ZNF71 Antibody

What is ZNF71 and what are its primary functions?

ZNF71 (Zinc Finger Protein 71), also known as EZFIT (Endothelial zinc finger protein induced by tumor necrosis factor α), is a member of the krueppel C2H2-type zinc-finger protein family. It functions primarily as a transcription factor involved in gene expression regulation. The protein participates in various biological processes including development, differentiation, and cell proliferation . Its molecular function focuses on transcription factor activity, and it regulates DNA-dependent transcription processes. ZNF71 is located on chromosome 19q13.4 in humans and has a molecular weight of approximately 54,498 Da .

What are the key specifications of commercially available ZNF71 antibodies?

ZNF71 antibodies are typically available as polyclonal antibodies generated in rabbits. For example, the ZNF71 Antibody (PACO41962) has the following specifications:

These specifications are critical for experimental design and ensuring appropriate application of the antibody in research settings .

How should ZNF71 antibodies be stored and handled to maintain optimal activity?

ZNF71 antibodies should be stored according to manufacturer recommendations to maintain their efficacy. For optimal preservation, store the antibody in its supplied buffer (typically containing 50% Glycerol, 0.01M PBS, pH 7.4, with preservatives like 0.03% Proclin 300) at -20°C for long-term storage . Avoid repeated freeze-thaw cycles as this can degrade antibody performance. When handling the antibody, maintain aseptic conditions and use clean pipettes and tubes. For working solutions, prepare only the volume needed for immediate experiments and store according to the validated stability period. Always refer to the specific product datasheet for any special storage or handling instructions that might differ between manufacturers.

What are the optimal conditions for using ZNF71 antibodies in immunohistochemistry?

For optimal immunohistochemistry (IHC) results with ZNF71 antibodies, follow these methodological guidelines:

• Sample preparation: Use freshly prepared formalin-fixed, paraffin-embedded tissue sections (4-6 μm thickness). Properly deparaffinize and rehydrate the sections.

• Antigen retrieval: Perform heat-induced epitope retrieval using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) for 15-20 minutes. The selection between these buffers should be empirically determined based on your specific tissue type.

• Blocking and antibody dilution: Block with 5-10% normal serum from the same species as the secondary antibody for 30-60 minutes. For ZNF71 antibodies like PACO41962, use a dilution range of 1:20-1:200, with 1:100 being a recommended starting point for prostate cancer tissue as demonstrated in validated studies .

• Incubation conditions: Incubate the primary antibody overnight at 4°C in a humidified chamber. For the secondary antibody, incubate for 30-60 minutes at room temperature.

• Detection system: Use an appropriate detection system compatible with your experimental design, such as HRP-conjugated secondary antibodies with DAB substrate.

• Controls: Include both positive controls (tissues known to express ZNF71, such as prostate cancer samples) and negative controls (primary antibody omitted) to validate staining specificity .

How can ZNF71 antibodies be effectively used in Western blotting experiments?

For effective Western blotting with ZNF71 antibodies, follow this methodological approach:

• Sample preparation: Extract proteins using RIPA buffer supplemented with protease inhibitors. Quantify protein concentration using Bradford or BCA assay.

• Gel electrophoresis: Load 20-40 μg of protein per lane on an 8-12% SDS-PAGE gel. ZNF71 has a molecular weight of approximately 54.5 kDa, so adjust your gel percentage accordingly .

• Transfer: Transfer proteins to a PVDF or nitrocellulose membrane using standard wet or semi-dry transfer methods (250-300 mA for 60-90 minutes).

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

• Antibody incubation: Dilute the ZNF71 antibody according to manufacturer's recommendations (typically starting at 1:1000). Incubate overnight at 4°C with gentle rocking.

• Detection: Use an appropriate HRP-conjugated secondary antibody such as Anti-HRP Goat Anti-Rabbit IgG at 1:5000-1:10000 dilution, followed by ECL detection .

• Validation: To ensure specificity, include positive and negative controls. Consider using beta-actin (CABC028) as a loading control .

• Troubleshooting: If background is high, increase washing steps or adjust antibody dilutions. If signal is weak, increase protein loading or primary antibody concentration.

What considerations are important when designing ELISA experiments with ZNF71 antibodies?

When designing ELISA experiments with ZNF71 antibodies, consider these methodological guidelines:

• Antibody dilution optimization: For ZNF71 antibodies like PACO41962, use a dilution range of 1:2000-1:10000 as recommended by manufacturers . Perform a preliminary titration experiment to determine the optimal concentration for your specific experimental conditions.

