RNF13 Antibody

What is RNF13 and why is it significant in research?

RNF13 (Ring Finger Protein 13) is a 381-amino acid protein with a molecular mass of 42.8 kDa that functions as an E3 ubiquitin-protein ligase. Its significance stems from its diverse subcellular localization (nucleus, lysosomes, and endoplasmic reticulum) and involvement in multiple cellular processes . Research has revealed RNF13's critical roles in mediating endoplasmic reticulum stress-induced apoptosis and protecting against nonalcoholic steatohepatitis, making it a valuable target for both basic and translational research .

The protein contains essential RING and transmembrane domains that are required for its function in signaling pathways, particularly in the IRE1α-TRAF2-JNK axis that regulates cellular stress responses . RNF13 has been associated with developmental and epileptic encephalopathy (EIEE73/DEE73), highlighting its importance in neurological research contexts .

Which applications are most suitable for RNF13 antibody detection?

The optimal applications for RNF13 antibody detection depend on your specific research objectives. Based on validated methodologies, Western blotting (WB), enzyme-linked immunosorbent assay (ELISA), and immunohistochemistry (IHC) represent the most widely employed and reliable applications .

For subcellular localization studies, immunofluorescence (IF) and immunocytochemistry (ICC) approaches using antibodies targeting specific domains of RNF13 are recommended . Flow cytometry (FACS) has also been validated for detecting RNF13 in cell populations, particularly with goat polyclonal antibodies targeting the amino acid region 51-150 .

The following table summarizes recommended applications based on antibody type and target species:

How can I validate the specificity of my RNF13 antibody?

Validating antibody specificity is crucial for reliable experimental outcomes. For RNF13 antibodies, implement a multi-tiered validation approach:

• Genetic validation: Compare antibody detection between wild-type and RNF13 knockdown/knockout cells or tissues. RNF13 siRNA-treated cells have demonstrated significantly reduced signal in validated antibody tests .

• Peptide competition assay: Pre-incubate the antibody with the immunizing peptide before application to your sample. Specific binding should be blocked, resulting in signal reduction.

• Cross-validation with multiple antibodies: Use antibodies targeting different epitopes of RNF13. Concordant results across different antibodies strengthen validity.

• Molecular weight verification: In Western blots, confirm the detected band corresponds to RNF13's expected molecular weight (42.8 kDa for canonical isoform), while accounting for post-translational modifications that may alter migration patterns .

• Subcellular localization confirmation: Verify that immunofluorescence signals align with RNF13's known localizations in the nucleus, endoplasmic reticulum, and lysosomes .

• Positive control tissues: Include tissues with known RNF13 expression, such as liver samples from NASH patients where RNF13 is upregulated .

How should I design experiments to investigate RNF13 function in the IRE1α-JNK apoptosis pathway?

When investigating RNF13's role in the endoplasmic reticulum stress-induced IRE1α-JNK apoptosis pathway, consider this comprehensive experimental design:

• Cellular model selection: SHSY-5Y neuroblastoma cells have been validated for studying RNF13-mediated apoptosis through the IRE1α-TRAF2-JNK pathway. These cells show differential sensitivity to staurosporine-induced apoptosis based on RNF13 status .

• Manipulate RNF13 expression:

  • Knockdown using validated siRNAs against RNF13

  • Overexpression constructs (ensure preservation of both the RING and transmembrane domains)

  • Domain-specific mutants to dissect functional regions

• Stress induction protocols:

  • Pharmacological ER stress inducers (thapsigargin, tunicamycin)

  • Staurosporine treatment (0.5-1 μM for 24-48 hours)

  • UV exposure (wavelength and intensity optimization required)

• Molecular interaction analysis:

  • Co-immunoprecipitation to confirm RNF13-IRE1α interaction

  • Proximity ligation assays for in situ interaction detection

  • Investigate the requirement for the intact RING domain

• Signaling cascade assessment:

  • Monitor IRE1α activation status

  • Measure JNK phosphorylation levels

  • Assess TRAF2 recruitment

  • Quantify downstream apoptotic markers (cleaved caspase-3)

• Domain functionality analysis: Create domain-specific mutants to determine the roles of RING and transmembrane domains in mediating interactions and apoptotic signaling .

