Recombinant Saccharomyces cerevisiae Uncharacterized protein YKL183C-A (YKL183C-A)

What is YKL183C-A in Saccharomyces cerevisiae and how was it identified?

YKL183C-A is an uncharacterized open reading frame (ORF) in the S. cerevisiae genome. Like many uncharacterized ORFs, it was likely identified through computational genome analysis methods. The systematic name follows yeast genome nomenclature where "Y" indicates yeast, "K" refers to chromosome 11, "L" represents the right arm of the chromosome, "183" indicates its relative position, "C" denotes transcription from the Crick (complementary) strand, and "A" suggests it was discovered as an additional or alternative ORF after initial annotation. Experimental validation of such ORFs typically involves RNA sequencing to confirm transcription and mass spectrometry to verify protein expression. For definitive validation, researchers should perform RT-PCR with gene-specific primers and conduct western blot analysis using tagged versions of the protein .

What approaches are most effective for determining the subcellular localization of YKL183C-A?

To determine subcellular localization of YKL183C-A, researchers should implement a multi-method approach:

• Fluorescent protein tagging: Create C-terminal or N-terminal GFP/mCherry fusions, being careful not to disrupt targeting sequences. Express these constructs from the native promoter to maintain physiological expression levels.

• Immunofluorescence microscopy: Generate antibodies against YKL183C-A or use epitope tags (HA, Myc, FLAG) for detection with commercial antibodies.

• Subcellular fractionation: Perform differential centrifugation to separate cellular compartments, followed by western blotting to detect the protein in specific fractions.

• Co-localization studies: Use known organelle markers (e.g., DAPI for nucleus, MitoTracker for mitochondria) to determine overlap with YKL183C-A signal.

• Validation with multiple tags: Confirm results using different tagging strategies to rule out tag-specific artifacts.

The choice between C-terminal and N-terminal tagging should be guided by computational predictions of targeting sequences and protein topology. Researchers should also examine localization under various growth conditions, as some yeast proteins exhibit condition-dependent localization patterns .

How can expression patterns of YKL183C-A be assessed across different growth conditions?

Analyzing expression patterns of YKL183C-A requires a systematic approach across various conditions:

• Transcriptional analysis:

  • RT-qPCR with specific primers for YKL183C-A

  • Northern blotting with gene-specific probes

  • RNA-seq analysis across multiple growth conditions (different carbon sources, stress responses, cell cycle stages)

  • Reporter gene assays using the YKL183C-A promoter driving expression of luciferase or β-galactosidase

• Protein-level analysis:

  • Western blotting with tagged versions or specific antibodies

  • Flow cytometry if using fluorescent protein fusions

  • Mass spectrometry-based proteomics

  • Protein half-life determination using cycloheximide chase experiments

• High-throughput approaches:

  • Analysis of existing datasets in SGD (Saccharomyces Genome Database)

  • Time-course experiments during environmental transitions

  • Comparison across growth phases (lag, log, diauxic shift, stationary)

For potentially low-abundance proteins like YKL183C-A, specialized techniques such as ribosome profiling can confirm active translation, while targeted proteomics approaches (SRM/MRM) may provide more sensitive protein detection than standard methods .

What is the most reliable strategy for creating a YKL183C-A deletion strain?

Creating a reliable YKL183C-A deletion strain requires careful design and thorough validation:

• Design considerations:

  • Evaluate the genomic context to avoid disrupting overlapping genes or regulatory elements

  • Design deletion cassettes that precisely remove the ORF without affecting flanking regions

  • Include unique barcode sequences for strain identification in pooled experiments

• Deletion methods:

  • PCR-based gene replacement with selectable markers (KanMX, HIS3, URA3)

  • Delitto perfetto method for scarless deletion

  • CRISPR-Cas9 approach for precise editing without selection markers

• Validation protocol:

  • Diagnostic PCR with primers outside the targeted region

  • Sequencing across deletion junctions

  • RT-PCR to confirm absence of transcript

  • Western blotting if antibodies or tagged versions are available

  • Complementation with wild-type gene to verify phenotype causality

• Controls:

  • Generate multiple independent deletion clones

  • Include isogenic wild-type controls in all experiments

  • Create marker-matched control strains

For small ORFs like YKL183C-A, verification is particularly important as conventional PCR verification may yield false positives due to the small size difference between wild-type and deleted regions .

