Recombinant Human Tumor necrosis factor receptor superfamily member 11B (TNFRSF11B) (Active)

What is TNFRSF11B and what are its primary functions in human physiology?

TNFRSF11B (Osteoprotegerin) functions as a decoy receptor for TNF superfamily ligands, particularly RANK Ligand (RANKL) and TRAIL (TNF-related apoptosis-inducing ligand). It is widely expressed and constitutively released as a homodimer by mesenchymal stem cells, fibroblasts, and endothelial cells .

The primary functions of TNFRSF11B include:

• Inhibition of osteoclastogenesis by preventing RANKL-RANK interactions

• Regulation of bone metabolism and homeostasis

• Protection against TRAIL-induced apoptosis

• Modulation of cartilage development and maintenance

TNFRSF11B deficiency can cause juvenile Paget's disease in humans, and insufficient levels relative to RANKL and RANK can produce osteoporosis and vascular calcification in both mice and humans .

How should researchers isolate and culture primary cells for TNFRSF11B studies?

Based on established protocols in the RAAK study , isolation and culture of primary articular chondrocytes for TNFRSF11B studies can be performed as follows:

• Obtain human primary articular chondrocytes (hPACs) from knee replacement surgeries (with appropriate ethical approval and informed consent).

• Culture cells in DMEM high glucose medium supplemented with 10% FCS, 100 U/ml penicillin, 100 μg/ml streptomycin, and 0.5 ng/ml bFGF-2.

• For 3D chondrogenic models, generate neo-cartilage from 250,000 cells in pellet culture for seven days.

• Pool two pellets together to generate independent biological replicates for downstream analyses.

• For RNA isolation, process total mRNA (150 ng) with appropriate cDNA synthesis kits.

This approach enables reliable assessment of TNFRSF11B function in a physiologically relevant context .

What methods are most effective for detecting TNFRSF11B expression in experimental models?

Multiple complementary techniques should be employed for comprehensive TNFRSF11B detection:

• RT-qPCR: Use for quantitative mRNA expression analysis. Housekeeping genes such as GAPDH and Acidic ribosomal phosphoprotein P0 (ARP) serve as reliable internal controls for normalization .

• Immunohistochemistry: Particularly useful for assessing protein localization and expression patterns in tissue sections.

• ELISA: Effective for quantifying secreted TNFRSF11B in culture media and biological fluids.

• Western Blot: Appropriate for assessing protein expression levels and post-translational modifications.

• RNA Sequencing: For comprehensive transcriptomic analysis and identification of co-expressed genes, as demonstrated in the RAAK study which identified 51 genes highly correlated with TNFRSF11B (r≥0.75) .

For optimal results, researchers should validate findings using at least two independent detection methods.

How can researchers effectively modulate TNFRSF11B expression in cellular models?

Based on published methodologies, TNFRSF11B expression can be modulated through several approaches:

For Overexpression:

• Lentiviral Transduction: Clone TNFRSF11B into lentiviral vectors (e.g., pLV.CMV.bc.eGFP) using appropriate restriction sites (AgeI and NheI have been successfully used) . Transduce target cells at a multiplicity of infection (MOI) of 1, which provides effective overexpression while minimizing cytotoxicity .

• Verification Protocol:

  • Confirm successful transduction by RT-qPCR

  • Validate protein expression by immunohistochemistry

  • Quantify secreted protein by ELISA

  • Assess functional changes through appropriate downstream assays

For Knockdown/Knockout:

• siRNA or shRNA approaches targeting specific regions of TNFRSF11B mRNA

• CRISPR-Cas9 genome editing for complete gene knockout

• Dominant negative constructs that interfere with protein dimerization

When evaluating the effects of modulation, it is critical to measure both intracellular and secreted TNFRSF11B, as the protein functions primarily in its secreted form .

What signaling pathways does TNFRSF11B regulate in different tissue contexts?

