Recombinant Human Tumor necrosis factor receptor superfamily member 1B protein (TNFRSF1B), partial (Active)

How is TNFRSF1B expression regulated in different cell types?

TNFRSF1B expression is more restricted than TNFR1, which is expressed by most cells . Significant TNFRSF1B expression occurs in highly suppressive T-regulatory cells (Tregs) in both mouse and human models . Additionally, TNFRSF1B has been detected in the central nervous system, cardiac myocytes, and thymocytes .

In T lymphocytes specifically, resting T cells lack binding capacity for TNF-alpha, but high-affinity TNF receptors (Kd 70 pM) are induced de novo upon primary activation . Comparing TNFR2 expression with IL-2 and interferon-gamma receptors reveals that maximum TNFR2 expression (approximately 5000 receptors per cell at day 6) occurs approximately 3 days after peak IL-2 receptor expression, followed by a subsequent decline . In contrast, IFN-gamma receptors maintain stable expression (300-400 receptors/cell) throughout T cell activation .

Which TNFRSF1B gene variants have been most extensively studied and what are their functional implications?

The most extensively studied TNFRSF1B variants include rs976881 and rs1061622, which were found to be in linkage equilibrium in all populations (R² = 0.022 and D' = 0.50; chi square = 108.34; p < 0.0001) . These SNPs are located within or near the TNFRSF1B gene in chromosome 1p36, with rs976881 in the intronic 5' region and rs1061622 representing a missense coding variant in exon 6 . These variants were selected based on their association with TNFRSF1B-related clinical outcomes and peripheral sTNFR2 levels .

For rs976881, the reference, heterozygous, and alternate alleles are T/T, T/C, and C/C, respectively. For rs1061622, they are G/G, G/T, and T/T, respectively . The rs976881 T/T reference genotype has been associated with higher levels of soluble TNFR2 (sTNFR2) .

How do promoter polymorphisms in the TNFRSF1B gene influence its expression and disease susceptibility?

Promoter polymorphisms in the TNFRSF1B gene may significantly influence disease susceptibility and potentially impact the response to therapies targeting TNFR2 . Copy number variation of key transcription factor binding sites can significantly affect TNFRSF1B promoter activity . This variation might influence the level of TNFR2 expression on T-regulatory cells, which could have implications for autoimmune diseases and cancer susceptibility .

The interaction between TNFRSF1B gene variant rs976881 and CSF sTNFR2 has been shown to affect CSF and MRI biomarkers of neurodegeneration, cognitive profiles, and rate of functional decline over 1 year in Alzheimer's disease patients . This suggests that certain genotypic variants could serve as markers of resilience in AD .

What are the recommended protocols for measuring soluble TNFR2 levels in clinical samples?

For accurate measurement of soluble TNFR2 (sTNFR2) in clinical samples such as cerebrospinal fluid (CSF), researchers should follow standardized protocols that include:

• Sample collection and processing: CSF samples should be collected via lumbar puncture following standard procedures, immediately processed, aliquoted, and stored at -80°C to prevent protein degradation .

• Quality control: All samples should undergo quality control checks according to established methodology for the relevant biobank or study (e.g., ADNI methodology) .

• Assay selection: The specific assay used for measuring sTNFR2 should be validated for the sample type being analyzed. For example, in the ADNI study, the Elecsys method was used for measuring AD biomarkers .

• Data normalization: Consider the need for normalization based on other biomarkers or patient characteristics.

• Statistical analysis: Account for covariates such as age, sex, APOE ε4 status, and potentially other biomarkers like Aβ42 levels when analyzing sTNFR2 data .

What experimental approaches are optimal for studying TNFR2 receptor induction on T cells?

To study TNFR2 receptor induction on T cells, the following methodological approaches are recommended:

• T cell isolation and activation: Isolate resting T cells from peripheral blood and activate them using appropriate stimuli. As demonstrated in previous research, resting T cells lack specific binding capacity for rTNF-alpha, but high-affinity TNF receptors are induced upon primary activation .

• Receptor binding assays: Use labeled recombinant TNF-alpha to measure binding capacity and determine receptor affinity (Kd values). Previous studies have established that activated T cells express high-affinity (Kd 70 pM) TNF receptors .

• Kinetic analysis: Monitor receptor expression over time (e.g., days 0-10 post-activation) to establish the temporal relationship between TNFR2 expression and other receptors like IL-2 receptors and IFN-gamma receptors .

