Recombinant Mouse RING finger protein 183 (Rnf183)

What is RNF183 and what is its basic structure?

RNF183 (Ring finger protein 183) belongs to the family of RING finger domain proteins. The full-length mouse RNF183 protein consists of 190 amino acids with a characteristic RING-finger domain in the N-terminal region . The amino acid sequence of mouse RNF183 begins with MSEPQGQELRAECPVCWNPFNNTFHTPKVLDCCHSFCVECLAHLSLVTPARRRLLCPLCR and continues through a transmembrane region and cytoplasmic tail . The RING finger domain (amino acids 1-60) is critical for its E3 ubiquitin ligase activity, as demonstrated by functional studies where deletion of this domain (RNF183Δt) substantially impaired its biological activities .

How should recombinant mouse RNF183 be stored and handled in laboratory settings?

For optimal stability and activity of recombinant mouse RNF183:

• Store the protein at -20°C for regular use, or at -80°C for extended storage periods

• The protein is typically supplied in a Tris-based buffer with 50% glycerol optimized for stability

• Avoid repeated freeze-thaw cycles as they can compromise protein integrity

• Working aliquots can be stored at 4°C for up to one week

• The standard concentration is typically 50 μg per vial, though other quantities may be available for specific experimental needs

What experimental models are most appropriate for studying RNF183 function?

Based on published research protocols:

• Cell culture models: ERα-positive Ishikawa cells have been effectively used to study RNF183's interaction with estrogen signaling pathways

• Genetic manipulation: RNA interference techniques using small interfering RNAs have successfully demonstrated RNF183's regulatory effects on target genes

• Reporter assays: ERα luciferase reporter systems have been instrumental in quantifying the functional impact of RNF183 on transcriptional regulation

• Xenograft models: Animal models have been used to evaluate RNF183's role in tumor growth and metastasis in vivo

How does RNF183 expression correlate with cancer prognosis across different malignancies?

RNF183 exhibits distinct prognostic patterns across different cancer types:

Colorectal Cancer (CRC):

This paradoxical role across different cancer types suggests context-dependent functions that warrant careful consideration when developing therapeutic strategies.

What mechanisms underlie RNF183's interaction with estrogen receptor signaling?

RNF183 exhibits significant regulatory effects on estrogen receptor signaling through several mechanisms:

• Transcriptional regulation:

  • RNF183 silencing noticeably reduces ESR1 mRNA levels, indicating regulation at the transcriptional level

  • Bioinformatics analysis via LinkedOmics revealed that RNF183 expression is markedly positively correlated with ESR1

• Protein-level interaction:

  • RNF183 depletion diminishes ERα protein levels under both basal and 17β-estradiol (E2) stimulated conditions

  • RNF183 overexpression enhances ERα reporter gene activity regardless of E2 stimulation

• E3 ubiquitin ligase activity involvement:

  • The zinc finger domain (amino acids 1-60) is essential for RNF183's effect on ERα activity

  • Truncated RNF183 lacking this domain (RNF183Δt) largely cancels the protein's ability to stimulate ERα reporter gene activity

• Downstream target regulation:

  • RNF183 depletion reduces expression of endogenous ERα target genes including TFF1, PGR, FOXA1, and XBP1 in an E2-dependent manner

  • These target genes show strong positive correlation with RNF183 in database analysis

How does RNF183 relate to tumor microenvironment and immune infiltration?

RNF183's relationship with the tumor microenvironment presents complex interactions:

• RNF183 expression negatively correlates with tumor purity in endometrial cancer (r = −0.063, p = 2.85E–01), though this correlation is relatively weak

• Negative correlations exist between RNF183 expression and infiltrating levels of:

  • CD4+ T cells (r = −0.064, p = 2.74E–01)

  • Neutrophils (r = −0.126, p = 3.17E–02)

  • Dendritic cells (r = −0.042, p = 4.78E–01)

• RNF183 copy number variations (CNVs) significantly correlate with infiltrating levels of:

  • CD8+ T cells

  • Macrophages

  • Dendritic cells

These findings suggest RNF183 may influence immune surveillance mechanisms within the tumor microenvironment, potentially affecting therapeutic responses to immunomodulatory treatments.