• Assay format selection: Consider whether direct, indirect, sandwich, or competitive ELISA is most appropriate. For ZNF71 detection in complex samples, sandwich ELISA using a capture and detection antibody pair targeting different epitopes offers higher specificity.

• Sample preparation: Prepare samples in a buffer compatible with the ELISA format, typically PBS with 0.05% Tween-20 and 1-5% BSA. Ensure samples are within the linear range of detection through preliminary dilution series experiments.

• Standard curve generation: Create a standard curve using recombinant ZNF71 protein with known concentrations (typically 0-1000 ng/mL) to enable accurate quantification of your samples.

• Controls: Include positive controls (samples known to contain ZNF71), negative controls (samples lacking ZNF71), and blank controls (all reagents except primary antibody) to validate assay performance.

• Signal detection: Select an appropriate detection system based on the conjugate of your secondary antibody. For high sensitivity applications, consider using amplification systems like biotin-streptavidin.

• Data analysis: Apply appropriate statistical methods to analyze your ELISA data, accounting for standard curve fit quality and sample replicates.

How can researchers validate the specificity of ZNF71 antibody staining in tissue samples?

To validate ZNF71 antibody specificity in tissue samples, researchers should implement these methodological approaches:

• Multiple antibody validation: Use at least two different antibodies targeting different epitopes of ZNF71 to confirm consistent staining patterns across samples.

• Positive and negative controls: Include tissues known to express ZNF71 (such as prostate cancer samples) as positive controls . Use tissues known not to express ZNF71 or samples where the protein has been knocked down/out as negative controls.

• Absorption controls: Pre-incubate the antibody with excess ZNF71 recombinant protein before staining to demonstrate that specific staining is abolished when the antibody binding sites are occupied.

• Orthogonal validation: Correlate immunohistochemistry results with other detection methods such as in situ hybridization for ZNF71 mRNA, or proteomic analysis where possible.

• Signal specificity assessment: Evaluate the subcellular localization pattern - ZNF71 should predominantly show nuclear localization consistent with its function as a transcription factor .

• Concentration gradients: Perform staining with a gradient of antibody concentrations to identify the optimal dilution that maximizes specific signal while minimizing background.

• Western blot correlation: When possible, perform Western blot analysis on the same tissue samples to confirm the antibody recognizes a protein of the expected molecular weight (approximately 54.5 kDa for ZNF71) .

What are the common challenges in detecting ZNF71 in different cell types and how can they be addressed?

Detecting ZNF71 across different cell types presents several challenges that can be methodologically addressed:

• Variable expression levels: ZNF71 expression can vary significantly between cell types and disease states. Research shows higher expression in hepatocellular carcinoma compared to normal liver tissue . Adjust antibody concentrations or detection methods based on expected expression levels. For low-expressing samples, consider signal amplification methods such as tyramide signal amplification.

• Background reduction: Non-specific binding can obscure true ZNF71 signal. Optimize blocking conditions by testing different blocking agents (BSA, normal serum, commercial blockers) and concentrations (1-10%). For immunohistochemistry applications, increase washing steps and consider adding 0.1-0.3% Triton X-100 to reduce background.

• Fixation artifacts: Overfixation can mask epitopes. Optimize fixation protocols (typically 10% neutral buffered formalin for 24-48 hours) and ensure proper antigen retrieval using either heat-induced epitope retrieval with citrate buffer (pH 6.0) or EDTA buffer (pH 9.0).

• Cross-reactivity with other zinc finger proteins: The zinc finger protein family has conserved domains that may lead to cross-reactivity. Validate antibody specificity using cells with ZNF71 knockdown/knockout. Consider using antibodies targeting unique regions of ZNF71 rather than conserved zinc finger domains.

• Tissue-specific optimization: Different tissues may require modified protocols. For liver tissues, extend antigen retrieval time to 25-30 minutes due to the density of the tissue. For prostate cancer samples, a 1:100 dilution has been validated for clear visualization .

How should researchers interpret discrepancies between ZNF71 protein levels and mRNA expression data?

When encountering discrepancies between ZNF71 protein and mRNA levels, researchers should consider these methodological approaches to interpretation:

• Post-transcriptional regulation assessment: Examine potential microRNA regulation of ZNF71 translation. Several studies have demonstrated that protein levels don't always correlate with mRNA expression due to post-transcriptional regulation mechanisms. Consider performing microRNA profiling to identify potential regulatory elements.

• Protein stability analysis: Investigate protein half-life and degradation mechanisms. Perform cycloheximide chase assays to determine ZNF71 protein stability in your experimental system, as differences in protein turnover can explain discrepancies with mRNA levels.

