PDCB3 Antibody

What is PDCL3 and what are its primary cellular functions?

PDCL3 (Phosducin-like protein 3), also known as VIAF1 (viral IAP-associated factor 1), PHLP3, or HTPHLP, functions primarily as a chaperone protein with multiple critical cellular roles. Its main functions include:

• Acting as a chaperone for the angiogenic VEGF receptor KDR/VEGFR2, increasing its abundance by inhibiting ubiquitination and degradation

• Inhibiting the folding activity of the chaperonin-containing T-complex (CCT), which leads to inhibition of cytoskeletal actin folding

• Functioning as a chaperone during heat shock alongside HSP90 and HSP40/70 chaperone complexes

• Modulating the activation of caspases during apoptosis

• Playing a crucial role in chaperone-assisted folding of proteins, particularly β Tubulin and Actin, which are essential for cell cycle regulation

For experimental work involving PDCL3, it's important to consider its subcellular localization primarily in the cytoplasm, where it associates with the cytosolic chaperonin complex to facilitate proper protein folding.

Which PDCL3 antibody types are available for research and what applications are they validated for?

Multiple types of PDCL3 antibodies are available for different research applications:

Monoclonal Antibodies:

• PDCL3 Antibody (F-4): A mouse monoclonal IgG2a kappa light chain antibody that detects PDCL3 protein of mouse, rat, and human origin

• Validated applications: Western blotting (WB), immunoprecipitation (IP), immunofluorescence (IF), and enzyme-linked immunosorbent assay (ELISA)

• Available in both non-conjugated and various conjugated forms, including agarose, horseradish peroxidase (HRP), phycoerythrin (PE), fluorescein isothiocyanate (FITC), and multiple Alexa Fluor conjugates

Polyclonal Antibodies:

• Rabbit polyclonal antibodies targeting specific regions such as the C-terminal domain of human PDCL3

• Validated primarily for Western blotting applications with human samples

• Available from multiple vendors with different immunogens and validation data

When selecting an antibody for your research, consider the species reactivity, validated applications, and the specific epitope recognized, as these factors will significantly impact experimental outcomes.

What are the optimal protocols for detecting PDCL3 in tissue samples using immunohistochemistry?

For optimal detection of PDCL3 in tissue samples using immunohistochemistry, researchers should follow these methodological considerations:

Sample Preparation:

• Formalin-fixed paraffin-embedded (FFPE) tissues have been successfully used to detect PDCL3 expression differences between cancer and adjacent normal tissues

• Consider constructing tissue microarrays for comparative analysis across multiple samples

Staining Protocol:

• Deparaffinize and rehydrate tissue sections through a graded ethanol series

• Perform antigen retrieval (heat-induced epitope retrieval using citrate buffer pH 6.0 is recommended)

• Block endogenous peroxidase activity with 3% H₂O₂

• Implement protein blocking to reduce background staining

• Incubate with validated PDCL3 primary antibody (optimal dilution should be determined empirically, typically 1:50 to 1:200)

• Apply appropriate secondary antibody system (HRP-conjugated secondary antibodies are commonly used)

• Develop with DAB substrate and counterstain with hematoxylin

Scoring Methods:

• Two quantitative scoring systems have been effectively employed for PDCL3 expression :

  • IRS (Immunoreactive Score): Combines intensity and percentage of positive cells

  • H-Score: Accounts for staining intensity and distribution

For differential expression analysis between tumor and normal tissues, both scoring methods have shown significantly higher PDCL3 expression levels in liver cancer tissues compared to adjacent tissues, providing statistical validation for expression differences .

How can Western blotting protocols be optimized for PDCL3 detection across different sample types?

