DCUN1D5 Antibody

What is DCUN1D5 and why is it significant for cancer research?

DCUN1D5 is a component of the neddylation E3 complex that promotes cullin neddylation. This process activates cullin-RING family E3 ubiquitin ligases, leading to increased proteasomal degradation of target proteins. Research demonstrates that DCUN1D5 is overexpressed in multiple cancers including lung adenocarcinoma, oral and lung squamous cell carcinomas, laryngeal squamous cell carcinoma, and breast cancer . Its oncogenic significance is highlighted by:

• Correlation of high expression with decreased disease-specific survival in oral and lung SCCs

• Evidence of oncogene addiction in cancer cells with elevated DCUN1D5 expression

• Demonstrated ability to transform fibroblasts (NIH-3T3 cells) in vitro

• In vitro studies showing DCUN1D5 promotes cellular migration (2.7-fold increase), invasion (67.5% increase), and proliferation (2.6-fold increase)

What applications are DCUN1D5 antibodies validated for?

Based on manufacturer data and published research, DCUN1D5 antibodies have been validated for multiple applications:

It's crucial to validate antibody specificity for your specific experimental conditions, as reactivity may vary between manufacturers .

How do I select the appropriate DCUN1D5 antibody for my specific cancer model?

When selecting a DCUN1D5 antibody for cancer research:

• Match species reactivity: Verify reactivity with your model species (human, mouse, rat). Most commercial antibodies show reactivity with human samples, with some cross-reactivity to mouse and rat .

• Consider application compatibility: Select antibodies validated for your specific application. For investigating DCUN1D5 in cancer tissues, prioritize antibodies validated for IHC-P and IF if conducting tissue analysis.

• Epitope consideration: For structure-function studies of DCUN1D5, select antibodies targeting relevant domains. Research shows both the PONY domain and nuclear localization sequence (NLS) are important for DCUN1D5 function .

• Validate in your model: Prior to full experiments, validate the antibody in your specific cancer model by comparing expression in paired normal vs. tumor samples, as DCUN1D5 overexpression has been documented in multiple cancer types .

What are the optimal protocols for detecting DCUN1D5 expression differences between cancer and normal tissues?

Based on published research methodologies , a comprehensive approach includes:

qRT-PCR Protocol:

• Extract total RNA from paired tumor and normal tissues

• Perform cDNA synthesis with reverse transcriptase

• Design primers specific to DCUN1D5 (researchers have used Primer3 program)

• Validate primer specificity by testing against all SCCRO paralogs

• Perform qRT-PCR in duplicate with appropriate housekeeping gene controls (GAPDH has been validated as stable in relevant tissues)

• Calculate relative expression using the comparative threshold cycle method with a standard curve generated from serial dilutions of cell line cDNA (MDA686 and MDA1186 have been used)

Western Blot Analysis:

• Extract protein from tissues and quantify

• Separate proteins using 12% SDS-PAGE

• Transfer to appropriate membrane

• Block and incubate with anti-DCUN1D5 antibody (1:500-1:2000 dilution)

• Detect using appropriate secondary antibodies and visualization systems

• Normalize to housekeeping proteins (GAPDH, actin, or α-tubulin)

Immunohistochemistry:

• Prepare tissue microarrays of paired tumor and normal samples

• Deparaffinize and perform antigen retrieval

• Block endogenous peroxidase activity

• Incubate with anti-DCUN1D5 antibody (1:200-1:500 dilution)

• Apply detection system and counterstain

• Score expression levels based on intensity and percentage of positive cells

How should I design DCUN1D5 knockdown experiments to investigate its oncogenic functions?

Based on successful published approaches :

• Selection of appropriate cell lines:

  • Choose multiple cell lines with differential DCUN1D5 expression levels

  • Include both high DCUN1D5-expressing cancer lines and low-expressing controls

  • Cancer cell lines previously used include oral and lung squamous cell carcinoma lines

• RNAi approach:

  • Design multiple siRNAs targeting different regions of DCUN1D5 mRNA

  • Transfect using optimized protocols with appropriate controls (scrambled siRNA)

  • Confirm knockdown efficiency by qRT-PCR and Western blot (72-96 hours post-transfection)

• Functional assays to assess oncogenic properties:

  • Cell viability: MTT or similar assays at 24h intervals

  • Cell cycle analysis: Flow cytometry with PI staining (knockdown of DCUN1D5 decreases S phase by ~10.2%)

  • Apoptosis: Annexin V/PI staining (knockdown increases apoptosis by ~11.7%)

  • Migration: Wound healing or transwell assays

  • Invasion: Matrigel-coated transwell assays

  • Glycolysis: Measure extracellular acidification rate and lactate production

• In vivo validation:

  • Consider xenograft models in nude mice to confirm in vitro findings

  • Design stable knockdown using shRNA for longer-term studies

  • Monitor tumor formation, growth rate, and analyze harvested tumors for pathway alterations

What controls and validation steps are necessary when using DCUN1D5 antibodies for immunohistochemistry in cancer tissues?