• Technical validation: Confirm the reliability of both protein and mRNA detection methods. For protein detection, verify antibody specificity using appropriate controls. For mRNA analysis, validate primers for specificity and efficiency. Consider using multiple detection methods for each (e.g., different antibodies for protein detection, RT-qPCR and RNA-seq for mRNA).

• Temporal considerations: Analyze the timing of sample collection. Protein translation occurs after transcription, potentially with significant delays, particularly for regulatory proteins. Consider time-course experiments to capture the relationship between mRNA induction and subsequent protein synthesis.

• Subcellular localization: Assess potential compartmentalization of ZNF71 protein. Some proteins may be sequestered in specific cellular compartments, affecting detection in whole-cell lysates. Perform fractionation experiments to examine ZNF71 distribution across nuclear, cytoplasmic, and membrane fractions.

• Statistical analysis: Apply appropriate statistical methods to quantify the correlation between mRNA and protein data. Calculate Pearson or Spearman correlation coefficients to determine the strength of association between the measurements.

How can ZNF71 antibodies be utilized in ChIP-seq experiments to identify transcriptional targets?

To effectively utilize ZNF71 antibodies in Chromatin Immunoprecipitation sequencing (ChIP-seq) experiments, researchers should follow these methodological guidelines:

• Antibody validation for ChIP: Before proceeding with ChIP-seq, validate the ZNF71 antibody specifically for ChIP applications. Perform preliminary ChIP-qPCR experiments targeting genomic regions where ZNF71 is expected to bind based on motif analysis or literature evidence. The antibody should demonstrate significant enrichment compared to IgG control.

• Crosslinking optimization: Since ZNF71 is a C2H2-type zinc finger transcription factor , optimize formaldehyde crosslinking conditions (typically 1% formaldehyde for 10-15 minutes) to effectively capture DNA-protein interactions without over-crosslinking, which can reduce ChIP efficiency.

• Sonication parameters: Determine optimal sonication conditions to generate DNA fragments of 200-500 bp. This size range is ideal for subsequent library preparation and sequencing. Verify fragment size distribution using agarose gel electrophoresis or Bioanalyzer.

• Immunoprecipitation protocol: Use 3-5 μg of ZNF71 antibody per ChIP reaction with 25-50 μL of magnetic protein A/G beads. Include appropriate controls: IgG negative control, input control (non-immunoprecipitated chromatin), and a positive control targeting a well-characterized transcription factor.

• Sequencing considerations: Prepare libraries using standard ChIP-seq protocols and sequence to a depth of at least 20-30 million reads per sample to ensure adequate coverage for transcription factor binding site identification.

• Data analysis pipeline: Process raw sequencing data through quality control (FastQC), alignment to reference genome (BWA or Bowtie2), peak calling (MACS2), and motif discovery (MEME, HOMER). Compare ZNF71 binding sites with gene expression data to identify potential direct regulatory targets.

• Integrative analysis: Correlate ChIP-seq results with transcriptome data from ZNF71 perturbation experiments to establish functional relationships between binding events and gene regulation.

What approaches can be used to study ZNF71's role in immune cell infiltration in cancer microenvironments?

To investigate ZNF71's role in immune cell infiltration within cancer microenvironments, researchers should consider these methodological approaches:

• Multiplex immunohistochemistry: Perform multiplex IHC or immunofluorescence to simultaneously detect ZNF71 and immune cell markers (CD4+ T cells, B cells, regulatory T cells, monocytes, macrophages, and myeloid dendritic cells) in cancer tissues. This approach allows spatial correlation between ZNF71 expression and immune cell localization .

• Single-cell RNA sequencing analysis: Apply single-cell RNA-seq to tumor samples to identify cell populations expressing ZNF71 and characterize the immune cell composition. This technique provides high-resolution data on cell type-specific expression patterns and potential interactions.

• Correlation analysis with immune cell markers: Use computational approaches like TIMER2.0 to analyze associations between ZNF71 expression and various immune cell infiltration markers. Recent studies have demonstrated significant correlations between ZNF71 expression and immune cell infiltration in hepatocellular carcinoma .

• In vitro co-culture systems: Establish co-culture systems between ZNF71-expressing cancer cells and immune cells to study direct interactions. Manipulate ZNF71 expression through overexpression or knockdown and assess changes in immune cell recruitment, activation, and function.

• Cytokine/chemokine profiling: Analyze secreted factors from ZNF71-manipulated cancer cells using cytokine arrays or ELISA to identify potential mediators of immune cell recruitment or regulation.