Optimizing Western blotting for PDCL3 detection requires attention to several critical parameters:

Sample Preparation:

• For tissue lysates: Use RIPA buffer supplemented with protease inhibitors

• For cell lysates: 1% Triton X-100 in PBS with protease inhibitors works effectively

• Protein concentration should be determined using BCA or Bradford assay and standardized across samples

Electrophoresis Parameters:

• PDCL3 has a predicted molecular weight of 28 kDa but may be observed at approximately 25 kDa

• Use 12-15% SDS-PAGE gels for optimal resolution

• Load 15-30 μg of protein per lane for adequate signal detection

Transfer and Detection Guidelines:

• Transfer to PVDF membrane (recommended over nitrocellulose for PDCL3)

• Block with 5% non-fat milk in TBST for 1 hour at room temperature

• Incubate with PDCL3 primary antibody at optimized concentration (typically 1 μg/mL for commercially available antibodies)

• Wash thoroughly with TBST (at least 3×10 minutes)

• Incubate with appropriate secondary antibody (HRP-conjugated anti-rabbit or anti-mouse depending on primary antibody)

• Develop using enhanced chemiluminescence (ECL) substrate

Validation Controls:

• Include positive control (human brain tissue lysate has been verified to express PDCL3)

• Consider using PDCL3 knockdown or knockout samples as negative controls

• β-actin or GAPDH should be used as loading controls

If weak signal is encountered, consider increasing antibody concentration or extending incubation time to overnight at 4°C.

How does PDCL3 expression correlate with immune infiltration in hepatocellular carcinoma (LIHC)?

PDCL3 expression shows significant correlations with immune infiltration in hepatocellular carcinoma, with important implications for tumor immunobiology:

Macrophage Infiltration Relationship:

• PDCL3 expression exhibits a strong negative correlation with macrophage infiltration (Rho = -0.481, p = 2.13e-21)

• Patients with high PDCL3 expression demonstrate significantly lower macrophage infiltration compared to those with low PDCL3 expression

• This negative correlation may partially explain the mechanism by which high PDCL3 expression contributes to tumor progression and adverse prognosis in LIHC

Immune Checkpoint Correlations:
PDCL3 shows positive correlations with multiple immune checkpoint markers:

• CD274 (PD-L1): cor = 0.243, p = 2.22e-06

• CTLA4: cor = 0.33, p = 6.85e-11

• HAVCR2: cor = 0.421, p = 0.00e+00

• PDCD1 (PD-1): cor = 0.276, p = 6.53e-08

• TIGIT: cor = 0.297, p = 5.40e-09

Prognostic Implications:

• In cases with high PDCL3 expression, decreased macrophage infiltration correlates with adverse prognosis

• No significant prognostic difference was observed in cases with low PDCL3 expression

To investigate these relationships in your research, implement multiparameter immunofluorescence or multiplex immunohistochemistry to simultaneously analyze PDCL3 expression and immune cell markers within the same tissue sections. Single-cell RNA sequencing of tumor samples can also provide valuable insights into the relationship between PDCL3 expression and the tumor immune microenvironment.

What methodologies should be employed to investigate PDCL3's role in protein folding and chaperone function?

To investigate PDCL3's roles in protein folding and chaperone function, researchers should consider these methodological approaches:

Protein-Protein Interaction Assays:

• Co-immunoprecipitation (Co-IP) to examine interactions between:

  • PDCL3 and cytosolic chaperonin complex (CCT)

  • PDCL3 and β-Tubulin or Actin

  • PDCL3 and VEGFR2/KDR

• Proximity ligation assay (PLA) to visualize and quantify interactions in situ

• Bioluminescence resonance energy transfer (BRET) or fluorescence resonance energy transfer (FRET) for real-time monitoring of interactions in living cells

Functional Assays for Chaperone Activity:

• ATPase activity assays to measure PDCL3's effect on CCT's ATPase activity

• Protein folding assays using purified components to assess the impact of PDCL3 on folding kinetics

• Thermal shift assays to evaluate PDCL3's effect on protein stability

Cell-Based Assays:

• PDCL3 knockdown/knockout followed by assessment of:

  • Cytoskeletal organization (immunofluorescence for actin and tubulin)

  • Cell cycle progression (flow cytometry)

  • Mitotic spindle formation (immunofluorescence)

• Stress response experiments:

  • Heat shock followed by analysis of client protein folding

  • Protein aggregation assays under stress conditions

Structural Biology Approaches:

• Cryo-electron microscopy to visualize PDCL3-chaperonin complexes

• X-ray crystallography or NMR for detailed structural analysis

• Hydrogen-deuterium exchange mass spectrometry to map interaction interfaces

When designing these experiments, it's important to include appropriate controls and to consider the use of tagged PDCL3 constructs that don't interfere with its chaperone function.

