Recombinant Saccharomyces cerevisiae Uncharacterized protein YAL065C (YAL065C)

What is known about the basic structure and characteristics of the YAL065C protein?

YAL065C is an uncharacterized protein in Saccharomyces cerevisiae (strain ATCC 204508/S288c), consisting of 128 amino acids. The protein sequence is: MNSATSETTTNTGAAETTTSTGAAETKTVVTSSISRFNHAETQTASATDVIGHSSSVVSVSETGNTKSLITSGLSTMSQQPRSTPASSIIGSSTASLEISTYVGIANGLLTNNGISVFISTVLLAIVW . It shows sequence similarity to FLO1 and other flocculins, suggesting a possible role in cell adhesion or flocculation processes . The protein's UniProt accession number is O13511, and it's encoded by the YAL065C gene located on chromosome I .

What are the recommended methods for expressing and purifying recombinant YAL065C protein?

Methodological approach:

• Expression system selection: Due to the protein's yeast origin, either a homologous (S. cerevisiae) or heterologous (E. coli, P. pastoris) expression system can be used. For native post-translational modifications, a yeast expression system is preferable.

• Vector design: Include appropriate tags (His, GST, or MBP) to facilitate purification. Based on available products, tags are typically determined during the production process .

• Purification protocol:

  • Use affinity chromatography based on the included tag

  • Follow with size exclusion chromatography

  • Store in Tris-based buffer with 50% glycerol at -20°C or -80°C for extended storage

• Quality control: Verify purity using SDS-PAGE and Western blotting; confirm identity via mass spectrometry.

Storage recommendations: Store at -20°C; for extended storage, conserve at -20°C or -80°C. Avoid repeated freezing and thawing. Working aliquots can be stored at 4°C for up to one week .

How can I identify potential functional domains in the uncharacterized YAL065C protein?

Methodological approach:

• Sequence homology analysis:

  • Begin with BLAST searches against known protein databases

  • Specific focus on comparison with flocculin family proteins, as YAL065C shows sequence similarity to FLO1

  • Utilize specialized fungal databases and BLASTP vs. fungi

• Structural prediction tools:

  • Apply secondary structure prediction (e.g., JPred, PSIPRED)

  • Use protein domain prediction tools (e.g., InterPro, Pfam)

  • Employ tertiary structure prediction using AlphaFold or RoseTTAFold

• Conserved motif analysis:

  • Identify conserved regions by multiple sequence alignment with related proteins

  • Focus on regions showing similarity to characterized flocculins

  • Based on flocculin similarities, analyze potential membrane-spanning domains

• Integrative approach:

  • Combine predictions with experimental data when available

  • Validate computational predictions through targeted mutagenesis experiments

What is the appropriate experimental design for RNA-Seq analysis to study YAL065C expression under different conditions?

Methodological approach:

• Replication strategy:

  • Include at least 3 biological replicates per condition for cell line studies

  • Technical replicates are unnecessary as technical variation is much lower than biological variation

  • For cell lines, biological replicates should be from different passages or independently grown cultures

• Sample preparation protocols:

  • Randomize RNA extraction batches to prevent confounding with variables of interest

  • NEVER extract RNA for all treated samples on one day and controls on another day, as this creates unfixable batch effects

  • If pooling is necessary, ensure each pool consists of distinct samples and maintain proper replication at the pool level

• Sequencing considerations:

  • Prepare barcoded libraries and pool all libraries before splitting across sequencing lanes to mitigate lane-to-lane variability

  • Ensure adequate sequencing depth based on expected expression levels

  • For detecting subtle expression changes, increase sample size rather than sequencing depth

• Statistical power:

  • Power and required sample size depends on:

    • Amount of variability (human >> mouse >> cell line)

    • Size of effect to be detected

    • Analysis method used

  • Resampling of pilot data gives the best estimates of power compared to theoretical approaches

How should I design experiments to study RNA interactions with YAL065C protein?

Methodological approach:

• In vitro interaction studies:

  • Begin with RNA Electrophoretic Mobility Shift Assays (EMSA) using purified recombinant YAL065C protein

  • Use RNA transcripts predicted to interact based on catRAPID prediction scores (e.g., NSR1 with score 14.14)

  • Include positive and negative controls for binding specificity

• Target RNA selection:

  • Based on prediction data from RNAct, focus on top candidates:

    RNA Transcript	Prediction Score
    NSR1 (YGR159C)	14.14
    YML009W-B	13.97
    NOP1 (YDL014W)	13.47
    MDJ1 (YFL016C)	12.83
    YKL036C	12.13

• Cross-linking methods:

  • Consider in vivo UV cross-linking followed by immunoprecipitation (CLIP-seq)

  • Alternatively, use RNA immunoprecipitation (RIP) followed by sequencing

• Data analysis:

  • Compare experimental results with computational predictions from RNAct

  • Validate findings using mutational analysis of key binding sites

What are the best methods to study the effects of genetic variations in YAL065C across different yeast strains?

