P4HA3 Antibody

What is P4HA3 and what is its primary function in cellular biology?

P4HA3 (Prolyl 4-hydroxylase subunit alpha-3) is an enzyme that catalyzes the post-translational formation of 4-hydroxyproline in -Xaa-Pro-Gly- sequences in collagens and other proteins . This hydroxylation is crucial for the proper folding and stability of the collagen triple helix structure. P4HA3 is one of the three isoforms of the alpha subunit of prolyl 4-hydroxylase, with P4HA3 being less extensively studied than its counterparts P4HA1 and P4HA2.

The protein functions within the endoplasmic reticulum where it modifies proline residues in procollagen chains. This enzymatic activity is essential for collagen biosynthesis and subsequent extracellular matrix formation. Under pathological conditions, increased P4HA3 activity can lead to excessive collagen deposition, contributing to fibrosis and tissue scarring, particularly in liver and lung conditions .

How does P4HA3 expression differ between normal tissues and cancer tissues?

P4HA3 demonstrates significantly higher expression in cancer tissues compared to adjacent normal tissues. Multiple studies have confirmed this differential expression pattern, particularly in colorectal cancer (CRC).

Analysis of The Cancer Genome Atlas (TCGA) database, GSE9348, GSE21815 datasets, and tissue microarray (TMA) samples consistently shows upregulation of P4HA3 in CRC tissues . This overexpression pattern is not limited to CRC but has been observed across multiple cancer types in pan-cancer analyses .

What are the common applications of P4HA3 antibodies in research?

P4HA3 antibodies are employed across various research applications to investigate its expression, localization, and role in normal and pathological conditions. The primary applications include:

• Western Blotting (WB): P4HA3 antibodies are used to detect and quantify P4HA3 protein expression in tissue lysates. For example, the ab101657 antibody has been validated for WB applications in human fetal kidney and liver lysates, with a predicted band size of 61 kDa .

• Immunohistochemistry on Paraffin-embedded tissues (IHC-P): This technique enables visualization of P4HA3 expression patterns within tissue architecture, allowing for spatial assessment of expression in tumor versus stromal components .

• Biomarker studies: P4HA3 antibodies are increasingly used to evaluate the potential of P4HA3 as a prognostic biomarker in various cancers, particularly colorectal cancer .

• Tumor microenvironment research: The antibodies help investigate correlations between P4HA3 expression and immune cell infiltration, immune checkpoint expression, and other features of the tumor microenvironment .

• Mechanistic studies: They facilitate research into the role of P4HA3 in epithelial-mesenchymal transition (EMT) and other cancer-related biological processes .

What are the optimal protocols for immunohistochemical staining of P4HA3 in formalin-fixed paraffin-embedded tissues?

When performing immunohistochemical (IHC) staining for P4HA3 in formalin-fixed paraffin-embedded (FFPE) tissues, researchers should consider the following optimized protocol:

• Tissue preparation: Standard FFPE processing with 10% neutral buffered formalin fixation for 24-48 hours.

• Antigen retrieval: Heat-induced epitope retrieval (HIER) using citrate buffer (pH 6.0) is typically effective. Heating at 95-98°C for 15-20 minutes followed by cooling to room temperature is recommended.

• Blocking: Use 3-5% normal serum (matching the species of secondary antibody) with 0.1% Triton X-100 in PBS for 1 hour at room temperature.

• Primary antibody incubation: P4HA3 antibody (e.g., ab101657) at 1/100 dilution has been validated for human fetal liver samples . For other tissue types, optimization may be necessary. Incubate overnight at 4°C.

• Detection system: HRP-conjugated secondary antibody followed by DAB (3,3'-diaminobenzidine) substrate is commonly used.

• Counterstaining: Hematoxylin counterstaining for 1-2 minutes provides good nuclear contrast.

• Scoring system: For research consistency, implement the histochemistry score (H-score) grading system: H-score = ∑(I × Pi), where I = intensity of staining (categorized as 0, 1, 2, 3 based on staining depth), and Pi = percentage of positive cells. The H-score ranges from 0 to 300, with larger values indicating stronger combined positive intensity .

This protocol has been successfully used in clinical studies examining P4HA3 expression in CRC tissues .

How should researchers optimize Western blot conditions for detecting P4HA3 protein?

