Recombinant Saccharomyces cerevisiae Uncharacterized protein YIL060W (YIL060W)

What is YIL060W protein and why is it significant for research?

YIL060W is an uncharacterized protein from Saccharomyces cerevisiae (baker's yeast) consisting of 144 amino acids. Its significance in research stems from its uncharacterized nature, making it a target for functional genomics studies aiming to elucidate previously unknown cellular mechanisms. The protein has UniProt ID P40519 and represents an opportunity to discover novel biochemical pathways or regulatory functions in yeast cells . As an uncharacterized protein, research on YIL060W contributes to completing our understanding of the yeast proteome, which serves as a model for eukaryotic cellular processes.

How is recombinant YIL060W protein typically produced for research applications?

Recombinant YIL060W protein is typically produced using E. coli expression systems. The full-length coding sequence (1-144 amino acids) is cloned into an expression vector that incorporates an N-terminal His-tag for purification purposes . The expression is induced under controlled conditions, followed by cell lysis and protein purification using affinity chromatography (typically Ni-NTA columns that bind the His-tag). The purified protein is then typically supplied as a lyophilized powder with greater than 90% purity as determined by SDS-PAGE . For research applications, the protein can be reconstituted in deionized sterile water to a concentration of 0.1-1.0 mg/mL, with the addition of 5-50% glycerol for long-term storage at -20°C/-80°C to prevent degradation through freeze-thaw cycles.

What are the recommended approaches for studying an uncharacterized protein like YIL060W?

When studying an uncharacterized protein like YIL060W, a systematic multi-omics approach is recommended. Begin with computational prediction of function based on sequence homology, protein domains, and evolutionary conservation. Follow with experimental validation including:

• Localization studies using GFP-fusion proteins to determine subcellular localization

• Phenotypic analysis of deletion strains (as available in yeast deletion collections)

• Chemical genomic profiling to identify genetic interactions and potential pathways

• Proteomics approaches to identify binding partners

• Transcriptomics to analyze expression patterns under various conditions

The experimental design should incorporate appropriate controls, including wild-type strains and strains with deletions of genes with known functions . A Latin Square Design can be particularly effective when testing multiple variables (such as different stress conditions) to identify functional contexts of the protein while controlling for experimental noise . This approach removes two sources of variation from the experimental error, making it more sensitive for detecting subtle phenotypes that might be associated with YIL060W function.

How should optimal storage and handling conditions be maintained for YIL060W protein preparations?

For optimal maintenance of YIL060W protein stability and activity, the following protocol should be followed:

• Store the lyophilized powder at -20°C/-80°C upon receipt

• Prior to opening, briefly centrifuge the vial to bring contents to the bottom

• Reconstitute in deionized sterile water to a concentration of 0.1-1.0 mg/mL

• Add glycerol to a final concentration of 5-50% (optimally 50%) for cryoprotection

• Aliquot into small volumes to avoid repeated freeze-thaw cycles

• For working stocks, maintain aliquots at 4°C for up to one week

• For long-term storage, keep at -20°C/-80°C

The protein is typically provided in a Tris/PBS-based buffer with 6% Trehalose at pH 8.0, which helps maintain stability . Repeated freeze-thaw cycles should be strictly avoided as they can lead to protein denaturation and loss of activity. For experimental applications, always verify protein integrity via SDS-PAGE before proceeding with functional assays.

How can researchers effectively design experiments to determine the function of YIL060W?

To effectively design experiments for functional characterization of YIL060W, researchers should implement a comprehensive strategy that integrates multiple approaches:

• Genetic Approach: Create precise gene deletions or modifications using techniques such as CRISPR-Cas9 or homologous recombination. Study the phenotypic consequences under various conditions (temperature, nutrient availability, stress) .

• Chemical Genomic Profiling: Expose heterozygous deletion strains (containing one functional copy of YIL060W) to various compounds and monitor growth over multiple generations (5-20). This can reveal sensitivity or resistance phenotypes that provide functional insights .

• Experimental Design Considerations:

  • Use Complete Randomized Design (CRD) for flexibility in treatment comparisons

  • Implement Randomized Block Design (RBD) when environmental variables need to be controlled

  • Consider Latin Square Design (LSD) when testing multiple factors simultaneously to reduce error variance

• Data Collection Timeline: Monitor growth and other phenotypes at multiple time points (e.g., after 5, 10, 15, and 20 generations) to capture both immediate and adaptive responses .

• Control Selection: Include both negative controls (wild-type strains) and positive controls (strains with deletions in genes of known function related to hypothesized pathways) .

