DDX50 Antibody

What is DDX50 and why is it significant for immunological research?

DDX50 is a DExD-Box RNA helicase that functions as a viral restriction factor by enhancing IRF3 activation and antiviral signaling. It shares 55.6% amino acid identity with DDX21 but has non-redundant functions in innate immune responses . DDX50 plays a critical role in restricting viral replication of diverse pathogens, including DNA viruses like vaccinia virus (VACV) and herpes simplex virus (HSV-1), as well as RNA viruses such as Zika virus (ZIKV) . Additionally, recent research has identified DDX50 as having glucose-binding capabilities that alter its conformation and impact cellular differentiation processes, making it a multifunctional protein of interest across several research areas .

Which experimental models are most suitable for studying DDX50 functions?

Based on published research, the following experimental models have proven effective for DDX50 studies:

For immunological studies, both human and mouse cell lines with CRISPR-mediated DDX50 knockout have been successfully used to investigate its role in antiviral responses .

How do I optimize DDX50 antibody detection in Western blot applications?

For optimal Western blot detection of DDX50:

• Use recommended dilutions of 1:500 to 1:2000 for primary antibody incubation

• Be aware that DDX50 has an observed molecular weight of approximately 83 kDa

• When working with tissue samples, include appropriate controls as DDX50 expression varies across cell types

• For co-immunoprecipitation experiments, rabbit monoclonal anti-Flag antibodies have been successfully used with DDX50-HA cell lines

• Consider using reduced-denatured protein samples as DDX50 can form dimers that may complicate band pattern interpretation

How can I effectively distinguish between DDX50 and DDX21 functions given their high sequence similarity?

Despite sharing 55.6% amino acid identity, DDX50 and DDX21 have distinct functions that can be experimentally differentiated through several approaches:

• RNA binding profiles: DDX50 binds a GC-rich SCSSSGCC RNA motif (S denotes G or C), while DDX21 preferentially binds the SCUGSDGC motif . CLIP-seq experiments can distinguish these binding patterns.

• Functional differentiation:

  • DDX50 binds long non-coding RNAs proportionally more than DDX21

  • DDX50 depletion has minimal effects on mRNA splicing, unlike DDX21

  • DDX50's role in differentiation is independent of its ATPase activity, while DDX21 functions typically depend on its helicase activity

• Selective knockdown: Design siRNAs targeting non-conserved regions between the two proteins to achieve selective depletion and measure pathway-specific outcomes.

• Specific mutants: Use point mutations like DDX50 K187R (ATPase deficient but glucose-binding intact) to selectively impair specific functions while maintaining others .

What are the optimal conditions for investigating DDX50's role in viral restriction?

For robust investigation of DDX50's viral restriction function:

• Infection parameters:

  • Use both high and low MOI (multiplicity of infection) conditions, as DDX50-mediated restriction is most evident at low MOI

  • For VACV, use MOI 5 (high) or 0.0001-0.0003 (low) in MEF or HEK293T cells

  • For HSV-1, MOI 0.01 is effective for observing DDX50's restriction effects

  • For ZIKV, MOI 1 (high) or 0.1 (low) has demonstrated differential restriction

• Readouts for viral restriction:

  • Plaque assays to measure viral yields (e.g., using Vero E6 cells for ZIKV)

  • qPCR for viral genome quantification

  • Immunofluorescence for viral protein expression

  • Cytokine/chemokine production (CXCL10, IL-6) as indirect measures of antiviral response

• Control conditions:

  • Include both wild-type and DDX50-knockout cells

  • Compare with known restriction factor knockouts (e.g., IRF3-deficient cells)

  • Include time course analyses (24-72 hours post-infection depending on virus)

What methodological approaches can detect DDX50-TRIF interactions in innate immune signaling?

To effectively characterize DDX50-TRIF interactions:

• Co-immunoprecipitation strategies:

  • Use anti-Flag antibodies for tagged DDX50 or TRIF constructs

  • Perform reciprocal immunoprecipitations to confirm interaction specificity

  • Include RNase treatment controls to determine if the interaction is RNA-dependent

• Stimulation conditions:

  • Transfection with polyIC (1 μg/ml) to induce dsRNA sensing pathways

  • Sendai virus infection to activate RIG-I pathways

  • HSV-1 or VACV infection for DNA virus-induced signaling

• Proximity-based assays:

  • Proximity ligation assay (PLA) to visualize endogenous interactions

  • FRET or BiFC (Bimolecular Fluorescence Complementation) for live-cell interaction dynamics

• Functional validation:

  • Measure IRF3 phosphorylation in the presence or absence of DDX50

  • Use TRIF mutants lacking key domains to map interaction regions

  • Compare wild-type DDX50 with the dimerization mutant (DDX50 562R) to assess if monomer formation affects TRIF binding

How do I differentiate between DDX50's role in RNA stability versus its antiviral functions?

