Recombinant Schizosaccharomyces pombe Uncharacterized protein C11D3.19 (SPAC11D3.19)

What experimental approaches are most suitable for initial characterization of SPAC11D3.19?

Initial characterization of SPAC11D3.19 should employ a systematic workflow combining genetic and biochemical techniques. Begin with sequence homology analysis against characterized proteins, followed by gene disruption experiments to observe phenotypic effects.

The recommended methodology for S. pombe genetic manipulation includes:

• Design primers to amplify selection markers (such as ura4+ or kanMX) flanked by sequences homologous to regions surrounding SPAC11D3.19

• Transform the PCR product into S. pombe cells using standard lithium acetate method

• Select transformants on appropriate selective media

• Verify gene deletion through PCR using primers binding outside the targeted region

• Perform phenotypic analysis under standard and stress conditions

Similar approaches have been successfully employed for characterizing other S. pombe genes. For instance, researchers studying laboratory-strain-specific loss-of-function variants have utilized comparable methodologies for genes like SPAC11D3.11c, which appears to be strain-specific .

How can I express and purify recombinant SPAC11D3.19 for biochemical studies?

For recombinant expression of SPAC11D3.19, a methodical approach incorporating multiple expression systems is recommended:

• PCR amplify the SPAC11D3.19 coding sequence from S. pombe genomic DNA

• Clone into expression vectors with different affinity tags (His6, GST, MBP) to improve solubility

• Test expression in multiple systems:

  • E. coli BL21(DE3) for bacterial expression

  • P. pastoris for yeast expression

  • Baculovirus-insect cell system for eukaryotic post-translational modifications

• Optimize expression conditions systematically:

  • Temperature (16°C, 25°C, 30°C)

  • Induction time (4h, 8h, overnight)

  • Inducer concentration

• Purify using affinity chromatography followed by size exclusion chromatography

When designing these expression experiments, ensure proper controls are included to validate protein functionality. Include both positive and negative controls to ensure reliability and validity of results .

What bioinformatic tools can predict functional domains in SPAC11D3.19?

To predict functional domains of SPAC11D3.19, employ a multi-tiered bioinformatic approach:

• Primary sequence analysis:

  • BLAST against characterized proteins in multiple organisms

  • PFAM, SMART, and InterPro for conserved domain identification

  • TMHMM for transmembrane region prediction

  • SignalP for signal peptide detection

• Secondary structure prediction:

  • PSIPRED for alpha helices and beta sheets

  • DISOPRED for disordered regions

  • NetPhos for potential phosphorylation sites

• Tertiary structure prediction:

  • AlphaFold2 for 3D structure modeling

  • ConSurf for evolutionary conservation mapping onto structure

  • CASTp for binding pocket prediction

• Comparative genomics:

  • Orthologs identification across fungal species

  • Synteny analysis to identify conserved genomic context

When analyzing uncharacterized proteins in S. pombe, comparative genomics approaches are particularly valuable. The extensive genomic mapping of S. pombe provides a robust framework for predicting functional relationships .

How can I determine the subcellular localization of SPAC11D3.19?

To determine subcellular localization, implement fluorescent protein tagging with appropriate controls:

• Design endogenous tagging construct:

  • C-terminal GFP/mCherry fusion using PCR-based gene targeting

  • Include a flexible linker (GGGGS)3 between protein and tag

  • Maintain the native promoter to preserve physiological expression levels

• Create control constructs:

  • Known nuclear protein (e.g., Gar2-mCherry)

  • Known ER marker (e.g., Erg11-GFP)

  • Known Golgi marker (e.g., Anp1-mCherry)

• Transformation and verification:

  • Transform into S. pombe using lithium acetate method

  • Select transformants on appropriate media

  • Verify correct integration by PCR and sequencing

  • Confirm protein functionality through complementation tests

• Microscopy and analysis:

  • Visualize cells at different cell cycle stages

  • Test multiple growth conditions (standard, stress)

  • Perform z-stack imaging for 3D localization

  • Quantify co-localization with organelle markers

This approach provides robust evidence for protein localization while ensuring the tagged protein maintains functionality. When designing these experiments, include appropriate controls for each experimental variable to ensure reliable results .

What approaches can identify potential interaction partners of SPAC11D3.19?

To identify protein interaction partners, implement complementary approaches:

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

  • Generate S. pombe strain expressing SPAC11D3.19 with tandem affinity tag (TAP)

  • Perform purification under native conditions

  • Analyze co-purifying proteins by LC-MS/MS

  • Filter against control purifications to identify specific interactors

• Proximity-dependent Biotin Identification (BioID):

  • Create SPAC11D3.19-BirA* fusion

  • Express in S. pombe and induce biotinylation

  • Purify biotinylated proteins and identify by mass spectrometry

  • Map interaction network through spatial proximity

• Yeast Two-Hybrid screening:

  • Use SPAC11D3.19 as bait against S. pombe cDNA library

  • Filter out auto-activators and false positives

  • Validate interactions through co-immunoprecipitation

• Data analysis and validation:

  • Compare results across different methods

  • Prioritize proteins identified in multiple approaches

  • Validate key interactions through reciprocal tagging

  • Perform functional assays to confirm biological relevance

When analyzing protein interaction data from S. pombe, compare your results with existing datasets to identify patterns and potential functional relationships .

