RNF6 Antibody

What applications are RNF6 antibodies validated for in research?

RNF6 antibodies have been validated for multiple applications including Western Blot (WB), Immunohistochemistry (IHC), Immunofluorescence/Immunocytochemistry (IF/ICC), Immunoprecipitation (IP), and Chromatin Immunoprecipitation (ChIP). When selecting an antibody, consider the validated applications reported by manufacturers and in peer-reviewed literature. For example, the Proteintech RNF6 antibody (20437-1-AP) has been successfully used in all these applications with recommended dilutions of 1:500-1:2000 for WB, 0.5-4.0 μg for 1.0-3.0 mg of total protein lysate for IP, and 1:50-1:500 for IF/ICC . The antibody's versatility across multiple applications makes it valuable for comprehensive RNF6 studies, particularly when investigating both its E3 ligase and transcriptional regulatory functions.

What RNF6 expression patterns should researchers expect across different cell types?

RNF6 expression varies significantly across cell and tissue types, with particularly high expression observed in various cancer cells. According to published studies, colorectal cancer cell lines DLD1 and SW480 express high levels of RNF6, while HCT116 and HT29 cells show lower endogenous RNF6 expression . Positive Western blot detection has been reported in mouse testis tissue and PC-3 prostate cancer cells . RNF6 overexpression has also been documented in leukemia cell lines (K562), multiple myeloma cells (RPMI-8226, LP1), and breast cancer tissue samples . This differential expression pattern makes it essential to select appropriate positive controls when validating RNF6 antibodies. For immunostaining applications, SW480 cells have shown strong positive signals in IF/ICC experiments .

How do different epitope targets of RNF6 antibodies affect their applications?

The choice of epitope target significantly impacts an RNF6 antibody's performance in different applications. Commercial RNF6 antibodies target various regions of the protein, including:

• N-terminal region antibodies (e.g., ABIN925857) that recognize the coiled-coil domain

• C-terminal region antibodies that may interact with the RING-H2 finger domain responsible for ubiquitin ligase activity

• Mid-region antibodies targeting amino acids 300-400 (ab204506)

For functional studies, antibodies targeting the C-terminal RING-H2 finger domain might interfere with ubiquitin ligase activity, making them less suitable for certain functional assays but potentially useful for inhibition studies. Antibodies against the N-terminal coiled-coil domain might affect protein-protein interactions. For ChIP applications, antibodies targeting DNA-binding regions might reduce efficiency by competing with DNA for binding. When studying RNF6's dual functions, using antibodies targeting different epitopes may help distinguish between its roles as an E3 ligase and transcription factor . Therefore, selecting antibodies with epitopes appropriate for the specific application is crucial for experimental success.

How can I validate the specificity of RNF6 antibody for chromatin immunoprecipitation (ChIP) experiments?

Validating RNF6 antibody specificity for ChIP experiments requires a multi-step approach:

• Positive and negative control regions: Based on published research, the SF3B2 promoter serves as a known RNF6 binding target for positive control . Design primers for this region and include non-target genomic regions as negative controls.

• RNF6 knockdown validation: Perform ChIP in both wild-type and RNF6-knockdown cells. The signal should be significantly reduced in knockdown cells, confirming antibody specificity.

• Supershift assay: Use electrophoretic mobility shift assay (EMSA) with nuclear extracts, the target DNA sequence as probe, and RNF6 antibody. As demonstrated in colorectal cancer studies, RNF6 antibody addition should cause a supershift of the RNF6-DNA complex, confirming specific binding .

• Quantitative assessment: Calculate enrichment using the percent input method as described in previous studies . Compare enrichment at known binding sites versus negative control regions.

• ChIP-seq validation: For genome-wide studies, validate peaks at known binding sites and identify the RNF6 binding motif (the core sequence "TTTCCT" has been previously identified from ChIP-seq data) .

These validation steps ensure that ChIP signals truly represent RNF6 binding rather than non-specific interactions or experimental artifacts.

What are the methodological considerations when investigating RNF6 auto-ubiquitination?

When studying RNF6 auto-ubiquitination, several methodological considerations are critical:

• Proteasome inhibition: Treat cells with proteasome inhibitors (MG132 or bortezomib) prior to analysis. Studies have shown that RNF6 protein accumulates markedly after proteasome inhibition but not with lysosomal inhibitors (chloroquine), confirming proteasomal degradation pathway involvement .