Methodological approach:

• Strain selection and genome analysis:

  • Include diverse strain backgrounds (laboratory, wine, beer, wild isolates)

  • Identify natural variants through whole genome sequencing

  • Pay particular attention to strains showing different flocculation phenotypes

• Variability quantification:

  • Use Normalized Difference (NormDiff) Score to quantify variable binding regions

  • Calculate the variance of NormDiff scores to identify highly variable positions

  • Consider top 2.5% of variable positions (variance > 10) as the focus for further study

• QTL mapping approach:

  • Utilize single marker regression with binding traits and genetic markers

  • Identify cis and trans markers affecting YAL065C expression or function

  • Focus on markers within 10kb of YAL065C for cis-effect analysis

• Phenotypic correlation analysis:

  • Correlate genetic variations with phenotypic differences

  • Consider admixture analysis in cases of recombination between different strain lineages

  • Analyze using local phylogenetic reconstruction in 10kb windows of computationally phased genotypes

How can I investigate the potential role of YAL065C in flocculin-related processes?

Methodological approach:

• Comparative analysis with characterized flocculins:

  • Perform detailed sequence and structural comparisons with known flocculins like FLO1

  • Identify shared domains and potential functional motifs

  • Analyze predicted protein topology, especially transmembrane regions (YAL065C contains "TVLLAIVW" at C-terminus which may be membrane-associated)

• Gene knockout and overexpression studies:

  • Generate YAL065C deletion strains using CRISPR-Cas9 or homologous recombination

  • Create controlled overexpression strains using inducible promoters

  • Assess changes in flocculation, cell adhesion, and biofilm formation

• Microscopy and phenotypic assays:

  • Use fluorescence microscopy with tagged YAL065C to determine subcellular localization

  • Perform flocculation assays under various environmental conditions (pH, ethanol, sugar concentration)

  • Investigate cell-cell and cell-surface adhesion properties

• Interaction studies:

  • Examine interactions with predicted functional partners:

    • YHR212W-A (score: 0.988)

    • YHR213W (score: 0.979)

    • YAR047C (score: 0.858)

  • Use pull-down assays, co-immunoprecipitation, or yeast two-hybrid screens

What approaches can identify regulatory networks and transcription factors affecting YAL065C expression?

Methodological approach:

• Promoter analysis:

  • Analyze the YAL065C promoter region for transcription factor binding sites

  • Perform sequence analysis of the region upstream of YAL065C for motif identification

  • Use YEASTRACT database to identify potential transcription factors that might regulate YAL065C

• Chromatin immunoprecipitation (ChIP) strategies:

  • Identify transcription factors bound to the YAL065C promoter using ChIP-seq

  • Analyze binding variability across different environmental conditions

  • Apply methods similar to those used in the study of genomic binding differences in yeast strains

• Environmental response profiling:

  • Monitor YAL065C expression under various stress conditions (temperature, osmotic stress, nutrient starvation)

  • Identify conditions that significantly alter expression levels

  • Correlate expression changes with activity of specific transcription factors

• Network analysis:

  • Construct co-expression networks using existing transcriptomic data

  • Identify gene clusters that correlate with YAL065C expression

  • Use this information to place YAL065C within broader regulatory pathways

How can I reconcile contradictory data regarding YAL065C function or expression?

Methodological approach:

• Systematic metadata analysis:

  • When facing contradictory results, first examine differences in:

    • Strain backgrounds used (laboratory vs. wild strains)

    • Growth conditions and media composition

    • Experimental procedures and analysis methods

  • Document all methodological differences between contradictory studies

• Reproducibility assessment:

  • Replicate key experiments using standardized protocols

  • Ensure adequate statistical power (minimum 3 replicates for cell cultures)

  • Control for batch effects by randomizing sample processing

• Integrative analysis techniques:

  • Apply meta-analysis methods to integrate results from multiple studies

  • Use Bayesian approaches to weight evidence based on study quality and sample size

  • Consider strain-specific effects that might explain apparent contradictions

• Resolution strategies:

  • For database inconsistencies (as seen in org.Sc.sgd.db package ):

    • Cross-reference with authoritative sources like SGD (Saccharomyces Genome Database)

    • Contact database maintainers to report inconsistencies

    • Document database versions in publications to ensure reproducibility

How should I design a research project to functionally characterize the uncharacterized YAL065C protein?

