Recombinant Human Tumor necrosis factor ligand superfamily member 13B (TNFSF13B), partial (Active)

What is the structural characterization of recombinant human TNFSF13B?

Recombinant human TNFSF13B (BAFF) is a 285-amino acid peptide glycoprotein that undergoes glycosylation at residue 124. It exists in both membrane-bound and soluble forms, with the soluble form generated through proteolytic cleavage. The protein belongs to the tumor necrosis factor (TNF) ligand family and functions as a cytokine primarily expressed in B-cell lineage cells . In its natural state, BAFF is expressed as a membrane-bound type II transmembrane protein on various cell types including monocytes, dendritic cells, and bone marrow stromal cells . Structurally, TNFSF13B can form both trimers and higher-order oligomers that influence its biological activity and receptor binding capabilities.

What receptors does TNFSF13B interact with and how are these interactions studied?

TNFSF13B interacts with three primary receptors: TNFRSF13C/BAFF-R, TNFRSF13B/TACI, and TNFRSF17/BCMA, each with differing binding affinities . These receptors are expressed mainly on mature B lymphocytes, with expression varying according to B-cell maturation stage. TACI is also found on a subset of T-cells while BCMA is predominantly expressed on plasma cells . The interactions can be studied using several techniques:

• Surface plasmon resonance to measure binding kinetics and affinity constants

• Co-immunoprecipitation to detect protein-protein interactions

• FRET (Fluorescence Resonance Energy Transfer) for real-time interaction analysis

• Receptor competition assays using recombinant soluble receptors

• Crystallography to determine structural binding interfaces

BAFF-R is primarily involved in positive regulation during B-cell development, while TACI has higher affinity for APRIL (a proliferation-inducing ligand similar to BAFF) .

What functional assays are used to evaluate recombinant TNFSF13B activity?

Key functional assays for evaluating recombinant TNFSF13B activity include:

• B-cell survival assays: Primary B cells cultured with TNFSF13B to measure anti-apoptotic effects using Annexin V/PI staining and flow cytometry

• Proliferation assays: Utilizing techniques like BrdU incorporation, CFSE dilution, or Ki-67 staining to assess BAFF-induced proliferation

• Signaling pathway activation: Western blot analysis for NF-κB, ERK, or AKT phosphorylation, or using reporter cell lines

• Immunoglobulin production: Measuring IgG, IgM, or IgA secretion from BAFF-stimulated B cells via ELISA or ELISpot

• B-cell differentiation: Monitoring expression of differentiation markers like CD38 and CD138 following BAFF exposure

• Receptor binding assays: Flow cytometry-based analysis of BAFF binding to cell surface receptors

When designing these assays, researchers should include appropriate positive controls (such as CD40L + IL-4 for B-cell stimulation) and negative controls (receptor-blocking antibodies) to validate specificity.

What methodologies are recommended for studying TNFSF13B gene polymorphisms?

Several methodologies are recommended for studying TNFSF13B gene polymorphisms:

• TaqMan allelic discrimination assay: Commonly used for SNP genotyping, particularly for variants like rs9514828 (−871 C > T)

• PCR-RFLP (Polymerase Chain Reaction-Restriction Fragment Length Polymorphism): Cost-effective for analyzing known restriction sites

• Sanger sequencing: For direct determination of DNA sequence in candidate regions

• Next-generation sequencing: For comprehensive analysis of the entire gene or multiple variants

• GWAS (Genome-Wide Association Studies): For discovering novel associations between TNFSF13B variants and disease phenotypes

When designing genetic association studies, researchers should consider adequate sample sizes, population stratification, and replication in independent cohorts . The BAFF-var insertion-deletion variant (GCTGT > A) has been genotyped using TaqMan allelic discrimination assay in multiple studies investigating autoimmune disease susceptibility .

How do TNFSF13B polymorphisms influence autoimmune disease susceptibility?

TNFSF13B polymorphisms have been associated with susceptibility to multiple autoimmune conditions:

• The BAFF-var variant (GCTGT > A) has been significantly associated with systemic lupus erythematosus (SLE) in Spanish (p = 0.001, OR = 1.41) and German cohorts (p = 0.030, OR = 1.86) . This insertion-deletion variant results in a shorter transcript that escapes microRNA inhibition, leading to increased soluble BAFF production .

• The same BAFF-var has been associated with rheumatoid arthritis (RA), suggesting it is a shared genetic risk factor for autoimmunity .

• The rs9514828 (−871 C > T) polymorphism in the TNFSF13B promoter region has been linked to SLE, RA, and primary Sjögren's syndrome (pSS) .

• The rs1041569 (−2701 A > T) variant has been associated with inflammatory bowel disease and primary Sjögren's syndrome .

These polymorphisms can influence disease susceptibility by altering BAFF expression levels, affecting B-cell homeostasis, and promoting autoantibody production .

How do TNFSF13B variants correlate with clinical outcomes and biomarkers?

