Recombinant Saccharomyces cerevisiae Uncharacterized plasma membrane protein YNL194C (YNL194C)

What is YNL194C and which protein family does it belong to?

YNL194C is an integral membrane protein in Saccharomyces cerevisiae that belongs to the Sur7 family of tetraspanners found in membrane compartment occupied by Can1 (MCC). This protein family includes Sur7 and the paralogous proteins Fmp45, Pun1, and YNL194C . As indicated in the genomic databases, YNL194C has a paralog, FMP45, that arose from whole genome duplication . The protein is required for sporulation and plasma membrane sphingolipid content, showing structural and functional similarity to SUR7 .

Research has demonstrated that YNL194C is part of the membrane compartment C, which contains a distinct set of proteins that are stably segregated within the yeast plasma membrane . This compartmentalization appears to serve specific biological functions related to membrane organization and protein turnover regulation, though the exact molecular mechanisms remain under investigation.

What experimental approaches are most effective for studying YNL194C localization and dynamics?

To effectively study YNL194C localization and dynamics, researchers should employ a combination of complementary techniques:

• Fluorescent protein tagging: Creating YNL194-GFP or YNL194-9myc fusion constructs has proven effective for visualizing the protein's distribution. Movie analysis of Ynl194-GFP has revealed that YNL194p patches, like Sur7p patches, are stationary and stable .

• Co-localization studies: Comparing Sur7-GFP and Ynl194-9myc patches through immunofluorescence microscopy provides insights into their spatial relationships . This approach can be extended to study relationships with other membrane proteins.

• Time-lapse microscopy: This method is essential for analyzing the dynamic behavior of YNL194C patches and determining their stability over time.

• Membrane fractionation: Biochemical isolation of membrane domains can complement microscopy approaches and provide quantitative data on protein distribution.

When conducting these experiments, researchers should carefully consider tag selection, as different tags may affect protein function or localization. For optimal results, both C-terminal and N-terminal tagging strategies should be tested to determine which preserves native protein behavior.

How is YNL194C expression regulated under different growth conditions?

YNL194C expression appears to be regulated by multiple environmental factors, as demonstrated by several independent studies:

• Response to DNA damage: The GFP-fusion protein is induced in response to the DNA-damaging agent methyl methanesulfonate (MMS), suggesting a potential role in stress response pathways .

• Carbon source dependence: YNL194C-GFP is more abundant at MCCs in the presence of glycerol and oleate compared to other carbon sources, indicating regulation by metabolic state .

• Response to osmotic stress: While not explicitly stated for YNL194C, related proteins in the same family show differential regulation under osmotic stress conditions. For example, AbPun1 (a related protein in a different yeast species) is induced by exposure to sorbitol .

This condition-dependent expression pattern suggests that YNL194C may have specialized functions under particular growth conditions or stress situations, rather than serving a constitutive housekeeping role.

What is the functional relationship between YNL194C and other MCC components?

YNL194C functions within a complex network of proteins that maintain the structure and function of Membrane Compartment occupied by Can1 (MCC). Understanding these relationships requires analysis at multiple levels:

The MCC contains at least 21 proteins, with nine being integral membrane proteins (including YNL194C) and 12 being cytosolic proteins that associate with these membrane patches . The integral membrane proteins include two families: the Sur7 family (Sur7, Pun1, YNL194C, and Fmp45) and the Nce102 family .

Functional analysis reveals distinct roles for different MCC components:

• While deletion of YNL194C itself does not appear to completely disrupt MCC formation, deletion of Nce102 causes homogeneous distribution of transporters like HUP1 and Can1 and makes Sur7 patches more diffuse .

• The eisosomal protein Pil1 is critical for MCC structure, as its deletion leads to dissipation of Lsp1 and Sur7 patches .

The specific contribution of YNL194C to MCC function likely involves maintenance of proper sphingolipid content in the plasma membrane, as indicated by its requirement for plasma membrane sphingolipid content . This suggests a role in lipid organization that may indirectly affect protein distribution and function within these membrane domains.

To further elucidate the functional relationship between YNL194C and other MCC components, researchers should conduct systematic genetic interaction studies and quantitative co-localization analyses under various environmental conditions.

How does mutation or deletion of YNL194C affect plasma membrane organization and function?

