IRF9 Antibody

What is IRF9 and what are its alternative names in the literature?

IRF9 (Interferon regulatory factor 9) functions as a critical transcription factor in the interferon signaling pathway. In the scientific literature, it appears under several alternative names which can complicate literature searches and data interpretation:

• IRF-9

• ISGF3 (Interferon-stimulated gene factor 3)

• ISGF3G

• p48

• IFN-alpha-responsive transcription factor subunit

• ISGF-3 gamma

The protein has a molecular weight of approximately 43.7 kilodaltons and can be found across multiple species with orthologs in canine, porcine, monkey, mouse, and rat models . When designing experiments or searching literature, it's essential to account for all these nomenclature variations to ensure comprehensive data collection.

What are the common applications for IRF9 antibodies in research?

IRF9 antibodies are employed across numerous experimental techniques in immunology, cell biology, and cancer research. Based on currently available commercial antibodies, the primary applications include:

When selecting an IRF9 antibody, it's crucial to validate its reactivity with your species of interest, as antibodies demonstrate varying cross-reactivity across human, mouse, and other models . For optimal results, antibody validation using positive and negative controls is strongly recommended.

How do I optimize Western blot protocols for IRF9 detection?

Optimizing Western blot protocols for IRF9 detection requires attention to several critical factors:

• Sample Preparation: For optimal IRF9 detection, lyse cells in RIPA buffer supplemented with protease inhibitors. Type I IFN stimulation (1000 U/ml for 12-24 hours) can serve as a positive control by upregulating IRF9 expression.

• Gel Selection: Use 10-12% polyacrylamide gels for optimal resolution of the 43.7 kDa IRF9 protein.

• Transfer Conditions: Semi-dry transfer at 15V for 30 minutes or wet transfer at 100V for 1 hour typically yields good results for IRF9.

• Blocking and Antibody Incubation:

  • Block with 5% non-fat milk in TBST for 1 hour at room temperature

  • Primary antibody dilution: typically 1:1000 in blocking buffer (overnight at 4°C)

  • Secondary antibody dilution: typically 1:5000 for 1 hour at room temperature

• Troubleshooting:

  • High background: Increase blocking time or use 5% BSA instead of milk

  • Weak signal: Increase primary antibody concentration or extend incubation time

  • Non-specific bands: Verify antibody specificity using IRF9 knockout cells or IRF9-depleted lysates as negative controls

Remember that phosphorylation status can affect IRF9 mobility on SDS-PAGE, potentially resulting in bands at approximately 48-50 kDa rather than the predicted 43.7 kDa.

What controls should I include when using IRF9 antibodies in my experiments?

Proper experimental controls are essential for valid interpretation of results when working with IRF9 antibodies:

Positive Controls:

• IFN-α/β treated cells (1000 U/ml for 12-24 hours) to upregulate IRF9 expression

• Cell lines known to express high levels of IRF9 (e.g., certain breast cancer cell lines like MCF-7)

• Recombinant IRF9 protein (for calibration curves or as Western blot standards)

Negative Controls:

• IRF9 knockout or knockdown cells (using CRISPR-Cas9 or siRNA)

• Cell lines with naturally low IRF9 expression

• Secondary antibody-only controls to assess non-specific binding

• Isotype controls for flow cytometry or immunohistochemistry applications

Specificity Controls:

• Antibody neutralization with blocking peptides

• Multiple antibodies targeting different IRF9 epitopes to confirm specificity

• Cross-validation using different detection techniques (e.g., confirm Western blot findings with immunofluorescence)

Implementing these controls helps ensure that observed signals genuinely represent IRF9 rather than experimental artifacts or cross-reactivity with related IRF family proteins.

How can I distinguish between different IRF family members when studying signaling pathways?

Distinguishing between IRF family members presents a significant challenge due to structural similarities and shared DNA-binding domains. To accurately characterize IRF9-specific functions:

• Antibody Selection: Choose antibodies raised against unique regions of IRF9 rather than conserved domains. Antibodies targeting the C-terminal region of IRF9 typically offer higher specificity compared to those targeting the N-terminal DNA-binding domain shared with other IRF family members.