• Functional assays: Assess the biological effects of TNF-alpha on activated, TNFR2-positive T cells by measuring:

  • Expression of activation markers (e.g., HLA-DR antigens)

  • High-affinity IL-2 receptor expression

  • Proliferative response to IL-2

  • IL-2-dependent IFN-gamma production

• Correlation with T cell subpopulations: Identify which T cell subsets express TNFR2 and at what levels, particularly focusing on T-regulatory cells which are known to express significant levels of TNFR2 .

How does the interaction between TNFRSF1B variants and sTNFR2 levels affect Alzheimer's disease biomarkers?

Research has demonstrated significant interactions between TNFRSF1B gene variants (particularly rs976881) and CSF sTNFR2 levels that modulate multiple Alzheimer's disease biomarkers and outcomes. Specific effects include:

• CSF tau biomarkers: The interaction between rs976881 and CSF sTNFR2 significantly modulates cerebrospinal fluid total tau (t-tau) and phosphorylated tau (p-tau) levels .

• Brain volume measures: This same interaction affects hippocampal and whole brain volumes as measured by MRI .

• Cognitive function: The interaction impacts specific cognitive domains, particularly performance on tests like the Digit Span Forwards subtest .

• Disease progression: A significant interaction between rs976881 and CSF sTNFR2 impacts Clinical Dementia Rating Sum of Boxes (CDR-SB) scores over 12 months, suggesting an effect on the rate of functional decline .

• Replication validation: The interaction's effect on CSF p-tau has been validated in an independent replication cohort of MCI patients with positive AD biomarkers .

These findings suggest that the interaction between TNFRSF1B variant rs976881 and CSF sTNFR2 levels modulates multiple AD-associated severity markers and cognitive domains, potentially impacting resilience-related clinical outcomes in AD .

What statistical models are most appropriate for analyzing TNFRSF1B data in neurodegenerative disease studies?

Based on Alzheimer's disease research methodology, several statistical models have proven effective for analyzing TNFRSF1B data in neurodegenerative disease studies:

Model 1: Analysis of CSF biomarkers

• Dependent variables: CSF t-tau and p-tau

• Main effects: CSF sTNFR2, TNFRSF1B SNP, and interaction effect between sTNFR2 and TNFRSF1B SNP

• Covariates: age, sex, CSF Aβ42, and APOE ε4 status

Model 2: Analysis of brain volumetric measures

• Dependent variables: hippocampal volume, ventricular volume, and whole brain volume

• Main effects: CSF sTNFR2, TNFRSF1B SNP, CSF t-tau, and interaction effects between sTNFR2 and TNFRSF1B SNP and between sTNFR2 and t-tau

• Covariates: age, sex, years of education, CSF Aβ42/p-tau ratio, APOE ε4 status, and baseline intracranial volume

Model 3: Analysis of cognitive outcomes

• Dependent variables: Logical Memory delayed recall score, Digit Span subtest score, Trail Making Test Part B score, category fluency test (animals), and Boston Naming Test score

• Main effects: CSF sTNFR2, TNFRSF1B SNP, CSF t-tau, and interaction effects between sTNFR2 and TNFRSF1B SNP and between sTNFR2 and t-tau

• Covariates: age, sex, years of education, CSF Aβ42/p-tau ratio, APOE ε4 status, and baseline MMSE score

Model 4: Analysis of longitudinal functional decline

• Dependent variable: CDR-SB scores (log-transformed if variance requirements are not met)

• Main effects: disease state (e.g., MCI or AD dementia), CSF t-tau, and interaction effect between sTNFR2 and TNFRSF1B SNP

• Covariates: age, sex, years of education, APOE ε4 status, baseline CDR-SB score, and CSF Aβ42/p-tau ratio

For all models, it is important to assess model linearity and fit using Levene's test of equality of error variances, lack-of-fit F-test, and residual plots. For multiple dependent variables, the Benjamini-Hochberg false discovery rate should be used to assess significance .

How does TNF-alpha signaling via TNFR2 affect T-regulatory cell function?

TNF-alpha signaling through TNFR2 has significant implications for T-regulatory cell (Treg) function. TNFR2 is expressed by a subset of highly suppressive T-regulatory cells in both mouse and human models . The interaction between TNF and TNFR2 on these Tregs appears to enhance their suppressive function, making TNFR2 an important therapeutic target for the treatment of autoimmune diseases and cancer .

