Recombinant Saccharomyces cerevisiae Uncharacterized protein YHR214C-D (YHR214C-D)

What is YHR214C-D and what are its fundamental properties?

YHR214C-D is a putative protein of unknown function from Saccharomyces cerevisiae. The protein consists of 97 amino acids in its full-length form . The protein was identified through gene-trapping, microarray-based expression analysis, and genome-wide homology searching . Current experimental evidence suggests subcellular localization to both the nucleus (when tagged with GFP) and the endoplasmic reticulum (when tagged with mCherry) .

The protein's basic properties include:

What experimental evidence supports YHR214C-D's ribosomal association?

YHR214C-D has been identified as a potential ribosome-interacting protein through systematic proteomic screens of yeast ribosomal complexes . Methodologically, researchers should approach this question using multiple complementary techniques:

• Sucrose gradient fractionation followed by immunoblotting to detect co-sedimentation with ribosomal subunits

• Affinity purification of ribosomal proteins followed by mass spectrometry

• EDTA-dependent association tests to determine the nature of the interaction

Such approaches have successfully identified 77 previously uncharacterized proteins as potential ribosome-interacting proteins, with several showing EDTA-dependent cosedimentation with ribosomes . When investigating YHR214C-D's ribosomal association, researchers should analyze the protein's distribution across 40S, 60S, 80S, and polysome fractions while including appropriate controls (e.g., known ribosomal and non-ribosomal proteins).

What expression systems and purification strategies are optimal for studying recombinant YHR214C-D?

For recombinant expression of YHR214C-D, E. coli has been successfully used as an expression host for producing His-tagged full-length protein . The methodological workflow should include:

Expression strategy:

• Clone the YHR214C-D coding sequence into a suitable expression vector with an N-terminal or C-terminal His-tag

• Transform into an E. coli expression strain (BL21(DE3) is commonly used)

• Optimize expression conditions (temperature, inducer concentration, duration)

Purification approach:

• Cell lysis under native or denaturing conditions

• Immobilized metal affinity chromatography (IMAC)

• Size exclusion chromatography for further purification

• Assessment of protein folding via circular dichroism

When designing experiments, researchers should consider testing multiple constructs with different affinity tags (His, GST, MBP) to identify the optimal combination for soluble expression and functional analysis.

What approaches can determine YHR214C-D's interaction network?

To determine YHR214C-D's protein interaction network, researchers should implement a multi-faceted approach:

• Tandem Affinity Purification (TAP):

  • Create strains expressing TAP-tagged YHR214C-D

  • Perform affinity purification followed by mass spectrometry

  • Calculate Purification Abundance Factors (PAFs) and Relative Abundance Factors (RAFs) to quantify interaction strength

  • Validate interactions via reciprocal TAP tagging of identified partners

• Yeast Two-Hybrid Screening:

  • Use YHR214C-D as bait against a yeast genomic library

  • Include appropriate controls to filter out false positives

  • Validate interactions via co-immunoprecipitation

• Proximity-based labeling:

  • Express YHR214C-D fused to a biotin ligase (BioID) or APEX2

  • Identify proximal proteins through streptavidin pulldown and mass spectrometry

The combination of these approaches provides complementary data to build confidence in identified interactions. For example, similar methodologies identified LSM12 interactions with PBP1 and PBP4, and TMA46 interactions with RBG1, a GTPase that interacts with translating ribosomes .

How can researchers assess phenotypes associated with YHR214C-D mutation or deletion?

Phenotypic analysis of YHR214C-D requires systematic approaches to detect potentially subtle effects:

Methodological workflow:

• Generate YHR214C-D deletion strains using standard gene replacement techniques

• Create conditional expression systems for studying essential functions

• Implement a hierarchical phenotyping strategy:

  Primary screens:

  • Growth rate analysis under various conditions

  • Microscopic examination for morphological abnormalities

  • Analysis of basic cellular processes (translation, transcription)

  Secondary screens:

  • Specific assays based on primary screen results or predicted functions

  • Ribosome profiling to assess translation patterns

  • RNA-seq to identify transcriptional changes

Available data suggests YHR214C-D deletion results in the following phenotypes:

These values indicate mild but measurable effects that should be validated through independent experimental approaches.

How does YHR214C-D compare to its paralog YAR069C?

