Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YGR293C (YGR293C)

What is YGR293C and why is it classified as a putative uncharacterized protein?

YGR293C is an open reading frame (ORF) in the Saccharomyces cerevisiae genome that has been identified through computational analysis but lacks experimental validation of its function. The classification as "putative uncharacterized" indicates that while bioinformatic evidence suggests it encodes a protein, its biological role remains unknown. This classification typically results from genome sequencing projects where ORF prediction tools identify potential protein-coding regions based on criteria such as length, presence of start/stop codons, and codon usage patterns .

The methodological approach to identifying such putative proteins involves:

• Computational gene prediction using Euclid distance discriminant methods analyzing single nucleotide frequencies at codon positions

• Comparison with known protein-coding regions to establish likelihood of expression

• Preliminary structural predictions that suggest protein-forming potential

• Absence of conclusive experimental data on function or expression

What experimental approaches are recommended for initial characterization of YGR293C?

Initial characterization of an uncharacterized protein like YGR293C should follow a systematic approach:

• Expression verification:

  • RT-PCR or Northern blotting to confirm transcription

  • Western blotting with epitope-tagged constructs to verify translation

  • Mass spectrometry to detect endogenous protein

• Subcellular localization:

  • Fluorescent protein tagging (GFP/RFP fusion constructs)

  • Immunofluorescence microscopy

  • Subcellular fractionation followed by Western blotting

• Preliminary phenotypic analysis:

  • Growth rate comparison of deletion strains under various conditions

  • Stress response profiling (temperature, oxidative, osmotic stress)

  • Sensitivity to antifungal agents or other compounds

• Basic bioinformatic analysis:

  • Sequence homology searches against characterized proteins

  • Structural prediction of domains and motifs

  • Evolutionary conservation analysis across fungal species

When designing expression verification experiments, researchers should consider creating multiple constructs with different fusion orientations (N and C-terminal tags) as protein function may be affected by tag position .

How can I generate a YGR293C deletion strain in Saccharomyces cerevisiae?

Creating a YGR293C deletion strain involves several methodological steps:

• Deletion cassette design:

  • Select a suitable marker gene (typically URA3, LEU2, HIS3, or KanMX for G418 resistance)

  • Design primers with 40-50bp homology to sequences flanking YGR293C

  • Amplify the marker gene with these chimeric primers

• Transformation:

  • Prepare competent yeast cells (typically using lithium acetate method)

  • Transform with the deletion cassette

  • Plate on selective media

• Verification of deletion:

  • PCR verification using primers outside the targeted region

  • DNA sequencing of junction points

  • RT-PCR to confirm absence of transcript

• Phenotypic assessment:

  • Compare growth rates with wild-type under standard conditions

  • Screen for phenotypes under various stress conditions

Table 1: Common selective markers for S. cerevisiae gene deletion

If the deletion has no obvious phenotype, consider utilizing the SATAY (Saturated Transposition) method, which can identify both essential genes and essential protein domains through transposon insertion patterns .

How can I determine if YGR293C forms protein complexes or interacts with other proteins?

Investigating protein-protein interactions for YGR293C requires multiple complementary approaches:

• Affinity purification coupled with mass spectrometry (AP-MS):

  • Create TAP-tagged or FLAG-tagged YGR293C constructs

  • Perform pull-down experiments under native conditions

  • Identify co-purifying proteins by mass spectrometry

  • Validate interactions with reciprocal pull-downs

• Yeast two-hybrid screening:

  • Use YGR293C as bait against a S. cerevisiae cDNA library

  • Test directed interactions with candidate partners

  • Perform domain mapping to identify interaction interfaces

• Proximity-based labeling:

  • BioID or TurboID fusion to YGR293C

  • Identify proximal proteins through biotinylation

  • Compare proximities under different conditions

• Co-localization studies:

  • Dual fluorescent tagging of YGR293C and putative interactors

  • Live-cell imaging and colocalization analysis

  • FRET or BiFC assays for direct interaction confirmation

When interpreting interaction data, it's critical to distinguish between stable complex members and transient interactors by varying extraction conditions and performing time-course analyses. Statistical validation should include appropriate controls and consideration of protein abundance to identify false positives .