Optimizing Western blot conditions for P4HA3 detection requires attention to several critical parameters:

• Sample preparation:

  • Use RIPA buffer with protease inhibitors for protein extraction

  • Load 20-30 μg of total protein per lane

  • Include positive controls such as human fetal kidney or liver lysates, which have demonstrated P4HA3 expression

• Gel electrophoresis:

  • Use 10-12% SDS-PAGE gels for optimal resolution of the 61 kDa P4HA3 protein

  • Run at 100-120V to ensure proper protein separation

• Transfer conditions:

  • Wet transfer at 100V for 60-90 minutes or 30V overnight at 4°C

  • Use PVDF membrane for better protein retention and signal

• Blocking and antibody dilutions:

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

  • Use primary P4HA3 antibody at 1/500 dilution (as validated for ab101657)

  • Incubate with primary antibody overnight at 4°C for best results

  • HRP-conjugated secondary antibody at 1:5000-1:10000 dilution for 1 hour at room temperature

• Detection strategy:

  • Enhanced chemiluminescence (ECL) detection systems work well for P4HA3

  • Expected band size: 61 kDa

  • Consider longer exposure times if signal is weak

• Troubleshooting considerations:

  • If multiple bands appear, increase blocking time/concentration

  • If signal is weak, decrease antibody dilution or increase protein loading

  • If background is high, increase washing steps or reduce antibody concentration

What controls should be included when evaluating P4HA3 expression in tumor samples?

When evaluating P4HA3 expression in tumor samples, including appropriate controls is crucial for result validation and interpretation:

• Positive tissue controls:

  • Human fetal liver and kidney tissues have been validated for P4HA3 expression

  • Include known P4HA3-expressing colorectal cancer samples from previous studies

  • Cell lines with confirmed P4HA3 expression can serve as positive controls for Western blot

• Negative controls:

  • Omission of primary antibody on duplicate sections to assess non-specific binding

  • Use of isotype controls to identify potential background issues

  • Normal adjacent tissue from the same patient for comparison with tumor tissue

• Internal controls:

  • Adjacent normal tissue serves as an internal reference for differential expression analysis

  • For tissue microarrays (TMAs), include normal tissue cores alongside tumor cores

• Technical controls:

  • For Western blotting, include loading controls such as β-actin, GAPDH, or total protein staining

  • For IHC, use standardized positive control sections in each staining batch

• Antibody validation controls:

  • Consider siRNA knockdown of P4HA3 in positive cell lines to confirm antibody specificity

  • If available, recombinant P4HA3 protein can be used for antibody validation

• Scoring controls:

  • For semi-quantitative analyses, include reference images for different staining intensities

  • Have multiple observers score samples independently to ensure reproducibility

How does P4HA3 expression correlate with clinical outcomes in colorectal cancer patients?

P4HA3 expression has demonstrated significant correlations with clinical outcomes in colorectal cancer (CRC) patients across multiple studies:

These findings collectively suggest that P4HA3 expression assessment could significantly enhance current prognostic stratification systems for CRC patients and potentially guide treatment decisions.

What is the relationship between P4HA3 expression and immune cell infiltration in the tumor microenvironment?

The relationship between P4HA3 expression and immune cell infiltration in the tumor microenvironment is complex and significant:

• Correlation with specific immune cell types: P4HA3 expression shows positive correlations with multiple tumor-infiltrating lymphocytes (TILs), including:

  • CD8+ T cells (cytotoxic T cells)

  • Foxp3+ T cells (regulatory T cells)

  • Th1 cells, Tem cells, Tgd cells, and Tcm cells

• Quantitative associations: Immunohistochemical staining has revealed a marked increase in CD8+ and Foxp3+ TILs in the tumor stroma of samples with high P4HA3 expression compared to those with low expression .

• Immune checkpoint correlation: P4HA3 expression positively correlates with the expression of immune checkpoint molecules, including:

  • PD-1 (PDCD1) and PD-L1 (CD274) (immunosuppressive checkpoints)

  • CTLA-4 (immunosuppressive checkpoint)

  • CD40, ICOS, and ICOSLG (immunostimulatory checkpoints)

• Stromal significance: The relationship between P4HA3 and immune markers is particularly evident in the tumor stroma rather than in tumor cells themselves. This suggests P4HA3 may influence the stromal compartment of the tumor microenvironment .

• Potential immunosuppressive role: The positive correlation between high P4HA3 expression and increased numbers of regulatory T cells, along with elevated PD-1 and PD-L1 expression, suggests that P4HA3 may contribute to an immunosuppressive tumor microenvironment, potentially facilitating immune evasion by tumor cells .

This intricate relationship between P4HA3 and immune components provides insights into potential therapeutic strategies targeting the P4HA3-immune axis in cancer.

How can researchers effectively quantify and analyze P4HA3 expression in clinical specimens?