• Appropriate lysis conditions: Use denaturing lysis conditions with SDS to disrupt protein-protein interactions and capture all ubiquitinated species.

• Controls: Include both RNF6 knockdown samples and RNF6 RING domain mutants as negative controls. The RING domain is essential for E3 ligase activity, and mutations in this domain should abolish auto-ubiquitination.

• Ubiquitin linkage analysis: Use antibodies specific for different ubiquitin linkages (K48, K63, K6, K27) to characterize the type of auto-ubiquitination. Studies have shown RNF6 can form different types of ubiquitin chains .

• In vitro ubiquitination assay: Perform cell-free assays with purified components (E1, E2, RNF6, and ubiquitin) to confirm direct auto-ubiquitination without other E3 ligases present .

• USP7 interaction analysis: Investigate the role of deubiquitinating enzyme USP7, which has been shown to prevent RNF6 auto-ubiquitination and stabilize the protein .

This methodological approach provides comprehensive evidence for RNF6 auto-ubiquitination and its regulation.

How can I optimize co-immunoprecipitation protocols to study RNF6 protein interactions?

Optimizing co-immunoprecipitation (Co-IP) for RNF6 protein interactions requires careful protocol design:

• Lysis buffer optimization: Use RIPA buffer for standard interactions, but consider milder NP-40 or Triton X-100 buffers for preserving weaker interactions. Include protease inhibitors and phosphatase inhibitors if phosphorylation might affect interactions.

• Proteasome inhibition: Pretreat cells with MG132 (15 μg/ml for 4-6 hours) before harvesting, especially when studying ubiquitination-related interactions. This prevents RNF6 and its partners from degradation .

• Antibody quantity optimization: Use 0.5-4.0 μg of RNF6 antibody for 1.0-3.0 mg of total protein lysate as a starting point, then optimize based on results .

• Controls: Include IgG control immunoprecipitations, input samples (5-10% of lysate), and RNF6 knockdown samples as controls . For known interactions like RNF6-USP7, include positive control samples.

• Reciprocal Co-IP: Confirm interactions by performing reciprocal Co-IP (IP with antibody against the interacting partner, then blot for RNF6) .

• Crosslinking: For transient or weak interactions, consider formaldehyde crosslinking followed by glycine quenching .

• Detection method: Use sensitive detection methods like enhanced chemiluminescence for Western blot detection of co-immunoprecipitated proteins.

These optimized protocols have successfully demonstrated RNF6 interactions with partners like USP7, USP9x, MST1, and others across different cancer types .

What experimental approaches should be used to distinguish between RNF6's E3 ligase and transcriptional functions?

To investigate RNF6's dual functions as both an E3 ligase and transcription factor, implement these specialized approaches:

• Subcellular fractionation: Separate nuclear and cytoplasmic fractions to analyze RNF6 distribution. Immunofluorescence staining has revealed RNF6 localization in both compartments, with transcriptional activity primarily in the nucleus .

• Domain-specific mutants: Generate constructs with mutations in:

  • RING domain (C-terminal) to disrupt E3 ligase activity while preserving DNA binding

  • DNA-binding domains to eliminate transcriptional activity while maintaining E3 ligase function

• Function-specific assays:

  • Transcriptional activity: ChIP-seq, RNA-seq of target genes (SF3B2, BIRC5, MCM7 in colorectal cancer; CCNA1/CREBBP in gastric cancer), reporter assays with promoter constructs, and EMSA

  • E3 ligase activity: Ubiquitination assays with potential substrates (MST1 in breast cancer), protein stability analyses, and in vitro ubiquitination

• Differential inhibition: Use proteasome inhibitors (MG132) which should block effects dependent on protein degradation but not transcriptional effects.

• Genetic rescue experiments: Perform rescue experiments with wild-type RNF6 versus domain-specific mutants to attribute phenotypes to specific functions.

This systematic approach has successfully distinguished RNF6's dual roles across multiple cancer types, revealing context-specific functions that may contribute to its oncogenic potential .

How do I design experiments to study RNF6's role in cancer progression using antibodies?

