TNFRSF10A Human

What is the fundamental role of TNFRSF10A in cellular pathways?

TNFRSF10A encodes a type 1 membrane protein that serves as a cell surface receptor primarily involved in apoptotic, necroptotic, and inflammatory cell-signaling pathways. It functions as a receptor for the cytokine TNF-related apoptosis-inducing ligand (TRAIL) in death receptor signaling pathways and has been demonstrated to activate NFκβ inflammatory signaling pathways . When trimeric TRAIL binds to TNFRSF10A, it induces receptor oligomerization, which exposes the cytoplasmic death domain and triggers FADD-dependent apoptotic pathways . This process is central to the receptor's function in programmed cell death regulation, particularly in conditions of cellular stress.

How is TNFRSF10A structurally and functionally related to other TNF receptor family members?

TNFRSF10A (also known as DR4 or TRAIL R1) shares significant structural homology with other TNF receptor family members, particularly TRAIL R2/DR5, with which it shares 55% amino acid sequence identity . The protein contains characteristic extracellular cysteine-rich domains, a transmembrane domain, and a cytoplasmic death domain essential for signal transduction . TNFRSF10A is part of a signaling network that includes additional TRAIL receptors - TRAIL R2/DR5 (which also transduces apoptosis signals) and two TRAIL decoy receptors that antagonize TRAIL-induced apoptosis . This structural organization allows for complex regulation of death receptor signaling dependent on receptor expression patterns and cellular context.

What are optimal approaches for studying TNFRSF10A-mediated cell death pathways?

When designing experiments to study TNFRSF10A-mediated cell death pathways, researchers should employ a multi-faceted approach combining both gene manipulation and cell viability assessments. Effective experimental designs include:

• Gene manipulation strategies: Use both knockdown (siRNA/shRNA) and overexpression approaches to modulate TNFRSF10A levels. In published studies, successful overexpression demonstrated approximately 4-fold increases in mRNA expression and 3-fold increases in protein levels as measured by ELISA .

• Cell death detection methods: Terminal Deoxynucleotidyl Transferase dUTP Nick End Labeling (TUNEL) assays combined with flow cytometry provide quantitative assessment of cell death. Include both stressed (e.g., tunicamycin-treated) and unstressed conditions to determine context-dependent effects .

• Protein activation monitoring: Include assays for downstream effectors such as cleaved caspase-3 (for apoptosis) and phosphorylated MLKL (for necroptosis) using sensitive detection methods like ELISA .

• Control conditions: Critical controls include non-transfected cells, vehicle-treated samples, and manipulation of related genes to establish specificity.

This comprehensive approach allows distinguishing between different cell death modalities and establishing causality between receptor activity and cellular outcomes.

How should researchers design experiments to investigate interactions between TNFRSF10A and the AC100861.1 lncRNA?

To properly investigate potential interactions between TNFRSF10A and AC100861.1 lncRNA, a carefully designed experimental approach should:

• Employ bidirectional manipulation: Systematically overexpress and knockdown each gene independently and in combination. Previous research achieved approximately 300-fold increases in AC100861.1 expression through overexpression and significant knockdown of both genes .

• Measure reciprocal expression effects: Quantify mRNA and protein levels of both genes following each manipulation using RT-qPCR for transcripts and ELISA for protein levels .

• Determine subcellular localization: Use RNA fluorescence in situ hybridization (RNA-FISH) to determine the subcellular localization of AC100861.1, which has been shown to localize primarily to the cytoplasm .

• Assess functional outcomes: Monitor changes in cell viability, apoptotic markers, necroptotic markers, and inflammatory signaling pathways to determine if either gene influences functional pathways regulated by the other .

• Examine shared promoter regulation: Use chromatin immunoprecipitation (ChIP) or reporter assays to investigate if the shared promoter region responds differently to various cellular stimuli.

This experimental framework allows researchers to comprehensively characterize the relationship between these genomically adjacent genes and determine whether their functions are interdependent or independent.

How does TNFRSF10A signaling intersect with necroptotic and inflammatory pathways?

