Recombinant Mouse Protein polybromo-1 (Pbrm1), partial

What is the domain structure of Pbrm1 and how does it function in chromatin remodeling?

Pbrm1 (also known as BAF180) contains six tandem bromodomains (BrDs), two bromo-adjacent homology domains (BAH), and a high-mobility group (HMG). This multi-domain architecture enables Pbrm1 to coordinate several essential functions within the PBAF complex . The bromodomains specifically recognize and bind to acetylated lysine residues on histone tails, with different bromodomains targeting distinct acetylation patterns. The BAH domains function as protein-interaction modules, while the HMG domain binds nucleosomal DNA .

This multifunctional structure allows Pbrm1 to target the PBAF complex to specific chromatin sites defined by discrete nucleosome acetylation patterns, recruit additional regulatory proteins, and directly participate in altering histone-DNA interactions to control genetic functions . Quantitative screening has revealed that several Pbrm1 bromodomains target specific acetyllysines within histone tail regions, suggesting that Pbrm1 recognizes a combinatorial histone acetylation code .

How is Pbrm1 expression regulated in different mouse tissues?

Pbrm1 expression can be studied using mouse models such as the Pbrm1 tm1a(EUCOMM)Wtsi ES cells. These models allow for β-galactosidase staining to determine Pbrm1 expression patterns across different tissues. For tissue-specific studies, various Cre-driver mouse lines can be used, including Sglt2-Cre, Villin-Cre, and Pax8-Cre mice, which enable conditional deletion of Pbrm1 in specific cell types .

Researchers have found that Pbrm1 is widely expressed across multiple tissues but has particularly important functions in renal cells. The studies with Pax8-Cre and Ksp-Cre driver lines have been instrumental in understanding Pbrm1's role in kidney development and renal cell carcinoma formation .

What are the main experimental applications for recombinant mouse Pbrm1 protein?

Recombinant mouse Pbrm1 protein is valuable for numerous experimental applications:

How does Pbrm1 deficiency affect IFNγ signaling and the tumor immune microenvironment?

Pbrm1 deficiency significantly impacts IFNγ signaling pathways and shapes the tumor immune microenvironment. Mechanistically, Pbrm1 loss reduces the binding of brahma-related gene 1 (BRG1) to the IFNγ receptor 2 (Ifngr2) promoter . This molecular alteration leads to decreased STAT1 phosphorylation and subsequent reduction in the expression of IFNγ target genes .

The consequences of this disrupted signaling are profound. Analysis across three independent patient cohorts and murine pre-clinical models has demonstrated that PBRM1 loss is consistently associated with:

• A less immunogenic tumor microenvironment (TME)

• Upregulated angiogenesis

• Increased resistance to immune checkpoint blockade therapy

In mouse models, Pbrm1-deficient Renca subcutaneous tumors showed greater resistance to immune checkpoint blockade compared to Pbrm1-intact tumors . This has significant implications for immunotherapy strategies in cancers with PBRM1 mutations.

What are the methodological considerations for generating Pbrm1 knockout mouse models?

Creating effective Pbrm1 knockout mouse models requires careful consideration of several methodological factors:

• Selection of appropriate ES cells: Pbrm1 tm1a(EUCOMM)Wtsi ES cells have been successfully used for generating germline transmission .

• Breeding strategy: Male chimeras should be mated with C57BL/6NTac female mice to generate germline transmission. Heterozygous mice can then be used for β-galactosidase staining to confirm Pbrm1 expression patterns .

• Conditional knockout approach: Floxed Pbrm1 mice can be generated by mating Pbrm1-LacZ/+ mice with FLP mice to excise the LacZ element .

• Selection of Cre drivers: Different tissue-specific Cre drivers yield distinct phenotypes:

  • Ksp-Cre drivers produce a phenotype dominated by large cysts

  • Pax8-Cre drivers result in less frequent cysts but more consistent tumor formation

  • The choice significantly affects tumor penetrance, size, and animal survival

• Co-targeting considerations: When studying renal cell carcinoma, co-targeting Vhl with Pbrm1 produces more robust tumor models than targeting either gene alone .

How do Pbrm1 mutations impact the efficacy of immunotherapy across different cancer types?

