CREB5 Antibody

What is CREB5 and what epitopes are targeted by most commercial antibodies?

CREB5, also known as CRE-BPA, is a 56.92 kDa transcription factor belonging to the cAMP response element binding protein family. It contains a bZIP (basic leucine zipper) domain critical for DNA binding and protein-protein interactions . Commercial antibodies typically target:

• Full-length recombinant CREB5 protein (most monoclonal antibodies)

• Middle region of CREB5 (some polyclonal antibodies)

• bZIP domain-specific epitopes (specialized research antibodies)

The bZIP domain contains critical leucine residues (e.g., L431, L434) that regulate transcriptional activity and heterodimerization with transcription factors like JUN and FOS .

What applications are CREB5 antibodies validated for?

Based on current literature and product information, CREB5 antibodies have been validated for:

Most antibodies show stronger reactivity with human CREB5 than with rodent orthologs .

How can I verify CREB5 antibody specificity in my experimental system?

Ensuring antibody specificity is critical for reliable results. Recommended validation approaches include:

• Positive controls: Use cell lines with known CREB5 expression (HepG2, HeLa, MCF-7)

• CREB5 overexpression: Compare transfected vs. non-transfected 293T cells

• CREB5 knockdown validation:

  • shRNA targeting the CREB5 3'UTR (58% reduction observed)

  • CRISPR/Cas9 targeting the CREB5 bZIP domain (77% reduction)

• Western blot: Confirm expected molecular weight (~57 kDa) and absence of non-specific bands

• Testing in CREB5-null systems as negative controls

How can I optimize CREB5 co-immunoprecipitation to study protein-protein interactions?

The interaction profile of CREB5 is critical for understanding its function in disease. Research has shown that CREB5 interacts with multiple proteins involved in transcriptional regulation . For optimal co-IP:

• Sample preparation protocol:

  • Use rapid immunoprecipitation and mass spectrometry of endogenous proteins (RIME) methodology for chromatin-bound interactions

  • Include protease and phosphatase inhibitors to preserve native interactions

  • Consider mild detergent conditions (0.1% NP-40 or 0.5% Triton X-100)

• Selective interaction partners to screen:

  • AR (androgen receptor) and FOXA1 - critical in prostate cancer resistance mechanisms

  • HOXB13, GRHL2, EP300, NFIC, and TBX3 - identified as CREB5-specific interactors

  • JUN/FOS family members - interact with the leucine zipper domain

• Critical controls:

  • Compare wild-type CREB5 to L434P mutant CREB5 (disrupts key interactions while maintaining others)

  • Use related family member CREB3 as specificity control (shares homology but has distinct interactome)

  • Include IgG control and input samples

Research shows CREB5 and FOXA1 share 504 protein interactions, with 335 of these being CREB5-specific when compared to CREB3 .

What are the best approaches for studying CREB5 phosphorylation states?

CREB5 contains two conserved N-terminal Threonine/Proline residues (T59 and T61) that are phosphorylation substrates for P38, JNK, and ERK kinases . For phosphorylation studies:

• Antibody selection:

  • Use phospho-specific antibodies targeting T61 phosphorylation

  • Pair with total CREB5 antibodies to determine phosphorylation/total ratio

• Experimental considerations:

  • Monitor phospho-P38 kinase levels, which correlate with CREB5 T61 phosphorylation

  • Perform kinase inhibitor studies (P38, JNK, ERK inhibitors) to determine regulatory pathways

  • Consider dephosphorylation controls (phosphatase treatment) to confirm specificity

• Cell type considerations:

  • Phosphorylated CREB5 (T61) is enriched in superficial zone chondrocytes compared to deep zone chondrocytes

  • Different cancer cell types may show variable phosphorylation patterns

How do I design ChIP-seq experiments to study CREB5 genomic binding sites?

Research has revealed that CREB5 binding sites change dramatically in response to treatments like enzalutamide . For effective ChIP-seq:

• Experimental design:

  • Use V5-tagged CREB5 for higher specificity ChIP when possible

  • Include pre- and post-treatment conditions to capture dynamic binding

  • Categorize binding sites as lost, retained, or gained based on treatment conditions

• Analysis approaches:

  • Use GIGGLE or similar tools to compare CREB5 binding with other transcription factors

  • Examine co-occupation with AR, FOXA1, HOXB13, and other predicted interactors

  • Classify binding sites by their genomic context (promoters, enhancers, etc.)

• Validation strategies:

  • Confirm key binding sites with ChIP-qPCR

  • Correlate binding with gene expression changes

  • Test functional relevance through reporter assays or CRISPR-based approaches

In LNCaP cells, enzalutamide treatment resulted in 5,392 lost, 12,432 retained, and 12,144 gained CREB5 binding sites .

Why might Western blot detection of CREB5 show variable results across tissue types?

