PDZD11 Antibody

What is PDZD11 and what cellular functions does it regulate?

PDZD11 (PDZ Domain Containing 11) is a protein involved in regulating adherens junctions and cell adhesion mechanisms. Research indicates that PDZD11 plays significant roles in cell-cell junction formation, apical plasma membrane organization, and tight junction assembly . Functionally, PDZD11 participates in the PLEKHA7-PDZD11 complex, which is crucial for epithelial cell adhesion and tissue connectivity . Additionally, PDZD11 has been implicated in cell proliferation pathways and immune responses, particularly in cancer microenvironments . Understanding these diverse functions provides the foundation for experimental applications utilizing PDZD11 antibodies in various research contexts.

What expression patterns of PDZD11 have been observed in cancer tissues?

PDZD11 exhibits notable expression differences between normal and cancerous tissues. In epithelial ovarian cancer (EOC), PDZD11 is significantly overexpressed compared to control tissues, as confirmed by both bioinformatic analyses of multiple databases (GEO, TCGA, GTEx) and immunohistochemical validation of 78 EOC cases versus 37 control tissues . The protein demonstrates remarkable diagnostic capacity with an AUC value of 0.85 in local EOC cases, while comprehensive analysis across multiple datasets revealed a SROC curve with AUC = 0.94, indicating high diagnostic accuracy . Interestingly, while PDZD11 is generally elevated in advanced cancer stages, its expression appears lower in EOC patients with lymph node metastasis compared to those without metastasis, suggesting context-dependent regulation . Similar upregulation patterns have been observed in hepatocellular carcinoma tissues compared to adjacent normal tissues .

How does PDZD11 correlate with clinical outcomes in cancer patients?

PDZD11 overexpression demonstrates significant association with adverse clinical outcomes. In epithelial ovarian cancer, elevated PDZD11 expression correlates with advanced cancer stages and poor prognosis, as indicated by survival analyses using Kaplan-Meier methods and log rank tests . The protein's expression is significantly associated with specific clinicopathological parameters, including cancer stage progression. Notably, in primary EOC tissues, PDZD11 protein levels are downregulated in patients with lymph node metastasis compared to those without, suggesting a complex relationship with metastatic potential . In hepatocellular carcinoma, elevated PDZD11 expression similarly correlates with poor prognosis . These clinical correlations establish PDZD11 as a potential prognostic biomarker, making antibodies against this protein valuable tools for translational research aimed at patient stratification and outcome prediction.

What are the optimal conditions for immunohistochemistry using PDZD11 antibodies?

For optimal immunohistochemistry (IHC) with PDZD11 antibodies, researchers should adhere to a validated protocol based on published studies. Begin with formalin-fixed, paraffin-embedded tissue sections (4-5 μm thickness). After deparaffinization and rehydration, perform heat-induced epitope retrieval using citrate buffer (pH 6.0) for 20 minutes. Endogenous peroxidase activity should be blocked with 3% hydrogen peroxide for 10 minutes. For primary antibody incubation, use anti-PDZD11 antibody at a 1:100-1:200 dilution (optimized through titration experiments) and incubate overnight at 4°C in a humidified chamber . Apply appropriate HRP-conjugated secondary antibody for 30 minutes at room temperature, followed by DAB chromogen development and hematoxylin counterstaining. Critical controls must include both positive controls (tissues known to express PDZD11, such as EOC tissues) and negative controls (primary antibody omission and normal ovarian or liver tissues depending on research focus) . For validation, compare staining patterns with the established expression profiles from published studies, which typically show membranous and cytoplasmic localization.

What methodologies are recommended for quantifying PDZD11 expression in tissue samples?