TNFRSF10A signaling exhibits complex intersections with both necroptotic and inflammatory pathways beyond its well-established role in apoptosis. Research indicates:

• Necroptosis activation: Manipulation of TNFRSF10A impacts molecules in the necroptotic pathway. Specifically, knockdown of TNFRSF10A leads to significant downregulation of phosphorylated MLKL (a key necroptosis effector) with fold changes of approximately 0.16 compared to controls . This suggests that baseline TNFRSF10A expression is necessary for maintaining necroptotic pathway sensitivity.

• Inflammatory pathway regulation: TNFRSF10A activates NFκβ inflammatory signaling pathways independent of its apoptotic functions . This dual functionality allows the receptor to modulate inflammatory responses while simultaneously regulating cell death decisions.

• Pathway crosstalk: The receptor's ability to influence both cell death and inflammatory signaling creates significant crosstalk between these pathways. The balance between these functions appears to be context-dependent, with cellular stress conditions (like tunicamycin treatment) potentially shifting the signaling preference .

• AC100861.1 influence: Interestingly, the lncRNA AC100861.1 at the TNFRSF10A locus also independently affects necroptotic and inflammatory signaling pathways. Overexpression of AC100861.1 upregulates phosphorylated MLKL (FC = 2.34), suggesting enhanced necroptotic pathway activation .

These findings indicate that TNFRSF10A functions within a complex signaling network that integrates various cell fate decisions and inflammatory responses, potentially explaining its involvement in diverse pathological conditions.

What methodologies are most effective for studying TRAIL receptor interactions with TNFRSF10A?

When investigating TRAIL receptor interactions with TNFRSF10A, researchers should employ a comprehensive suite of methodologies to capture the complex dynamics of these interactions:

• Expression correlation analysis: RT-qPCR should be used to simultaneously measure expression of TNFRSF10A and related receptors (TNFRSF10B, TNFRSF10C, and TNFRSF10D) to identify potential compensatory expression changes. Research has shown that manipulation of TNFRSF10A can alter expression of TNFRSF10D while leaving TNFRSF10B and TNFRSF10C relatively unchanged .

• Protein-protein interaction studies: Co-immunoprecipitation combined with western blotting can verify direct interactions between TRAIL and TNFRSF10A, and potentially identify novel interaction partners.

• Functional neutralization assays: Human TRAIL R1/Fc chimeras can be employed to neutralize TRAIL binding capacity, allowing researchers to distinguish between TRAIL-dependent and independent functions of TNFRSF10A .

• Flow cytometry for receptor quantification: Flow cytometry using specific antibodies (such as Mouse Anti-Human TRAIL R1/TNFRSF10A APC-conjugated Monoclonal Antibody) provides accurate quantification of surface receptor expression levels .

• Receptor oligomerization assessment: Since TRAIL-induced apoptosis likely requires receptor oligomerization, techniques such as blue native PAGE or proximity ligation assays can be used to evaluate receptor complex formation after ligand binding .

These methodological approaches allow researchers to thoroughly characterize the interactions between TRAIL and TNFRSF10A at both the molecular and functional levels.

How do TNFRSF10A SNPs correlate with corticosteroid response in Duchenne muscular dystrophy?

Research on TNFRSF10A SNPs has revealed significant correlations with corticosteroid treatment responses in Duchenne muscular dystrophy (DMD) patients. The findings show:

• Identification of response-associated SNPs: Through discriminant analysis of principal components (DAPC) of 205 DMD-related genes, researchers identified two response-associated SNPs in the TNFRSF10A gene that distinguished between high responders (HR) and low responders (LR) to corticosteroid therapy .

• CT haplotype significance: The CT haplotype in TNFRSF10A was significantly associated with high responder status in DMD patients. This association was validated across multiple cohorts, including 46 DMD patients on corticosteroid therapy and 150 non-ambulant DMD patients never treated with corticosteroids .

• Response definition parameters: High responders were defined as patients who maintained walking ability after 15 years of age, while low responders lost ambulation before 10 years of age despite corticosteroid therapy .

• Statistical validation: The association between the CT haplotype and treatment response was confirmed across a total validation cohort of 207 DMD patients, providing robust statistical support for this genetic correlation .

These findings suggest TNFRSF10A genetic variants could potentially serve as biomarkers for predicting corticosteroid treatment efficacy in DMD patients, allowing for more personalized treatment approaches in this severe muscular dystrophy.