• Sequential Immunoprecipitation: For co-immunoprecipitation studies, use sequential immunoprecipitation with antibodies against different components of the ISGF3 complex (IRF9, STAT1, STAT2) to confirm specific interactions.

• Chromatin Binding Profiles: IRF9-dominant regions contain both ISRE and GAS motifs, often without other motifs typically associated with IRF3 or IRF5. This distinctive binding pattern can differentiate IRF9 activity from other IRF proteins .

• Functional Validation: IRF9 uniquely forms the ISGF3 complex with STAT1 and STAT2 in response to type I IFNs. Detecting ISGF3 formation through co-immunoprecipitation or proximity ligation assays can distinguish IRF9-specific signaling from other IRF pathways.

• Genetic Approaches: Use IRF9-specific knockouts or knockdowns while monitoring the expression of unique downstream targets (see comparative table below).

This integrated approach enables the specific attribution of signaling events to IRF9 rather than other IRF family members in complex experimental systems .

What role does IRF9 play in autoimmune disease models, and how can IRF9 antibodies help study these mechanisms?

IRF9 plays a critical role in autoimmune disease pathogenesis, particularly in systemic lupus erythematosus (SLE). Strategic application of IRF9 antibodies can elucidate these mechanisms:

• Autoantibody Production: Studies using the pristane-induced mouse model of SLE demonstrate that IRF9 is required for IgG autoantibody production against nucleic acid-associated antigens. IRF9-deficient mice develop high titers of IgM autoantibodies but significantly reduced IgG autoantibodies, suggesting IRF9's critical role in isotype switching .

• B Cell Activation: IRF9-deficient B cells show impaired activation through TLR7, a key receptor recognizing single-stranded RNA. This defect links IRF9 to B cell activation in autoimmunity, which can be studied using IRF9 antibodies in conjunction with B cell activation markers .

• Analysis Techniques Using IRF9 Antibodies:

  • ChIP-seq to map IRF9 binding sites on autoimmunity-related gene promoters

  • Co-immunoprecipitation to identify IRF9 interaction partners in autoimmune models

  • Immunohistochemistry to examine IRF9 expression in lymphoid organs during disease progression

  • Phospho-specific antibodies to track IRF9 activation status

• IFN Signaling Integration: IRF9 appears to function upstream of TLR signaling in autoimmune B cell activation, positioning the IFN-I pathway as a critical mediator in autoimmunity. Multi-parameter flow cytometry using antibodies against IRF9, STAT1, and TLR7 can map this signaling cascade in various immune cell populations .

The table below summarizes key findings from pristane-induced SLE model comparing wild-type and IRF9-deficient mice:

These findings highlight IRF9 as a potential therapeutic target in SLE and other autoimmune diseases characterized by excessive type I IFN signaling .

How does IRF9 overexpression influence drug resistance in cancer, and what experimental approaches can investigate this phenomenon?

IRF9 overexpression has been implicated in drug resistance, particularly to antimicrotubule agents in breast cancer. This emerging role can be investigated through several experimental approaches:

• Expression Correlation Studies: Approximately half of breast and uterine tumors show IRF9 overexpression, suggesting its importance in these cancer types. Immunohistochemistry with IRF9 antibodies on tissue microarrays can establish correlations between IRF9 expression levels and clinical outcomes, including drug response patterns .

• Mechanistic Analysis: Transient overexpression of IRF9 in breast adenocarcinoma cells (MCF-7) induces resistance to paclitaxel (13-fold) and vinblastine (3.3-fold), but not to doxorubicin. This resistance occurs through an IFN-independent mechanism, as neither Stat1/Stat2 overexpression nor IFNα treatment reproduces this effect .

• Downstream Pathway Identification: IRF9 overexpression induces expression of several IFN-responsive genes, including Stat1, Stat2, and MHC class I, but not IRF1. This selective gene activation pattern differs from classical IFN responses and can be mapped using:

  • RT-qPCR for transcript analysis

  • Western blotting with antibodies against pathway components

  • ChIP-seq to identify direct IRF9 binding targets in resistant cells

• Experimental Models: Several approaches can investigate IRF9-mediated drug resistance:

• Resistance Measurements: To quantify IRF9-mediated resistance, researchers can compare IC50 values between IRF9-overexpressing and control cells. For example, paclitaxel resistance increased 13-fold in IRF9-transfected MCF-7 cells compared to vector controls .