For rigorous IHC validation:

• Positive controls:

  • Include tissues with known DCUN1D5 expression (HeLa cells, lung adenocarcinoma tissues)

  • Use paired normal/tumor samples to demonstrate differential expression

• Negative controls:

  • Omit primary antibody while maintaining secondary antibody

  • Use tissues known to have minimal DCUN1D5 expression

  • Include isotype control antibodies

• Antibody validation:

  • Confirm specificity through Western blot analysis of tissues to be analyzed by IHC

  • Use multiple antibodies targeting different epitopes when possible

  • Correlate IHC results with mRNA expression data from the same samples

• Quantification methodology:

  • Implement standardized scoring systems (H-score, Allred score)

  • Consider digital image analysis for objective quantification

  • Have multiple pathologists score independently to ensure reproducibility

• Technical considerations:

  • Optimize antigen retrieval methods (pH, buffer composition, time)

  • Determine optimal antibody dilution through titration (1:200-1:500 has been reported)

  • Include appropriate blocking steps to minimize background

How can researchers investigate the dual requirements of PONY domain and nuclear localization for DCUN1D5's oncogenic function?

Structure-function studies of DCUN1D5 reveal fascinating insights into domain requirements . To investigate this:

• Generate domain-specific mutants:

  • Create expression constructs with mutations in the PONY domain alone

  • Design NLS mutants with disrupted nuclear localization

  • Develop double mutants affecting both domains

  • Include full-length wild-type DCUN1D5 as control

• Biochemical neddylation assays:

  • Perform in vitro neddylation assays using recombinant components

  • Measure cullin neddylation with wild-type versus mutant DCUN1D5

  • Analyze reaction kinetics to assess processivity changes

  • Research shows PONY domain alone is sufficient for in vitro neddylation

• Subcellular localization studies:

  • Use immunofluorescence with appropriate markers to track localization

  • Employ cell fractionation followed by Western blotting

  • Correlate localization with functional outcomes

  • Previous studies show nuclear localization is required for in vivo function despite being dispensable for in vitro neddylation

• Transformation assays:

  • Transfect NIH-3T3 cells with wild-type and mutant constructs

  • Assess soft agar colony formation

  • Measure proliferation rates and contact inhibition

  • Previous work indicates both PONY domain and NLS are required for transformation

• In vivo validation:

  • Develop xenograft models expressing domain mutants

  • Monitor tumor formation and progression

  • Analyze harvested tumors for pathway alterations

What are the challenges in distinguishing DCUN1D5 from other DCUN1D family members, and how can researchers overcome them?

The DCUN1D family presents challenges for specific detection:

• Sequence homology issues:

  • DCUN1D family members (DCUN1D1-5) share conserved PONY domains

  • Design primers and antibodies targeting unique regions

  • Test antibody cross-reactivity against all family members

  • Validate primer specificity by testing against all SCCRO paralogs

• Functional redundancy analysis:

  • Perform simultaneous knockdown of multiple family members

  • Use rescue experiments with individual members to isolate specific functions

  • Implement CRISPR/Cas9 to generate clean knockouts of individual genes

• Expression pattern differentiation:

  • Compare tissue-specific expression patterns of family members

  • Create heatmaps showing relative expression across tissues and cancer types

  • Analyze subcellular localization differences (SCCRO5/DCUN1D5 contains NLS domain)

• Antibody selection strategies:

  • Use epitopes outside the conserved PONY domain

  • Validate antibody specificity against recombinant proteins of all family members

  • Consider developing monoclonal antibodies targeting unique epitopes

• Bioinformatic approaches:

  • Use RNA-seq data to design isoform-specific detection strategies

  • Implement computational methods to distinguish highly similar proteins

  • Design discriminatory peptides for mass spectrometry analysis

How can researchers integrate DCUN1D5 expression data with immune infiltration and glycolysis pathway analysis in cancer studies?

