Recombinant Saccharomyces cerevisiae Uncharacterized protein YDL211C (YDL211C)

What is known about the YDL211C gene in Saccharomyces cerevisiae?

YDL211C is an uncharacterized open reading frame (ORF) in the Saccharomyces cerevisiae genome. While its precise function remains to be fully elucidated, the gene is documented in the Saccharomyces Genome Database (SGD) with basic sequence-derived information including length, molecular weight, and isoelectric point . The gene has been subject to both computational prediction and experimental validation approaches in efforts to determine if it encodes a transcription factor, similar to characterization efforts for other uncharacterized yeast genes . Current research suggests it may play a role in regulatory networks, though definitive functional assignments require further experimental validation.

What computational approaches can predict potential functions of YDL211C?

Several computational prediction methods are valuable for generating hypotheses about YDL211C's function:

• Homology-based algorithms like TFpredict can assess the likelihood of YDL211C being a transcription factor based on sequence similarity to known TFs .

• Structural prediction tools such as SWISS-MODEL can generate homology models to infer potential functional domains and oligomeric states .

• GO (Gene Ontology) computational annotations provide predicted functional categories based on sequence features .

• Comparative genomics across fungal species can identify conserved domains suggesting preserved functions.

When applying these methods to YDL211C, researchers should evaluate multiple templates and consider inference scores to existing complexes of similar sequence identity. Integration of UniProt annotations with visualization tools like VMD provides a comprehensive structural prediction framework .

How can I experimentally validate whether YDL211C functions as a transcription factor?

To determine if YDL211C functions as a transcription factor, implement the following experimental validation pipeline:

• Chromatin Immunoprecipitation (ChIP-exo): Tag YDL211C with a myc epitope and perform ChIP-exo to identify potential genome-wide binding sites. Use antibodies specific to the myc tag (e.g., 9E10, Santa Cruz Biotechnology) with Dynabeads Pan Mouse IgG magnetic beads for immunoprecipitation .

• Differential Gene Expression Analysis: Generate a YDL211C deletion strain and perform RNA-seq analysis comparing the mutant to wild-type under various conditions. Consider using the Wald statistical test for differential expression analysis, with adjusted p-values < 0.05 and log2 fold-change ≥ 2.0 as thresholds for significance .

• DNA-Binding Domain Validation: Employ electrophoretic mobility shift assays (EMSAs) to verify direct DNA binding to identified target sequences.

• Transcriptional Reporter Assays: Clone potential target promoters into reporter constructs and measure activity with and without YDL211C expression.

For the ChIP-exo procedure specifically, grow cells to OD600 = 0.5 in glucose minimal medium, fix with 1% formaldehyde for 25 minutes at room temperature, quench with 2.5M glycine, wash with ice-cold TBS, and lyse with Ready-lyse lysozyme solution before sonication to generate 300-500bp DNA fragments .

What strategies should I use to create YDL211C mutant strains in S. cerevisiae?

Creating YDL211C mutant strains requires systematic genetic engineering approaches:

For Gene Deletion:

• Use PCR-based gene disruption with selectable markers (e.g., kanMX4 cassette) to replace the YDL211C coding sequence .

• Consider the BY4741 background strain (MATa his3-Δ1 leu2Δ0 met15Δ0) as a suitable genetic background, similar to approaches used for other yeast genes .

• Verify deletions through PCR confirmation and phenotypic analysis.

For Overexpression:

• Clone the YDL211C ORF into a multicopy vector like p426ADH (ATCC 87377) which contains the β-lactamase gene, URA3 selection marker, and uses the strong constitutive ADH1 promoter and CYC1 terminator .

• Transform the construct into appropriate laboratory strains such as CEN.PK 113-11C (MATa his3-Δ1 ura3-52) .

• Select transformants on media lacking uracil and confirm expression levels via RT-qPCR or Western blotting.

For integrative approaches, linearize plasmids at unique restriction sites within selection markers (e.g., PstI in the HIS3 locus) to promote homologous recombination at specific genomic locations .

How can I measure the expression levels of YDL211C under different growth conditions?

To quantify YDL211C expression across varying conditions:

• RT-qPCR Analysis: Design primers specific to YDL211C and reference genes (e.g., ACT1, TDH3). Extract RNA using acid phenol methods, synthesize cDNA, and perform qPCR with SYBR Green chemistry. Calculate relative expression using the 2-ΔΔCt method.

