hoxa9a Antibody

What is HOXA9 and why is it significant in hematological research?

HOXA9 (Homeobox protein A9) is a critical DNA-binding transcription factor that plays essential roles in both normal hematopoiesis and leukemia development. The canonical HOXA9 protein is 272 amino acids in length, weighs approximately 30.2 kDa, and has a nuclear subcellular localization . It belongs to the Abd-B homeobox protein family and functions primarily in DNA-binding transcription factor activity.

HOXA9 is particularly significant in hematological research because:

• It is required for normal hematopoiesis and contributes to myeloid blood cell differentiation

• Overexpression is found in more than 50% of acute myeloid leukemia (AML) cases and strongly correlates with poor prognosis

• It functions as a pioneer factor at de novo enhancers and can reprogram the epigenetic landscape

• It serves as a potential therapeutic target for leukemia with HOXA9 overexpression

What are the primary applications for HOXA9 antibodies in research?

Based on the available research, HOXA9 antibodies are commonly used in several experimental applications:

When designing experiments, researchers should consider that HOXA9 functions primarily as a nuclear transcription factor, with specific binding patterns that vary between cell lineages .

How should researchers select the appropriate HOXA9 antibody for their experiments?

When selecting a HOXA9 antibody for research, consider these critical factors:

• Reactivity: Confirm antibody reactivity with your species of interest. HOXA9 antibodies have been validated for human and mouse samples, with others available for additional species like rat and bovine .

• Antibody type:

  • Monoclonal antibodies: Offer high specificity against a single epitope, reducing background signal but potentially limiting detection if the epitope is masked.

  • Polyclonal antibodies: Recognize multiple epitopes, increasing detection sensitivity but potentially increasing background.

• Application compatibility: Verify the antibody has been validated for your intended application (WB, ChIP, IHC, etc.) .

• Immunogen information: Check if the antibody was raised against relevant protein regions. For example, some HOXA9 antibodies target the N-terminal region .

• Validation data: Look for antibodies with published validation data, especially in ChIP-seq experiments where specificity is crucial .

• Storage buffer compatibility: Consider if the antibody's buffer components (often containing sodium azide, glycerol, etc.) are compatible with your experimental system .

How does HOXA9 function as a pioneer factor in enhancer regulation?

Research has demonstrated that HOXA9 acts as a pioneer transcription factor that can reprogram the enhancer landscape in hematopoietic cells. This function has significant implications for understanding leukemogenesis:

• Enhancer reorganization: HOXA9 overexpression leads to significant enhancer reorganizations with prominent emergence of leukemia-specific de novo enhancers (DE) .

• Chromatin binding patterns: ChIP-seq analysis reveals that the majority of HOXA9 binding sites (>90% of 6,578 peaks in myeloid cells) occur at intergenic or intronic regions, rather than at promoters .

• Lineage-specific binding: Despite some common core targets, HOXA9 binds distinct lineage-specific enhancers:

  • In myeloid cells: Enriched for binding sites of myeloid-specific transcription factors (CEBP, ETS family)

  • In B cells: Enriched for binding sites of B cell-specific factors (EBF1, TCF3)

• Epigenetic modification: HOXA9 binding sites are highly enriched for active enhancer signatures including H3K4me1 and H3K27ac marks .

• Recruitment of cofactors: HOXA9 recruits critical cofactors to establish enhancer function:

  • The MLL3/MLL4 complex (responsible for H3K4 methylation)

  • CEBPα (in myeloid cells)

  • These recruitments are either mutually dependent or sequential in a locus-specific manner

When designing experiments to study HOXA9's pioneer factor activity, researchers should consider ChIP-seq approaches targeting both HOXA9 and enhancer-associated histone modifications.

What are the protein interactions of HOXA9 and how can they be studied?

HOXA9 forms functional complexes with various proteins that modulate its activity and specificity. Understanding these interactions is crucial for deciphering HOXA9's role in normal development and disease:

Key HOXA9 interacting partners:

• MEIS1: A cofactor that accelerates HOXA9-mediated leukemogenesis. The HOXA9-MEIS1 complex is frequently co-expressed in AML .

• MLL3/MLL4 complex: HOXA9 interacts with KMT2C and KMT2D (MLL3 and MLL4) and their cofactor PTIP. This interaction is important for establishing H3K4me1 at de novo enhancers .

• CEBPα: In myeloid cells, HOXA9 and CEBPα co-occupy genomic regions, with their recruitment being interdependent .

• SAFB: Scaffold/matrix attachment region binding proteins that interact with HOXA9 in AML cells. SAFB cobinds with HOXA9 at one-third to two-thirds of HOXA9 binding sites .

Methods to study HOXA9 interactions:

When designing interaction studies, researchers should consider using endogenous immunoprecipitation approaches rather than tagged overexpression systems to avoid artifacts .

