RECQL Antibody

Basic Research Questions

• What is RECQL and why is it important in cancer research?

  RECQL (also known as RECQ1 and RECQL1) is a DNA helicase that plays critical roles in DNA damage repair and genome stability. It has gained significant attention in cancer research due to its association with patient survival outcomes, particularly in breast cancer. Studies have shown that high levels of RECQL protein in breast cancer tumor cells correlate with better patient survival . Additionally, RECQL prevents replication fork collapse during replication stress, which is common in cancer cells . This makes it both a potential biomarker and therapeutic target in oncology research.

• What are the primary applications of RECQL antibodies in molecular biology research?

  RECQL antibodies are utilized across multiple experimental techniques including:

  • Western blotting (WB) for protein expression quantification

  • Immunohistochemistry (IHC) for tissue localization

  • Immunoprecipitation (IP) for protein-protein interaction studies

  • Immunofluorescence (IF) for subcellular localization

  • ELISA for quantitative protein detection

  These applications enable researchers to investigate RECQL expression, localization, and interactions in various experimental contexts . Selection of the appropriate application depends on your research question, with WB commonly used for expression studies and IHC valuable for clinical sample analysis.

• How do I select the appropriate RECQL antibody for my experiment?

  Consider these key factors when selecting a RECQL antibody:

  • Target specificity: Verify the antibody recognizes your specific RECQL epitope

  • Species reactivity: Ensure compatibility with your experimental model (human, mouse, rat)

  • Application validation: Confirm the antibody has been validated for your intended application

  • Clonality: Monoclonal antibodies offer higher specificity; polyclonal antibodies typically provide stronger signals

  • Epitope location: N-terminal, C-terminal, or central region targeting may affect recognition of splice variants

  Critically review provided validation data such as Western blot images showing the expected molecular weight (approximately 73 kDa) . Cross-referencing with literature using the same antibody can provide additional confidence in antibody performance.

Advanced Research Questions

• How can RECQL antibodies be used to investigate the role of RECQL in replication stress response?

  Methodological approach:

  1. Induce replication stress using appropriate agents (hydroxyurea, aphidicolin, etc.)

  2. Perform immunofluorescence to detect RECQL localization at stalled replication forks

  3. Combine with other replication stress markers (γH2AX, RPA, etc.)

  4. Use chromatin immunoprecipitation (ChIP) with RECQL antibodies to identify genomic binding sites

  5. Implement proximity ligation assays (PLA) to detect interactions with other replication stress response proteins

  Research has shown that RECQL plays a critical role in protecting stalled replication forks against MRE11-dependent double-strand break formation . When investigating this phenomenon, consider combining RECQL antibody staining with pulsed DNA labeling techniques (EdU, BrdU) to specifically examine active replication sites.

• What methodological considerations should be applied when using RECQL antibodies in breast cancer patient samples?

  When analyzing breast cancer samples with RECQL antibodies:

  1. Sample preparation: Use formalin-fixed paraffin-embedded (FFPE) tissue with appropriate antigen retrieval

  2. Antibody optimization: Determine optimal dilution (typically 1:200-1:500 for IHC)

  3. Scoring system: Establish clear parameters for high versus low/medium expression

     • Research indicates the 75th percentile as an effective cutoff for prognostic significance

  4. Controls: Include both positive (known RECQL-expressing tissues) and negative controls

  5. Correlation analysis: Stratify by ER status, as RECQL associations differ between ER-positive and ER-negative patients

  In one study with 933 breast cancer patients, women with RECQL protein levels above the 75th percentile demonstrated better 15-year disease-specific survival among ER-positive patients (62.5% vs. 48.7%, HR=0.72, 95%CI=0.52-0.98, p=0.04) . This differential impact based on ER status highlights the importance of appropriate stratification in analysis.

• How should researchers address potential cross-reactivity issues with RECQL antibodies?

  To minimize and identify cross-reactivity problems:

  1. Validation controls:

     • Use RECQL knockout/knockdown samples as negative controls

     • Test across multiple cell lines with varying RECQL expression

     • Validate results with multiple antibodies targeting different epitopes

  2. Specificity testing:

     • Perform peptide competition assays with the immunizing peptide

     • Consider testing reactivity with other RECQ family members (WRN, BLM, RECQL4, RECQL5)

     • Apply stringent washing conditions to reduce non-specific binding

  3. Technical optimization:

     • Adjust blocking conditions to minimize background

     • Titrate antibody concentrations to determine optimal signal-to-noise ratio

     • Use appropriate species-specific secondary antibodies

  Cross-reactivity is particularly concerning given the five RecQ helicases in mammals with conserved helicase domains .

