Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YML012C-A (YML012C-A)

What experimental approaches are recommended for initial characterization of YML012C-A?

Initial characterization of YML012C-A should follow a systematic approach beginning with in silico analysis followed by experimental validation. Start with sequence-based predictions of protein structure and potential functions using tools like BLAST, Pfam, and InterPro. For experimental work, gene deletion studies using the widely available yeast knockout collections represent a fundamental first step .

For a robust experimental design:

• Generate clean deletion strains in multiple genetic backgrounds

• Perform phenotypic profiling under various growth conditions

• Compare growth rates, morphology, and stress responses between wild-type and deletion strains

• Maintain consistent experimental conditions across all trials with appropriate controls3

When measuring phenotypic responses, ensure you're changing only one experimental variable at a time (such as temperature, media composition, or stressor concentration) while measuring one dependent variable (such as growth rate)3. This controlled approach increases confidence in establishing cause-effect relationships between YML012C-A deletion and observed phenotypes.

How can gene expression analysis be optimized for studying YML012C-A?

For optimal gene expression analysis of YML012C-A, quantitative PCR (qPCR) represents the most accessible and reliable method. According to established protocols in S. cerevisiae research, the following methodology produces reproducible results:

• Extract high-quality RNA using hot phenol or commercial kits optimized for yeast

• Validate RNA integrity using gel electrophoresis or Bioanalyzer analysis

• Perform reverse transcription with oligo-dT or random hexamer primers

• Design qPCR primers spanning exon junctions when possible

• Normalize expression data to established housekeeping genes (ACT1, TDH3, or ALG9)

For analyzing YML012C-A expression under various conditions, follow this experimental design matrix:

For more comprehensive analysis, RNA-seq can provide genome-wide context for YML012C-A expression patterns across conditions and developmental stages .

What protein localization techniques are most effective for YML012C-A studies?

For determining the subcellular localization of YML012C-A, fluorescent protein tagging provides the most direct visualization method. The experimental approach should include:

• C-terminal and N-terminal GFP fusion constructs (to account for potential interference with localization signals)

• Validation of fusion protein functionality through complementation tests

• Co-localization studies with established organelle markers

• Live-cell imaging under various growth conditions and stress responses

When designing GFP fusion constructs, include a flexible linker sequence (typically 3-5 glycine residues) to minimize interference with protein folding. For consistent results, maintain cells in log-phase growth and standardize imaging parameters across experiments. Compare localization patterns in both normal and stress conditions to identify potential condition-dependent relocalization .

How should researchers approach genetic interaction studies for YML012C-A?

Comprehensive genetic interaction mapping represents a powerful approach for predicting YML012C-A function. The Synthetic Genetic Array (SGA) methodology has been extensively validated in S. cerevisiae for systematic genetic interaction screening .

The experimental design should follow these steps:

• Generate a query strain carrying the YML012C-A deletion marked with a selectable marker

• Cross this strain with the yeast deletion collection (~4,800 non-essential gene deletions)

• Select for double mutants using appropriate markers

• Quantify growth defects to identify synthetic interactions

• Use hierarchical clustering to group interactions by functional similarity

For more focused studies, create double deletions with genes in pathways of interest based on preliminary phenotypic or localization data. Genetic interactions provide crucial functional context - genes with similar genetic interaction profiles typically participate in related biological processes. According to large-scale studies in yeast, approximately 170,000 genetic interactions have been mapped, creating a comprehensive functional network that can help contextualize novel genes .

When analyzing genetic interaction data, calculate both the expected and observed fitness of double mutants. Significant deviation from expected fitness (calculated from the product of single mutant fitness values) indicates genetic interaction .

What approaches resolve conflicting data in YML012C-A functional studies?

