Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YDR183C-A (YDR183C-A)

What is the predicted function of the putative uncharacterized protein YDR183C-A in Saccharomyces cerevisiae?

The putative uncharacterized protein YDR183C-A represents an open reading frame in Saccharomyces cerevisiae with limited functional annotation. Similar to other uncharacterized proteins like YMR318C (later characterized as ADH6), YDR183C-A may contain conserved sequence motifs that provide preliminary clues about its potential function . Initial bioinformatic analysis should include examination of its amino acid sequence for conserved domains, structural predictions, and comparison to characterized proteins in closely related yeast species. Protein homology modeling and phylogenetic analysis can provide valuable insights into potential enzymatic activity or cellular roles.

It is critical to note that putative function predictions should be treated as hypotheses requiring experimental validation. While YDR183C-A remains uncharacterized, methodical biochemical characterization following approaches similar to those used for proteins like YMR318C may reveal unexpected functional roles in cellular metabolism, stress response, or other biological processes.

What are the optimal expression conditions for studying YDR183C-A in recombinant Saccharomyces cerevisiae systems?

Expression of uncharacterized proteins like YDR183C-A requires systematic optimization of growth conditions, vector systems, and regulatory elements. Based on protocols used for expressing recombinant proteins in S. cerevisiae, researchers should consider several key parameters. First, selection of an appropriate expression vector with a suitable promoter (constitutive or inducible) based on the experimental objectives . For initial characterization studies, placing YDR183C-A under control of a strong inducible promoter such as GAL1 allows for controlled expression.

Growth conditions significantly impact expression levels and should be systematically optimized, including temperature (typically 30°C for standard growth), media composition (YPD for rich media or defined minimal media for controlled conditions), and induction parameters if using inducible systems . Expression can be verified through techniques such as Western blotting with epitope tags or antibodies against the protein of interest. For challenging expression scenarios, codon optimization based on S. cerevisiae preference may improve yields. Additionally, strain selection is crucial—laboratory strains with well-characterized genetics (BY4741/BY4742) are often preferred for initial studies, while specialized strains may be necessary for specific applications.

How can I verify successful expression of YDR183C-A in my recombinant system?

Verification of YDR183C-A expression requires a multi-faceted approach combining molecular and biochemical techniques. The most direct verification method involves fusion of YDR183C-A with epitope tags (such as FLAG, HA, or 6xHis) at either the N- or C-terminus, followed by Western blot analysis using commercially available antibodies against these tags . If developing antibodies specific to YDR183C-A, consider targeting unique peptide sequences within the protein to ensure specificity.

RNA-level verification through RT-PCR or RNA-Seq can confirm transcription but does not guarantee protein expression. For subcellular localization studies, fluorescent protein fusions (such as GFP) can be employed, though care must be taken to ensure that the tag does not interfere with protein function or localization. Mass spectrometry offers a tag-free approach for verification, particularly selected reaction monitoring (SRM) or parallel reaction monitoring (PRM) methods targeting unique peptides from YDR183C-A. For functional verification, complementation assays can be performed if deletion phenotypes are identified, where re-introduction of YDR183C-A should rescue the observed phenotype.

What are the most effective techniques for purifying recombinant YDR183C-A protein for in vitro studies?

Purification of uncharacterized proteins like YDR183C-A requires a strategic approach that leverages affinity tags and optimized chromatography techniques. Based on protocols used for similar yeast proteins such as YMR318C, a multi-step purification strategy is recommended . Initially, expression with an affinity tag (6xHis, GST, or MBP) facilitates first-pass purification using affinity chromatography. For 6xHis-tagged constructs, immobilized metal affinity chromatography (IMAC) with Ni-NTA resin provides an efficient first step, while GST-tagged proteins can be captured using glutathione agarose.