For rigorous quantification of PDZD11 expression in tissue samples, a multi-parameter approach is recommended. Immunohistochemistry scoring should employ a comprehensive semi-quantitative method that evaluates both staining intensity (0=negative, 1=weak, 2=moderate, 3=strong) and percentage of positive cells (0-100%) . Calculate a final H-score (0-300) by multiplying intensity score by percentage of positive cells. For increased objectivity, implement digital image analysis using software like ImageJ or QuPath to quantify staining patterns. For comparative analysis across patient groups, statistical approaches should include Student's t-test, Mann-Whitney test, or one-way ANOVA depending on data distribution characteristics . When analyzing PDZD11 expression in relation to clinical outcomes, utilize standardized mean difference (SMD) calculations, as performed in published studies examining PDZD11 in EOC . For normalization in western blot analyses, reference proteins such as GAPDH or β-actin should be used, with densitometric analysis performed to generate relative expression values.

How can researchers effectively validate PDZD11 antibody specificity?

Comprehensive validation of PDZD11 antibody specificity requires a multi-tiered approach. Begin with western blot analysis using both positive control cells known to express PDZD11 (e.g., ovarian cancer cell lines for EOC research) and negative control samples (knockdown or knockout models) . The antibody should detect a single band at the expected molecular weight of PDZD11 (~16 kDa). Additionally, perform peptide competition assays by pre-incubating the antibody with excess immunizing peptide, which should abolish specific staining. Cross-reactivity assessment is critical - test the antibody on tissues or cells known to lack PDZD11 expression. For functional validation, correlate antibody staining patterns with mRNA expression data from qRT-PCR or RNA-seq analyses . Furthermore, compare results from at least two antibodies targeting different epitopes of PDZD11 to confirm consistent staining patterns. Cell line validation can be enhanced by creating PDZD11 overexpression and knockdown models, which should demonstrate corresponding changes in antibody signal intensity . Document all validation steps systematically, including lot numbers and detailed experimental conditions, to ensure reproducibility across studies.

How does PDZD11 expression correlate with immune cell infiltration in cancer?

PDZD11 expression demonstrates significant correlations with immune cell infiltration in cancer microenvironments. Analysis using the TIMER database revealed that PDZD11 expression positively correlates with infiltration abundances of multiple immune cell types in epithelial ovarian cancer, including neutrophils, macrophages, dendritic cells, CD8+ T cells, and CD4+ T cells . More detailed analysis using the CIBERSORT algorithm on 379 EOC samples from TCGA showed that tumors with high PDZD11 expression had significantly elevated infiltration of CD8+ T cells, follicular helper T cells, gamma delta T cells, and M1 macrophages compared to low PDZD11 expression samples . Conversely, resting natural killer cells and M0 macrophages showed lower infiltration in high PDZD11 expression tumors. These findings suggest PDZD11 may play an important role in modulating the immune microenvironment of tumors, making it a promising target for immunotherapy-focused research.

What techniques can researchers use to investigate PDZD11's relationship with immune checkpoints?

To investigate PDZD11's relationship with immune checkpoints, researchers should employ a multi-modal approach combining bioinformatic and experimental methods. Begin with correlation analyses using databases such as TIMER to evaluate associations between PDZD11 and immune checkpoint genes (TIGIT, HAVCR2/TIM3, LAG3, CTLA4, PDCD-1/PD-1, CD274/PD-L1) . For experimental validation, utilize multiplex immunofluorescence to simultaneously detect PDZD11 and immune checkpoint proteins in tissue sections, enabling spatial relationship analysis. Flow cytometry of dissociated tumor samples can assess co-expression patterns in specific cell populations. At the functional level, co-immunoprecipitation experiments can identify potential physical interactions between PDZD11 and checkpoint molecules . For mechanistic studies, implement PDZD11 knockdown or overexpression in relevant cell lines, followed by assessment of immune checkpoint expression using qRT-PCR, western blotting, and flow cytometry. Additionally, chromatin immunoprecipitation (ChIP) assays can determine if PDZD11 is involved in transcriptional regulation of immune checkpoint genes. The data from published studies already indicates that PDZD11 expression positively correlates with TIGIT, TIM3, LAG3, CTLA4, and PD-1 expression, but not with PD-L1, providing a foundation for more detailed mechanistic investigations .

What is the potential significance of PDZD11 in immunotherapy response prediction?