The effects of YNL194C mutation or deletion on plasma membrane organization can be studied through several complementary approaches:

• Microscopic analysis: In YNL194C deletion strains, researchers should examine the distribution patterns of other MCC components (like Sur7, Can1, and Nce102) using fluorescence microscopy. Changes in patch number, size, intensity, or distribution would indicate a role for YNL194C in organizing these domains.

• Lipidomic analysis: Since YNL194C is required for plasma membrane sphingolipid content , comprehensive lipidomic profiling of wild-type versus deletion strains would reveal specific lipid changes that result from YNL194C absence.

• Functional assays: Researchers should assess membrane-associated functions such as nutrient transport (especially arginine uptake via Can1), stress response, and endocytosis rates in YNL194C mutants.

• Genome-wide screens: To place YNL194C in a broader functional context, researchers can perform synthetic genetic array (SGA) analysis to identify genes that show genetic interactions with YNL194C, particularly those involved in membrane organization or trafficking.

A comprehensive analysis should consider both direct effects of YNL194C deletion on membrane structure and indirect effects on cellular processes that depend on proper membrane compartmentalization. The search results indicate that among the 28 mutants showing disturbed MCC formation in a genome-wide visual screen, several were involved in vesicle-mediated transport (9/28 strains, 32%; background frequency of 4.9%; p-value of 4.7 × 10^-5) , suggesting connections between MCC formation and membrane trafficking pathways.

What experimental approaches are most effective for identifying protein-protein interactions involving YNL194C?

Identifying the protein interaction network of YNL194C requires specialized approaches suitable for membrane proteins:

• Proximity-based labeling methods: BioID or APEX2 fusion proteins can label proteins in close proximity to YNL194C in living cells, overcoming challenges associated with traditional co-immunoprecipitation of membrane proteins.

• Split-protein complementation assays: Techniques such as split-GFP or split-ubiquitin systems are particularly suitable for detecting membrane protein interactions in their native environment.

• Co-immunoprecipitation with membrane solubilization: While challenging for membrane proteins, careful optimization of detergent conditions can allow for co-immunoprecipitation of YNL194C complexes.

• Genetic interaction mapping: Comprehensive genetic interaction screens can identify functional relationships that may reflect physical interactions or pathway connections.

The BioGRID database reports 89 interactors and 98 interactions for YNL194C , providing a foundation for validation studies and network analysis. When interpreting these data, researchers should prioritize interactions that are supported by multiple approaches or that involve proteins known to co-localize with YNL194C in MCCs.

How can researchers differentiate between the functions of YNL194C and its paralog FMP45?

Distinguishing the specific functions of paralogs like YNL194C and FMP45 requires systematic comparative analysis:

• Phenotypic characterization of single and double mutants: Compare growth, stress resistance, membrane organization, and specific processes like sporulation in ynl194c∆, fmp45∆, and ynl194c∆ fmp45∆ strains to assess functional redundancy or specialization.

• Localization patterns: Determine whether YNL194C and FMP45 localize to the same membrane domains or show distinct distributions under various conditions.

• Expression analysis: Compare the expression patterns of both genes across different growth conditions, stress treatments, and developmental stages using RNA-seq or quantitative PCR.

• Complementation studies: Test whether expression of one paralog can rescue phenotypes associated with deletion of the other.

• Domain swap experiments: Create chimeric proteins containing domains from both paralogs to identify regions responsible for specific functions.

The creation of strains with disruptions of YNL194, as mentioned in the search results , provides a foundation for such comparative analyses. For example, strain YJC2122 with disruptions of SUR7, YNL194, and YDL222 could be particularly valuable for studying functional relationships between these related proteins.

What are the optimal approaches for generating recombinant YNL194C constructs for functional studies?

Creating functional recombinant YNL194C constructs requires careful consideration of this membrane protein's properties:

• Vector selection: For expression in yeast, researchers should consider vectors with different promoters:

  • Native promoter constructs for physiological expression levels

  • Inducible promoters (GAL1, CUP1) for controlled expression

  • Constitutive promoters (GPD, TEF) for high-level expression

• Tagging strategies:

  • C-terminal tags are often preferable as they less frequently interfere with signal sequences

  • Consider using small epitope tags (HA, FLAG, Myc) for immunodetection

  • Fluorescent protein tags (GFP, mCherry) for localization studies

  • Multiple tagging approaches should be tested as tag position can affect function

• Purification approaches: If protein purification is required:

  • Include affinity tags (His6, GST, TAP)

