DDX58 Antibody

What is DDX58 and what is its function in cellular immunity?

DDX58, also known as RIG-I (Retinoic acid-Inducible Gene I), belongs to the DExD/H-box helicase family and functions as a pattern recognition receptor in innate immunity. It specifically recognizes and binds to viral double-stranded RNAs (dsRNAs) in the cytoplasm during infection. Upon binding to viral RNA, DDX58 undergoes conformational changes that release its signaling domains (CARDs), which then interact with the mitochondrial antiviral signaling protein (MAVS). This interaction triggers a signaling cascade leading to the production of type I interferons and proinflammatory cytokines, ultimately activating antiviral immune responses . DDX58 is also involved in recognizing endogenous dsRNAs that accumulate during cellular stress, such as from chemotherapy treatments, which can lead to inflammation and apoptosis .

What are the common applications for DDX58 antibodies in research?

DDX58 antibodies are versatile tools employed in multiple research applications. Western Blotting (WB) is the most common application, with recommended dilutions typically ranging from 1:1000 to 1:6000, allowing for protein expression quantification . Immunohistochemistry (IHC) applications (dilutions 1:100-1:1200) enable visualization of DDX58 in tissue sections, with validated results in human colon and heart tissues . Immunoprecipitation (IP) is effective using 0.5-4.0 μg of antibody for 1.0-3.0 mg of total protein lysate, facilitating protein-protein interaction studies .

Additional applications include immunofluorescence (IF) for subcellular localization studies, ELISA for quantitative detection, and flow cytometry for single-cell analysis. Research publications have documented successful use of these applications in studying DDX58's role in viral defense, cancer progression, and inflammatory responses .

What is the molecular weight of DDX58 and how does this affect antibody selection?

DDX58 has a calculated molecular weight of approximately 106 kDa (925 amino acids), though observed molecular weights in experimental conditions typically range between 101-106 kDa . This variation may reflect post-translational modifications or tissue-specific processing. When selecting antibodies, researchers should verify that the manufacturer's reported detection matches this expected range.

Understanding the molecular weight is crucial for properly interpreting Western blot results, especially in complex samples where multiple bands might appear. Antibodies targeting different epitopes of DDX58 may show slight variations in detected molecular weight. For example, product data sheets indicate that anti-DDX58 antibodies typically detect bands around 102 kDa in THP-1 cells and between 101-106 kDa in other cell types including A431, HeLa, and NIH/3T3 cells . These considerations are important for experimental design and validation.

What sample types have been validated for DDX58 antibody detection?

DDX58 antibodies have been validated across diverse sample types. For cell lines, successful detection has been reported in human epithelial A431 cells, cervical HeLa cells, murine fibroblast NIH/3T3 cells, and human monocytic THP-1 cells . In tissue samples, positive immunohistochemistry results have been documented in human colon and heart tissues . Some antibodies show cross-reactivity between human and mouse samples, making them suitable for comparative studies.

Researchers have also successfully detected DDX58 in breast cancer cell lines (MDA-MB-231, MCF-7, SKBR3), liver cancer cells (HepG2), and Sertoli cells (TM4) . Additionally, DDX58 antibodies have been used in clinical samples, including tissue microarrays from triple negative breast cancer patients. This broad validation across sample types enables researchers to select appropriate antibodies for their specific experimental models.

How does DDX58 expression correlate with chemotherapy resistance in triple negative breast cancer?

Recent research demonstrates that DDX58 expression levels significantly correlate with chemotherapy response in triple negative breast cancer (TNBC). Low DDX58 expression is associated with poor prognosis and reduced pathological complete response (pCR) rates to neoadjuvant chemotherapy . Mechanistically, DDX58 deficiency promotes resistance to multiple chemotherapeutic agents, including paclitaxel, doxorubicin (Dox), and 5-fluorouracil through several pathways.

The data from gene expression datasets (GSE20194, GSE22093, and GSE163882) confirms that DDX58 expression is significantly higher in patients who achieved pathological complete response, supporting its role as a potential predictive biomarker for TNBC treatment response .

What mechanisms underlie DDX58's role in inflammatory signaling pathways?

DDX58 orchestrates inflammatory responses through multiple interconnected signaling pathways. Upon detection of double-stranded RNAs (dsRNAs), DDX58 undergoes conformational changes that release its caspase activation and recruitment domains (CARDs), which then interact with the mitochondrial antiviral signaling protein (MAVS) . This interaction serves as a critical junction point, activating several downstream pathways.