What approaches can be used to determine the function of YGR293C using genetic interaction screens?

Genetic interaction screening provides powerful insights into gene function:

• Systematic genetic interaction mapping:

  • Create YGR293C deletion strain in a SGA (Synthetic Genetic Array) compatible background

  • Cross with genome-wide deletion collection

  • Identify synthetic lethal, synthetic sick, or suppressor interactions

  • Analyze interaction networks for functional pathways

• SATAY (Saturated Transposition) screening:

  • Perform transposon mutagenesis in wild-type and YGR293C mutant backgrounds

  • Identify genes with differential transposon insertion patterns

  • Map both positive and negative genetic interactions

  • Detect functional protein domains through insertion patterns

• Chemical genetic profiling:

  • Screen YGR293C mutants against compound libraries

  • Identify differential sensitivity/resistance profiles

  • Map chemical-genetic interactions to biological pathways

• Conditional mutant analysis:

  • Create temperature-sensitive or auxin-inducible degron variants

  • Perform RNA-seq or proteomics after conditional inactivation

  • Identify rapid transcriptional or protein-level responses

Table 2: Comparison of genetic interaction screening methods

When analyzing genetic interaction data, cluster genes with similar interaction profiles to identify functional relationships and potential redundant pathways .

How can I approach structural characterization of the YGR293C protein?

Structural characterization requires a multi-level approach:

• Protein expression and purification:

  • Express in heterologous systems (E. coli, insect cells)

  • Design constructs with varying boundaries based on predicted domains

  • Optimize solubility using fusion tags (MBP, SUMO, GST)

  • Implement rigorous purification protocols with quality control

• Crystallography and cryo-EM approaches:

  • Screen crystallization conditions systematically

  • Optimize crystal quality through additives and seeding

  • Consider single-particle cryo-EM for challenging targets

  • Validate structures with complementary methods

• Biophysical characterization:

  • Circular dichroism for secondary structure content

  • Size-exclusion chromatography with multi-angle light scattering for oligomeric state

  • Differential scanning fluorimetry for stability assessment

  • Small-angle X-ray scattering for solution structure

• Computational structure prediction:

  • Apply modern deep learning approaches (AlphaFold2, RoseTTAFold)

  • Validate predictions with experimental data

  • Use structural information to generate functional hypotheses

For membrane-associated proteins, consider nanodiscs or detergent screening to maintain native conformation. If the protein proves challenging to express, domain analysis and construct optimization based on bioinformatic predictions may identify stable fragments for structural studies .

What methods are available to investigate the essentiality of YGR293C in Saccharomyces cerevisiae?

Determining gene essentiality requires multiple complementary approaches:

• Classical deletion analysis:

  • Attempt gene deletion in diploid strains

  • Induce sporulation and analyze tetrad viability patterns

  • Quantify growth defects in haploid deletions if viable

• Conditional systems:

  • Implement tetracycline-repressible promoters

  • Create temperature-sensitive alleles

  • Develop auxin-inducible degron systems for protein depletion

  • Use CRISPR interference for targeted repression

• Transposon-based analysis:

  • Apply SATAY (Saturated Transposition) methodology

  • Identify regions with low transposon insertion density

  • Map essential protein domains through insertion patterns

  • Compare with wild-type insertion profiles

• High-resolution functional mapping:

  • Perform systematic mutagenesis (alanine scanning, domain deletion)

  • Create point mutations at conserved residues

  • Develop complementation assays with mutant variants

  • Map functionally critical regions at amino acid resolution

If YGR293C proves essential, researchers should consider analyzing its functional relationship to other essential genes through suppressor screens and synthetic rescue experiments. The SATAY approach is particularly valuable as it can distinguish between a gene that is essential as a whole versus specific essential domains within a gene .

How can RNA sequencing data be used to investigate YGR293C function?