Effective quantification and analysis of P4HA3 expression in clinical specimens requires standardized approaches across different methodologies:

• Immunohistochemical (IHC) scoring:

  • Implement the histochemistry score (H-score) grading system: H-score = ∑(I × Pi), where I = intensity of staining (0-3) and Pi = percentage of positive cells

  • The resulting H-score range of 0-300 provides a semi-quantitative assessment of expression

  • For comparative analyses, use the average H-score as a threshold to categorize samples into high and low expression groups

• Western blot quantification:

  • Normalize P4HA3 band intensity to loading controls (β-actin, GAPDH)

  • Use densitometry software (ImageJ, Image Lab) for objective measurement

  • Compare relative expression levels across samples using fold-change calculations

• mRNA expression analysis:

  • qRT-PCR with appropriate reference genes (GAPDH, ACTB, 18S rRNA)

  • RNA-Seq data analysis with proper normalization (TPM, FPKM)

  • For public database analysis (e.g., TCGA), use established bioinformatic pipelines and normalization methods

• Spatial analysis in tumor samples:

  • Distinguish between expression in tumor cells versus stromal compartments

  • Consider digital pathology approaches with automated scoring algorithms

  • Multiplex IHC to simultaneously assess P4HA3 and immune markers

• Statistical approaches:

  • For comparing expression between groups: t-test/Wilcoxon rank sum test

  • For correlation analysis: Spearman correlation coefficient

  • For survival analysis: Cox regression (when variables meet proportional hazards assumption) or log-rank test

  • Set significance threshold at p < 0.05

• Reporting standards:

  • Clearly document antibody details, dilutions, and protocols

  • Provide representative images of different expression patterns

  • Report both raw data and normalized/transformed values where appropriate

What molecular mechanisms might explain the association between P4HA3 overexpression and cancer progression?

Several molecular mechanisms potentially explain the association between P4HA3 overexpression and cancer progression:

• Enhanced collagen modification and deposition:

  • P4HA3 catalyzes proline hydroxylation in collagen, enhancing collagen stability and promoting desmoplastic reactions in tumors

  • Excessive collagen deposition creates a stiffer tumor microenvironment, which can promote cancer cell invasion and metastasis

  • Modified extracellular matrix composition may provide favorable tracks for cancer cell migration

• Epithelial-mesenchymal transition (EMT) promotion:

  • Gene enrichment analyses have identified a strong association between P4HA3 and EMT-related pathways

  • EMT enables cancer cells to acquire mesenchymal traits that facilitate invasion and metastasis

  • Tumor cells with mesenchymal phenotypes are less susceptible to attack by immune cells, promoting immune evasion

• Immunosuppressive microenvironment development:

  • P4HA3 expression correlates with increased infiltration of regulatory T cells (Tregs) and expression of immune checkpoint molecules (PD-1, PD-L1)

  • This immunosuppressive milieu can inhibit effective anti-tumor immune responses

  • The immunosuppressive environment may operate in a feed-forward loop with EMT, as mesenchymal tumor cells show higher PD-L1 positivity

• Hypoxia adaptation:

  • As a hypoxia-responsive gene family member, P4HA3 may enable tumor adaptation to hypoxic conditions

  • Hypoxia is a known driver of tumor progression and treatment resistance

• Increased cancer stemness:

  • Collagen modifications may contribute to cancer stem cell niche formation

  • EMT and stemness are interconnected processes that can be influenced by extracellular matrix modifications

These mechanisms likely operate in concert rather than in isolation, collectively driving cancer progression in P4HA3-overexpressing tumors.

How might P4HA3 antibodies be integrated into multiplex immunohistochemistry panels for comprehensive tumor microenvironment assessment?

Integration of P4HA3 antibodies into multiplex immunohistochemistry (mIHC) panels offers powerful insights into tumor microenvironment dynamics:

• Optimal panel design considerations:

  • Select compatible antibodies raised in different species to avoid cross-reactivity

  • Include P4HA3 alongside key immune markers (CD8, CD4, Foxp3, PD-1, PD-L1) based on established correlations

  • Consider including EMT markers (E-cadherin, vimentin, N-cadherin) given the association between P4HA3 and EMT

  • Add collagen markers to assess relationship with P4HA3 function

• Technical implementation strategies:

  • Sequential staining with tyramide signal amplification (TSA) allows detection of multiple targets

  • Spectral unmixing systems (e.g., Vectra, Akoya Biosciences) can separate overlapping fluorophore signals

  • For chromogenic mIHC, use distinct substrates and careful antibody stripping between rounds

• Spatial relationship assessment:

  • Analyze co-localization patterns between P4HA3 and immune cell markers

  • Quantify distances between P4HA3-expressing cells and various immune cell populations

