DDX47 Antibody, HRP conjugated

What are the primary applications for DDX47 antibody, HRP conjugated?

HRP-conjugated DDX47 antibodies are primarily optimized for Western blotting (WB) and enzyme-linked immunosorbent assay (ELISA) applications. While unconjugated DDX47 antibodies have been validated for immunofluorescence and immunoprecipitation, the HRP conjugation specifically enhances detection sensitivity in applications requiring enzymatic signal amplification. The direct HRP conjugation eliminates the need for secondary antibodies, reducing background and improving signal-to-noise ratios in Western blot and ELISA protocols. Based on validation data, DDX47 antibodies show strong reactivity with human samples and can detect the protein at its expected molecular weight of approximately 48-50 kDa .

What species reactivity can be expected with DDX47 antibodies?

DDX47 antibodies have been validated for reactivity with human samples across multiple manufacturers . Based on sequence homology analysis, these antibodies are also predicted to recognize DDX47 in multiple species including mouse, rat, dog, rabbit, horse, cow, pig, bat, monkey, and chicken with 100% sequence identity, and Xenopus laevis with 92% identity . When using HRP-conjugated versions of these antibodies, species cross-reactivity is expected to be maintained as conjugation typically does not affect the antigen-binding region of the antibody.

How can I optimize signal detection when using DDX47 antibody, HRP conjugated, for detecting low abundance targets?

For low abundance DDX47 detection, implement a multi-faceted optimization strategy. First, increase protein loading (up to 50 μg of whole cell lysate as validated in HeLa samples) while using a more concentrated antibody dilution (1:1000). Second, employ enhanced chemiluminescence (ECL) substrates specifically designed for high sensitivity, such as those used in validation studies with 3-minute exposure times . Third, consider membrane blocking optimization using 5% non-fat milk or BSA in TBST buffer with extended incubation periods (1-2 hours at room temperature or overnight at 4°C). Finally, incorporate signal enhancement through extended exposure times or digital integration methods without compromising signal-to-noise ratios. This approach has been successfully used to detect DDX47 in nuclear and nucleolar fractions where it functions in ribosome biogenesis and R-loop resolution .

What controls should be included when studying DDX47's involvement in R-loop resolution using HRP-conjugated antibodies?

When investigating DDX47's function in R-loop resolution, a comprehensive set of controls is essential. First, include both positive and negative controls: siDDX47-treated cells versus siControl cells as demonstrated in previous studies showing increased R-loop formation upon DDX47 depletion . Second, implement RNase H treatment controls which specifically degrade RNA-DNA hybrids to confirm R-loop specificity. Third, use S9.6 antibody (which specifically recognizes RNA-DNA hybrids) in parallel assays to validate R-loop detection. Fourth, analyze standard human genes known to form R-loops (APOE and RPL13A) alongside your genes of interest. Fifth, complement Western blot data with orthogonal approaches like DNA-RNA immunoprecipitation (DRIP) or proximity ligation assay (PLA) using anti-DDX47 and S9.6 antibodies to verify protein-hybrid interactions in situ . This multi-layered control system provides robust validation of DDX47's direct involvement in R-loop biology.

How does DDX47 subcellular localization affect antibody selection and experimental design?

DDX47 exhibits dynamic shuttling between the nucleus and cytoplasm, with significant enrichment in nucleolar regions where it functions in ribosome biogenesis . This complex localization pattern necessitates careful experimental design when using HRP-conjugated antibodies. For accurate subcellular analysis, implement cellular fractionation protocols that effectively separate nucleolar, nucleoplasmic, and cytoplasmic fractions before Western blotting. Use compartment-specific markers as controls: fibrillarin for nucleoli, lamin B for nuclear envelope, and GAPDH for cytoplasm. When studying DDX47's nucleolar functions in R-loop resolution or ribosome biogenesis, complement Western blot data with immunofluorescence using unconjugated antibodies to visualize precise localization patterns. Studies have shown DDX47 association with transcriptionally active chromatin regions through chromatin immunoprecipitation (ChIP) analysis , suggesting that detection strategies should account for this chromatin association when designing experimental workflows.