On activated TNFR2-positive T cells, TNF-alpha exerts multiple stimulatory activities:

• Enhanced expression of activation markers: TNF increases the expression of HLA-DR antigens on activated T cells .

• Upregulation of cytokine receptors: TNF promotes increased expression of high-affinity IL-2 receptors .

• Amplified proliferative response: As a consequence of increased IL-2 receptor expression, TNF-treated T cells show an enhanced proliferative response to IL-2 .

• Cytokine production: TNF-alpha acts as a co-stimulator of IL-2-dependent IFN-gamma production .

These effects indicate that TNF-alpha can regulate growth and functional activities of T cells, including Tregs, through TNFR2 signaling. The genetic variation in the promoter of the TNFRSF1B gene may significantly impact Treg number and function, which could influence autoimmune disease susceptibility and potential responses to TNFR2-directed therapies .

What is the relationship between TNFR2 receptor density and T cell activation status?

The relationship between TNFR2 receptor density and T cell activation status is dynamic and follows specific patterns:

• Receptor induction: Resting T cells lack specific binding capacity for TNF-alpha, but high-affinity (Kd 70 pM) TNF receptors are de novo induced upon primary activation of T cells .

• Temporal expression pattern: TNFR2 expression follows a specific timeline during T cell activation:

  • Receptor expression increases after activation

  • Maximum expression (approximately 5000 receptors/cell) occurs around day 6 post-activation

  • This peak occurs approximately 3 days after the peak of IL-2 receptor expression

  • Following the peak, TNFR2 expression subsequently declines

• Comparison with other receptors: Unlike TNFR2, IFN-gamma receptors maintain stable expression (300-400 receptors/cell, Kd 10 pM) throughout the course of T cell activation, highlighting the unique regulation of TNFR2 .

• Functional consequences: The increased TNFR2 expression coincides with enhanced responsiveness to TNF-alpha, including increased HLA-DR expression, IL-2 receptor upregulation, enhanced proliferative response to IL-2, and increased IFN-gamma production .

This relationship suggests that TNFR2 expression is specifically regulated during T cell activation, with expression density correlating with functional responsiveness to TNF-alpha. The timing of TNFR2 upregulation after IL-2 receptor expression suggests a potential regulatory role in later stages of T cell activation.

How can TNFR2-selective agonists be designed for therapeutic applications in neurodegenerative diseases?

Designing TNFR2-selective agonists for therapeutic applications in neurodegenerative diseases requires a sophisticated approach that builds on current understanding of TNFR2 signaling and structure:

• Structural basis for selective targeting: The more restricted expression of TNFR2 compared to TNFR1 makes it a more attractive molecular target for drug development . TNFR2-selective agonists should be designed to specifically bind TNFR2 without activating TNFR1 to avoid the pleiotropic effects and severe side effects associated with systemic TNFR1 activation .

• Targeting neuroprotective pathways: Since TNFR2 promotes neuronal survival downstream , agonists should be designed to activate specific pathways that enhance this neuroprotective effect. The activation of TNFR2 signaling has been posited as a promising strategy for AD therapy .

• Consideration of genetic variants: The design of TNFR2 agonists should account for genetic variations in TNFRSF1B, particularly rs976881, which affects the interaction with sTNFR2 and modulates multiple AD-associated severity markers . Personalized approaches based on patient genotype might optimize therapeutic efficacy.

• Biomarker-guided development: Target engagement and efficacy should be monitored using established biomarkers, such as CSF t-tau and p-tau levels, MRI measures of brain volume, and functional assessments like CDR-SB scores .

• Cell-specific delivery strategies: Since TNFR2 is expressed in multiple tissues including the central nervous system , delivery strategies that enhance CNS penetration while minimizing peripheral effects should be considered to maximize efficacy and reduce potential side effects.

What experimental approaches can distinguish between TNFR1 and TNFR2 signaling in complex cellular systems?

Distinguishing between TNFR1 and TNFR2 signaling in complex cellular systems requires sophisticated experimental approaches:

• Receptor-selective ligands: Utilize engineered variants of TNF or novel compounds that selectively bind and activate either TNFR1 or TNFR2. This approach allows for the specific stimulation of one receptor pathway without activating the other.