What is the co-expression profile of RNF183 and how can this inform research approaches?

Comprehensive co-expression analysis provides valuable insights for experimental design:

• In the UCEC cohort analysis, 8,777 genes showed significant positive correlations with RNF183 while 11,121 genes showed significant negative associations (FDR < 0.01)

• Notable positive correlations include estrogen-responsive genes and pathways:

  • ESR1 (estrogen receptor alpha)

  • TFF1, PGR, FOXA1, and XBP1 (established ERα target genes)

• Protein-protein interaction (PPI) network analysis through GeneMANIA has identified functional associations that can guide research into RNF183's biological mechanisms

Researchers should consider these co-expression patterns when:

• Designing pathway analysis experiments

• Selecting appropriate controls and comparative genes

• Interpreting experimental results within the broader network context

• Developing hypotheses about potential compensatory mechanisms

What methodological approaches are recommended for assessing RNF183's role in cancer progression?

For comprehensive evaluation of RNF183's role in cancer:

Gene expression manipulation techniques:

• RNA interference using targeted siRNAs for knockdown studies

• Overexpression vectors with full-length or truncated RNF183 (RNF183Δt) to assess domain-specific functions

Functional assays:

• Luciferase reporter assays to quantify effects on transcriptional regulation

• RT-qPCR for measuring effects on target gene expression

• Western blotting for protein level assessment under various conditions (e.g., with/without E2 stimulation)

Bioinformatic approaches:

• Kaplan-Meier survival analysis based on "best cut-off" expression values

• TIMER database analysis for immune infiltration correlations

• LinkedOmics for co-expression network identification

Clinical correlation studies:

• Stratification of patients by tumor subtype, grade, and genetic characteristics (e.g., TP53 mutation status)

• Combined analysis of RNF183 with established prognostic markers

How can conflicting data on RNF183's role in different cancer types be reconciled?

The contrasting roles of RNF183 in endometrial cancer (favorable prognosis) versus colorectal cancer (poor prognosis) necessitate careful methodological approaches:

• Tissue-specific context evaluation:

  • Always analyze RNF183 expression in relation to tissue-specific molecular subtypes

  • Consider hormonal context, particularly in hormone-responsive tissues where RNF183 interacts with estrogen signaling

• Integration of multiple endpoints:

  • Combine survival data (OS, PFS) with mechanistic endpoints

  • Assess both tumor cell-intrinsic effects and influences on the tumor microenvironment

• Genetic background consideration:

  • Stratify analyses by key genetic alterations (e.g., TP53 mutation status)

  • Perform pathway enrichment analysis to identify context-dependent signaling networks

• Standardized expression thresholds:

  • Apply consistent "best cut-off" values when classifying high versus low expression

  • Validate findings across multiple datasets when available

What are the critical quality control considerations for RNF183 experimental studies?

To ensure reproducibility and validity:

• Protein quality assessment:

  • Verify recombinant protein activity before experimental use

  • Consider tag position and type, as these may affect protein function

  • Use appropriate storage conditions to maintain stability (store at -20°C to -80°C)

• Expression validation:

  • Confirm RNF183 knockdown or overexpression efficiency at both mRNA and protein levels

  • Include appropriate controls when using truncated variants (e.g., RNF183Δt)

• Technical considerations for E3 ligase studies:

  • Include domain-specific mutants to distinguish between scaffold and enzymatic functions

  • Control for potential off-target effects when manipulating expression levels

• Translational relevance:

  • Correlate in vitro findings with human sample data whenever possible

  • Consider species differences when translating findings from mouse models