What are common sources of background when using HRP-conjugated DDX47 antibodies and how can they be minimized?

Background issues with HRP-conjugated DDX47 antibodies typically stem from multiple sources that require specific interventions. First, non-specific binding can be addressed by optimizing blocking conditions—testing both 5% BSA and 5% non-fat milk in TBST to determine which provides optimal signal-to-noise ratio for DDX47 detection at 48 kDa . Second, membrane overexposure often causes background; reduce exposure times based on empirical testing—validation studies indicate optimal exposures between 3-10 minutes for DDX47 detection . Third, inadequate washing contributes significantly to background; implement 5-6 washes with TBST for 5-10 minutes each following antibody incubation. Fourth, cross-reactivity with similar DEAD-box helicases can occur; verify antibody specificity by testing multiple cell lines (HeLa, HepG2, Jurkat, SH-SY5Y) and confirm band migration at the predicted 48-50 kDa mark. Fifth, endogenous peroxidase activity in samples can cause false positives; incorporate a peroxidase quenching step (3% H₂O₂ treatment for 10 minutes) before blocking when working with tissue samples.

How can I validate DDX47 antibody specificity in the context of studying R-loop resolution mechanisms?

Validating DDX47 antibody specificity for R-loop resolution studies requires a multi-step approach. First, perform siRNA-mediated knockdown of DDX47 and confirm protein depletion by Western blot, which should show significant reduction in the 48 kDa band intensity . Second, conduct rescue experiments by expressing siRNA-resistant DDX47 constructs to reverse phenotypes, confirming antibody detection of both endogenous and exogenous protein. Third, implement PLA using anti-DDX47 and S9.6 (DNA-RNA hybrid) antibodies to confirm direct association with R-loops in situ as demonstrated in previous studies . Fourth, compare immunoprecipitation results between HRP-conjugated and unconjugated DDX47 antibodies to ensure conjugation doesn't affect specificity. Fifth, utilize orthogonal detection methods by employing antibodies targeting different epitopes of DDX47—comparing results from antibodies recognizing N-terminal (aa 1-50) versus C-terminal regions . This comprehensive validation strategy ensures reliable detection of DDX47 specifically in the context of its R-loop resolution function.

What methodological considerations are critical when studying DDX47's role in transcription-replication conflicts using HRP-conjugated antibodies?

Investigating DDX47's involvement in transcription-replication conflicts (TRCs) requires specialized methodological approaches. First, implement cell synchronization protocols (thymidine block) to enrich for S-phase cells where TRCs predominantly occur, as previous studies showed no significant cell cycle progression differences in siDDX47 cells following synchronization . Second, design dual-detection systems combining DDX47 antibodies with replication markers (PCNA) and transcription machinery components (RNAPI/II) through sequential blotting or multiplexed fluorescent Western blots. Third, incorporate proximity ligation assay (PLA) using anti-RNAPI and anti-PCNA antibodies to quantify transcription-replication conflicts, which significantly increase in DDX47-depleted cells . Fourth, complement protein detection with functional assays measuring R-loop accumulation by DRIP-qPCR at specific genomic regions, particularly rDNA loci (18S and 28S) where DDX47 shows strong functional impact . Fifth, integrate nascent RNA synthesis measurements using EU labeling under conditions of RNA polymerase inhibition (α-amanitin) to distinguish between DDX47's effects on RNAPI versus RNAPII transcription in the nucleolus . This multi-layered approach provides mechanistic insights into how DDX47 prevents harmful R-loops that lead to TRCs.

How should I interpret differences in DDX47 molecular weight detection across different experimental systems?