What are the optimal methods for detecting RNF13 in liver tissues to study its role in NAFLD/NASH?

To effectively investigate RNF13's protective role against nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH), apply these methodological approaches:

• Tissue preparation: For human and mouse liver samples, optimal fixation with 4% paraformaldehyde followed by paraffin embedding preserves RNF13 epitopes. For frozen sections, rapid freezing in OCT compound prevents degradation .

• Antigen retrieval optimization: Heat-induced epitope retrieval in citrate buffer (pH 6.0) has been validated for RNF13 detection in liver sections. Optimize retrieval conditions to balance signal intensity with tissue morphology preservation .

• Antibody selection strategy:

  • For human samples: Rabbit polyclonal antibodies against AA 204-381 at 1:40-1:200 dilution

  • For multi-species studies: Antibodies with broad reactivity across human, mouse, and rat

  • For co-localization studies: Antibodies compatible with multiplexed immunofluorescence

• NAFLD-specific markers: Pair RNF13 detection with markers for:

  • Lipid accumulation (Oil Red O staining)

  • Inflammatory signaling (TNF-α, IL-6)

  • STING pathway components (to validate the RNF13-STING regulatory axis)

• Quantification approaches: Implement computational image analysis using standardized scoring systems for:

  • RNF13 expression levels (intensity measurements)

  • Subcellular distribution patterns

  • Co-localization with STING or other pathway components

• Complementary methods: Supplement immunohistochemistry with Western blot analysis of tissue lysates and RT-qPCR to correlate protein levels with transcriptional regulation during disease progression .

How can I effectively use RNF13 antibodies in co-immunoprecipitation experiments?

Co-immunoprecipitation (Co-IP) is essential for studying RNF13's protein-protein interactions, particularly with IRE1α in the ER stress response pathway. Follow these optimized protocols:

• Lysis buffer optimization: Use RIPA buffer supplemented with deubiquitinase inhibitors (N-ethylmaleimide) and protease inhibitor cocktail to preserve RNF13's post-translational modifications and interaction states .

• Antibody selection criteria:

  • Choose antibodies recognizing native conformations

  • Antibodies targeting regions outside interaction interfaces

  • Consider using epitope-tagged RNF13 constructs and tag-specific antibodies for difficult interactions

• Pre-clearing strategy: Pre-clear lysates with protein A/G beads to minimize non-specific binding, particularly important for tissues with high lipid content such as steatotic liver samples .

• Crosslinking considerations: For transient or weak interactions, implement reversible crosslinking with DSP (dithiobis(succinimidyl propionate)) before cell lysis .

• Validation controls:

  • Input controls (5-10% of lysate)

  • IgG negative controls

  • Reciprocal Co-IPs (using antibodies against interaction partners)

  • Domain mutant controls (particularly RING domain mutants for IRE1α interactions)

• Detection strategy: For Western blot detection of co-immunoprecipitated proteins, use antibodies from different host species than the immunoprecipitating antibody to avoid detection of immunoglobulin heavy chains .

What approaches should I use to study the E3 ubiquitin ligase activity of RNF13?

Investigating RNF13's E3 ubiquitin ligase function requires specialized techniques focusing on its enzymatic activity and substrate specificity:

• In vitro ubiquitination assays: Reconstitute the ubiquitination reaction using:

  • Purified recombinant RNF13 (focus on the RING domain)

  • E1 and E2 enzymes (test multiple E2s to identify optimal partners)

  • Ubiquitin (consider tagged versions for easier detection)

  • ATP regeneration system

  • Candidate substrates (STING has been validated)

• Cell-based ubiquitination analysis:

