TNFRSF17 Human, Sf9

What is TNFRSF17 and what are its alternative designations in scientific literature?

TNFRSF17 (Tumor Necrosis Factor Receptor Superfamily Member 17) is also known as BCMA (B-cell maturation protein), CD269, and BCM in scientific literature. It belongs to the TNF receptor superfamily and functions primarily in B-cell development and immune response regulation .

What are the structural characteristics of TNFRSF17 protein?

TNFRSF17 is a type III membrane protein containing one extracellular cysteine-rich domain. Within the TNF receptor superfamily, it shares the highest homology with TACI. The native human protein contains approximately 184 amino acids, with the extracellular domain spanning amino acids 78-184 . The Sf9-produced recombinant version typically contains 296 amino acids (including tags) with a molecular mass of approximately 33.1 kDa .

What are the primary biological functions of TNFRSF17?

TNFRSF17 serves as a receptor for TNFSF13B (BAFF/BLyS) and TNFSF13 (APRIL). Its primary functions include:

• Promoting B-cell survival

• Regulating humoral immunity

• Activating NF-kappa-B and JNK signaling pathways

• Supporting B-cell development

• Transducing signals for cell survival and proliferation

The binding of TNFRSF17 to APRIL or BAFF has been shown to stimulate IgM production in peripheral blood B cells and increase the survival of cultured B cells .

What are the key differences between TNFRSF17 expressed in E. coli versus Sf9 insect cells?

The Sf9-expressed version more closely mimics the native human protein due to post-translational modifications (glycosylation) .

What optimal storage conditions should be used for TNFRSF17 recombinant proteins?

For TNFRSF17 recombinant proteins, storage conditions depend on the intended usage timeframe:

• Short-term use (2-4 weeks): Store at 4°C

• Long-term storage: Store frozen at -20°C

• For extended stability: Add a carrier protein (0.1% HSA or BSA) for long-term storage

• Avoid multiple freeze-thaw cycles

The protein is typically supplied in a buffer containing either:

• 10% glycerol and Phosphate-Buffered Saline (pH 7.4) for the Sf9-expressed version

• 20mM Tris-HCl buffer (pH 8.0), 0.15M NaCl, 10% glycerol and 1mM DTT for the E. coli version

What purification strategies are most effective for TNFRSF17 recombinant proteins?

The effective purification of TNFRSF17 typically involves multiple chromatographic steps:

• For Sf9-expressed TNFRSF17:

  • Initial capture via the C-terminal His-tag using immobilized metal affinity chromatography

  • Secondary purification using proprietary chromatographic techniques

  • Final purity confirmation through SDS-PAGE (>95% purity)

• For TNF receptor family proteins in general:

  • Refolding from inclusion bodies may be necessary for E. coli-expressed proteins

  • Ion exchange chromatography using Uno-S columns has proven effective

  • 0.2 μm filtration to ensure sterility and remove aggregates

How can surface plasmon resonance be optimized for studying TNFRSF17 binding interactions?

For optimal surface plasmon resonance (SPR) studies with TNFRSF17:

• Immobilization:

  • Immobilize TNF family ligands on CM5 sensorchips using amine coupling (NHS/EDC)

  • Dilute ligands in 10 mM acetate buffer

  • Cap excess free amine groups with 1 M ethanolamine

  • Use flow rates of approximately 10 μL/min during immobilization

• Binding assays:

  • Prepare soluble TNFRSF17 in appropriate assay buffer (e.g., with 0.05% BSA)

  • Test multiple concentrations to establish dose-response relationships

  • Use flow rates of 30-50 μL/min for association/dissociation monitoring

  • Include underivatized surfaces as background controls

• Surface regeneration:

  • Optimize regeneration conditions (e.g., 10 mM H₃PO₄ or 50 mM glycine pH 4.0 with 500 mM NaCl)

  • Verify surface stability through multiple regeneration cycles

  • Monitor for any decrease in baseline signal or binding capacity

• Data analysis:

  • Perform "double referencing" by subtracting both underivatized surface data and buffer-only controls

  • Fit data to appropriate kinetic binding models to determine kon, koff, and KD values

How can researchers investigate TNFRSF17's role in plasma cell longevity and survival?

Investigating TNFRSF17's role in plasma cell longevity requires multi-faceted approaches:

• Transcriptional analysis:

  • Use single-cell RNA sequencing (scRNA-seq) to analyze TNFRSF17 expression in plasma cells

  • Sort plasma cells based on longevity markers like CD19 and CD45

  • Compare expression patterns between short-lived, intermediate, and long-lived plasma cell populations

• Differential expression analysis:

  • Examine whether TNFRSF17 expression correlates with established longevity markers such as CD27

  • Look for co-expression with genes involved in metabolism (AMPD1, QPCT) and apoptosis inhibition (BIRC3)

• Disease context investigations:

  • Compare TNFRSF17 expression between patient cohorts (e.g., celiac disease patients) and controls

  • Analyze expression patterns in both untreated and treated disease states

Recent research has identified wide expression of TNFRSF17 (B cell maturation antigen) in intestinal plasma cells, with differential expression patterns correlating with plasma cell longevity markers .

What experimental approaches can distinguish between the effects of different TNFRSF17 ligands?