Researchers often encounter variability in CREB5 detection. Common issues and solutions include:

• Expression level variations:

  • CREB5 is differentially expressed across tissues and cancer types

  • Enrich samples through nuclear extraction protocols for low-expressing tissues

  • Load 50-80 μg of total protein for tissues with lower expression

• Isoform considerations:

  • Human CREB5 has multiple isoforms that may appear as different bands

  • Select antibodies targeting conserved regions when studying multiple isoforms

  • Consider using isoform-specific primers for RT-qPCR validation

• Technical optimizations:

  • Use fresh samples when possible (CREB5 may be sensitive to degradation)

  • Optimize transfer conditions for higher molecular weight proteins

  • Consider longer blocking times (2-3 hours) to reduce background

How can contradictory findings about CREB5 function be reconciled methodologically?

Research has shown seemingly contradictory roles for CREB5 in different contexts . To address these contradictions:

• Context-dependent experimental design:

  • In standard culture conditions, CREB5 overexpression reduced viability (Z = -1.3)

  • In enzalutamide-treated conditions, CREB5 promoted survival (Z = +14.5)

  • Design experiments that specifically test context-dependent functions

• Pathway analysis approach:

  • Examine CREB5 in relation to specific pathways (Wnt, EMT, AR signaling)

  • Use GSEA to identify pathways associated with CREB5 expression

  • Consider parallel analysis of clinical samples to validate cell line findings

• Mutation-based functional studies:

  • Generate and test specific CREB5 mutants (L431P, L434P) that disrupt specific interactions

  • Use domain-deletion approaches to isolate functional regions

  • Correlate mutant function with protein interaction profiles

What controls are crucial when studying CREB5 in cancer models?

CREB5 has been implicated in multiple cancer types, including prostate cancer and glioblastoma . Critical experimental controls include:

• Cellular models:

  • AR-dependent vs. AR-independent cell lines (LNCaP vs. PC3/DU145)

  • Include both hormone-sensitive and resistant cell variants

  • Compare primary vs. metastatic cell models when available

• Expression modulation controls:

  • Use both overexpression and knockdown/knockout approaches

  • Include closely related family members (CREB3) as specificity controls

  • Test mutant variants (L431P, L434P) that disrupt specific functions

• In vivo validation:

  • Orthotopic xenograft models (as used for GSC11 glioblastoma cells)

  • Monitor both tumor growth and survival endpoints

  • Include matched patient-derived samples when possible

Research has shown that CREB5 knockdown in glioblastoma stem cells significantly decreased tumorigenic potential and increased survival in mouse models .

How can CREB5 antibodies be used to predict therapy response in cancer patients?

Recent research suggests CREB5 may predict response to androgen receptor-targeted therapies in prostate cancer :

• Clinical sample considerations:

  • Use tissue microarrays with adequate controls

  • Optimize staining protocols for FFPE vs. frozen tissue

  • Consider co-staining with AR, FOXA1, and EMT markers

• Technical approach:

  • Quantitative image analysis of nuclear CREB5 staining

  • Correlation with phospho-CREB5 levels

  • Multivariate analysis with clinical parameters and treatment history

• Validation methods:

  • Correlate IHC findings with transcriptomic data

  • Compare pre- and post-treatment samples when available

  • Validate findings across multiple patient cohorts

Analysis of the SU2C/PCF mCRPC cohort (n = 209) showed correlation between CREB5 expression and EMT/β-catenin pathway genes in metastatic castration-resistant prostate cancer .

What emerging technologies might enhance CREB5 functional studies?

Several cutting-edge approaches show promise for advancing CREB5 research:

• Proximity labeling approaches:

  • BioID or TurboID fusion proteins to map the CREB5 interactome in living cells

  • APEX2-based approaches for temporal resolution of interactions

  • Comparison of interactomes under different treatment conditions

• Single-cell applications:

  • Single-cell CUT&Tag to map CREB5 genomic binding at single-cell resolution

  • Correlation of CREB5 protein levels with transcriptional programs

  • Spatial transcriptomics to map CREB5 activity in tissue context

• Therapeutic targeting approaches:

  • Degrader technologies (PROTACs) targeting CREB5

  • Disruption of specific protein-protein interactions

  • Combinatorial approaches targeting CREB5 and interacting proteins

These technologies may help resolve the complex and context-dependent functions of CREB5 in disease progression and therapy resistance.

How can integrated multi-omics approaches enhance CREB5 research?

Understanding CREB5 function requires integration of multiple data types:

• Recommended integration strategy:

  • Combine ChIP-seq, RIME, and RNA-seq data from matched samples

  • Correlate genomic binding with protein interactions and transcriptional outcomes

  • Use systems biology approaches to model network effects

• Analytical frameworks:

  • Pathway enrichment analysis using tools like Enrichr

  • GSEA for correlation with known gene signatures

  • Network analysis to identify key nodes and vulnerabilities

• Validation in clinical cohorts:

  • Analyze transcriptomic data from patient cohorts (e.g., SU2C/PCF mCRPC)

  • Compute correlation coefficients between CREB5 and pathway genes

  • Stratify patients based on CREB5-associated signatures

Integration of ChIP-seq and RIME data has already revealed extensive reprogramming of CREB5 nuclear interactions in response to enzalutamide treatment .