TNFRSF11B regulates multiple signaling pathways in a tissue-specific manner:

In Bone/Cartilage:

• Inhibits RANKL-RANK signaling, thereby suppressing NF-κB activation and osteoclast differentiation

• Upregulates MMP13 (14.76-fold increase upon overexpression), indicating matrix remodeling effects

• Increases expression of COL2A1 (4.77-fold) and COL1A1 (1.88-fold), suggesting anabolic matrix effects

• Enhances mineralization markers including RUNX2 (1.68-fold) and ASPN (2.61-fold)

• Strongly upregulates BMP6 (9.34-fold), suggesting activation of BMP signaling pathways

In Cancer:

• Activates Wnt/β-catenin signaling in gastric cancer, promoting cell proliferation, migration, and invasion

• Directly interacts with GSK-3β, a key regulator of β-catenin stability

• Inhibits apoptosis in cancer cells

These differential effects highlight the context-dependent functions of TNFRSF11B and the importance of tissue-specific experimental design.

What are the optimal experimental conditions for studying TNFRSF11B protein-protein interactions?

For robust analysis of TNFRSF11B protein-protein interactions:

• Co-immunoprecipitation (Co-IP):

  • Use mild lysis buffers containing 1% NP-40 or 0.5% Triton X-100 to preserve protein complexes

  • Include protease and phosphatase inhibitors to prevent degradation

  • Cross-validation with reciprocal pull-downs is essential (e.g., IP with anti-TNFRSF11B and blot for interacting partner, then IP with antibody against partner and blot for TNFRSF11B)

  • This approach has successfully demonstrated TNFRSF11B interaction with GSK3β in gastric cancer cells

• Proximity Ligation Assay (PLA):

  • Useful for detecting protein interactions in situ with high sensitivity

  • Particularly valuable for confirming interactions in tissue sections

• Surface Plasmon Resonance (SPR):

  • For quantitative analysis of binding kinetics between purified TNFRSF11B and potential binding partners

  • Requires recombinant proteins of high purity (>95%)

• FRET/BRET Assays:

  • For real-time monitoring of protein interactions in living cells

  • Requires fusion of fluorescent/luminescent tags to TNFRSF11B and partner proteins

When reporting interaction data, researchers should include appropriate negative controls and quantitative measurements of binding strength.

How does TNFRSF11B contribute to osteoarthritis pathogenesis?

TNFRSF11B has emerged as a significant factor in osteoarthritis (OA) development through several mechanisms:

• Matrix Remodeling Effects:

  • TNFRSF11B is among the highest upregulated genes in lesioned OA cartilage (RAAK study)

  • Overexpression significantly increases MMP13 (14.76-fold), a key matrix-degrading enzyme in OA

  • Simultaneously upregulates both COL2A1 (4.77-fold) and COL1A1 (1.88-fold), suggesting altered matrix composition

• Mineralization Promotion:

  • Increases expression of mineralization and osteoblast characteristic markers including RUNX2, ASPN, and OGN

  • May contribute to cartilage calcification, a hallmark of advanced OA

• Novel Signaling Pathways:

  • Strongly upregulates BMP6 (9.34-fold), suggesting activation of previously unknown downstream pathways in cartilage

  • Downregulates SLC15A3 (2.5-fold), though the functional significance requires further investigation

• Genetic Evidence:

  • Gain-of-function mutations in TNFRSF11B have been identified in families with early-onset OA with chondrocalcinosis

  • Mutations in osteoprotegerin account for the CCAL1 locus in calcium pyrophosphate deposition disease

These findings indicate that TNFRSF11B plays a complex role in OA, affecting both anabolic and catabolic processes in cartilage.

What are the challenges in interpreting TNFRSF11B-related data across different experimental systems?

