Recombinant Kluyveromyces lactis Inheritance of peroxisomes protein 2 (INP2)

What is INP2 and what is its function in yeast cells?

INP2 (Inheritance of peroxisomes protein 2) is a protein that plays a crucial role in controlling the inheritance of peroxisomes into daughter cells during yeast cell division. While its counterpart, Inp1, is responsible for peroxisome retention in mother cells, INP2 specifically mediates the transport and inheritance of peroxisomes into daughter cells . This coordinated system ensures proper distribution of the peroxisome population between mother and daughter cells during cell division, which is essential for maintaining cellular function in both cells. In Kluyveromyces lactis, the INP2 protein consists of 666 amino acids and is encoded by the INP2 gene (also known as KLLA0E01694g) .

How does INP2 cooperate with other proteins in the peroxisome inheritance process?

INP2 functions as part of a coordinated system with Inp1 to ensure proper peroxisome distribution during cell division. While these proteins have opposing functions, their balanced activities are essential for maintaining peroxisome populations in both mother and daughter cells . The molecular mechanism involves:

• Inp1 tethering peroxisomes to the cell cortex in mother cells, ensuring retention

• INP2 binding to the myosin motor protein Myo2, facilitating peroxisome transport along actin cables to the daughter cell

• Temporal regulation of both proteins' expression and activity during the cell cycle

This balanced system ensures that peroxisomes are properly distributed, with some retained in the mother cell (via Inp1) and others transported to the daughter cell (via INP2). Disruption of either protein leads to inheritance defects, with inp2 mutants showing reduced peroxisome inheritance in daughter cells .

What are the optimal conditions for expressing recombinant K. lactis INP2 in E. coli?

For optimal expression of recombinant K. lactis INP2 in E. coli, researchers should consider the following parameters:

Expression System:

• E. coli strains: BL21(DE3) or Rosetta for difficult-to-express eukaryotic proteins

• Expression vectors with inducible promoters (T7, tac)

• N-terminal His-tag for purification, as successfully implemented in commercial preparations

Culture Conditions:

• Initial growth at 37°C until OD600 reaches 0.6-0.8

• Reduce temperature to 18-25°C during induction to enhance proper folding

• IPTG concentration: 0.1-0.5 mM

• Extended post-induction time (16-18 hours) at lower temperature

Media and Supplements:

• Rich media (LB or TB) for higher protein yields

• Glucose (0.5-1%) to minimize leaky expression

• Appropriate antibiotics based on the expression vector

Harvest and Lysis:

• Mechanical disruption via sonication or high-pressure homogenization

• Lysis buffer: Tris-based buffer (pH 8.0) with protease inhibitors

This approach has been successfully used to produce recombinant full-length K. lactis INP2 (1-666aa) with N-terminal His-tag , yielding protein with greater than 90% purity as determined by SDS-PAGE.

How should recombinant INP2 be stored and handled to maintain activity?

Proper storage and handling of recombinant INP2 is crucial for maintaining its structural integrity and functional activity:

Short-term Storage:

• Store working aliquots at 4°C for up to one week

• Use Tris/PBS-based buffer (pH 8.0) with 6% trehalose as a stabilizing agent

Long-term Storage:

• Store at -20°C/-80°C upon receipt

• Add glycerol to a final concentration of 50% (recommended default)

• Aliquot before freezing to avoid repeated freeze-thaw cycles

Reconstitution of Lyophilized Protein:

• Briefly centrifuge vial before opening to bring contents to the bottom

• Reconstitute in deionized sterile water to a concentration of 0.1-1.0 mg/mL

• For long-term storage, add 5-50% glycerol (final concentration)

Critical Handling Considerations:

• Avoid repeated freeze-thaw cycles as this significantly reduces activity

• When thawing, place on ice and use immediately

• Consider adding stabilizing agents like BSA (0.1%) for dilute solutions

• Monitor protein stability through activity assays before experimental use

These storage recommendations are based on established protocols for recombinant INP2, which is typically supplied as a lyophilized powder that requires proper reconstitution and storage to maintain activity .

How can I design experiments to study INP2's role in peroxisome inheritance?