DDX50 has dual roles in RNA stability and antiviral response that can be experimentally distinguished:

• For RNA stability assessment:

  • Perform actinomycin D chase experiments to measure mRNA decay rates in DDX50-depleted versus control cells

  • Focus on specific pro-differentiation RNAs such as JUN, OVOL1, CEBPB, PRDM1, and TINCR

  • Analyze DDX50-STAU1 interactions using co-immunoprecipitation followed by RNA sequencing

  • Measure changes in RNA structure using SHAPE (Selective 2′-hydroxyl acylation analyzed by primer extension) in the presence and absence of DDX50

• For antiviral function assessment:

  • Measure IRF3 phosphorylation and nuclear translocation

  • Quantify expression of IRF3-dependent genes like Isg56 and Ifnb

  • Analyze cytokine production (CXCL10, IL-6) in response to viral infection

  • Perform viral replication assays under different glucose conditions to dissect metabolic from antiviral functions

• Comparative analysis:

  • Create a dataset comparing DDX50-dependent genes in differentiation versus viral infection

  • Perform pathway enrichment analysis to identify shared and distinct pathways

  • Use DDX50 mutants that selectively affect one function but not the other (e.g., glucose-binding mutants)

What analytical approaches are recommended for interpreting conflicting results with DDX50 in HIV-1 studies?

The literature shows contradictory roles for DDX50 in HIV-1 replication. To resolve these contradictions:

• Systematically compare experimental conditions:

  • Cell type differences: Primary CD4+ T cells versus cell lines may show different DDX50 functions

  • Infection parameters: MOI, viral strains, and infection duration

  • Knockdown methods: siRNA versus CRISPR, acute versus stable depletion

  • Measure multiple aspects of viral replication: Early versus late replication events

• Analyze pathway context:

  • DDX50 has both proviral functions (enhancing replication) in genome-wide RNAi screens and antiviral functions (through IRF3 activation)

  • Compare DDX50's role to other DExD/H-box helicases in HIV-1 replication

  • Measure interactions with specific HIV-1 proteins and RNA elements

• Data integration matrix:

How can I effectively analyze the interplay between glucose binding and DDX50's function in both differentiation and antiviral responses?

To systematically analyze this complex relationship:

• Experimental design matrix:

  • Compare DDX50 functions under normal glucose (5.5 mM), high glucose (25 mM), and glucose restriction conditions

  • Use glucose analogs like 3-O-methyl-D-glucose (3OMG) that bind DDX50 but aren't metabolized

  • Compare effects of galactose (which doesn't bind DDX50) versus glucose

• Functional readouts across conditions:

  • Measure DDX50 dimerization status using native PAGE or crosslinking approaches

  • Assess DDX50-STAU1 complex formation using co-immunoprecipitation

  • Quantify binding to target RNAs using RNA immunoprecipitation

  • Monitor antiviral response markers (IRF3 phosphorylation, ISG expression)

  • Track differentiation markers in parallel experiments

• Use DDX50 mutants strategically:

  • Glucose-binding mutants (V227W, K187G) to disrupt glucose sensing

  • Dimerization mutant (DDX50 562R) to assess monomer-dependent functions

  • ATPase-deficient mutant (K187R) to distinguish helicase-dependent and independent functions

  • RNA-binding deficient mutant (6M) to assess RNA-dependent functions

What strategies can address non-specific binding issues with DDX50 antibodies in co-immunoprecipitation experiments?

When troubleshooting non-specific binding:

• Optimize antibody conditions:

  • Titrate antibody concentration (start with 1-5 μg for IP)

  • Try different antibody clones or host species

  • Consider using epitope-tagged DDX50 constructs if specificity issues persist

• Modify binding and washing conditions:

  • Increase salt concentration (150-500 mM NaCl) to reduce non-specific interactions

  • Add detergents (0.1-0.5% NP-40 or Triton X-100) to reduce hydrophobic interactions

  • Include competitors like BSA (0.1-1%) to block non-specific binding sites

  • Use more stringent washing steps (increase number or duration)

• Special considerations for DDX50:

  • Be aware that DDX50 forms dimers that can be disrupted by glucose

  • DDX50's interactions with RNA may influence co-IP results; consider RNase treatment controls

  • DDX50 shares high homology with DDX21 (55.6%); validate antibody specificity against both proteins

  • DDX50 shuttles between nucleus and cytoplasm; consider cell fractionation to enrich for relevant pools

How can I optimize detection of endogenous DDX50 in tissue samples with variable expression levels?