• RNA-Seq Profiling: Perform strand-specific RNA-seq on cells grown under different conditions. Process data through standard pipelines including quality filtering, alignment to the S. cerevisiae genome, and differential expression analysis using DESeq2 or similar tools .

• Proteomics Approach: Use targeted mass spectrometry with isotope-labeled reference peptides to quantify YDL211C protein levels.

• Reporter Constructs: Fuse the YDL211C promoter to fluorescent protein genes and measure fluorescence intensity under varying conditions.

For chemostat-based expression studies, maintain cultures at controlled dilution rates (e.g., 0.1 h-1) with defined mineral media supplemented with appropriate carbon sources (e.g., 10 g glucose/liter, 10 g xylose/liter) . Monitor expression across different growth phases and in response to environmental stressors to identify regulatory patterns.

What techniques can identify potential protein interaction partners of YDL211C?

Identifying YDL211C interaction partners requires multiple complementary approaches:

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

  • Tag YDL211C with epitopes (FLAG, HA, or TAP tag)

  • Perform immunoprecipitation under gentle conditions

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

  • Filter against control purifications to remove non-specific binders

• Yeast Two-Hybrid Screening:

  • Clone YDL211C as bait in appropriate Y2H vectors

  • Screen against genomic or ORFeome prey libraries

  • Validate positive interactions with targeted tests and orthogonal methods

• Proximity-Dependent Biotin Identification (BioID):

  • Fuse YDL211C to a promiscuous biotin ligase (BirA*)

  • Express in yeast and allow biotinylation of proximal proteins

  • Purify biotinylated proteins and identify via mass spectrometry

• Co-Immunoprecipitation with Candidate Partners:

  • Based on computational predictions of functional partners

  • Create epitope-tagged versions of both YDL211C and candidates

  • Perform reciprocal co-IPs with appropriate controls

When analyzing results, implement stringent statistical criteria and validate high-confidence interactions through multiple methods to minimize false positives. Consider using SAINT (Significance Analysis of INTeractome) scoring for probabilistic assessment of true interactions.

How does deletion of YDL211C affect the transcriptional profile of S. cerevisiae?

Deletion of YDL211C can be analyzed through comprehensive transcriptomic profiling:

• Construct isogenic wild-type and YDL211C deletion strains using methods described above.

• Culture both strains under identical conditions (e.g., minimal media with glucose as carbon source, harvested at mid-log phase).

• Perform RNA-seq analysis with biological triplicates to ensure statistical robustness.

• Process sequencing data through standard bioinformatic pipelines:

  • Quality filtering and adapter trimming

  • Alignment to S. cerevisiae reference genome

  • Quantification of transcript abundance

  • Differential expression analysis using DESeq2 or similar tools

  • Multiple testing correction using Benjamini-Hochberg procedure

• Consider genes with log2 fold-change ≥ 2.0 and adjusted p-value < 0.05 as significantly differentially expressed .

• Perform Gene Ontology enrichment analysis to identify biological processes affected by YDL211C deletion.

• Validate key findings using RT-qPCR on selected differentially expressed genes.

Based on approaches used for other uncharacterized transcription factors, focus analysis on genes that may be involved in metabolic pathways, stress responses, or cell cycle regulation, as these are common targets for yeast transcriptional regulators .

What phenotypic changes can be observed in YDL211C mutant strains?

For comprehensive phenotypic characterization of YDL211C mutants:

• Growth Analysis:

  • Measure growth rates in various media (rich, minimal, different carbon sources)

  • Perform growth curve analysis using automated plate readers

  • Calculate specific growth rates (μ) for each condition

• Stress Response Profiling:

  • Test sensitivity to oxidative stress (H₂O₂, menadione)

  • Evaluate osmotic stress tolerance (NaCl, sorbitol)

  • Assess temperature sensitivity (heat shock, cold shock)

  • Examine cell wall integrity (Congo Red, Calcofluor White)

• Metabolic Phenotyping:

  • Analyze carbon source utilization profiles

  • Measure key metabolite production using HPLC

  • Conduct 13C-flux analysis to identify altered metabolic pathways

• Microscopic Analysis:

  • Examine cell morphology and cell cycle progression

  • Investigate subcellular structures using fluorescent markers

  • Monitor protein localization using tagged constructs

Document both qualitative observations and quantitative measurements as a comprehensive phenotype annotation, specifying the observable trait, qualifier (e.g., "abnormal"), mutant type, strain background, and reference . Classify annotations as either classical genetics or high-throughput approaches, consistent with SGD annotation standards.