This multi-faceted approach maximizes the probability of identifying the biological role of YIL060W while minimizing false positives through rigorous experimental design and appropriate controls.

How can chemical genomic profiling be applied to study YIL060W function?

Chemical genomic profiling represents a powerful approach for investigating the function of uncharacterized proteins like YIL060W through systematic analysis of genetic interactions. The methodology involves the following steps:

• Strain Preparation: Utilize heterozygous S. cerevisiae diploid strains where one copy of each predicted ORF has been replaced by an antibiotic resistance marker (KANMX4), with unique DNA barcodes identifying each deletion .

• Growth Conditions: Expose strain pools to the IC20 concentration of test compounds or control conditions. Culture at 30°C with shaking at 200 rpm in 48-well plates with an initial OD600 of 0.1 .

• Generational Analysis: Allow growth for approximately 5 generations (12 hours), then subculture (1/20 dilution) to obtain pools representing ~10, 15, and 20 generations of growth under selective pressure .

• DNA Extraction and Barcode Sequencing:

  • Harvest cells at each generational timepoint

  • Extract genomic DNA using appropriate kits (e.g., Wizard Genomic DNA Purification Kit)

  • Amplify molecular barcodes via PCR using hybrid primers containing Illumina preadaptors

  • Sequence using next-generation platforms (e.g., Illumina HiSeq 2500)

• Data Analysis: Rank heterozygous strains based on compound sensitivity after 20 generations, identifying those most depleted in treated populations compared to untreated controls .

This approach can reveal genetic interactions that suggest potential pathways in which YIL060W might function, particularly if deletion of YIL060W alters sensitivity to specific compounds or stress conditions.

What advanced techniques can be used to investigate protein-protein interactions involving YIL060W?

Investigating protein-protein interactions involving an uncharacterized protein like YIL060W requires sophisticated methodologies that can detect both stable and transient interactions. The following advanced approaches are recommended:

• Affinity Purification coupled with Mass Spectrometry (AP-MS):

  • Express His-tagged YIL060W protein in yeast cells

  • Perform in vivo crosslinking to capture transient interactions

  • Purify using Ni-NTA affinity chromatography

  • Identify binding partners via mass spectrometry

  • Validate interactions through reciprocal pull-downs

• Proximity-based Labeling:

  • Create fusion proteins with BioID or APEX2 enzymes

  • Express in yeast to allow in vivo biotinylation of proximal proteins

  • Purify biotinylated proteins and identify via mass spectrometry

  • This approach captures both direct interactions and proteins in close proximity

• Yeast Two-Hybrid (Y2H) Screening:

  • Create bait constructs with YIL060W fused to DNA-binding domain

  • Screen against a library of yeast proteins fused to activation domain

  • Validate positive interactions with complementary approaches

• Fluorescence Resonance Energy Transfer (FRET):

  • Generate fluorescent protein fusions with YIL060W and candidate interactors

  • Measure energy transfer as indication of protein proximity

  • Particularly useful for studying dynamics of interactions in living cells

For all these approaches, appropriate controls are crucial, including using unrelated proteins as negative controls and known interacting pairs as positive controls. The combination of multiple complementary techniques provides the strongest evidence for genuine biological interactions.

What microarray analysis approaches are most effective for studying YIL060W's role in gene expression?

Microarray analysis offers powerful insights into the transcriptional consequences of YIL060W function or deletion. To effectively implement this approach, researchers should follow these methodological steps:

• Experimental Design Considerations:

  • Compare wild-type strains to YIL060W deletion or overexpression strains

  • Include multiple biological replicates (minimum 3) to ensure statistical robustness

  • Test under various conditions to identify context-dependent functions

  • Consider time-course experiments to capture dynamic responses

• RNA Isolation Protocol:

  • Harvest cells at appropriate growth phase (log phase recommended)

  • Extract total RNA using specialized kits for yeast

  • Verify RNA integrity using RNA Integrity Number (RIN) detection

  • Only proceed with samples showing RIN values >8.0

• Sample Preparation and Hybridization:

  • Prepare cDNA from RNA samples

  • Label with appropriate fluorophores

  • Hybridize to microarrays following manufacturer protocols

  • Include appropriate controls (e.g., housekeeping genes, spike-in controls)

• Data Analysis Pipeline:

  • Scanning and initial data capture

  • Background correction and normalization

  • Statistical analysis to identify differentially expressed genes

  • Pathway and Gene Ontology enrichment analysis

  • Network analysis to identify co-regulated gene clusters

• Validation Steps:

  • Confirm key findings using RT-qPCR

  • Correlate transcriptional changes with phenotypic observations

  • Test predictions through targeted genetic or biochemical experiments

This comprehensive approach allows for the identification of genes and pathways affected by YIL060W, providing crucial insights into its biological function and regulatory networks.