For detecting DDX50 across tissues with varying expression:

• Sample preparation optimization:

  • Use tissue-specific extraction buffers that account for varying protein content

  • Consider protease inhibitor cocktails optimized for each tissue type

  • Normalize loading based on total protein rather than housekeeping genes

  • For tissues with low expression, increase sample concentration or use immunoprecipitation before Western blot

• Signal enhancement strategies:

  • Use high-sensitivity detection systems (ECL Prime or fluorescent secondaries)

  • Consider signal amplification methods (like biotin-streptavidin systems)

  • Optimize exposure times based on preliminary tissue expression data

  • For immunohistochemistry, use polymer detection systems with DAB enhancement

• Expression reference guide:
  Based on research findings, expected relative DDX50 expression levels:

  Tissue/Cell Type	Relative Expression	Detection Notes
  Epidermal tissue	High	Important in differentiation
  Adipocytes	Moderate-High	Increases during differentiation
  Immune cells	Moderate	Involved in antiviral responses
  Cervical epithelia	Moderate	Required for differentiation
  HEK293T cells	Moderate	Well-characterized model system
  Mouse embryonic fibroblasts	Moderate	Established knockout models available

How can DDX50's glucose-binding capacity be leveraged to study metabolic influences on antiviral immunity?

Recent discovery of DDX50 as a glucose-binding protein opens new research avenues:

• Experimental approaches:

  • Compare antiviral responses under different glucose concentrations

  • Use glucose-binding mutants (V227W, K187G) to dissect metabolic from non-metabolic functions

  • Measure DDX50 dimerization status as a readout of glucose binding in infected cells

  • Assess whether viral infection alters glucose binding to DDX50

• Research questions to address:

  • Does hyperglycemia or hypoglycemia alter DDX50-mediated viral restriction?

  • Can glucose analogs modulate DDX50's antiviral functions?

  • Do viruses target DDX50-glucose binding as an immune evasion strategy?

  • Is DDX50 glucose binding altered in metabolic diseases, affecting antiviral immunity?

• Methodological considerations:

  • Use cellular thermal shift assays (CETSAs) to measure DDX50-glucose binding during infection

  • Apply fluorescence quenching assays to quantify binding under different conditions

  • Employ microscale thermophoresis to measure binding affinities of DDX50 mutants

  • Develop biosensors to monitor DDX50 conformational changes in real-time

What approaches can investigate the therapeutic potential of targeting DDX50 in viral infections?

To explore DDX50 as a therapeutic target:

• Screen for DDX50 modulators:

  • Develop high-throughput assays measuring DDX50-dependent IRF3 activation

  • Screen small molecule libraries for compounds that enhance DDX50 activity

  • Identify compounds that stabilize DDX50 monomers, mimicking glucose binding effects

  • Use fragment-based drug discovery focusing on the ATP-binding domain

• Therapeutic strategy validation:

  • Test candidate compounds against multiple viruses (VACV, HSV-1, ZIKV) to assess broad-spectrum potential

  • Evaluate compounds in both prevention and treatment models

  • Assess effects on normal cellular differentiation to identify potential side effects

  • Determine if combination with established antivirals produces synergistic effects

• Translational considerations:

  • Compare DDX50 sequence conservation across species to guide animal model selection

  • Develop tissue-specific delivery strategies for DDX50 modulators

  • Assess potential for resistance development through viral mutation

  • Consider repurposing glucose analogs or metabolic drugs that may interact with DDX50

What methodological approaches can best characterize the structural changes in DDX50 during glucose binding and their impact on protein-protein interactions?

To investigate structural dynamics:

• Advanced structural biology techniques:

  • Cryo-electron microscopy to visualize DDX50 conformational states with/without glucose

  • Hydrogen-deuterium exchange mass spectrometry to map conformational changes

  • Nuclear magnetic resonance (NMR) to track dynamic changes in protein structure

  • X-ray crystallography of DDX50 in different binding states

• Protein interaction mapping:

  • Use BioID or APEX proximity labeling to identify DDX50 interaction partners in different glucose conditions

  • Apply crosslinking mass spectrometry to capture transient interactions

  • Perform yeast two-hybrid screens with DDX50 mutants mimicking different conformational states

  • Use protein complementation assays to visualize interaction dynamics in live cells

• Computational approaches:

  • Molecular dynamics simulations to model glucose-induced conformational changes

  • Protein-protein docking predictions for DDX50-STAU1 and DDX50-TRIF interactions

  • Machine learning analysis of binding site conservation across DDX family members

  • Systems biology modeling of DDX50's role in signaling networks under different metabolic conditions