How can HOXA9 inhibitors be effectively utilized in research studies?

HOXA9 inhibitors represent valuable tools for investigating HOXA9-dependent processes and evaluating therapeutic potential. The DB818 inhibitor has shown promise in research settings:

Research applications for HOXA9 inhibitors:

• Cellular phenotyping: DB818 treatment suppresses growth, induces apoptosis, and alters differentiation in AML cell lines with HOXA9 overexpression .

• Target gene analysis: HOXA9 inhibition with DB818 has been shown to:

  • Down-regulate HOXA9 transcriptional targets: MYB, MYC, and BCL2

  • Up-regulate FOS expression

• Comparison with genetic approaches: Studies can compare inhibitor effects with HOXA9 knockdown to:

  • Identify on-target vs. off-target effects (e.g., MYC expression differed between DB818-treated and HOXA9-deficient OCI/AML3 cells)

  • Validate biological significance of observations

• Cell line selection: DB818 shows variable efficacy across different cell lines, with particular effectiveness in AML cell lines harboring gene mutations that up-regulate HOXA9 expression (OCI/AML3, MV4-11, and THP-1) .

• Mechanistic studies: Inhibitors can help elucidate the relationship between HOXA9 and disease progression, as demonstrated by DB818's ability to suppress leukemia cell growth .

When designing experiments with HOXA9 inhibitors, researchers should include appropriate controls and validation steps to distinguish on-target effects from off-target effects.

What are the optimal protocols for HOXA9 ChIP-seq experiments?

ChIP-seq is a powerful technique for mapping HOXA9 binding sites genome-wide. Based on published studies, here are key considerations for successful HOXA9 ChIP-seq experiments:

Antibody selection:

• Use validated ChIP-grade antibodies that have demonstrated specificity in ChIP applications

• The antibody used in Collins et al. (2014) was referenced as successfully enriching endogenous HOXA9 in ChIP-seq experiments

Experimental design:

• Cross-linking: Standard formaldehyde fixation (typically 1% for 10 minutes)

• Sonication: Optimize to achieve DNA fragments of 200-500 bp

• Immunoprecipitation: Use appropriate antibody amounts (typically 2-5 μg per sample)

• Controls:

  • Input chromatin (essential)

  • IgG negative control

  • Positive control regions known to bind HOXA9

Analysis considerations:

• Peak calling: Use appropriate algorithms (e.g., MACS2) to identify binding sites

• Motif analysis: Analyze HOXA9 binding sites for enriched sequence motifs:

  • Expect lineage-specific motifs (CEBP and ETS family in myeloid cells; EBF1 and TCF3 in B cells)

• Genomic annotation: Characterize binding site distribution:

  • Expect >90% at intergenic/intronic regions in myeloid cells

  • ~6% at promoters or other transcribed regions

• Integration with histone marks: Compare with H3K4me1, H3K27ac, and H3K27me3 to identify active enhancers

Validation approaches:

• Confirm selected binding sites by ChIP-qPCR

• Test functional importance through reporter assays or CRISPR-mediated deletion

How should researchers optimize antigen retrieval for HOXA9 immunohistochemistry?

Effective antigen retrieval is critical for successful HOXA9 immunohistochemistry. Based on published protocols:

Recommended antigen retrieval protocols:

• Heat-induced epitope retrieval (HIER):

  • Primary option: TE buffer at pH 9.0

  • Alternative option: Citrate buffer at pH 6.0

  • Typically performed by microwaving tissue sections for approximately 8 minutes

• Step-by-step protocol:

  • Deparaffinize and dehydrate sections

  • Incubate with 3% H₂O₂ for 10 minutes to block endogenous peroxidase

  • Rinse twice in distilled water

  • Perform microwave-based antigen retrieval (8 minutes)

  • Allow sections to cool to room temperature

  • Proceed with blocking and antibody incubation

• Antibody incubation parameters:

  • Primary antibody dilution: 1:200-1:2000 (optimization recommended)

  • Incubation: Overnight at 4°C

  • Secondary antibody: Biotinylated secondary antibodies (1:1000)

  • Visualization: 3,3'-diaminobenzidine (DAB) as chromogen

• Controls and counterstaining:

  • Negative control: PBS without primary antibody

  • Counterstain: Weak hematoxylin counterstaining

  • Mounting: Standard slide mounting techniques

Troubleshooting tips:

• If signal is weak, increase antibody concentration or extend incubation time

• If background is high, increase blocking time or reduce antibody concentration

• Consider testing both pH 6.0 and pH 9.0 buffers to determine optimal retrieval conditions

What controls should be included in HOXA9 knockdown or inhibition experiments?

