Recombinant Chicken Coiled-coil domain-containing protein 43 (CCDC43)

What is CCDC43 and what structural features characterize this protein?

CCDC43 (Coiled-coil domain-containing protein 43) is a protein characterized by the presence of coiled-coil structural motifs. Coiled-coil domains consist of two to five amphipathic α-helices that associate into a left-handed superhelix . The amino acid sequences of coiled-coil domains are arranged in distinctive (abcdefg)n heptad repeats, where positions a and d are preferentially occupied by non-polar residues forming a hydrophobic core, while the remaining positions are usually hydrophilic .

Analysis of CCDC protein domain architecture using computational tools like the Simple Modular Architecture Research Tool (SMART) reveals that CCDC proteins contain multiple coiled-coil domains. Similar to FAM81 proteins discussed in the literature, CCDC43 likely contains regions predicted to be intrinsically disordered in addition to its coiled-coil domains .

What expression systems are typically used for producing recombinant chicken proteins?

Recombinant chicken proteins can be produced using several expression systems:

The production process typically involves :

• Gene cloning: Isolating the chicken CCDC43 gene and inserting it into appropriate vectors

• Transformation: Introducing the vector into host cells

• Expression: Culturing transformed cells under optimal conditions

• Purification: Isolating the recombinant protein using affinity chromatography

For chicken proteins specifically, total RNA is extracted from chicken tissues (such as spleen) using reagents like TRIzol, followed by cDNA preparation with first-strand synthesis kits. Target sequences are then amplified using appropriate primers and cloned into expression vectors (e.g., pCold) using seamless cloning techniques .

How do researchers optimize the purification of recombinant coiled-coil domain proteins?

Purification of coiled-coil domain proteins presents specific challenges due to their structural properties:

• Initial considerations: Coiled-coil domains can self-associate, leading to aggregation during expression and purification. Buffer optimization is critical to maintain protein solubility.

• Affinity tags selection: His-tags are commonly used, as demonstrated in the purification of recombinant chicken proteins such as chIFN-γ and chCD154 . The Western blot results confirmed successful expression of these recombinant proteins, displaying single bands at their expected molecular weights.

• Buffer optimization:

  • pH optimization: Most coiled-coil proteins show pH-dependent stability, with pKa values for glutamic acid residues spanning a range between 4.0 and 4.7

  • Salt concentration: Higher ionic strength can reduce non-specific interactions

  • Addition of stabilizing agents: Glycerol or low concentrations of detergents may prevent aggregation

• Quality control methods:

  • SDS-PAGE and Western blotting to confirm purity and identity

  • Circular dichroism spectroscopy to verify proper folding of the coiled-coil domain

  • Analytical ultracentrifugation to assess oligomerization state

• Scale-up considerations: Typically, 1 mg of pure recombinant protein can be prepared from optimized expression systems, as demonstrated with other chicken recombinant proteins .

What experimental approaches are used to characterize the structure of coiled-coil domain proteins?

Several complementary approaches are used to characterize coiled-coil domain proteins:

• Circular dichroism (CD) spectroscopy: Essential for analyzing α-helical content and thermal stability. CD spectroscopy can determine melting temperatures (Tm) of coiled-coil domains and identify stability control regions .

• Analytical ultracentrifugation: Used to determine the oligomerization state of coiled-coil domains (e.g., dimeric, trimeric) .

• X-ray crystallography: Provides high-resolution structural information. The first high-resolution X-ray structure of a parallel two-stranded α-helical coiled-coil was determined for GCN4 .

• Computational prediction tools: Algorithms like STABLECOIL (http://biomol.uchsc.edu/researchFacilities/ComputationalCore/stablecoil/index.html) predict coiled-coil regions in protein sequences based on experimentally derived stability data .

• Truncation analysis: Creating C-terminal or N-terminal truncation constructs helps identify stability control regions and essential structural elements, as demonstrated in tropomyosin studies .

How does CCDC43 expression correlate with disease states, and what are the implications for using recombinant CCDC43 in disease studies?

CCDC43 expression has been strongly correlated with disease states, particularly in cancer:

• Cancer correlation: CCDC43 is overexpressed in gastric cancer tissues, with expression closely related to tumor differentiation, lymph-node-metastasis, and poor prognosis .

• Mechanistic insights:

  • CCDC43 promotes proliferation, invasion, and metastasis of gastric cancer cells

  • CCDC43 may upregulate and stabilize ADRM1, resulting in the construction of the ubiquitin-mediated proteasome

  • Transcription factor YY1 directly binds to CCDC43 gene promoters, leading to over-expression

• Hepatocellular carcinoma:

  • CCDC43 expression is reduced after Tian Yang Wan (TYW) administration

  • CCDC43 transcript abundance is positively correlated with HCC Grade and Stage

  • Higher CCDC43 expression correlates with decreased survival rates in HCC patients

  • CCDC43 is a risk factor for death in HCC, as shown by Cox analyses

• Immune system connections:

  • CCDC43 expression correlates with abundance of various immune cells in the tumor microenvironment

  • Higher CCDC43 expression correlates with anti-PD-1 immunotherapy response

• Cell death pathway connections:

  • 82.8% of ferroptosis genes and all cuproptosis genes studied were significantly associated with CCDC43

  • Positive correlation with cell death factors including ABCC1, ATG5, TP53, and PDHA1

These findings suggest that recombinant chicken CCDC43 could be valuable for comparative oncology studies, immune response research, and as a potential therapeutic target.

