Recombinant Mouse Inhibitor of nuclear factor kappa-B kinase-interacting protein (Ikbip)

What is the molecular structure and function of mouse Ikbip?

Mouse Inhibitor of Nuclear Factor Kappa-B Kinase-Interacting Protein (Ikbip) is a protein with a predicted molecular mass of approximately 28.7 kDa, though its accurate molecular mass is reported as 29 kDa . Functionally, Ikbip interacts with the NF-κB signaling pathway, which plays a critical role in immune response regulation.

The protein has an isoelectric point of 6.8 and is primarily localized to the endoplasmic reticulum lumen . Recombinant versions typically contain the sequence from Thr78 to Arg301, often with an N-terminal His Tag for purification purposes .

In terms of its biological function, Ikbip appears to be involved in cellular processes related to immune response and potentially cancer progression, as evidenced by its expression patterns and correlations with various immune parameters in tumor contexts .

How does mouse Ikbip compare to human IKBIP?

Though the search results don't provide a direct comparison between mouse Ikbip and human IKBIP, research indicates that IKBIP functions are likely conserved across species. Human IKBIP has been extensively studied in pan-cancer analyses, where it shows variable expression across tumor and non-tumoral tissues and during various stages of tumor development .

The conservation of function is suggested by similar correlation patterns with immune parameters. For example, human IKBIP expression has been linked to tumor mutational burden (TMB), microsatellite instability (MSI), and immune checkpoint genes (ICGs) across multiple cancer types . Researchers working with mouse models should consider these similarities when translating findings between species while acknowledging potential species-specific differences.

What are the expression patterns of Ikbip in normal mouse tissues versus disease models?

While the search results don't provide specific information about Ikbip expression in normal mouse tissues, studies on human IKBIP indicate that expression patterns vary significantly between normal and cancerous tissues . By extension, researchers can anticipate similar variations in mouse models.

In human cancer studies, IKBIP is "highly expressed in most cancers and is negatively associated with the prognosis of several major cancer types" . When designing experiments with mouse models, researchers should consider:

• Establishing baseline expression in relevant normal tissues

• Comparing expression levels across different disease stages

• Examining tissue-specific variations in expression

• Correlating expression with pathological features

Methodologically, quantitative PCR, western blotting, and immunohistochemistry are recommended for characterizing expression patterns in both normal and disease tissues.

What are the optimal conditions for expressing and purifying recombinant mouse Ikbip?

For optimal expression of recombinant mouse Ikbip, prokaryotic expression systems using E. coli are commonly employed . Based on available information and standard protocols for similar proteins, the following methodological approach is recommended:

Expression System:

• Host: E. coli (BL21 or equivalent strain)

• Vector: pET-based expression vector with N-terminal His-tag

• Induction: 0.5-1.0 mM IPTG at OD600 0.6-0.8

• Temperature: 16-18°C for overnight expression (to enhance solubility)

Purification Protocol:

• Cell lysis using sonication or French press in PBS buffer containing protease inhibitors

• Clarification by centrifugation (20,000 × g, 30 min, 4°C)

• Affinity chromatography using Ni-NTA resin

• Washing with increasing imidazole concentrations (10-40 mM)

• Elution with 250-300 mM imidazole

• Buffer exchange to PBS, pH 7.4

Quality Control:

• SDS-PAGE analysis to confirm purity (>90% is typically achieved)

• Western blot confirmation using anti-His or specific anti-Ikbip antibodies

• Endotoxin testing (<1.0 EU per 1μg using LAL method)

How should recombinant mouse Ikbip be stored to maintain stability and activity?

Proper storage of recombinant mouse Ikbip is critical for maintaining its stability and activity. Based on the provided information, the following storage guidelines are recommended :

Short-term Storage:

• Store at 2-8°C for up to one month

• Buffer: PBS (pH 7.4) at a concentration of 0.1-1.0 mg/mL

• Addition of 0.01% SKL and 5% trehalose as stabilizers

Long-term Storage:

• Aliquot the protein to avoid repeated freeze/thaw cycles

• Store at -80°C for up to 12 months

• For lyophilized protein, reconstitute in 10mM PBS (pH 7.4)

• Avoid vortexing during reconstitution to prevent protein denaturation

Stability Assessment:
The thermal stability of recombinant Ikbip can be evaluated through accelerated thermal degradation testing at 37°C for 48h. Properly stored protein should show less than 5% degradation under these conditions .