What are common challenges in working with YIL060W and how can they be addressed?

Researchers working with the uncharacterized protein YIL060W encounter several technical challenges. Here are common issues and their methodological solutions:

• Low Protein Solubility:

  • Challenge: His-tagged YIL060W may form inclusion bodies during expression

  • Solution: Optimize expression conditions by lowering induction temperature (16-20°C), reducing IPTG concentration, or using specialty E. coli strains designed for membrane protein expression

  • Alternative: Consider using different fusion tags (MBP, GST) that can enhance solubility

• Protein Stability Issues:

  • Challenge: Rapid degradation after reconstitution

  • Solution: Add protease inhibitors to all buffers, maintain strict temperature control, and consider adding stabilizing agents like trehalose (already present in storage buffer at 6%)

  • Recommended: Always perform fresh reconstitution before critical experiments

• Inconclusive Phenotypes in Deletion Strains:

  • Challenge: Subtle or context-dependent phenotypes that are difficult to detect

  • Solution: Implement stress conditions to reveal conditional phenotypes, and use sensitive growth monitoring instruments capable of detecting minor growth differences

  • Advanced approach: Employ genetic interaction screens to identify synthetic phenotypes

• Contradictory Data Interpretation:

  • Challenge: Different experimental approaches yielding inconsistent results

  • Solution: Implement rigorous statistical analysis, increase biological replicates, and verify findings with complementary methods

  • Critical practice: Document all experimental conditions meticulously to identify potential sources of variability

These methodological solutions should be implemented systematically, with careful documentation of outcomes to build a consistent understanding of YIL060W's properties and function.

How should researchers analyze and interpret genetic stability data for YIL060W mutants?

Analyzing and interpreting genetic stability data for YIL060W mutants requires a systematic methodological approach:

• Experimental Setup for Stability Testing:

  • Culture YIL060W mutant strains for multiple generations (typically 20+ generations)

  • Periodically sample and assess phenotypic characteristics and genotypic markers

  • Include appropriate controls (wild-type strains cultured under identical conditions)

• Analytical Framework:

  • Quantify phenotypic stability by measuring variance in growth rates, stress resistance, or other relevant phenotypes across generations

  • Apply statistical tests to determine if observed changes exceed experimental noise:

    • ANOVA to compare means across multiple generations

    • Regression analysis to identify trends over time

    • Variance component analysis to partition sources of variation

• Interpretation Guidelines:

  • Stable phenotype: Consistent phenotypic measurements across generations with variations within statistical bounds

  • Unstable phenotype: Significant drift in measurements that exceeds experimental error

  • Adaptive changes: Directional shifts in phenotype that may indicate compensatory mechanisms

• Decision Matrix for Data Interpretation:

  Observation Pattern	Statistical Significance	Interpretation	Recommended Action
  Consistent phenotype	p > 0.05 across time points	Genetically stable mutation	Proceed with functional characterization
  Gradual phenotype loss	p < 0.05 with time-dependent trend	Genetic instability or suppressor mutations	Sequence for secondary mutations; restart with fresh isolates
  Sudden phenotype change	p < 0.05 with step change	Contamination or major genetic event	Verify strain identity; reestablish from frozen stocks
  Oscillating phenotype	Variable significance	Epigenetic regulation or measurement error	Increase sampling frequency; control environmental variables

• Advanced Analysis:

  • Whole genome sequencing to identify any secondary mutations that may arise

  • Transcriptome analysis to detect compensatory changes in gene expression

  • Epigenetic profiling if phenotypic changes occur without genetic alterations

This methodological framework ensures reliable interpretation of genetic stability data, which is crucial for understanding the true functions of YIL060W without confounding effects from genetic drift or compensatory adaptations.

What statistical approaches are most appropriate for analyzing cross-resistance data in YIL060W studies?