RNA-seq provides powerful insights into gene function through transcriptomic analysis:

• Differential expression analysis:

  • Compare YGR293C deletion/overexpression strains with wild-type

  • Identify significantly altered genes and pathways

  • Perform time-course experiments to capture dynamic responses

  • Analyze condition-dependent transcriptomic changes

• Co-expression network analysis:

  • Build correlation networks from multiple RNA-seq datasets

  • Identify genes co-regulated with YGR293C

  • Map YGR293C to specific transcriptional modules

  • Infer function through guilt-by-association principles

• Ribosome profiling integration:

  • Combine RNA-seq with ribosome profiling data

  • Assess translational impacts of YGR293C manipulation

  • Identify post-transcriptional regulatory effects

  • Map potential translational control mechanisms

• Condition-specific analysis:

  • Perform RNA-seq under various stress conditions

  • Identify differentially affected pathways in mutants

  • Map condition-dependent functional requirements

  • Generate testable hypotheses about environmental roles

When designing RNA-seq experiments, include appropriate biological replicates (minimum of 3) and consider time-course sampling to capture primary versus secondary effects. Data analysis should include pathway enrichment, transcription factor binding site analysis, and comparison with existing datasets from related mutants .

What approaches can be used to determine if YGR293C has enzymatic activity?

Investigating potential enzymatic functions involves:

• Bioinformatic prediction:

  • Search for conserved catalytic motifs and domains

  • Compare with characterized enzyme families

  • Identify potential substrate-binding sites

  • Generate hypotheses about possible reaction mechanisms

• Activity-based screening:

  • Purify recombinant protein for in vitro assays

  • Screen against substrate libraries

  • Monitor potential catalytic activities using spectroscopic methods

  • Apply mass spectrometry to identify reaction products

• Metabolomic profiling:

  • Compare metabolite profiles between wild-type and YGR293C mutants

  • Identify differentially abundant metabolites

  • Trace isotope-labeled precursors to map metabolic flows

  • Correlate metabolic changes with phenotypic effects

• Structure-guided functional analysis:

  • Identify potential active site residues from structural data

  • Create point mutations at candidate catalytic residues

  • Perform complementation tests with mutant variants

  • Correlate structural features with biochemical activities

Table 3: Common enzyme activity detection methods

When investigating novel enzymatic activities, consider substrate promiscuity by testing related compound families and varying reaction conditions (pH, metal cofactors, temperature) to maximize discovery potential .

How can I use CRISPR-Cas9 to study YGR293C in Saccharomyces cerevisiae?

CRISPR-Cas9 offers powerful approaches for precise genome manipulation:

• Gene knockout strategies:

  • Design sgRNAs targeting YGR293C coding sequence

  • Incorporate repair templates with selectable markers

  • Screen transformants for successful editing

  • Validate edits with sequencing and expression analysis

• Base editing applications:

  • Apply cytosine or adenine base editors for precise mutations

  • Create specific amino acid substitutions without DSBs

  • Engineer regulatory sequences with minimal disruption

  • Introduce premature stop codons for truncation analysis

• CRISPRi/CRISPRa systems:

  • Use catalytically dead Cas9 (dCas9) fused to repressors (Mxi1) for CRISPRi

  • Implement dCas9-activator fusions (VP64) for CRISPRa

  • Achieve tunable expression modulation

  • Apply for temporal control of YGR293C expression

• Multiplexed editing:

  • Simultaneously target YGR293C and related genes

  • Create combinatorial mutant libraries

  • Implement synthetic genetic interaction screens

  • Engineer complex pathway modifications

When designing CRISPR experiments in yeast, optimize sgRNA selection for specificity and efficiency, consider chromosomal accessibility factors, and implement appropriate selection strategies. For quantitative phenotyping, barcode integration can facilitate pooled screens with next-generation sequencing readouts .

What proteomics approaches are most effective for studying the role of YGR293C?