What experimental approaches can determine if YKL183C-A has a role in DNA damage response pathways?

To investigate potential roles of YKL183C-A in DNA damage response:

• Sensitivity assays:

  • Spot dilution assays on media containing DNA damaging agents (MMS, UV, hydroxyurea, phleomycin)

  • Quantitative survival curves using colony forming unit (CFU) counting

  • Continuous growth monitoring in liquid media with sublethal concentrations of damaging agents

• Genetic interaction analysis:

  • Generate double mutants with known DNA damage response genes (RAD9, RAD17, RAD24)

  • Quantify epistatic relationships through growth rate measurements

  • Synthetic genetic array (SGA) screening against DNA repair mutant collection

• Checkpoint functionality:

  • Flow cytometry to analyze cell cycle distribution before and after damage

  • Microscopic examination of nuclear and cellular morphology

  • Western blotting for Rad53 phosphorylation status

• Molecular assays:

  • Comet assay to directly measure DNA strand breaks

  • Pulse-field gel electrophoresis to assess chromosome integrity

  • ChIP assays if YKL183C-A is suspected to associate with chromatin

When performing these experiments, it's essential to include appropriate positive controls (e.g., rad9Δ strains show specific sensitivity to MMS) and to quantify results using computational image analysis or plate reader measurements rather than relying on visual assessment alone .

What strategies can be employed to express and purify recombinant YKL183C-A for structural studies?

For structural studies of YKL183C-A, optimize expression and purification as follows:

• Expression system selection:

  • Homologous expression in S. cerevisiae under control of strong promoters (GAL1, TDH3)

  • P. pastoris for higher yield of properly folded eukaryotic proteins

  • E. coli with solubility-enhancing fusion partners (MBP, SUMO, Trx)

  • Cell-free systems for potentially toxic proteins

• Construct design:

  • Codon optimization for the chosen expression system

  • N- and C-terminal affinity tags (His6, GST, FLAG) with TEV/PreScission protease sites

  • Consider multiple constructs with varying N/C-terminal boundaries

• Purification strategy:

  • Initial capture via affinity chromatography

  • Secondary purification by ion exchange or size exclusion chromatography

  • Buffer optimization through thermal shift assays (TSA/DSF)

  • Detergent screening if membrane association is suspected

• Quality control:

  • SEC-MALS to determine oligomeric state

  • CD spectroscopy for secondary structure assessment

  • Dynamic light scattering for homogeneity evaluation

  • Mass spectrometry to confirm identity and modifications

• Crystallization screening:

  • Commercial sparse matrix screens at multiple temperatures

  • In situ proteolysis to remove flexible regions

  • Surface entropy reduction for crystallization-resistant proteins

For challenging proteins like YKL183C-A, consider screening multiple constructs in parallel and implementing high-throughput approaches to identify optimal conditions for structural studies .

How can protein-protein interactions of YKL183C-A be comprehensively mapped?

For comprehensive mapping of YKL183C-A protein interactions:

• Affinity purification-mass spectrometry (AP-MS):

  • Tandem affinity purification (TAP) tagging of YKL183C-A

  • SILAC labeling for quantitative interaction analysis

  • Crosslinking before lysis to capture transient interactions

  • Comparison across multiple conditions to identify context-specific interactions

• Proximity labeling approaches:

  • BioID or TurboID fusion to YKL183C-A for in vivo proximity labeling

  • APEX2 tagging for rapid, spatially-restricted labeling

  • Quantitative analysis of labeled proteins by mass spectrometry

• Yeast two-hybrid screens:

  • Genome-wide screening against ordered arrays

  • Split-ubiquitin system for membrane-associated interactions

  • Cytosolic and nuclear-targeted variants to overcome spatial constraints

• Protein complementation assays:

  • Split fluorescent protein (BiFC) for visualizing interactions in vivo

  • Split luciferase assays for quantitative interaction measurement

  • Protein-fragment complementation assay (PCA) for in vivo screening

• Biophysical interaction validation:

  • Isothermal titration calorimetry (ITC)

  • Surface plasmon resonance (SPR)

  • Microscale thermophoresis (MST)

After identifying potential interactors, validation should include reciprocal pulldowns, co-localization studies, and functional assays that test the biological relevance of the interaction .

What computational approaches can predict the structure and function of YKL183C-A?

Modern computational biology offers powerful tools for predicting features of uncharacterized proteins:

• Sequence-based predictions:

  • PSI-BLAST, HHpred, and HMMER for detecting remote homologs

  • InterProScan for identifying conserved domains

  • TMHMM and TOPCONS for predicting transmembrane regions

  • SignalP for signal peptide detection

  • NetPhos for phosphorylation site prediction

• Structural predictions:

  • AlphaFold2 for accurate ab initio 3D structure prediction

  • SWISS-MODEL for homology modeling if templates exist

  • I-TASSER for threading-based modeling

  • MolProbity for structure validation

  • Molecular dynamics simulations to assess stability

• Function prediction:

  • Gene Ontology (GO) term prediction using tools like PANNZER2

  • Protein-protein interaction prediction using STRING

  • Phylogenetic profiling to identify co-evolving genes

  • Genomic context methods (gene neighborhood analysis)

  • Enzyme active site prediction with COFACTOR

• Integrative approaches:

  • Multiple evidence integration platforms like FungiDB

  • Bayesian networks to combine diverse predictive features

  • Structural similarity searches against function-annotated proteins

When implementing these approaches, it's important to critically evaluate prediction confidence scores and seek multiple lines of computational evidence before designing experimental validation studies .

How can researchers investigate if YKL183C-A has a role in mitochondrial function?

To investigate potential mitochondrial functions of YKL183C-A:

• Growth and respiratory competence analysis:

  • Compare growth of wild-type and ykl183c-aΔ strains on fermentable vs. non-fermentable carbon sources

  • Quantitative growth curves in glycerol/ethanol media

  • Petite frequency determination (rate of respiratory-deficient colony formation)

• Mitochondrial morphology and dynamics:

  • Fluorescence microscopy with mitochondrial markers (MitoTracker, mito-GFP)

  • Quantitative image analysis of morphology parameters (length, branching, volume)

  • Time-lapse imaging to assess fusion/fission dynamics

• Respiratory chain function:

  • Oxygen consumption measurements using respirometry

  • Enzymatic assays for individual respiratory complexes

  • Membrane potential assessment using potentiometric dyes (TMRM, JC-1)

  • ROS production measurement with fluorescent indicators

• Mitochondrial protein import:

  • In vitro import assays with isolated mitochondria

  • Blue native PAGE to analyze complex assembly

  • Pulse-chase experiments to track import kinetics

• Genetic interaction analysis:

  • Screen for genetic interactions with known mitochondrial genes

  • Test for synthetic phenotypes with respiratory chain complex components

  • Assess mtDNA stability in the absence of YKL183C-A

Similar to uncharacterized ORF YCR095W-A, which was found to affect mitochondrial morphology and oxygen consumption in certain conditions, YKL183C-A may show subtle mitochondrial phenotypes that require specialized assays for detection .

How can researchers address challenges in detecting expression of small ORFs like YKL183C-A?