TNFSF13B variants correlate with several clinical outcomes and biomarkers:

• The BAFF-var allele is associated with increased levels of total IgG and IgM and with reduced monocyte counts .

• Serum soluble BAFF (s-BAFF) levels are significantly elevated in patients with active SLE compared to those with inactive disease (953.17 pg/ml vs 781.4 pg/mL; p 0.008), showing a weak positive correlation with disease activity (r 0.311; p 0.001) .

• In SLE patients, certain TNFSF13B polymorphisms are associated with higher disease activity, though some studies like that by LSO-057 found no differences in genotype distribution between active and inactive disease groups .

• In cancer research, TNFSF13B expression is correlated with poor prognosis in kidney renal clear cell carcinoma (KIRC), with high expression leading to worse survival outcomes (p = 0.00019) .

• TNFSF13B expression showed maximum partial Spearman's correlation with dendritic cells (partial cor = 0.634, p = 1.35E−52) in tumor microenvironment analysis .

What are the optimal conditions for expressing and purifying recombinant human TNFSF13B?

Optimal conditions for expressing and purifying recombinant human TNFSF13B include:

• Expression systems:

  • E. coli (BL21 strain) for high yield but lacks post-translational modifications

  • Mammalian systems (HEK293 or CHO cells) for properly glycosylated protein

  • Insect cell systems (Sf9, Hi5) as a compromise between yield and modifications

• Expression strategies:

  • Use of fusion tags (His6, GST, or MBP) to enhance solubility and facilitate purification

  • Codon optimization for the host expression system

  • Temperature optimization (typically 16-25°C for E. coli) to minimize inclusion body formation

• Purification methods:

  • Affinity chromatography (Ni-NTA for His-tagged proteins) as initial capture step

  • Ion exchange chromatography to separate charged variants

  • Size-exclusion chromatography for final polishing and buffer exchange

  • Endotoxin removal using specialized resins for cell-based applications

• Quality control:

  • SDS-PAGE and Western blotting to confirm purity and identity

  • Mass spectrometry to verify molecular weight and modifications

  • Functional assays (B-cell proliferation) to confirm biological activity

How can researchers effectively measure TNFSF13B expression in clinical samples?

Researchers can effectively measure TNFSF13B expression in clinical samples using several complementary approaches:

• For mRNA quantification:

  • Quantitative RT-PCR using validated reference genes for normalization

  • RNA-seq for comprehensive transcriptomic profiling

  • In situ hybridization for tissue localization

• For protein detection:

  • ELISA for quantifying soluble BAFF in serum, plasma, or synovial fluid

  • Flow cytometry for measuring membrane-bound BAFF on cell surfaces

  • Immunohistochemistry for tissue localization and cell-specific expression

  • Western blotting for total protein quantification

In SLE studies, serum soluble BAFF levels are commonly measured using validated ELISA kits with careful attention to sample collection and storage conditions . When analyzing TNFSF13B in RA patients, researchers examine both synovial tissue and peripheral blood samples to understand local and systemic expression patterns .

What approaches are used to study TNFSF13B in the tumor microenvironment?

Several approaches are used to study TNFSF13B in the tumor microenvironment:

• Computational methods:

  • The ESTIMATE algorithm to analyze tumor gene expression data and identify differentially expressed genes

  • Protein-protein interaction network analysis to identify functional clusters

  • Gene Set Enrichment Analysis (GSEA) to explore immune signaling pathways

  • Tumor Immune Estimation Resource (TIMER) analysis to assess lymphocyte infiltration

• Experimental methods:

  • Immunohistochemistry to visualize TNFSF13B expression in tumor tissues

  • Flow cytometry to identify BAFF-producing cells within the tumor microenvironment

  • Single-cell RNA sequencing to characterize expression at cellular resolution

  • Multiplex cytokine assays to measure BAFF alongside other immune mediators

In kidney renal clear cell carcinoma (KIRC), TNFSF13B expression is positively correlated with immune cell infiltration, particularly dendritic cells, suggesting its role in tumor immunity . High expression of TNFSF13B in tumors is associated with poor prognosis (p = 0.00019), indicating its potential value as a prognostic biomarker .

How can recombinant TNFSF13B be used to evaluate potential BAFF-targeting therapies?

Recombinant TNFSF13B serves as a valuable tool for evaluating potential BAFF-targeting therapies through multiple approaches:

• In vitro binding assays:

  • ELISA-based competition assays to assess binding affinity of therapeutic antibodies

  • Surface plasmon resonance to determine binding kinetics and epitope specificity

  • Fluorescence-based thermal shift assays to evaluate compound binding

• Functional assays:

  • B-cell survival assays to measure neutralization of BAFF activity

  • Reporter cell systems expressing BAFF receptors linked to luciferase or fluorescent proteins

  • Signaling pathway inhibition assessment via phospho-flow cytometry or Western blotting

• Target validation:

  • Ex vivo testing using primary cells from patients with autoimmune diseases

  • Combination studies with other targeted therapies to identify synergistic effects

  • Patient stratification based on BAFF levels or genetic variants

These approaches have been instrumental in developing therapies like belimumab, the first BAFF-targeted therapy approved for SLE .