Studying INP2's role in peroxisome inheritance requires a multi-faceted experimental approach:

Genetic Manipulation Strategies:

• Generate INP2 deletion strains in K. lactis to establish baseline inheritance defects

• Create a series of INP2 mutants with targeted modifications in predicted functional domains

• Implement regulatable promoters for temporal control of INP2 expression

Visualization Approaches:

• Fluorescently tag INP2 (e.g., INP2-GFP) to track its localization

• Label peroxisomes with fluorescent markers (e.g., RFP-SKL) to monitor distribution

• Use time-lapse microscopy to track peroxisome movement during cell division

Quantitative Analysis Framework:
For rigorous analysis, implement the following experimental design:

Quantification Metrics:

• Peroxisome count ratio between mother and daughter cells

• Percentage of buds containing peroxisomes at different cell cycle stages

• Velocity and directionality of peroxisome movement toward the bud

This comprehensive approach provides robust data on INP2's role while accounting for experimental variation through appropriate statistical design .

What analytical approaches should be used to resolve conflicting data on INP2 function?

When faced with conflicting data regarding INP2 function, a systematic analytical approach is essential:

Source Assessment Framework:

• Evaluate methodological differences between studies:

  • Different yeast species (S. cerevisiae vs. K. lactis)

  • Variations in protein expression systems and tags

  • Differences in growth conditions and media composition

• Measurement Parameter Analysis:

  • Identify differences in quantification methods

  • Evaluate statistical approaches and sample sizes

  • Consider sensitivity and specificity of detection techniques

Reconciliation Strategies:

• Design direct comparison experiments:

  • Reproduce conflicting results under identical conditions

  • Test both methodological approaches in parallel

• Implement expanded analysis:

  • Include additional time points or environmental conditions

  • Consider single-cell analysis to account for cell-to-cell variation

  • Evaluate dynamic changes rather than endpoint measurements

Statistical Reconciliation:
Apply variance components analysis to partition observed variation into:

• Biological variation (true effect)

• Technical variation (methodology-dependent)

• Random error

This statistical approach helps determine whether conflicting results arise from methodological differences or reflect true biological complexity .

By systematically analyzing methodological differences and designing targeted validation experiments, apparently conflicting data can often be reconciled into a more comprehensive understanding of INP2 function.

How can advanced imaging approaches be used to study INP2 dynamics?

Advanced imaging approaches provide powerful tools for studying INP2 dynamics during peroxisome inheritance:

Live-Cell Imaging Strategies:

• Multi-channel time-lapse microscopy:

  • INP2-GFP for protein localization

  • RFP-SKL for peroxisome tracking

  • Cell cycle markers (e.g., nuclear markers) for correlating with cell division stages

• Super-resolution techniques:

  • Structured Illumination Microscopy (SIM) for improved spatial resolution

  • Single-molecule localization microscopy for precise protein positioning

Quantitative Image Analysis:

• Tracking algorithms to follow individual peroxisomes over time

• Intensity analysis to quantify INP2 association with peroxisomes

• Co-localization analysis to determine interaction with cytoskeletal elements

Experimental Design for Imaging:
To ensure robust statistical analysis of imaging data, implement:

• Block design with minimum 4 replicates to control for day-to-day variation

• Randomization of samples to minimize bias

• Mixed-effects models to account for nested data structure (peroxisomes within cells, cells within experiments)

This integrated imaging approach, combined with rigorous experimental design and statistical analysis, provides comprehensive insights into the dynamic behavior of INP2 during peroxisome inheritance.

What statistical approaches are most appropriate for analyzing INP2 functional data?

Analyzing INP2 functional data requires sophisticated statistical approaches to account for experimental variation and complex data structures:

Recommended Statistical Framework:

• Linear Mixed-Effects Models:

  • Account for hierarchical experimental structure

  • Include both fixed effects (treatments) and random effects (experimental blocks)

• Variance Components Analysis:

  • Partition variance into biological and technical sources

  • Estimate variance parameters using REML (Restricted Maximum Likelihood)

  • Calculate Mean Variance of Difference (MVD) for treatment comparisons

Multiple Testing Correction:
When analyzing multiple variants or conditions:

• Apply FDR (False Discovery Rate) for large-scale comparisons

• Use Bonferroni correction for smaller, targeted comparisons

• Report both raw and adjusted p-values for transparency

Sample Size Considerations:

• Minimum 4 biological replicates recommended based on power analysis

• For imaging studies, analyze at least 30-50 cells per condition

• Power analysis should target detection of 1.5-fold changes with 80% power

This rigorous statistical approach ensures robust interpretation of INP2 functional data while accounting for experimental variation through appropriate experimental design .