Researchers face several challenges when interpreting TNFRSF11B data:

• Context-Dependent Functions:

  • TNFRSF11B exhibits different, sometimes contradictory functions depending on tissue context

  • In bone, it primarily inhibits osteoclastogenesis and preserves bone mass

  • In cancer, it can promote proliferation and invasion through Wnt/β-catenin signaling

  • In cartilage, it simultaneously upregulates both matrix-building (COL2A1) and matrix-degrading (MMP13) factors

• Temporal Dynamics:

  • Expression regulation by estrogen, parathyroid hormone, and cytokines is complex and changes with age

  • Short-term versus long-term effects may differ significantly

• Interaction Complexity:

  • Multiple binding partners with different affinities (RANKL, TRAIL, syndecan-1 heparin sulfates)

  • Both intracellular and extracellular regulatory mechanisms exist

  • Within osteoblasts, OPG interacts with RANKL in the Golgi to inhibit RANKL secretion

  • Extracellularly, OPG-RANKL binding results in clathrin-mediated internalization and degradation of both proteins

• Methodological Considerations:

  • 2D versus 3D culture systems yield different results

  • Species differences in TNFRSF11B function and regulation

  • Importance of proper normalization and controls

To address these challenges, researchers should:

• Use multiple complementary models and approaches

• Include appropriate time-course analyses

• Consider both cell-autonomous and non-cell-autonomous effects

• Validate findings across different experimental systems

How can researchers evaluate TNFRSF11B as a therapeutic target in disease models?

When evaluating TNFRSF11B as a therapeutic target, researchers should consider:

• Target Validation Approaches:

  • Genetic modulation (overexpression, knockdown) in relevant cell types

  • Pharmacological modulation using recombinant protein or inhibitors

  • Assessment of target engagement and downstream pathway modulation

  • Correlation of TNFRSF11B levels with disease progression markers

• In Vitro Assays:

  • For osteoarthritis: 3D pellet culture models with primary articular chondrocytes

  • For cancer: Cell proliferation, migration, invasion assays, and apoptosis assessment

  • For bone disorders: Osteoclast differentiation and activity assays

• In Vivo Models:

  • Transgenic models with tissue-specific TNFRSF11B modulation

  • Xenograft tumor models for cancer studies

  • Surgical or chemical induction of osteoarthritis followed by TNFRSF11B modulation

• Biomarker Assessment:

  • Measure effects on established disease biomarkers

  • Monitor changes in TNFRSF11B-associated gene expression signatures

  • Assess matrix components (GAGs, collagens) in osteoarthritis models

The effective concentration of recombinant TNFRSF11B protein in bioactivity assays is typically 8-24 ng/mL , which can serve as a starting point for dosing studies.

What clinical biomarker potential does TNFRSF11B hold in different disease contexts?

TNFRSF11B has emerging potential as a clinical biomarker in multiple diseases:

In Osteoarthritis:

• Significantly upregulated in lesioned compared to preserved OA cartilage

• May indicate advanced disease with mineralization and matrix remodeling

• Could potentially identify patients with specific OA subtypes (e.g., those with chondrocalcinosis)

In Cancer:

• High expression in gastric cancer associates with poor patient outcomes

• Potential marker for tumors with activated Wnt/β-catenin signaling

• May identify cancers likely to show aggressive growth and invasion behaviors

In Bone Disorders:

• TNFRSF11B deficiency is linked to juvenile Paget's disease

• Altered TNFRSF11B/RANKL ratios associate with osteoporosis risk

• May predict risk of vascular calcification

To establish TNFRSF11B as a reliable biomarker, researchers should:

• Determine normal reference ranges in relevant biological fluids

• Assess sensitivity and specificity for disease detection

• Evaluate correlation with disease severity and progression

• Compare performance against existing clinical biomarkers

• Validate across multiple independent patient cohorts

What are the best approaches for analyzing large-scale data to identify TNFRSF11B-associated gene networks?