Small ORFs present unique detection challenges that require specialized approaches:

• Transcriptional detection strategies:

  • Custom RT-qPCR with highly specific primers and probes

  • Northern blotting with LNA (locked nucleic acid) probes for increased sensitivity

  • 5' and 3' RACE to confirm transcript boundaries

  • Nanopore direct RNA sequencing to capture full-length transcripts

• Translational verification:

  • Ribosome profiling to confirm active translation

  • Polysome association analysis through sucrose gradient fractionation

  • N-terminal sequencing to confirm translation start site

  • Mass spectrometry with targeted acquisition methods (PRM/SRM)

• Protein detection optimization:

  • Tricine-SDS-PAGE for better resolution of small proteins

  • Customized western blot conditions (membrane type, transfer parameters)

  • Multiple epitope tags to enhance signal

  • Sample preparation optimization to prevent degradation during extraction

• Specialized quantification methods:

  • Digital PCR for absolute quantification of low-abundance transcripts

  • Label-free proteomics with spiked-in standards

  • Fluorescent reporter fusions with sensitive detection systems

When working with small ORFs, it's crucial to include appropriate positive controls and to perform thorough validation with orthogonal methods to confirm genuine expression .

What strategies can resolve contradictory data about YKL183C-A function?

When faced with contradictory results regarding YKL183C-A function:

• Systematic analysis of experimental variables:

  • Create a detailed comparison table of contradictory studies

  • Replicate key experiments with identical conditions in the same laboratory

  • Test multiple strain backgrounds to identify genetic modifiers

  • Thoroughly document media composition, growth phase, and environmental parameters

• Technical validation approaches:

  • Confirm genetic modifications by sequencing

  • Verify antibody specificity with appropriate controls

  • Use multiple independent detection methods

  • Quantify protein expression levels to rule out dosage effects

• Condition-dependent function assessment:

  • Test function across a matrix of environmental conditions

  • Perform time-course experiments to detect transient effects

  • Examine stress-specific or cell cycle-specific functions

  • Consider redundancy with paralogs or functionally related proteins

• Resolution through advanced methods:

  • Single-cell analysis to detect population heterogeneity

  • Genome-wide approaches to place contradictions in context

  • Mathematical modeling to reconcile apparently conflicting observations

  • Collaboration with laboratories reporting conflicting results

When publishing findings about poorly characterized proteins like YKL183C-A, researchers should explicitly address prior contradictory results and provide a framework that may explain discrepancies .

What are best practices for creating precise genomic modifications to study YKL183C-A without disrupting adjacent features?

For precise genomic modification of YKL183C-A:

• Strategic CRISPR-Cas9 implementation:

  • Design guide RNAs with minimal off-target potential

  • Create repair templates with extended homology arms (>500 bp)

  • Use Cas9 variants with increased specificity (eSpCas9, HiFi Cas9)

  • Consider base editing or prime editing for minimal genomic disruption

• Scarless modification techniques:

  • Delitto perfetto method for marker-free modifications

  • URA3 pop-in/pop-out strategy with 5-FOA counter-selection

  • Two-step CRISPR strategy with transient selection

• Careful tag integration:

  • Analyze ORF context for overlapping genes or regulatory elements

  • Use small epitope tags (3xFLAG, mini-AID) to minimize disruption

  • Consider flexible linkers between the protein and tag

  • Place tags at positions least likely to disrupt function based on structure prediction

• Thorough validation procedures:

  • PCR and sequencing to confirm desired modifications

  • RT-qPCR of neighboring genes to verify normal expression

  • RNA-seq to detect potential transcriptome-wide effects

  • Complementation tests to confirm phenotype specificity

• Control strategies:

  • Generate multiple independent modification clones

  • Create synonymous mutations as controls

  • Consider reversible systems (AID, anchor-away) to confirm phenotype specificity

Given the limited information about YKL183C-A, researchers should conduct modification design with conservative approaches that minimize disruption to surrounding genomic features .

How can systems biology approaches integrate YKL183C-A into broader cellular networks?