How can bulk segregant analysis be applied to study SPAC11D3.19 function?

Bulk segregant analysis (BSA) provides a powerful approach for linking phenotypes to genetic variants:

• Experimental setup:

  • Cross SPAC11D3.19 mutant strain with wild-type of opposite mating type

  • Induce sporulation and isolate random spores

  • Phenotype progeny and pool based on phenotypic extremes

  • Extract DNA from pools for whole-genome sequencing

• Sequencing and bioinformatic pipeline:

  • Perform paired-end sequencing (>30x coverage)

  • Map reads to reference genome using BWA-MEM

  • Remove duplicate reads using SAMtools' rmdup command

  • Perform variant calling with SAMtools (options: -B -q 10 -m 3 -F 0.2)

  • Filter variants based on quality (score ≥30) and read depth (10-200)

• Allele frequency analysis:

  • Calculate reference allele frequencies at each SNP position

  • Generate scatter plots of allele frequencies between pools

  • Apply LOESS regression to visualize trends (span parameter ~60kb)

  • Identify genomic regions with skewed segregation

This methodology has been validated for uncovering trait-gene relationships in fission yeast strains . When implementing BSA, careful experimental design and rigorous statistical analysis are essential to identify significant genetic associations.

What ChIP-seq approaches can characterize genomic binding sites if SPAC11D3.19 has DNA-binding properties?

If bioinformatic analysis suggests DNA-binding properties for SPAC11D3.19, implement ChIP-seq with these methodological considerations:

• Experimental design:

  • Generate strain with epitope-tagged SPAC11D3.19 (HA, FLAG)

  • Include untagged control and input samples

  • Prepare minimum 2 biological replicates

  • Test multiple growth conditions if appropriate

• ChIP protocol optimization:

  • Crosslink cells with 1% formaldehyde for 15 minutes

  • Lyse cells and sonicate to 200-500bp fragments

  • Immunoprecipitate using antibodies against tag

  • Include mock IP controls

  • Reverse crosslinks and purify DNA

• Sequencing and analysis pipeline:

  • Prepare libraries and sequence (minimum 20M reads)

  • Map reads to S. pombe genome

  • Call peaks with at least 2-fold enrichment over input

  • Identify enriched motifs using MEME-ChIP

  • Correlate binding sites with gene expression data

Based on ChIP-seq studies in S. pombe, most transcription factor binding sites reside in accessible regions with low histone H3 levels and elevated H3K14ac . The number of binding sites can vary widely between different DNA-binding proteins, ranging from 1 to 356 sites per factor .

How can CRISPR-Cas9 be employed for precise genome editing of SPAC11D3.19?

For precise genome editing of SPAC11D3.19, implement CRISPR-Cas9 with these methodological considerations:

• Guide RNA design:

  • Identify target sites using S. pombe-specific CRISPR design tools

  • Select guides with minimal off-target effects

  • Design multiple guides targeting different regions

  • Include positive control guides targeting known sites

• Repair template design:

  • For point mutations: 60-80bp homology arms surrounding the mutation

  • For insertions/tags: longer homology arms (>500bp)

  • Include silent mutations in PAM site to prevent re-cutting

• Transformation and screening:

  • Co-transform Cas9, gRNA, and repair template

  • Use drug selection markers when possible

  • Screen transformants by PCR, restriction digest, or sequencing

  • Verify off-target effects in top predicted sites

• Validation of edited strains:

  • Sequence the entire target locus

  • Confirm expression levels are maintained

  • Verify protein function through complementation tests

  • Test phenotypes under multiple conditions

This approach allows for precise modifications including point mutations, deletions, insertions, or tagging without introducing selection markers that might affect adjacent genes.

What controls are essential when studying phenotypic effects of SPAC11D3.19 deletion?

When studying phenotypic effects, implement these essential controls:

• Strain controls:

  • Wild-type parental strain

  • Independent deletion clones (minimum 3)

  • Complementation strain (deletion with reintroduced functional gene)

  • Marker-only control (selection marker at neutral locus)

• Experimental controls:

  • Positive control (strain with known phenotype)

  • Negative control (strain without phenotype)

  • Technical replicates for quantitative measurements

  • Biological replicates (minimum 3 independent experiments)

• Phenotypic analysis matrix:

  • Standard conditions (YES, EMM media)

  • Stress conditions (temperature, pH, oxidative, osmotic)

  • Cell cycle analysis (synchronization by nitrogen starvation)

  • Growth rate determination (growth curves, spot assays)

• Statistical analysis:

  • Determine appropriate statistical tests based on data distribution

  • Calculate p-values with multiple testing correction

  • Report effect sizes alongside statistical significance

  • Use power analysis to determine sample size

This comprehensive control strategy ensures that observed phenotypes are specifically attributable to SPAC11D3.19 deletion rather than background effects or experimental artifacts .