For comprehensive analysis of TNFRSF11B-associated gene networks:

• Co-expression Network Analysis:

  • The RAAK study demonstrated successful identification of TNFRSF11B-correlated genes in OA cartilage

  • Focus on genes with strong correlation coefficients (r≥0.75 has proven effective)

  • The highest positively correlated genes identified include CDH19 (r=0.88), ATP1A1 (r=0.87), and DIXDC1 (r=0.85)

  • Inversely correlated genes include SLC15A3 (r=-0.81), MAPK11 (r=-0.81), and HLA-E (r=-0.8)

• Functional Enrichment Analysis:

  • Group co-expressed genes by biological pathways and cellular components

  • Tools like DAVID, GSEA, or Metascape can identify enriched functional categories

  • Focus on unexpected pathways that may reveal novel TNFRSF11B functions

• Protein-Protein Interaction Networks:

  • Combine co-expression data with protein interaction databases (e.g., STRING)

  • Prioritize genes for validation based on both correlation and functional connections

  • This approach successfully identified 30 genes for validation from an initial set of 51 correlated genes

• Experimental Validation:

  • Test effects of TNFRSF11B modulation on expression of network genes

  • From 30 tested genes correlated with TNFRSF11B, 8 were confirmed to be significantly affected by TNFRSF11B overexpression

  • BMP6 showed the strongest response (9.34-fold increase), suggesting a previously unknown regulatory relationship

This multi-layer approach enables identification of both known and novel TNFRSF11B-associated pathways in specific disease contexts.

What are the optimal storage and handling conditions for recombinant TNFRSF11B protein?

For maintaining optimal activity of recombinant human TNFRSF11B:

• Storage Recommendations:

  • Store lyophilized recombinant protein at -20°C to -80°C

  • After reconstitution, prepare single-use aliquots to avoid freeze-thaw cycles

  • Store reconstituted protein at -80°C for long-term storage or at 4°C for up to one week

• Reconstitution Guidelines:

  • Reconstitute in sterile, buffer-appropriate solutions (PBS or manufacturer-recommended buffer)

  • For cell culture applications, filter-sterilize using 0.22 μm filters

  • Prepare at concentrations ≥100 μg/mL for stability

  • Include carrier proteins (0.1-1% BSA) for dilute solutions to prevent adsorption to tubes

• Quality Control:

  • Verify protein purity (≥95% is optimal for research applications)

  • Confirm activity using established bioassays

  • The effective concentration in bioactivity assays is typically 8-24 ng/mL

• Working Solution Preparation:

  • Dilute to working concentration immediately before use

  • Maintain sterile conditions throughout handling

  • Consider carrier-free preparations for applications sensitive to additional proteins

Proper handling ensures maximal protein activity and reproducible experimental results.

What controls should be included in experiments evaluating TNFRSF11B function?

Comprehensive control strategies for TNFRSF11B experiments include:

• Expression Modulation Studies:

  • Positive controls: Empty vector controls for overexpression studies

  • Dose-response: Test multiple concentrations/expression levels

  • Time-course: Evaluate acute vs. chronic effects

  • Specificity controls: Include related proteins from TNF receptor family

  • Rescue experiments: Restore phenotype by re-expression after knockdown

• Functional Assays:

  • Cellular context controls: Test effects in multiple relevant cell types

  • Pathway validation: Measure downstream signaling activation and inhibition

  • Biological validation: Correlate in vitro findings with in vivo and clinical observations

• Interaction Studies:

  • Binding specificity: Include known binding partners (RANKL, TRAIL) as positive controls

  • Domain mapping: Test interaction with truncated or mutated versions

  • Competition assays: Demonstrate specificity through competitive binding

  • Non-binding controls: Include proteins not expected to interact with TNFRSF11B

• Technical Controls:

  • Housekeeping genes: Use multiple reference genes for RT-qPCR (e.g., GAPDH and ARP)

  • Loading controls: For protein expression analysis

  • Antibody validation: Confirm specificity with recombinant protein and knockout/knockdown samples

Proper controls ensure reliable and reproducible results that can be confidently interpreted in the context of TNFRSF11B biology.

How does TNFRSF11B contribute to cancer development and progression?