• Experimental Design Considerations:

  • Implement a randomized complete block design or Latin square design to control for experimental variability

  • Include sufficient biological replicates (minimum n=3, preferably n≥5)

  • Test multiple concentrations of each stressor to establish dose-response relationships

• Primary Statistical Analyses:

  • Two-way ANOVA: To assess the effects of strain (YIL060W mutant vs. wild-type) and stressor type, plus their interaction

  • Repeated measures ANOVA: When testing the same strains across multiple conditions

  • Mixed-effects models: To account for random and fixed effects in complex experimental designs

• Post-hoc Testing and Multiple Comparisons:

  • Tukey's HSD test: For pairwise comparisons between multiple strains or conditions

  • Dunnett's test: When comparing multiple treatment groups against a single control

  • Bonferroni or Benjamini-Hochberg corrections: To control for family-wise error rate or false discovery rate

• Cross-Resistance Correlation Analysis:

  • Calculate Pearson or Spearman correlation coefficients between resistance profiles

  • Apply hierarchical clustering to identify patterns of cross-resistance

  • Perform principal component analysis (PCA) to reduce dimensionality and identify key variables driving resistance patterns

• Data Visualization Approaches:

  • Heat maps: To display resistance patterns across multiple stressors

  • Dose-response curves: To compare sensitivity thresholds

  • Network diagrams: To illustrate relationships between different resistance phenotypes

Table: Example Framework for Statistical Analysis of Cross-Resistance Data

This comprehensive statistical approach ensures rigorous analysis of cross-resistance data, enabling researchers to identify genuine biological patterns related to YIL060W function rather than statistical artifacts.

What are the most promising areas for future research on YIL060W function?

Based on current knowledge and technological capabilities, several promising research directions could significantly advance our understanding of YIL060W function:

• Systematic Genetic Interaction Mapping:

  • Implement CRISPR-based genetic interaction screens to identify synthetic lethal or synthetic rescue interactions

  • Create comprehensive genetic interaction profiles to position YIL060W within cellular pathways

  • Compare interaction profiles with those of characterized proteins to infer function through similarity

• Structural Biology Approaches:

  • Determine the three-dimensional structure using X-ray crystallography, cryo-EM, or NMR spectroscopy

  • Identify potential binding pockets or catalytic sites

  • Perform structure-guided mutagenesis to test functional hypotheses

• Systems Biology Integration:

  • Combine transcriptomics, proteomics, and metabolomics data to create a holistic view of YIL060W's impact

  • Apply machine learning approaches to identify patterns across multiple data types

  • Develop predictive models of cellular responses to YIL060W perturbation

• Evolutionary Conservation Analysis:

  • Conduct comparative genomics across fungal species to identify conserved domains or motifs

  • Perform complementation studies with orthologs from other species

  • Trace the evolutionary history to identify potential functional constraints

• Single-Cell Analysis:

  • Implement single-cell transcriptomics to identify cell-to-cell variability in responses to YIL060W deletion

  • Use microfluidic approaches to monitor individual cell behaviors over time

  • Identify potential phenotypic heterogeneity masked in population-level studies

These research directions, particularly when pursued in parallel, offer the greatest potential for elucidating the biological role of this uncharacterized protein and integrating it into our understanding of cellular function.

How can advanced computational approaches enhance the study of YIL060W?

Advanced computational approaches offer powerful methods to accelerate understanding of uncharacterized proteins like YIL060W. Researchers should consider implementing these methodological strategies:

• Deep Learning for Function Prediction:

  • Apply neural network architectures trained on characterized proteins to predict YIL060W function

  • Utilize models that integrate sequence, structure prediction, and evolutionary conservation

  • Validate computational predictions with targeted experimental approaches

• Molecular Dynamics Simulations:

  • Generate structural models based on homology or ab initio predictions

  • Simulate protein dynamics in different environments

  • Identify potential ligand binding sites or conformational changes

  • Test hypotheses about protein-protein interactions through in silico docking

• Network-Based Function Inference:

  • Construct protein-protein interaction networks incorporating YIL060W

  • Apply network analysis algorithms to predict function based on connection patterns

  • Identify network motifs that suggest specific cellular roles

  • Use graph neural networks to predict functional associations

• Integrative Multi-omics Analysis:

  • Develop computational pipelines that integrate data across platforms:

    • Transcriptomics (RNA-seq, microarray)

    • Proteomics (mass spectrometry)

    • Metabolomics (NMR, mass spectrometry)

    • Genetic interaction screens

  • Apply dimensionality reduction techniques to identify key patterns

  • Implement Bayesian networks to model causal relationships

• Text Mining and Literature-Based Discovery:

  • Apply natural language processing to extract indirect connections from scientific literature

  • Identify proteins with similar profiles in published research

  • Generate testable hypotheses based on literature-derived associations

When implementing these computational approaches, researchers should maintain rigorous validation protocols, including experimental verification of key predictions and critical assessment of model limitations. The integration of multiple computational methods provides the most robust framework for function prediction.