What statistical methods are appropriate for analyzing differential expression data related to SPAC11D3.19?

For robust analysis of differential expression data:

• Pre-processing and quality control:

  • Filter low-count genes (minimum 10 reads in at least 3 samples)

  • Assess sample-to-sample variation with PCA

  • Identify and handle batch effects

  • Normalize counts appropriately (DESeq2, TMM, or quantile normalization)

• Statistical testing framework:

  • For RNA-seq: negative binomial models (DESeq2, edgeR)

  • For proteomics: linear models with empirical Bayes (limma)

  • Multiple testing correction (Benjamini-Hochberg FDR)

  • Significance thresholds (padj < 0.05, |log2FC| > 1)

• Advanced analytical approaches:

  • Gene set enrichment analysis (GSEA)

  • Pathway analysis (ReactomeFI, STRING)

  • Co-expression network analysis (WGCNA)

  • Integration with ChIP-seq or proteomics data

• Visualization and reporting:

  • MA plots for global expression changes

  • Volcano plots for significance vs. magnitude

  • Heatmaps for gene clusters

  • Pathway diagrams for functional context

When working with S. pombe expression data, data.table provides efficient methods for manipulating large datasets . For example:

This approach allows for efficient computation across specific gene columns grouped by experimental conditions .

How can I integrate proteomics and transcriptomics data to understand SPAC11D3.19 function?

To integrate multi-omics data for comprehensive functional characterization:

• Data collection and standardization:

  • Ensure comparable experimental conditions across platforms

  • Transform data to comparable scales

  • Account for different dynamic ranges between technologies

  • Address missing values appropriately

• Correlation analysis:

  • Calculate protein-mRNA correlation coefficients

  • Identify discordant expression patterns

  • Cluster genes/proteins with similar behavior

  • Test for post-transcriptional regulation

• Pathway and network integration:

  • Map data onto known pathways

  • Construct integrated networks

  • Identify functional modules

  • Predict regulatory relationships

• Visualization and interpretation:

  • Multi-layered network visualization

  • Integrated heatmaps

  • Biological context mapping

  • Hypothesis generation

When integrating data for S. pombe proteins, context from existing datasets is valuable. For instance, compare your findings with the list of genes showing significant transcriptional changes under relevant conditions, as illustrated in Table 7 from genomic studies in S. pombe :

What approaches can determine if SPAC11D3.19 functions within a larger protein complex?

To investigate potential protein complex involvement:

• Size exclusion chromatography with western blotting:

  • Prepare native cell extracts from tagged SPAC11D3.19 strain

  • Fractionate by size exclusion chromatography

  • Analyze fractions by western blotting

  • Compare elution profile with known complex markers

• Co-immunoprecipitation with mass spectrometry:

  • Optimize lysis conditions to maintain complex integrity

  • Perform IP with antibodies against tagged SPAC11D3.19

  • Identify co-purifying proteins by mass spectrometry

  • Compare with control IPs to identify specific interactors

• Blue native PAGE analysis:

  • Prepare native complexes from tagged strains

  • Separate on blue native gels

  • Identify complex components by mass spectrometry

  • Perform supershift assays with specific antibodies

• Density gradient ultracentrifugation:

  • Prepare cell extracts under native conditions

  • Separate complexes by sucrose or glycerol gradient

  • Analyze fractions for SPAC11D3.19 and potential complex members

  • Compare with known complex markers

When analyzing potential protein complexes, compare elution profiles or interaction partners with known S. pombe complexes to identify functional relationships .

What best practices should be followed when publishing research on SPAC11D3.19?

When publishing research on uncharacterized proteins like SPAC11D3.19, follow these best practices:

• Comprehensive characterization approach:

  • Combine multiple methodologies (genetic, biochemical, -omics)

  • Provide both in vivo and in vitro evidence

  • Include thorough controls and statistical analysis

  • Address potential functional redundancy

• Data sharing and reproducibility:

  • Deposit raw data in appropriate repositories (GEO, ProteomeXchange)

  • Provide detailed protocols with specific reagent information

  • Make strains available through repositories (Yeast Genetic Resource Center)

  • Include sufficient methodological detail for reproduction

• Nomenclature and annotation:

  • Follow community standards for gene/protein naming

  • Provide evidence levels for functional assignments

  • Update database entries with new findings

  • Clearly distinguish between direct observations and predictions

• Contextual interpretation:

  • Relate findings to known biological processes

  • Discuss evolutionary conservation

  • Compare with related proteins in S. pombe

  • Connect to broader biological significance