TNFRSF11B has emerging roles in cancer biology beyond its canonical function in bone metabolism:

• Wnt/β-catenin Pathway Activation:

  • In gastric cancer, TNFRSF11B directly combines with GSK-3β, leading to upregulation of active β-catenin

  • This activation promotes cell proliferation, migration, and invasion in vitro and tumorigenic ability in vivo

  • The proportion of nuclear active β-catenin shows positive correlation with TNFRSF11B expression

• Anti-apoptotic Effects:

  • TNFRSF11B inhibits gastric cancer cell apoptosis

  • It can inhibit TRAIL-induced apoptosis in multiple cell types

  • TRAIL decreases the release of OPG from cells that express it, creating a regulatory feedback loop

• Clinical Correlation:

  • High TNFRSF11B expression in the cytoplasm of gastric cancer cells is associated with poor patient outcomes

  • May serve as a prognostic biomarker in multiple cancer types

• Potential Mechanisms in Other Cancers:

  • In multiple myeloma, binding of OPG by syndecan-1 heparin sulfates on cancer cells results in OPG internalization and degradation, contributing to bone loss

  • May contribute to resistance against anticancer kinase inhibitors in certain cancers

These findings suggest TNFRSF11B as a potential therapeutic target in multiple cancer types, particularly those showing aberrant Wnt/β-catenin signaling.

What novel methodologies are emerging for studying TNFRSF11B in complex disease models?

Several cutting-edge approaches are advancing TNFRSF11B research:

• Organoid Models:

  • 3D culture systems that better recapitulate tissue architecture and cellular heterogeneity

  • Allow for investigation of TNFRSF11B function in complex microenvironments

  • Can be derived from patient samples for personalized disease modeling

• Single-Cell Analysis:

  • Single-cell RNA sequencing to identify cell-specific TNFRSF11B expression patterns

  • Spatial transcriptomics to map TNFRSF11B expression in tissue context

  • Mass cytometry (CyTOF) for high-dimensional protein analysis at single-cell resolution

• CRISPR-Based Approaches:

  • CRISPR activation/inhibition for precise modulation of TNFRSF11B expression

  • CRISPR screens to identify synthetic lethal interactions with TNFRSF11B

  • Base editing for modeling disease-associated TNFRSF11B variants

• Advanced Imaging Techniques:

  • Live-cell imaging of TNFRSF11B trafficking and secretion

  • Super-resolution microscopy for detailed localization studies

  • Intravital microscopy for in vivo visualization of TNFRSF11B function

• Computational Approaches:

  • Machine learning for predicting TNFRSF11B functional networks

  • Systems biology modeling of TNFRSF11B signaling dynamics

  • Integration of multi-omics data to understand TNFRSF11B in disease contexts

These emerging methodologies promise to provide deeper insights into TNFRSF11B biology and its role in complex diseases.

How can contradictory findings about TNFRSF11B function be reconciled in different disease contexts?

Reconciling contradictory findings about TNFRSF11B requires consideration of several factors:

• Tissue-Specific Signaling Networks:

  • TNFRSF11B interacts with different partners depending on cellular context

  • In bone, it primarily functions as a decoy receptor for RANKL

  • In cancer, it may activate Wnt/β-catenin signaling through GSK-3β interaction

  • In cartilage, it simultaneously promotes both anabolic (COL2A1, COL1A1) and catabolic (MMP13) responses

• Concentration-Dependent Effects:

  • Low versus high concentrations may activate different signaling pathways

  • The effective concentration in bioactivity assays (8-24 ng/mL) may differ from physiological or pathological concentrations in vivo

• Post-Translational Modifications:

  • Different glycosylation patterns may affect binding partners and function

  • Proteolytic processing may generate fragments with distinct activities

• Experimental Model Considerations:

  • 2D versus 3D culture systems (e.g., the 3D pellet culture used in the RAAK study)

  • Acute versus chronic exposure

  • Recombinant protein versus endogenous expression

• Integrated Analysis Approach:

  • Compare findings across multiple experimental systems

  • Develop computational models that incorporate context-dependent parameters

  • Design experiments specifically to test contradictory hypotheses in the same model

By systematically addressing these factors, researchers can develop a more nuanced understanding of TNFRSF11B's multifaceted roles in health and disease.