DNAJA4 Antibody

What is DNAJA4 and why is it significant in molecular research?

DNAJA4 (DnaJ homolog subfamily A member 4) is a heat shock protein that functions as a co-chaperone with HSP70 to protect damaged cells. It belongs to the DNAJ/HSP40 family with a molecular weight of approximately 40-45 kDa. DNAJA4 is highly expressed in heart and testis tissue, accounting for approximately 1% of total protein in mouse heart. It plays critical roles in various cellular processes including protein folding, cholesterol synthesis, and stress responses. Recent research has demonstrated its significance in cancer progression and metastasis, making it an important target for molecular studies .

What applications are DNAJA4 antibodies suitable for in research?

DNAJA4 antibodies have been validated for multiple research applications:

• Western Blotting (WB): Most commercial antibodies are validated for WB at dilutions ranging from 1:500-1:10000

• Immunohistochemistry (IHC): Both paraffin-embedded and frozen sections

• Flow Cytometry: Particularly for intracellular detection

• Enzyme-Linked Immunosorbent Assay (ELISA)

• Immunoprecipitation (IP)

The optimal application depends on the specific antibody clone, host species, and experimental design. For example, antibody ab185553 has been validated for IP, WB, and Flow Cytometry with human samples at specific dilutions .

How do I select the appropriate DNAJA4 antibody for my specific experiment?

Selection criteria should be based on:

• Target epitope: Different antibodies target different regions of DNAJA4 (N-terminal, C-terminal, or specific amino acid sequences). For example, some antibodies target amino acids 276-302 at the C-terminus, while others target regions between amino acids 210-260 .

• Species reactivity: Verify cross-reactivity with your species of interest. Some antibodies react only with human DNAJA4, while others cross-react with mouse, rat, or multiple species .

• Application compatibility: Ensure the antibody is validated for your specific application. Check validation data showing expected molecular weight (typically 45-48 kDa).

• Clonality: Monoclonal antibodies offer higher specificity but narrower epitope recognition, while polyclonal antibodies provide stronger signals but potential cross-reactivity .

• Detection of isoforms: At least three isoforms of DNAJA4 are known to exist; confirm whether the antibody detects all isoforms if this is relevant to your research .

How can I optimize Western blot protocols for DNAJA4 detection?

Western blot optimization for DNAJA4 requires careful consideration of several parameters:

• Protein extraction: DNAJA4 is found in various cellular compartments, so extraction buffer composition is critical. Use buffers containing protease inhibitors to prevent degradation.

• Sample preparation: The predicted band size for DNAJA4 is approximately 45 kDa, but observed molecular weights can vary between 45-68 kDa depending on post-translational modifications and isoforms .

• Blocking conditions: 5% non-fat dry milk (NFDM) in TBST has been successfully used for DNAJA4 antibodies .

• Antibody dilutions: Optimized dilutions vary by manufacturer:

  • ab185553: 1/10000 dilution

  • ABIN1536998: 1-2 μg/ml

  • 12806-1-AP: 1:500-1:1000

• Detection system: HRP-conjugated secondary antibodies are commonly used, typically at 1:1000-1:1500 dilution .

• Positive controls: Human fetal brain tissue lysate, HeLa cell lysate, and human colon tissue lysate have been used as positive controls for DNAJA4 detection .

What are the recommended approaches for studying DNAJA4 in cancer research models?

Based on recent research methodologies:

• Expression analysis:

  • IHC scoring systems (0-3+) have been utilized to evaluate DNAJA4 protein expression in clinical samples, with samples grouped as negative (0) and positive (1+, 2+, 3+) .

  • RNA-seq and microarray data analysis can be performed using platforms such as Bc-GenExMiner v4.8 for survival correlation studies .

• Functional studies:

  • DNAJA4 overexpression models have been used to study its effect on migration, invasion, and epithelial-mesenchymal transition (EMT) in cancer cell lines .

  • DNAJA4-knockout (KO) cell lines created using CRISPR-Cas9 have been employed to study its role in hyperthermia response and cytoskeletal regulation .

• Mechanistic investigations:

  • Co-immunoprecipitation with DNAJA4 antibodies to identify interacting proteins (e.g., MYH9, PSMD2) .

  • Protein degradation studies to examine DNAJA4's role in the ubiquitin-proteasome pathway .

  • F-actin co-localization studies using immunofluorescence .

How can I effectively analyze DNAJA4 expression patterns in different cancer subtypes?

Research on DNAJA4 in cancer has employed several analytical approaches:

How do I interpret contradictory results regarding DNAJA4's role in different cancer types?

The literature shows that DNAJA4 exhibits context-dependent roles in different cancers:

• Cancer-specific effects:

  • In nasopharyngeal carcinoma (NPC), DNAJA4is downregulated due to promoter hypermethylation and functions as a metastasis suppressor by inhibiting EMT .

  • In breast cancer, DNAJA4 is upregulated with reduced promoter methylation, and high expression correlates with poor survival outcomes .

To reconcile these contradictions:

• Compare experimental models: Cell lines vs. patient samples may show different results.

• Examine tissue specificity: DNAJA4 may interact with tissue-specific factors.

• Consider genetic background: Cancer-specific mutations may influence DNAJA4 function.

• Analyze protein interactions: DNAJA4 interacts with different partners in different contexts (e.g., MYH9 in NPC, cytoskeletal elements in other contexts) .

• Evaluate epigenetic regulation: Hypermethylation vs. hypomethylation in different cancers .

What are common issues in DNAJA4 immunodetection and how can they be resolved?

Several technical challenges may affect DNAJA4 detection:

• Multiple band detection:

  • Issue: Detection of bands at unexpected molecular weights.

  • Solution: Confirm antibody specificity using blocking peptides or knockout controls. Multiple bands could represent isoforms or post-translational modifications .