Proper controls are essential for interpreting HOXA9 knockdown or inhibition experiments. Based on published studies, researchers should include:

Essential controls for HOXA9 knockdown experiments:

• Vector controls:

  • Empty vector control for overexpression studies

  • Non-targeting shRNA/siRNA with similar GC content for knockdown studies

  • Doxycycline-treated controls for inducible systems without shRNA

• Expression validation:

  • Western blot to confirm HOXA9 protein reduction

  • qPCR to confirm mRNA knockdown

  • Time course analysis (important as HOXA9 knockdown may take 3-4 days to achieve significant reduction)

• Phenotypic controls:

  • Rescue experiments (re-expression of HOXA9 should reverse knockdown phenotypes)

  • Comparison with known HOXA9 inhibitors (e.g., DB818) to distinguish on-target from off-target effects

• Parallel approaches:

  • Use multiple knockdown methods (shRNA, CRISPR-Cas9) targeting different regions

  • Use different cell lines with variable HOXA9 dependence

Controls for HOXA9 inhibitor experiments:

• Dose-response analysis: Test multiple concentrations to establish dose-dependent effects

• Time course: Determine temporal dynamics of inhibitor effects

• Comparison with genetic approaches: Compare with shRNA/CRISPR to identify potential off-target effects

• Target engagement: Confirm inhibitor directly affects HOXA9 activity through reporter assays

Readouts to assess effectiveness:

How should researchers analyze bimodal HOXA9 expression patterns in patient data?

HOXA9 has been observed to exhibit bimodal expression in certain cancer datasets, including AML, which has important implications for patient stratification and prognosis . Here's a methodological approach for analyzing such patterns:

Identifying bimodal expression:

• Visualization techniques:

  • Kernel density plots to visualize distribution

  • Histogram analysis with appropriate binning

  • Q-Q plots to detect deviation from normal distribution

• Statistical methods to confirm bimodality:

  • Hartigan's dip test for unimodality

  • Gaussian mixture modeling

  • Akaike Information Criterion (AIC) to compare unimodal vs. bimodal fits

Patient stratification approaches:

• Cohort division:

  • Example from Zhong et al.: 31 patients in low expression peak, 80 patients in high expression peak

  • Use statistical methods like Gaussian mixture modeling to determine cutoff values

• Survival analysis:

  • Kaplan-Meier survival curves comparing high vs. low expression groups

  • Log-rank test to assess statistical significance

  • Hazard ratio calculation (e.g., HR 0.29 for low expression in AML)

  • Cox proportional hazards model for multivariate analysis

• Clinical correlation:

  • Analyze distribution across FAB subtypes (e.g., high HOXA9 expression correlates with M0 and M5 subtypes)

  • Assess molecular classification patterns (e.g., MLL-induced leukemia shows high HOXA9)

  • Correlate with clinical parameters like age, white blood cell count, and blast percentage

• Biological validation:

  • Correlation with other bimodally expressed genes (e.g., APP, IGSF10)

  • Investigating causal mechanisms (e.g., positive feedback loops)

When analyzing bimodal expression patterns, researchers should consider potential biological switches or positive feedback mechanisms that might explain the observed distribution patterns.

How can researchers integrate HOXA9 ChIP-seq data with gene expression profiles?

Integrating HOXA9 binding data with transcriptomic changes provides powerful insights into direct vs. indirect regulation. Here's a methodological approach based on published studies:

Data integration workflow:

• Binding site annotation:

  • Assign HOXA9 binding sites to potential target genes

  • Consider both proximal (promoter) and distal (enhancer) binding

  • Use tools like GREAT or nearest gene approaches with appropriate distance thresholds

• Expression correlation:

  • Compare HOXA9 binding with differential expression after HOXA9 manipulation

  • Generate scatter plots of binding strength vs. expression change

  • Calculate correlation coefficients (Pearson/Spearman)

• Enhancer-gene linking:

  • For distal binding sites, use chromatin conformation data (Hi-C, ChIA-PET)

  • Consider using correlation of enhancer activity with gene expression across cell types

  • Validate key enhancer-gene connections experimentally

• Pathway analysis:

  • Perform Gene Ontology (GO) and KEGG pathway enrichment analysis

  • Use tools like LinkedOmics to screen co-expressed genes

  • Integrate with protein-protein interaction networks

Analytical approaches:

• Direct target identification:

  • Motif analysis at binding sites (expect lineage-specific motifs)

  • Integration with data from HOXA9 inhibitors like DB818

  • Comparison of rapid vs. delayed expression changes

• Cofactor analysis:

  • Overlay HOXA9 binding with cofactor binding (MEIS1, CEBPα, SAFB)

  • Identify sites with co-occupancy vs. solo binding

  • Correlate with different expression patterns

• Visualization strategies:

  • Generate heatmaps of binding and expression across conditions

  • Use genome browsers to visualize specific loci

  • Create network diagrams showing key regulatory hubs

• Validation approaches:

  • CRISPR-mediated deletion of binding sites

  • Reporter assays for enhancer activity

  • Comparison of genetic perturbation with inhibitor studies

This integrated approach can help identify direct HOXA9 targets and separate them from secondary effects in complex transcriptional networks.