  • Consider detergent screening for optimal solubilization

  • Evaluate nanodiscs or other membrane mimetics for maintaining native structure

• Validation methods:

  • Functional complementation of ynl194c∆ phenotypes

  • Proper localization to MCCs

  • Correct molecular weight by Western blotting

  • Mass spectrometry confirmation of protein identity

The search results indicate successful creation of YNL194-GFP and YNL194-9myc constructs , demonstrating the feasibility of these approaches. Additionally, transposon insertion approaches have been used to create in-frame fusions, such as inserting a transposon with LACZ in frame at codon 137 of YNL194 (YJC2700) followed by Cre-mediated excision to leave an in-frame, 93-codon, 3HA tag .

What techniques are most appropriate for analyzing YNL194C function in sporulation and membrane organization?

Since YNL194C is required for sporulation and plasma membrane sphingolipid content , specialized techniques are needed to analyze these functions:

• Sporulation analysis:

  • Quantitative sporulation assays comparing wild-type and ynl194c∆ strains

  • Time-course analysis of meiotic progression using DNA staining

  • Electron microscopy to examine spore wall formation

  • Gene expression profiling during sporulation to identify affected pathways

• Membrane lipid analysis:

  • Lipidomic profiling using mass spectrometry to quantify sphingolipid species

  • Fluorescent lipid probes to visualize membrane domain organization

  • Detergent resistance assays to assess membrane domain integrity

  • Membrane fluidity measurements using fluorescence anisotropy or FRAP

• Protein dynamics in membrane domains:

  • Single-particle tracking of fluorescently tagged membrane proteins

  • Fluorescence correlation spectroscopy (FCS) to measure diffusion coefficients

  • Structured illumination microscopy or other super-resolution techniques for detailed visualization of membrane domains

• Functional consequences of membrane organization:

  • Endocytosis assays using fluorescent cargo proteins

  • Transporter activity measurements for Can1 and other MCC-associated transporters

  • Stress response assays, particularly for conditions affecting membrane integrity

These approaches should be applied comparatively to wild-type, ynl194c∆, and complemented strains to establish causal relationships between YNL194C function and observed phenotypes.

How should researchers design and interpret genetic interaction studies involving YNL194C?

Genetic interaction studies can provide valuable insights into YNL194C function:

• Experimental design considerations:

  • Select appropriate array of query genes focusing on membrane organization, lipid metabolism, and sporulation pathways

  • Include known MCC components (SUR7, PUN1, NCE102) and eisosome proteins (PIL1, LSP1)

  • Use quantitative measures of interaction strength rather than binary growth/no-growth scoring

  • Include appropriate controls for plate position effects and batch variation

• Analysis approaches:

  • Calculate genetic interaction scores using established methods (e.g., ε-score = observed fitness - expected fitness)

  • Cluster genes based on similarity of genetic interaction profiles

  • Perform enrichment analysis for biological processes within interaction networks

  • Compare interaction profiles with those of paralogous genes (FMP45, SUR7)

• Interpretation frameworks:

  • Positive genetic interactions (better than expected growth in double mutants) often indicate compensatory pathways

  • Negative genetic interactions (worse than expected growth) frequently reflect functions in parallel pathways or complex relationships

  • Similar genetic interaction profiles suggest similar functions

Based on the search results, YNL194C has functional relationships with various cellular processes, including membrane organization and vesicle-mediated transport. A genome-wide visual screen identified 28 mutants with disturbed MCC formation, with significant enrichment for genes involved in vesicle-mediated transport (9/28 strains, 32%; background frequency of 4.9%; p-value of 4.7 × 10^-5) . This suggests that researchers should pay particular attention to interactions between YNL194C and genes involved in membrane trafficking pathways.

How should researchers analyze and interpret changes in YNL194C patch distribution under different experimental conditions?

