Recombinant Schizosaccharomyces pombe Uncharacterized protein C15A10.09c (SPAC15A10.09c)

What is SPAC15A10.09c and why is it classified as a "priority unstudied" protein?

SPAC15A10.09c is an uncharacterized protein in Schizosaccharomyces pombe that has been classified as a "priority unstudied" protein based on several key criteria. It belongs to a group of 135 conserved proteins that show clear human orthologs and strong conservation profiles across 100 metazoans, suggesting they have fundamental biological roles conserved over approximately 1000 million years of evolution . Despite this conservation, these proteins have not been directly studied in any organism.

Notably, SPAC15A10.09c is among the 49 proteins (approximately 36% of these priority unstudied proteins) that are conserved from fission yeast to humans but lack clear orthologs in budding yeast (Saccharomyces cerevisiae) . This distinctive evolutionary pattern suggests it may perform functions that were either lost or significantly altered in the S. cerevisiae lineage after divergence from the common ancestor with S. pombe . The significant human conservation makes this protein particularly interesting from both evolutionary and medical perspectives.

What phenotypes are associated with SPAC15A10.09c deletion?

Deletion of SPAC15A10.09c results in several notable phenotypes that provide clues to its cellular function:

• Chronological lifespan extension: The SPAC15A10.09c deletion mutant exhibits a strong longevity phenotype in stationary-phase cells, suggesting a role in cellular aging pathways . Interestingly, this contrasts with reports showing reduced chronological lifespan in nitrogen-limited quiescent cells, indicating context-dependent effects on aging .

• Respiratory deficiency: The mutant grows slowly on non-fermentable media, indicating impaired mitochondrial respiration . This suggests SPAC15A10.09c plays a role in respiratory metabolism.

• Network associations: Phenotypic clustering places SPAC15A10.09c in a tight correlation network with genes involved in respiration and carbon metabolism, including:

  • otg1 (galactosyltransferase)

  • cox20 (cytochrome c oxidase assembly protein)

  • sdh8 (mitochondrial respiratory chain complex II assembly factor)

  • sdh3 (succinate dehydrogenase cytochrome b subunit)

  • emi5 (succinate dehydrogenase complex assembly)

• DNA damage repair: Computational predictions suggest SPAC15A10.09c functions in DNA damage repair processes, which aligns with its potential role in aging given the connection between DNA damage accumulation and cellular senescence .

These phenotypes collectively point to functions related to mitochondrial metabolism, stress responses, and cellular aging pathways.

What experimental methods are available for studying SPAC15A10.09c?

Several complementary experimental approaches are effective for studying SPAC15A10.09c:

• Gene deletion and modification:

  • PCR-based gene deletion using antibiotic resistance markers

  • Conditional expression systems using regulatable promoters (e.g., nmt1)

  • Epitope tagging for protein detection and localization

  • CRISPR-Cas9 for precise genome editing

• Protein expression and analysis:

  • Recombinant protein expression (commercially available)

  • Western blotting using epitope tags or specific antibodies

  • Subcellular fractionation to determine localization

  • Co-immunoprecipitation to identify interaction partners

• Phenotypic analysis:

  • Growth assays on different carbon sources

  • Chronological lifespan determination

  • DNA damage sensitivity tests

  • Stress response assays

  • Cell morphology and cell cycle analysis

• Omics approaches:

  • Transcriptome analysis using RNA-seq and tools like MultiRNAflow

  • Proteomics to identify changes in protein abundance

  • Metabolomics focusing on mitochondrial metabolism

  • Genetic interaction mapping

• Microscopy techniques:

  • Fluorescence microscopy with tagged proteins

  • Live-cell imaging to monitor dynamics

  • Electron microscopy for ultrastructural analysis

• Mitochondrial function assays:

  • Oxygen consumption measurements

  • Membrane potential analysis

  • ROS production quantification

  • Mitochondrial morphology assessment

These methods can be combined to build a comprehensive understanding of SPAC15A10.09c function.

How should I design gene deletion experiments to study SPAC15A10.09c function?