What are the key considerations when designing fusion proteins containing coiled-coil domains for chicken immunology studies?

Based on successful fusion protein studies in chickens, key considerations include:

• Domain selection and orientation:

  • Functional domains should be selected based on complementary activities

  • The order of domains can significantly impact fusion protein functionality

  • Linker design between domains is critical for maintaining proper folding

• Expression system selection:

  • For chicken immunology studies, the pCold expression vector system has proven effective

  • Total RNA extraction from chicken tissues (e.g., spleen) followed by cDNA preparation enables cloning of target sequences

• Validation methods:

  • Western blot analysis to confirm expression of the fusion protein at the expected molecular weight

  • Functional assays to verify that both domains retain activity

  • In vivo testing in chicken models to evaluate biological effects

• Design example from literature:

  • A successful fusion protein combining chicken IFN-γ (chIFN-γ) and CD154 (chCD154) showed enhanced protective effects against Salmonella infection compared to either protein alone

  • The fusion protein improved survival rates, reduced bacterial loads, and lessened tissue damage

  • The fusion construct was generated by inserting chCD154 into NotI- and BamHI-digested pCold-chIFN-γ vector using seamless cloning

• Experimental design considerations:

  • Pretreatment protocols typically involve administration of the recombinant protein (e.g., 40 μg/chicken) via oral route for three consecutive days before challenge

  • Control groups should include both negative controls and groups receiving individual components of the fusion protein

What bioinformatic approaches are most effective for analyzing CCDC43 function and evolutionary conservation?

Effective bioinformatic approaches for CCDC43 analysis include:

• Sequence conservation analysis:

  • Comparison of sequence identity across species (e.g., human FAM81A has >90% sequence identity with its mouse ortholog, while showing only 34-37% identity with zebrafish orthologs)

  • Evolutionary rate analysis to identify rapidly evolving regions

• Domain prediction and structure modeling:

  • SMART (Simple Modular Architecture Research Tool) for domain identification

  • Prediction of intrinsically disordered regions

  • Homology modeling based on known coiled-coil structures

• Transcriptomic analysis:

  • Differential expression analysis across tissues and conditions

  • Single-cell RNA sequencing (scRNAseq) for cell-type specific expression patterns

• Pathway analysis:

  • Gene Set Enrichment Analysis (GSEA) to identify pathways influenced by CCDC43

  • Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis

• Correlation analysis with disease markers:

  • Methods used in HCC studies include:

    • ESTIMATE algorithm for immune and stromal cell abundance

    • TIMER2.0 for immune cell infiltration analysis

    • MCPcounter algorithm for tumor microenvironment cell identification

    • Single sample genomic enrichment analysis (ssGSEA) for immune cell typing

• Mutation and genetic variation analysis:

  • Somatic mutation analysis using tools like maftools

  • Tumor mutation burden (TMB) calculation

How can structural biology approaches be applied to optimize stability of recombinant coiled-coil domain proteins?

Structural biology insights can guide optimization of recombinant coiled-coil protein stability:

• Identification of stability control regions:

  • Truncation studies reveal that specific regions can significantly impact protein stability

  • For example, in tropomyosin, a stability control region between residues 97 and 118 increases thermal stability by 16°C (ΔTm)

• Strategic residue modification:

  • Hydrophobic core positions (a and d in the heptad repeat) are critical for stability

  • Single amino acid substitutions in these positions can contribute ~7 kcal/mole to stability

  • Electrostatic interactions through charged residues at positions e and g can form interhelical ion pairs

• Cluster identification:

  • Identification of stabilizing clusters (3-6 consecutive stabilizing residues)

  • Analysis of destabilizing clusters (3-5 consecutive destabilizing residues)

  • Context-dependent analysis of hydrophobicity in the hydrophobic core

• pH optimization:

  • Analysis of pKa values of acidic residues (e.g., glutamic acid residues with pKa values between 4.0 and 4.7)

  • Small contributions to stability from ionization of acidic residues

• Structural validation methods:

  • Circular dichroism spectroscopy to monitor folding and stability

  • Thermal denaturation studies to determine melting temperatures

  • Calorimetric and pressure denaturation experiments to identify independent domains with different stabilities

What are the current methodological challenges in studying protein-protein interactions involving CCDC43?

