Recombinant Saccharomyces cerevisiae Putative uncharacterized protein YBL071C (YBL071C)

What is YBL071C and why is it significant for research?

YBL071C is a putative uncharacterized protein in Saccharomyces cerevisiae (baker's yeast) consisting of 102 amino acids. Its significance stems from being part of the broader study of uncharacterized yeast proteins, which often reveal fundamental cellular processes. As with many putative proteins, researchers approach YBL071C using systematic characterization methods to determine its structure, localization, and function. The methodological approach begins with bioinformatic analysis (sequence homology, structural predictions) followed by experimental validation through techniques such as fluorescent tagging for localization studies, affinity purification for interaction partners, and phenotypic analysis of deletion strains . The protein's small size (102 amino acids) suggests it may function as a regulatory element rather than an enzymatic protein, making it potentially significant for understanding cellular signaling pathways in eukaryotes.

What expression systems are available for recombinant YBL071C production?

The primary expression system utilized for recombinant YBL071C production is E. coli, which allows for high-yield protein expression with His-tagging for purification purposes . Methodologically, researchers should consider several factors when selecting an expression system:

• For structural studies requiring post-translational modifications, yeast-based expression systems (P. pastoris or S. cerevisiae) may provide advantages over bacterial systems

• For high-throughput functional analysis, bacterial systems offer cost-effective production

• For interaction studies, mammalian expression systems might provide more relevant folding environments

When designing expression constructs, consider:

The methodological workflow typically involves cloning the YBL071C sequence with appropriate tags, optimizing expression conditions (temperature, induction timing, media composition), and establishing a purification protocol specific to the expressed construct .

What are the predicted structural features of YBL071C?

While definitive structural data for YBL071C is limited, several methodological approaches can be employed to predict its structural features. Computational prediction algorithms suggest YBL071C likely contains 1-2 alpha-helical regions based on its amino acid composition, with potential disordered regions that may facilitate protein-protein interactions. The methodological approach should involve:

• Primary sequence analysis using tools like BLAST, Pfam, and SMART to identify conserved domains

• Secondary structure prediction using PSIPRED or JPred

• 3D structure prediction using AlphaFold2 or RoseTTAFold

• Disorder prediction using IUPred or PONDR

Experimentally, circular dichroism spectroscopy can verify secondary structure content, while NMR or X-ray crystallography would provide definitive structural information for this relatively small protein. The absence of well-defined domains in database searches suggests YBL071C may represent a lineage-specific adaptation in Saccharomyces yeasts, requiring comparative genomics approaches across related fungal species to identify evolutionary patterns .

How can I design experiments to determine if YBL071C plays a role in cold adaptation in S. cerevisiae?

Designing experiments to investigate YBL071C's potential role in cold adaptation requires a systematic approach comparing wild-type and YBL071C-deficient strains. Based on established cold adaptation research methodologies, follow this experimental design:

• Generate YBL071C deletion strains using CRISPR-Cas9 or traditional homologous recombination techniques

• Design temperature challenge experiments:

  • Growth curve analysis at optimal (30°C) versus cold temperatures (10-15°C)

  • Colony formation efficiency comparisons at various temperatures

  • Competitive growth assays with wild-type and mutant strains

• Assess molecular responses:

  • Transcriptome analysis (RNA-seq) comparing gene expression patterns

  • Proteome profiling to identify compensatory protein expression

  • Mitochondrial function assessment (oxygen consumption, membrane potential)

Include appropriate controls such as known cold-sensitive mutants (e.g., YME1 or DRS2) and cold-tolerant strains . Statistical analysis should employ two-way ANOVA to evaluate interaction effects between genotype and temperature.

Follow cold adaptation experimental protocols established for yeast, carefully controlling media composition, starter culture conditions, and growth phase at temperature shift to minimize experimental variability .

What approaches can be used to identify potential interaction partners of YBL071C?