When designing gene deletion experiments for SPAC15A10.09c, follow these methodological steps:

• Deletion strategy planning:

  • Use PCR-based gene deletion with kanMX (G418 resistance) or another selectable marker

  • Design primers with 80-100bp homology to regions flanking SPAC15A10.09c

  • Include verification primers to confirm successful deletion

  • Consider retaining the possibility for marker recycling if multiple deletions are planned

• Essential controls:

  • Wild-type parental strain with identical genetic background

  • Complementation control (SPAC15A10.09c deletion with reintroduced gene)

  • Positive controls with known phenotypes similar to predictions (e.g., mitochondrial or DNA repair mutants)

• Transformation and verification:

  • Transform S. pombe cells using lithium acetate method

  • Select transformants on appropriate antibiotic media

  • Verify deletion by PCR from both ends of the integration site

  • Confirm absence of the SPAC15A10.09c transcript by RT-PCR

• Phenotypic characterization matrix:

  Condition Category	Specific Tests	Measurements	Controls
  Growth conditions	YES, EMM, non-fermentable carbon	Growth rate, lag phase, max OD	Wild-type, known respiratory mutants
  Stress responses	H₂O₂, heat shock, nutrient limitation	Survival rate, recovery time	Wild-type, known stress-sensitive mutants
  DNA damage	UV, MMS, hydroxyurea, camptothecin	Survival curves, recovery rate	Wild-type, rad51Δ
  Aging studies	Chronological lifespan	Viability over time (CFU)	Wild-type, long-lived mutants (e.g., sck2Δ)
  Cell cycle	Cell size, division timing	Flow cytometry, microscopy	Wild-type

• Data analysis approach:

  • Apply appropriate statistical tests for each phenotypic assay

  • Use methods like those in the pyphe package for high-throughput phenotypic analysis

  • Consider both the magnitude and statistical significance of phenotypic differences

This design ensures comprehensive characterization of SPAC15A10.09c function through its deletion phenotypes.

What approaches should I use to determine SPAC15A10.09c localization?

To determine the subcellular localization of SPAC15A10.09c, implement multiple complementary approaches:

• Fluorescent protein tagging:

  • Create C-terminal and N-terminal GFP fusion constructs, as transmembrane topology may affect tag accessibility

  • Express from the native promoter to maintain physiological expression levels

  • Ensure the tag doesn't disrupt function by confirming the fusion protein complements deletion phenotypes

  • Co-localize with established organelle markers (mitochondria, ER, plasma membrane)

• Immunofluorescence microscopy:

  • Generate antibodies against SPAC15A10.09c or use epitope tags (HA, FLAG, Myc)

  • Fix cells using methods optimized for membrane proteins

  • Use confocal microscopy for high-resolution imaging

  • Perform co-staining with organelle-specific markers

• Subcellular fractionation:

  • Separate cellular components through differential centrifugation

  • Create pure fractions of different organelles (mitochondria, ER, plasma membrane)

  • Detect SPAC15A10.09c in different fractions by Western blotting

  • Include controls for each cellular compartment (e.g., Cox2 for mitochondria)

• Protease protection assays:

  • Determine membrane topology by treating intact organelles with proteases

  • Compare accessibility of N- and C-terminal tags to determine orientation

  • Use selective membrane permeabilization to access different compartments

• Proximity labeling:

  • Fuse SPAC15A10.09c with BioID or APEX2 enzymes

  • Allow in vivo biotinylation of proximal proteins

  • Identify labeled proteins through mass spectrometry

  • Map the protein's microenvironment within the cell

Based on phenotypic correlations with mitochondrial genes and growth defects on non-fermentable media, special attention should be paid to potential mitochondrial localization . The protein's sequence features suggest it may be a membrane protein, possibly with multiple transmembrane domains, which should inform the design of fusion constructs.

How should I design experiments to investigate SPAC15A10.09c's role in cellular aging?

