Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YPR077C (YPR077C)

What is YPR077C and what is currently known about its function?

YPR077C is a systematic open reading frame (ORF) name for a putative uncharacterized protein in the yeast Saccharomyces cerevisiae. Current knowledge about this protein is limited, with no conclusive functional characterization in the Saccharomyces Genome Database. Expression data analysis shows minimal expression patterns across various conditions, suggesting it may have specialized functions or be expressed under specific conditions not commonly tested in standard laboratory environments .

The protein is located on chromosome XVI and has been preserved throughout evolution in various Saccharomyces species, suggesting potential biological significance despite its uncharacterized status. When investigating uncharacterized proteins like YPR077C, researchers typically begin with sequence analysis, searching for conserved domains, structural predictions, and phylogenetic relationships to proteins with known functions.

What experimental approaches are recommended for initial characterization of YPR077C?

For initial characterization of YPR077C, a multi-faceted approach is recommended:

• Gene deletion studies: Create knockout strains using standard methods like PCR-mediated gene disruption with selectable markers. Phenotypic analysis under various stress conditions (temperature, pH, osmotic pressure, nutrient limitation) can reveal functional hints .

• Protein localization: Generate GFP-tagged versions of YPR077C to determine subcellular localization using fluorescence microscopy, providing clues about potential function.

• Expression profiling: Analyze expression under different environmental conditions using RNA-seq or microarray techniques to identify conditions where YPR077C is significantly up or down-regulated .

• Protein-protein interaction studies: Employ yeast two-hybrid screens or co-immunoprecipitation followed by mass spectrometry to identify interaction partners.

• Comparative genomics: Analyze conservation patterns across related yeast species to identify potential functional motifs or domains.

The experimental workflow should begin with bioinformatic analysis to guide wet-lab experiments, followed by genetic manipulation techniques and finally physiological and biochemical characterization.

How can I generate recombinant strains expressing modified versions of YPR077C?

To generate recombinant strains expressing modified versions of YPR077C, several methodological approaches are available:

PCR-based integration method:

• Design primers that contain 40-50bp homology to regions flanking the YPR077C gene and include desired modifications (tags, mutations).

• PCR amplify your modified gene construct with a selectable marker.

• Transform yeast cells with the PCR product which will integrate through homologous recombination.

• Select transformants on appropriate media and verify integration by PCR and sequencing.

CRISPR-Cas9 approach:

• Design guide RNA targeting YPR077C.

• Prepare a repair template containing your modified version of the gene.

• Co-transform yeast with Cas9 expression plasmid, guide RNA, and repair template.

• Select and verify transformants.

Plasmid-based expression:
For expressing modified versions alongside the native gene, clone the modified YPR077C into a suitable expression vector (e.g., pRS series) under a constitutive (e.g., TEF1, GPD) or inducible (e.g., GAL1, CUP1) promoter .

Table 1: Comparison of methods for generating YPR077C recombinant strains

How can I investigate potential roles of YPR077C in meiotic recombination?

Investigating YPR077C's potential role in meiotic recombination requires specialized approaches:

• DSB mapping: Since meiotic recombination in S. cerevisiae is initiated by double-strand breaks (DSBs), map these events in wild-type and YPR077C deletion strains using ChIP-seq with antibodies against Spo11 (the protein that creates DSBs) or using methods that directly detect DNA breaks .

• Recombination frequency analysis: Measure recombination rates between genetic markers in wild-type and YPR077C deletion strains. Position markers at known hotspots and coldspots to detect local effects.

• Synthetic genetic array (SGA) analysis: Cross your YPR077C mutant with deletion mutants of known recombination factors to identify genetic interactions.

• Immunofluorescence microscopy: Track the formation of recombination intermediates (e.g., Rad51 foci) during meiosis in the presence and absence of YPR077C.

The experimental design should account for the distribution pattern of recombination hotspots and coldspots. Recombination hotspots in S. cerevisiae are nonrandomly associated with regions of high G+C base composition, while coldspots are nonrandomly associated with centromeres and telomeres . If YPR077C influences recombination, you might observe altered patterns in these distributions in your mutant strains.

What approaches can be used to investigate YPR077C's potential role in transcriptional regulatory networks?

To investigate YPR077C's potential role in transcriptional regulatory networks, implement these methodological approaches:

• Genome-wide expression profiling: Compare transcriptomes of wild-type and YPR077C deletion strains using RNA-seq or microarrays under multiple conditions to identify genes differentially regulated in the absence of YPR077C .