Identifying interaction partners of uncharacterized proteins like YBL071C requires complementary approaches that balance depth, specificity, and functional relevance. Implement the following methodological workflow:

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

  • Express His-tagged YBL071C in S. cerevisiae

  • Perform crosslinking to capture transient interactions

  • Purify using nickel affinity chromatography

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

  • Filter against appropriate negative controls

• Yeast two-hybrid (Y2H) screening:

  • Clone YBL071C as both bait and prey constructs

  • Screen against genomic libraries and targeted candidates

  • Validate interactions through co-immunoprecipitation

• Proximity labeling approaches:

  • BioID or TurboID fusions to YBL071C

  • In vivo biotinylation of proximal proteins

  • Streptavidin pulldown and mass spectrometry

• Computational prediction and co-expression analysis:

  • Network analysis using STRING, BioGRID

  • Co-expression datasets from stress response studies

When analyzing interaction data, prioritize proteins that appear across multiple methods, show stoichiometric levels in AP-MS, or have functional annotations related to your hypothesis . Interactions should be categorized based on confidence levels and verified through reciprocal tagging experiments.

How does YBL071C expression change under various stress conditions?

Analyzing YBL071C expression under diverse stress conditions provides crucial insights into its potential functional roles. A comprehensive methodological approach involves:

• Transcriptional profiling:

  • RT-qPCR analysis of YBL071C mRNA levels

  • RNA-seq to place changes in genome-wide context

  • Time-course analysis to capture transient responses

• Protein-level analysis:

  • Western blot with antibodies against tagged YBL071C

  • Quantitative proteomics (SILAC or TMT labeling)

  • Protein stability assessment with cycloheximide chase

• Stress conditions to test:

  • Temperature stress (heat shock, cold shock)

  • Oxidative stress (H₂O₂, menadione)

  • Osmotic stress (high salt, sorbitol)

  • Nutrient limitation (nitrogen, carbon, phosphate)

  • DNA damage (UV, MMS)

Proper experimental design requires precise control of growth conditions, synchronized cultures, and multiple biological replicates. Analysis should include comparison to known stress-responsive genes as internal controls. Expression changes should be correlated with phenotypic observations in deletion strains under the same conditions to establish functional relevance . Where appropriate, use GFP-fusion proteins to track subcellular localization changes under stress conditions.

What controls should be included when studying potential cold sensitivity phenotypes of YBL071C mutants?

• Positive controls:

  • Known cold-sensitive mutants (e.g., DRS2, which shows cold sensitivity on both YPD and YPEG media)

  • YME1 mutants (theoretical cold sensitivity due to inactivation of redundant genes)

  • Include heterozygous and homozygous diploid strains when available

• Negative controls:

  • Wild-type parent strain

  • Non-cold-sensitive deletion strains (e.g., SPO7, SWC3, CCR4)

  • Empty vector controls for complementation experiments

• Media controls:

  • Test both fermentable (YPD) and non-fermentable (YPEG) carbon sources

  • Minimal media and complete media comparisons

• Temperature regime controls:

  • Standardized temperature points (30°C as permissive, 15°C as cold)

  • Temperature shift protocols with controlled rates

  • Recovery experiments at permissive temperature

• Strain background controls:

  • Use multiple strain backgrounds if possible

  • Ensure genetic markers are consistent across test strains

This experimental design allows for clear differentiation between general growth defects and specific cold-sensitive phenotypes . The table of results should follow the format shown in search result , capturing growth comparisons across different media and temperatures for multiple strain types.

How should I design experiments to characterize YBL071C's potential role in cellular pathways?

Characterizing YBL071C's role in cellular pathways requires a systematic experimental approach that integrates genetic, biochemical, and computational methods. The methodological framework should include:

• Genetic interaction mapping:

  • Synthetic genetic array (SGA) analysis

  • Targeted epistasis experiments with related genes

  • Suppressor screens to identify compensatory pathways

  • Quantitative scoring of genetic interactions

• Pathway perturbation experiments:

  • Chemical inhibition of major cellular pathways

  • Monitor YBL071C deletion effects on pathway outputs

  • Measure biochemical endpoints relevant to hypothesized pathways

• Systems-level analysis:

  • Multi-omics integration (transcriptomics, proteomics, metabolomics)

  • Network analysis to place YBL071C in known pathway maps

  • Enrichment analysis for biological processes

• Variable manipulation following experimental design principles:

  • Independent variables: genetic background, environmental conditions

  • Dependent variables: growth rates, biochemical readouts, stress responses

  • Controlled variables: media composition, temperature, cell density

Implement randomization to control for batch effects and systematic biases, with appropriate statistical analysis using methods such as ANOVA for multi-factorial designs or regression analysis for continuous variables . Document all experimental conditions meticulously to ensure reproducibility, and consider both loss-of-function and gain-of-function approaches to fully characterize the protein's role.