PDZD11's potential significance in immunotherapy response prediction stems from its established correlations with multiple immune checkpoints and immune cell infiltration patterns. Research has demonstrated that PDZD11 expression positively correlates with critical immune checkpoint molecules including TIGIT, TIM3, LAG3, CTLA4, and PD-1 in epithelial ovarian cancer . This association suggests that tumors with high PDZD11 expression may have an immunosuppressive microenvironment that could impact response to immune checkpoint blockade therapy. The increased infiltration of CD8+ T cells, follicular helper T cells, and gamma delta T cells in PDZD11-high tumors further supports its potential role in immune response modulation . For clinical application, researchers should develop and validate a PDZD11-based predictive signature by analyzing retrospective cohorts of patients who have received immune checkpoint inhibitors, correlating PDZD11 expression levels with objective response rates and survival outcomes. Additionally, investigating how PDZD11 expression changes during treatment could provide insights into acquired resistance mechanisms. The documented relationship between PDZD11 and PARP inhibitor efficacy pathways also suggests potential value in predicting responses to combination therapies involving both PARP inhibitors and immune checkpoint inhibitors .

How should researchers design experiments to investigate PDZD11's role in cell adhesion and metastasis?

To investigate PDZD11's role in cell adhesion and metastasis, researchers should implement a comprehensive experimental framework. Begin with gene manipulation approaches, establishing stable PDZD11 knockdown and overexpression in relevant cancer cell lines using CRISPR-Cas9 or shRNA technologies . For adhesion studies, perform cell-cell adhesion assays measuring the formation and strength of adherens junctions through techniques like dispase-based dissociation assays or atomic force microscopy. Evaluate the interaction between PDZD11 and the PLEKHA7 complex using co-immunoprecipitation and proximity ligation assays . For metastasis investigations, conduct transwell migration and invasion assays, comparing PDZD11-modified cells with controls. Additionally, implement 3D organoid culture systems to assess morphological changes and invasive capacity. In vivo metastasis models using xenografts with cell lines of varying PDZD11 expression levels can provide system-level insights. Importantly, correlate experimental findings with clinical observations, particularly the noted relationship between PDZD11 downregulation and lymphatic metastasis in EOC . Molecular pathway analysis should focus on cadherin binding, tight junction formation, and cell adhesion mediator activity, which were identified in GO analyses as significantly associated with PDZD11 function .

How can researchers design experiments to elucidate the molecular mechanisms underlying PDZD11's functions in cancer?

To elucidate the molecular mechanisms underlying PDZD11's functions in cancer, researchers should implement a systematic experimental approach incorporating multiple molecular biology techniques. Begin with comprehensive transcriptomic and proteomic profiling of PDZD11 knockout and overexpression models in relevant cancer cell lines to identify differentially expressed genes and proteins . Pathway enrichment analysis should focus on the cellular processes already implicated in PDZD11 function, including cell adhesion, cell proliferation, and immune response pathways . For mechanistic validation, perform chromatin immunoprecipitation sequencing (ChIP-seq) to identify direct transcriptional targets if PDZD11 is found to have nuclear localization. To elucidate protein interaction networks, use proximity-dependent biotin identification (BioID) or immunoprecipitation followed by mass spectrometry to identify PDZD11 binding partners beyond the known PLEKHA7 interaction . For functional studies, design rescue experiments in PDZD11 knockout models by reintroducing either wild-type or mutant forms of PDZD11 lacking specific domains. To investigate PDZD11's role in immune modulation, co-culture PDZD11-modified cancer cells with immune cell populations, evaluating changes in immune checkpoint expression, cytokine production, and immune cell activation status . For in vivo validation, develop conditional PDZD11 knockout mouse models in relevant cancer backgrounds, analyzing tumor development, progression, and immune infiltration. These comprehensive approaches will provide mechanistic insights into how PDZD11 contributes to cancer development and progression.

How should researchers address inconsistent PDZD11 staining patterns in immunohistochemistry?