Gene Set Enrichment Analysis (GSEA) reveals that DDX58 activation primarily triggers:

• The Type I interferon (IFN) pathway, inducing expression of interferon-stimulated genes that regulate inflammation and apoptosis

• The NFκB signaling pathway, promoting expression of pro-inflammatory cytokines including IL-6, IL-18, and IL-1β

• The TOLL-like receptor signaling pathway, which acts synergistically with DDX58 signaling

• The NOD-like receptor signaling pathway, enhancing inflammasome activation

• The JAK-STAT signaling pathway, particularly IL6-JAK-STAT3 and IL2-STAT5 signaling, which further amplify inflammatory responses

In pathological contexts such as D-galactose-induced cell damage, DDX58 knockdown significantly reduces expression of p65 (a key NFκB component) and inflammatory cytokines, demonstrating its central role in maintaining inflammatory states . These mechanisms position DDX58 as a master regulator connecting RNA sensing to broad inflammatory responses.

How can researchers distinguish between specific and non-specific binding in DDX58 antibody applications?

Distinguishing between specific and non-specific binding in DDX58 antibody applications requires systematic validation through multiple complementary approaches. For Western blotting, researchers should first verify that the detected band falls within the expected 101-106 kDa range for DDX58 . Multiple antibodies targeting different epitopes should be compared to establish consensus detection patterns.

Critical controls include:

• Positive controls: Cell lines with validated DDX58 expression (A431, HeLa, NIH/3T3, or THP-1 cells)

• Negative controls: DDX58 knockout or siRNA-treated samples

• Peptide competition assays: Pre-incubation of antibody with immunizing peptide should eliminate specific bands

• Loading controls: To normalize protein loading and distinguish from non-specific background

For immunohistochemistry and immunofluorescence, background can be reduced through optimized blocking (5% normal serum from the secondary antibody species) and including isotype control antibodies processed identically to experimental samples . Secondary-only controls help identify non-specific secondary antibody binding.

In specialized applications like co-immunoprecipitation, isotype-matched IgG controls are essential to identify non-specific protein pulldown . When analyzing patient-derived or heterogeneous samples, orthogonal validation using RT-qPCR for DDX58 mRNA can help confirm antibody specificity.

What controls should be included when using DDX58 antibodies for Western blotting?

When using DDX58 antibodies for Western blotting, a comprehensive set of controls should be incorporated to ensure reliable and interpretable results. Positive controls should include cell lines with validated DDX58 expression, such as A431, HeLa, NIH/3T3, or THP-1 cells, which have been consistently documented to express detectable levels of DDX58 . For induced expression studies, poly(I:C) treatment or viral infection models serve as functional positive controls that upregulate DDX58.

Negative controls should include DDX58 knockout or knockdown samples when available, which are essential for confirming antibody specificity . Loading controls (typically housekeeping proteins like β-actin, GAPDH, or α-tubulin) are crucial for normalizing expression levels across samples.

Additionally, antibody specificity controls such as pre-absorption with immunizing peptide or using secondary antibody-only lanes help identify non-specific signals. When studying DDX58 activation, include unstimulated versus stimulated samples (e.g., before and after doxorubicin treatment) to capture changes in expression or modification state . Finally, molecular weight markers are essential for confirming that the detected band falls within the expected 101-106 kDa range for DDX58, especially important when examining potential post-translational modifications that may alter migration patterns .

How can researchers optimize immunohistochemistry protocols for DDX58 detection in different tissue types?

Optimizing immunohistochemistry (IHC) protocols for DDX58 detection across different tissue types requires systematic adjustment of several parameters. Firstly, antigen retrieval is critical—for DDX58, manufacturers recommend TE buffer (pH 9.0) as the primary method, with citrate buffer (pH 6.0) as an alternative . Testing both methods on serial sections can determine which works best for specific tissue types.

Antibody dilution ranges typically from 1:100 to 1:1200 for DDX58 IHC, requiring titration experiments to determine optimal concentration for each tissue . Blocking solutions should be optimized based on tissue characteristics—tissues with high endogenous biotin (like liver or kidney) benefit from avidin-biotin blocking steps.