Current methodological challenges include:

• Expression system limitations:

  • Ensuring proper folding and post-translational modifications of chicken proteins

  • Balancing yield with authenticity of modifications

  • Potential toxicity of overexpressed recombinant proteins in host systems

• Interaction detection methods:

  • Yeast two-hybrid analysis has been used successfully for coiled-coil protein interactions but may produce false positives

  • Immunoprecipitation/immunodepletion approaches require specific antibodies which may not be readily available for chicken CCDC43

  • Biochemical studies may be hampered by protein stability issues

• Proximity labeling approaches:

  • TurboID technology can identify proteins in close proximity to CCDC43

  • Requires fusion of a biotinylating protein with CCDC43

  • Purification using streptavidin agarose followed by mass spectrometry identification

• Structural characterization challenges:

  • Coiled-coil domains can form multimeric assemblies that complicate structural studies

  • Dynamic nature of some interactions may require multiple complementary approaches

• Validation in avian systems:

  • Limited availability of chicken-specific reagents and cell lines

  • Need for gene editing approaches (e.g., CRISPR-Cas9) to study function in chicken cells

  • Example: XCR1-iCaspase9-RFP chickens were generated using CRISPR-Cas9 knockin transgenesis to study protein function in situ

How can recombinant CCDC43 be used in therapeutic development, based on its role in cellular processes?

Therapeutic development potential includes:

• Cancer treatment applications:

  • CCDC43 inhibition shows promise as a therapeutic strategy for HCC and gastric cancer

  • CCDC43 expression correlates with sensitivity to multiple chemotherapeutic agents

  • Drug sensitivity prediction using the GDSC database can identify compounds effective against CCDC43-overexpressing cells

• Immunotherapy connections:

  • CCDC43 expression correlates with immune checkpoint responses

  • Higher CCDC43 expression correlates with anti-PD-1 immunotherapy response

  • Recombinant CCDC43 could be used to study and potentially modulate immune responses

• Cell death pathway modulation:

  • Strong correlation between CCDC43 and cell death pathway genes (ferroptosis and cuproptosis)

  • Potential to target CCDC43 to promote cell death in cancer therapy

  • Recombinant CCDC43 could be used to screen for compounds that modulate these pathways

• Delivery system development:

  • Fusion of CCDC43 with other therapeutic proteins may enhance targeted delivery

  • Similar to successful chIFN-γ-chCD154 fusion proteins that showed enhanced protective effects

• Experimental considerations:

  • In vivo validation using animal models is essential

  • Administration protocols must be optimized (e.g., dosage, route, timing)

  • Long-term effects require extended observation periods

  • Combined therapy approaches may yield synergistic effects

What are the optimal experimental conditions for verifying CCDC43 function in chicken cell models?

Based on successful studies with related proteins in chicken models:

• Cell model selection:

  • Primary chicken cells derived from relevant tissues

  • Chicken primordial germ cells (PGCs) for genetic modification studies

  • Established chicken cell lines with appropriate tissue origin

• Gene editing approaches:

  • CRISPR-Cas9 system has been successfully used for chicken gene editing

  • Co-transfection with guide RNAs (sgRNAs) and donor plasmids

  • PX459 V2.0 vector has been used effectively for chicken gene editing

• Expression verification methods:

  • Western blot analysis to confirm protein expression

  • Immunofluorescence for cellular localization

  • qRT-PCR for transcript level analysis

• Functional assays:

  • Proliferation, invasion, and migration assays (if studying cancer-related functions)

  • Apoptosis assays to assess cell death effects

  • Protein-protein interaction studies using co-immunoprecipitation

• In vivo validation:

  • Gene-edited chicken lines can be generated by PGC transfection followed by germline transmission

  • Reporter systems (e.g., RFP tagging) enable visualization of protein expression

  • Conditional approaches (e.g., inducible systems) allow temporal control of gene function

How should researchers design studies to investigate contradictory findings regarding CCDC43 function?

When investigating contradictory findings:

• Systematic literature analysis:

  • Meta-analysis of published data (similar to the analysis of PSD proteome studies)

  • Quantitative analysis of combined results from multiple investigations

  • Assessment of methodological differences that might explain contradictions

• Experimental design considerations:

  • Use multiple complementary methods to verify findings

  • Include appropriate controls (positive, negative, and isotype controls)

  • Conduct experiments in different cell types/tissues to assess context-dependency

• Statistical approaches:

  • Apply robust statistical methods (e.g., cox regression analysis for survival data)

  • Utilize multiple statistical tests to validate findings

  • Establish clear significance thresholds (p < 0.05, p < 0.01, p < 0.001, p < 0.0001)

• Reproducibility measures:

  • Perform experiments in multiple biological replicates

  • Use different experimental approaches to verify the same finding

  • Consider inter-laboratory validation for controversial findings

• Data visualization and interpretation:

  • Generate comprehensive heat maps using tools like complexHeatmap

  • Create correlation plots to visualize relationships between variables

  • Use Kaplan-Meier curves to estimate survival in disease studies