When encountering contradictory results in YML012C-A characterization, implement a systematic troubleshooting and validation strategy:

• Verify strain background effects by testing phenotypes in multiple genetic backgrounds (BY4741, W303, RM11-1a, YPS163)

• Examine allele-specific effects through reciprocal hemizygosity analysis

• Validate gene deletions by PCR and sequencing to confirm precise removal without affecting adjacent genes

• Test for the presence of suppressor mutations using tetrad analysis from heterozygous diploids

For resolving conflicting data regarding stress response phenotypes:

When contradictory data emerges from different laboratories, standardize experimental conditions including media composition, growth phase, and stress application methods. Quantitative rather than qualitative assessments should be prioritized, with clear statistical analysis to determine significance of observed differences .

How can proteomics approaches be optimized for studying YML012C-A interactions?

For comprehensive characterization of YML012C-A protein interactions, implement a multi-faceted proteomic strategy:

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

  • Tag YML012C-A with epitopes such as TAP, FLAG, or HA

  • Perform pulldowns under native conditions

  • Analyze co-purifying proteins by LC-MS/MS

  • Include appropriate controls (untagged strains, irrelevant tagged proteins)

• Proximity-dependent labeling

  • Fuse YML012C-A to BioID or APEX2

  • Allow in vivo labeling of proximal proteins

  • Purify biotinylated proteins and identify by MS

  • Map spatial interaction networks

• Crosslinking mass spectrometry (XL-MS)

  • Apply chemical crosslinkers to stabilize transient interactions

  • Digest and analyze crosslinked peptides

  • Identify direct binding partners and interaction interfaces

For each approach, perform biological triplicates and implement stringent statistical filtering to distinguish true interactions from contaminants. Validation of key interactions should be performed using orthogonal methods such as co-immunoprecipitation or yeast two-hybrid assays .

What experimental designs best elucidate YML012C-A's potential role in oxidative stress response?

To investigate YML012C-A's potential involvement in oxidative stress response, implement a multi-tiered experimental approach:

• Phenotypic profiling under oxidative stressors:

  • Test growth in the presence of hydrogen peroxide, paraquat, and menadione

  • Compare wild-type, YML012C-A deletion, and complemented strains

  • Measure dose-response curves and calculate IC50 values

  • Analyze growth kinetics using time-course measurements

• Gene expression analysis during oxidative stress:

  • Perform RNA-seq or qPCR after oxidative stress exposure

  • Compare expression profiles between wild-type and YML012C-A deletion strains

  • Focus on known oxidative stress response genes (e.g., CTT1, TSA1, TSA2, SOD1)

  • Analyze temporal expression patterns during stress and recovery phases

• Genetic interaction mapping with oxidative stress genes:

  • Create double mutants with key oxidative stress regulators (YAP1, SKN7)

  • Test epistatic relationships through phenotypic analysis

  • Determine if YML012C-A functions upstream or downstream of known regulators

The experimental design should control for variables such as cell density, growth phase, and precise application of oxidative stressors. For hydrogen peroxide experiments, prepare fresh solutions for each experiment and verify concentrations spectrophotometrically. Include appropriate positive controls such as TSA1/TSA2 deletions, which show known sensitivity to oxidative stress .

How can recombinational repair studies incorporate YML012C-A analysis?

To investigate potential roles of YML012C-A in DNA recombination and repair, design experiments that examine its relationship with established recombination pathways:

• Sensitivity testing to DNA damaging agents:

  • Compare survival of wild-type and YML012C-A deletion strains after exposure to:

    • UV radiation (DNA crosslinking)

    • Methyl methanesulfonate (alkylating agent)

    • 8-methoxypsoralen-plus-UVA (interstrand crosslinks)

  • Quantify survival rates and recovery kinetics

• Recombination rate measurement:

  • Implement genetic recombination assays (direct-repeat, ectopic, and allelic)

  • Compare spontaneous and induced recombination frequencies

  • Analyze with and without exposure to DNA damaging agents

  • Determine if YML012C-A affects mitotic or meiotic recombination, or both

• Epistasis analysis with recombination genes:

  • Create double mutants with key recombination genes (RAD52, RAD51, MRE11)

  • Compare phenotypes to determine pathway relationships

  • Analyze by measuring both spontaneous and induced recombination rates

If YML012C-A functions in recombinational repair, deletion strains would show increased sensitivity to DNA damaging agents and potentially altered recombination frequencies. Context this research within S. cerevisiae's established role as a model for understanding DNA repair mechanisms in eukaryotes .

