Recombinant Mouse Tumor necrosis factor ligand superfamily member 9 (Tnfsf9)

What is Tnfsf9 and what are its alternative nomenclatures?

Tumor necrosis factor ligand superfamily member 9 (Tnfsf9) is a costimulatory molecule first identified in mice in 1989, with the human homolog discovered in the 1990s. It is also known by several alternative names including 4-1BB ligand (4-1BBL), CD137L, CD157L, and LY63L. The mouse Tnfsf9 protein corresponds to accession number P41274 and gene ID 21950 . Tnfsf9 exerts costimulatory actions as part of the tumor necrosis factor receptor superfamily (TNFSF) and plays significant roles in T cell activation and proliferation .

What is the molecular structure and basic properties of recombinant mouse Tnfsf9?

Recombinant mouse Tnfsf9 exists in multiple isoforms due to alternative splicing. RT-PCR analysis using specific primers produces 771 and 636 bp amplicons representing the transmembrane and secreted forms of Tnfsf9, respectively . The protein contains specific domains that enable it to interact with its receptor (TNFRSF9, also known as 4-1BB or CD137). The protein primarily functions as a ligand that binds to TNFRSF9, which is expressed on activated CD8+ T cells, activated CD4+ T cells, natural killer (NK) cells, and endotheliocytes .

What methods are most effective for detecting and quantifying mouse Tnfsf9 in different sample types?

Several methodological approaches can be employed for detecting and quantifying mouse Tnfsf9:

• ELISA (Enzyme-Linked Immunosorbent Assay): Sandwich-based ELISA kits offer quantitative detection of mouse Tnfsf9 in serum, plasma, and cell culture supernatants with a sensitivity of approximately 0.75 ng/ml and a detection range of 0.75-200 ng/ml .

• Quantitative RT-PCR (qRT-PCR): Using specific oligonucleotide primers (5'-TTGGGAACATTTAATGACCAGA-3' and 5'-TCCCGGTCTTAAGCACAGAC-3') designed based on GenBank accession number NM_011612 with an annealing temperature of 62°C, producing a 91 base pair amplicon common to both splice variants .

• RT-PCR for splice variant detection: Using specific primers that yield 771 bp and 636 bp amplicons from the alternatively spliced transmembrane and secreted forms of Tnfsf9, respectively .

• In situ hybridization: For localization of Tnfsf9 mRNA in tissue sections using digoxigenin (DIG)-labeled riboprobes generated from amplicons using specific primers (5'-AGGTGGACAGCCGAACTGTAACAT-3' and 5'-TTCTTCTTCCTGTGGACATCGGCA-3') .

What is the tissue expression profile of Tnfsf9 in mice under normal physiological conditions?

Tnfsf9 expression varies across different tissues and developmental stages in mice. In normal physiology, Tnfsf9 mRNA is detected at varying levels in multiple tissues. During pregnancy in mice, Tnfsf9 mRNA levels are low on day 3.5, just prior to implantation. After implantation begins, expression increases significantly in implantation sites (IS) on days 4.5-8.5 compared to pre-implantation tissue .

The expression pattern during implantation shows tissue-specific regulation:

This temporal and spatial expression pattern suggests a role for Tnfsf9 in uterine decidualization during pregnancy .

How can researchers reliably distinguish between membrane-bound and soluble forms of mouse Tnfsf9?

Distinguishing between membrane-bound and soluble forms of mouse Tnfsf9 requires specific methodological approaches:

• RT-PCR with splice variant-specific primers: Primers that yield different amplicon sizes (771 bp for transmembrane and 636 bp for secreted forms) can be used to distinguish between the two forms at the mRNA level .

• Western blotting with domain-specific antibodies: Antibodies targeting domains specific to either the membrane-bound or soluble form can differentiate between the two protein variants.

• Flow cytometry: For detecting membrane-bound Tnfsf9 on cell surfaces.

• ELISA with specific capture antibodies: Different ELISA configurations can be optimized to preferentially detect either soluble or total Tnfsf9 in sample preparations.

When designing experiments requiring this distinction, researchers should carefully select the appropriate methodology based on their specific research questions and sample types.

How does Tnfsf9 modulate T cell responses in mouse models?

Tnfsf9 plays critical roles in modulating T cell responses through its interaction with its receptor TNFRSF9 (4-1BB). The co-stimulatory signaling mediated by this interaction triggers signaling cascades within T cells that:

• Promotes T cell proliferation

• Enhances secretion of cytokines

• Increases resistance to activation-induced cell death (AICD)

• Supports the development of memory T cells

In clear cell renal cell carcinoma (ccRCC) models, TNFRSF9+ CD8+ T cells express higher levels of both exhaustion markers (PD-1, TIM-3, CTLA-4, and TIGIT) and effector markers (IFN-γ, GZMB, CD107a, and Ki-67) compared to their TNFRSF9-negative counterparts. In silico analysis demonstrates that TNFRSF9 expression significantly correlates with IFNG, GZMK, MKI-67, PDCD1, HAVCR2, TIGIT, and CTLA-4 in CD8+ T cells .