What is the significance of TNFSF13B polymorphisms for predicting treatment response?

TNFSF13B polymorphisms hold significant potential for predicting treatment response:

• The BAFF-var insertion-deletion (GCTGT > A) leads to increased BAFF production, potentially identifying patients more likely to respond to BAFF-targeted therapies like belimumab .

• Promoter polymorphisms such as rs9514828 affect BAFF expression levels and may influence response to B-cell depleting therapies or BAFF inhibitors .

• Genetic variants could help explain why BAFF-targeting therapies show varying efficacy in different autoimmune conditions - effective in SLE but only moderately effective in RA patients .

• Patient stratification based on TNFSF13B genotype could improve clinical trial outcomes by reducing heterogeneity in study populations.

Research suggests that "patients stratified by TNFSF13B BAFF-var status may show a differential benefit from anti-BAFF therapies" . This highlights the potential role of TNFSF13B polymorphisms in personalized medicine approaches for autoimmune diseases.

How does TNFSF13B interact with other cytokines in autoimmune pathogenesis?

TNFSF13B interacts with multiple cytokines and immune pathways in autoimmune pathogenesis:

• Type I interferons: Upregulate BAFF expression in myeloid cells, creating a feed-forward loop in SLE pathogenesis

• TNF-α: Can either enhance or suppress BAFF expression depending on the cellular context, potentially explaining some paradoxical effects of anti-TNF therapy

• IL-6: Synergizes with BAFF to promote plasma cell differentiation and autoantibody production

• T-cell derived cytokines: Regulate BAFF expression in target tissues, connecting T-cell and B-cell pathways

These interactions create a complex regulatory network that contributes to disease heterogeneity and treatment response variability . In RA, BAFF system dysregulation plays a role in pathogenesis with abnormal levels detected in serum, synovial fluid, and saliva from patients . Understanding these complex interactions is crucial for developing combination therapies that target multiple pathways simultaneously.

What single-cell technologies can advance understanding of TNFSF13B biology?

Single-cell technologies that can advance our understanding of TNFSF13B biology include:

• Single-cell RNA sequencing (scRNA-seq): Identifies specific cellular sources of BAFF and characterizes heterogeneity within producer populations such as monocytes and dendritic cells

• Mass cytometry (CyTOF): Simultaneously measures multiple protein markers including BAFF and its receptors at single-cell resolution

• Spatial transcriptomics: Maps BAFF expression within tissue microenvironments while preserving spatial context

• Single-cell ATAC-seq: Investigates chromatin accessibility to understand transcriptional regulation of TNFSF13B

• CITE-seq: Combines surface protein and transcriptome analysis to correlate BAFF receptor expression with cellular phenotypes

• Single-cell proteomics: Measures BAFF-induced signaling changes across multiple pathways

These technologies can help identify specific cellular populations that produce or respond to BAFF in different disease contexts, potentially revealing new therapeutic targets .

What are the challenges in measuring TNFSF13B in biological samples?

Several challenges exist in measuring TNFSF13B in biological samples:

• Distinguishing forms: Differentiating between membrane-bound and soluble BAFF requires specific antibodies and appropriate sample processing

• Glycosylation heterogeneity: Variable glycosylation patterns can affect antibody recognition and quantification

• Complex formation: BAFF can form heterocomplexes with APRIL or be bound to soluble receptors, potentially masking epitopes

• Reference standards: Lack of standardized reference materials for BAFF quantification

• Pre-analytical variables: Sample collection, processing, and storage conditions can significantly impact BAFF stability and measurements

• Circadian variations: BAFF levels may fluctuate throughout the day, requiring standardized collection times

• Medication effects: Treatments, particularly corticosteroids, can alter BAFF expression

In clinical studies, standardized protocols for sample collection, processing, and storage are essential for accurate and reproducible BAFF measurements .

How can TNFSF13B research inform personalized medicine approaches?

TNFSF13B research can inform personalized medicine approaches in several ways:

• Genetic stratification: TNFSF13B polymorphisms like BAFF-var can identify patient subgroups most likely to benefit from BAFF-targeted therapies .

• Biomarker development: Serum BAFF levels can serve as biomarkers for disease activity monitoring and treatment response prediction .

• Therapeutic selection: Understanding the relative contribution of BAFF to individual patient pathology can guide selection between BAFF-targeted therapies versus other approaches.

• Combination therapy design: Insights into BAFF's interactions with other cytokines can inform rational combination therapy approaches.

• Dosing optimization: Patient-specific BAFF levels may help optimize dosing of BAFF-targeted therapies.

Research has shown that BAFF-inhibitor drugs like belimumab are "only moderately effective in a small number of patients with RA and SLE" . Because these diseases are heterogeneous with different biological subsets, patient stratification based on TNFSF13B status could significantly improve treatment outcomes by matching the right therapy to the right patient.