Following initial affinity purification, secondary purification steps improve purity. Ion exchange chromatography is particularly effective, with the choice between cation or anion exchange dependent on the protein's theoretical isoelectric point (pI). Size exclusion chromatography serves as an excellent final polishing step, simultaneously providing information about oligomeric state and removing aggregates. Throughout the purification process, optimization of buffer conditions (pH, salt concentration, reducing agents) is essential for maintaining protein stability and activity. For challenging proteins prone to insolubility, addition of stabilizing agents (glycerol, specific ions, or mild detergents) may be necessary. Purification should be performed at 4°C where possible to minimize degradation, and protease inhibitors should be included in initial lysis buffers.

How can I design deletion mutants to investigate YDR183C-A function in Saccharomyces cerevisiae?

Designing deletion mutants for investigating YDR183C-A function requires careful consideration of targeting strategy, marker selection, and verification methods. The most widely used approach involves PCR-based gene replacement with selectable markers . This method utilizes primers containing 40-50 bp homology to sequences flanking the YDR183C-A open reading frame, with the central portion of the primers annealing to a selectable marker cassette (such as KanMX, HygMX, or auxotrophic markers).

Following transformation, selection on appropriate media, and isolation of potential deletion strains, rigorous verification is essential. This should include diagnostic PCR using primers that anneal outside the integration site paired with marker-specific primers to confirm correct integration. Whole-genome sequencing provides comprehensive verification, particularly important for uncharacterized genes where phenotypic confirmation may not be immediately available . For functional complementation testing, the wild-type gene should be reintroduced (potentially under control of its native promoter) to verify that any observed phenotypes are specifically due to YDR183C-A deletion.

When working with essential or near-essential genes, conditional deletion systems may be necessary. These include placing YDR183C-A under control of regulatable promoters (like GAL1 or tetO) or using degron-based systems for conditional protein degradation. In all cases, potential effects on neighboring genes should be considered, as compact yeast genomes often contain overlapping regulatory elements.

What high-throughput screening approaches can identify potential interaction partners of YDR183C-A?

Several complementary high-throughput screening approaches can effectively identify potential interaction partners of uncharacterized proteins like YDR183C-A. Yeast two-hybrid (Y2H) screening remains a powerful method for detecting binary protein-protein interactions, where YDR183C-A is fused to a DNA-binding domain and screened against a library of prey proteins fused to an activation domain . Split-ubiquitin systems provide an alternative for membrane or nuclear proteins that may not be suitable for classical Y2H.

Affinity purification coupled with mass spectrometry (AP-MS) offers a more physiological approach to identifying protein complexes. This involves expressing epitope-tagged YDR183C-A, purifying under mild conditions to maintain protein-protein interactions, and identifying co-purifying proteins by mass spectrometry . For detecting transient or weak interactions, proximity-dependent labeling methods such as BioID or APEX can be employed, where YDR183C-A is fused to a biotin ligase or peroxidase that biotinylates proteins in close proximity.

Genetic interaction screening using synthetic genetic array (SGA) methodology provides functional insights by systematically creating double mutants between YDR183C-A and the entire yeast deletion collection. Synthetic lethal or synthetic sick interactions often indicate functional relationships between genes. Additionally, high-content microscopy screening can identify proteins with similar localization patterns or proteins whose localization is affected by YDR183C-A deletion. For all screening approaches, thorough validation of identified interactions through orthogonal methods is essential to minimize false positives.

How can I differentiate between direct and indirect effects when characterizing the phenotype of YDR183C-A deletion mutants?

Domain-specific mutations provide a powerful approach for dissecting protein function. By introducing point mutations in specific domains or motifs rather than deleting the entire gene, researchers can attribute particular phenotypes to specific protein functions . Conditional expression systems allow for temporal control of YDR183C-A levels, helping to distinguish immediate responses from adaptive changes. Acute depletion using degron tags or regulated promoters can reveal primary phenotypes before compensatory mechanisms engage.

Multi-omics approaches (transcriptomics, proteomics, metabolomics) can map the cascade of changes following YDR183C-A deletion, with direct effects typically appearing as hub nodes in network analyses. For mechanistic understanding, biochemical validation is essential—if YDR183C-A is proposed to have a specific enzymatic function, direct in vitro biochemical assays should demonstrate this activity . Finally, evolutionary analysis examining the conservation of YDR183C-A and associated phenotypes across related yeast species can provide insights into functional significance.