What methodological approaches should be used when investigating TNFRSF10A in disease contexts?

When investigating TNFRSF10A in disease contexts, researchers should implement a comprehensive methodological framework that addresses multiple dimensions of gene function:

• Genotype-phenotype correlation studies: For genetic association studies, researchers should prioritize genes of interest based on interactome mapping and sequence across these genes in well-characterized patient cohorts with clearly defined phenotypes . For DMD studies, researchers successfully sequenced 205 genes in a discovery cohort before validating findings in larger cohorts .

• Functional validation experiments: Cell culture models should incorporate both gene manipulation (overexpression/knockdown) and cellular stress conditions (e.g., tunicamycin treatment) to recapitulate disease environments . These should be combined with assays measuring cell viability, death pathway activation, and inflammatory signaling.

• Molecular pathway analysis: Comprehensive analysis of downstream pathways using techniques like RT-qPCR for transcript analysis and ELISAs for protein detection can reveal how TNFRSF10A dysfunction impacts cellular pathways . Important targets include phosphorylated MLKL for necroptosis and NFκβ pathway components.

• Biomarker development approaches: When developing potential biomarkers, validation in multiple independent cohorts is essential, as demonstrated in the DMD studies which validated findings across three separate patient groups .

• Therapeutic targeting considerations: For therapeutic development, researchers should evaluate potential impacts on all functions of TNFRSF10A, including apoptotic, necroptotic, and inflammatory roles, as targeting one pathway may have unintended consequences on others .

This methodological framework ensures rigorous investigation of TNFRSF10A in disease contexts while addressing the complex multifunctional nature of this receptor.

What are the optimal approaches for detecting and quantifying TNFRSF10A in experimental systems?

Detection and quantification of TNFRSF10A in experimental systems requires a multi-faceted approach targeting both transcript and protein levels with appropriate controls:

• RT-qPCR for transcript quantification: For accurate mRNA quantification, researchers should design primers spanning exon-exon junctions to avoid genomic DNA amplification. Studies have successfully measured TNFRSF10A overexpression showing approximately 4-fold increases in expression relative to controls (FC = 4.12 ± 0.36) .

• Protein quantification via ELISA: Enzyme-linked immunosorbent assays provide sensitive protein quantification with fold changes in TNFRSF10A-overexpressing samples of approximately 2.94 ± 0.27 compared to controls . These assays should include standard curves with recombinant proteins for absolute quantification.

• Flow cytometry for surface expression: APC-conjugated monoclonal antibodies against TNFRSF10A (such as the Mouse Anti-Human TRAIL R1/TNFRSF10A APC-conjugated Monoclonal Antibody) allow for quantitative assessment of surface receptor density . This approach is particularly valuable for assessing receptor accessibility to ligands.

• Immunoblotting for protein isoforms: Western blotting with isoform-specific antibodies can distinguish between different protein variants or post-translationally modified forms of TNFRSF10A.

• Immunohistochemistry for tissue localization: For tissue samples, immunohistochemical staining with validated antibodies enables assessment of expression patterns within complex tissues and cell populations.

How can researchers effectively modulate TNFRSF10A expression for functional studies?

Effective modulation of TNFRSF10A expression for functional studies requires careful consideration of experimental design and validation approaches:

• Overexpression strategies:

  • Plasmid-based overexpression using mammalian expression vectors containing the TNFRSF10A coding sequence under strong promoters has achieved approximately 4-fold increases in transcript levels and 3-fold increases in protein levels .

  • Validation of overexpression should include both RT-qPCR for mRNA and ELISA or western blotting for protein levels .

  • Consider inducible overexpression systems for temporal control of expression.

• Knockdown approaches:

  • siRNA or shRNA targeting TNFRSF10A has been successfully employed in cell culture models .

  • Multiple siRNA sequences should be tested to identify optimal knockdown efficiency and specificity.

  • Validate knockdown at both mRNA and protein levels to confirm target reduction.

• Genome editing technologies:

  • CRISPR-Cas9 can be used for complete gene knockout or for introducing specific mutations.

