Recombinant Human Tumor necrosis factor receptor superfamily member 10C (TNFRSF10C), partial (Active)

What is the basic function of TNFRSF10C in cellular biology?

TNFRSF10C, also known as TRAIL-R3 or DcR1 (Decoy Receptor 1), functions as a decoy receptor for the TNF-related apoptosis-inducing ligand (TRAIL). Unlike functional death receptors, TNFRSF10C binds to TRAIL but lacks the death domain necessary for apoptosis signaling. Consequently, it inhibits TRAIL-induced apoptosis by competing with death receptors (DR4 and DR5) for TRAIL binding . This competitive inhibition creates a regulatory mechanism for TRAIL-mediated cell death pathways, allowing cells to modulate their sensitivity to apoptotic signals.

When designing experiments to study TNFRSF10C function, researchers should consider:

• Co-expression levels of other TRAIL receptors (DR4, DR5, and DcR2)

• The ratio of decoy to functional receptors in the specific cell type

• Post-translational modifications that might affect binding affinity

• Tissue-specific expression patterns that may influence experimental outcomes

How is TNFRSF10C structurally different from other TRAIL receptors?

TNFRSF10C possesses unique structural characteristics that distinguish it from other TRAIL receptors:

• It contains the extracellular TRAIL-binding domain

• It lacks a functional intracellular death domain

• It is anchored to the cell membrane through a glycosylphosphatidylinositol (GPI) linkage rather than a transmembrane domain

This structural arrangement allows TNFRSF10C to bind TRAIL without initiating apoptotic signaling cascades, effectively sequestering the ligand and preventing its interaction with death-domain-containing receptors.

What are the optimal expression systems for producing recombinant TNFRSF10C?

When producing recombinant TNFRSF10C for research applications, several expression systems have been successfully employed, each with distinct advantages:

For functional studies requiring proper receptor-ligand interactions, mammalian expression systems are generally recommended due to their ability to produce correctly folded and glycosylated proteins. For structural studies where glycosylation is less critical, bacterial systems may be suitable after optimization of solubilization and refolding protocols.

What purification strategies yield the highest activity for recombinant TNFRSF10C?

Purification of active recombinant TNFRSF10C requires careful consideration of protein stability and functional integrity. A multi-step purification approach is typically recommended:

• Initial capture: Affinity chromatography using His-tag, FLAG-tag, or immunoaffinity approaches

• Intermediate purification: Ion exchange chromatography to separate charged variants

• Polishing: Size exclusion chromatography to remove aggregates and ensure monodispersity

Critical considerations include:

• Maintaining physiological pH (7.2-7.4) throughout purification to preserve native conformation

• Including stabilizing agents (glycerol 10-15%, low concentrations of non-ionic detergents)

• Minimizing freeze-thaw cycles which can lead to activity loss

• Confirming biological activity through TRAIL binding assays post-purification

How does TNFRSF10C methylation status correlate with cancer progression?

TNFRSF10C promoter hypermethylation has been observed in multiple cancer types and correlates with disease progression. In non-small cell lung cancer (NSCLC), methylation levels of TNFRSF10C in tumor tissues are significantly higher compared to distant non-tumor tissues (P=0.013) . Similar hypermethylation patterns have been documented in glioblastoma, prostate cancer, and breast cancer .

Methylation analysis reveals interesting sex-specific and smoking status-dependent patterns:

• Male patients show higher TNFRSF10C methylation in tumor tissues compared to females (P=0.013)

• Non-smokers exhibit significant differences in TNFRSF10C methylation between tumor and non-tumor tissues (P=0.031)

• In male non-smokers, a unique pattern of hypomethylation was observed (P=0.03)

These findings suggest that TNFRSF10C methylation status could serve as a potential biomarker for cancer diagnosis and might explain differential responses to TRAIL-based therapies across patient demographics.

What methodologies provide the most reliable assessment of TNFRSF10C methylation status?