Methodological approach:

• Research question formulation:

  • Develop focused research questions that are:

    • Researchable using primary and/or secondary sources

    • Feasible to answer within practical constraints

    • Specific enough to answer thoroughly

    • Complex enough to develop the answer over the space of a paper

    • Relevant to your field of study

• Experimental strategy planning:

  • Begin with computational characterization:

    • Sequence analysis and homology prediction

    • Structural modeling and domain prediction

    • Evolutionary conservation analysis

  • Progress to biochemical characterization:

    • Expression and purification optimization

    • Basic biochemical properties (oligomerization, stability)

    • Interaction partner identification (proteins, RNA, DNA)

  • Advance to functional studies:

    • Gene knockout/knockdown phenotypic analysis

    • Localization studies

    • Response to environmental stressors

• Timeline and resource planning:

  • Prioritize experiments based on logical dependencies

  • Plan for iterative refinement of hypotheses

  • Include contingency plans for unexpected results

• Collaboration strategy:

  • Identify potential collaborators with complementary expertise

  • Plan for data sharing and integrated analysis

What bioinformatic pipelines are recommended for analyzing RNA-seq data to study YAL065C expression patterns?

Methodological approach:

• Preprocessing and quality control:

  • Use FastQC for initial quality assessment

  • Apply Trimmomatic or similar tools for adapter removal and quality trimming

  • Assess rRNA contamination and filter if necessary

• Read mapping and quantification:

  • Map reads to S. cerevisiae reference genome using STAR or HISAT2

  • Quantify expression using featureCounts or salmon

  • For strain-specific analysis, consider using strain-specific reference genomes

• Differential expression analysis:

  • Popular Bioconductor packages for differential expression include:

    • DESeq2, based on negative binomial model fitted to gene counts

    • edgeR, based on negative binomial model fitted to gene counts

    • limma-voom, based on weighted linear models fitted to log-transformed counts per million reads

  • limma-voom is recommended as it better controls the false discovery rate at the nominal rate

• Visualization and interpretation:

  • Create MA plots to visualize differential expression patterns

  • Use volcano plots to highlight significantly changed genes

  • Perform pathway analysis and gene set enrichment analysis to contextualize results

  • Compare YAL065C expression patterns with known flocculins and functionally related genes

What strategies can help integrate proteomics, transcriptomics, and genomics data for comprehensive analysis of YAL065C?

Methodological approach:

• Data collection and standardization:

  • Ensure consistent experimental conditions across different omics platforms

  • Use the same strain backgrounds for all experiments

  • Standardize data formats and normalization procedures

• Multi-omics integration techniques:

  • Correlation networks: Identify relationships between transcript levels, protein abundance, and genomic variations

  • Pathway mapping: Overlay multi-omics data on known biological pathways

  • Machine learning approaches: Use supervised and unsupervised learning to identify patterns across datasets

• Functional validation of integrated findings:

  • Design targeted experiments to test hypotheses generated from integrated analysis

  • Prioritize validation experiments based on consistency across multiple data types

  • Use CRISPR-Cas9 genome editing to validate predicted functional relationships

• Visualization and analysis tools:

  • Use specialized tools for multi-omics data integration:

    • Mixomics for multivariate analysis

    • Cytoscape for network visualization

    • Galaxy platform for accessible workflow creation

  • Develop custom pipelines for S. cerevisiae-specific analysis when necessary

How conserved is YAL065C across different yeast species and what does this suggest about its function?

Methodological approach:

• Ortholog identification:

  • Perform BLASTP searches against fungal genomes

  • Use specialized databases like FungiDB or SGD for fungal-specific searches

  • Apply reciprocal best hit approaches to confirm true orthologs

• Evolutionary analysis:

  • Conduct multiple sequence alignment of identified orthologs

  • Generate phylogenetic trees to understand evolutionary relationships

  • Calculate selection metrics (dN/dS ratios) to identify conserved regions under purifying selection

• Functional inference from conservation patterns:

  • Highly conserved domains suggest fundamental functions

  • Rapidly evolving regions may indicate species-specific adaptations

  • Pay special attention to conservation patterns in regions similar to known flocculins

• Structural conservation analysis:

  • Compare predicted structural features across species

  • Identify conserved structural motifs that may have functional importance

  • Use this information to guide site-directed mutagenesis experiments

How can I analyze the impact of genetic variation in YAL065C on phenotypic differences between yeast strains?

