Recombinant Human Tumor necrosis factor receptor superfamily member 4 (TNFRSF4)

What is the basic structure of human TNFRSF4 and how does it compare to its orthologs in model organisms?

TNFRSF4 (OX40) is a type I transmembrane protein belonging to the tumor necrosis factor receptor superfamily. The protein structure typically consists of a signal peptide, an extracellular domain containing multiple TNFR-cysteine rich repeats, a transmembrane domain, and a cytoplasmic region. Mouse OX40, for example, is a 256 amino acid residue protein with a 19 aa signal peptide, 192 aa extracellular domain containing 4 TNFR-cysteine rich repeats, a 25 aa transmembrane domain, and a 36 aa cytoplasmic region .

Human TNFRSF4 shares approximately 63% amino acid sequence identity with its mouse counterpart, while mouse and rat orthologs have approximately 90% sequence identity . This conservation across species facilitates translational research using animal models.

Methodological approach for structural analysis:

• X-ray crystallography or cryo-electron microscopy for detailed 3D structure

• Sequence alignment tools for comparative analysis across species

• Domain prediction algorithms to identify functional regions

• Molecular dynamics simulations to understand structural dynamics

Which cell types express TNFRSF4 and how is its expression regulated during immune responses?

TNFRSF4 is predominantly expressed on activated T cells, particularly CD4+ T cells, but is also expressed on activated CD8+ T cells in both humans and mice . Expression is typically low or undetectable in resting T cells and increases following T cell receptor stimulation.

To investigate expression patterns and regulation, researchers should employ multiple complementary approaches:

For flow cytometry detection, activated mouse splenocytes can be stained with fluorescently labeled antibodies such as Goat Anti-Mouse OX40/TNFRSF4 PE-conjugated Antigen Affinity-purified Polyclonal Antibody alongside markers like CD3 to identify T cell populations .

How is TNFRSF4 implicated in tumor microenvironment dynamics and cancer progression?

TNFRSF4 has emerged as a key player in tumor immunology, particularly in modulating the tumor microenvironment (TME). Research indicates TNFRSF4 is positively correlated with tumor-infiltrating immune cells (TICs), primarily CD4+ T cells, CD8+ T cells, and regulatory T cells (Tregs) . This correlation was verified using both bioinformatic analyses from The Cancer Genome Atlas (TCGA) database and validation in clinical samples .

Methodological approaches for investigating TNFRSF4 in cancer:

• Computational deconvolution of immune infiltrates:

  • CIBERSORT algorithm for estimation of immune cell abundance

  • Single sample Gene Set Enrichment Analysis (ssGSEA) to investigate immune infiltration landscapes

  • Correlation analysis between TNFRSF4 expression and immune cell signatures

• Multiplex tissue analysis:

  • Tissue microarrays (TMAs) for high-throughput analysis

  • Multiplex immunohistochemistry (m-IHC) to simultaneously visualize TNFRSF4 with immune markers like CD4, CD8, and FOXP3

  • Spatial transcriptomics to map expression patterns within the TME

• Functional studies:

  • In vitro co-culture systems to model T cell-tumor interactions

  • RNA interference or CRISPR-based approaches to modulate TNFRSF4 expression

  • Patient-derived xenograft models to evaluate in vivo relevance

What is the prognostic significance of TNFRSF4 expression in cancer, and how can it be effectively assessed?

TNFRSF4 has demonstrated significant prognostic value in cancers like endometrial cancer (EC). Research indicates it is not only associated with patient survival but also serves as a crucial indicator of TME remodeling .

For robust prognostic assessment:

• Clinical correlation methods:

  • Kaplan-Meier survival analysis with log-rank tests to determine association with patient outcomes

  • Disease-specific survival (DSS) calculations, defined as time between surgery and cancer-related death

  • Multivariate Cox regression analysis to establish independence from other prognostic factors

• Expression quantification approaches:

  • Standardized immunohistochemistry scoring systems

  • Receiver operating characteristic (ROC) curve analysis to determine diagnostic performance

  • Meta-analysis of expression data across multiple cohorts

• Integrated biomarker evaluation:

  • Correlation with established clinicopathologic features

  • Combined analysis with other immune markers (CD4, CD8, FOXP3)

  • Validation across independent patient cohorts

For comprehensive assessment, researchers should integrate expression data from TCGA databases with validation using clinical samples, as demonstrated in studies that have identified TNFRSF4 as a potential biomarker for prognosis prediction and immunomodulation of endometrial cancer .

How do genetic polymorphisms in TNFRSF4 influence susceptibility to infectious diseases?

Genetic variants in TNFRSF4 and related genes have been associated with susceptibility to infectious diseases, particularly viral infections such as hepatitis C virus (HCV). Research has identified specific single nucleotide polymorphisms (SNPs) that may affect disease risk and outcomes .