To comprehensively investigate RNF6's role in cancer progression, design experiments that address expression, function, and mechanism:

• Expression profiling:

  • Analyze RNF6 expression across cancer stages using IHC with tissue microarrays

  • Correlate expression with patient outcomes using Kaplan-Meier survival analysis

  • Studies have shown RNF6 upregulation correlates with poor prognosis in breast cancer

• Functional studies:

  • Generate stable RNF6 knockdown and overexpression cell lines using lentiviral vectors

  • For knockdown, validated siRNA sequences include: sense 5′-CCCGAACAAUGGAGAGUUUTT-3′ and antisense 5′-AAACUCUCCAUUGUUCGGGTT-3′

  • Assess effects on:

    • Proliferation (cell counting, MTT assays)

    • Migration (scratch/wound healing assays)

    • Invasion (transwell assays)

    • Apoptosis (TUNEL staining, PARP/Caspase-3 cleavage)

• Mechanistic investigations:

  • Identify downstream effectors using:

    • ChIP-seq for transcriptional targets

    • Co-IP for interaction partners

    • Western blot for pathway activation

  • Validate mechanisms with rescue experiments (e.g., SF3B2 re-expression rescues effects of RNF6 knockout)

• In vivo validation:

  • Xenograft models with RNF6-modulated cells

  • Transgenic mouse models (e.g., Rnf6 transgenic mice)

  • Combine with therapeutic interventions to assess clinical potential

This comprehensive experimental design has revealed RNF6's promotion of various cancer types through mechanisms including SF3B2 upregulation in colorectal cancer , YAP pathway regulation in breast cancer , and CCNA1/CREBBP regulation in gastric cancer .

Why might I observe multiple bands when using RNF6 antibody in Western blot?

Multiple bands when using RNF6 antibody in Western blot may occur for several reasons:

• Post-translational modifications: The most common cause is ubiquitination of RNF6 itself. Studies have shown that proteasome inhibitors like MG132 cause accumulation of polyubiquitinated RNF6, appearing as higher molecular weight bands or smears . The primary band should appear at 78 kDa, with ubiquitinated forms at higher molecular weights.

• Proteolytic degradation: RNF6 may undergo partial degradation during sample preparation. To prevent this, use fresh samples, keep them cold throughout processing, and include protease inhibitor cocktails in lysis buffers.

• Alternative splicing: While not specifically documented for RNF6 in the search results, alternative splicing can produce protein variants of different sizes. Confirm with RT-PCR whether your experimental system expresses RNF6 splice variants.

• Non-specific binding: Some antibodies may cross-react with other RING finger proteins due to structural similarities. Validate specificity using RNF6 knockdown samples as negative controls and RNF6-overexpressing samples as positive controls .

• Antibody quality: Batch-to-batch variation or antibody degradation may cause non-specific binding. Always use antibodies within their recommended shelf-life and validate each new lot.

To determine which bands represent true RNF6, compare results with multiple antibodies targeting different epitopes and include appropriate knockdown controls.

How can I optimize immunohistochemistry protocols for RNF6 detection in tissue samples?

For optimal RNF6 detection in immunohistochemistry, follow these methodological guidelines:

• Fixation and processing:

  • Fix tissue specimens with 10% neutral formaldehyde for 4-6 hours at room temperature

  • Process with standard dehydration, waxing, and sectioning at 4-6 μm thickness

  • Bake sections at 60°C overnight

• Antigen retrieval:

  • Perform microwave antigen retrieval in 0.01 mol/l citrate buffer (pH 6.0) for 10 minutes

  • This step is critical as formalin fixation can mask RNF6 epitopes

• Background reduction:

  • Block endogenous peroxidase with 0.3% hydrogen peroxide in methanol for 15 minutes

  • Block non-specific binding with 2% BSA for 1 hour at room temperature

  • Use species-appropriate serum to minimize background reactivity

• Antibody optimization:

  • Start with manufacturer's recommended dilutions and optimize as needed

  • Incubate primary antibody overnight at 4°C for optimal binding

  • Use appropriate detection systems (HRP-polymer or avidin-biotin complexes)

• Controls:

  • Include positive controls (tissues known to express RNF6, such as testicular tissue)

  • Include negative controls (primary antibody omitted or isotype control antibody)

  • Include RNF6-low tissues as additional controls

• Counterstaining and evaluation:

  • Use hematoxylin for nuclear counterstaining

  • Evaluate both staining intensity and percentage of positive cells

  • Consider scoring systems used in published studies for consistency

These optimized protocols have successfully demonstrated RNF6 expression in various cancer tissues, revealing its potential as a prognostic biomarker .

