Recombinant Saccharomyces cerevisiae Uncharacterized protein YGL194C-A (YGL194C-A)

What is YGL194C-A and where is it localized in S. cerevisiae cells?

YGL194C-A is a putative protein of unknown function in Saccharomyces cerevisiae. It was identified through comparative genomic analysis of six Saccharomyces species. According to localization studies using fluorescent protein tags, both SWAT-GFP and mCherry fusion proteins of YGL194C-A localize to the endoplasmic reticulum . This subcellular localization provides an important clue for potential functional characterization, suggesting possible roles in ER-associated processes such as protein folding, quality control, or secretory pathway functions.

To further investigate YGL194C-A localization:

• Validate ER localization using co-localization with known ER markers

• Perform time-lapse microscopy to determine if localization changes under stress conditions

• Use fractionation techniques to biochemically confirm ER association

How is YGL194C-A classified in genome databases?

YGL194C-A is classified as a "Core" gene in the Saccharomyces cerevisiae pangenome, meaning it is present across the S. cerevisiae strains analyzed in the Peter et al. (2018) Nature study . The protein is cataloged with:

• PanORF ID: 3837-YGL194C-A

• SGD systematic Name: YGL194C-A

• UniProt accession: Q2V2P6

• Classification: Core gene (conserved across strains)

• Expression Region: 1-80 (full-length protein)

The protein lacks a standard name, which is typically assigned when functional characterization has been performed, further highlighting its uncharacterized status .

What experimental approaches are recommended for initial characterization of YGL194C-A?

For initial characterization of YGL194C-A, a multi-faceted approach is recommended:

• Transcriptional analysis:

  • Analyze YGL194C-A expression under different growth conditions using DNA microarray or RNA-seq approaches

  • Monitor expression changes in response to various stressors (e.g., alkylating agents like methyl methanesulfonate)

  • Consider two-dimensional transcriptome analysis in chemostat cultures to identify conditions where YGL194C-A is differentially expressed

• Protein interaction studies:

  • Perform co-immunoprecipitation followed by mass spectrometry to identify interacting partners

  • Consider yeast two-hybrid screening to detect direct protein-protein interactions

  • Apply co-evolutionary analysis methods to predict potential interacting domains with other proteins

• Gene knockout/knockdown:

  • Create YGL194C-A deletion strains and assess phenotypic changes across multiple conditions

  • Use conditional expression systems for essential genes if conventional knockouts are not viable

How should experimental design be optimized for studying an uncharacterized protein like YGL194C-A?

When designing experiments for YGL194C-A characterization, consider the following methodological framework:

• Control selection:

  • Include positive controls with known ER proteins that share similar characteristics

  • Use negative controls such as cytosolic proteins to validate localization specificity

  • Include technical replicates to ensure reproducibility6

• Variable isolation:

  • Design experiments that isolate one variable at a time to establish clear cause-effect relationships

  • When examining stress responses, carefully control for secondary effects such as growth rate changes

  • Measure multiple parameters simultaneously to capture complex phenotypes

• Data validation:

  • Validate high-throughput findings (e.g., from transcriptomics) using targeted methods like qPCR

  • Cross-validate protein-protein interactions using multiple methodologies

  • Follow up computational predictions with biochemical validation6

• Uncertainty reduction:

  • Perform multiple independent trials under identical conditions

  • Establish appropriate sample sizes based on statistical power analysis

  • Use appropriate statistical methods to analyze data and determine significance6

How can transcriptome analysis inform our understanding of YGL194C-A function?

Transcriptome analysis provides valuable insights into the functional context of YGL194C-A:

• Co-expression analysis:
  Global transcriptional profiling under different conditions can reveal genes with expression patterns similar to YGL194C-A, suggesting functional relationships. For example, using DNA chip technology similar to that employed in the alkylating agent response study could identify if YGL194C-A is co-expressed with genes involved in specific cellular processes or stress responses .

• Condition-specific expression:
  By analyzing YGL194C-A expression across multiple nutrient limitations (carbon, nitrogen, phosphorus, sulfur) and oxygen availability conditions, researchers can identify specific conditions that trigger expression changes, providing functional clues .

• Promoter analysis:
  Examining the promoter region of YGL194C-A for regulatory elements can predict transcription factor binding sites. The presence of specific elements (like those listed in Table II of search result ) can suggest regulation by known transcription factors and integration into specific regulatory networks.

What comparative genomic approaches would be useful for studying YGL194C-A?

Comparative genomics offers powerful ways to gain insights into YGL194C-A:

• Ortholog identification:
  Identifying YGL194C-A orthologs in other fungal species can provide evolutionary context. The protein was initially identified based on genome comparisons across six Saccharomyces species .

• Structural homology:
  When sequence-based approaches yield limited results, structural homology modeling can identify proteins with similar three-dimensional structures despite low sequence similarity.

• Synteny analysis:
  Examining the genomic context of YGL194C-A across related species can reveal conserved gene neighborhoods, suggesting functional relationships or operonic organization.

• Evolutionary rate analysis:
  Calculating the rate of evolutionary change can indicate selective pressures and functional constraints on YGL194C-A.

How might YGL194C-A function be predicted using computational approaches?

Several computational strategies can help predict YGL194C-A function:

• Domain prediction and analysis:
  Although YGL194C-A is small (80 amino acids), analyzing potential functional domains or motifs may reveal similarity to known functional elements. Tools like PFAM, SMART, or InterPro can identify conserved domains even in small proteins.