To investigate SPAC15A10.09c's role in cellular aging, design a comprehensive experimental approach:

• Chronological lifespan (CLS) analysis:

  • Measure CLS in both wild-type and ΔSPAC15A10.09c strains using colony forming unit (CFU) counts over time

  • Compare different growth media conditions:

    • Glucose-limited (stationary phase)

    • Nitrogen-limited (quiescence)

  • Include appropriate controls:

    • Long-lived mutants (Δsck2, Δpka1)

    • Short-lived mutants (Δsod2)

    • Complemented strain (ΔSPAC15A10.09c + SPAC15A10.09c)

• Aging biomarker assessment:

  • Measure ROS levels using fluorescent probes (e.g., dihydroethidium)

  • Assess mitochondrial function throughout aging (membrane potential, respiration)

  • Quantify ATP levels during chronological aging

  • Measure oxidative damage to proteins, lipids, and DNA

• Genetic interaction analysis:

  • Create double mutants with genes in known aging pathways:

    • TOR pathway (tor1, sck2)

    • Stress response (sty1, atf1)

    • Mitochondrial function (cit1, sdh genes)

  • Determine epistatic relationships to position SPAC15A10.09c in aging pathways

  • Test whether human orthologs can complement the phenotype

• Transcriptome and proteome analysis:

  • Compare expression profiles between wild-type and mutant during aging

  • Use time-course analysis to capture dynamic changes

  • Apply the MultiRNAflow package for temporal transcriptome analysis

  • Identify differentially regulated pathways during aging

• Metabolic profiling:

  • Analyze key metabolites in central carbon metabolism

  • Focus on TCA cycle intermediates given the mitochondrial connection

  • Monitor NAD+/NADH ratios throughout lifespan

  • Examine amino acid and lipid metabolism changes

The experimental design should account for the observation that SPAC15A10.09c deletion has opposite effects in stationary-phase versus quiescent cells , indicating context-dependent functions that require careful experimental setup to properly characterize.

What controls are essential when analyzing DNA damage response in SPAC15A10.09c mutants?

When analyzing the predicted DNA damage response functions of SPAC15A10.09c, include these essential controls:

• Strain controls:

  • Wild-type strain with identical genetic background

  • SPAC15A10.09c deletion strain

  • Complemented strain (deletion with reintroduced gene)

  • Known DNA repair mutants as positive controls:

    • Homologous recombination: Δrad51, Δrad52

    • Checkpoint control: Δrad3, Δchk1

    • Base excision repair: Δapn1

    • Nucleotide excision repair: Δrad13

    • Non-homologous end joining: Δpku70

• Treatment controls:

  • Untreated samples for each strain

  • Dose response curves for each damaging agent

  • Different classes of DNA damaging agents:

    • Double-strand breaks: ionizing radiation, bleomycin

    • Replication stress: hydroxyurea, camptothecin

    • Base modifications: MMS, UV radiation

  • Recovery time course measurements

• Analytical controls:

  • Technical replicates (minimum of 3)

  • Biological replicates (minimum of 3 independent experiments)

  • Multiple timepoints to capture repair kinetics

  • Internal standards for quantitative assays

• Pathway-specific controls:

  • Checkpoint activation markers (Chk1 phosphorylation)

  • Recombination markers (Rad52 foci)

  • DNA damage markers (γ-H2A phosphorylation)

  • Cell cycle synchronization controls

• Experimental design controls:

  • Blind scoring where applicable

  • Randomized sample ordering

  • Appropriate statistical tests for data analysis

  • Inclusion of multiple phenotypic readouts:

    • Survival assays

    • Mutation rates

    • Repair kinetics

    • Checkpoint activation/release

The experimental approach should investigate whether SPAC15A10.09c is directly involved in DNA repair processes or whether it influences repair indirectly through its apparent roles in mitochondrial function and metabolism . This requires careful comparative analysis with mutants in established DNA repair pathways.

How can I design experiments to investigate potential connections between SPAC15A10.09c and mitochondrial function?