  • Co-express RNF13 with tagged ubiquitin

  • Immunoprecipitate candidate substrates under denaturing conditions

  • Analyze ubiquitination patterns by Western blot

  • Compare wild-type RNF13 with RING domain mutants as negative controls

• Degradation kinetics assessment:

  • Cycloheximide chase assays to monitor substrate half-life

  • Compare substrate stability in control versus RNF13-overexpressing or RNF13-depleted cells

  • Confirm proteasome dependency using MG132 treatment

• Binding site mapping:

  • Create domain truncation mutants of both RNF13 and substrates

  • Perform systematic interaction analyses to identify minimal binding regions

  • Validate critical residues through point mutations

• Post-translational modification sites:

  • Mass spectrometry analysis to identify ubiquitination sites on substrates

  • Mutate identified lysine residues to arginine to confirm functional significance

  • Distinguish between different ubiquitin chain topologies (K48 vs. K63-linked chains)

Why might I observe multiple bands when performing Western blot for RNF13?

Multiple bands in RNF13 Western blots can result from several biological and technical factors:

• Isoform detection: Up to two different isoforms of RNF13 have been reported in the literature. When using antibodies that recognize epitopes common to both isoforms, multiple bands may represent these distinct protein variants .

• Post-translational modifications: RNF13 undergoes various modifications including ubiquitination and glycosylation. These modifications alter the molecular weight and can produce additional bands. Deglycosylation experiments using PNGase F can help confirm glycosylation-related bands .

• Proteolytic processing: Evidence suggests RNF13 may undergo regulated intramembrane proteolysis, potentially generating fragments of different sizes. This is particularly relevant when studying its transmembrane domain functions .

• Cross-reactivity considerations: Some antibodies may cross-react with related RING finger proteins. Validate specificity using RNF13 knockdown controls to identify which bands represent specific RNF13 detection .

• Degradation products: RNF13, as an E3 ligase, can undergo auto-ubiquitination and subsequent degradation. Proteasome inhibitor treatment (MG132) before sample collection can help determine if lower molecular weight bands represent degradation products .

• Technical optimizations:

  • Adjust sample preparation (add fresh protease inhibitors)

  • Optimize gel percentage for better resolution

  • Test different antibody concentrations and incubation conditions

How can I accurately interpret RNF13 expression patterns in diseased versus normal tissues?

Interpreting RNF13 expression patterns in pathological contexts requires careful consideration of multiple factors:

• Establish baseline expression: RNF13 is widely expressed across many tissue types. Determine normal expression levels and patterns in your tissue of interest before comparing to disease states .

• Consider cell type heterogeneity: In complex tissues like liver, different cell populations (hepatocytes, Kupffer cells, stellate cells) may express RNF13 at different levels. Single-cell approaches or cell type-specific markers can resolve this complexity .

• Distinguish expression changes from relocalization: RNF13's subcellular distribution may change under stress conditions without altering total protein levels. Compare total protein by Western blot with localization by immunofluorescence .

• Contextualize with pathway markers: In NAFLD/NASH studies, correlate RNF13 expression with:

  • STING pathway activation markers

  • Inflammatory cytokine levels

  • Lipid accumulation metrics

  • Fibrosis markers

• Quantification approaches:

  • Use digital image analysis with standardized thresholds

  • Implement histological scoring systems

  • Always blind observers to sample identity

  • Include technical and biological replicates

• Validate at multiple levels: Confirm protein expression changes with mRNA quantification and consider epigenetic regulation mechanisms for full mechanistic understanding .

What are the potential pitfalls in designing RNF13 knockout and overexpression models?