Comprehensive proteomic analysis requires multiple specialized techniques:

• Global proteome analysis:

  • Compare protein expression profiles between wild-type and YGR293C mutants

  • Implement SILAC, TMT, or label-free quantification

  • Identify differentially expressed proteins

  • Map affected pathways through enrichment analysis

• Post-translational modification mapping:

  • Enrich for phosphopeptides, ubiquitinated peptides, or other PTMs

  • Identify differentially modified proteins in YGR293C mutants

  • Map regulatory networks affected by YGR293C

  • Correlate modifications with functional outcomes

• Protein-protein interaction proteomics:

  • Perform immunoprecipitation-mass spectrometry with tagged YGR293C

  • Implement BioID or APEX proximity labeling

  • Apply crosslinking mass spectrometry for transient interactions

  • Integrate interactome data with functional information

• Spatial proteomics:

  • Perform subcellular fractionation followed by proteomics

  • Implement hyperLOPIT for high-resolution spatial mapping

  • Track protein relocalization in response to YGR293C manipulation

  • Correlate localization changes with functional effects

When designing proteomics experiments, consider appropriate sample preparation methods for yeast cells (such as mechanical disruption or enzymatic cell wall digestion), implement rigorous statistical analysis, and validate key findings with orthogonal techniques like Western blotting or fluorescence microscopy .

How can I establish if YGR293C is involved in TORC1 signaling pathways?

Investigating potential TORC1 pathway involvement requires specialized approaches:

• Rapamycin sensitivity analysis:

  • Test growth of YGR293C mutants under varying rapamycin concentrations

  • Compare with known TORC1 pathway mutants

  • Analyze growth in nutrient limitation conditions

  • Perform epistasis analysis with established TORC1 components

• TORC1 activity monitoring:

  • Assess phosphorylation status of TORC1 substrates (Sch9, Npr1)

  • Monitor autophagy induction through GFP-Atg8 processing

  • Analyze ribosomal protein gene expression as TORC1 readout

  • Track nuclear localization of stress-responsive transcription factors

• Genetic interaction mapping:

  • Cross YGR293C mutants with TORC1 pathway mutants (tor1Δ, tco89Δ, ego1Δ)

  • Analyze growth phenotypes of double mutants

  • Perform suppressor screens with YGR293C overexpression

  • Map pathway position through epistasis relationships

• Protein association analysis:

  • Test direct interaction with TORC1 components

  • Analyze co-localization with TORC1 at vacuolar membrane

  • Investigate potential regulatory relationships with Pib2

  • Assess impact on TORC1 complex formation and stability

Recent research has identified Pib2 as a master regulator of TORC1 with both activating and inhibiting activities located on opposite ends of the protein . If YGR293C interacts with any TORC1 pathway components, it would be valuable to investigate its relationship with Pib2 specifically, as this could provide insights into nutrient sensing and growth regulation mechanisms.

How should I design experiments to investigate potential phenotypes of YGR293C mutants?

Comprehensive phenotypic analysis requires systematic experimental design:

• Growth condition screening:

  • Test multiple carbon sources (glucose, galactose, glycerol, ethanol)

  • Vary nitrogen sources (ammonium, amino acids, poor nitrogen)

  • Screen different temperatures (16°C, 30°C, 37°C, 42°C)

  • Challenge with stressors (oxidative, osmotic, pH, cell wall)

• High-throughput phenotyping:

  • Implement automated growth curve analysis

  • Use colony size measurement on solid media arrays

  • Apply fluorescent reporters for stress responses

  • Develop custom image analysis pipelines for morphological features

• Single-cell analysis:

  • Perform flow cytometry for cell cycle analysis

  • Implement microfluidics for long-term single-cell tracking

  • Analyze cell-to-cell variability in response to perturbations

  • Correlate phenotypic heterogeneity with expression levels

• Systematic condition interaction mapping:

  • Create condition-by-genotype interaction matrices

  • Identify condition-specific requirements for YGR293C

  • Map environmental sensitivities to specific cellular processes

  • Generate functional hypotheses from pattern recognition

When designing phenotypic screens, include appropriate controls (both positive and negative) for each condition, implement biological replicates (minimum of 3), and consider potential strain background effects. For quantitative phenotyping, standardize inoculation density, growth phase of starter cultures, and measurement parameters .