What strategies can resolve contradictory data when characterizing YDR183C-A function?

Resolving contradictory data during YDR183C-A characterization requires systematic investigation of potential sources of variability and methodological differences. First, standardize experimental conditions across studies, including strain background, growth conditions, and assay parameters . Strain background effects are particularly important in yeast, as different laboratory strains may harbor genetic differences that influence phenotypic outcomes. Side-by-side comparisons using multiple strain backgrounds can identify strain-specific effects.

Employ dose-dependent approaches to assess whether contradictory results stem from differences in expression levels or activity thresholds. If YDR183C-A is expressed at different levels across studies, phenotypic differences may reflect dose-dependent functions rather than contradictory roles . Consider conditional or environmental influences, as some protein functions only manifest under specific conditions (stress, nutrient limitation, cell cycle stage). Systematic environmental testing can reveal context-dependent functions.

Investigate post-translational modifications or alternative splicing that might generate protein isoforms with different activities. Proteomic approaches can identify modifications that may be present in some experimental contexts but not others. For contradictions between in vitro and in vivo results, examine whether key cofactors or interaction partners present in vivo may be missing from in vitro systems. Finally, collaborative cross-validation between laboratories using identical materials and protocols can help resolve whether contradictions stem from methodological differences or reflect genuine biological complexity.

How can computational approaches enhance functional prediction for YDR183C-A?

Computational approaches provide powerful tools for predicting functions of uncharacterized proteins like YDR183C-A. Structure prediction using tools like AlphaFold2 or RoseTTAFold can generate high-confidence structural models that may reveal structural similarity to characterized proteins even in the absence of significant sequence homology . These structural predictions can suggest potential binding pockets, catalytic sites, or interaction interfaces.

Integrative network analysis incorporates diverse data types (protein-protein interactions, genetic interactions, co-expression patterns, and subcellular localization) to position YDR183C-A within cellular networks . Proteins occupying similar network positions often share functional characteristics. Comparative genomics approaches examining the evolutionary conservation, gene neighborhood, and phylogenetic profile of YDR183C-A can provide functional insights through guilt-by-association principles. Genes with similar evolutionary patterns often participate in related biological processes.

Machine learning approaches trained on multiple features (sequence, structure, localization, expression patterns) can generate integrated functional predictions. These methods are particularly valuable for multifunctional proteins where single features may miss important aspects of functionality. Text mining of scientific literature using natural language processing can extract implicit functional associations that may not be captured in formal annotations. For all computational predictions, confidence scoring is essential, and experimental validation of the most promising predictions should be prioritized.

What crossing designs maximize genetic variation when creating recombinant Saccharomyces cerevisiae strains for YDR183C-A functional studies?

When designing recombinant S. cerevisiae strains for YDR183C-A functional studies, crossing strategy significantly impacts genetic diversity and experimental outcomes. Research comparing different crossing methods demonstrates that pairwise crossing designs (designated as "S-type" populations) produce significantly higher genetic variation than simple mixing of strains . These S-type populations involve systematically crossing haploid strains in pairs, isolating the resulting diploid colonies, inducing sporulation, and harvesting the meiotic products before pooling them for subsequent mating.

The number of parental strains directly correlates with genetic diversity in the resulting population, with 8-12 founder strains typically providing a robust genetic background for functional studies . This diversity is crucial for detecting gene-environment and gene-gene interactions that might influence YDR183C-A function. When selecting founding strains, prioritize genetic diversity over convenience—including wild isolates alongside laboratory strains increases the likelihood of capturing natural variation in genetic backgrounds that may interact with YDR183C-A.

Maintenance of genetic diversity through multiple rounds of crossing requires careful protocol design. Studies show that after 12 cycles of outcrossing, properly constructed S-type populations maintain significantly higher genetic diversity than populations created through simple mixing . This sustained diversity provides a more robust platform for functional genomics studies, particularly when investigating proteins with context-dependent functions or subtle phenotypes.