How can I correlate INP2 expression levels with peroxisome inheritance efficiency?

Correlating INP2 expression levels with peroxisome inheritance efficiency requires integrated quantitative approaches:

Experimental Setup:

• Create an expression gradient:

  • Use inducible promoters with different inducer concentrations

  • Generate strains with varied INP2 expression levels through promoter replacements

• Synchronized measurement:

  • Measure INP2 levels and inheritance simultaneously in the same cells

  • Apply cell synchronization to control for cell cycle variation

Correlation Analysis Framework:

• Quantify both parameters:

  • INP2 expression: Western blotting or fluorescence intensity of tagged protein

  • Inheritance efficiency: Ratio of peroxisomes in daughter vs. mother cells

• Statistical correlation:

  • Calculate Pearson's or Spearman's correlation coefficients

  • Perform regression analysis (linear or non-linear as appropriate)

  • Apply mixed-effects models to account for experimental blocks

Advanced Analysis Options:

By implementing this rigorous analytical framework, researchers can establish causal relationships between INP2 expression levels and peroxisome inheritance, while controlling for experimental variation through appropriate statistical design .

What methods can be used to characterize functional domains of INP2?

Characterizing the functional domains of INP2 requires a comprehensive approach combining mutational analysis with functional assays:

Domain Mapping Strategy:

• Generate a series of truncation mutants:

  • N-terminal truncations to identify peroxisome-binding regions

  • C-terminal truncations to identify domains involved in cytoskeletal interactions

  • Internal deletions of predicted functional regions

• Site-directed mutagenesis:

  • Target conserved residues identified through sequence alignment

  • Focus on predicted motifs for protein-protein interactions

  • Create alanine scanning mutations across regions of interest

Functional Assays:

• Peroxisome inheritance assay:

  • Express mutant variants in inp2Δ background

  • Quantify rescue of inheritance defects

  • Measure peroxisome distribution between mother and daughter cells

• Protein localization:

  • Visualize GFP-tagged mutants

  • Determine if mutations affect peroxisome association

  • Assess co-localization with cytoskeletal elements

Experimental Design Considerations:
Implement a robust experimental design to ensure reliable results:

• Use incomplete block design with at least 4 replicates

• Include wild-type INP2 and deletion controls in each block

• Apply statistical models that account for block structure when analyzing results

This systematic approach provides comprehensive insights into the structure-function relationships of INP2 while controlling for experimental variation through appropriate statistical design.

How can recombinant INP2 be used to study peroxisome-cytoskeleton interactions?

Recombinant INP2 provides a powerful tool for studying peroxisome-cytoskeleton interactions through in vitro and reconstitution approaches:

In Vitro Binding Assays:

• Actin binding assays:

  • Use purified recombinant INP2

  • Determine direct binding to F-actin through co-sedimentation

  • Measure binding affinity and kinetics

• Myosin motor interaction:

  • Test binding of recombinant INP2 to myosin motor proteins

  • Perform pull-down assays with purified components

  • Use surface plasmon resonance to measure binding kinetics

Reconstitution Systems:

• Minimal motility assays:

  • Attach recombinant INP2 to artificial liposomes

  • Observe movement along actin filaments in the presence of myosin

  • Quantify transport efficiency and directionality

• Peroxisome-mimetic systems:

  • Create liposomes with peroxisomal membrane composition

  • Incorporate recombinant INP2 into these membranes

  • Test interactions with cytoskeletal elements

Experimental Design and Analysis:
For rigorous characterization:

• Use multiple protein preparations to ensure reproducibility

• Implement factorial experimental design to test multiple conditions

• Apply appropriate statistical analysis accounting for experimental blocks

This in vitro approach complemented with cellular studies provides mechanistic insights into how INP2 mediates peroxisome-cytoskeleton interactions during inheritance.