What is the role of Tnfsf9 in macrophage polarization and how can researchers experimentally manipulate this process?

Tnfsf9 has been implicated in macrophage polarization, particularly in promoting M2 polarization of macrophages in the context of pancreatic cancer . To experimentally manipulate and study this process, researchers can:

• Use recombinant Tnfsf9 protein: Apply purified recombinant mouse Tnfsf9 to macrophage cultures to observe direct effects on polarization markers.

• Employ genetic manipulation: Use CRISPR-Cas9 or siRNA approaches to knock down or overexpress Tnfsf9 in macrophages or tumor cells to analyze the subsequent effects on macrophage polarization.

• Co-culture systems: Establish co-cultures of macrophages with Tnfsf9-expressing or Tnfsf9-depleted tumor cells to examine the paracrine effects on macrophage phenotype.

• In vivo models: Generate conditional knockout mice or use neutralizing antibodies to modulate Tnfsf9 activity in tumor microenvironments and analyze the resulting macrophage polarization status.

• Flow cytometry analysis: Quantify M1 and M2 markers on macrophages following Tnfsf9 manipulation using flow cytometry panels that include CD80, CD86, MHC II (M1 markers) and CD206, CD163, Arginase-1 (M2 markers).

How does Tnfsf9 expression correlate with cancer progression and patient survival in different tumor models?

Tnfsf9 expression demonstrates complex relationships with cancer progression and patient outcomes that vary by cancer type:

These findings highlight the context-dependent roles of Tnfsf9 in different cancer types and treatment scenarios, emphasizing the need for cancer-specific investigations.

What experimental approaches can determine the mechanistic role of Tnfsf9 in tumor microenvironment modulation?

To investigate the mechanistic role of Tnfsf9 in tumor microenvironment modulation, researchers can employ several experimental approaches:

• Single-cell RNA sequencing: To characterize the transcriptional profiles of Tnfsf9-expressing and Tnfsf9-responsive cells within the tumor microenvironment at high resolution.

• Spatial transcriptomics: To map the spatial distribution of Tnfsf9 and its receptor within tumor tissues and correlate with various immune cell populations.

• Chromatin immunoprecipitation (ChIP) sequencing: To identify transcription factors regulating Tnfsf9 expression in different cell types within the tumor microenvironment.

• Multiplex immunofluorescence imaging: To visualize the co-localization of Tnfsf9 with various immune cell markers and signaling proteins in tumor sections.

• Pathway analysis using inhibitors: To dissect the downstream signaling pathways activated by Tnfsf9-TNFRSF9 interaction in various cell types using specific pathway inhibitors.

• In vivo tumor models with genetic manipulation: To examine the effects of Tnfsf9 knockout or overexpression on tumor growth, immune infiltration, and response to therapies.

• Ex vivo tumor slice cultures: To manipulate Tnfsf9 signaling in intact tumor microenvironments while preserving spatial relationships between cells.

How can Tnfsf9 signaling be therapeutically targeted in cancer models, and what are the current experimental approaches?

Targeting Tnfsf9 signaling in cancer models involves several experimental therapeutic approaches:

• Agonistic antibodies: Developing antibodies that enhance Tnfsf9-TNFRSF9 interaction to boost anti-tumor immune responses, particularly in contexts where this stimulates effective T cell responses.

• Antagonistic antibodies: In cancer types where Tnfsf9 promotes tumor progression (such as pancreatic cancer), blocking antibodies could inhibit its tumor-promoting functions .

• Recombinant Tnfsf9 protein engineering: Modified versions of Tnfsf9 with enhanced receptor binding or extended half-life could provide more potent immunostimulatory effects.

• Combination with immune checkpoint inhibitors: Based on the finding that higher TNFRSF9 signature correlates with better response to nivolumab, combination approaches could enhance efficacy of existing immunotherapies .

• CAR-T cells with Tnfsf9 co-stimulatory domains: Engineering chimeric antigen receptor T cells with Tnfsf9 signaling domains could enhance their persistence and anti-tumor activity.

• Small molecule modulators: Developing compounds that can selectively enhance or inhibit downstream signaling pathways activated by Tnfsf9-TNFRSF9 interaction.

• Gene therapy approaches: Localized delivery of Tnfsf9-encoding vectors to tumors could enhance anti-tumor immunity in the tumor microenvironment.

How can single-cell analysis technologies be applied to study Tnfsf9-mediated immune responses in complex tissues?