How can CRISPR-Cas9 technology be optimized for precise manipulation of YDR183C-A in Saccharomyces cerevisiae?

CRISPR-Cas9 technology offers unprecedented precision for manipulating YDR183C-A in S. cerevisiae, but requires optimization for maximum efficiency and specificity. Guide RNA (gRNA) design is the foundation of successful editing, with several yeast-specific considerations. Tools optimized for S. cerevisiae should be used for gRNA design, considering the AT-rich nature of the yeast genome and potential off-target sites . For maximum efficiency, target sequences with GC content between 40-60% and avoid sites with secondary structure that might impede Cas9 binding.

Delivery method significantly impacts editing efficiency. While plasmid-based systems offer convenience, ribonucleoprotein (RNP) delivery of pre-assembled Cas9-gRNA complexes often provides higher efficiency with reduced off-target effects . For precise modifications such as point mutations within YDR183C-A, repair template design is critical. Homology arms of 35-60bp are typically sufficient in yeast, though longer arms may increase efficiency for challenging loci.

Screening strategies should be tailored to the specific modification. For gene disruption, direct selection markers provide the most straightforward approach, while precise edits without selectable markers require PCR-based screening followed by sequencing verification. To enhance HDR efficiency for precise edits, consider synchronizing cells in G2/M phase when homologous recombination is most active. For multiplex editing affecting YDR183C-A and potential interacting partners simultaneously, expressing multiple gRNAs from a single construct improves efficiency compared to co-transformation of multiple plasmids.

What methods effectively verify genetic stability in recombinant strains expressing modified YDR183C-A constructs?

Verifying genetic stability in recombinant strains expressing modified YDR183C-A constructs requires multi-layered approaches addressing genomic, transcriptomic, and phenotypic stability. At the genomic level, whole genome sequencing provides comprehensive verification of construct integration and detection of any unintended mutations . For routine monitoring, targeted PCR and Sanger sequencing of the modified YDR183C-A locus and flanking regions can detect common instabilities such as recombination events or deletions.

Southern blotting remains valuable for confirming copy number and integration stability, particularly for constructs intended to be present as single copies. Quantitative PCR targeting YDR183C-A and adjacent sequences provides a more accessible method for copy number verification. For expression stability, quantitative RT-PCR monitoring of YDR183C-A transcript levels across multiple generations and growth conditions can identify silencing or expression drift . Similarly, Western blotting for tagged YDR183C-A variants allows protein-level stability monitoring.

Phenotypic stability testing involves subjecting strains to varied growth conditions and multiple passages, then reassessing key phenotypes. Flow cytometry can effectively monitor expression stability in populations when fluorescent reporters are incorporated into YDR183C-A constructs. For long-term studies, establishing frozen stock archives at defined generations provides reference points for stability assessment. When instability is detected, its molecular basis should be characterized, as understanding the mechanism (homologous recombination, transcriptional silencing, selective pressure) can inform improved design of future constructs.

How can metabolic profiling be used to elucidate the role of YDR183C-A in Saccharomyces cerevisiae?

Metabolic profiling offers powerful insights into the functional role of uncharacterized proteins like YDR183C-A by revealing perturbations in cellular metabolism resulting from genetic manipulation. Untargeted metabolomics using high-resolution mass spectrometry provides a comprehensive snapshot of metabolic changes in YDR183C-A deletion or overexpression strains compared to wild-type controls . This approach can identify affected metabolic pathways without prior hypotheses about protein function, making it particularly valuable for uncharacterized proteins.

Stable isotope labeling experiments using 13C-glucose or 15N-ammonium sulfate can reveal flux changes through specific pathways, providing dynamic information beyond static metabolite levels. By tracking isotope incorporation patterns, researchers can identify primary metabolic impacts of YDR183C-A manipulation. For suspected enzymatic functions, targeted metabolomics focusing on specific metabolite classes can provide higher sensitivity and quantitative precision than untargeted approaches . This is particularly useful when bioinformatic analysis suggests involvement in specific pathways.