What are the optimal conditions for expressing recombinant YML012C-A protein?

For optimal recombinant expression of YML012C-A, consider both homologous (S. cerevisiae) and heterologous (E. coli) expression systems:

For S. cerevisiae expression:

• Use strong inducible promoters (GAL1, CUP1) for controlled expression

• Include epitope tags (His6, FLAG, MBP) for purification and detection

• Optimize codon usage for enhanced expression

• Culture at 30°C in selective media with appropriate carbon source

• For induction, use 2% galactose (GAL1 promoter) or 0.5mM CuSO4 (CUP1 promoter)

For E. coli expression:

• Optimize codon usage for bacterial expression

• Test multiple fusion tags (His6, GST, MBP, SUMO) to improve solubility

• Screen expression conditions (temperature, IPTG concentration, media composition)

• Consider co-expression with yeast chaperones for proper folding

Expression optimization should use a factorial design approach to systematically test variables:

Verify expression by Western blotting and assess protein solubility through fractionation experiments. For purification, compare affinity, ion exchange, and size exclusion chromatography to obtain the highest purity .

How can researchers design effective CRISPR-Cas9 experiments for YML012C-A editing?

For precise CRISPR-Cas9 genetic manipulation of YML012C-A, implement the following methodological approach:

• Guide RNA design:

  • Select target sites with minimal off-target potential

  • Avoid regions with secondary structure that may inhibit Cas9 binding

  • Design at least 3-4 candidate gRNAs targeting different regions

  • Prioritize target sites near the start codon for gene disruption

• Repair template design:

  • For gene deletions: homology arms of 40-60bp flanking the target site

  • For point mutations: ~80bp homology arms surrounding the desired mutation

  • For tag insertions: homology arms plus in-frame tag sequence

  • Include silent mutations in the PAM site to prevent re-cutting

• Delivery methods:

  • Transform assembled Cas9-gRNA ribonucleoprotein complexes

  • Alternatively, use plasmid-based expression with appropriate selectable markers

  • Include repair templates as single-stranded or double-stranded DNA

• Verification strategy:

  • PCR screening of transformants

  • Sanger sequencing of the modified locus

  • Phenotypic validation where applicable

  • Whole genome sequencing to check for off-target effects

When designing experiments, consider the specific genetic background of your strain, as efficiency can vary. For complex modifications, sequential editing may be required. Always include appropriate controls such as wild-type strains and transformations without gRNA to establish baseline transformation efficiency3.

What statistical approaches best analyze high-throughput data for YML012C-A studies?

When analyzing high-throughput data generated from YML012C-A studies, implement these statistical approaches:

• For RNA-seq differential expression analysis:

  • Apply DESeq2 or edgeR for count normalization and statistical testing

  • Use false discovery rate (FDR) control with q < 0.05 as significance threshold

  • Perform Gene Set Enrichment Analysis (GSEA) to identify affected pathways

  • Validate key genes with qPCR using at least 3 biological replicates

• For proteomics data:

  • Implement MaxQuant or Proteome Discoverer for peptide identification

  • Use MSstats or Perseus for statistical analysis

  • Apply SAINT algorithm for filtering interaction proteomics data

  • Consider both fold-change and statistical significance (volcano plot analysis)

• For genetic interaction screens:

  • Calculate genetic interaction scores (ε) as deviation from multiplicative model

  • Apply thresholds of |ε| > 0.08 and p < 0.05 for significance

  • Perform hierarchical clustering of interaction profiles

  • Generate network visualizations using Cytoscape with appropriate layouts

• For multi-omics integration:

  • Apply dimensionality reduction techniques (PCA, t-SNE)

  • Use canonical correlation analysis to identify relationships between data types

  • Implement network-based approaches to reveal functional modules

  • Validate predictions with targeted experiments

For all analyses, ensure appropriate normalization, batch effect correction, and multiple testing adjustment. Power analysis should be performed prior to experiments to determine adequate sample sizes. For complex designs, consult with a biostatistician to develop appropriate analysis plans .