To differentiate the effects of BAFF versus APRIL binding to TNFRSF17:

• Binding kinetics analysis:

  • Immobilize each ligand separately on biosensor surfaces

  • Measure association/dissociation rates and equilibrium constants

  • Compare binding profiles at different pH and salt concentrations

• Signaling pathway analysis:

  • Monitor activation of downstream pathways (NF-κB, JNK)

  • Examine phosphorylation of specific pathway components

  • Compare signaling kinetics and magnitude between ligands

• Competitive binding assays:

  • Pre-incubate TNFRSF17 with one ligand before exposure to the second

  • Determine whether binding is competitive, non-competitive, or allosteric

• Mutagenesis studies:

  • Generate site-directed mutants of the TNFRSF17 cysteine-rich domain

  • Test how specific mutations affect binding of each ligand differently

How can researchers analyze the impact of glycosylation on TNFRSF17 function?

To assess glycosylation effects on TNFRSF17:

• Comparative binding studies:

  • Use SPR to compare binding kinetics of Sf9-expressed (glycosylated) versus E. coli-expressed (non-glycosylated) TNFRSF17

  • Calculate and compare association/dissociation rates and equilibrium constants

• Enzymatic deglycosylation:

  • Treat Sf9-expressed TNFRSF17 with glycosidases (PNGase F, Endo H)

  • Compare functionality before and after deglycosylation

• Glycosylation site mapping:

  • Identify glycosylation sites using mass spectrometry

  • Generate site-directed mutants with altered glycosylation sites

• Functional assays:

  • Compare the ability of glycosylated versus non-glycosylated forms to activate signaling pathways

  • Assess thermal stability and resistance to proteolytic degradation

What roles does TNFRSF17 play in autoimmune and inflammatory conditions?

Evidence from transcriptional studies suggests TNFRSF17 has important roles in immune-mediated conditions:

• In celiac disease:

  • Differential expression of TNFRSF17 has been observed between untreated celiac disease patients, treated patients, and controls

  • Expression patterns correlate with disease activity

  • TNFRSF17 shows coordinated expression with other immune regulatory genes

• Potential mechanisms in autoimmunity:

  • Support for long-lived antibody-producing plasma cells

  • Regulation of B-cell survival and antibody production

  • Modulation of NF-κB signaling pathways implicated in inflammation

• Research approaches:

  • Single-cell transcriptomics to identify TNFRSF17-expressing cell subsets in disease tissues

  • Correlation of expression levels with disease severity markers

  • Functional studies examining how TNFRSF17 signaling affects inflammatory mediator production

How can researchers develop functional assays to evaluate TNFRSF17-targeted therapeutics?

For developing TNFRSF17-targeted therapeutic evaluation assays:

• Cell-based assays:

  • Establish B-cell lines expressing defined levels of TNFRSF17

  • Develop reporter systems with NF-κB or AP-1 response elements

  • Design assays measuring survival, proliferation, or activation markers

• Binding displacement assays:

  • Set up competition assays between therapeutic candidates and natural ligands

  • Use labeled ligands and measure displacement by potential therapeutics

• Signaling pathway analysis:

  • Monitor effects on downstream signaling (phospho-specific flow cytometry)

  • Assess pathway inhibition through Western blotting or ELISA-based methods

• Validation strategies:

  • Include positive controls (validated TNFRSF17 antagonists)

  • Incorporate specificity controls (targeting related TNF receptors)

  • Ensure reproducibility through standardized protocols and statistical validation

What strategies can address protein stability and aggregation issues with TNFRSF17?

To overcome stability challenges with TNFRSF17 recombinant proteins:

• Buffer optimization:

  • Test various stabilizing agents (glycerol, trehalose, low concentrations of non-ionic detergents)

  • Adjust pH and ionic strength to identify optimal conditions

  • Consider adding reducing agents (DTT, TCEP) at appropriate concentrations

• Storage strategies:

  • Aliquot protein solutions to minimize freeze-thaw cycles

  • For long-term storage, add carrier proteins (BSA, HSA)

  • Monitor protein stability using analytical SEC or DLS techniques

• Handling protocols:

  • Maintain cold chain during experiments

  • Centrifuge protein solutions before use to remove potential aggregates

  • Validate activity periodically using standardized binding assays

How can non-specific binding be minimized in TNFRSF17 binding assays?

To reduce non-specific binding in TNFRSF17 assays:

• Buffer modifications:

  • Include blocking agents (0.05-0.1% BSA or HSA)

  • Adjust salt concentration (150-500 mM NaCl)

  • Add low concentrations of surfactants (0.0005% P-20)

• Experimental design:

  • Employ proper reference surfaces in SPR experiments

  • Perform double-referencing during data analysis

  • Include appropriate negative controls (unrelated proteins of similar size/structure)

• Sample preparation:

  • Pre-clear samples by centrifugation or filtration

  • Consider pre-running samples through a blank surface to remove components with high non-specific binding potential

  • Dialyze or buffer-exchange samples to remove potential interfering substances

What statistical approaches are recommended for analyzing TNFRSF17 experimental data?

For robust statistical analysis of TNFRSF17 data:

• For binding studies:

  • Apply global fitting to multiple concentration datasets simultaneously

  • Calculate 95% confidence intervals for all binding parameters

  • Validate binding models using residual analysis and chi-square tests

• For comparative studies:

  • Use paired Wilcoxon signed-rank tests for comparing different specific vs. non-specific binding within samples

  • Apply unpaired Wilcoxon rank-sum tests for comparing between different sample groups (e.g., disease vs. control)

• General statistical considerations:

  • Adjust p-values for multiple testing to control false discovery rates

  • Calculate one-way ANOVA to assess global differences between means

  • Apply appropriate regression models to analyze relationships between variables

• Data reporting standards:

  • Report p-value significance levels with appropriate notation (* p<0.05, ** p<0.01, etc.)

  • Include sufficient replicates (typically n≥3) for statistical validity

  • Provide clear descriptions of statistical methods in research publications