  • Evaluate P4HA3 expression in tumor compartments versus stromal regions

• Advanced analysis approaches:

  • Machine learning algorithms for pattern recognition in complex mIHC datasets

  • Apply spatial statistics to quantify cell distribution patterns

  • Develop custom scoring systems that incorporate P4HA3 together with immune cell densities

• Validation and quality control:

  • Include single-stain controls for each antibody

  • Perform antibody titration to determine optimal concentrations for multiplex applications

  • Validate multiplex findings with traditional single-plex IHC on consecutive sections

• Clinical correlation framework:

  • Correlate mIHC patterns with patient outcomes

  • Develop integrated scoring systems combining P4HA3 expression patterns with immune contexture

  • Identify patterns predictive of response to immunotherapy or other treatment modalities

What experimental approaches would best elucidate the functional relationship between P4HA3 expression and immune checkpoint regulation?

To elucidate the functional relationship between P4HA3 expression and immune checkpoint regulation, several experimental approaches would be valuable:

• In vitro co-culture systems:

  • Co-culture P4HA3-overexpressing or knockdown cancer cells with immune cells (T cells, macrophages)

  • Measure changes in immune checkpoint expression (PD-1, PD-L1, CTLA-4) in both cancer and immune cells

  • Assess functional parameters like T cell proliferation, cytokine production, and cytotoxicity

  • Analyze conditioned media to identify secreted factors mediating P4HA3-immune interactions

• 3D organoid models:

  • Develop patient-derived organoids with variable P4HA3 expression

  • Incorporate immune components into the organoid microenvironment

  • Evaluate spatial organization and functional interactions under controlled conditions

  • Test effects of immune checkpoint inhibitors in relation to P4HA3 expression levels

• Genetic manipulation strategies:

  • CRISPR/Cas9-mediated knockout or overexpression of P4HA3 in cancer cell lines

  • Inducible expression systems to study temporal effects of P4HA3 modulation

  • Rescue experiments with catalytically inactive P4HA3 mutants to distinguish enzymatic from non-enzymatic functions

  • Combination with immune checkpoint gene manipulations to test functional interactions

• In vivo mouse models:

  • Generate syngeneic mouse models with P4HA3 knockdown/overexpression

  • Characterize immune infiltration patterns using flow cytometry and IHC

  • Test response to immune checkpoint inhibitors in relation to P4HA3 status

  • Perform adoptive transfer experiments with labeled immune cells to track recruitment and function

• Multi-omics approaches:

  • Integrate transcriptomics, proteomics, and epigenomics analyses

  • Identify regulatory networks connecting P4HA3 with immune checkpoint genes

  • Map signaling pathways potentially linking collagen modification to immune regulation

  • Employ single-cell technologies to resolve heterogeneity in tumor-immune interactions

• Pharmacological intervention studies:

  • Test P4HA inhibitors alone and in combination with immune checkpoint blockers

  • Evaluate effects on tumor growth and immune infiltration

  • Analyze changes in collagen modification, EMT markers, and immune checkpoint expression

  • Determine sequence-dependent effects of combined targeting strategies

These complementary approaches would provide mechanistic insights into how P4HA3 influences immune checkpoint regulation and could inform novel therapeutic strategies targeting this axis.

What are the common challenges in P4HA3 antibody validation and how can researchers overcome them?

Researchers face several challenges when validating P4HA3 antibodies, with corresponding solutions:

• Cross-reactivity with other P4HA isoforms:

  • Challenge: P4HA1, P4HA2, and P4HA3 share sequence homology, potentially causing cross-reactivity

  • Solution: Validate antibody specificity using knockout/knockdown approaches

  • Solution: Select antibodies targeting unique regions of P4HA3, such as those raised against immunogens within amino acids 100-400 of human P4HA3

  • Solution: Confirm results with orthogonal methods (e.g., mass spectrometry)

• Variability in fixation sensitivity:

  • Challenge: Fixation conditions can affect epitope availability

  • Solution: Optimize antigen retrieval protocols specifically for P4HA3

  • Solution: Compare multiple fixatives if working with fresh specimens

  • Solution: Validate antibody performance across different fixation times

• Batch-to-batch variability:

  • Challenge: Different lots of the same antibody may show performance differences

  • Solution: Request lot-specific validation data from manufacturers

  • Solution: Maintain reference samples for comparative testing of new lots

  • Solution: Consider monoclonal antibodies for greater consistency

• Determining appropriate positive controls:

  • Challenge: Identifying reliable positive control tissues/cells

  • Solution: Use validated positive controls such as human fetal kidney and liver tissues