How might YML012C-A function be related to cytokinesis in S. cerevisiae?

Investigating potential roles of YML012C-A in cytokinesis requires careful experimental design focusing on key cytokinesis processes in S. cerevisiae:

• Actomyosin ring (AMR) dynamics:

  • Tag key AMR components (Myo1, Iqg1, Mlc1) with fluorescent proteins

  • Compare ring assembly, constriction, and disassembly in wild-type and YML012C-A deletion strains

  • Perform time-lapse microscopy to measure cytokinesis timing and dynamics

  • Quantify aberrant cytokinesis events and multinucleate cell formation

• Septum formation analysis:

  • Examine primary and secondary septum formation using calcofluor white staining

  • Measure chitin deposition patterns at the bud neck

  • Analyze septum ultrastructure using transmission electron microscopy

  • Quantify timing of septum formation relative to AMR constriction

• Septin organization:

  • Visualize septin ring dynamics using Cdc3-GFP or other tagged septins

  • Compare septin hourglass-to-ring transition timing

  • Analyze septin ring stability using FRAP (Fluorescence Recovery After Photobleaching)

  • Determine if YML012C-A affects septin recruitment or organization

When examining potential cytokinesis defects, remember that in S. cerevisiae, cytokinesis begins with budding in late G1 and is not completed until about halfway through the next cell cycle. Unlike higher eukaryotes, spindle assembly can occur before S phase completion, and the G2 phase is not clearly defined . These unique aspects of yeast cell division should be considered when interpreting results.

What approaches can determine if YML012C-A has a role in stress-responsive gene regulation?

To investigate potential roles of YML012C-A in stress-responsive gene regulation, implement a comprehensive transcriptional analysis strategy:

• Global transcriptional profiling:

  • Perform RNA-seq comparing wild-type and YML012C-A deletion strains under:

    • Normal growth conditions

    • Oxidative stress (H₂O₂, menadione)

    • Nutrient limitation

    • Temperature stress

  • Identify differentially expressed genes and enriched GO categories

  • Focus particularly on known stress-responsive gene networks

• Chromatin association studies:

  • Perform ChIP-seq if YML012C-A contains potential DNA-binding domains

  • Use epitope-tagged YML012C-A expressed at endogenous levels

  • Analyze binding patterns under both normal and stress conditions

  • Identify potential DNA binding motifs through motif enrichment analysis

• Transcription factor interaction studies:

  • Test for physical interactions with known stress-responsive transcription factors (Msn2/4, Yap1, Hsf1)

  • Perform genetic interaction studies with stress response regulators

  • Analyze epistatic relationships through gene expression analysis

  • Determine if YML012C-A functions as a co-activator, co-repressor, or chromatin remodeler

When analyzing gene expression data, cluster differentially expressed genes and compare to established stress response signatures. If YML012C-A functions in gene regulation, deletion strains should show altered expression patterns for specific gene sets under stress conditions. Validate key findings with reporter gene assays and targeted qPCR .

What models best explain contradictory data regarding YML012C-A function?