Methodological framework for genetic association studies:

• SNP selection strategy:

  • Candidate SNP screening using criteria of minor allele frequency (MAF) ≥ 0.05 and correlation coefficient r² ≥ 0.8

  • Analysis of regulatory regions, including 2,000 bp upstream and downstream of transcription initiation sites

  • Prioritization using functional prediction databases such as RegulomeDB and UCSC Genome Browser

• Genotyping methodology:

  • TaqMan real-time PCR assay for high-throughput, accurate genotyping

  • Quality control measures including >95% success rate and repeat testing of 10% of samples for consistency verification

  • Blinded genotyping relative to clinical data

• Statistical analysis approaches:

  • Multiple genetic models (dominant, recessive, additive) to comprehensively assess genetic effects

  • Adjustment for confounding factors including demographic and clinical variables

  • Correction for multiple testing using methods like False Discovery Rate (FDR)

• Interaction analysis:

  • Evaluation of gene-environment interactions

  • Assessment of multiplicative interactions between SNPs and factors such as gender

  • Stratified analysis to identify subgroup-specific effects

What functional effects do TNFRSF4-related genetic variants have on molecular mechanisms and protein function?

Understanding the functional consequences of genetic variants requires detailed molecular investigations. For TNFRSF4-related genes, variants such as rs7514229 in TNFSF4 (which encodes the ligand for TNFRSF4) have demonstrated functional effects on RNA structure and stability .

Methodological approaches for functional characterization:

• In silico prediction:

  • RNA secondary structure analysis using tools like RNAfold to predict structural changes

  • Calculation of minimum free energy (MFE) differences between wild-type and variant sequences

  • For rs7514229, the T allele alters mRNA secondary structure and increases MFE from -17.30 kcal/mol to -16.60 kcal/mol compared to the wild-type G allele

• miRNA binding analysis:

  • Identification of variants within putative microRNA binding sites using tools like SNPinfo

  • Experimental validation using luciferase reporter assays

  • Assessment of miRNA-mediated regulation changes

• Expression quantitative trait loci (eQTL) analysis:

  • Correlation between genotypes and gene expression levels in relevant tissues

  • Integration with public databases such as GTEx

  • Cell type-specific effects assessment

• Protein function assessment:

  • For coding variants, evaluation of effects on protein structure and function

  • Signaling pathway analysis downstream of receptor activation

  • Cell-based functional assays measuring activation, proliferation, or cytokine production

What are the optimal methods for detecting and quantifying TNFRSF4 expression in different experimental systems?

Detection of TNFRSF4 requires careful consideration of experimental system and research question. Multiple complementary approaches provide the most comprehensive characterization:

• Flow cytometry optimization:

  • Antibody selection: Both monoclonal and polyclonal antibodies (e.g., Goat Anti-Mouse OX40/TNFRSF4 PE-conjugated Antigen Affinity-purified Polyclonal Antibody) can be used

  • Proper controls: Include isotype controls (e.g., Normal Goat IgG Phycoerythrin Control) to assess non-specific binding

  • Co-staining with lineage markers such as CD3 for T cell identification

  • Titration of antibodies to determine optimal concentration for maximum signal-to-noise ratio

• Immunohistochemistry considerations:

  • Tissue fixation optimization to preserve epitope recognition

  • Antigen retrieval methods evaluation

  • Multiplex approaches for co-localization studies

  • Quantitative scoring systems development

• Transcript detection methods:

  • Primer design spanning exon-exon junctions to avoid genomic DNA amplification

  • Reference gene selection for accurate normalization

  • Digital PCR for absolute quantification

  • Single-cell RNA sequencing for heterogeneous populations

Note: As stated in research protocols, "Optimal dilutions should be determined by each laboratory for each application" .

How can researchers design and optimize flow cytometry panels for TNFRSF4 detection in complex immune cell populations?

Flow cytometry is a primary method for TNFRSF4 detection in immune cells. Optimizing multi-parameter panels requires systematic approach:

• Panel design strategy:

  • Core markers for lineage identification (CD3, CD4, CD8)

  • Activation markers to contextualize TNFRSF4 expression

  • Viability dye to exclude dead cells

  • Fluorochrome selection to minimize spectral overlap

• Sample preparation optimization:

  • For mouse splenocytes: activation conditions to induce TNFRSF4 expression

  • Enzymatic digestion protocols for tissue samples

  • Fixation/permeabilization if intracellular markers are included

  • Fc receptor blocking to prevent non-specific binding

• Controls and validation:

  • Fluorescence minus one (FMO) controls

  • Isotype controls such as Normal Goat IgG Phycoerythrin Control

  • Biological controls (unstimulated vs. stimulated cells)

  • Titration of each antibody to determine optimal concentration

• Analytical considerations:

  • Proper compensation setup for multi-color panels

  • Gating strategy standardization

  • Quantification methods (percent positive, mean/median fluorescence intensity)

  • Statistical approaches for comparing populations

For example, in the detection of OX40/TNFRSF4 in mouse splenocytes, researchers used Rat Anti-Mouse CD3 APC-conjugated Monoclonal Antibody alongside Goat Anti-Mouse OX40/TNFRSF4 PE-conjugated Antigen Affinity-purified Polyclonal Antibody .