Methodological approach:

• Strain collection and genotyping:

  • Assemble diverse S. cerevisiae strains from different ecological niches

  • Sequence YAL065C and surrounding genomic regions

  • Identify SNPs, indels, and structural variations

• Phenotypic characterization:

  • Assess flocculation, adhesion, and biofilm formation across strains

  • Measure growth rates under various environmental conditions

  • Quantify stress responses (temperature, ethanol, osmotic stress)

• Genotype-phenotype correlation:

  • Perform QTL mapping in segregating populations

  • Use methods similar to those described in the study of binding variation in yeast

  • Apply single marker regression with binding traits and markers

• Functional validation:

  • Use CRISPR-Cas9 to introduce specific variants into a reference strain

  • Perform allele swapping experiments between strains

  • Measure the phenotypic impact of specific variations to confirm causality

What are the challenges in working with uncharacterized proteins like YAL065C and how can they be addressed?

Methodological approach:

• Expression and purification challenges:

  • Test multiple expression systems (E. coli, yeast, insect cells)

  • Optimize codon usage for expression host

  • Try various solubility and purification tags (His, GST, MBP, SUMO)

  • Consider expression of domains rather than full-length protein if expression is problematic

• Functional prediction limitations:

  • Combine multiple computational approaches:

    • Homology-based predictions

    • De novo structure prediction

    • Gene neighborhood analysis

    • Co-expression network analysis

  • Validate predictions with targeted experiments

• Phenotypic analysis challenges:

  • Use sensitive assays that can detect subtle phenotypes

  • Test multiple environmental conditions to find those where the protein function is important

  • Consider redundancy with related proteins that may mask knockout phenotypes

  • Utilize overexpression to potentially amplify functional effects

• Publication and data sharing:

  • Document negative results to help other researchers

  • Contribute data to relevant databases even for uncharacterized proteins

  • Consider preprints to rapidly share findings about previously uncharacterized proteins

What statistical considerations are important when analyzing experiments with YAL065C?

Methodological approach:

• Power analysis and sample size determination:

  • Power depends on:

    • Amount of variability (human >> mouse >> cell line)

    • Size of effect to be detected

    • Analysis method used

  • Resampling of pilot data provides the best estimates of power

  • For cell line studies, a minimum of 3 replicates per group is typically required

• Multiple testing correction:

  • When performing genome-wide analyses:

    • Control family-wise error rate (Bonferroni) or false discovery rate (Benjamini-Hochberg)

    • Consider local false discovery rate methods for specific applications

    • Report both raw and adjusted p-values

• Batch effect management:

  • Randomize RNA extraction and sample processing

  • Include batch as a covariate in statistical models

  • NEVER process all samples of one condition on one day and another condition on a different day

• Specialized statistical methods:

  • For RNA-seq data, use methods that properly model count data:

    • DESeq2, edgeR, or limma-voom are recommended packages

    • limma-voom better controls the false discovery rate at the nominal rate

  • For QTL mapping, use appropriate methods for handling linkage disequilibrium

What are the key databases and resources for studying YAL065C?

Resource catalog:

• General yeast resources:

  • Saccharomyces Genome Database (SGD): https://www.yeastgenome.org/ - Comprehensive resource with gene information, phenotypes, and literature

  • YEASTRACT: https://yeastract.com/ - Transcription regulatory associations and tools for regulatory network analysis

• Protein-specific resources:

  • UniProt (O13511): Comprehensive protein information including sequence, domains, and functional annotations

  • STRING database: Protein-protein interaction network showing YAL065C interactions with confidence scores

  • RNAct: Protein-RNA interaction predictions including scores for YAL065C interactions with various RNAs

• Expression databases:

  • SPELL (Serial Pattern of Expression Levels Locator): Tool for identifying genes with similar expression profiles

  • Gene Expression Omnibus (GEO): Repository of expression data including yeast datasets

• Bioinformatic tools and packages:

  • Bioconductor: R packages for analyzing genomic data (e.g., org.Sc.sgd.db, DESeq2, edgeR, limma)

  • SGD BLAST tools: Specialized BLAST implementations for yeast sequences