What approaches should be used to validate HOXA9 antibody specificity?

Antibody validation is critical for ensuring reliable HOXA9 research results. Based on best practices, here are comprehensive validation approaches:

Essential validation methods:

• Western blot validation:

  • Confirm detection of expected molecular weight (30.2 kDa canonical size, often observed at ~40 kDa)

  • Test multiple cell lines with variable HOXA9 expression

  • Include positive controls like K-562 nuclear extract

• Genetic validation:

  • Test antibody in HOXA9 knockout/knockdown samples

  • Observe loss of signal in Western blot, IHC, or ChIP

  • Published knockdown studies can serve as reference

• Peptide competition assays:

  • Pre-incubate antibody with immunizing peptide

  • Verify signal reduction/elimination

• Cross-reactivity assessment:

  • Test against related proteins (other HOX family members)

  • Evaluate specificity across species if doing cross-species research

Application-specific validation:

• For ChIP applications:

  • Verify enrichment at known HOXA9 binding sites

  • Confirm motif enrichment in peaks

  • Compare with published ChIP-seq datasets

  • The HOXA9 antibody referenced in Blood (2023) has been validated for enriching endogenous HOXA9 in ChIP-seq

• For IHC applications:

  • Include positive and negative control tissues

  • Compare staining patterns with mRNA expression data

  • Use multiple antibodies targeting different epitopes

• For flow cytometry:

  • Include appropriate isotype controls

  • Validate with cells expressing variable HOXA9 levels

Documentation and reporting:

Comprehensive validation ensures reproducibility and reliability of results across different experimental platforms and between laboratories.

What emerging technologies might advance HOXA9 antibody-based research?

Several cutting-edge technologies show promise for expanding HOXA9 antibody applications in research:

• CUT&RUN and CUT&Tag:

  • More sensitive alternatives to traditional ChIP-seq

  • Require fewer cells (as low as 1,000 cells)

  • Could enable HOXA9 binding studies in rare cell populations or primary patient samples

  • Potential for single-cell applications to study HOXA9 binding heterogeneity

• Proximity labeling approaches:

  • TurboID or APEX2 fusions to HOXA9 to map protein neighborhoods

  • Could provide temporal dynamics of HOXA9 interactions

  • Complement traditional co-immunoprecipitation approaches used in current studies

• Integrative epigenomic profiling:

  • Simultaneous profiling of HOXA9 binding with chromatin accessibility (ATAC-seq)

  • Co-ChIP approaches to simultaneously map HOXA9 and cofactor binding

  • Integration with DNA methylation data to understand regulatory mechanisms

• Live-cell imaging of HOXA9:

  • Development of nanobodies or scFv fragments against HOXA9

  • Potential for visualizing HOXA9 dynamics in living cells

  • Could provide insights into HOXA9 nuclear translocation and binding kinetics

• Therapeutic antibody development:

  • Antibody-drug conjugates targeting cell-surface proteins regulated by HOXA9

  • Intrabodies or protac approaches to target HOXA9 directly

  • Building on existing inhibitor approaches like DB818

These emerging technologies could help address current limitations in understanding HOXA9 function at single-cell resolution and in limited primary samples.

How might HOXA9 antibodies facilitate translational research in cancer therapeutics?

HOXA9 antibodies offer significant potential for advancing translational cancer research:

• Biomarker development:

  • IHC-based detection of HOXA9 expression for patient stratification

  • High HOXA9 expression is associated with poor prognosis in multiple cancers:

    • Acute myeloid leukemia (AML)

    • Hepatocellular carcinoma (HCC)

    • Nasopharyngeal carcinoma (NPC)

  • Potential for developing standardized diagnostic assays

• Therapeutic response prediction:

  • Monitoring HOXA9 target gene expression following treatment

  • Identifying patient subgroups likely to respond to HOXA9 inhibitors

  • Stratification based on bimodal expression patterns

• Mechanism-based combination therapies:

  • Using HOXA9 antibodies to identify rational drug combinations

  • Example: HOXA9 recruits the MLL3/MLL4 complex - potential for combination with epigenetic modulators

  • Targeting HOXA9 alongside cofactors like MEIS1 or SAFB

• Resistance mechanisms:

  • Investigating HOXA9 expression or localization changes in resistant cases

  • Understanding feedback mechanisms after HOXA9 inhibition

  • Utilizing antibodies to monitor drug-induced HOXA9 complex changes

• Drug screening platforms:

  • Development of high-throughput screening assays using HOXA9 antibodies

  • Identification of compounds that disrupt HOXA9 interactions or binding

  • Building on the success of existing HOXA9 inhibitors like DB818