These findings reveal a novel role for IRF9 as a potential biomarker for predicting response to antimicrotubule agents in breast cancer and suggest targeting IRF9 as a strategy to overcome drug resistance.

What are the optimal approaches for studying IRF9 binding to DNA and chromatin in different cellular contexts?

Studying IRF9 binding to DNA and chromatin requires specialized techniques that can capture both direct binding events and contextual influences:

• ChIP-seq Analysis for IRF9 Binding Patterns:

  • Use high-quality ChIP-grade IRF9 antibodies (validate specificity with IRF9-deficient cells)

  • Optimal crosslinking: 1% formaldehyde for 10 minutes at room temperature

  • Sonication conditions: typically 10-12 cycles (30s ON/30s OFF) to achieve 200-500bp fragments

  • Include input controls and IgG controls to filter non-specific binding

  • For unbiased analysis, integrate with ATAC-seq to correlate binding with chromatin accessibility

• Motif Analysis for IRF9 Binding Specificity:

  • IRF9-dominant regions frequently contain both ISRE (Interferon-Stimulated Response Element) and GAS (Gamma-Activated Sequence) motifs

  • The consensus sequence 5′-WBVGGAAANNGAAACT-3′ and its variants are enriched in IRF9-dominant clusters

  • GAS motif enrichment suggests that STAT1 homodimers activated by type I IFN signaling are important determinants for IRF9-dominant binding

• Integrated Analysis Approaches:

  • Combine ChIP-seq with RNA-seq to correlate binding events with transcriptional outcomes

  • Use sequential ChIP (Re-ChIP) to identify regions where IRF9 co-binds with STAT1 and STAT2

  • Employ CRISPR-based techniques to functionally validate the importance of identified binding sites

• Differential Binding Analysis:

  • Compare IRF9 binding profiles across different:

    • Cell types (immune cells vs. cancer cells)

    • Stimulation conditions (IFN-treated vs. untreated)

    • Disease states (normal vs. autoimmune or cancer models)

• Advanced Technologies:

  • Cut&Run or CUT&Tag for higher resolution and lower background compared to traditional ChIP

  • HiChIP to identify long-range interactions between IRF9-bound enhancers and target promoters

  • Single-cell approaches to resolve cell-to-cell variability in IRF9 binding

These approaches enable comprehensive characterization of IRF9's genomic binding landscape and its context-dependent functions in different cellular states and disease models .

How can I design experiments to differentiate between IFN-dependent and IFN-independent functions of IRF9?

Differentiating between IFN-dependent and IFN-independent functions of IRF9 requires strategic experimental design:

• Cell System Selection:

  • Use IFNAR1/2 knockout cells to eliminate type I IFN receptor signaling

  • Compare with IRF9 knockout cells to identify differential phenotypes

  • Consider U5A cells (IFNAR2-deficient) as established research tools for IFN-independent studies

• IRF9 Mutant Analysis:

  • Generate IRF9 constructs with mutations in domains required for STAT interaction

  • Create IRF9 variants with altered nuclear localization signals

  • Test these constructs in rescue experiments with IRF9-deficient cells

• Temporal Analysis:

  • Compare acute vs. chronic effects of IRF9 expression

  • In cancer drug resistance studies, IRF9 overexpression confers resistance to antimicrotubule agents through mechanisms distinct from those activated by IFNα treatment

• Pathway Validation:

  • Monitor expression of downstream genes using RT-qPCR or RNA-seq

  • Compare IRF9-induced gene expression profiles with those induced by IFN treatment

  • Use statistical approaches (principal component analysis, hierarchical clustering) to identify unique gene signatures

• Combined Interventions:

  • Compare IRF9 overexpression alone, IFN treatment alone, and combined conditions

  • Test if IFN neutralizing antibodies block IRF9-dependent phenotypes

The table below highlights differences observed between IFN-dependent and IFN-independent effects in a breast cancer model:

This comparative approach clearly demonstrates that IRF9 can mediate distinct cellular effects independent of the classical IFN signaling pathway, particularly in the context of cancer drug resistance .