What experimental approaches can detect if YBL071C has post-translational modifications?

Detecting post-translational modifications (PTMs) of YBL071C requires specialized techniques that can identify and characterize specific chemical modifications. The methodological workflow should include:

• Initial PTM prediction:

  • Computational analysis using tools like NetPhos, UbPred, and SUMOplot

  • Sequence motif analysis for established modification sites

  • Structural modeling to identify surface-accessible residues

• Mass spectrometry-based approaches:

  • Immunoprecipitation of tagged YBL071C

  • Sample preparation optimized for specific PTM types:

    • Phosphorylation: TiO₂ enrichment

    • Ubiquitination: Tryptic digestion preserving GG remnants

    • Glycosylation: Lectin enrichment or PNGase treatment

  • LC-MS/MS analysis with neutral loss scanning

  • Data analysis with appropriate search parameters for modifications

• Biochemical validation:

  • Phospho-specific antibodies if available

  • Mobility shift assays (Phos-tag gels for phosphorylation)

  • Enzymatic treatments (phosphatases, deubiquitinases)

  • Site-directed mutagenesis of predicted modification sites

• Dynamic PTM analysis:

  • Time-course experiments during stress responses

  • Inhibitor treatments to block specific modification pathways

Document the mass accuracy, peptide coverage, and statistical confidence for each identified modification. Present results in tabular format with modification sites, flanking sequences, and detection scores. Validate key findings with orthogonal methods whenever possible.

How can I resolve contradictory data regarding YBL071C's function across different experimental approaches?

Resolving contradictory data about YBL071C's function requires systematic evaluation of experimental evidence and methodological differences. Apply this analytical framework:

• Evaluate methodological differences:

  • Compare experimental conditions (strains, media, temperatures)

  • Assess measurement techniques (direct vs. indirect readouts)

  • Consider temporal aspects (acute vs. chronic responses)

  • Evaluate specific reagents used (antibodies, constructs)

• Implement contradiction detection approaches:

  • Systematically catalog all findings with standardized formats

  • Weight evidence based on methodological rigor

  • Identify specific points of contradiction vs. complementary findings

• Resolution strategies:

  • Design focused experiments targeting contradiction points

  • Implement orthogonal approaches to validate key findings

  • Consider context-dependent functions (condition-specific roles)

  • Evaluate potential strain background effects

• Computational integration:

  • Bayesian analysis to weight contradictory evidence

  • Machine learning approaches to identify patterns across datasets

  • Network analysis to place contradictory results in pathway context

When presenting contradictory findings, use a structured comparison table highlighting differences in experimental conditions, measured variables, and statistical significance . This approach acknowledges that apparent contradictions may represent context-dependent functions rather than experimental errors.

What statistical approaches are most appropriate for analyzing YBL071C phenotypic data?

Selecting appropriate statistical approaches for YBL071C phenotypic data analysis depends on experimental design and data characteristics. The methodological framework should include:

• Experimental design considerations:

  • Use power analysis to determine sample sizes

  • Plan for appropriate replication (biological and technical)

  • Implement randomization and blinding where possible

  • Consider factorial designs to assess interaction effects

• Data preprocessing:

  • Assess normality with Shapiro-Wilk or Kolmogorov-Smirnov tests

  • Transform data if necessary (log, square root)

  • Identify and address outliers with standardized approaches

  • Normalize to appropriate controls

• Statistical test selection:

  • For comparing two conditions: t-test (parametric) or Mann-Whitney (non-parametric)

  • For multiple conditions: ANOVA with appropriate post-hoc tests

  • For growth curves: area under curve analysis or growth rate comparisons

  • For genetic interaction data: multiplicative or additive interaction models

• Multiple testing correction:

  • Apply Bonferroni correction for stringent analysis

  • Use Benjamini-Hochberg for controlling false discovery rate

  • Report both raw and adjusted p-values

• Effect size reporting:

  • Include Cohen's d, fold change, or percent difference

  • Present confidence intervals alongside p-values

  • Use standardized plotting formats (box plots with data points)

When reporting results, clearly state all statistical parameters, including test types, degrees of freedom, test statistics, and precise p-values . This transparency enables proper evaluation of findings and facilitates meta-analysis across studies.