Recent research reveals DCUN1D5's correlation with glycolysis and immune infiltration in tumors . To investigate these connections:

• Multi-omics approach:

  • Integrate transcriptomics (RNA-seq) with proteomics and metabolomics

  • Correlate DCUN1D5 expression with glycolysis gene signatures

  • Perform Gene Set Enrichment Analysis (GSEA) to identify pathway enrichment

  • Recent studies show strong correlation between DCUN1D5 and glycolysis-related genes

• Immune profiling methodology:

  • Use immunohistochemistry multiplex to assess immune cell populations

  • Implement flow cytometry for detailed immune cell subtyping

  • Correlate DCUN1D5 levels with specific immune cell signatures

  • Research shows DCUN1D5 affects tumor immune cell infiltration

• Functional validation experiments:

  • Assess glycolytic capacity using Seahorse analysis in DCUN1D5-manipulated cells

  • Measure key glycolytic metabolites by mass spectrometry

  • Conduct co-culture experiments with immune cells and DCUN1D5-modified cancer cells

  • In vitro studies show decreased glycolysis when DCUN1D5 is downregulated

• Clinical correlation analysis:

  • Develop nomograms incorporating DCUN1D5, glycolysis markers, and immune signatures

  • Analyze patient survival based on combined biomarker panels

  • Stratify patients based on DCUN1D5 expression and immune infiltration patterns

  • Research shows DCUN1D5 has prognostic value in lung adenocarcinoma

• Therapeutic implications:

  • Test combination approaches targeting DCUN1D5 and glycolysis

  • Evaluate immune checkpoint inhibitors in models with variable DCUN1D5 expression

  • Design rational combination strategies based on pathway interactions

What are the most effective approaches for studying DCUN1D5's role in DNA damage response in cancer cells?

Based on published findings showing DCUN1D5's involvement in DNA damage response :

• DNA damage induction protocols:

  • Apply genotoxic agents (UV, ionizing radiation, chemotherapy agents)

  • Create controlled DNA damage using CRISPR/Cas9-based techniques

  • Monitor DCUN1D5 expression in a time-dependent manner after damage

  • Research shows endogenous DCUN1D5 decreases in a time-dependent manner after genotoxic stress

• DNA repair pathway analysis:

  • Assess key DNA repair proteins (γH2AX, RAD51, 53BP1) in DCUN1D5-manipulated cells

  • Measure DNA repair kinetics using comet assay or repair reporter systems

  • Determine which repair pathways (HR, NHEJ, etc.) are affected by DCUN1D5

• Cell cycle checkpoint investigation:

  • Synchronize cells and analyze checkpoint activation after damage

  • Measure CDK activity and checkpoint protein phosphorylation

  • Flow cytometry analysis to determine cell cycle distribution changes

  • Silencing DCUN1D5 decreases S phase cells by 10.2% and increases apoptosis by 11.7%

• Mechanistic studies:

  • Identify DCUN1D5 interacting partners during DNA damage using IP-MS

  • Assess neddylation of DNA repair factors in the presence/absence of DCUN1D5

  • Determine if DCUN1D5's role in DNA damage is neddylation-dependent

• Therapeutic implications:

  • Test sensitivity to DNA damaging agents in cells with variable DCUN1D5 expression

  • Evaluate combination with neddylation inhibitors (e.g., MLN4924)

  • Explore synthetic lethality approaches in cells with DCUN1D5 overexpression

What are common technical challenges when using DCUN1D5 antibodies, and how can researchers address them?

Based on technical information from antibody manufacturers and research papers :

• Non-specific binding:

  • Optimize blocking conditions (5% BSA or 5% non-fat milk)

  • Titrate antibody concentrations (start with 1:500 for WB, 1:100 for IHC/IF)

  • Include additional washing steps with variable salt concentrations

  • Pre-absorb antibody with recombinant protein if needed

• Variable signal intensity:

  • Optimize protein extraction methods for complete solubilization

  • Ensure consistent protein loading (28 kDa is the observed molecular weight)

  • Consider antigen retrieval optimization for IHC applications

  • Test multiple antibody clones or suppliers if consistent issues occur

• Background in immunofluorescence:

  • Use appropriate negative controls (secondary only, isotype control)

  • Optimize fixation method (4% paraformaldehyde, methanol)

  • Include additional permeabilization steps if needed

  • Counter-stain with DAPI to visualize nuclei and assess DCUN1D5 localization

• Inconsistent IP results:

  • Optimize lysis conditions to preserve protein-protein interactions

  • Use 0.5-4.0 μg antibody per 1.0-3.0 mg of total protein lysate

  • Consider crosslinking antibody to beads to avoid IgG contamination

  • Include appropriate controls (IgG IP, input)

How can researchers design experiments to resolve contradictory data regarding DCUN1D5 function in different cancer types?