When working with the protein, researchers should thaw aliquots on ice and use them immediately after thawing for optimal results. Any unused reconstituted protein should not be refrozen.

What functional assays can validate the activity of recombinant mouse Ikbip?

Validating the functional activity of recombinant mouse Ikbip is essential for ensuring experimental reliability. While the search results don't provide specific assays for Ikbip activity, the following approaches are recommended based on its known functions and standard practices for similar proteins:

Binding Assays:

• Co-immunoprecipitation (Co-IP): To verify Ikbip's interaction with IKKβ or other known binding partners

• Surface Plasmon Resonance (SPR): For quantitative measurement of binding kinetics

• ELISA-based interaction assays: Using immobilized Ikbip to capture binding partners

Functional Assays:

• NF-κB Reporter Assays: To assess the impact of Ikbip on NF-κB signaling pathway activation

• Phosphorylation Analysis: Using kinase assays to determine how Ikbip affects phosphorylation events in the NF-κB pathway

• Cellular Localization: Fluorescently tagged Ikbip can be used to confirm endoplasmic reticulum localization

Quality Control Assays:

• Western Blotting: For verification of protein integrity and identity

• Mass Spectrometry: To confirm the exact molecular weight and potential post-translational modifications

• Circular Dichroism: To assess proper protein folding

These assays should be adapted to the specific research question and experimental system being used.

How does Ikbip expression correlate with tumor mutational burden and microsatellite instability?

Comprehensive analysis of human IKBIP expression has revealed significant correlations with both tumor mutational burden (TMB) and microsatellite instability (MSI) across multiple cancer types, suggesting similar relationships may exist in mouse models .

Correlations with TMB:
In human cancers, IKBIP expression shows variable correlations with TMB depending on cancer type:

• Positive correlation in 8 cancer types: ACC, COAD, KIRC, LGG, LUAD, SARC, SKAM, and UCEC

• Negative correlation in 5 cancer types: CESC, ESCA, HNSC, PRAD, and THCA

Correlations with MSI:
Similar cancer-specific patterns are observed with MSI:

• Positive correlation in 4 cancer types: ACC, COAD, READ, and UCEC

• Negative correlation in 4 cancer types: CHOL, LGG, LUAD, and LUSC

These findings have significant implications for immunotherapy, as both TMB and MSI are established biomarkers for predicting response to immune checkpoint inhibitors. Researchers studying mouse models should consider examining similar correlations to determine if Ikbip could serve as a surrogate marker for TMB or MSI status in experimental settings.

What is the relationship between Ikbip and immune cell infiltration in tumors?

Research indicates that IKBIP expression correlates significantly with immune cell infiltration across multiple cancer types . When working with mouse models, researchers should consider the following relationships:

Correlation with Specific Immune Cell Types:
Human IKBIP expression shows substantial correlation with infiltrating immune cells:

• B cells in 12 cancer types

• CD4+ T cells in 13 cancer types

• CD8+ T cells in 23 cancer types

• Macrophages in 23 cancer types

• Neutrophils in 24 cancer types

• Dendritic cells in 24 cancer types

Immune Cell Subtype Relationships:
IKBIP expression demonstrates cancer-specific correlations with immune cell subtypes:

• Negative correlation in COAD, LGG, BLCA, PRAD, STAD, BRCA, and READ

• Positive correlation in THYM, OV, and LAML tissues

• Strongest correlations observed with Th2 cells and CLP cells

Methodological Approaches for Analysis:
For researchers studying these relationships in mouse models, several approaches are recommended:

• Flow cytometry to quantify immune cell populations in Ikbip-expressing tumors

• Immunohistochemistry to visualize spatial relationships between Ikbip expression and immune infiltrates

• Single-cell RNA sequencing to correlate Ikbip expression with immune cell phenotypes

• ESTIMATE algorithm application for stromal and immune score calculations

How can Ikbip be utilized as a biomarker for immunotherapy response?