What are common technical challenges when working with PDCL3 antibodies and how can they be addressed?

Researchers may encounter several technical challenges when working with PDCL3 antibodies that can be addressed through specific methodological adjustments:

Challenge: Non-specific binding in Western blots
Solutions:

• Increase blocking time or concentration (consider 5% BSA instead of milk for phospho-specific applications)

• Optimize primary antibody dilution (perform titration experiments)

• Increase washing duration and frequency (5×10 minutes with TBST)

• Pre-absorb antibody with blocking peptide if available

• Test alternative antibody clones that target different epitopes of PDCL3

Challenge: Weak or absent signal in immunohistochemistry
Solutions:

• Optimize antigen retrieval methods (test both citrate and EDTA-based buffers)

• Explore signal amplification systems (tyramide signal amplification)

• Extend primary antibody incubation (overnight at 4°C)

• Ensure tissue fixation is appropriate (overfixation can mask epitopes)

• Verify antibody compatibility with your fixation method

Challenge: Discrepancy in molecular weight on Western blots
Solutions:

• Be aware that PDCL3 has a predicted molecular weight of 28 kDa but is sometimes observed at 25 kDa

• Consider post-translational modifications that might alter migration

• Verify specificity with positive and negative controls

• Consider possible isoforms or degradation products

Challenge: Inconsistent results across different lots of antibody
Solutions:

• Request lot-specific validation data from suppliers

• Maintain detailed records of antibody lot numbers and performance

• Validate each new lot against your established protocols

• Consider developing an in-house reference standard

For each new application or sample type, perform thorough validation including specificity controls (knockout/knockdown samples), positive controls (tissues known to express PDCL3), and negative controls (primary antibody omission).

How can researchers reconcile contradictory findings about PDCL3 expression patterns across different cancer types?

When faced with contradictory findings regarding PDCL3 expression patterns across different cancer types, researchers should implement a systematic approach to reconcile these discrepancies:

Methodological Standardization:

• Compare antibody clones and detection methods used across studies

• Standardize tissue processing and fixation protocols

• Implement quantitative scoring systems (e.g., IRS or H-Score) rather than qualitative assessments

• Utilize multiple detection methods (IHC, IF, Western blot) to cross-validate expression patterns

Data Analysis Considerations:

• Perform statistical analysis with appropriate tests and corrections for multiple comparisons

• Consider sample size and power calculations when evaluating conflicting results

• Evaluate whether contradictions may be explained by:

  • Different cancer subtypes or stages

  • Varying patient demographics

  • Distinct molecular subtypes within the same cancer

Biological Explanations for Contradictions:

• PDCL3 may have context-dependent roles in different cancer types

• Expression may vary based on:

  • Tumor microenvironment (particularly immune infiltration)

  • Specific genetic alterations co-occurring in the tumor

  • Differentiation status of cancer cells

Integration Strategies:

• Conduct meta-analysis of existing data across multiple studies

• Utilize public databases (TCGA, GTEx) to perform independent verification

• Consider single-cell RNA sequencing to resolve cell-type specific expression patterns

• Develop tissue microarrays containing multiple cancer types for direct comparison under identical conditions

When analyzing PDCL3 expression in hepatocellular carcinoma, researchers should be particularly attentive to its negative correlation with macrophage infiltration and positive correlation with immune checkpoint markers, which may explain some of the observed biological effects and contradictions in prognostic associations .

What validation methods are required to establish PDCL3 as a reliable prognostic biomarker for hepatocellular carcinoma?