Variations in DDX47's detected molecular weight require systematic interpretation. DDX47's calculated molecular weight is 51 kDa, but it typically appears around 48 kDa in Western blots . This discrepancy may result from several factors: First, post-translational modifications affect migration patterns—examine potential phosphorylation sites using phosphatase treatments before blotting. Second, alternative splicing can generate different isoforms—validate using RT-PCR with isoform-specific primers alongside protein detection. Third, protein degradation during sample preparation may produce lower molecular weight bands—incorporate protease inhibitors and compare fresh versus stored samples. Fourth, cell type-specific differences can occur—the antibody has been validated in multiple cell lines (HeLa, HepG2, Jurkat, SH-SY5Y) , allowing for comparative analysis across these systems. Fifth, different SDS-PAGE systems (gradient gels vs. fixed percentage) can affect migration patterns—standardize gel systems when comparing across experiments. When analyzing DDX47 in nucleolar versus nucleoplasmic fractions, these considerations become particularly important as compartment-specific modifications may alter the detected molecular weight.

How can I quantitatively analyze DDX47's association with R-loops across different experimental conditions?

Quantitative analysis of DDX47's association with R-loops demands precise methodological approaches. First, implement densitometric analysis of PLA signals between DDX47 and S9.6 antibodies across different conditions, normalizing to nuclear area and cell number . Second, perform DRIP-qPCR at specific genomic loci (APOE, RPL13A, 18S and 28S rDNA) with and without RNase H treatment, calculating R-loop enrichment as the ratio of signals between untreated and RNase H-treated samples . Third, conduct ChIP-qPCR to quantify DDX47 recruitment to chromatin, calculating percent input at R-loop-forming regions versus non-forming regions . Fourth, apply colocalization analysis in immunofluorescence imaging, calculating Pearson's correlation coefficients between DDX47 and R-loop signals. Fifth, develop a standardized scoring system combining these multiple parameters to generate an "R-loop association index" that integrates protein-hybrid interaction strength, chromatin recruitment, and functional impact on R-loop levels. This quantitative framework provides robust metrics for comparing DDX47's R-loop association across experimental variables such as cell types, stress conditions, or disease models.

What comparative analysis should be performed when evaluating DDX47 in relation to other DEAD-box helicases involved in R-loop resolution?

Comprehensive comparative analysis of DDX47 versus other R-loop resolving helicases requires a structured analytical framework. First, implement side-by-side Western blot analysis of DDX47 alongside functionally related helicases (DHX9, DDX21, DDX39B) using HRP-conjugated antibodies with matched concentrations and exposure conditions. Second, conduct substrate specificity assays comparing the helicases' activities on identical R-loop substrates in vitro, measuring resolution efficiency through quantitative biochemical assays. Third, perform epistasis analysis through sequential and simultaneous knockdown/knockout experiments to establish functional hierarchies and potential redundancies between DDX47 and other helicases. Fourth, analyze subcellular distribution patterns through fractionation and immunolocalization studies, focusing on nucleolar enrichment where DDX47 shows distinctive localization . Fifth, compare differential impacts on specific gene classes by performing genome-wide R-loop mapping (DRIP-seq) after individual helicase depletion, with particular attention to rDNA loci where DDX47 shows pronounced effects on both RNAPI and RNAPII-driven transcripts . This comprehensive comparative approach reveals both unique and overlapping functions of DDX47 within the broader context of cellular R-loop management mechanisms.

How can DDX47 antibodies be utilized to investigate its role in cancer progression and as a potential biomarker?

DDX47 has emerging significance in cancer biology, functioning as a potential prognostic marker in multiple cancer types. To investigate this role, implement a multi-faceted research strategy: First, conduct tissue microarray analysis using DDX47 antibodies across cancer progression stages, quantifying expression levels through digital pathology approaches. Second, correlate DDX47 protein levels with R-loop accumulation markers in matched tumor samples using multiplexed immunohistochemistry or sequential Western blotting with HRP-conjugated antibodies. Third, implement chromatin immunoprecipitation sequencing (ChIP-seq) to identify cancer-specific chromatin binding patterns of DDX47, focusing on oncogenes and tumor suppressor loci. Fourth, analyze DDX47's protein interaction network in normal versus cancer cells through co-immunoprecipitation followed by mass spectrometry, identifying cancer-specific binding partners. Fifth, monitor DDX47's expression and localization changes in response to chemotherapeutic agents using time-course Western blot analysis and subcellular fractionation. Recent evidence has indicated that DDX47 can serve as a poor prognostic predictor for renal cell carcinoma and as a diagnostic biomarker for breast cancer and chronic myeloid leukemia , highlighting the importance of these approaches in understanding DDX47's role in cancer biology.