• ChIP-seq analysis: If YPR077C is suspected to be a transcription factor or chromatin-associated protein, perform chromatin immunoprecipitation followed by sequencing to identify its binding sites throughout the genome.

• Network analysis: Construct transcriptional regulatory networks using existing databases and your experimental data, then analyze how YPR077C fits within these networks.

• Epistasis analysis: Create double mutants of YPR077C with known transcription factors to identify genetic interactions suggesting shared pathways.

• Motif analysis: If YPR077C has DNA-binding properties, identify potential binding motifs in promoter regions of differentially expressed genes.

Analysis of transcriptional data should focus on identifying clusters of genes with coordinated expression changes, as these may represent functional modules regulated by YPR077C. Pay particular attention to genes involved in stress responses, metabolic processes, or cell cycle regulation, as these are common targets of regulatory networks in yeast .

How can synthetic recombinant populations be utilized to study YPR077C function in diverse genetic backgrounds?

Synthetic recombinant populations provide powerful tools for studying YPR077C function across diverse genetic backgrounds:

• Population construction strategies:

  • K-type populations: Create by mass mating of haploid strains with different genetic backgrounds, followed by sporulation cycles and selection .

  • S-type populations: Build through more controlled crossing designs where founding lines are crossed in pairs in a round-robin design, allowing better representation of founder genotypes .

• Experimental evolution approach:

  • Create replicate populations with and without functional YPR077C.

  • Subject populations to selection pressures relevant to hypothesized YPR077C function.

  • Sequence populations at regular intervals (e.g., cycles 0, 6, and 12) to track allele frequency changes .

  • Use these data to identify genetic interactions with YPR077C through patterns of epistasis.

• QTL mapping in segregant populations:

  • Cross strains with and without functional YPR077C to diverse genetic backgrounds.

  • Phenotype segregants for traits of interest.

  • Identify QTLs that interact with YPR077C genotype.

When designing synthetic populations for YPR077C studies, consider that S-type populations typically exhibit higher levels of genetic variation and more equal founder haplotype representation, making them better suited for detecting subtle phenotypic effects . This approach requires approximately 15 asexual generations between each outcrossing cycle, with a minimum of 180 cell doublings over 12 cycles of outcrossing .

What methodologies are recommended for investigating protein-protein interactions involving YPR077C?

For investigating protein-protein interactions involving YPR077C, employ these methodological approaches:

• Yeast two-hybrid (Y2H) screening:

  • Clone YPR077C as both bait (DNA-binding domain fusion) and prey (activation domain fusion).

  • Screen against a yeast genomic library or an array of known proteins.

  • Validate positive interactions through complementary methods.

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

  • Generate strains expressing tagged versions of YPR077C (e.g., TAP-tag, FLAG-tag).

  • Perform affinity purification under native conditions.

  • Identify co-purifying proteins by mass spectrometry.

  • Quantify interaction stoichiometry using methods like SILAC.

• Proximity-dependent biotin identification (BioID):

  • Create a fusion of YPR077C with a promiscuous biotin ligase (BirA*).

  • Induce biotinylation of proximal proteins in vivo.

  • Purify biotinylated proteins and identify by mass spectrometry.

• Bimolecular fluorescence complementation (BiFC):

  • Fuse YPR077C and candidate interactors to complementary fragments of a fluorescent protein.

  • Co-express in yeast and visualize reconstituted fluorescence at sites of interaction.

• Co-immunoprecipitation validation:

  • Co-express differentially tagged versions of YPR077C and interacting proteins.

  • Immunoprecipitate one partner and detect the other by western blotting.

When analyzing interaction data, integrate results from multiple approaches to build confidence in the interactions and construct interaction networks. Consider that certain interactions may be condition-dependent or occur only during specific cell cycle stages or stress conditions.

How can CRISPR-Cas9 genome editing be optimized for studying YPR077C function?

Optimizing CRISPR-Cas9 genome editing for studying YPR077C involves several methodological considerations:

• Guide RNA design optimization:

  • Select guide RNAs with high on-target efficiency and minimal off-target effects.

  • Design multiple guide RNAs targeting different regions of YPR077C.

  • Consider using algorithms like CHOPCHOP or E-CRISP specifically optimized for S. cerevisiae.

  • Avoid regions with secondary structures that might impede Cas9 access.

• Repair template strategy:

  • For gene disruption: Use short (50-60bp) homology arms flanking a selectable marker.

