TNFRSF10D Human

What is TNFRSF10D and what are its alternative nomenclatures in research literature?

TNFRSF10D is known by several names in scientific literature, including TRAIL Receptor-4 (TRAIL-R4), Decoy receptor 2 (DCR2), TNF-related apoptosis-inducing ligand receptor 4, TRUNDD, and CD264. It belongs to the tumor necrosis factor receptor superfamily and is characterized as a TRAIL receptor with a truncated death domain . When conducting literature searches, researchers should use multiple search terms to ensure comprehensive coverage of relevant publications.

What are the key structural characteristics of TNFRSF10D?

TNFRSF10D contains three key structural components: a truncated cytoplasmic death domain, an extracellular TRAIL-binding domain, and a transmembrane domain . The truncated nature of its death domain is particularly significant as it differentiates TNFRSF10D from other TRAIL receptors and explains its functional role. In recombinant form, human TNFRSF10D protein typically consists of 395 amino acids (spanning positions 56-211 of the native sequence) with a molecular mass of approximately 73.8 kDa, though it may appear at 40-57 kDa on SDS-PAGE due to glycosylation patterns .

How does TNFRSF10D function in relation to TRAIL signaling pathways?

TNFRSF10D functions primarily as a decoy receptor that can prevent TRAIL-mediated apoptosis on cells expressing TRAIL R1 and/or TRAIL R2 . Unlike the death-inducing TRAIL receptors, TNFRSF10D can bind TRAIL ligand but cannot transmit the apoptotic signal due to its truncated death domain. In experimental settings, Recombinant Human TRAIL R4/TNFRSF10D Fc Chimera has been shown to inhibit Recombinant Human TRAIL/TNFSF10-induced cytotoxicity in the L-929 mouse fibroblast cell line in a dose-dependent manner . This inhibitory effect can be neutralized by Human TRAIL R4/TNFRSF10D Monoclonal Antibody, with typical ND50 values ranging from 0.3-1.8 μg/mL .

What are effective methods for detecting TNFRSF10D expression in tissue and cell samples?

Several validated methods exist for detecting TNFRSF10D expression:

• Flow Cytometry: Flow cytometry represents an effective approach for detecting TNFRSF10D/DcR2 expression on cell surfaces. Research has employed this technique to measure expression levels on various cell lines including Colo205 and HCT116 cells . Detection typically employs anti-FLAG-PE for visualization of bound molecules. Flow cytometry allows researchers to simultaneously assess TNFRSF10D alongside other markers such as EGFR, HER2, HER3, and EpCAM.

• Sandwich Immunoassay: This method provides quantitative detection of TNFRSF10D. Optimal antibody dilutions should be determined by each laboratory for specific applications .

• Neutralizing Assays: Biological activity of TNFRSF10D can be measured in a neutralizing assay using Jurkat human T lymphocytes. The ED50 for TNFRSF10D effect is typically less than or equal to 10 ng/mL in the presence of 2 ng/mL TRAIL .

What are the recommended protocols for analyzing TNFRSF10D DNA methylation in clinical samples?

For TNFRSF10D DNA methylation analysis in clinical samples, researchers have established the following protocol:

• DNA Extraction from FFPE Tissues: Extract DNA from small punch-biopsies from formalin-fixed paraffin-embedded (FFPE) tissue samples.

• Bisulfite Conversion: Perform bisulfite conversion on extracted DNA to preserve methylation information.

• MethyLight PCR: Analyze the methylation status using MethyLight PCR technique .

This methodology has been successfully applied to melanoma specimens, demonstrating that TNFRSF10D DNA-methylation analysis from small tissue-punches from archival FFPE is feasible and provides valuable prognostic information . The approach is particularly valuable as it can be conducted on readily available archived specimens, enhancing its utility in retrospective studies where fresh tissue is unavailable.

How can researchers effectively design experiments to study the inhibitory effects of TNFRSF10D on TRAIL-induced apoptosis?

When designing experiments to study TNFRSF10D's inhibitory effects on TRAIL-induced apoptosis, researchers should consider:

• Cell Line Selection: Choose cell lines with known TRAIL sensitivity, such as L-929 mouse fibroblasts or Jurkat human T lymphocytes .