Several methodologies have been employed to analyze TNFRSF10C methylation with varying degrees of sensitivity and specificity:

For TNFRSF10C specifically, qMSP has been successfully employed using primers targeting CG-rich regions in the promoter. The primer sequences reported in the literature are:

• TNFRSF10C forward: 5′-AGGGTGCGATTTAGGATTTAG-3′

• TNFRSF10C reverse: 5′-CGATAACGACGACGAACTT-3′

When designing methylation studies for TNFRSF10C, researchers should consider:

• Including both tumor and matched non-tumor tissues when possible

• Stratifying analysis by relevant clinical parameters (sex, smoking status, etc.)

• Validating methylation findings with gene expression analysis

• Confirming functional relevance through in vitro demethylation experiments

How does promoter methylation mechanistically affect TNFRSF10C expression?

The relationship between TNFRSF10C promoter methylation and gene expression has been demonstrated through multiple lines of evidence:

• Inverse correlation between methylation and expression: Analysis of lung cancer data from cBioPortal database revealed that TNFRSF10C methylation levels were negatively correlated with its mRNA expression (r=-0.379, P=0.008) .

• Demethylation experiments: Treatment of lung cancer cell lines (A549 and NCI-H1299) with 5′-aza-deoxycytidine resulted in significant increases in TNFRSF10C mRNA expression:

  • A549 cells: 8-fold increase (P=0.0001)

  • NCI-H1299 cells: 3.16-fold increase (P=1.143×10^-5)

• Promoter activity assays: Dual luciferase reporter assays using the TNFRSF10C promoter fragment (-121 to +363 bp) demonstrated enhanced transcriptional activity (fold-change=1.570, P=0.032) , confirming this region's role in regulating gene expression.

Mechanistically, methylation of CpG islands in the TNFRSF10C promoter likely interferes with the binding of transcription factors necessary for gene expression. This epigenetic silencing mechanism appears to be particularly relevant in specific cancer contexts and patient subgroups.

How does TNFRSF10C dysregulation impact TRAIL-based cancer therapies?

The dysregulation of TNFRSF10C through epigenetic mechanisms has significant implications for TRAIL-based cancer therapeutics:

• Hypermethylation and silencing of TNFRSF10C could potentially enhance cancer cell sensitivity to TRAIL-induced apoptosis by reducing the decoy receptor's competitive inhibition .

• Conversely, in some contexts, TNFRSF10C silencing might represent a mechanism through which tumors disrupt normal apoptotic signaling, contributing to immune evasion.

• Patient stratification considerations: The observed sex-specific and smoking status-dependent methylation patterns suggest that response to TRAIL-based therapies might vary across patient demographics.

When designing TRAIL-based therapeutic studies, researchers should consider:

• Evaluating TNFRSF10C methylation status as a potential predictive biomarker

• Assessing the ratio of functional to decoy TRAIL receptors in target tissues

• Exploring combination approaches using demethylating agents with TRAIL-based therapies

• Investigating tissue-specific and demographic-specific responses

What are the optimal cell models for studying TNFRSF10C function in cancer?

Selecting appropriate cell models is critical for studying TNFRSF10C function in cancer contexts:

When designing experiments:

• Include multiple cell lines to account for heterogeneity

• Consider the baseline expression of all TRAIL receptors

• Verify methylation status of the specific cell line batch

• Assess the impact of culture conditions on TNFRSF10C expression and methylation

How can single-cell approaches enhance our understanding of TNFRSF10C function in heterogeneous tumors?

Traditional bulk analysis methods may obscure important cellular heterogeneity in TNFRSF10C expression and methylation. Single-cell approaches offer several advantages:

• Resolving intratumoral heterogeneity:

  • Identifying subpopulations with differential TNFRSF10C expression

  • Correlating single-cell methylation with expression at the individual cell level

  • Mapping TRAIL sensitivity to receptor expression patterns

• Methodological considerations:

  • Single-cell RNA sequencing can reveal expression patterns across tumor populations

  • Single-cell ATAC-seq can identify chromatin accessibility at the TNFRSF10C locus

  • Single-cell bisulfite sequencing, while technically challenging, can directly assess methylation

• Data integration strategies:

  • Combining single-cell RNA-seq with protein-level measurements (CyTOF, CITE-seq)

  • Integrating spatial information to understand the tumor microenvironment context

  • Correlating with clinical outcomes to identify prognostically relevant subpopulations

How might TNFRSF10C methylation be developed as a clinical biomarker?