  • For SNP studies, homology-directed repair with CRISPR-Cas9 can introduce specific variants identified in clinical studies, such as those associated with corticosteroid response in DMD .

• Complementary approaches:

  • Receptor neutralization using antibodies or TNFRSF10A-Fc chimeric proteins provides functional blockade without altering expression levels .

  • Ligand manipulation (TRAIL) offers an alternative approach to modulate signaling through the receptor.

• Control conditions:

  • Include appropriate vector-only controls for overexpression studies.

  • Use non-targeting siRNA/shRNA sequences as controls for knockdown experiments.

  • Always validate specificity by measuring related genes (e.g., TNFRSF10B, TNFRSF10C, TNFRSF10D) to ensure targeted effects .

These approaches provide researchers with a comprehensive toolkit for modulating TNFRSF10A expression across various experimental systems.

How should researchers interpret conflicting data regarding TNFRSF10A functions in different cellular contexts?

When encountering conflicting data regarding TNFRSF10A functions across different cellular contexts, researchers should implement a systematic analytical framework:

• Context-dependent signaling analysis: Carefully evaluate cellular contexts, as TNFRSF10A signaling outcomes vary significantly between stressed and unstressed conditions. In some studies, TNFRSF10A knockdown increased cell death only under stress conditions (tunicamycin treatment), while overexpression decreased viability regardless of stress state .

• Pathway activation profiling: Analyze multiple downstream pathways simultaneously, as TNFRSF10A can activate both apoptotic and non-apoptotic pathways. Measure markers such as cleaved caspase-3 (apoptosis), phosphorylated MLKL (necroptosis), and NFκβ pathway components (inflammation) to determine which pathways predominate in specific contexts .

• Expression level considerations: Quantify absolute receptor expression levels across experimental systems, as threshold effects may explain functional differences. Flow cytometry for surface expression provides critical information about ligand accessibility .

• Ligand availability assessment: Determine whether differences result from varying levels of TRAIL or other ligands in different experimental systems .

• Receptor complex composition analysis: Investigate expression of other TRAIL receptors (TNFRSF10B, TNFRSF10C, TNFRSF10D) as their relative abundance may shift signaling outcomes through competitive binding or heteromeric receptor complex formation .

• Genetic background evaluation: Consider genetic variations such as SNPs that might influence receptor function, as seen in the differential corticosteroid response in DMD patients with specific TNFRSF10A haplotypes .

This analytical framework enables researchers to reconcile apparently conflicting data by identifying the specific cellular and molecular contexts in which different TNFRSF10A functions predominate.

What statistical approaches are most appropriate for analyzing TNFRSF10A genetic variation in clinical studies?

For rigorous analysis of TNFRSF10A genetic variation in clinical studies, researchers should employ a comprehensive statistical framework:

• Discriminant Analysis of Principal Components (DAPC): This approach effectively identified TNFRSF10A SNPs correlated with corticosteroid response in DMD patients by prioritizing 2 response-associated SNPs from an initial set of 43 discriminating SNPs .

• Multi-cohort validation strategy: To establish robust associations, findings should be validated across multiple independent cohorts. In DMD research, initial discoveries in 21 patients were validated in two additional cohorts (46 and 150 patients), strengthening confidence in the observed correlations .

• Phenotype definition precision: Clear, quantitative definitions of phenotypic categories are essential. For corticosteroid response, high responders were defined as maintaining ambulation beyond age 15, while low responders lost ambulation before age 10 despite treatment .

• Haplotype analysis: Rather than focusing on individual SNPs, analyzing haplotypes (such as the CT haplotype in TNFRSF10A) can capture combinatorial genetic effects that individual SNP analysis might miss .

• Gene prioritization methods: Using interactome mapping to prioritize candidate genes (as done with 205 DMD-related genes) provides a biologically informed approach to reduce multiple testing burden and increase discovery power .

• Appropriate controls for population stratification: Genetic association studies should account for population structure to avoid false positive associations due to ancestry differences between case and control groups.

• Functional validation of statistical associations: Statistical correlations should be followed by functional validation experiments to establish biological plausibility of observed associations.

This statistical framework balances discovery potential with rigorous validation, enabling reliable identification of clinically relevant TNFRSF10A genetic variants.