Could YDL211C play a role in xylose metabolism similar to other transcriptional regulators in S. cerevisiae?

The potential role of YDL211C in xylose metabolism can be systematically investigated:

• Comparative Expression Analysis:

  • Culture wild-type S. cerevisiae on different carbon sources including glucose, xylose, and mixed substrates

  • Measure YDL211C expression levels across these conditions

  • Compare expression patterns to known xylose metabolism regulators

• Functional Testing in Recombinant Xylose-Utilizing Strains:

  • Introduce YDL211C deletion or overexpression into recombinant xylose-utilizing S. cerevisiae strains (similar to TMB 3399 and TMB 3400)

  • Evaluate changes in xylose consumption rates, growth rates, and byproduct formation

  • Measure activities of key xylose metabolism enzymes including xylose reductase (XR), xylitol dehydrogenase (XDH), and xylulokinase (XK)

• Transcriptional Impact Assessment:

  • Perform RNA-seq on YDL211C mutants grown on xylose

  • Focus analysis on pentose phosphate pathway genes (TKL1, TAL1, etc.) and xylose metabolism genes (XKS1)

  • Look for altered expression of other transcriptional regulators involved in carbon metabolism

Recent studies on improved xylose-utilizing S. cerevisiae strains have identified several transcriptional regulators affecting xylose metabolism, including those encoded by YCR020C, YBR083W, and YPR199C . Using similar experimental approaches to those applied for these regulators would be appropriate for investigating YDL211C's potential role.

What structural features can be predicted for the YDL211C protein?

Structural prediction for YDL211C can employ multiple computational approaches:

• Homology Modeling:

  • Implement the SWISS-MODEL pipeline to generate structural models

  • Evaluate multiple templates based on sequence identity and coverage

  • Assess the quality of models using QMEAN and MolProbity scores

  • Infer oligomeric state based on interface conservation scores to existing complexes

• Domain Architecture Analysis:

  • Identify conserved domains using InterPro, Pfam, and SMART databases

  • Predict DNA-binding domains, protein interaction motifs, and regulatory regions

  • Annotate the structure using UniProt information

• Visualization and Analysis:

  • Use Visual Molecular Dynamics (VMD) or PyMOL for structural visualization

  • Predict potential DNA-binding interfaces through electrostatic surface mapping

  • Identify potential post-translational modification sites

• Molecular Dynamics Simulations:

  • Perform molecular dynamics simulations to assess structural stability

  • Identify flexible regions and potential conformational changes

  • Evaluate ligand binding potential through virtual docking

The structural analysis should focus on identifying features common to transcription factors, such as helix-turn-helix motifs, zinc finger domains, or leucine zippers, which would support a regulatory function for YDL211C.

How conserved is YDL211C across different yeast species and what does this suggest about its function?

A comprehensive evolutionary analysis of YDL211C includes:

• Sequence Conservation Analysis:

  • Perform BLAST searches against fungal genomes to identify orthologs

  • Create multiple sequence alignments using MUSCLE or MAFFT

  • Calculate conservation scores for individual residues

  • Identify highly conserved regions likely important for function

• Phylogenetic Profiling:

  • Construct phylogenetic trees to visualize evolutionary relationships

  • Map the presence/absence of YDL211C orthologs across fungal lineages

  • Correlate conservation patterns with known phenotypic traits

• Synteny Analysis:

  • Examine genomic context conservation across related species

  • Identify consistently co-localized genes that may suggest functional relationships

  • Analyze promoter regions for conserved regulatory elements

• Functional Prediction Based on Conservation:

  • Apply the "guilt by association" principle to predict function based on:

    • Conserved co-expression patterns

    • Presence in similar metabolic or regulatory pathways

    • Conservation of protein-protein interaction networks

Higher conservation across species generally suggests functional importance. If YDL211C shows strong conservation in specific lineages adapted to particular environmental niches, this may provide clues about its specialized functions in those conditions.

