Recombinant Mouse Tumor necrosis factor ligand superfamily member 11 (Tnfsf11), partial (Active)

What is the molecular structure of mouse TNFSF11?

Mouse TNFSF11 is a type II transmembrane protein comprising 316 amino acids, with a predicted cytoplasmic domain of 48 amino acids and an extracellular domain of 247 amino acids. The extracellular domain contains two potential N-linked glycosylation sites. The protein functions as a homotrimer, which is essential for receptor binding. Mouse and human TNFSF11 share approximately 85% amino acid identity . The protein is expressed in two forms: a membrane-bound form and a soluble form, both of which are biologically active .

Which tissues and cells primarily express TNFSF11?

TNFSF11 is primarily expressed in T cells and T cell-rich organs such as the thymus and lymph nodes . Additionally, osteoblasts and osteocytes are major producers of TNFSF11 in the skeletal system. Activated T-cells also contribute significantly to TNFSF11 production, particularly in inflammatory conditions . This tissue-specific expression pattern reflects the protein's diverse roles in both the immune and skeletal systems.

How is TNFSF11 gene expression regulated?

The expression of TNFSF11 is controlled by several factors, including vitamin D, parathyroid hormone, and various cytokines . In humans, the TNFSF11 gene is mapped to chromosome 13q14 . Epigenetic regulation through DNA methylation of the TNFSF11 promoter region has also been identified as an important regulatory mechanism, with hypomethylation associated with increased expression in certain pathological conditions . The promoter regulatory region spans from −260 to +615 bp of the transcription start site (TSS) of isoform 1 .

What is the RANK/RANKL/OPG axis and how does it function?

The RANK/RANKL/OPG axis is a crucial regulatory system for bone remodeling. RANKL (TNFSF11) binds to its receptor RANK (expressed on osteoclast precursor cells), initiating a series of events that results in osteoclast differentiation and activation. Osteoprotegerin (OPG), synthesized by osteoblasts and other cells, acts as a decoy receptor that binds to RANKL, preventing its interaction with RANK and thereby inhibiting osteoclastogenesis. The proper balance between RANKL and OPG levels is essential for maintaining bone homeostasis, and imbalances in this system have been implicated in the pathogenesis of osteoporosis and other bone disorders .

What are the key molecular signaling pathways activated by TNFSF11?

When TNFSF11 binds to RANK on target cells, it triggers several intracellular signaling cascades. This binding initiates the activation of NF-κB, MAPKs, and calcium signaling pathways. At the molecular level, the RANKL-RANK interaction involves recruitment of TRAF6, which leads to a series of phosphorylation and ubiquitination processes. These molecular events ultimately result in the activation of transcription factors that control osteoclast differentiation . During osteoclast differentiation, TNFSF11 induces activation of CREB1 and mitochondrial ROS generation in a TMEM64 and ATP2A2-dependent manner, which is necessary for proper osteoclast development .

Beyond bone metabolism, what other biological functions does TNFSF11 influence?

TNFSF11 has multiple functions beyond bone metabolism. It enhances T cell growth and dendritic cell function, augmenting the ability of dendritic cells to stimulate naive T-cell proliferation. TNFSF11 may be an important regulator of interactions between T-cells and dendritic cells, playing a role in the regulation of T-cell-dependent immune responses. Additionally, it is involved in lymph node organogenesis and may play an important role in enhanced bone-resorption in humoral hypercalcemia of malignancy . These diverse functions highlight TNFSF11's role as a pleiotropic cytokine affecting multiple physiological systems.

How can recombinant mouse TNFSF11 be used in osteoclast differentiation assays?

Recombinant mouse TNFSF11 can effectively induce osteoclast differentiation of RAW 264.7 mouse monocyte/macrophage cell lines, with an ED50 (effective dose for 50% response) of 0.5-2 ng/mL . For experimental protocols, researchers should:

• Culture RAW 264.7 cells in appropriate medium (typically DMEM with 10% FBS)

• Seed cells at optimal density (approximately 1-2 × 10⁴ cells/well in 96-well plates)

• Add recombinant mouse TNFSF11 at concentrations ranging from 0.1-100 ng/mL (include a dose-response curve)

• Incubate for 4-7 days, replacing medium and TNFSF11 every 2-3 days

• Assess osteoclast formation by TRAP (tartrate-resistant acid phosphatase) staining and counting multinucleated TRAP-positive cells

This methodology allows for quantitative assessment of osteoclastogenic potential and can be adapted to test inhibitors or enhancers of this process.

What methods are recommended for analyzing TNFSF11 methylation patterns?

To analyze TNFSF11 promoter methylation patterns, a recommended methodology involves bisulfite conversion followed by methylation-specific PCR (MSP). The protocol should include:

• DNA extraction and bisulfite conversion (which converts unmethylated cytosines to uracils while leaving methylated cytosines unchanged)

• PCR amplification using primers specific for methylated and unmethylated DNA in the TNFSF11 promoter region (spanning from −260 to +615 bp of the TSS)

• Internal normalization with optimized primers for known genomic regions (e.g., TSH2B for methylated DNA and GAPDH for unmethylated DNA)

• Use of positive controls (human bisulfite-converted methylated and unmethylated DNA controls)

• Quantification using real-time PCR and calculation of methylation percentages

The ΔΔCq method can be used to assess the difference in relative methylation levels between samples, with reference genes for normalization . This approach provides insights into epigenetic regulation of TNFSF11 expression.

What quality control parameters should be assessed for recombinant mouse TNFSF11 in research applications?