When confronted with contradictory data regarding YML012C-A function, consider these explanatory models and testing strategies:

• Condition-dependent function model:

  • YML012C-A may have different roles under different environmental conditions

  • Test function under a systematic array of conditions (nutrition, temperature, pH, stressors)

  • Measure phenotypes across complete growth curves rather than endpoints

  • Identify specific conditions where phenotypes emerge or disappear

• Genetic background influence model:

  • Function may differ across strain backgrounds due to genetic modifiers

  • Test phenotypes in multiple strain backgrounds (BY, W303, RM11-1a, YPS163)

  • Perform QTL mapping to identify modifier loci

  • Create allele replacements to test causality of specific variants

• Redundancy and compensation model:

  • Paralogs or functionally related proteins may mask phenotypes

  • Identify potential redundant genes through sequence or interaction analysis

  • Create double/triple mutants to overcome redundancy

  • Analyze expression changes of related genes in YML012C-A deletion strains

• Pleiotropic function model:

  • YML012C-A may have multiple distinct cellular roles

  • Separate functions spatially (through localization studies)

  • Separate functions temporally (through inducible expression/depletion)

  • Create separation-of-function mutations targeting specific domains

For each model, design experiments that can distinguish between alternative hypotheses. Use quantitative rather than qualitative measurements, and implement appropriate statistical analysis to determine significance. Consider performing suppressor screens to identify genes that can rescue deletion phenotypes, potentially revealing functional pathways .

How can multi-omics data integration enhance understanding of YML012C-A function?

Integrating multiple types of omics data provides a comprehensive systems-level view of YML012C-A function. Implement this multi-layered approach:

• Data generation and integration strategy:

  • Generate matched samples for transcriptomics, proteomics, and metabolomics

  • Include both wild-type and YML012C-A deletion strains

  • Sample across multiple conditions and time points

  • Process data through standardized pipelines for compatibility

• Network-based integration:

  • Construct protein-protein interaction networks incorporating YML012C-A

  • Overlay transcriptomic data to identify condition-specific modules

  • Map genetic interactions to identify functional relationships

  • Apply algorithms like WGCNA to identify co-regulated gene modules

• Pathway enrichment and ontology mapping:

  • Perform GO enrichment across all data types

  • Apply pathway analysis using KEGG, Reactome, or yeast-specific databases

  • Identify common pathways enriched across multiple data types

  • Use hierarchical ontology mapping to identify functional themes

• Data visualization and modeling:

  • Implement multi-dimensional visualization techniques

  • Create integrative network visualizations using Cytoscape

  • Develop predictive models of YML012C-A function

  • Validate key predictions experimentally

This integrated approach has proven successful in characterizing previously uncharacterized yeast genes. For example, in studies of oxidative stress responses, integration of transcriptomic and genetic interaction data has identified key regulatory networks and revealed new functional relationships . The same approach can uncover the biological context in which YML012C-A operates.

What experimental validation approaches best confirm computational predictions about YML012C-A?

To rigorously validate computational predictions about YML012C-A function, implement this hierarchical validation strategy:

• Phenotypic validation:

  • If computational analysis predicts pathway involvement, test relevant phenotypes

  • Design quantitative assays measuring specific cellular processes

  • Compare phenotypes under normal and stress conditions

  • Validate across multiple genetic backgrounds to ensure robustness

• Physical interaction validation:

  • For predicted protein interactions, perform co-immunoprecipitation

  • Use proximity ligation assays to confirm interactions in vivo

  • Test direct binding with purified proteins using biophysical methods

  • Confirm co-localization using fluorescence microscopy

• Genetic modification validation:

  • Create point mutations in predicted functional domains

  • Perform structure-function analysis based on computational models

  • Generate chimeric proteins to test domain-specific functions

  • Use complementation studies with mutant variants

• Systems-level validation:

  • Test whether interventions in predicted pathways affect YML012C-A-related phenotypes

  • Measure effects of YML012C-A modification on predicted cellular systems

  • Perform epistasis analysis with key genes in predicted pathways

  • Use metabolic flux analysis if metabolic functions are predicted

For each validation experiment, include appropriate positive and negative controls. Design experiments with sufficient statistical power, typically requiring at least three biological replicates. Implement blinding procedures when possible to eliminate observer bias, particularly for phenotypic assessments3 .