Single-cell analysis technologies offer powerful approaches to dissect Tnfsf9-mediated immune responses in complex tissues:

• Single-cell RNA sequencing (scRNA-seq): Enables comprehensive transcriptional profiling of individual cells expressing Tnfsf9 or its receptor, revealing heterogeneity within seemingly uniform populations. This approach can identify novel cell subsets involved in Tnfsf9 signaling and uncover unexpected expression patterns in non-immune cells.

• Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq): Combines surface protein detection with transcriptome analysis, allowing simultaneous assessment of Tnfsf9 protein expression and transcriptional state at single-cell resolution.

• Single-cell ATAC-seq: Reveals chromatin accessibility landscapes in individual cells, providing insights into the epigenetic regulation of Tnfsf9 expression in different cell types.

• Imaging mass cytometry (IMC): Allows multiplexed protein detection in tissue sections while preserving spatial information, enabling visualization of Tnfsf9-expressing cells relative to other immune and stromal components.

• Spatial transcriptomics: Maps the spatial distribution of Tnfsf9 mRNA in tissue sections, revealing localized expression patterns that may indicate specialized microenvironmental niches.

When designing single-cell experiments, researchers should consider:

• Tissue disaggregation protocols that preserve Tnfsf9 expression

• Including appropriate markers to identify all relevant cell populations

• Computational analysis pipelines capable of detecting subtle changes in expression patterns

• Validation of findings using orthogonal methods such as flow cytometry or immunohistochemistry

What are the optimal experimental designs for studying Tnfsf9 in mouse models of autoimmunity or inflammation?

Designing optimal experiments for studying Tnfsf9 in mouse models of autoimmunity or inflammation requires careful consideration of several factors:

• Mouse model selection:

  • Genetic models (e.g., Tnfsf9 knockout, conditional knockout, or transgenic overexpression)

  • Induced models of autoimmunity (e.g., experimental autoimmune encephalomyelitis, collagen-induced arthritis)

  • Inflammation models (e.g., DSS-induced colitis, LPS-induced systemic inflammation)

• Temporal considerations:

  • Early initiation phase of autoimmunity/inflammation

  • Established disease phase

  • Resolution/chronic phase

  • Longitudinal tracking of Tnfsf9 expression and immune responses

• Methodological approaches:

  • Flow cytometry panels for comprehensive immune phenotyping

  • Tissue-specific analysis of Tnfsf9 expression using qRT-PCR and immunohistochemistry

  • Functional assays for assessing T cell activation and cytokine production

  • In vivo imaging for tracking inflammatory processes in real-time

• Intervention strategies:

  • Administration of recombinant Tnfsf9 at different disease stages

  • Blocking antibodies against Tnfsf9 or its receptor

  • Cell-specific deletion using Cre-loxP systems

  • Adoptive transfer experiments with Tnfsf9-deficient or overexpressing immune cells

• Control groups:

  • Wild-type littermates

  • Isotype control antibodies

  • Vehicle controls for recombinant protein administration

  • Sham-operated controls for surgical models

How can computational approaches be integrated with experimental data to predict Tnfsf9 signaling outcomes in different immunological contexts?

Integrating computational approaches with experimental data provides powerful frameworks for understanding and predicting Tnfsf9 signaling outcomes:

• Network analysis and pathway modeling:

  • Construction of signaling networks based on experimental data

  • Identification of key nodes and potential feedback loops in Tnfsf9 signaling

  • Prediction of system-wide effects of perturbations to Tnfsf9 signaling

• Machine learning for biomarker identification:

  • Development of algorithms to predict treatment response based on Tnfsf9 expression patterns

  • Identification of gene signatures associated with favorable or unfavorable outcomes in Tnfsf9-high contexts

  • Classification of immune cell states based on Tnfsf9 signaling activity

• Multi-omics data integration:

  • Correlation of Tnfsf9 expression with proteomic, metabolomic, and epigenomic datasets

  • Identification of molecular mechanisms linking Tnfsf9 signaling to cellular phenotypes

  • Construction of predictive models incorporating multiple data types

• Agent-based modeling of immune cell interactions:

  • Simulation of Tnfsf9-mediated cellular interactions in virtual tissue environments

  • Prediction of emergent properties of immune responses based on Tnfsf9 signaling rules

  • In silico testing of therapeutic interventions targeting Tnfsf9 pathways

• Structural biology and molecular dynamics:

  • Prediction of binding interfaces between Tnfsf9 and its receptor

  • Simulation of conformational changes induced by receptor binding

  • Virtual screening for potential small molecule modulators of Tnfsf9-receptor interactions

A comprehensive computational workflow might include:

• Initial data generation through experimental approaches

• Data preprocessing and normalization

• Feature selection to identify relevant variables

• Model training and validation using cross-validation

• Experimental testing of computational predictions

• Model refinement based on new experimental data