Based on human cancer studies, IKBIP shows promise as a biomarker for immunotherapy response due to its correlations with established predictive factors . Researchers working with mouse models can explore similar applications through the following approaches:

Potential as Immunotherapy Response Predictor:

• IKBIP expression correlates with TMB and MSI, both established predictors of immunotherapy efficacy

• Positive correlations between IKBIP and immune checkpoint genes in several cancer types suggest relevance to checkpoint inhibitor therapy

• The relationship between IKBIP and tumor immune microenvironment indicates potential utility in stratifying responders versus non-responders

Methodological Framework for Biomarker Validation:

• Establish baseline Ikbip expression in the tumor model of interest

• Correlate expression with response to immunotherapy in preclinical models

• Perform multivariate analysis including Ikbip expression, TMB, MSI, and immune cell infiltration

• Develop cutoff values for "high" versus "low" Ikbip expression

• Validate findings across multiple tumor models

Research Design Considerations:

• Use paired tumor samples (pre- and post-treatment)

• Include appropriate controls (IgG-treated or vehicle-treated)

• Consider combination therapies that might synergize with Ikbip-targeting approaches

• Validate findings using both in vitro and in vivo systems

What bioinformatic approaches are recommended for analyzing Ikbip-related phosphoproteomic data?

Phosphoproteomic analysis can provide valuable insights into Ikbip function and regulation. For researchers analyzing such data, the following bioinformatic approaches are recommended based on recent methodologies:

Database Resources:

• iKiP-DB (in vitro Kinase-to-Phosphosite database): A specialized database that can predict kinase activity in phosphoproteomic data by expanding knowledge of kinase-to-phosphosite annotation

• PTMsigDB: A complementary resource for analyzing kinase-substrate associations

Analysis Workflow:

• Data Preprocessing:

  • Normalization using rank sample normalization

  • Quality control with minimum overlap requirements (e.g., 5 sites)

  • Appropriate permutation testing (e.g., 1000 permutations)

• Statistical Analysis:

  • Kolmogorov-Smirnov statistic with appropriate weighting (e.g., 0.75)

  • Area under curve calculations

  • Normalized enrichment score (NES) as output score

• Integration with Other Data Types:

  • Correlation with transcriptomic data

  • Pathway enrichment analysis

  • Network analysis to identify key interactors

Visualization Techniques:

• Heat maps of phosphorylation patterns

• Network visualization of kinase-substrate relationships

• Pathway enrichment visualization

• Correlation plots between Ikbip and phosphorylation events

How can researchers resolve contradictory data regarding Ikbip's role in different cancer types?

As observed in human IKBIP studies, the protein's role can vary significantly between cancer types, leading to apparently contradictory data . Researchers should employ the following strategies to address such contradictions:

Methodological Approaches to Resolve Contradictions:

• Context-Specific Analysis:

  • Separate data by cancer type, stage, and molecular subtype

  • Consider tissue-specific functions and microenvironments

  • Analyze correlations within specific genetic backgrounds

• Mechanistic Investigations:

  • Perform pathway analysis to identify differing downstream effects

  • Examine protein interaction networks in different cellular contexts

  • Investigate post-translational modifications that might alter function

• Integrated Analysis Frameworks:

  • Meta-analysis approaches with random-effects models

  • Bayesian hierarchical modeling to account for context-specific effects

  • Machine learning approaches to identify patterns across datasets

Data Interpretation Guidelines:

• Avoid generalizing findings across all cancer types

• Consider the tumor microenvironment's influence on Ikbip function

• Examine the role of genetic background in modifying Ikbip effects

• Integrate multiple data types (genomic, transcriptomic, proteomic) for a comprehensive view

The differential correlations of IKBIP with TMB and MSI across cancer types exemplify this complexity, with positive correlations in some cancers and negative in others .

What statistical methods are most appropriate for analyzing correlations between Ikbip and immune parameters?

When analyzing correlations between Ikbip and immune parameters, researchers should employ robust statistical methods tailored to the specific data types. Based on approaches used in human IKBIP studies, the following methods are recommended:

Correlation Analysis Approaches:

• For Continuous Variables:

  • Pearson correlation for normally distributed data

  • Spearman's rank correlation for non-parametric relationships

  • Partial correlation to control for confounding factors

• For Categorical or Mixed Data:

  • Point-biserial correlation for continuous vs. binary variables

  • Kendall's tau for ordinal data

  • Multiple correlation analysis for relationships with multiple variables

Statistical Significance and Validation:

• Adjust p-values for multiple testing (e.g., Benjamini-Hochberg procedure)

• Implement bootstrapping for confidence interval estimation

• Use cross-validation to assess the robustness of findings

• Consider sample size and power calculations for proper interpretation

Visualization and Reporting:

• Create correlation matrices with heatmap visualization

• Use scatter plots with regression lines for key relationships

• Report effect sizes alongside p-values

• Provide forest plots for meta-analysis of correlations across studies

As demonstrated in human IKBIP research, these approaches can reveal complex relationships between protein expression and immune parameters, such as the variable correlations between IKBIP and immune cell infiltration across different cancer types .