• Co-evolutionary analysis:
  As described in search result , co-evolutionary analysis of domains in interacting proteins can predict potential protein-protein interactions. This approach could identify proteins that may interact with YGL194C-A based on correlated evolutionary patterns.

• Structural prediction:
  Modern protein structure prediction tools like AlphaFold can generate high-confidence structural models that may reveal functional insights based on structural similarity to characterized proteins.

• Network-based approaches:
  Integrating multiple data types (transcriptomic, proteomic, genetic interaction) into network models can place YGL194C-A in a functional context based on its network neighborhood.

What challenges arise when studying membrane-associated uncharacterized proteins?

Studying membrane-associated proteins like YGL194C-A presents specific challenges:

• Solubilization difficulties:
  ER membrane proteins often require specialized detergents or amphipols for extraction while maintaining native conformation, complicating biochemical analyses.

• Structural determination:
  Membrane proteins are notoriously difficult for structural studies due to their hydrophobic nature and requirement for lipid environments.

• Functional redundancy:
  Small membrane proteins may have redundant functions, making single-gene knockout phenotypes subtle or absent. Consider creating multiple gene deletions of related proteins.

• Context-dependent function:
  Membrane protein function may depend on specific lipid compositions or interactions that vary with growth conditions or cellular compartments.

How can researchers address experimental challenges when YGL194C-A shows no obvious phenotype upon deletion?

Many uncharacterized yeast proteins show no obvious phenotype when deleted under standard laboratory conditions. To address this challenge:

• Comprehensive phenotyping:
  Test the deletion strain under hundreds of different conditions, including various stressors, carbon sources, temperatures, and chemical agents. High-throughput phenotyping approaches can screen multiple conditions simultaneously.

• Synthetic genetic interactions:
  Perform synthetic genetic array (SGA) analysis to identify genes that, when mutated in combination with YGL194C-A deletion, produce aggravated or rescued phenotypes.

• Overexpression studies:
  Complement deletion studies with overexpression analysis, as some phenotypes only emerge when a protein is present at higher-than-normal levels.

• Condition-specific essentiality:
  Some genes become essential only under specific conditions. Systematically test the YGL194C-A deletion strain under various stress conditions similar to those examined in the global response to alkylating agents .

How might YGL194C-A be involved in cellular stress responses?

Given its ER localization, YGL194C-A may participate in stress response pathways:

• ER stress response:
  YGL194C-A might function in the unfolded protein response (UPR) or ER-associated degradation (ERAD) pathways. To test this hypothesis, researchers could monitor YGL194C-A expression and localization during ER stress induced by agents like tunicamycin or DTT.

• DNA damage response:
  The global transcriptional response study of S. cerevisiae to alkylating agents revealed numerous genes involved in DNA damage response . Researchers could determine if YGL194C-A expression changes after exposure to DNA-damaging agents like methyl methanesulfonate (MMS).

• Protein quality control:
  As described in search result , there is evidence for a program to eliminate and replace alkylated proteins after exposure to alkylating agents. Given its ER localization, YGL194C-A might participate in protein quality control mechanisms.

How can researchers determine if YGL194C-A interacts with known protein complexes in the ER?

To investigate potential interactions with known ER protein complexes:

• Proximity labeling:
  Use techniques like BioID or APEX2 proximity labeling to identify proteins in close proximity to YGL194C-A in living cells.

• Co-immunoprecipitation with known complexes:
  Perform targeted co-IP experiments with components of major ER complexes such as the Sec61 translocon, oligosaccharyltransferase complex, or ERAD machinery.

• Genetic interaction with complex components:
  Create double mutants of YGL194C-A with components of known ER complexes and assess synthetic phenotypes that might indicate functional relationships.

• Fluorescence resonance energy transfer (FRET):
  Use FRET to detect direct protein-protein interactions between YGL194C-A and candidate interacting partners in living cells.

What techniques could be employed to express and purify recombinant YGL194C-A for structural studies?

For successful expression and purification of recombinant YGL194C-A:

• Expression system selection:

  • Consider using specialized yeast expression systems for membrane proteins

  • E. coli systems with fusion tags (MBP, SUMO) to enhance solubility

  • Cell-free expression systems that can directly incorporate the protein into nanodiscs or liposomes

• Purification strategy:

  • Optimize detergent selection for membrane protein extraction

  • Consider nanodiscs or amphipol reconstitution for maintaining native structure

  • Use affinity chromatography followed by size exclusion chromatography for high purity

• Quality control:

  • Verify proper folding using circular dichroism spectroscopy

  • Assess homogeneity using dynamic light scattering

  • Validate function through binding assays with predicted interactors

How can high-throughput approaches accelerate functional characterization of YGL194C-A?

Modern high-throughput techniques offer efficient paths to functional insights:

• CRISPR screening:
  Perform genome-wide CRISPR screens in YGL194C-A deletion background to identify synthetic lethal or synthetic rescue interactions that provide functional context.

• Proteome-wide interaction mapping:
  Use techniques like BioID-MS to identify the complete interactome of YGL194C-A under different conditions.

• Metabolomic profiling:
  Compare metabolite profiles between wild-type and YGL194C-A deletion strains to identify altered metabolic pathways that might indicate function.

• Parallel phenotyping: Use automated high-throughput growth assays across hundreds of conditions to identify specific conditions where YGL194C-A becomes important for cellular fitness.