How can I distinguish between RNF6's nuclear and cytoplasmic functions when interpreting experimental data?

Differentiating between RNF6's nuclear and cytoplasmic functions requires specialized experimental approaches and careful data interpretation:

• Subcellular fractionation analysis:

  • Perform biochemical fractionation to separate nuclear and cytoplasmic components

  • Verify fraction purity using compartment-specific markers (e.g., GAPDH for cytoplasm, Lamin B for nucleus)

  • Quantify relative RNF6 distribution between compartments under different conditions

• Immunofluorescence imaging:

  • Perform high-resolution confocal microscopy to visualize RNF6 localization

  • Studies have shown RNF6 localization in both nucleus and cytoplasm of colorectal cancer cells

  • Quantify nuclear/cytoplasmic signal ratios across cell populations

• Domain-specific mutant analysis:

  • Generate RNF6 constructs with mutated nuclear localization signals (NLS) or nuclear export signals (NES)

  • Compare phenotypes of cells expressing these mutants versus wild-type RNF6

• Compartment-specific interaction studies:

  • Identify interaction partners unique to each compartment (e.g., SF3B2 in nucleus , MST1 in cytoplasm )

  • Use proximity ligation assays to visualize where these interactions occur within cells

• Function-specific readouts:

  • Nuclear function: Analyze transcriptional targets (SF3B2, CCNA1) via mRNA expression

  • Cytoplasmic function: Examine ubiquitination and degradation of cytoplasmic substrates

• Temporal dynamics analysis:

  • Track RNF6 localization changes in response to cellular stress, cell cycle stages, or drug treatments

  • Correlate localization shifts with changes in nuclear vs. cytoplasmic functions

This approach helps untangle the complex dual roles of RNF6 and interpret seemingly contradictory experimental results across different cancer models.

What statistical approaches are appropriate for analyzing RNF6 expression in relation to clinical outcomes?

For robust statistical analysis of RNF6 in clinical contexts, employ these methodological approaches:

• Expression comparison between tissue types:

  • Use paired t-tests for matched normal/tumor tissue samples

  • Use unpaired Student's t-test to evaluate significance between two independent groups

  • Apply one-way ANOVA followed by Tukey's test for multiple group comparisons

• Correlation with clinicopathological features:

  • Use Fisher's exact test or Chi-square test to analyze the association between RNF6 expression and categorical clinicopathological characteristics (tumor stage, grade, etc.)

  • Use Spearman's or Pearson's correlation for continuous variables

• Survival analysis:

  • Employ Kaplan-Meier method to examine the correlation between RNF6 expression and patient survival

  • Use log-rank tests to determine statistical significance between high and low expression groups

  • Define cutoff values for high vs. low expression based on:

    • Median value

    • ROC curve analysis

    • Established scoring systems from literature

• Multivariate analysis:

  • Perform Cox proportional hazards regression to determine if RNF6 is an independent prognostic factor

  • Include established prognostic variables (stage, grade, age) in models

  • Studies have shown RNF6 overexpression to be an independent predictor for poor outcome in breast cancer patients

• Multiple testing correction:

  • Apply Benjamini-Hochberg or Bonferroni correction when performing multiple comparisons

  • Set significance threshold at P<0.05 after correction

• Software and presentation:

  • Use established statistical software like SPSS for analysis

  • Present graphs using GraphPad Prism

  • Report data as mean ± SD from at least three independent experiments

These rigorous statistical approaches have successfully demonstrated RNF6's clinical significance across multiple cancer types.

How can I interpret conflicting results across different model systems in RNF6 research?

When encountering conflicting RNF6 results across experimental models, consider these systematic interpretation approaches:

• Baseline expression analysis:

  • Compare endogenous RNF6 expression levels across models

  • Studies show variability: DLD1 and SW480 cells express high RNF6 levels while HCT116 and HT29 show lower expression

  • High baseline expression may make cells less responsive to further overexpression

• Context-dependent interaction networks:

  • Examine model-specific RNF6 interaction partners:

    • SF3B2 in colorectal cancer

    • MST1 in breast cancer

    • CCNA1/CREBBP in gastric cancer

  • These different interaction networks may explain tissue-specific functions

• Genetic background differences:

  • Analyze mutation profiles of RNF6 regulators (e.g., PBX1 ) and targets across models

  • Examine expression of deubiquitinating enzymes like USP7 that regulate RNF6 stability

• Methodological differences:

  • Compare knockdown/overexpression efficiencies across studies

  • Evaluate timing differences (acute vs. stable modulation)

  • Assess assay sensitivity and specificity

• Functional redundancy:

  • Consider compensatory mechanisms by other RING finger proteins

  • Evaluate adaptive responses to long-term RNF6 modulation

• Integrated data analysis:

  • Conduct meta-analysis across multiple studies

  • Focus on consistent patterns despite model-specific variations

  • Create pathway models that account for context-dependent functions

This systematic approach transforms seemingly conflicting results into a more comprehensive understanding of RNF6's context-dependent functions across cancer types.