• Use of Metabolic Inhibitors: Consider including metabolic inhibitors such as actinomycin D, which can enhance TRAIL sensitivity in certain cell types .

• Dose-Response Analysis: Implement a dose-dependent experimental design where Recombinant Human TRAIL R4/TNFRSF10D Fc Chimera concentrations are varied against a fixed concentration of Recombinant Human TRAIL/TNFSF10. For example, studies have used 90 ng/mL of TRAIL R4/TNFRSF10D Fc Chimera against 20 ng/mL of TRAIL/TNFSF10 .

• Neutralization Assessment: Include conditions where Human TRAIL R4/TNFRSF10D Monoclonal Antibody is used to neutralize the inhibitory effect of TNFRSF10D, with typical ND50 values of 0.3-1.8 μg/mL .

• Detection Methods: Employ multiple readouts for apoptosis, including cell viability assays, caspase activation measurements, and flow cytometry for apoptotic markers.

What is the prognostic significance of TNFRSF10D DNA methylation in cancer patients?

TNFRSF10D DNA methylation has emerged as a significant prognostic marker in cancer, particularly in melanoma. Research indicates:

This data suggests that TNFRSF10D methylation status might serve as a more robust prognostic indicator than traditional clinicopathological parameters in melanoma patients.

How does TNFRSF10D methylation status compare with other established prognostic factors in melanoma?

The comparison between TNFRSF10D methylation and other established prognostic factors reveals important insights:

What are the implications of TNFRSF10D methylation for personalized treatment approaches in cancer?

While the search results don't directly address personalized treatment approaches based on TNFRSF10D methylation, several implications can be inferred:

• Risk Stratification: Patients could be stratified into risk groups based on TNFRSF10D methylation status, potentially allowing for more intensive surveillance or adjuvant therapy for high-risk patients (those with methylated TNFRSF10D).

• Treatment Selection: The strong prognostic value of TNFRSF10D methylation suggests it might help identify patients more likely to benefit from aggressive therapies or novel treatment approaches.

• Combination with Interferon Therapy: The data indicates a potential interaction between interferon alpha therapy and prognostic outcomes. This suggests that TNFRSF10D methylation status might help identify patients who would benefit most from interferon therapy .

• Epigenetic Therapy Potential: Given that DNA methylation is potentially reversible, patients with methylated TNFRSF10D might benefit from epigenetic therapies that could restore normal TNFRSF10D expression.

What are the molecular mechanisms linking TNFRSF10D methylation to cancer progression?

The exact molecular mechanisms connecting TNFRSF10D methylation to cancer progression are not explicitly detailed in the search results, but based on its function, several mechanisms can be proposed:

• Apoptosis Resistance: TNFRSF10D functions as a decoy receptor that can inhibit TRAIL-mediated apoptosis . Methylation of TNFRSF10D could potentially silence its expression, altering the balance between death receptors and decoy receptors, thereby affecting cellular sensitivity to TRAIL-mediated apoptosis.

• Immune Evasion: TRAIL is produced by immune cells and plays a role in tumor surveillance. Alterations in TNFRSF10D expression via methylation could impact how cancer cells respond to immune-mediated TRAIL signaling.

• Cell Survival Signaling: Beyond its role in apoptosis, TRAIL receptor signaling has been implicated in non-apoptotic pathways including NF-κB activation and proliferation. TNFRSF10D methylation might influence these alternative signaling outputs.

• Interaction with Other Prognostic Factors: The correlation between TNFRSF10D methylation and other prognostic factors like age, Clark level, and mitotic rate suggests potential interactions with fundamental processes governing melanoma biology .

Future research should focus on elucidating these mechanisms to better understand the biological basis of TNFRSF10D methylation's prognostic impact.

How might heterogeneity in TNFRSF10D expression affect experimental outcomes and clinical interpretations?

Heterogeneity in TNFRSF10D expression can significantly impact both experimental results and clinical interpretations:

• Cellular Heterogeneity: Expression levels of TNFRSF10D can vary between different cell types and even within a single tumor. Flow cytometry studies have shown variable expression levels of TRAIL receptors including TNFRSF10D across different cell lines and treatments . This heterogeneity might affect the interpretation of bulk tissue analysis.