  • Solution: Include cell lines with known P4HA3 expression levels

  • Solution: Create transgenic cell lines overexpressing P4HA3 as definitive controls

• Distinguishing specific from non-specific signals:

  • Challenge: Background staining can complicate interpretation

  • Solution: Include isotype controls and antibody omission controls

  • Solution: Perform peptide competition assays

  • Solution: Apply multiple antibodies targeting different epitopes of P4HA3

• Quantification standardization:

  • Challenge: Ensuring consistent scoring across experiments and observers

  • Solution: Implement standardized scoring systems like the H-score

  • Solution: Use digital image analysis software for objective quantification

  • Solution: Conduct multi-observer validation to establish scoring reliability

How can researchers integrate P4HA3 analysis with other molecular markers for comprehensive tumor profiling?

Integrating P4HA3 analysis with other molecular markers creates a comprehensive tumor profile:

• Multi-marker panel design:

  • Combine P4HA3 with established CRC prognostic markers (KRAS, BRAF, MSI status)

  • Include markers from complementary pathways: EMT markers (E-cadherin, vimentin), immune checkpoints (PD-1, PD-L1), and collagen-related proteins

  • Stratify panels by biological processes: tumor cells, stromal activation, immune contexture

• Multi-platform integration approaches:

  • Correlate P4HA3 IHC with genomic alterations from NGS panels

  • Link protein expression to transcriptomic signatures

  • Develop integrated algorithms incorporating multiple data types

• Spatial context preservation:

  • Employ digital spatial profiling technologies

  • Use multiplex immunofluorescence to map P4HA3 in relation to other markers

  • Correlate with spatial transcriptomics data for comprehensive tissue architecture understanding

• Functional correlation strategies:

  • Design functional assays to test relationships between P4HA3 and other markers

  • Develop co-expression network analyses to identify functionally related markers

  • Use systems biology approaches to map interactions between different marker pathways

• Clinical implementation considerations:

  • Develop clinically applicable scoring systems combining P4HA3 with other markers

  • Create decision trees incorporating multiple marker results

  • Validate integrated panels in prospective clinical cohorts

• Data analysis frameworks:

  • Apply machine learning algorithms to identify optimal marker combinations

  • Use dimensionality reduction techniques to visualize complex multi-marker data

  • Develop predictive models incorporating P4HA3 with other established biomarkers

This integrated approach allows researchers to position P4HA3 within the broader molecular landscape of tumors, enhancing both biological understanding and clinical utility.

What are the emerging technologies that might enhance P4HA3 detection and functional analysis in future research?

Several emerging technologies hold promise for advancing P4HA3 detection and functional analysis:

• Advanced imaging technologies:

  • Mass cytometry imaging (Imaging CyTOF) for highly multiplexed protein detection

  • Super-resolution microscopy to visualize subcellular P4HA3 localization and interactions

  • Light-sheet microscopy for 3D visualization of P4HA3 distribution in tissue structures

  • Spatial transcriptomics to correlate P4HA3 protein with mRNA expression patterns

• Single-cell analysis platforms:

  • Single-cell proteomics to examine P4HA3 expression heterogeneity

  • Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to simultaneously profile P4HA3 protein and transcriptome

  • Single-cell spatial analysis to map P4HA3-expressing cells within the tumor architecture

• Protein interaction technologies:

  • Proximity ligation assays to detect P4HA3 interactions with other proteins in situ

  • Bio-ID or APEX2 proximity labeling to identify novel P4HA3 interaction partners

  • Förster resonance energy transfer (FRET) imaging to visualize dynamic protein interactions

• Functional genomics approaches:

  • CRISPR activation/interference screens to identify genes affecting P4HA3 expression

  • Base editing and prime editing for precise modification of P4HA3 regulatory elements

  • Pooled CRISPR screening in immune-cancer co-culture systems to identify mediators of P4HA3-immune interactions

• Protein engineering and synthetic biology:

  • Split protein complementation systems to monitor P4HA3 activity in living cells

  • Engineered P4HA3 variants with altered substrate specificity or catalytic properties

  • Optogenetic or chemically inducible P4HA3 systems for temporal control of activity

• Artificial intelligence applications:

  • Deep learning algorithms for automated P4HA3 quantification in complex tissues

  • AI-driven prediction of P4HA3 structure-function relationships

  • Machine learning integration of multi-modal data to predict P4HA3 functional impact

These emerging technologies will enable more comprehensive characterization of P4HA3's role in normal physiology and disease pathogenesis, potentially revealing new therapeutic opportunities.