DDX27 Antibody

What is DDX27 and what are its key biological functions?

DDX27 (DEAD-box helicase 27) belongs to the DEAD-box RNA helicases family, characterized by conserved D-E-A-D (Asp-Glu-Ala-Asp) sequences. This ATP-dependent helicase plays critical roles in:

• Ribosome biogenesis, specifically regulating 47S ribosomal RNA formation

• Association with the PeBow-complex in ribosome synthesis pathways

• RNA transportation and degradation processes

• Influencing ribosome RNA maturation during skeletal muscle myogenesis

• Various cellular metabolic processes including glucose and lipid metabolism

Beyond these normal functions, DDX27 has been implicated in tumorigenesis and cancer development, particularly in breast, hepatocellular, and gastrointestinal cancers .

What are the recommended applications for DDX27 antibodies in laboratory research?

Based on validated protocols, DDX27 antibodies are primarily utilized in:

• Western Blot (WB): Successfully employed for detection in multiple sample types including:

  • Cell lines: RT-4, U-251 MG

  • Biological fluids: Human plasma

  • Tissues: Liver, tonsil

• Immunohistochemistry (IHC): Particularly effective in formalin/PFA-fixed paraffin-embedded sections showing:

  • Strong nucleolar and cytoplasmic positivity in Purkinje cells of human cerebellum (at 1:10-1:20 dilution)

  • Primarily nuclear localization in breast cancer tissues

These applications enable researchers to assess DDX27 expression levels, compare expression between normal and pathological samples, analyze subcellular localization, and evaluate correlations with other biological markers .

What are the key specifications of commercially available DDX27 antibodies?

Commercial DDX27 antibodies (such as PAB24527) typically have the following specifications:

Note that optimal working dilutions should be determined by the end user for specific applications and experimental conditions .

How can researchers validate the specificity of DDX27 antibodies in experimental applications?

Validating antibody specificity is crucial for ensuring reliable experimental results. For DDX27 antibodies, consider these validation approaches:

• Positive and negative control samples:

  • Positive controls: Use breast cancer tissues or cell lines with known high DDX27 expression

  • Negative controls: Include normal breast tissue (lower expression) or DDX27-knockout cells

• Critical validation experiments:

  • Western blot analysis confirming a single band at the expected molecular weight

  • Pre-absorption with immunizing peptide to demonstrate binding specificity

  • DDX27 knockdown/knockout experiments comparing staining patterns with wild-type cells

  • Correlation of protein detection with mRNA expression levels from RT-qPCR or RNA-seq data

• Multi-antibody verification:

  • Compare staining patterns using antibodies targeting different DDX27 epitopes

  • Verify consistent subcellular localization patterns (nucleolar/nuclear)

• Staining pattern assessment:

  • In cerebellum: Verify nucleolar and cytoplasmic positivity in Purkinje cells

  • In breast tissue: Confirm primarily nuclear localization with expected differential expression between cancer and normal tissue

How is DDX27 expression correlated with cancer progression and what are the implications for cancer research?

DDX27 expression shows significant correlations with cancer progression parameters:

• Expression profile:

  • Significantly higher expression in breast cancer compared to normal breast tissue

  • 58.8% of breast cancer samples (97/165) show high DDX27 expression versus only 27.5% (11/40) in normal breast tissue

• Clinical correlation analysis:

These findings suggest DDX27 may serve as a potential prognostic biomarker and therapeutic target in breast cancer research .

What protocol optimizations are necessary for DDX27 immunohistochemistry in different tissue types?

Successful DDX27 immunohistochemistry requires careful protocol optimization:

• Tissue fixation and processing:

  • Recommended: Formalin/PFA fixation with standard paraffin embedding

  • Section thickness: 4 μm sections for optimal staining

• Antigen retrieval protocol:

  • Method: Citrate buffer with high-pressure heat-induced epitope retrieval

  • Critical step: Complete cooling to room temperature before antibody application

• Blocking and antibody dilution:

  • Blocking: 3% hydrogen peroxide to inhibit endogenous peroxidase activity

  • Primary antibody: 1:10-1:20 dilution range for PAB24527 antibody

  • Incubation: 4°C overnight for primary antibody; room temperature for 1 hour for secondary

• Detection system:

  • Recommended: Diaminobenzidine (DAB) with hematoxylin counterstaining

  • Expected patterns: Nuclear localization in breast cancer; nucleolar and cytoplasmic positivity in Purkinje cells

• Standardized evaluation system:

  • Intensity scoring: Deep (3), medium (2), light (1), negative (0)

  • Percentage scoring: 0 (0-5%), 1 (6-25%), 2 (26-50%), 3 (51-75%), 4 (76-100%)

  • Score calculation: Intensity × percentage

  • Classification: High expression (≥4); low expression (≤3)

For tissues with high background or weak signal, further optimization of antibody concentration, incubation time, and antigen retrieval conditions may be necessary .