How can I design ChIP-seq experiments to identify novel RNF6 transcriptional targets?

To design robust ChIP-seq experiments for discovering RNF6 transcriptional targets:

• Antibody validation:

  • Verify antibody specificity for RNF6 ChIP using known targets (SF3B2 promoter)

  • Test multiple antibodies to select the one with highest specificity and efficiency

  • Include RNF6 knockdown/knockout controls

• Experimental design:

  • Use at least 3-4 biological replicates per condition

  • Include input controls and IgG ChIP negative controls

  • Consider using spike-in normalization for quantitative comparisons

• Protocol optimization:

  • Fix cells with 1% formaldehyde for cross-linking at room temperature

  • Quench with glycine to stop cross-linking

  • Optimize sonication conditions to achieve 200-500bp fragments

  • Immunoprecipitate protein-DNA complexes with anti-RNF6 antibody

  • Include protein A/G magnetic beads for capture

• Sequencing considerations:

  • Aim for 20-30 million uniquely mapped reads per sample

  • Use paired-end sequencing for better peak resolution

  • Include appropriate sequencing controls

• Integrative analysis:

  • Combine with RNA-seq in RNF6 knockdown/overexpression cells

  • Identify direct targets by integrating binding sites with differential expression data

  • Previous studies identified candidates by restricting to RNF6 binding sites within 5000bp of transcription start sites

• Validation approaches:

  • Confirm binding with ChIP-qPCR on selected targets

  • Verify motifs with EMSA (previous studies identified "TTTCCT" as an RNF6 binding motif)

  • Validate functional relevance with reporter assays and gene expression studies

This comprehensive approach has successfully identified important RNF6 transcriptional targets in colorectal cancer (SF3B2) and gastric cancer (CCNA1/CREBBP) , revealing its context-specific regulatory networks.

What are the key considerations for developing RNF6 knockout or knockdown models?

When developing RNF6 loss-of-function models, consider these methodological aspects:

• Selection of knockdown/knockout strategy:

  • siRNA: Provides rapid, transient knockdown. Validated sequences include:

    • 5′-CCCGAACAAUGGAGAGUUUTT-3′ (sense) and 5′-AAACUCUCCAUUGUUCGGGTT-3′ (antisense)

    • 5′-TCAGGCAATTACCTTGCAT-3′ and 5′-ATAACAGTTCCTCTTCGTA-3′

  • shRNA: For stable knockdown delivered via lentiviral vectors

  • CRISPR-Cas9: For complete knockout, target exons encoding functional domains

• Validation of gene modification:

  • Genomic validation: PCR and sequencing of targeted region

  • Transcript analysis: RT-PCR and qRT-PCR with primers:

    • Forward 5′-CATCAGTGGCTCTTCGGTCA-3′

    • Reverse 5′-ATGCTCATAGTGCCTGGTGG-3′

  • Protein validation: Western blot analysis with validated antibodies

• Controls:

  • Negative controls: Non-targeting siRNA/shRNA or scrambled guide RNA

  • Rescue controls: Re-expression of RNF6 to confirm specificity of observed phenotypes

  • Domain mutants: Expression of specific domain mutants for functional dissection

• Phenotypic characterization:

  • Proliferation: Cell counting, MTT assays

  • Migration/Invasion: Scratch assays, transwell assays

  • Apoptosis: TUNEL staining, PARP/Caspase-3 cleavage analysis

  • EMT markers: E-cadherin and N-cadherin expression analysis

• Target validation:

  • Assess expression of known RNF6 targets (SF3B2, MST1, CCNA1)

  • Perform ubiquitination assays on known substrates

  • Evaluate transcriptional activity at target promoters

• In vivo validation:

  • Xenograft models with knockdown/knockout cells

  • Comparison of tumor growth, invasion, and metastasis

Studies using these approaches have demonstrated that RNF6 depletion suppresses cancer cell proliferation and invasion across multiple cancer types, confirming its oncogenic roles .