To investigate connections between SPAC15A10.09c and mitochondrial function, design experiments following this methodological framework:

• Respiratory capacity assessment:

  • Measure oxygen consumption rates in wild-type vs. ΔSPAC15A10.09c cells

  • Compare growth on fermentable (glucose) vs. non-fermentable (glycerol, ethanol) carbon sources

  • Quantify respiration in different growth phases

  • Test specific respiratory chain complex activities (I-IV)

• Mitochondrial integrity analysis:

  • Examine mitochondrial morphology using fluorescence microscopy

  • Measure mitochondrial membrane potential (ΔΨm) using potentiometric dyes

  • Assess mitochondrial genome stability and copy number

  • Analyze ROS production using specific probes

• Genetic interaction studies:

  • Create double mutants with genes in the phenotype-correlation network:

    • cox20 (cytochrome c oxidase assembly)

    • sdh8 (complex II assembly factor)

    • sdh3 (succinate dehydrogenase component)

    • emi5 (complex II assembly)

  • Test interactions with mitochondrial protein import machinery

  • Examine effects of mitochondrial stress response pathway mutations

• Biochemical assays:

  • Measure activities of TCA cycle enzymes

  • Analyze respiratory chain complex assembly by Blue Native PAGE

  • Assess mitochondrial protein import efficiency

  • Quantify ATP production in isolated mitochondria

• Transcriptome and proteome analysis:

  • Compare expression of nuclear-encoded mitochondrial genes

  • Analyze mitochondrial proteome composition

  • Examine retrograde signaling pathway activation

  • Use techniques from MultiRNAflow to analyze temporal expression patterns

• Metabolic profiling:

  Metabolic Parameter	Method	Expected Outcome if Mitochondrial Function Affected
  TCA cycle intermediates	LC-MS	Altered levels in ΔSPAC15A10.09c
  NAD+/NADH ratio	Enzymatic assay	Disrupted redox balance
  Oxygen consumption	Respirometry	Reduced in ΔSPAC15A10.09c
  Mitochondrial membrane potential	JC-1 or TMRM staining	Altered in mutant
  ROS production	DHE, MitoSOX	Increased in ΔSPAC15A10.09c
  ATP/ADP ratio	Luciferase assay	Reduced with respiratory substrates

The tight phenotypic correlation between SPAC15A10.09c and multiple mitochondrial proteins, combined with the growth defect on non-fermentable media, strongly suggests a functional connection to mitochondria that warrants thorough investigation .

How can network analysis help predict SPAC15A10.09c function?

Network analysis provides powerful approaches for predicting SPAC15A10.09c function:

• Phenotypic correlation network analysis:

  • Analyze the tight phenotype-correlation network that includes SPAC15A10.09c and ten other genes, particularly focusing on the four with known chronological lifespan phenotypes

  • Calculate correlation coefficients between phenotypic profiles across multiple conditions

  • Apply clustering methods like k-medoids with Pearson correlation distance, as used in the phenomics study

  • Position SPAC15A10.09c within the eight phenotypic clusters identified in broad phenotypic profiling studies

• Functional enrichment analysis:

  • Perform GO term enrichment on the network genes

  • Identify overrepresented pathways using tools like g:Profiler

  • Generate lollipop graphs showing the most significant enriched terms, ranked by -log10(p-value)

  • Create Manhattan plots to visualize enrichment across functional categories

• Cross-species network comparison:

  • Apply co-inertia analysis to compare networks across species

  • Use the Hungarian algorithm for ortholog matching in network comparisons

  • Examine whether human orthologs maintain similar network positions

  • Apply k-means clustering and majority voting to identify conserved modules

• Integrative network approaches:

  • Combine multiple data types (genetic interactions, physical interactions, co-expression)

  • Apply machine learning approaches like random forests for predicting function

  • Utilize network embeddings and FunFams for improved prediction accuracy

  • Integrate growth phenotype data as features for function prediction

• Network visualization and analysis:

  • Create network diagrams highlighting SPAC15A10.09c's position

  • Calculate network metrics (centrality, betweenness) to assess importance

  • Identify network modules using community detection algorithms

  • Apply back-transformation methods to map predictions to the original space

The phenomics study already provides strong network-based evidence linking SPAC15A10.09c to mitochondrial function, DNA repair, and aging processes . Further network analysis could refine these predictions and identify specific pathways and processes within these broader functional areas.