What are the current approaches for targeting TNFRSF4 in immunotherapy research, and how is efficacy assessed?

TNFRSF4 represents a promising immunotherapeutic target, particularly in cancer. The protein's role in T cell activation and its correlation with tumor-infiltrating immune cells make it an attractive candidate for immune modulation strategies.

Methodological approaches for therapeutic development and assessment:

• Targeting strategies:

  • Agonistic antibodies to enhance co-stimulatory signaling

  • Recombinant ligands (OX40L/TNFSF4) for receptor activation

  • Bispecific antibodies linking TNFRSF4 engagement to tumor-targeting

  • CAR-T cells incorporating TNFRSF4 signaling domains

• Preclinical efficacy evaluation:

  • In vitro T cell functional assays (proliferation, cytokine production)

  • Co-culture systems with tumor cells to assess cytotoxicity

  • Syngeneic mouse models with established tumors

  • Humanized mouse models for human-specific agents

• Mechanistic assessment:

  • Changes in tumor-infiltrating lymphocyte profiles

  • Immunophenotyping by flow cytometry or mass cytometry

  • Transcriptomic analysis of treated tumors

  • Spatial analysis of immune cell distribution and activation states

• Combination therapy approaches:

  • Rational design with complementary immunomodulatory agents

  • Sequential vs. concurrent administration optimization

  • Dose-finding studies to maximize efficacy while minimizing toxicity

  • Pharmacodynamic biomarker identification

How can researchers investigate TNFRSF4 signaling pathways and their cross-talk with other immune regulatory networks?

Understanding TNFRSF4 signaling requires sophisticated approaches that capture pathway dynamics and network interactions:

• Signaling cascade mapping:

  • Phosphoproteomic analysis following receptor engagement

  • Temporal profiling to capture signaling kinetics

  • Inhibitor studies to establish pathway dependencies

  • CRISPR screening to identify essential pathway components

• Transcriptional response characterization:

  • RNA-seq following TNFRSF4 stimulation at multiple time points

  • ChIP-seq to identify transcription factor binding events

  • ATAC-seq to assess chromatin accessibility changes

  • Single-cell approaches to capture population heterogeneity

• Network interaction analysis:

  • Computational modeling of pathway cross-talk

  • Simultaneous modulation of multiple pathways

  • Protein-protein interaction studies using proximity labeling

  • Systems biology approaches integrating multiple data types

• Physiological outcome assessment:

  • T cell metabolic reprogramming analysis

  • Memory formation and maintenance evaluation

  • Exhaustion marker expression monitoring

  • In vivo functional studies in disease models

What novel technologies are advancing our understanding of TNFRSF4 biology and its therapeutic applications?

Emerging technologies are revolutionizing TNFRSF4 research, offering unprecedented resolution and insight:

• Single-cell multi-omics:

  • Integrated analysis of transcriptome, proteome, and epigenome at single-cell resolution

  • CITE-seq for simultaneous surface protein and transcript analysis

  • Single-cell TCR sequencing paired with TNFRSF4 expression analysis

  • Spatial transcriptomics to map expression in tissue context

• Advanced imaging techniques:

  • Super-resolution microscopy for receptor clustering analysis

  • Intravital imaging to track TNFRSF4+ cells in vivo

  • Multiplex ion beam imaging (MIBI) for highly multiplexed protein detection

  • Live-cell imaging of signaling dynamics

• Protein engineering approaches:

  • Structure-guided design of TNFRSF4-targeted biologics

  • Conditionally active TNFRSF4 agonists for tumor-specific activation

  • Proteolysis-targeting chimeras (PROTACs) for targeted degradation

  • Synthetic receptors incorporating TNFRSF4 signaling domains

• Computational and AI-driven methods:

  • Machine learning for biomarker identification and patient stratification

  • Network inference to identify novel signaling interactions

  • Virtual screening for small molecule modulators

  • Digital pathology for automated quantification in tissues

What are the key methodological challenges in translating TNFRSF4 research findings from preclinical models to clinical applications?

Translational research on TNFRSF4 faces several methodological challenges that require systematic approaches:

• Model system limitations:

  • Species differences in TNFRSF4 expression and function (63% sequence identity between human and mouse)

  • Development of humanized models that better recapitulate human immune responses

  • Validation across multiple model systems to enhance robustness

  • Patient-derived organoids and ex vivo systems for direct human relevance

• Biomarker development considerations:

  • Standardization of TNFRSF4 detection methods for clinical samples

  • Identification of companion diagnostics for therapeutic targeting

  • Integration with other prognostic markers as demonstrated in endometrial cancer research

  • Validation in prospective clinical studies

• Therapeutic development challenges:

  • Optimization of pharmacokinetics and tissue distribution

  • Management of immune-related adverse events

  • Rational design of combination regimens

  • Patient selection strategies based on genetic or expression profiles

• Clinical trial design innovations:

  • Adaptive designs for rapid assessment of activity

  • Incorporation of pharmacodynamic endpoints

  • Integration of immune monitoring to understand mechanisms

  • Platform trials to evaluate multiple combinations efficiently