• Genetic approaches:

  • CRISPR/Cas9-mediated receptor knockout: Generate cell lines or animal models with selective deletion of either TNFR1 or TNFR2

  • RNA interference: Use siRNA or shRNA to selectively downregulate either receptor

  • Overexpression studies: Introduce wild-type or mutant forms of each receptor to examine specific signaling effects

• Pathway-specific readouts: Monitor specific downstream effects characteristic of each receptor:

  • TNFR1-specific: Measure activation of TNFR1-associated death domain, Fas-associated death domain, and apoptotic markers

  • TNFR2-specific: Assess TRAF2 recruitment, NF-κB activation, and proliferative responses

• Context-dependent analysis: Since IL-2 stimulation can alter TNFR2 signaling outcomes in T cells (shifting from proliferative to apoptotic) , experimental designs should control for and investigate the cellular context and activation state.

• Time-resolved analysis: Given the temporal differences in receptor expression (e.g., peak TNFR2 expression occurring approximately 3 days after peak IL-2 receptor expression in T cells) , time-resolved experiments are essential to distinguish between early TNFR1-dominated responses and later TNFR2-mediated effects.

• Single-cell approaches: Use single-cell RNA-seq or CyTOF to resolve heterogeneous responses in mixed cell populations, allowing for the identification of cell-specific signaling patterns through each receptor.

How might genotype-based stratification improve clinical trials targeting the TNFR2 pathway?

Genotype-based stratification for TNFRSF1B variants could significantly enhance clinical trials targeting the TNFR2 pathway:

• Identification of responsive populations: Given that TNFRSF1B variants like rs976881 and rs1061622 affect sTNFR2 levels and disease outcomes , stratification based on these variants could identify patient subgroups more likely to respond to TNFR2-targeted therapies.

• Biomarker-guided trial design: The interaction between rs976881 and CSF sTNFR2 levels modulates multiple AD biomarkers , suggesting that trials should measure both genotype and sTNFR2 levels to predict treatment response. This approach enables:

  • More homogeneous trial cohorts

  • Potentially smaller sample sizes due to reduced variability

  • Clear biological rationale for expected treatment effects

• Reduction in trial failure rates: By excluding genetic variants associated with poor treatment response or increased adverse effects, trials may demonstrate efficacy that would otherwise be masked in heterogeneous populations.

• Personalized dosing strategies: Different genotypes may require different dosing regimens to achieve optimal receptor engagement and therapeutic effect, which could be incorporated into adaptive trial designs.

• Combination therapy approaches: Genotype data could inform which patients might benefit from combination approaches targeting both TNFR2 and complementary pathways based on their specific genetic profile.

What are the key methodological challenges in studying the role of TNFRSF1B variants in different disease contexts?

Researchers face several methodological challenges when studying TNFRSF1B variants across disease contexts:

• Linkage disequilibrium complexities: TNFRSF1B variants such as rs976881 and rs1061622 have been found to be in linkage equilibrium (R² = 0.022) , but this may vary across populations. Comprehensive haplotype analysis rather than single SNP analysis may be necessary to capture the full genetic effect.

• Tissue-specific expression patterns: Since TNFR2 expression is relatively restricted compared to TNFR1 , disease-relevant tissues must be appropriately sampled. For example, studies of neurodegenerative diseases require CSF or brain tissue samples, while immunological studies may focus on specific T cell populations.

• Interaction effects: As demonstrated by the interaction between rs976881 and CSF sTNFR2 levels affecting multiple AD biomarkers , complex interaction analyses are required rather than simple association studies. This necessitates:

  • Larger sample sizes to achieve adequate statistical power

  • More sophisticated statistical models to detect interaction effects

  • Multiple testing corrections like the Benjamini-Hochberg false discovery rate

• Longitudinal assessment requirements: Since TNFRSF1B variants may affect disease progression over time rather than just baseline characteristics , longitudinal study designs with appropriate time points are essential but logistically challenging.

• Integration of multiple data types: Comprehensive understanding requires integration of genomic, transcriptomic, proteomic, and clinical data, which presents significant bioinformatic and analytical challenges.

• Functional validation: Moving beyond association to causality requires functional validation of variant effects through techniques like gene editing, reporter assays for promoter variants, and protein expression/function studies for coding variants.