What bioinformatic approaches can predict SPAC15A10.09c function?

Multiple bioinformatic approaches can be integrated to predict SPAC15A10.09c function:

• Sequence-based prediction:

  • Protein domain prediction using Pfam, SMART, and InterPro

  • Transmembrane topology prediction using TMHMM or Phobius

  • Motif identification using MEME or GLAM2

  • Signal peptide and targeting sequence prediction

• Structural approaches:

  • 3D structure prediction using AlphaFold or RoseTTAFold

  • Structural similarity searches against known proteins

  • Binding site and functional residue prediction

  • Molecular dynamics simulations to assess conformational properties

• Machine learning methods:

  • Random forest models as used in the phenomics study

  • Features based on:

    • Network embeddings

    • FunFams (functional families)

    • Growth phenotype data across conditions

  • Cross-validation strategies to prevent overfitting

  • Area under precision-recall curve (AUPR) for performance evaluation

• Evolutionary analysis:

  • Ortholog identification across species using OMA

  • Phylogenetic profiling to identify co-evolving genes

  • Evolutionary rate analysis to identify functional constraints

  • Identification of conserved residues that may be functionally important

• Integrative approaches:

  • Combining predictions from multiple algorithms

  • Weighted integration based on algorithm performance

  • Bayesian integration of heterogeneous data types

  • Meta-predictors that combine multiple prediction tools

The phenomics study implemented random forest models using the DecisionTree.jl package in Julia v1.5, with forests of 500 trees and hyperparameter optimization through grid search . This approach successfully predicted GO terms for SPAC15A10.09c, including its role in DNA damage repair, which was subsequently validated experimentally through the observation of longevity phenotypes .

How should I analyze transcriptomic data for SPAC15A10.09c studies?

For analyzing transcriptomic data in SPAC15A10.09c studies, implement the following methodological approach:

• Experimental design considerations:

  • Include proper biological replicates (minimum of 3)

  • Consider time-course experiments to capture dynamic responses

  • Include appropriate controls (wild-type, complemented strains)

  • Consider different growth conditions (fermentable vs. non-fermentable media)

• Data preprocessing and normalization:

  • Use the DATAprepSE() function from MultiRNAflow for initial preprocessing

  • Apply appropriate normalization methods:

    • rle transformation for differential expression analysis

    • rlog or vst transformation for unsupervised analysis

  • Verify data quality using boxplots of normalized counts

• Exploratory data analysis:

  • Perform Principal Component Analysis using PCAanalysis() from MultiRNAflow

  • Apply Hierarchical Clustering on Principal Components with HCPCanalysis()

  • Use MFUZZanalysis() for temporal clustering of expression patterns

  • Visualize gene expression profiles with DATAplotExpressionGenes()

• Differential expression analysis:

  • Use DEanalysisGlobal() for statistical analysis of transcriptional responses

  • Apply DESeq2 methods for identifying differentially expressed genes

  • Create volcano plots and MA plots with DEplotVolcanoMA()

  • Generate heatmaps of differentially expressed genes with DEplotHeatmaps()

• Functional enrichment analysis:

  • Perform Gene Ontology analysis using GSEAQuickAnalysis()

  • Generate inputs for external tools using GSEApreprocessing()

  • Create lollipop graphs showing significant GO terms

  • Focus on processes related to mitochondrial function and DNA repair

• Integration with phenotypic data:

  • Correlate expression changes with phenotypic effects

  • Compare transcriptomic profiles with those of related mutants

  • Look for gene expression signatures characteristic of mitochondrial dysfunction

  • Analyze expression of genes in the phenotype-correlation network with SPAC15A10.09c

The MultiRNAflow R package provides a unified framework specifically designed for temporal RNA-seq data with multiple biological conditions, making it particularly suitable for SPAC15A10.09c studies where time-dependent effects and multiple conditions are likely relevant .

What is known about SPAC15A10.09c in relation to cellular aging mechanisms?