What are the recommended troubleshooting approaches for weak or non-specific DDX27 signals?

When encountering signal issues with DDX27 antibodies, consider these methodological solutions:

For weak signals:

• Antibody optimization:

  • Decrease dilution ratio (increase concentration) within recommended ranges

  • For IHC, start with 1:10 dilution rather than 1:20

  • Extend primary antibody incubation time (24-48 hours at 4°C)

• Antigen retrieval enhancement:

  • Increase retrieval duration or pressure

  • Ensure complete cooling before antibody application

  • Consider alternative buffers if citrate buffer yields insufficient results

• Detection system sensitivity:

  • Use amplification-based detection systems

  • For Western blot, employ more sensitive chemiluminescent substrates

  • Increase exposure time while monitoring background levels

For non-specific signals:

• Background reduction strategies:

  • Increase blocking time or concentration

  • Use more stringent washing protocols (more washes with higher detergent concentration)

  • Ensure proper quenching of endogenous peroxidase with 3% hydrogen peroxide

• Antibody specificity improvement:

  • Increase antibody dilution to reduce non-specific binding

  • Pre-absorb antibody with non-specific proteins

  • Consider using a more specific monoclonal antibody alternative

• Sample-specific adjustments:

  • For Western blot, reduce protein loading amount

  • For IHC, optimize section thickness (4 μm recommended)

  • Implement tissue-specific blocking reagents for high-background tissues

How can researchers reliably quantify DDX27 expression across different experimental platforms?

Standardized quantification approaches ensure reliable DDX27 expression measurements:

• Western blot quantification:

  • Normalize to appropriate loading controls (β-actin, GAPDH)

  • Use densitometric analysis with consistent exposure settings

  • Include internal reference samples across all blots for inter-experimental normalization

  • Perform replicate experiments (minimum n=3) and report means with appropriate statistical measures

• IHC quantification:

  • Implement the standardized scoring system described in section 2.3

  • Employ multiple independent observers for objective scoring

  • Consider digital image analysis for more objective quantification

  • Analyze multiple regions to account for tumor heterogeneity

• mRNA expression analysis:

  • For RT-qPCR: Use validated reference genes with stable expression

  • For RNA-seq: Implement proper normalization as described in research protocols:

    • Use edgR package for normalization (as used in TCGA data analysis)

    • Optimize data from the same patients and exclude formalin-fixed samples

    • Apply appropriate normalization methods (TPM, FPKM)

• Cross-platform normalization:

  • Focus on relative changes rather than absolute values

  • Use rank-based methods or Z-score transformations

  • Report both statistical significance and effect sizes

How does DDX27 contribute to stem cell-like properties in cancer, and what methodologies can assess this relationship?

DDX27's role in cancer stemness can be investigated through these methodological approaches:

• Expression correlation analysis:

  • DDX27 positively correlates with established stemness markers:

    • OCT4 (p < 0.0001)

    • SOX2 (p = 0.0032)

  • IHC co-staining shows positive association between DDX27 and OCT4 expression (p < 0.0001, r = 0.428)

• Functional analysis in cancer stem cell models:

  • DDX27 expression is significantly elevated in mammosphere models:

    • Higher expression in MCF-7 MS compared to parental MCF-7 cells

    • Higher expression in T47D MS compared to parental T47D cells

  • This suggests association with stem cell-like phenotypes

• Overexpression studies:

  • DDX27 overexpression in breast cancer cells results in:

    • Upregulation of stemness biomarkers SOX2 and OCT4

    • Enhanced proliferation (measurable by CCK-8 assay)

    • Increased migration capability (quantifiable by Transwell assay)

• Clinical correlation methodologies:

  • Analyze correlation between DDX27 expression and:

    • Cancer recurrence rates

    • Therapy resistance

    • Metastatic potential

    • Patient survival metrics (OS, DFS)

These approaches can establish DDX27's mechanistic role in cancer stemness and potential utility as a therapeutic target in cancer treatment strategies .