Creating reliable RNF13 genetic models requires careful consideration of these potential challenges:

• Knockout design considerations:

  • Complete versus conditional knockouts: RNF13 plays roles in multiple tissues; tissue-specific conditional knockouts may be necessary to avoid confounding effects

  • Compensatory upregulation: Other RING finger proteins may compensate for RNF13 loss, potentially masking phenotypes

  • Knockout verification: Confirm at both genomic DNA, mRNA and protein levels using validated antibodies

• Overexpression system selection:

  • Tagged versus untagged constructs: Tags may interfere with RNF13 function, particularly the RING and transmembrane domains

  • Inducible versus constitutive: Constitutive overexpression may select for compensatory adaptations

  • Expression level control: Physiologically relevant versus supraphysiological levels

• Domain integrity preservation:

  • Both RING and transmembrane domains are essential for RNF13 function

  • Mutations intended to disrupt one function may have unintended effects on protein folding or stability

  • Include domain-specific mutants as controls in functional studies

• Subcellular localization verification:

  • Confirm proper localization to the ER, nucleus and lysosomes

  • Mislocalized protein may create gain-of-function or dominant-negative effects

  • Use compartment-specific markers to validate distribution patterns

• Phenotypic interpretation caveats:

  • In NAFLD models, dietary conditions strongly influence outcomes; standardize feeding protocols

  • For apoptosis studies, use multiple cell death markers to distinguish apoptosis from other death modes

  • Age and sex differences may influence RNF13-dependent phenotypes

How can RNF13 antibodies be used to explore its role in developmental and epileptic encephalopathy?

The association between RNF13 and developmental and epileptic encephalopathy (DEE73/EIEE73) opens important avenues for neurodevelopmental research :

• Tissue-specific expression mapping: Use validated RNF13 antibodies for comprehensive immunohistochemical analysis of:

  • Developmental brain tissue sections (human and model organisms)

  • Region-specific expression patterns during critical neurodevelopmental windows

  • Co-localization with neuronal, glial, and progenitor cell markers

• Patient-derived sample analysis:

  • Compare RNF13 expression levels and patterns in available patient samples

  • Correlate expression changes with specific mutations and clinical phenotypes

  • Implement phospho-specific antibodies to assess activation state in patient tissues

• Seizure model applications:

  • Monitor dynamic changes in RNF13 expression before, during, and after seizure activity

  • Examine subcellular redistribution in response to electrical activity

  • Assess correlation between RNF13 levels and seizure susceptibility

• Mechanistic investigation approaches:

  • Determine if RNF13's role in ER stress responses contributes to neuronal vulnerability

  • Investigate potential neuronal substrates for RNF13's E3 ligase activity

  • Explore interactions with ion channels or synaptic proteins implicated in epilepsy

• Developmental timeline analysis:

  • Map RNF13 expression throughout neurodevelopment using stage-specific markers

  • Correlate expression patterns with critical periods for circuit formation

  • Compare with established neurodevelopmental regulators

What strategies can improve detection of post-translational modifications on RNF13?

Detecting and characterizing RNF13's post-translational modifications (PTMs) presents unique challenges requiring specialized approaches:

• Modification-specific antibodies:

  • Phospho-specific antibodies targeting predicted regulatory sites

  • Ubiquitination-state specific antibodies

  • Glycosylation-sensitive detection methods

• Enrichment strategies before analysis:

  • For ubiquitination: Tandem ubiquitin binding entities (TUBEs) to capture ubiquitinated proteins

  • For phosphorylation: Phosphopeptide enrichment using TiO₂ or immobilized metal affinity chromatography

  • For glycosylation: Lectin affinity chromatography

• Mass spectrometry approaches:

  • Sample preparation optimized for membrane proteins

  • Middle-down proteomics to maintain PTM combinations on larger peptides

  • Targeted parallel reaction monitoring for known modification sites

• Site-specific mutation validation:

  • Create point mutations at identified or predicted PTM sites

  • Assess functional consequences on localization, activity, and protein interactions

  • Compare wildtype and mutant proteins using antibody-based detection methods

• Dynamic regulation analysis:

  • Stress-induced changes in modification patterns (especially during ER stress)

  • Cell cycle-dependent modification dynamics

  • Modification crosstalk (how one modification influences others)

• Proteolytic processing detection:

  • Domain-specific antibodies to detect cleavage products

  • Comparisons between N-terminal and C-terminal targeting antibodies

  • Protease inhibitor panels to identify responsible proteases