  • For precise edits: Incorporate longer homology arms (200-500bp) and silent mutations in the PAM site or seed region to prevent re-cutting.

  • For domain analysis: Design repair templates with specific domain deletions or substitutions.

• Transformation optimization:

  • Use lithium acetate/PEG/ssDNA carrier method with heat shock.

  • Pre-express Cas9 from a plasmid before introducing guide RNA and repair template.

  • Consider using repair templates with positive and negative selection markers.

• Screening and verification:

  • Design PCR screening strategies that can detect successful editing events.

  • Verify edits by Sanger sequencing.

  • Check for off-target effects at computationally predicted sites.

  • Perform whole-genome sequencing on critical strains to ensure no unintended mutations.

Table 2: CRISPR-Cas9 editing optimization parameters for YPR077C studies

How should RNA-seq data be analyzed to identify potential regulatory targets of YPR077C?

RNA-seq data analysis for identifying potential YPR077C regulatory targets requires a systematic approach:

• Data preprocessing and quality control:

  • Assess sequencing quality using FastQC.

  • Trim adapters and low-quality reads using Trimmomatic or similar tools.

  • Filter out rRNA reads if not depleted during library preparation.

• Read alignment and quantification:

  • Align reads to the S. cerevisiae reference genome using HISAT2, STAR, or similar tools.

  • Quantify gene expression using tools like featureCounts or HTSeq.

  • Normalize counts to account for sequencing depth using TPM, FPKM, or other methods.

• Differential expression analysis:

  • Compare YPR077C mutant to wild-type using DESeq2, edgeR, or limma-voom.

  • Apply appropriate statistical thresholds (adjusted p-value < 0.05, fold change > 1.5).

  • Account for batch effects and technical variables.

• Functional enrichment analysis:

  • Perform GO term enrichment, KEGG pathway analysis, or similar functional annotations.

  • Use tools like GSEA to identify coordinated changes in gene sets.

  • Consider yeast-specific databases like SGD for functional annotations.

• Network analysis:

  • Construct co-expression networks from expression data.

  • Identify modules of co-regulated genes.

  • Integrate with existing protein-protein interaction or genetic interaction data.

• Motif analysis:

  • Extract promoter sequences of differentially expressed genes.

  • Identify enriched sequence motifs using MEME, HOMER, or similar tools.

  • Compare with known transcription factor binding sites.

When interpreting results, focus on patterns that suggest direct versus indirect regulation, and cross-reference with chromatin immunoprecipitation data if available to distinguish direct binding targets.

How can phenotypic data from YPR077C mutants be properly analyzed and interpreted?

Proper analysis and interpretation of phenotypic data from YPR077C mutants requires:

• Experimental design considerations:

  • Include multiple biological replicates (minimum of 3-5).

  • Use appropriate controls (wild-type, isogenic strains with unrelated gene deletions).

  • Consider epistasis by analyzing double mutants with related genes.

  • Test multiple environmental conditions relevant to hypothesized functions.

• Growth phenotype analysis:

  • Measure growth parameters in liquid culture (lag phase, doubling time, maximal OD).

  • Analyze growth curves using specialized software like Growthcurver .

  • Compare fitness in competition assays using barcode sequencing or fluorescent markers.

• Stress response phenotyping:

  • Test sensitivity to various stressors (oxidative, osmotic, temperature, pH, nutrient limitation).

  • Quantify colony size or growth inhibition zones.

  • Apply appropriate statistical tests (ANOVA, t-tests) with multiple testing correction.

• Metabolic phenotyping:

  • Measure consumption/production of key metabolites.

  • Analyze metabolic flux using labeled substrates.

  • Consider integrating with transcriptome data to identify regulatory mechanisms.

• Microscopy-based phenotyping:

  • Quantify cell morphology, organelle distribution, or protein localization.

  • Use automated image analysis to avoid bias.

  • Consider time-lapse imaging for dynamic processes.

When interpreting phenotypic data, consider several factors: genetic background effects, potential secondary mutations, growth phase-specific effects, and environmental condition specificity. Subtle phenotypes may only be apparent under specific conditions or in competition assays, so broad phenotypic testing is recommended.

What statistical approaches are appropriate for analyzing complex genetic interactions involving YPR077C?

For analyzing complex genetic interactions involving YPR077C, several statistical approaches are appropriate:

• Quantitative genetic interaction mapping:

  • Calculate genetic interaction scores (ε) as the difference between observed and expected double mutant phenotypes.