YHR214C-D has a paralog, YAR069C, that arose from a segmental duplication . Researchers investigating functional redundancy should:

• Generate single and double deletion strains (Δyhr214c-d, Δyar069c, and Δyhr214c-d Δyar069c)

• Compare phenotypes across multiple conditions to identify:

  • Shared phenotypes (suggesting redundancy)

  • Unique phenotypes (suggesting divergent functions)

• Perform complementation experiments by expressing each paralog in the deletion background of the other

• Compare protein-protein interaction networks and subcellular localization

The evolutionary relationship between these paralogs can provide insight into functional conservation or divergence. Researchers should analyze:

• Sequence conservation at both protein and nucleotide levels

• Conservation of key structural motifs

• Evolutionary rates (Ka/Ks ratios) as indicators of selection pressure

• Expression patterns across different conditions and growth phases

What experimental design strategies are optimal for characterizing uncharacterized proteins like YHR214C-D?

Characterization of uncharacterized proteins requires careful experimental design. Researchers should implement:

• Multi-omics integration approach:

  • Combine proteomics, transcriptomics, and functional genomics

  • Use network analysis to place YHR214C-D in functional context

  • Implement machine learning to predict functions from disparate data types

• Task-driven experimental design:

  • Define specific hypotheses based on preliminary data

  • Select experimental channels that provide complementary information

  • Optimize data collection to maximize information gain

As demonstrated by the TADRED (TAsk-DRiven Experimental Design) approach, researchers can simultaneously optimize experimental design and train machine learning models to execute user-specified analysis tasks . This allows identification of the most informative experimental conditions while minimizing resource expenditure.

• Evolutionary profiling:

  • Compare YHR214C-D across species to identify conserved features

  • Use comparative genomics to identify co-evolving genes

  • Analyze phylogenetic profiles to infer functional relationships

How can researchers resolve contradictory data regarding YHR214C-D function?

When faced with contradictory data about YHR214C-D function, researchers should:

• Implement systematic validation:

  • Replicate experiments under identical conditions

  • Vary key parameters to identify condition-dependent effects

  • Use orthogonal approaches to test the same hypothesis

• Consider context-dependence:

  • Test function across different genetic backgrounds

  • Examine condition-specific effects (nutrient availability, stress)

  • Investigate cell cycle-dependent functions

• Address technical considerations:

  • Evaluate the impact of different tagging strategies on protein function

  • Compare results from different expression systems

  • Assess the sensitivity and specificity of detection methods

• Implement Bayesian integration:

  • Assign confidence scores to different data sources

  • Update functional hypotheses as new evidence emerges

  • Explicitly model uncertainty in functional assignments

What bioinformatic approaches can predict potential functions of YHR214C-D?

For predicting functions of uncharacterized proteins like YHR214C-D, researchers should employ:

• Sequence-based prediction:

  • Profile-based methods (PSI-BLAST, HMMer)

  • Detection of conserved domains and motifs

  • Secondary structure prediction and disorder analysis

  • Transmembrane topology prediction

• Structure-based prediction:

  • Homology modeling and fold recognition

  • Molecular dynamics simulations

  • Binding site prediction

  • Protein-protein docking

• Network-based approaches:

  • Guilt-by-association analysis

  • Gene neighborhood analysis

  • Co-expression network analysis

  • Functional interaction networks

• Integration of experimental data:

  • Incorporation of localization data (nucleus and ER)

  • Analysis of potential ribosome association

  • Evaluation of phenotypic data from knockout studies

How can yeast models contribute to understanding RNA-mediated processes that might involve YHR214C-D?

Saccharomyces cerevisiae offers significant advantages for studying RNA-mediated processes:

• Methodological strengths:

  • Well-characterized genome and transcriptome

  • Ease of genetic manipulation

  • Economic utility and rapid generation time

  • Highly conserved protein functions across eukaryotes

• Specific approaches for RNA-related functions:

  • RNA immunoprecipitation to identify RNA binding partners

  • CRAC (crosslinking and analysis of cDNAs) for mapping RNA-protein interactions

  • Ribosome profiling to assess translation efficiency

  • RNA-seq to identify transcriptional changes

Given YHR214C-D's potential association with ribosomes and nuclear localization , researchers should investigate its role in:

• Translation regulation

• RNA processing and stability

• Ribosome biogenesis

• Nuclear-cytoplasmic RNA transport

Yeast models have successfully elucidated mechanisms of RNA-mediated processes relevant to human diseases, from aberrant RNA-binding proteins in amyotrophic lateral sclerosis to translation regulation in cancer .