How should I design in vivo experiments to validate RNF6's role in tumor progression?

For robust in vivo validation of RNF6's role in tumorigenesis, implement these design elements:

• Model selection:

  • Xenograft models: Inject RNF6-modulated cancer cells subcutaneously or orthotopically

    • "K562 cells were injected subcutaneously into the right dorsal flank of 6-week-old male Balb/c nude mice"

    • Use 1-3×10^6 cells per injection site

  • Genetic models: Generate tissue-specific RNF6 transgenic or knockout mice

    • "Rnf6 tg mice (pCAG-loxp-stop-loxp-Rosa26-RNF6) were generated"

    • Cross with tissue-specific Cre lines: "Rosa26-RNF6 mice were crossed to CDX2-CreER T2"

• Experimental design:

  • Group size: Use power analysis to determine minimum sample size (typically 8-10 animals per group)

  • Randomization: Randomly assign animals to experimental groups when tumors reach ~50mm³

  • Blinding: Ensure investigators measuring outcomes are blinded to group assignment

  • Controls: Include appropriate vector controls, wild-type littermates, and sham-treated groups

• Tumor induction approaches:

  • Cell line xenografts: Monitor tumor growth from injected RNF6-modulated cells

  • Chemical induction in genetic models: "After tamoxifen at 2 months of age to activate RNF6 tg expression, mice were treated with AOM to induce colorectal tumorigenesis"

• Measurement parameters:

  • Tumor volume: Measure every 2-3 days using calipers; calculate volume using formula: length×(width)²/2

  • Tumor multiplicity: Count number of tumors per animal

  • Tumor burden: Measure total tumor volume per animal

  • Survival analysis: Monitor for humane endpoints and construct Kaplan-Meier curves

• Histological and molecular analyses:

  • H&E staining: Assess histopathology and dysplasia grade

  • Immunohistochemistry: Evaluate proliferation (Ki-67, PCNA) , apoptosis (TUNEL, cleaved caspase-3)

  • Molecular markers: Analyze RNF6 target expression in tumor tissues

• Therapeutic intervention studies:

  • Test RNF6-targeted approaches or combinations

  • "The combination of 5-fluorouracil (5-FU) plus pladienolide B exerted synergistic effects in CRC with high RNF6 expression, leading to tumor regression in xenograft models"

These comprehensive in vivo approaches have successfully demonstrated RNF6's promotion of tumor growth, providing strong preclinical evidence for its oncogenic functions .

How can I design experiments to study the regulation of RNF6 expression and stability?

To comprehensively investigate RNF6 regulation at transcriptional and post-translational levels:

• Transcriptional regulation analysis:

  • Promoter mapping: Create a series of truncated RNF6 regulatory region constructs in luciferase reporter systems

  • Key regulatory elements: Studies identified the region between −144 and −99 upstream of the RNF6 transcription start site as critical, containing a PBX1 recognition element (PRE)

  • Transcription factor identification: "PBX1 modulated RNF6 expression by binding to the specific PRE. When PRE was mutated, RNF6 transcription was completely abolished"

  • Co-regulator analysis: "PBX1 collaborated with PREP1 but not MEIS1 to modulate RNF6 expression"

• Post-translational regulation studies:

  • Protein stability assessment: Treat cells with cycloheximide to block protein synthesis and monitor RNF6 degradation kinetics

  • Degradation pathway identification: Compare effects of proteasome inhibitors (MG132, bortezomib) versus lysosomal inhibitors (chloroquine)

    • "RNF6 protein was markedly increased by proteasome inhibitors but not by lysosomal inhibitor chloroquine"

  • Ubiquitination analysis: Immunoprecipitate RNF6 and probe for ubiquitin by Western blot

    • "MG132 also strikingly accumulated RNF6 polyubiquitination in a concentration-dependent manner"

• Auto-ubiquitination mechanism:

  • In vitro ubiquitination assays: Use purified components to demonstrate direct RNF6 auto-ubiquitination

  • RING domain mutants: Create mutants lacking E3 ligase activity to abolish auto-ubiquitination

• Deubiquitinating enzyme studies:

  • DUB identification: "Both USP7 and USP9x proteins were found in the RNF6 immunoprecipitates"