To address contradictions in DCUN1D5 research:

• Standardized methodology approach:

  • Implement identical experimental protocols across cancer types

  • Use the same antibody clones and detection methods

  • Establish common positive and negative controls

  • Analyze multiple cell lines from each cancer type simultaneously

• Context-dependent function analysis:

  • Investigate tissue-specific binding partners through IP-MS

  • Assess differential expression of neddylation machinery components

  • Examine cancer-specific mutations or isoform expression patterns

  • Consider microenvironment factors that might influence function

• Integrated multi-omics:

  • Perform parallel transcriptomic, proteomic, and functional studies

  • Create comprehensive pathway models for each cancer type

  • Identify common and divergent signaling nodes

  • Use systems biology approaches to model context-specific functions

• Genetic manipulation validation:

  • Use multiple knockdown/knockout approaches (siRNA, shRNA, CRISPR)

  • Implement rescue experiments with wild-type and mutant constructs

  • Test function in isogenic cell line pairs

  • Validate in patient-derived models

What are best practices for quantifying and analyzing DCUN1D5 expression data across heterogeneous cancer samples?

For consistent DCUN1D5 quantification:

• Normalization strategies:

  • Use multiple reference genes for qRT-PCR (GAPDH has been validated)

  • Employ spike-in controls for absolute quantification

  • Implement digital PCR for more precise quantification

  • Create standard curves using recombinant DCUN1D5

• Image analysis for IHC/IF:

  • Use digital pathology software for objective quantification

  • Develop machine learning algorithms for consistent scoring

  • Implement color deconvolution for DAB/hematoxylin separation

  • Score both intensity and percentage of positive cells

• Heterogeneity assessment:

  • Analyze multiple regions within each tumor sample

  • Consider single-cell approaches for highly heterogeneous tumors

  • Correlate with histopathological features and tumor subregions

  • Use tissue microarrays with multiple cores per tumor

• Statistical approaches:

  • Implement appropriate statistical methods for non-normally distributed data

  • Use paired analyses for tumor/normal comparisons

  • Correct for multiple testing in large datasets

  • Consider bootstrapping for robust confidence intervals

What novel approaches could researchers use to target DCUN1D5 therapeutically in cancer?

Based on current understanding of DCUN1D5 biology :

• Direct inhibition strategies:

  • Design small molecule inhibitors targeting the PONY domain

  • Develop peptide-based inhibitors disrupting DCUN1D5-cullin interactions

  • Create degraders (PROTACs) targeting DCUN1D5 for proteasomal degradation

  • Consider RNAi-based therapeutics for selective knockdown

• Pathway-based approaches:

  • Target neddylation pathway using MLN4924 or similar inhibitors

  • Explore synthetic lethality with DNA damage response inhibitors

  • Develop glycolysis inhibitors for tumors with high DCUN1D5 expression

  • Consider immune-based approaches for DCUN1D5-overexpressing tumors

• Combination strategies:

  • Pair DCUN1D5/neddylation inhibition with standard chemotherapies

  • Test synergy with DNA damage-inducing agents based on DCUN1D5's role in DNA damage response

  • Combine with cell cycle checkpoint inhibitors

  • Explore combinations with immune checkpoint inhibitors

• Biomarker-driven approaches:

  • Develop companion diagnostics to identify DCUN1D5-dependent tumors

  • Create response prediction models integrating DCUN1D5 with pathway markers

  • Design clinical trials with DCUN1D5 expression as stratification factor

  • Consider neoadjuvant window studies to assess pharmacodynamic effects

How might single-cell technologies advance our understanding of DCUN1D5's role in tumorigenesis and cancer progression?

Single-cell approaches offer new insights into DCUN1D5 biology:

• Single-cell transcriptomics applications:

  • Map DCUN1D5 expression across tumor cell subpopulations

  • Correlate with stemness, EMT, and drug resistance signatures

  • Identify rare cell populations with extreme DCUN1D5 expression

  • Track expression changes during tumor evolution and treatment response

• Spatial transcriptomics/proteomics:

  • Visualize DCUN1D5 expression in the context of tumor architecture

  • Map relationship between DCUN1D5-expressing cells and immune infiltrates

  • Analyze expression at tumor-stroma interface

  • Correlate with hypoxic or glycolytic microenvironments

• Single-cell functional assays:

  • Implement CRISPR screens with single-cell readouts

  • Use CyTOF to correlate DCUN1D5 with signaling states

  • Develop reporter systems for neddylation activity at single-cell level

  • Track cell fate after DCUN1D5 modulation

• Computational integration:

  • Develop algorithms to integrate single-cell multi-omics data

  • Create predictive models of DCUN1D5 function at cellular resolution

  • Map DCUN1D5-dependent gene regulatory networks

  • Model cell-cell interactions in the context of DCUN1D5 expression