SPAC15A10.09c shows significant connections to cellular aging mechanisms, with several key findings:

• Strong longevity phenotype:

  • SPAC15A10.09c deletion mutants show a strong longevity phenotype in stationary-phase cells (glucose limitation)

  • This suggests the protein normally functions as a negative regulator of chronological lifespan

  • Interestingly, the same deletion reportedly reduces lifespan in quiescent cells (nitrogen limitation), indicating context-dependent effects

• Mitochondrial connections:

  • SPAC15A10.09c forms a tight phenotype-correlation network with mitochondrial genes

  • These include components of respiratory chain complex II (sdh3, sdh8, emi5) and cytochrome c oxidase assembly (cox20)

  • The mutant grows slowly on non-fermentable media, indicating respiratory deficiencies

  • Mitochondrial function is a well-established determinant of aging in yeast and other organisms

• DNA repair associations:

  • Computational prediction suggested a role in DNA damage repair

  • DNA damage accumulation is a hallmark of aging

  • Impaired DNA repair typically reduces lifespan, making the longevity phenotype particularly interesting

• Pathway connections:

  • The gene was identified in the "aging/cell death" category by NET-FF predictions

  • It shows phenotypic similarity to other genes involved in aging and cellular stress responses

  • The effects may relate to metabolic regulation, as suggested by growth phenotypes on different carbon sources

• Conservation implications:

  • As a protein conserved from fission yeast to humans but absent in budding yeast, SPAC15A10.09c may represent an evolutionarily conserved aging regulator

  • The human ortholog could have relevance for understanding human aging processes

  • The absence in S. cerevisiae suggests possible alternative aging regulatory mechanisms in different yeast species

The opposing effects on chronological lifespan in different metabolic states (glucose-limited vs. nitrogen-limited) are particularly interesting and suggest SPAC15A10.09c may function at the intersection of nutrient sensing, metabolic regulation, and stress response pathways that influence aging .

What is the evolutionary significance of SPAC15A10.09c's conservation pattern?

The conservation pattern of SPAC15A10.09c—present in S. pombe and humans but absent in S. cerevisiae—has several significant evolutionary implications:

• Ancestral gene presence:

  • SPAC15A10.09c likely existed in the last common ancestor of S. pombe and humans

  • Its absence in S. cerevisiae represents gene loss rather than recent acquisition in the S. pombe lineage

  • This pattern is consistent with the finding that 3% of S. pombe ORFs have homologs in C. elegans that appear to have disappeared from the S. cerevisiae lineage

• Divergent evolution of cellular processes:

  • The conservation pattern suggests divergent evolution of the processes in which SPAC15A10.09c functions

  • S. cerevisiae may have developed alternative mechanisms for these functions

  • This aligns with known divergence in mitochondrial metabolism between the two yeast species, with S. pombe being more similar to humans in several aspects

• Model organism implications:

  • This conservation pattern highlights why S. pombe is sometimes a better model for human biology than S. cerevisiae

  • For studying SPAC15A10.09c function, S. pombe clearly provides insights that cannot be gained from S. cerevisiae

  • The comparison between the three organisms offers a natural evolutionary experiment

• Functional constraints:

  • Conservation over approximately 1 billion years of evolution indicates strong functional constraints

  • The protein likely performs a fundamental cellular function in organisms that retained it

  • The loss in S. cerevisiae suggests either functional redundancy or environmental adaptation

• Medical relevance:

  • The human ortholog may have medical significance, particularly in pathways related to mitochondrial function and aging

  • Understanding SPAC15A10.09c function in S. pombe could provide insights into human disease mechanisms

  • It represents a potential target for therapeutic interventions with no counterpart in commensal yeasts

This evolutionary pattern makes SPAC15A10.09c particularly valuable for studying both conserved eukaryotic functions and evolutionary divergence in fundamental cellular processes . The protein belongs to a set of genes acquired or lost since the divergence of S. pombe and S. cerevisiae from their common ancestor, representing an excellent example of the dynamic nature of genome evolution .