To establish PDCL3 as a reliable prognostic biomarker for hepatocellular carcinoma, researchers should implement a multi-faceted validation approach:

Clinical Cohort Validation:

Statistical Validation Metrics:

• Area under ROC curve (AUC) analysis for diagnostic potential

  • PDCL3 has demonstrated an AUC of 0.944 for diagnosing LIHC, indicating strong biomarker potential

• Kaplan-Meier survival analysis with log-rank test for prognostic significance

• Cox proportional hazards regression for hazard ratio calculation

• Determination of optimal cut-off values for high vs. low expression

Molecular and Functional Validation:

• Correlation with established molecular markers in HCC

• Functional studies in cell line and animal models to establish causality:

  • PDCL3 overexpression and knockdown experiments have confirmed its role in promoting:

    • Tumor cell proliferation

    • Migration

    • Invasion

    • Colony-forming ability

• Mechanistic studies linking PDCL3 to specific oncogenic pathways

Technical Validation:

• Standardization of detection methods across laboratories

• Analytical validation including:

  • Reproducibility assessment

  • Inter- and intra-observer variability testing

  • Analytical sensitivity and specificity determination

Immune Context Consideration:

• Evaluate PDCL3 expression in conjunction with immune infiltration markers

• Analyze the relationship with immune checkpoint molecules (CD274, CTLA4, HAVCR2, PDCD1, TIGIT)

• Consider stratification based on immune phenotypes

The combined evidence from these validation approaches would provide a comprehensive assessment of PDCL3's value as a prognostic biomarker for hepatocellular carcinoma.

How can researchers design experiments to elucidate the mechanism by which PDCL3 influences immune infiltration in the tumor microenvironment?

To investigate the mechanisms by which PDCL3 influences immune infiltration in the tumor microenvironment, researchers should design experiments that address both causality and molecular pathways:

In Vitro Co-Culture Systems:

• Establish co-culture systems between:

  • PDCL3-manipulated hepatocellular carcinoma cells (overexpression/knockdown)

  • Primary human macrophages or macrophage cell lines

• Measure:

  • Macrophage migration and chemotaxis toward tumor cells

  • Polarization states (M1 vs M2) via flow cytometry and qRT-PCR

  • Cytokine/chemokine production profiles using multiplex assays

• Perform transwell migration assays to assess if PDCL3 expression affects macrophage recruitment

Secretome Analysis:

• Collect conditioned media from PDCL3-overexpressing and control hepatocellular carcinoma cells

• Perform proteomic analysis to identify differentially secreted factors

• Validate key candidates using ELISA and functional neutralization experiments

In Vivo Models:

• Develop orthotopic or subcutaneous hepatocellular carcinoma models with:

  • PDCL3 overexpression

  • PDCL3 knockdown/knockout (CRISPR-Cas9)

  • Inducible PDCL3 expression systems

• Analyze:

  • Tumor immune infiltration by flow cytometry and immunohistochemistry

  • Temporal dynamics of immune cell recruitment

  • Response to immune checkpoint inhibitors

Molecular Mechanistic Studies:

• Perform RNA-seq and pathway analysis on PDCL3-manipulated tumor cells

• Investigate:

  • NF-κB pathway activation

  • STAT3 signaling

  • Cytokine/chemokine expression profiles

• Chromatin immunoprecipitation (ChIP) to identify transcription factors regulated by PDCL3

Clinical Correlation:

• Analyze patient samples for:

  • PDCL3 expression

  • Macrophage markers (CD68, CD163)

  • Checkpoint molecule expression (CD274, CTLA4, HAVCR2, PDCD1, TIGIT)

• Perform spatial transcriptomics or multiplex immunofluorescence to map the physical relationship between PDCL3-expressing cells and immune infiltrates

The observed negative correlation between PDCL3 expression and macrophage infiltration (Rho = -0.481, p = 2.13e-21) provides a strong foundation for these mechanistic studies, and may reveal novel therapeutic opportunities targeting the PDCL3-immune axis in hepatocellular carcinoma.