Integration of metabolomics with transcriptomics and proteomics data creates a multi-omics perspective that can distinguish between direct metabolic effects and compensatory responses. Network analysis of these integrated datasets can position YDR183C-A within the metabolic network hierarchy. For validation, in vitro enzymatic assays using purified YDR183C-A with potential substrates identified through metabolomics can confirm direct biochemical functions . Additionally, metabolic profiling under varied environmental conditions (carbon sources, stress, nutrient limitation) can reveal condition-specific functions that might be missed under standard laboratory conditions.

What approaches can determine if YDR183C-A is involved in stress response pathways in yeast?

Determining YDR183C-A's potential role in stress response pathways requires systematic exposure to diverse stressors combined with comprehensive phenotypic and molecular analyses. Growth assays comparing YDR183C-A deletion or overexpression strains with wild-type controls under various stressors (oxidative, osmotic, temperature, pH, nutrient limitation, heavy metals, DNA damage) can identify condition-specific phenotypes . High-throughput phenotyping using liquid growth assays with automated monitoring provides quantitative measurements of growth parameters (lag phase, growth rate, maximum density) that may reveal subtle phenotypes.

Transcriptional response analysis using RNA-Seq or microarrays comparing YDR183C-A mutants to wild-type under stress conditions can identify dysregulated stress-response genes. If YDR183C-A functions in stress signaling, characteristic expression signatures of stress response pathways may be altered. Protein localization studies using fluorescently-tagged YDR183C-A can reveal stress-induced translocation, as many stress response proteins relocalize upon activation . Time-course experiments tracking localization changes following stress induction may provide insights into activation kinetics.

Post-translational modification analysis is particularly important as many stress response proteins are regulated through phosphorylation, ubiquitination, or other modifications. Mass spectrometry-based phosphoproteomics or ubiquitinomics comparing stressed and unstressed cells can identify regulatory modifications on YDR183C-A. For potential transcription factor functions, chromatin immunoprecipitation followed by sequencing (ChIP-seq) can map YDR183C-A binding sites genome-wide, particularly under stress conditions where many transcription factors show altered binding patterns . Epistasis analysis placing YDR183C-A in the context of known stress response pathways can determine whether it functions upstream, downstream, or parallel to established components.

How can synthetic genetic interaction screening inform the biological context of YDR183C-A function?

Synthetic genetic interaction screening provides a powerful approach for positioning uncharacterized proteins like YDR183C-A within functional networks by systematically mapping genetic interactions across the genome. The Synthetic Genetic Array (SGA) methodology enables creation of double mutants combining a YDR183C-A deletion with each non-essential gene deletion in the yeast genome . Quantitative scoring of genetic interactions identifies synthetic lethal/sick interactions (negative genetic interactions) and suppressive interactions (positive genetic interactions) that reveal functional relationships.

Network analysis of the resulting genetic interaction profile generates a functional map where genes with similar profiles often participate in related processes. This "guilt by association" principle allows inference of YDR183C-A function based on characterized genes with similar interaction patterns . For more focused analysis, targeted epistasis studies with genes in specific pathways of interest can provide higher resolution mapping within those pathways, determining whether YDR183C-A functions upstream, downstream, or in parallel to known components.

Chemical-genetic profiling, exposing the YDR183C-A mutant to a library of small molecules or stressors, generates a chemical-genetic interaction profile that complements conventional genetic interactions. Compounds producing sensitivity phenotypes may target processes related to YDR183C-A function. Dosage suppression screening, where a library of overexpression plasmids is introduced into a YDR183C-A mutant showing a growth defect, can identify genes that when overexpressed rescue the phenotype, often indicating proteins functioning in the same pathway or process. For all genetic interaction data, integration with physical interaction data (protein-protein, protein-DNA) strengthens functional inferences and helps distinguish direct from indirect relationships.