Analyzing YNL194C patch dynamics requires robust quantitative approaches:

• Image acquisition and processing:

  • Use consistent imaging parameters across conditions

  • Apply appropriate deconvolution or background subtraction

  • Ensure proper segmentation of cells and patches

• Quantitative metrics to measure:

  • Number of patches per cell

  • Patch size distribution

  • Fluorescence intensity per patch

  • Spatial distribution pattern (random vs. clustered)

  • Co-localization coefficients with other markers

• Statistical analysis:

  • Apply appropriate statistical tests based on data distribution

  • Consider cell-to-cell variability within populations

  • Use multiple biological and technical replicates

  • Implement robust outlier detection methods

• Interpretation frameworks:

  • Compare observed changes to known membrane reorganization patterns

  • Consider potential membrane lipid alterations affecting domain formation

  • Evaluate functional consequences of distribution changes

The search results indicate that YNL194C-GFP is more abundant at MCCs in the presence of glycerol and oleate , and movie analysis has shown that YNL194p patches are stationary and stable . When analyzing patch distribution under different conditions, researchers should determine whether changes affect patch number, intensity, stability, or all of these parameters to gain insights into the regulatory mechanisms involved.

What approaches should be used to resolve potentially contradictory findings regarding YNL194C function?

When faced with contradictory findings regarding YNL194C function or localization, researchers should implement a systematic resolution strategy:

• Methodological reconciliation:

  • Compare experimental conditions in detail (strain backgrounds, growth media, temperature)

  • Evaluate differences in construct design (tag position, promoter strength)

  • Assess sensitivity and specificity of detection methods

  • Consider temporal aspects (growth phase, induction time)

• Biological explanations:

  • Test for condition-dependent functions or localizations

  • Investigate potential functional redundancy with paralogs

  • Examine strain-specific genetic modifiers

  • Consider post-translational modifications affecting function

• Experimental resolution approaches:

  • Conduct side-by-side comparisons using standardized protocols

  • Employ multiple complementary techniques to validate findings

  • Develop more specific reagents (e.g., antibodies against specific domains)

  • Use CRISPR-based genome editing to create identical mutations across strain backgrounds

The scientific method requires researchers to develop testable hypotheses that explain apparent contradictions. For example, if different studies report varying localization patterns for YNL194C, researchers might hypothesize that post-translational modifications affect its distribution and design experiments to test this hypothesis.

How can researchers effectively integrate localization, interaction, and functional data to develop models of YNL194C activity?

Building comprehensive models of YNL194C function requires integration of multiple data types:

• Data integration approaches:

  • Create unified databases of YNL194C-related findings

  • Develop visualization tools to overlay different data types

  • Apply network analysis methods to identify functional modules

  • Use machine learning to identify patterns across datasets

• Model development:

  • Start with descriptive models that summarize observed phenomena

  • Progress to mechanistic models that propose specific molecular interactions

  • Develop predictive models that can be tested experimentally

  • Iterate between model refinement and experimental validation

• Validation strategies:

  • Design experiments that specifically test model predictions

  • Collaborate with groups using different techniques or approaches

  • Compare model predictions with large-scale datasets

• Contextual considerations:

  • Assess how YNL194C function fits within broader membrane organization principles

  • Compare with similar proteins in other organisms

  • Consider evolutionary aspects of membrane compartmentalization

The search results provide elements that should be integrated into such models, including YNL194C's membership in the Sur7 family of tetraspanners , its localization to MCCs , its role in sporulation and plasma membrane sphingolipid content , and its relationship with eisosome components . A comprehensive model would explain how these diverse observations are mechanistically connected.

What experimental design principles should researchers follow when investigating the effects of YNL194C overexpression or deletion?

Designing rigorous experiments to study YNL194C function requires careful consideration of several factors:

• Genetic manipulation strategies:

  • Use markerless deletion techniques when possible to avoid marker effects

  • Complement deletions with wild-type gene to confirm phenotype specificity

  • For overexpression, consider both constitutive and inducible systems

  • Include appropriate empty vector controls

• Control selection:

  • Use isogenic strains differing only in YNL194C status

  • Include deletion of paralogs (FMP45) for comparison

  • Consider double mutants with other MCC components

• Experimental conditions:

  • Test multiple growth conditions (different carbon sources, stress agents)

  • Include conditions known to affect MCC organization (e.g., glycerol, oleate)

  • Consider developmental processes like sporulation where YNL194C functions

• Phenotypic analysis:

  • Use quantitative rather than qualitative measures

  • Implement time-course experiments to capture dynamic processes

  • Combine population-level and single-cell measurements

  • Apply multiple independent methods to assess each phenotype

The search results describe successful disruption of YNL194C in various strains (e.g., YJC2044), with confirmation by PCR and sequencing , demonstrating the feasibility of these genetic approaches.

When reporting results, researchers should follow the structure outlined in published guidelines for experimental research, as exemplified in the search results regarding research question formulation :