What experimental design is optimal for studying DDX47's function across different cellular compartments?

DDX47's ability to shuttle between nuclear and cytoplasmic compartments necessitates a specialized experimental approach. First, implement subcellular fractionation protocols that separate five key compartments: cytoplasmic, nucleoplasmic, chromatin-bound, nucleolar, and membrane fractions—followed by Western blot analysis with HRP-conjugated DDX47 antibodies. Second, design live-cell imaging experiments with fluorescently tagged DDX47 to track its dynamic movement between compartments in real-time, complemented by fixed-cell analysis with antibody detection. Third, conduct biochemical activity assays using DDX47 isolated from different cellular compartments to determine if its RNA helicase and R-loop resolvase activities vary by location. Fourth, map DDX47's protein interaction networks in each compartment through proximity labeling approaches (BioID or APEX) followed by mass spectrometry. Fifth, analyze post-translational modifications on DDX47 from different compartments using mass spectrometry to identify compartment-specific regulatory mechanisms. This comprehensive approach reveals how DDX47's functions are spatially regulated, particularly its dual roles in nucleolar ribosome biogenesis and nucleoplasmic R-loop resolution .

How can research methodology be optimized when investigating the impact of DDX47 on genome stability through R-loop regulation?

Investigating DDX47's impact on genome stability requires specialized methodologies focused on DNA damage detection and R-loop dynamics. First, implement γH2AX foci quantification alongside comet assays to measure DNA damage levels in DDX47-depleted versus control cells, establishing baseline genome instability phenotypes. Second, design DNA-RNA hybrid immunoprecipitation followed by sequencing (DRIP-seq) experiments in DDX47-depleted cells with appropriate RNase H controls to map genome-wide R-loop accumulation patterns. Third, utilize genomic DNA combing techniques to visualize replication fork progression rates and identify fork stalling events that may result from DDX47-dependent R-loop accumulation. Fourth, implement CRISPR-based scarless tagging of endogenous DDX47 with proximity labeling enzymes to identify its interaction partners specifically at sites of DNA damage. Fifth, conduct transcription-replication conflict analysis through sequential EdU and EU labeling combined with proximity ligation assays between PCNA and RNAPI/II as previously demonstrated . This methodological framework provides mechanistic insights into how DDX47's R-loop resolvase activity directly impacts genome stability through preventing harmful transcription-replication conflicts at specific genomic loci.

What emerging technologies could enhance DDX47 detection and functional analysis in the context of R-loop biology?

Emerging technologies promise to revolutionize DDX47 research in R-loop biology. First, CRISPR-based endogenous tagging with split fluorescent proteins or enzymatic tags will enable live visualization of DDX47 at endogenous levels without antibody-based detection limitations. Second, implementation of proximity-dependent biotin identification (BioID) or APEX2 tagging of DDX47 will map its protein interaction network specifically at R-loop resolution sites. Third, integration of multiplexed ion beam imaging (MIBI) or co-detection by indexing (CODEX) systems will allow simultaneous visualization of DDX47, R-loops, and numerous other factors in single cells with subcellular resolution. Fourth, application of nascent RNA sequencing techniques (NET-seq, TT-seq) in DDX47-depleted cells will reveal transcriptional impacts of R-loop accumulation with nucleotide resolution. Fifth, development of engineered DDX47 variants with controllable activity (optogenetic or chemical-inducible systems) will enable temporal manipulation of R-loop resolution capacity. These technological advances will provide unprecedented insights into the spatial, temporal, and mechanistic aspects of DDX47's function in R-loop biology, moving beyond the limitations of conventional antibody-based detection methods.