• Low signal intensity:

  • Issue: Weak detection of DNAJA4 despite proper technique.

  • Solution: Optimize protein extraction methods, reduce antibody dilution, extend incubation times, or use signal enhancement systems .

• High background:

  • Issue: Non-specific signal obscuring DNAJA4-specific bands.

  • Solution: Increase blocking duration/concentration, adjust antibody concentration, use more stringent washing procedures, or try alternative blocking agents .

• Cross-reactivity:

  • Issue: Antibody recognizing non-DNAJA4 proteins.

  • Solution: Use monoclonal antibodies for higher specificity or validate with orthogonal methods (e.g., mass spectrometry) .

• Variable results in different samples:

  • Issue: Inconsistent detection across tissue/cell types.

  • Solution: Consider tissue-specific expression levels and optimize protocols for each sample type .

How can I validate the specificity of DNAJA4 antibodies for my experimental system?

Comprehensive validation approaches include:

• Positive and negative controls:

  • Use samples with known DNAJA4 expression (e.g., heart tissue, testis, HeLa cells) .

  • Include DNAJA4 knockout or knockdown samples as negative controls .

• Blocking peptide experiments:

  • Pre-incubate antibody with the immunizing peptide before application to samples.

  • Specific signals should be abolished or significantly reduced .

• Multiple antibody approach:

  • Use antibodies targeting different epitopes of DNAJA4 to confirm consistent results .

• Orthogonal methods:

  • Complement protein detection with mRNA analysis (RT-qPCR, RNA-seq).

  • Correlate protein levels with functional outcomes in biological assays .

• Mass spectrometry validation:

  • For ultimate confirmation, immunoprecipitate DNAJA4 and verify by mass spectrometry.

How is DNAJA4 involved in cancer progression mechanisms?

Research has revealed multiple mechanisms of DNAJA4 involvement in cancer:

What are the most effective protocols for studying DNAJA4's interaction with the cytoskeleton during stress responses?

Based on published methodologies:

• Immunofluorescence co-localization:

  • Fix cells using 4% paraformaldehyde after desired treatment (e.g., hyperthermia at 44°C for 30 min).

  • Permeabilize with 0.1% Triton X-100.

  • Co-stain with DNAJA4 antibody and cytoskeletal markers (e.g., anti-F-actin antibodies).

  • Use confocal microscopy to analyze co-localization patterns .

• Co-immunoprecipitation:

  • Lyse cells in buffer containing protease inhibitors.

  • Immunoprecipitate with DNAJA4 antibody (e.g., ab185553 at 1/50 dilution).

  • Analyze precipitates for cytoskeletal proteins by Western blot .

• Flow cytometry for quantitative analysis:

  • Fix cells with paraformaldehyde and permeabilize.

  • Stain with DNAJA4 antibody and cytoskeletal markers.

  • Analyze using flow cytometry to quantify co-expression patterns .

• Functional studies using DNAJA4 knockout models:

  • Generate DNAJA4-KO cell lines using CRISPR-Cas9.

  • Compare cytoskeletal organization between wild-type and knockout cells under stress conditions.

  • Analyze recovery dynamics after stress removal .

How do different DNAJA4 antibodies compare in terms of specificity and sensitivity across applications?

A comparative analysis of commonly used DNAJA4 antibodies shows:

Selection considerations:

• For highest specificity in human samples: ab185553 (monoclonal)

• For multi-species studies: ABIN7247618 or A10527

• For C-terminal specific detection: ABIN1536998

• For blocking peptide controls: A10527

What are the advanced considerations for using DNAJA4 antibodies in studying differential expression across cancer subtypes?

Advanced methodological considerations include:

• Tissue microarray analysis:

  • Optimize staining protocols for tissue-specific characteristics.

  • Implement standardized scoring systems (0-3+) for quantitative comparison.

  • Include proper controls (normal adjacent tissue, positive and negative controls) .

• Subtype stratification:

  • Use established molecular classification systems (e.g., PAM50 for breast cancer).

  • Correlate DNAJA4 expression with subtype-specific markers.

  • Conduct separate survival analyses for each subtype to identify differential prognostic value .

• Integration with multi-omics data:

  • Correlate protein expression (IHC) with transcriptomic data (RNA-seq).

  • Analyze methylation patterns in conjunction with expression data.

  • Investigate genetic alterations (mutations, CNVs) in relation to expression levels .

• Validation across multiple cohorts:

  • Use independent patient cohorts to validate findings.

  • Compare results from different detection methods (IHC, WB, RNA-seq).

  • Consider clinical and pathological variables in multivariate analyses .

How do epigenetic modifications affect DNAJA4 expression, and how can this be studied effectively?

Methodological approaches for studying DNAJA4 epigenetic regulation:

• Promoter methylation analysis:

  • Bisulfite sequencing of the CpG island in DNAJA4 promoter region.

  • Methylation-specific PCR to quantify methylation status.

  • Use of in-silico tools like UALCAN to analyze existing methylation datasets (TCGA) .

• Experimental manipulation of methylation:

  • Treatment with DNA methyltransferase inhibitors (e.g., 5-aza-2'-deoxycytidine) to reverse methylation.

  • Analysis of DNAJA4 expression changes after demethylation treatment.

  • Correlation of expression changes with functional outcomes .

• Chromatin immunoprecipitation (ChIP):

  • ChIP assays to investigate histone modifications around the DNAJA4 promoter.

  • Analysis of transcription factor binding associated with epigenetic states.

• Integrated analysis:

  • Compare promoter methylation with expression levels across cancer subtypes.

  • Establish correlation between methylation status and clinical outcomes.

  • Investigate the role of specific transcription factors (e.g., SREBF2) in regulating DNAJA4 expression .