What experimental approaches could determine if YBL071C interacts with nuclear pore complexes?

Based on potential parallels with other yeast proteins that interact with nuclear pore complexes, several methodological approaches can determine if YBL071C has similar functions:

• Fluorescence microscopy approaches:

  • Generate YBL071C-GFP fusion proteins

  • Co-localization studies with known nuclear pore markers (e.g., Nup188)

  • FRAP analysis to measure dynamics at the nuclear envelope

  • Live cell imaging during stress responses

• Biochemical interaction studies:

  • Immunoprecipitation with nuclear pore complex proteins

  • Proximity labeling using BioID fused to NPC components

  • In vitro binding assays with recombinant proteins

  • Crosslinking mass spectrometry to capture interaction interfaces

• Genetic interaction analysis:

  • Epistasis analysis with NPC mutants (e.g., NUP188, SAC3)

  • Synthetic genetic array screening focused on nuclear transport

  • Phenotypic analysis of double mutants under stress conditions

• Functional transport assays:

  • Nuclear import/export reporter assays in YBL071C mutants

  • mRNA export efficiency measurements

  • Protein localization studies under various conditions

Special attention should be given to cold temperature conditions, as some nuclear pore interactions may be conditional or stress-specific . Design experiments to test if YBL071C impacts dynactin-mediated transport to the nucleus, similar to the relationships observed with Arp1p and Nip100p .

How might YBL071C function be related to mitochondrial processes under cold stress?

Investigating YBL071C's potential role in mitochondrial processes under cold stress requires a methodological approach that builds on existing knowledge of yeast cold adaptation:

• Mitochondrial phenotype characterization:

  • Electron microscopy to assess mitochondrial morphology

  • Mitochondrial membrane potential measurements

  • Oxygen consumption rates at different temperatures

  • mtDNA stability and copy number analysis

• Genetic interaction studies:

  • Generate double mutants with known mitochondrial genes

  • Focus on cold-sensitive mitochondrial proteins (e.g., YME1)

  • Test growth on non-fermentable carbon sources (YPEG)

  • Assess respiratory competence through TTC overlay assays

• Targeted biochemical analyses:

  • Measure oxidative stress markers in YBL071C mutants

  • Assess levels of reactive oxygen species

  • Quantify mitochondrial protein import efficiency

  • Analyze mitochondrial translation products

• Transcriptome and proteome analysis:

  • RNA-seq comparing wild-type and ΔyBL071C responses to cold

  • Quantitative proteomics of purified mitochondria

  • Focus on retrograde signaling pathways

Similar to studies with YME1, investigate if YBL071C deletion causes accumulation of oxidative stress byproducts specifically at cold temperatures . Design experiments to test the theory that YBL071C may have redundant functions with other genes that become inactive at cold temperatures, thus creating conditional phenotypes only observable under specific stress conditions.

What are the most promising research directions for further characterizing YBL071C?

Based on current knowledge and research trends, the most promising directions for further characterizing YBL071C include:

• Comprehensive phenomics screening:

  • Systematic analysis across diverse conditions

  • Integration with existing datasets

  • Particular focus on stress responses and temperature adaptation

• High-resolution structural studies:

  • Cryo-EM or X-ray crystallography of the full protein

  • NMR for dynamic regions and interaction surfaces

  • Hydrogen-deuterium exchange mass spectrometry

• Evolutionary analysis:

  • Comparative genomics across fungal species

  • Selection pressure analysis of sequence conservation

  • Identification of co-evolving partners

• Systems-level integration:

  • Multi-omics data integration

  • Network-based function prediction

  • Machine learning approaches leveraging existing functional data