When working with recombinant mouse TNFSF11, researchers should evaluate several quality control parameters:

• Purity assessment: Use SDS-PAGE under reducing conditions (should show a single band at approximately 19 kDa)

• Biological activity: Verify through osteoclast differentiation assays using RAW 264.7 cells (ED50 of 0.5-2 ng/mL)

• Endotoxin levels: Should be tested and maintained below acceptable thresholds for cell culture work

• Protein concentration: Accurately determine using established protein quantification methods

• Stability testing: Assess activity retention after reconstitution under recommended storage conditions

These parameters ensure experimental reproducibility and reliability when working with recombinant TNFSF11.

How does the rs1021188 polymorphism influence TNFSF11 function and disease susceptibility?

The rs1021188 polymorphism has been associated with altered TNFSF11 expression and disease susceptibility. Research has shown:

• This polymorphism is located in the regulatory region of the TNFSF11 gene

• It has been linked to otosclerosis (OTSC), with significant differences in genotype frequencies between patients and healthy controls

• The variant may affect transcription factor binding sites and alter gene expression levels

• There appears to be an independent effect of the rs1021188 polymorphism and DNA hypomethylation of the TNFSF11 promoter in OTSC

To study this association, researchers should consider genotyping using direct counting methods, Hardy-Weinberg Equilibrium testing for genotype frequency deviation in control groups, and statistical comparison of genotypes between case and control groups using Chi-square tests .

What is the relationship between TNFSF11 expression, genetic variation, and bone disorders?

TNFSF11 plays a critical role in bone metabolism, and alterations in its expression or function are associated with several bone disorders:

• Osteoporosis: Imbalances in the RANK/RANKL/OPG axis contribute to pathological bone loss

• Otosclerosis (OTSC): Associated with rs1021188 polymorphism and DNA hypomethylation of the TNFSF11 promoter

• Osteopetrosis: TNFSF11 deficiency has been linked to Autosomal Recessive Osteopetrosis 2 and Autosomal Recessive Malignant Osteopetrosis

Research approaches should include genotype-phenotype correlation studies, functional analyses of genetic variants, and investigation of epigenetic modifications affecting TNFSF11 expression in disease states.

How can regulatory regions of the TNFSF11 gene be analyzed for evolutionary conservation?

Analysis of evolutionary conservation of TNFSF11 regulatory regions, particularly CpG islands, provides insights into functionally important elements. A methodological approach includes:

• Utilize the University of California, Santa Cruz Genome Browser interface (GRCh37/hg19 assembly)

• Perform multiple alignments across 100 vertebrate species

• Measure evolutionary conservation using phastCons and phyloP programs

• Classify CpG-rich regions according to their evolutionary dynamics using parameter-rich evolutionary models and clustering analysis

• Evaluate chromatin non-condensed DNaseI Hypersensitivity sites in multiple cell types

What approaches can be used to study the epigenetic regulation of TNFSF11 in different disease models?

To investigate epigenetic regulation of TNFSF11 across disease models, researchers should employ:

• Bisulfite sequencing: For comprehensive DNA methylation profiling of the TNFSF11 promoter

• Chromatin immunoprecipitation (ChIP): To identify histone modifications and transcription factor binding at the TNFSF11 locus

• CRISPR-based epigenome editing: To causally test the impact of specific epigenetic modifications

• Single-cell approaches: To characterize cell-type specific epigenetic states

• Integrative bioinformatics: To correlate epigenetic patterns with gene expression and disease phenotypes

Comparing methylation patterns between disease and healthy samples requires statistical evaluation using non-parametric tests (e.g., Mann-Whitney) and potentially multivariate analysis with hierarchical clustering .

What are the challenges in developing highly specific inhibitors or modulators of the RANKL-RANK interaction?

Developing specific modulators of the RANKL-RANK interaction presents several challenges:

• Structural considerations: The protein functions as a homotrimer, requiring modulators that can interfere with trimerization or receptor clustering

• Specificity: Ensuring that modulators target only the RANKL-RANK interaction without affecting other TNF family members

• Tissue-specific targeting: Developing approaches that can target specific tissues (e.g., bone vs. immune system) to limit off-target effects

• Functional redundancy: Addressing potential compensatory mechanisms in the TNF superfamily

• Dosing and pharmacokinetics: Determining optimal dosing regimens that effectively modulate the pathway without complete inhibition

Research approaches should include structure-based drug design, high-throughput screening of compound libraries, and development of tissue-specific delivery systems.

What are emerging areas of TNFSF11 research beyond traditional bone and immune applications?

Emerging research directions for TNFSF11 include:

• Role in neuroinflammation and neurodegenerative disorders

• Involvement in metabolic diseases and adipose tissue biology

• Functions in cancer progression, particularly in tumor-induced bone disease

• Potential as a biomarker for disease progression in various conditions

• Interactions with the microbiome and subsequent effects on bone and immune homeostasis

These expanding areas highlight the pleiotropic nature of TNFSF11 and suggest new therapeutic targets and diagnostic approaches.

How can systems biology approaches enhance our understanding of TNFSF11 in health and disease?

Systems biology approaches offer powerful tools for understanding TNFSF11's complex roles:

• Network analysis: Mapping TNFSF11 interactions within broader signaling networks

• Multi-omics integration: Combining genomics, transcriptomics, proteomics, and metabolomics data

• Mathematical modeling: Developing predictive models of RANKL-RANK-OPG dynamics

• In silico screening: Computational identification of potential modulators

• Pathway enrichment analysis: Identifying biological processes affected by TNFSF11 dysregulation