Incubation conditions, including temperature (4°C overnight versus room temperature for shorter periods) and incubation time, should be systematically tested. Detection systems require consideration—for tissues with low DDX58 expression, amplification systems like tyramide signal amplification may improve sensitivity. Counterstaining intensity should be balanced to provide contextual cellular information without obscuring specific DDX58 staining.

Validation across tissue types is essential—human colon and heart tissues have been verified as reliable positive controls for DDX58 staining . Finally, quantification methods should be standardized, using digital image analysis when possible to provide consistent scoring across different tissue samples.

What approaches can be used to study DDX58-RNA interactions in cellular models?

Studying DDX58-RNA interactions in cellular models requires multifaceted approaches that capture both physical binding and functional outcomes. RNA immunoprecipitation (RIP) is fundamental—using anti-DDX58 antibodies (typically 0.5-4.0 μg for 1.0-3.0 mg lysate) to pull down DDX58-bound RNAs, followed by RT-qPCR or RNA sequencing to identify associated transcripts .

Immunofluorescence co-localization, as demonstrated in doxorubicin studies, can visualize DDX58 and dsRNA interactions—using the J2 antibody (specific for dsRNAs) alongside DDX58 antibodies reveals spatial associations . For functional analyses, luciferase reporter assays with DDX58-responsive elements (like IFN-β promoters) measure signaling outcomes of RNA binding.

Cell-based stimulation models provide physiological context—poly(I:C) transfection serves as a positive control, while doxorubicin treatment (which increases endogenous dsRNAs 8-fold) creates a clinically relevant model . Genetic approaches complement these methods—comparing DDX58 wild-type versus mutant constructs with altered RNA-binding domains can determine binding specificity.

These techniques are particularly valuable in understanding how DDX58 recognizes both viral RNAs during infection and endogenous dsRNAs that accumulate during cellular stress conditions like chemotherapy treatment .

How does DDX58 expression change in heart failure models and what are the implications?

DDX58 expression undergoes significant changes in heart failure (HF) models, with important mechanistic and clinical implications. In ischemic heart failure, weighted gene co-expression network analysis identified DDX58 as a key immune-related gene specifically associated with macrophage function . Gene Set Enrichment Analysis (GSEA) reveals that DDX58 upregulation in heart failure activates multiple immune pathways, including TOLL-like receptor signaling, complement and coagulation cascades, NOD-like receptor signaling, FCγR-mediated phagocytosis, and chemokine signaling pathways .

At the cellular level, DDX58 expression in cardiac tissue correlates strongly with macrophage infiltration, particularly with pro-inflammatory M1 macrophages, suggesting a role in cardiac inflammation during heart failure progression. Functionally, DDX58 activation in cardiac tissue leads to Type I interferon responses, triggering inflammatory cytokine production that can exacerbate cardiac damage and remodeling .

Notably, DDX58 also serves as a molecular link between heart failure and cancer—analysis of The Cancer Genome Atlas (TCGA) data revealed that DDX58 expression correlates with immune infiltration scores across multiple cancer types, with particularly strong associations in bladder cancer, colorectal adenocarcinoma, and head and neck squamous cell carcinoma . These findings suggest that DDX58 may represent a therapeutic target for modulating inflammatory responses in heart failure, with potential implications for cancer comorbidity management in heart failure patients.

What is the relationship between DDX58 and tumor immune infiltration in different cancer types?

DDX58 exhibits complex relationships with tumor immune infiltration that vary substantially across cancer types. Analysis using the Tumor Immune Evaluation Resource (TIMER) database and CIBERSORT algorithm reveals that DDX58 expression correlates significantly with immune cell infiltration in multiple cancers . The strongest correlations with immune scores were observed in Bladder Urothelial Carcinoma (BLCA), Colon Adenocarcinoma (COAD), and Head and Neck Squamous Cell Carcinoma (HNSC), suggesting cancer-specific immune regulatory functions.

Among immune cell subtypes, DDX58 expression most prominently associates with M1 macrophage infiltration across various tumors, consistent with its role in promoting pro-inflammatory responses . DDX58 expression also strongly correlates with immune checkpoint gene expression, potentially influencing immunotherapy responses.

In triple negative breast cancer (TNBC), low DDX58 expression associates with reduced pathological complete response rates to chemotherapy, suggesting impaired immune-mediated tumor clearance . Mechanistically, DDX58 activation in tumors can trigger Type I interferon production, which enhances antigen presentation, promotes T-cell recruitment, and potentiates cytotoxic T-cell activity.