  • ε = Phenotype(AB) - [Phenotype(A) × Phenotype(B)]

  • Negative values suggest functional redundancy or parallel pathways.

  • Positive values suggest suppression or within-pathway interactions.

• Hierarchical clustering analysis:

  • Cluster genes based on similarity of genetic interaction profiles.

  • This identifies groups of genes with similar functions.

  • YPR077C can be positioned in this functional landscape.

• Bayesian network inference:

  • Model directed genetic relationships.

  • Infer probable pathway structures from genetic interaction data.

  • Incorporate prior knowledge from literature.

• Random forest and machine learning approaches:

  • Train models to predict genetic interactions.

  • Identify key features that determine interaction outcomes.

  • Cross-validate predictions with experimental data.

• Network analysis metrics:

  • Calculate centrality measures to determine YPR077C's importance in interaction networks.

  • Identify network motifs involving YPR077C.

  • Compare network properties before and after perturbation.

When analyzing genetic interactions in synthetic recombinant populations, it's essential to account for population structure, linkage disequilibrium, and drift effects. Statistical power calculations should guide experimental design, particularly for detecting subtle interaction effects .

What emerging technologies might enhance our understanding of YPR077C function?

Several emerging technologies show promise for enhancing our understanding of YPR077C function:

• Single-cell genomics and transcriptomics:

  • Reveal cell-to-cell variability in YPR077C expression and function.

  • Identify rare cell populations where YPR077C may have specialized roles.

  • Map cellular trajectories in response to YPR077C perturbation.

• Spatial transcriptomics and proteomics:

  • Determine subcellular localization patterns of YPR077C.

  • Map spatial co-localization with interaction partners.

  • Identify local translation or degradation patterns.

• CRISPR interference/activation (CRISPRi/CRISPRa):

  • Enable tunable repression or activation of YPR077C.

  • Target specific promoter or enhancer elements to dissect regulatory mechanisms.

  • Create allelic series for dose-response studies.

• Synthetic genomics approaches:

  • Replace YPR077C with orthologues from related species.

  • Design and test synthetic variants with altered domain structures.

  • Incorporate non-canonical amino acids for specialized functionalities.

• Cryo-electron microscopy and AlphaFold predictions:

  • Determine high-resolution structures of YPR077C and its complexes.

  • Validate structural predictions from computational models.

  • Guide rational design of protein variants.

• Long-read sequencing technologies:

  • Characterize complex structural variants affecting YPR077C.

  • Analyze epigenetic modifications in the YPR077C locus.

  • Investigate isoform diversity and alternative splicing.

These technologies can be integrated into systematic approaches for functional characterization, potentially revealing unexpected roles for this uncharacterized protein and placing it in the context of broader cellular networks.

How can evolutionary analysis of YPR077C across yeast species inform functional hypotheses?

Evolutionary analysis of YPR077C across yeast species can inform functional hypotheses through several methodological approaches:

• Sequence conservation analysis:

  • Perform multiple sequence alignments of YPR077C orthologues across yeast species.

  • Calculate conservation scores for individual residues and domains.

  • Identify highly conserved regions likely to be functionally important.

  • Map conservation onto predicted protein structures.

• Phylogenetic profiling:

  • Determine the presence/absence pattern of YPR077C across species.

  • Correlate this pattern with specific traits or environmental adaptations.

  • Identify genes with similar phylogenetic profiles that may function together.

• Evolutionary rate analysis:

  • Calculate dN/dS ratios to identify signatures of selection.

  • Compare evolutionary rates across different lineages.

  • Identify accelerated evolution in specific yeast clades, suggesting functional adaptation.

• Synteny analysis:

  • Examine conservation of gene order around YPR077C.

  • Identify co-evolving gene clusters that may represent functional units.

  • Detect genomic rearrangements that might affect YPR077C regulation.

• Experimental validation in diverse species:

  • Test complementation of YPR077C function across species.

  • Measure phenotypic effects of orthologous genes from different yeast species.

  • Create chimeric proteins to map functional domains.

Table 3: Evolutionary conservation of YPR077C across select Saccharomyces species

Evolutionary analysis can reveal whether YPR077C performs a conserved function across yeasts or has undergone functional diversification in specific lineages. This information can generate testable hypotheses about protein function based on the ecological and metabolic features of species where the gene is highly conserved versus those where it has diverged.