• Treatment-Induced Changes: Research has demonstrated that treatment with agents like BZB (250 ng/ml for 16h) can alter TNFRSF10D expression levels . This dynamic regulation means that expression at a single timepoint may not represent the full biological context.

• Technical Variability: Different detection methods (flow cytometry, immunoassays, etc.) might yield different results regarding TNFRSF10D expression levels, necessitating method standardization for cross-study comparisons.

• Methylation Heterogeneity: The binary classification of TNFRSF10D as "methylated" versus "unmethylated" may oversimplify a more complex pattern of partial or heterogeneous methylation within a tumor sample.

Researchers should account for these sources of heterogeneity through approaches such as single-cell analysis, spatial profiling, longitudinal sampling, and robust technical replicates.

What are the critical considerations when interpreting contradictory results in TNFRSF10D research?

When faced with contradictory results in TNFRSF10D research, researchers should consider:

Researchers should explicitly address these considerations when designing studies, analyzing data, and interpreting conflicting literature in the field.

What novel approaches could enhance the clinical utility of TNFRSF10D as a biomarker?

Several innovative approaches could enhance TNFRSF10D's clinical utility:

• Liquid Biopsy Development: Developing methods to detect TNFRSF10D methylation in circulating tumor DNA from blood samples could allow for non-invasive longitudinal monitoring of patients.

• Integrated Multi-Marker Panels: Combining TNFRSF10D methylation with other molecular markers, immunohistochemical features, and clinical parameters into integrated prognostic models might improve predictive accuracy.

• Artificial Intelligence Applications: Machine learning approaches could help identify subtle patterns in TNFRSF10D methylation levels or distribution that correlate with outcomes and might not be apparent through conventional statistical analysis.

• Standardized Assays: Development of clinically validated, standardized assays for TNFRSF10D methylation would facilitate implementation in diagnostic laboratories and enable cross-study comparisons.

• Spatial Methylation Analysis: Techniques that preserve spatial information about TNFRSF10D methylation within the tumor microenvironment could provide insights into the relationship between methylation patterns and local tumor-immune interactions.

What experimental approaches would best elucidate the functional consequences of TNFRSF10D methylation?

To better understand the functional impact of TNFRSF10D methylation, researchers should consider:

• CRISPR-Based Epigenetic Editing: Using CRISPR-Cas9 coupled with DNA methyltransferases or demethylases to specifically modify TNFRSF10D methylation status in cell lines would allow for controlled experiments examining the direct consequences of methylation changes.

• Patient-Derived Xenografts: Creating xenograft models from patient tumors with known TNFRSF10D methylation status would enable in vivo studies of tumor behavior and treatment response.

• Single-Cell Analysis: Applying single-cell techniques to analyze TNFRSF10D methylation, expression, and functional outcomes would help address the impact of intratumoral heterogeneity.

• Dynamic Monitoring: Longitudinal studies tracking changes in TNFRSF10D methylation during disease progression and in response to therapy would clarify its role in disease evolution.

• Mechanistic Studies: Detailed biochemical and cell biological investigations of how TNFRSF10D methylation affects protein expression, localization, and interaction with TRAIL and other signaling partners would illuminate the molecular basis of its clinical significance.

How might TNFRSF10D research contribute to developing novel therapeutic strategies?

TNFRSF10D research could contribute to therapeutic innovation through several avenues:

• Epigenetic Drug Development: Understanding the mechanisms and consequences of TNFRSF10D methylation could inform the development of targeted epigenetic therapies aimed at restoring normal TNFRSF10D expression patterns.

• Combination Therapy Optimization: The prognostic value of TNFRSF10D methylation in the context of treatments like interferon alpha therapy suggests potential for optimizing combination regimens based on methylation status .

• TRAIL Pathway Modulation: Given TNFRSF10D's role as a decoy receptor in TRAIL signaling, research could inform strategies to modulate the TRAIL pathway in cancer, potentially enhancing the efficacy of TRAIL-based therapeutics by accounting for TNFRSF10D status.

• Immunotherapy Biomarkers: Exploring the relationship between TNFRSF10D methylation and response to immunotherapies could yield predictive biomarkers, particularly given TRAIL's role in immune surveillance.

• Precision Medicine Approaches: The strong prognostic value of TNFRSF10D methylation suggests its potential utility in precision medicine algorithms for treatment selection and intensity, particularly in melanoma patients.