What experimental design considerations are important when investigating DDX27's role in ribosome biogenesis?

When investigating DDX27's function in ribosome biogenesis, researchers should implement these experimental design principles:

• Subcellular localization analysis:

  • Perform immunofluorescence co-localization with nucleolar markers

  • Conduct subcellular fractionation followed by Western blot analysis

  • Verify nucleolar accumulation pattern in Purkinje cells and other relevant cell types

• Ribosome biogenesis assessment:

  • Analyze pre-rRNA processing through Northern blot or qRT-PCR

  • Measure 47S rRNA formation rates with and without DDX27 manipulation

  • Evaluate polysome profiles following DDX27 depletion or overexpression

• Interaction studies:

  • Investigate DDX27's association with the PeBow complex

  • Perform co-immunoprecipitation to identify interacting partners

  • Consider proximity labeling approaches (BioID, APEX) to identify the DDX27 interactome

• Functional manipulation approaches:

  • Use RNA interference (siRNA, shRNA) for DDX27 knockdown

  • Apply CRISPR/Cas9 for DDX27 knockout models

  • Develop DDX27 overexpression systems with appropriate controls

  • Assess the effects of DDX27 mutations on ribosome synthesis

• Translation efficiency measurement:

  • Conduct polysome profiling following DDX27 manipulation

  • Measure global protein synthesis using techniques like puromycin incorporation

  • Analyze translation of specific mRNAs important in cancer progression

These approaches enable systematic investigation of DDX27's mechanistic role in ribosome biogenesis and its downstream effects on cellular physiology .

What are the potential therapeutic implications of targeting DDX27 in cancer treatment?

Given DDX27's established role in cancer progression, potential therapeutic approaches include:

• Direct targeting strategies:

  • Small molecule inhibitors of DDX27's helicase activity

  • Peptide inhibitors disrupting DDX27 interactions with the PeBow complex

  • Targeted degradation approaches (PROTACs) specific to DDX27

• Therapeutic rationale based on research findings:

  • DDX27 correlates with poor prognosis (OS: p = 0.0087; DFS: p = 0.0235)

  • Enhances cancer stem cell properties that contribute to therapy resistance

  • Associated with aggressive clinicopathological features:

• Combinatorial therapy approaches:

  • Combining DDX27 inhibition with conventional chemotherapy

  • Targeting DDX27 alongside cancer stemness pathways

  • Using DDX27 expression as a biomarker for therapy selection

• Translational research considerations:

  • Development of clinically viable detection methods for DDX27 expression

  • Identification of patient populations most likely to benefit from DDX27-targeted therapies

  • Design of rational drug combinations based on DDX27's molecular interactions

What novel methodologies show promise for investigating DDX27's molecular mechanisms in cancer?

Advanced methodologies for elucidating DDX27's cancer-related mechanisms include:

• Single-cell analysis approaches:

  • Single-cell RNA sequencing to identify DDX27-high subpopulations

  • Mass cytometry (CyTOF) for simultaneous detection of DDX27 and stemness markers

  • Spatial transcriptomics to analyze DDX27 expression in the tumor microenvironment

• High-throughput functional genomics:

  • CRISPR-Cas9 screens to identify synthetic lethal interactions with DDX27

  • RNA-seq following DDX27 manipulation to identify downstream effectors

  • Ribosome profiling to assess translation impacts of DDX27 activity

• Advanced imaging technologies:

  • Super-resolution microscopy for detailed DDX27 localization

  • Live-cell imaging with fluorescently tagged DDX27 to track dynamics

  • FRET-based approaches to study DDX27 protein interactions in real-time

• Structural biology approaches:

  • Cryo-EM studies of DDX27 in ribosome biogenesis complexes

  • Structure-based drug design for DDX27 inhibitors

  • Hydrogen-deuterium exchange mass spectrometry to map DDX27 interaction domains

• In vivo models:

  • Patient-derived xenografts with DDX27 manipulation

  • Genetically engineered mouse models with tissue-specific DDX27 alterations

  • Evaluation of DDX27 inhibitors in preclinical cancer models

How can researchers distinguish the oncogenic mechanisms of DDX27 from other DEAD-box helicases?