• What techniques can be employed to study RECQL protein interactions using RECQL antibodies?

  Methodological approaches include:

  1. Co-immunoprecipitation (Co-IP):

     • Use RECQL antibodies to pull down protein complexes

     • Analyze interacting partners via mass spectrometry or Western blot

     • Consider native versus crosslinked conditions based on interaction strength

  2. Proximity Ligation Assay (PLA):

     • Visualize protein interactions in situ with single-molecule resolution

     • Combine RECQL antibody with antibodies against suspected interacting partners

  3. Chromatin Immunoprecipitation (ChIP):

     • Identify DNA regions bound by RECQL and associated proteins

     • Combine with sequencing (ChIP-seq) for genome-wide interaction maps

  4. FRET/BRET assays:

     • Use antibody-based FRET to detect protein proximity in live cells

     • Requires fluorophore-conjugated antibodies or recombinant proteins

  Research has identified interactions between RECQL and key proteins such as PARP1, XRCC1, and APE1 in the base excision repair pathway . When designing interaction studies, consider that buffer conditions (salt concentration, detergents) can significantly impact detected interactions.

• How can researchers quantitatively assess RECQL protein levels in clinical samples for correlation with patient outcomes?

  Quantitative assessment methods:

  1. Immunohistochemistry scoring:

     • Use tissue microarrays (TMAs) with multiple cores per sample

     • Apply H-score or Allred scoring systems for semi-quantitation

     • Consider automated image analysis for more objective quantification

  2. Quantitative protein analysis:

     • Use quantitative Western blotting with recombinant protein standards

     • Apply reverse phase protein arrays (RPPA) for high-throughput analysis

     • Consider mass spectrometry-based quantitation for absolute measurements

  3. Statistical considerations:

     • Determine optimal cutpoints using statistical methods (ROC curves, quartiles)

     • Apply multivariate analysis to account for confounding variables

     • Use Kaplan-Meier analysis with log-rank tests for survival outcomes

  A study examining RECQL protein levels in 933 breast cancer patients demonstrated statistical significance when using the 75th percentile as a cutoff, with hazard ratios of 0.72 (95% CI: 0.52-0.98) for ER-positive patients receiving tamoxifen treatment .

• What experimental controls are essential when using RECQL antibodies to study its function in DNA repair processes?

  Essential controls include:

  1. Antibody validation controls:

     • RECQL knockout/knockdown samples

     • Blocking peptide controls to confirm specificity

     • Multiple antibodies targeting different epitopes

  2. Experimental design controls:

     • Positive controls: Cells with DNA damage induced by known agents

     • Negative controls: DNA repair-proficient cells without damage induction

     • Time course experiments to capture repair kinetics

  3. Functional validation controls:

     • Complementation experiments with wild-type versus mutant RECQL

     • Parallel analysis with other DNA repair proteins (e.g., PARP1, XRCC1)

     • Analysis in different genetic backgrounds (e.g., p53-proficient vs. deficient)

  When studying RECQL's role in protecting stalled replication forks, researchers have shown that knockdown of RECQL in different cancer cells increased the level of DNA double-strand breaks, supporting its critical function in DNA repair .

• How can RECQL antibodies be employed in studies investigating the relationship between RECQL and estrogen receptor signaling in breast cancer?

  Methodological approaches:

  1. Co-localization studies:

     • Dual immunofluorescence to visualize RECQL and ER localization

     • Super-resolution microscopy for detailed spatial relationship analysis

  2. Expression correlation:

     • IHC on sequential tissue sections for RECQL and ER

     • Dual staining protocols with appropriate controls for specificity

     • Quantitative analysis of expression correlation

  3. Functional relationship studies:

     • ChIP experiments following estrogen stimulation

     • RECQL immunoprecipitation after hormonal treatment

     • Analysis of RECQL recruitment to ER target genes

  4. Clinical correlation analysis:

     • Stratify patient cohorts by ER and RECQL status

     • Analyze treatment outcomes based on combined biomarker status

  Research has shown differential survival impact of RECQL expression between ER-positive and ER-negative breast cancer patients. High RECQL protein levels were associated with better survival among ER-positive patients (HR=0.72, p=0.04) but not among ER-negative patients (HR=1.07, p=0.79) . For tamoxifen-treated ER-positive patients, the survival benefit was even more pronounced (HR=0.64, p=0.04), suggesting RECQL might be a predictive marker for tamoxifen treatment efficacy.