How can high-throughput technologies be applied to elucidate YDL211C function?

Advanced high-throughput approaches for YDL211C functional characterization include:

• Multiplexed ChIP-exo Analysis:

  • Tag YDL211C with a myc epitope

  • Perform ChIP with specific antibodies against the myc tag

  • Ligate unique barcoded adapters to each sample

  • Pool samples for remaining library preparation steps

  • Sequence and demultiplex based on barcodes

  • Map binding sites genome-wide at high resolution

• Systematic Genetic Interaction Mapping:

  • Create YDL211C deletion in query strain background

  • Cross with genome-wide deletion collection (or hypomorphic essential gene collection)

  • Score genetic interactions based on colony size/growth rate

  • Identify functional relationships through clustering of genetic interaction profiles

• Metabolomics Profiling:

  • Compare metabolite profiles between wild-type and YDL211C mutants

  • Use LC-MS/MS or GC-MS for comprehensive metabolite detection

  • Analyze data using multivariate statistical methods

  • Identify significantly altered metabolic pathways

• CRISPRi/CRISPRa Approaches:

  • Implement CRISPR interference to repress YDL211C expression

  • Use CRISPR activation to enhance expression

  • Create titratable expression systems to study dosage effects

  • Combine with reporters to monitor transcriptional outcomes

For multiplexed ChIP-exo specifically, formaldehyde crosslinking should be performed for 25 minutes at room temperature, followed by glycine quenching (2.5M) for 5 minutes, with subsequent washing steps using ice-cold TBS and cell lysis with lysozyme and sonication to generate 300-500bp DNA fragments .

What regulatory networks might YDL211C participate in, and how can we map these interactions?

Mapping YDL211C's potential regulatory network requires integrated approaches:

• Integrative Network Reconstruction:

  • Combine ChIP-exo binding data with RNA-seq differential expression data

  • Identify direct targets (genes with both binding evidence and expression changes)

  • Map relationships to known transcriptional regulators

  • Construct network models using Cytoscape or similar tools

• Perturbation-Based Network Mapping:

  • Create combinatorial deletion/overexpression strains with YDL211C and other regulators

  • Perform RNA-seq under multiple conditions

  • Apply network inference algorithms (ARACNE, CLR, GENIE3)

  • Validate key network connections experimentally

• Temporal Network Dynamics:

  • Implement time-course experiments after environmental shifts

  • Use mathematical modeling to infer causal relationships

  • Identify feed-forward and feedback regulatory motifs

  • Characterize network robustness through perturbation analysis

• Single-Cell Approaches:

  • Perform single-cell RNA-seq to identify cell-to-cell variability in network states

  • Use reporter constructs to monitor real-time dynamics

  • Implement optogenetic control of YDL211C to probe network responses

When analyzing network models, prioritize validation of hub connections and unexpected regulatory relationships that might reveal novel biological insights.

What are the implications of YDL211C for understanding evolution of transcriptional regulation in fungi?

The evolutionary significance of YDL211C for transcriptional regulation can be investigated through:

• Comparative Functional Genomics:

  • Complement YDL211C deletion with orthologs from diverse fungal species

  • Test functional conservation through phenotypic rescue experiments

  • Compare binding specificity and regulatory targets across species

  • Map evolutionary trajectories of regulatory network rewiring

• Ancestral Sequence Reconstruction:

  • Infer ancestral sequences of YDL211C at key evolutionary nodes

  • Synthesize and characterize these reconstructed proteins

  • Identify critical mutations that altered function during evolution

  • Test evolutionary hypotheses through directed mutagenesis

• Cis-Regulatory Evolution Analysis:

  • Compare YDL211C binding sites across species

  • Identify conserved and diverged regulatory elements

  • Characterize the co-evolution of transcription factors and their binding sites

  • Map the emergence of new regulatory connections

• Fitness Landscape Mapping:

  • Create libraries of YDL211C variants through random or directed mutagenesis

  • Assess fitness effects under different selection pressures

  • Identify evolutionary constraints and adaptive trajectories

  • Model the evolution of transcriptional networks

This research direction connects YDL211C characterization to broader questions in evolutionary biology, potentially revealing general principles of regulatory network evolution in eukaryotes.

How can contradictory data about YDL211C function be reconciled?