Integrating YKL183C-A into cellular networks requires multi-omics approaches:

• Network analysis strategies:

  • Protein-protein interaction mapping via AP-MS or BioID

  • Genetic interaction networks through SGA or CRISPR screens

  • Transcriptomic profiling of ykl183c-aΔ mutants

  • Metabolomic analysis to detect biochemical changes

  • Integration with existing databases (BioGRID, STRING, SGD)

• Network visualization and analysis:

  • Cytoscape for network visualization with custom layouts

  • Weighted gene correlation network analysis (WGCNA)

  • Gene Set Enrichment Analysis (GSEA) for pathway identification

  • Network centrality measures to assess importance in cellular systems

• Multi-omics data integration:

  • Correlation analysis across transcriptomic, proteomic and metabolomic datasets

  • Machine learning approaches to identify patterns across diverse data types

  • Integration of genetic and physical interaction networks

  • Time-series analysis to detect dynamic responses

• Experimental validation of network predictions:

  • CRISPR-based perturbation of predicted network nodes

  • Synthetic lethality screens to test predicted interactions

  • Dynamic response measurements after targeted interventions

  • Controlled environmental perturbations to test network robustness

For uncharacterized proteins like YKL183C-A, network-based approaches can provide function hypotheses based on the principle of guilt by association, identifying biological processes in which the protein may participate .

How can single-cell techniques provide insights into YKL183C-A function that population-level studies might miss?

Single-cell approaches reveal unique insights about cell-to-cell variability:

• Single-cell transcriptomics applications:

  • scRNA-seq to detect cell-specific expression patterns

  • smFISH for spatial localization of YKL183C-A transcripts

  • Live-cell RNA imaging using MS2/PP7 systems to track transcript dynamics

  • Correlation of YKL183C-A expression with cell cycle or stress response markers

• Single-cell protein analysis:

  • Flow cytometry or microscopy of YKL183C-A-fluorescent protein fusions

  • Time-lapse imaging to track expression dynamics in individual lineages

  • Single-cell western blotting for protein abundance quantification

  • Mass cytometry for multiplexed protein detection

• Functional heterogeneity assessment:

  • Microfluidic platforms for controlled single-cell perturbations

  • Cell tracking combined with reporter systems to link expression to phenotype

  • Single-cell metabolomics to connect YKL183C-A to metabolic states

  • Lineage tracking with genetic barcodes to detect selection effects

• Computational analysis approaches:

  • Trajectory inference methods to map potential cellular states

  • Clustering algorithms to identify subpopulations

  • Network analysis to identify co-regulated gene modules

  • Information theory approaches to quantify heterogeneity

Single-cell techniques are particularly valuable when studying proteins like YKL183C-A that may have condition-specific functions or where population averages might mask important biological phenomena .

What novel genetic approaches could be applied to elucidate the function of YKL183C-A in S. cerevisiae?

Cutting-edge genetic approaches for functional characterization:

• Advanced CRISPR technologies:

  • CRISPRi for titratable gene repression without DNA modification

  • CRISPRa for upregulation of YKL183C-A expression

  • CRISPR-based saturation mutagenesis for comprehensive variant analysis

  • Base editing to introduce specific point mutations

• Synthetic biology approaches:

  • FACS-based genetic selection systems linked to YKL183C-A function

  • Synthetic genetic circuits to create artificial dependencies

  • Optogenetic control of YKL183C-A expression or localization

  • Engineered protein scaffolds to detect functional interactions

• Specialized screening technologies:

  • Barcode-based parallel fitness assays across hundreds of conditions

  • Perturb-seq combining CRISPR perturbation with single-cell RNA-seq

  • Transposon insertion profiling for identification of functional domains

  • Deep mutational scanning to map sequence-function relationships

• Comparative genomics approaches:

  • Systematic analysis across Saccharomyces species to identify conserved features

  • Humanized yeast systems if human homologs are identified

  • Horizontal gene transfer experiments to test function in diverse backgrounds

  • Experimental evolution to identify compensatory mutations in ykl183c-aΔ backgrounds

These approaches can provide complementary insights into YKL183C-A function, particularly when conventional methods yield limited results. The integration of multiple orthogonal technologies offers the most robust path toward functional characterization of this uncharacterized protein .