When encountering inconsistent PDZD11 staining patterns in immunohistochemistry, a systematic troubleshooting approach is essential. First, evaluate fixation conditions—overfixation or underfixation can significantly alter epitope accessibility. PDZD11 antibodies may require specific antigen retrieval optimization; compare citrate buffer (pH 6.0) versus EDTA buffer (pH 9.0) retrieval methods, as different epitopes may respond differently . Antibody concentration requires careful titration; prepare a dilution series (1:50, 1:100, 1:200, 1:500) on positive control tissues to determine optimal signal-to-noise ratio. If batch variation occurs, implement standardized positive controls across experiments and consider antibody validation using western blot to confirm specificity . Tissue heterogeneity is a significant consideration for PDZD11, as its expression varies with cancer stage and metastatic status; ensure representative sampling across tumor regions and document precise anatomical locations . For quantification consistency, implement digital image analysis with standardized parameters and have multiple trained observers score samples independently. When comparing results across studies, note that different PDZD11 antibody clones may recognize distinct epitopes, potentially leading to varied staining patterns. Finally, correlate protein expression with mRNA levels from the same samples when possible to validate expression patterns through orthogonal methods.

What are the key considerations when interpreting PDZD11 expression data in relation to immune infiltration?

When interpreting PDZD11 expression data in relation to immune infiltration, several critical factors must be considered for accurate analysis. First, acknowledge the tissue-specific context of immune infiltration patterns; PDZD11 correlations with immune cell types in epithelial ovarian cancer (positive associations with neutrophils, macrophages, dendritic cells, CD8+ T cells, and CD4+ T cells) may differ in other cancer types . Sample purity significantly impacts interpretation—tumor samples with higher stromal or immune cell content may show altered PDZD11 expression levels that don't accurately reflect cancer cell expression. Use computational methods like ESTIMATE or histological assessment to account for tumor purity in analyses . The polarization state of immune cells is crucial; while PDZD11 high-expression tumors show elevated infiltration of CD8+ T cells and M1 macrophages, these cells may exhibit exhausted or suppressed phenotypes, requiring functional characterization beyond mere quantification . Spatial distribution analysis through multiplex immunofluorescence should complement bulk expression data to determine whether PDZD11-expressing cells directly interact with immune cells or influence them through paracrine mechanisms. When analyzing correlations between PDZD11 and immune checkpoints (TIGIT, TIM3, LAG3, CTLA4, PD-1), consider both the statistical significance and the biological magnitude of these relationships . Finally, integrate immune infiltration data with clinical outcomes to determine whether PDZD11-associated immune patterns have prognostic implications, providing context for therapeutic development.

How can researchers resolve contradictory findings between PDZD11 mRNA and protein expression levels?

To resolve contradictory findings between PDZD11 mRNA and protein expression levels, researchers should implement a comprehensive analytical strategy addressing multiple biological and technical factors. First, examine post-transcriptional regulation mechanisms that may affect the mRNA-protein correlation, including miRNA targeting, RNA stability, and alternative splicing of PDZD11 transcripts . Quantify specific PDZD11 transcript variants using isoform-specific qRT-PCR to determine if discrepancies arise from differential isoform expression. For technical validation, employ multiple antibodies targeting different PDZD11 epitopes to confirm protein expression patterns and rule out antibody-specific artifacts . Similarly, utilize different mRNA quantification methods (qRT-PCR, RNA-seq, NanoString) on the same samples to verify transcriptional findings. Consider temporal dynamics—protein half-life and degradation rates may create time-shifted expression patterns compared to mRNA levels. Implement polysome profiling to assess translational efficiency of PDZD11 mRNA, which could explain discrepancies between abundant transcript and low protein levels or vice versa. In tissue samples, cellular heterogeneity must be addressed; single-cell RNA-seq paired with multiplexed protein analysis can reveal cell type-specific expression patterns that might be masked in bulk analyses . For clinical samples showing contradictory patterns, analyze matched fresh-frozen and FFPE samples from the same patient to rule out preservation method effects. Finally, integrate data from multiple patient cohorts across different studies to determine whether contradictions are consistent or represent cohort-specific phenomena.