine appropriate replicate numbers

  • Sensitivity analysis to establish detection thresholds

  • Integration with existing datasets using consistent normalization methods

  • Machine learning approaches to identify subtle expression patterns

This systematic approach maximizes the chance of identifying conditions where YGR073C might have functional relevance, even if expression is rare or context-specific.

How can contradictory findings regarding the functionality of dubious ORFs like YGR073C be reconciled with current genome annotation paradigms?

Reconciling contradictory findings about dubious ORFs requires rethinking genome annotation frameworks:

• Evidence integration framework:

  • Establish tiered classification system based on multiple evidence types

  • Develop quantitative metrics for functional probability

  • Create annotation confidence scores that integrate diverse data types

  • Implement regular reassessment cycles as new data emerges

• Expanded functional definitions:

  • Recognize regulatory roles of seemingly non-coding regions

  • Consider evolutionary conservation of non-protein functions

  • Acknowledge context-dependent functionality

  • Incorporate developmental or condition-specific roles

• Methodological reconciliation:

  • Standardize experimental approaches for dubious ORF validation

  • Develop technology-specific quality metrics and confidence thresholds

  • Establish minimum reporting requirements for dubious ORF studies

  • Create centralized repositories for conflicting evidence

• Community consensus building:

  • Implement expert curation panels for contentious genomic features

  • Develop clear nomenclature for different degrees of functional evidence

  • Establish thresholds for reclassification of dubious ORFs

  • Create transparent processes for genome annotation updates

This framework allows the scientific community to accommodate emerging data on dubious ORFs while maintaining annotation rigor and acknowledging uncertainty where appropriate.