How might Ikbip function in non-cancer disease models?

While the provided search results focus primarily on IKBIP's role in cancer, its involvement in the NF-κB pathway suggests potential relevance to other disease models. Researchers interested in exploring Ikbip in non-cancer contexts should consider:

Potential Disease Contexts:

• Inflammatory Disorders: Given the central role of NF-κB in inflammation, Ikbip may influence inflammatory bowel disease, rheumatoid arthritis, or other autoimmune conditions

• Infectious Diseases: The correlation with immune parameters suggests potential roles in host response to viral or bacterial infections

• Neurodegenerative Diseases: NF-κB signaling is implicated in neuroinflammation associated with conditions like Alzheimer's and Parkinson's disease

• Metabolic Disorders: Potential involvement in metabolic inflammation and insulin resistance

Research Design Considerations:

• Establish baseline Ikbip expression in relevant tissues

• Use knockout or knockdown approaches to assess functional significance

• Examine Ikbip regulation during disease progression

• Consider temporal dynamics of expression and activation

Translational Potential:
The demonstrated association between IKBIP and immune parameters in cancer contexts suggests that similar mechanisms might operate in other disease states, potentially opening new therapeutic avenues .

What novel technologies can advance the study of Ikbip protein interactions?

Advanced technologies can provide deeper insights into Ikbip's protein interactions and functional networks. Researchers should consider these cutting-edge approaches:

Emerging Technological Approaches:

• Proximity Labeling Techniques:

  • BioID or TurboID fusions with Ikbip to identify proximal proteins in living cells

  • APEX2-based proximity labeling for temporal control of interaction mapping

  • Split-BioID for detecting conditional or stimulus-dependent interactions

• Advanced Imaging Approaches:

  • Super-resolution microscopy to visualize Ikbip distribution with nanometer precision

  • Live-cell FRET or BRET to monitor dynamic interactions in real-time

  • Correlative light and electron microscopy to connect function with ultrastructure

• Protein Engineering Methods:

  • Optogenetic control of Ikbip activity or localization

  • CRISPR-based tagging at endogenous loci for physiological expression levels

  • Nanobody-based detection systems for improved specificity

• Systems Biology Integration:

  • Multi-omics approaches combining proteomics, transcriptomics, and metabolomics

  • Network analysis to position Ikbip within signaling cascades

  • Machine learning for predicting functional consequences of Ikbip interactions

These technologies can help resolve controversies about Ikbip's function and potentially identify novel therapeutic targets in the NF-κB pathway.

How can the understanding of Ikbip phosphorylation dynamics improve therapeutic strategies?

Phosphorylation plays a critical role in protein function and signaling pathway regulation. For Ikbip, understanding its phosphorylation dynamics could significantly advance therapeutic strategies:

Phosphorylation Analysis Approaches:

• Identification of Phosphorylation Sites:

  • Mass spectrometry-based phosphoproteomics to map Ikbip phosphorylation sites

  • Phospho-specific antibodies for monitoring specific sites

  • Mutagenesis studies (e.g., phospho-mimetic or phospho-deficient mutants)

• Kinase-Substrate Relationships:

  • Utilize iKiP-DB to predict kinases that might phosphorylate Ikbip

  • In vitro kinase assays to validate predicted relationships

  • Inhibitor studies to assess functional significance

• Dynamic Regulation:

  • Temporal analysis of phosphorylation under different stimuli

  • Integration with signaling pathway analysis

  • Correlation with functional outcomes

Therapeutic Applications:

• Identification of druggable kinases that regulate Ikbip function

• Development of phosphorylation state-specific inhibitors

• Combination therapies targeting both Ikbip and its regulatory kinases

• Biomarker development based on phosphorylation status

The demonstrated importance of kinase activity in various cancer contexts suggests that understanding Ikbip phosphorylation could provide valuable insights for therapeutic development .