To differentiate DDX27's cancer-specific functions from other DEAD-box family members:

• Comparative expression analysis:

  • Systematically analyze expression patterns of multiple DEAD-box helicases across cancer types

  • Identify cancer types where DDX27 shows unique expression patterns

  • Perform correlation analysis between DDX27 and other family members

• Functional redundancy assessment:

  • Conduct rescue experiments using other DEAD-box helicases after DDX27 depletion

  • Compare phenotypic effects of knockdown/overexpression of multiple family members

  • Identify DDX27-specific cellular functions not complemented by other helicases

• Domain-specific investigations:

  • Create chimeric proteins swapping domains between DDX27 and other DEAD-box helicases

  • Perform mutagenesis of conserved versus unique DDX27 regions

  • Develop domain-specific antibodies for functional studies

• Interactome mapping:

  • Compare protein interaction networks of DDX27 with other family members

  • Identify DDX27-specific interaction partners relevant to cancer progression

  • Analyze unique vs. shared cellular pathways affected by different helicases

• Cancer-specific relevance:

  • Compare prognostic significance of DDX27 versus other family members

  • Analyze correlation with stemness markers across DEAD-box family

  • Investigate differential response to therapy based on expression patterns

What is the optimal Western blot protocol for detecting DDX27 in various sample types?

The following optimized Western blot protocol is recommended for DDX27 detection:

• Sample preparation:

  • Extract proteins using RIPA buffer supplemented with protease inhibitors

  • For tissue samples: Homogenize thoroughly in cold buffer

  • For cell lines: Lyse directly in wells after PBS washing

  • Quantify protein concentration using BCA or Bradford assay

  • Prepare samples in Laemmli buffer with reducing agent (50-100 μg total protein)

• Gel electrophoresis and transfer:

  • Separate proteins on 10% SDS-PAGE gel

  • Transfer to PVDF membrane (0.45 μm) at 100V for 90 minutes in cold transfer buffer

• Immunoblotting:

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

  • Incubate with primary DDX27 antibody at 1:1000 dilution in blocking buffer overnight at 4°C

  • Wash 3× with TBST, 10 minutes each

  • Incubate with HRP-conjugated secondary antibody (1:5000) for 1 hour at room temperature

  • Wash 3× with TBST, 10 minutes each

• Development and analysis:

  • Develop using ECL substrate and appropriate imaging system

  • Expected band size: ~89.8 kDa

  • Normalize to loading control (β-actin or GAPDH)

• Validated sample types:

  • Cell lines: RT-4, U-251 MG (shown in lane 1 and lane 2)

  • Tissues: Human liver and tonsil (shown in lane 4 and lane 5)

  • Biological fluids: Human plasma (shown in lane 3)

How should researchers design immunohistochemistry experiments to accurately assess DDX27 expression in clinical samples?

For rigorous IHC assessment of DDX27 in clinical samples:

• Specimen preparation protocol:

  • Fix tissues in 10% neutral buffered formalin for 24-48 hours

  • Process and embed in paraffin following standard protocols

  • Section at 4 μm thickness

  • Use positive control tissues in each batch (breast cancer with known high DDX27)

• Optimized IHC protocol:

  • Deparaffinize sections in xylene and rehydrate through graded alcohols

  • Perform antigen retrieval in citrate buffer (pH 6.0) using high-pressure method

  • Block endogenous peroxidase with 3% hydrogen peroxide for 10 minutes

  • Apply primary DDX27 antibody at 1:10-1:20 dilution

  • Incubate overnight at 4°C in humidified chamber

  • Apply appropriate secondary antibody for 1 hour at room temperature

  • Develop with DAB and counterstain with hematoxylin

• Standardized evaluation method:

  • Intensity scoring: deep (3), medium (2), light (1), negative (0)

  • Percentage scoring: 0 (0-5%), 1 (6-25%), 2 (26-50%), 3 (51-75%), 4 (76-100%)

  • Calculate final score = intensity × percentage

  • Define high expression: score ≥4; low expression: score ≤3

• Quality control measures:

  • Include technical negative control (primary antibody omitted)

  • Use multi-tissue controls with known DDX27 expression

  • Have two independent pathologists score blindly

  • Document staining pattern (nuclear, nucleolar, cytoplasmic)

• Data analysis recommendations:

  • Correlate DDX27 expression with clinicopathological parameters

  • Perform survival analysis (Kaplan-Meier) based on expression levels

  • Analyze correlation with stemness markers (OCT4, SOX2)