What considerations are important when expressing recombinant YGR293C for functional studies?

Optimal recombinant protein production requires careful experimental design:

• Expression system selection:

  • E. coli: Simple, high-yield but may lack proper folding for yeast proteins

  • S. cerevisiae: Native environment but potentially lower yields

  • Pichia pastoris: High-yield eukaryotic expression with proper folding

  • Insect cells: Complex glycosylation and chaperone-assisted folding

• Construct design optimization:

  • Test multiple affinity tags (His, GST, MBP, SUMO)

  • Vary tag position (N-terminal vs C-terminal)

  • Consider codon optimization for expression host

  • Design domain constructs based on bioinformatic predictions

• Expression condition optimization:

  • Screen induction parameters (temperature, inducer concentration)

  • Test different media formulations and supplements

  • Optimize growth phase for induction

  • Implement chaperone co-expression strategies

• Purification strategy development:

  • Design multi-step purification protocols

  • Optimize buffer conditions for stability

  • Implement quality control by SEC and DLS

  • Validate proper folding through activity assays

Table 4: Comparison of expression systems for recombinant yeast proteins

When expressing proteins of unknown function like YGR293C, implement parallel approaches with different systems, as the optimal expression strategy cannot be predicted in advance. Consider small-scale screening before scaling up, and always validate protein identity with mass spectrometry .

How should I analyze high-throughput sequencing data to understand YGR293C function?

Effective analysis of sequencing data requires robust computational approaches:

• RNA-seq analysis pipeline:

  • Implement quality control and read filtering

  • Perform alignment to reference genome (STAR, HISAT2)

  • Quantify expression levels (featureCounts, Salmon)

  • Apply differential expression analysis (DESeq2, edgeR)

  • Conduct pathway enrichment analysis

• ChIP-seq/CUT&RUN analysis:

  • Process and align reads to reference genome

  • Call peaks (MACS2) and annotate genomic features

  • Perform motif discovery and enrichment analysis

  • Integrate with expression data and chromatin state information

  • Compare binding profiles with transcription factors or chromatin modifiers

• SATAY data analysis:

  • Map transposon insertion sites to reference genome

  • Calculate insertion density across genes and domains

  • Identify regions with significant deviation from random distribution

  • Compare profiles between wild-type and mutant backgrounds

  • Integrate with other functional genomics datasets

• Integrative multi-omics analysis:

  • Implement correlation networks across data types

  • Apply machine learning for pattern recognition

  • Perform clustering to identify functionally related genes

  • Develop predictive models for gene function

When analyzing high-throughput sequencing data, account for technical biases, implement appropriate normalization methods, and apply rigorous statistical testing with multiple hypothesis correction. Visualization of data (genome browsers, heatmaps, networks) is essential for interpretation and hypothesis generation .

How can comparative genomics help understand the function of YGR293C?

Leveraging evolutionary information provides functional insights:

• Ortholog identification and analysis:

  • Identify YGR293C orthologs across fungal species

  • Extend search to distant eukaryotic lineages

  • Analyze patterns of conservation and divergence

  • Map conserved domains and critical residues

• Synteny analysis:

  • Examine gene order conservation around YGR293C

  • Identify functionally linked gene clusters

  • Analyze co-evolution with neighboring genes

  • Map genomic rearrangements across species

• Selection pressure analysis:

  • Calculate dN/dS ratios across protein regions

  • Identify sites under positive or purifying selection

  • Correlate evolutionary constraints with structural features

  • Map functionally important regions through conservation patterns

• Phylogenetic profiling:

  • Construct presence/absence matrices across species

  • Identify genes with similar phylogenetic profiles

  • Infer functional relationships through co-evolution

  • Analyze patterns of gene gain/loss in relation to ecology

When performing comparative genomics analysis, consider the quality of genome assemblies and annotations, implement appropriate sequence alignment methods, and use phylogenetically aware statistical approaches. Integration with functional data from model organisms can significantly enhance interpretation .