Resolving contradictory findings about YDL211C requires systematic approaches:

• Strain Background Analysis:

  • Test YDL211C function across multiple genetic backgrounds (lab strains vs. wild isolates)

  • Document strain-specific effects that might explain contradictory results

  • Consider genetic interactions unique to specific backgrounds

• Condition-Dependent Functionality:

  • Systematically vary experimental conditions (media composition, temperature, growth phase)

  • Map condition-specific roles that could explain seemingly contradictory data

  • Identify environmental triggers for different functional states

• Methodological Reconciliation:

  • Compare experimental techniques used in contradictory studies

  • Evaluate sensitivity, specificity, and limitations of each method

  • Implement orthogonal approaches to validate key findings

  • Consider technical artifacts and appropriate controls

• Computational Integration:

  • Apply Bayesian integration of multiple data types

  • Weight evidence based on methodological rigor

  • Develop testable hypotheses to resolve contradictions

  • Implement meta-analysis approaches when appropriate

When presented with contradictory findings, prioritize designing critical experiments that can directly test competing hypotheses rather than simply accumulating more descriptive data.

What are the optimal conditions for studying YDL211C activity in vitro?

For optimal in vitro characterization of YDL211C:

• Protein Expression and Purification:

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

  • Optimize with various solubility tags (MBP, SUMO, GST)

  • Implement multi-step purification protocols (affinity, ion exchange, size exclusion)

  • Verify protein integrity by mass spectrometry and activity assays

• Buffer Optimization:

  • Systematically test buffer conditions:

    • pH range (typically 6.5-8.0 for yeast proteins)

    • Salt concentration (50-500 mM NaCl)

    • Reducing agents (DTT, β-mercaptoethanol)

    • Stabilizing additives (glycerol, trehalose)

  • Employ thermal shift assays to identify stabilizing conditions

• DNA-Binding Activity Assays:

  • For electrophoretic mobility shift assays (EMSAs):

    • Use native PAGE (6-8%) in 0.5× TBE buffer

    • Maintain consistent temperature (4°C)

    • Include poly(dI-dC) as non-specific competitor

    • Use fluorescently labeled or radioisotope-labeled DNA probes

  • For microscale thermophoresis or bio-layer interferometry:

    • Optimize protein:DNA ratios

    • Control for non-specific binding

    • Include appropriate positive controls

• Co-factor Requirements:

  • Test potential co-factors:

    • Divalent metal ions (Mg²⁺, Zn²⁺, Mn²⁺)

    • Small molecule ligands

    • Protein partners

  • Monitor activity changes with co-factor titrations

Develop a standardized activity assay that provides quantitative readouts for comparing wild-type and mutant variants or for studying condition-dependent activities.

What technological limitations currently hinder full characterization of YDL211C, and how might they be addressed?

Current technological challenges and potential solutions include:

• Protein Structural Analysis Limitations:

  • Challenge: Difficulty obtaining crystal structures of full-length transcription factors

  • Solutions:

    • Implement cryo-electron microscopy for flexible proteins

    • Use integrative structural biology combining multiple data types

    • Develop improved computational prediction methods

    • Apply hydrogen-deuterium exchange mass spectrometry for conformational dynamics

• Transient Interaction Detection:

  • Challenge: Capturing weak or transient protein-DNA and protein-protein interactions

  • Solutions:

    • Implement crosslinking mass spectrometry (XL-MS)

    • Apply proximity labeling approaches (BioID, APEX)

    • Develop single-molecule techniques to observe individual binding events

    • Use microfluidic approaches for high-throughput interaction screens

• Context-Dependent Function:

  • Challenge: Replicating native cellular environment in vitro

  • Solutions:

    • Create reconstituted chromatin templates for in vitro studies

    • Develop improved cell extract systems

    • Implement advanced live-cell imaging with minimal perturbation

    • Apply optogenetic approaches for precise temporal control

• Computational Prediction Limitations:

  • Challenge: Accuracy of ab initio functional prediction

  • Solutions:

    • Develop improved machine learning algorithms trained on diverse data types

    • Implement physics-based modeling of protein-DNA interactions

    • Create integration frameworks for heterogeneous data sources

    • Apply evolutionary coupling analysis for co-evolving partners

By addressing these technological limitations, researchers can develop more comprehensive insights into YDL211C function and regulation.