The potential of TNFRSF10C methylation as a clinical biomarker is supported by several lines of evidence:

• Cancer-specific hypermethylation: TNFRSF10C shows differential methylation between tumor and non-tumor tissues across multiple cancer types .

• Non-invasive detection potential: While tissue-based methylation analysis provides the current standard, investigating TNFRSF10C methylation in liquid biopsies (circulating tumor DNA, exosomes) could provide less invasive diagnostic opportunities .

• Demographic considerations: The observed sex-specific and smoking status-dependent methylation patterns suggest potential for developing targeted screening approaches.

For clinical biomarker development, researchers should consider:

• Standardizing methylation detection methodologies for clinical application

• Establishing robust cutoff values for positive/negative results

• Conducting large-scale validation studies across diverse patient populations

• Evaluating the combination of TNFRSF10C methylation with other biomarkers for improved sensitivity and specificity

What are the most promising therapeutic strategies targeting the TRAIL/TNFRSF10C axis?

Several therapeutic strategies targeting the TRAIL/TNFRSF10C axis show promise for cancer treatment:

• Epigenetic modifiers:

  • DNA methyltransferase inhibitors (5-azacytidine, decitabine) to reverse TNFRSF10C hypermethylation

  • Histone deacetylase inhibitors to enhance expression of silenced genes

• TRAIL-based therapeutics:

  • Recombinant TRAIL or TRAIL-mimetic agents

  • Agonistic antibodies against death receptors (DR4, DR5)

  • Bispecific antibodies targeting both tumor antigens and TRAIL receptors

• Combination approaches:

  • Demethylating agents + TRAIL to enhance sensitivity

  • Conventional chemotherapy + TRAIL for synergistic effects

  • Immune checkpoint inhibitors + TRAIL pathway activation

When designing therapeutic studies:

• Characterize baseline TNFRSF10C methylation and expression

• Consider sex and smoking status as potential modifiers of treatment response

• Monitor changes in methylation status during treatment

• Develop companion diagnostics for patient stratification

How can researchers address challenges in recombinant TNFRSF10C stability and activity?

Recombinant TNFRSF10C presents several technical challenges that researchers should consider:

• Stability issues:

  • Protein aggregation during storage

  • Loss of activity upon freeze-thaw cycles

  • Structural changes affecting TRAIL binding

• Recommended solutions:

  • Addition of stabilizers: 10-15% glycerol, low concentrations of non-ionic detergents

  • Single-use aliquots to avoid freeze-thaw cycles

  • Storage at -80°C for long-term or 4°C for short-term (1-2 weeks)

  • Quality control testing of each batch for TRAIL binding activity

• Functional validation approaches:

  • Competitive binding assays with labeled TRAIL

  • Surface plasmon resonance to measure binding kinetics

  • Cell-based assays measuring inhibition of TRAIL-induced apoptosis

What are the critical controls needed for TNFRSF10C methylation studies?

Robust methylation analysis requires careful consideration of controls:

• Technical controls:

  • Fully methylated and unmethylated DNA standards for qMSP calibration

  • Multiple primer sets targeting different CpG regions when possible

  • Non-bisulfite converted controls to verify conversion efficiency

• Biological controls:

  • Matched tumor/normal tissue pairs from the same patient

  • Cell lines with known methylation status

  • Tissues from different demographic groups (considering sex and smoking status variables)

• Validation approaches:

  • Correlation with gene expression data

  • Functional studies using demethylating agents

  • Promoter activity assays to confirm the relevance of specific CpG regions

When reporting methylation data, researchers should clearly describe:

• The specific genomic region analyzed (including genome coordinates)

• The methodology employed with detailed protocols

• The quantification and normalization approaches

• The statistical methods used for data analysis