These findings suggest that DDX58 expression could serve as a biomarker for immune infiltration status and potentially predict immunotherapy responsiveness. The dual role of DDX58 in promoting both anti-tumor immunity and inflammation highlights the need for cancer-specific therapeutic strategies.

What is the significance of DDX58's interaction with dsRNAs in doxorubicin treatment of cancer?

The interaction between DDX58 and double-stranded RNAs (dsRNAs) represents a critical mechanism underlying doxorubicin's anti-tumor effects, particularly in triple negative breast cancer (TNBC). Research demonstrates that doxorubicin treatment significantly increases endogenous dsRNA levels in cancer cells—immunofluorescence studies revealed an 8-fold increase in dsRNA expression following 6 hours of doxorubicin exposure .

These dsRNAs serve as danger-associated molecular patterns (DAMPs) that are recognized by DDX58, which typically functions as a viral RNA sensor. Upon binding to dsRNAs, DDX58 undergoes conformational changes that enable interaction with the mitochondrial antiviral signaling protein (MAVS), as confirmed by co-immunoprecipitation and co-localization studies . This interaction activates the DDX58-Type I interferon (IFN) signaling pathway, including significant upregulation of downstream genes like IRF3, IRF7, IFNB1, and ISG15, ultimately promoting apoptosis in cancer cells.

The significance of this pathway is underscored by findings that DDX58 knockout cells exhibit a 25% reduction in tumor growth inhibition rate after doxorubicin treatment compared to wild-type cells in mouse models . TUNEL staining confirms significantly reduced apoptosis in DDX58-deficient tumors following chemotherapy. These findings reveal a novel mechanism where chemotherapy-induced dsRNAs trigger innate immune signaling pathways to enhance cancer cell death, a mechanism distinct from doxorubicin's well-established DNA damage effects.

Why might DDX58 antibodies show different banding patterns in different cell types?

Different banding patterns observed with DDX58 antibodies across cell types stem from multiple biological and technical factors. Post-translational modifications represent a primary cause—DDX58 undergoes various modifications including ubiquitination, phosphorylation, and SUMOylation that differ between cell types and activation states, potentially altering antibody binding or protein migration. While the calculated molecular weight of DDX58 is 106 kDa, observed weights range from 101-106 kDa across cell lines .

Alternative splicing generates DDX58 isoforms with tissue-specific expression patterns—some antibodies may detect all isoforms while others recognize specific variants depending on epitope location. Cell-type specific protein-protein interactions can affect epitope accessibility, particularly for antibodies targeting regions involved in MAVS binding or RNA recognition.

Activation state influences conformation—DDX58 exists in closed (inactive) and open (active) conformations, with certain antibodies preferentially detecting one state . Technical factors also contribute—differences in sample preparation (particularly lysis buffers and denaturation conditions) can expose different epitopes.

To address these variations, researchers should validate results using multiple antibodies targeting different epitopes and include appropriate positive controls like THP-1 or A431 cells when comparing expression across experimental systems .

How can researchers validate DDX58 knockout/knockdown models using antibody-based methods?

Validation of DDX58 knockout (KO) or knockdown (KD) models requires a comprehensive approach combining multiple antibody-based methods to ensure complete and specific target depletion. Western blotting represents the primary validation method, using DDX58 antibodies at 1:1000-1:6000 dilution to confirm protein absence in KO or reduction in KD models . Multiple antibodies targeting different epitopes should be employed to rule out potential residual truncated proteins.

Immunofluorescence or immunohistochemistry provides spatial verification, confirming loss of DDX58 expression at the cellular level using antibody dilutions of 1:100-1:1200 . Flow cytometry can quantify DDX58 depletion efficiency across cell populations, particularly valuable for heterogeneous samples or incomplete knockdown systems.

Functional validation is equally crucial—measuring the expression of DDX58-dependent genes (like type I interferons and ISGs) via RT-qPCR after stimulation with poly(I:C) or viral challenge confirms signaling pathway disruption . Immunoprecipitation of DDX58 binding partners (such as MAVS) should show reduced or abolished interaction in KO/KD models.

Additionally, rescue experiments reintroducing DDX58 should restore both protein detection by antibodies and functional readouts, confirming phenotype specificity. Finally, phenotypic validation, such as demonstrating the expected 25% reduction in doxorubicin sensitivity in DDX58-KO versus wild-type tumors, provides the ultimate confirmation of model validity and biological relevance .