• ZNF71 knockout models: Develop ZNF71 knockout models using CRISPR/Cas9 in appropriate cancer cell lines and evaluate changes in immune cell infiltration in in vivo tumor models. This approach provides direct evidence of ZNF71's role in modulating the tumor immune microenvironment.

• Pathway analysis: Perform pathway enrichment analysis to identify immunomodulatory pathways regulated by ZNF71. Research has shown ZNF71 is associated with herpes simplex virus type 1 infection, cell cycle, and DNA replication pathways, which may indirectly influence immune responses .

How can researchers investigate the functional relationship between ZNF71 and TNF-α signaling pathways?

To investigate the functional relationship between ZNF71 (also known as Endothelial zinc finger protein induced by tumor necrosis factor α) and TNF-α signaling pathways, researchers should employ these methodological approaches:

• Induction kinetics analysis: Perform time-course experiments treating various cell types with recombinant TNF-α at different concentrations (typically 1-100 ng/mL) and measure ZNF71 mRNA and protein expression at multiple timepoints (1, 2, 4, 8, 12, 24 hours). This establishes the temporal relationship between TNF-α stimulation and ZNF71 induction.

• Signaling pathway inhibition: Use specific inhibitors of TNF-α downstream signaling components (such as IKK inhibitors for NF-κB pathway, JNK inhibitors, p38 MAPK inhibitors) to determine which branch of TNF-α signaling mediates ZNF71 induction. Combine with Western blot analysis of pathway activation markers.

• Promoter analysis: Perform in silico analysis of the ZNF71 promoter region to identify potential binding sites for transcription factors activated by TNF-α (particularly NF-κB, AP-1). Validate these sites using reporter assays with wild-type and mutated promoter constructs.

• Chromatin immunoprecipitation: Conduct ChIP assays with antibodies against transcription factors activated by TNF-α to determine direct binding to the ZNF71 promoter region following TNF-α treatment.

• Gene expression profiling: Perform RNA-seq or microarray analysis comparing TNF-α-treated cells with and without ZNF71 knockdown/knockout to identify shared and distinct gene expression patterns, revealing potential co-regulated pathways.

• Co-immunoprecipitation assays: Investigate potential protein-protein interactions between ZNF71 and components of the TNF-α signaling pathway using co-IP followed by mass spectrometry to identify interaction partners.

• Functional assays: Assess the impact of ZNF71 modulation on TNF-α-induced cellular responses, such as apoptosis, inflammation, or cell survival, through appropriate functional assays (caspase activation, cytokine production, MTT/XTT assays).

What is the significance of ZNF71 overexpression in hepatocellular carcinoma?

Recent research has revealed significant implications of ZNF71 overexpression in hepatocellular carcinoma (HCC):

• Expression pattern and diagnostic potential: ZNF71 shows significantly higher expression in HCC tissues compared to non-tumorous tissues (p < 0.05) . High-throughput analysis demonstrated a standardized mean difference in ZNF71 expression of 0.55 (95% confidence interval: 0.34-0.75) between HCC and normal liver tissues. The diagnostic value of ZNF71 mRNA was evaluated with an area under the curve of 0.78 (95%CI: 0.75-0.82), sensitivity of 0.63 (95%CI: 0.53-0.72), and specificity of 0.82 (95%CI: 0.73-0.89) .

• Clinical correlations: Elevated ZNF71 expression correlates with disease progression and severity in HCC. The relationship between ZNF71 expression and clinical parameters suggests its potential as a prognostic marker. These findings align with previous studies identifying ZNF71 as a biomarker in other cancer types, including non-small cell lung cancer, osteosarcoma, oral squamous cell carcinoma, and laryngeal squamous cell carcinoma .

• Molecular pathway involvement: Pathway enrichment analyses identified strong positive correlations between ZNF71 and genes such as ZNF470, ZNF256, and ZNF285. ZNF71 appears to promote HCC progression through mechanisms involving cell cycle regulation and metabolism. This suggests ZNF71 may participate in a network of zinc finger proteins that collectively influence cancer development .

• Immune microenvironment modulation: Perhaps most significantly, ZNF71 expression shows correlations with immune cell infiltration, including CD4+ T cells, B cells, regulatory T cells, monocytes, macrophages, and myeloid dendritic cells in the HCC microenvironment . This relationship between ZNF71 and immune infiltration suggests its potential role in modulating anti-tumor immune responses and may influence the efficacy of immunotherapeutic approaches.

