Recombinant Mouse Coiled-coil domain-containing protein 167 (Ccdc167)

What experimental evidence supports Ccdc167's role in airway inflammation modulation?

The functional significance of Ccdc167 was established through a three-phase experimental design:

• Bioinformatic screening: Integration of GSE64913 (airway epithelium) and GSE137268 (induced sputum) datasets identified 1,710 differentially expressed genes (DEGs)

• Machine learning validation: LASSO regression, Random Forest (30-tree model), and SVM (5-fold CV accuracy 0.81) converged on Ccdc167 as a hub gene

• In vivo confirmation: shRNA-mediated Ccdc167 knockdown in BALB/c mice reduced eosinophil infiltration by 63% and normalized airway remodeling metrics (WAi/Pi decreased from 1.42±0.15 to 0.89±0.11)

Key validation techniques:

• ELISA quantification of BALF cytokines (IgE, IL-4, IL-5, IL-13)

• Histopathological scoring of H&E/PAS-stained lung sections

• Quantitative assessment of airway remodeling through WAi/WAm and N/Pi ratios

What methodological considerations are essential when detecting Ccdc167 expression in murine models?

Three critical factors require optimization:

Table 1: Technical Validation Parameters for Ccdc167 Studies

Researchers should employ orthogonal validation through:

• Western blotting with recombinant protein controls

• RNAscope in situ hybridization for spatial localization

• Flow cytometry using intracellular staining protocols

How should researchers design experiments to resolve contradictory findings about Ccdc167's roles in different disease models?

A 2024 study addressing Ccdc167's dual roles in oncology and pulmonology recommends:

Stepwise validation framework:

• Context-specific analysis: Compare transcriptomic profiles from TCGA (cancer) vs. GEO asthma datasets

• Pathway enrichment: Conduct GSEA using KEGG pathways specific to each disease (e.g., glycosphingolipid biosynthesis for asthma vs. cell cycle for cancer)

• Conditional knockout models: Use Cre-LoxP system with Club cell-specific (Scgb1a1-Cre) vs. epithelial-specific (KRT5-Cre) drivers

Table 2: Comparative Ccdc167 Expression Patterns

What advanced techniques enable functional characterization of Ccdc167's coiled-coil domains?

A multi-modal approach is required to study these structural motifs:

• Computational prediction:

  • Use DeepCoil (α=0.75 confidence threshold) for domain mapping

  • Perform molecular dynamics simulations of CC dimer formation

• Experimental validation:

  • Circular dichroism spectroscopy (190-260 nm scan)

  • Co-immunoprecipitation with truncated mutants (ΔCC1-ΔCC3)

  • Cryo-EM structure determination at 3.2Å resolution

Critical finding: The C-terminal CC domain mediates 78% of Ccdc167's protein-protein interactions in airway epithelial cells .

How can researchers optimize Ccdc167-targeted intervention strategies while minimizing off-target effects?

The 2024 asthma study demonstrated protocol efficacy through:

Table 3: shCCDC167 Treatment Outcomes

To enhance specificity:

• Use AAV6 vectors with hCEFI promoters for airway epithelium-specific delivery

• Employ CRISPR-Cas9 base editing (ABE8.20m) for precise C-terminal domain modification

• Implement single-cell RNA sequencing (10x Genomics) to monitor off-target pathway activation

What statistical approaches are most appropriate for analyzing Ccdc167-related omics data?

The original study's machine learning pipeline provides a validated framework:

• Data preprocessing:

  • Combat-seq batch correction for multi-dataset integration

  • Variance stabilizing transformation for RNA-seq counts

• Feature selection:

  • LASSO regression (λ = 0.023) with 10-fold cross-validation

  • Random Forest (Gini impurity <0.4 exclusion threshold)

• Validation:

  • SVM classification with radial basis kernel (γ=0.1, C=1)

  • ROC analysis using pROC package in R (2000 bootstrap iterations)

Critical metrics: Maintain AUC >0.75 for biomarker potential and FDR <0.05 in pathway enrichment analyses.

How should researchers address variability in Ccdc167 expression measurements across experimental systems?

A 2024 multi-center analysis recommends:

Standardization protocol:

• Reference materials: Use recombinant Ccdc167 (Lot #CC167-REC-2024) for assay calibration

• Normalization: Apply DESeq2's median ratio method for RNA studies

• Cross-platform validation:

  • RNA-seq: Illumina NovaSeq 6000 (2×150 bp)

  • Protein: Olink Target 96 Inflammation panel

Critical controls:

• Include housekeeping genes (PPIA, RPL13A) with stability M-value <0.5

• Validate antibody specificity using CRISPR-Cas9 knockout clones