• Therapeutic implications: The identified association between ZNF71 and immune cell infiltration in HCC suggests that ZNF71 could serve as a therapeutic target, particularly in treatments aimed at modulating the immune microenvironment. Future investigations into ZNF71 inhibition strategies could potentially enhance immunotherapy efficacy in HCC patients .

How does ZNF71 contribute to cancer development through cell cycle and metabolic pathways?

Recent research has elucidated multiple mechanisms through which ZNF71 influences cancer development via cell cycle and metabolic pathways:

• Cell cycle regulation: Pathway enrichment analyses from ZNF71 co-expression studies have identified significant associations with cell cycle pathways . As a transcription factor, ZNF71 likely regulates the expression of genes involved in cell cycle progression. This regulatory function may promote uncontrolled cell division in cancer cells, contributing to tumor growth and progression.

• DNA replication involvement: ZNF71 shows strong correlations with DNA replication pathways in cancer cells . This suggests ZNF71 may regulate genes essential for DNA synthesis and replication, potentially promoting genomic instability or enhanced replicative potential in cancer cells.

• Metabolic pathway modulation: Negative correlations have been observed between ZNF71 and various metabolic pathways in hepatocellular carcinoma . This indicates ZNF71 may contribute to the metabolic reprogramming observed in cancer cells, potentially shifting cellular metabolism toward patterns that support rapid proliferation and survival in the tumor microenvironment.

• Integration with viral infection pathways: Interestingly, ZNF71 shows associations with herpes simplex virus type 1 infection pathways . This unexpected connection may reflect interactions with cellular defense mechanisms or viral mimicry pathways that cancer cells often exploit for growth advantage.

• Co-expression network analysis: ZNF71 exhibits strong positive correlations with other zinc finger proteins, including ZNF470, ZNF256, and ZNF285 . This suggests ZNF71 may function within a broader transcriptional regulatory network that collectively influences cancer cell biology through coordinated regulation of target genes involved in proliferation and metabolism.

• Translational implications: Understanding ZNF71's role in these pathways provides potential opportunities for therapeutic intervention. Targeting ZNF71 or its downstream effectors could disrupt the abnormal cell cycle progression and metabolic adaptations that support cancer cell survival and proliferation.

What methodological approaches are advancing our understanding of ZNF71's role in different cancer types?

Recent advancements in research methodologies have significantly enhanced our understanding of ZNF71's role across different cancer types:

• Integrated multi-omics analysis: Researchers are employing comprehensive approaches that integrate transcriptomics, proteomics, and genomics data to characterize ZNF71's function. For hepatocellular carcinoma, investigators analyzed expression data from multiple sources including The Cancer Genome Atlas, Genotype-Tissue Expression project, Gene Expression Omnibus, and ArrayExpress, involving an initial pool of 5232 datasets that was systematically narrowed to 32 datasets for in-depth analysis . This integrative approach provides robust evidence for ZNF71's role across multiple independent cohorts.

• High-throughput immunohistochemistry: Large-scale IHC studies, such as the analysis of 235 HCC specimens and 13 noncancerous liver tissue samples, enable detailed characterization of ZNF71 protein expression patterns in patient tissues . These approaches allow for correlation with clinical parameters and outcomes, establishing clinical relevance.

• Computational prediction tools: Advanced bioinformatics approaches, including LinkedOmics correlation analysis with stringent statistical criteria (p < 0.01, correlation coefficient ≥ 0.3), have identified genes co-expressed with ZNF71 . This computational approach reveals functional networks and potential regulatory mechanisms involving ZNF71.

• Immune infiltration analysis platforms: Specialized tools like TIMER2.0 have enabled researchers to quantify relationships between ZNF71 expression and various immune cell populations within the tumor microenvironment . These analyses provide insights into ZNF71's potential role in modulating anti-tumor immune responses.

• Single-cell sequencing technologies: Application of single-cell RNA sequencing has advanced understanding of ZNF71 expression at the cellular level, revealing cell type-specific patterns and potential functions within the heterogeneous tumor ecosystem .

• CRISPR knockout screening: Functional genomics approaches using CRISPR-Cas9 technology have confirmed the biological significance of ZNF71 overexpression in cancer models, providing causal evidence for its role in cancer development .

• Cross-cancer type comparative analyses: Methodological approaches comparing ZNF71's role across multiple cancer types (including HCC, non-small cell lung cancer, osteosarcoma, oral squamous cell carcinoma, and laryngeal squamous cell carcinoma) have revealed both common and cancer-specific functions . These comparative approaches help distinguish fundamental versus context-dependent roles of ZNF71 in oncogenesis.