Pbrm1 mutations have emerged as potential biomarkers for immunotherapy response across multiple cancer types. Comprehensive analysis of clinical data reveals that patients with PBRM1 mutations exhibit:

• Improved progression-free survival (PFS) (HR = 0.51, 95% CI: 0.28–0.95, p = 0.030)

• Higher objective response rates (ORR) (47.92% vs. 28.21%, p = 0.0044)

• Better disease control rates (DCR) (72.92% vs. 47.53%, p = 0.0008)

These benefits have been observed across multiple cancer types. In non-small cell lung cancer specifically, patients with PBRM1 mutations demonstrated:

• Significantly longer PFS (HR = 0.268, 95% CI: 0.084–0.854, p = 0.04)

• Higher ORR (55.56% vs. 20.00%, p = 0.027)

• Improved DCR (100% vs. 75.20%)

Gene set enrichment analysis (GSEA) reveals that PBRM1 mutations are closely related to immune efficacy and immune microenvironment, including enhanced killer cell-mediated immunity regulation, cell cytokine production, CD8+ T-cell activation, and MHC protein binding processes .

What approaches can be used to evaluate the binding specificity of Pbrm1's bromodomains to acetylated histones?

Evaluating the binding specificity of Pbrm1's bromodomains requires a multifaceted experimental approach:

How can researchers effectively compare phenotypes between different Pbrm1 knockout mouse models?

Effective comparison of phenotypes between different Pbrm1 knockout mouse models requires standardized methodology:

• Consistent genetic background: Use mice with identical or closely related genetic backgrounds to minimize variation unrelated to Pbrm1 status.

• Standardized phenotypic assessment: Develop comprehensive evaluation protocols for:

  • Tumor incidence, multiplicity, size, and grade

  • Histopathological analysis with standardized grading systems

  • Survival analysis (Kaplan-Meier curves)

  • Molecular profiling (RNA-seq, proteomics)

  • Immunohistochemical characterization of the TME

• Cell-of-origin tracking: Use lineage tracing approaches (e.g., with Rosa26-CAG-loxP-stop-loxP-tdTomato mice) to identify the cellular origins of arising tumors .

• Comparison across different Cre-drivers: When comparing models with different Cre-drivers (e.g., Ksp-Cre vs. Pax8-Cre), document:

  • Penetrance of phenotype (percentage of mice developing tumors)

  • Timing of tumor onset

  • Size and number of tumors per animal

  • Presence of other phenotypes (e.g., cysts)

• Molecular and signaling pathway analysis: Assess activation of key signaling pathways (e.g., mTORC1) across different models to identify conserved mechanisms .

What are the optimal experimental designs to investigate Pbrm1's role in immune checkpoint blockade resistance?

To investigate Pbrm1's role in immune checkpoint blockade resistance, researchers should consider these experimental design elements:

• In vivo tumor models:

  • Syngeneic tumor models (e.g., Renca cells) with Pbrm1 knockout via CRISPR/Cas9

  • Genetically engineered mouse models with tissue-specific Pbrm1 deletion

  • Patient-derived xenografts from PBRM1-mutant and wild-type tumors

• Treatment protocols:

  • Compare multiple checkpoint inhibitors (anti-PD-1, anti-CTLA-4, combinations)

  • Include control arms (isotype antibodies)

  • Test combination approaches (checkpoint inhibitors plus angiogenesis inhibitors)

• Immune profiling:

  • Flow cytometry to quantify tumor-infiltrating immune cells

  • Single-cell RNA sequencing to characterize immune populations

  • Spatial transcriptomics to map immune cell locations within the tumor microenvironment

  • Cytokine/chemokine profiling

• Mechanistic investigations:

  • ChIP-seq to analyze BRG1 binding to IFNGR2 and other promoters

  • Phospho-flow cytometry or Western blotting for STAT1 phosphorylation

  • ATAC-seq to assess chromatin accessibility changes

  • Rescue experiments (e.g., IFNGR2 overexpression in Pbrm1-deficient cells)

• Clinical correlation:

  • Analysis of patient samples with known PBRM1 status and immunotherapy response

  • Multi-omic profiling (genomics, transcriptomics, proteomics)

  • Development and validation of predictive biomarker signatures

How can Pbrm1 mutation status be utilized as a predictive biomarker for immunotherapy response?

Pbrm1/PBRM1 mutation status has significant potential as a predictive biomarker for immunotherapy response based on multiple lines of evidence:

What experimental approaches can determine the functional consequences of specific Pbrm1 mutations?

To determine the functional consequences of specific Pbrm1 mutations, researchers can employ these experimental approaches:

• Domain-specific mutation analysis:

  • Create a panel of recombinant Pbrm1 proteins with mutations in specific domains (bromodomains, BAH domains, HMG domain)

  • Test each mutant's ability to bind acetylated histones, interact with other PBAF components, and remodel chromatin

  • Compare naturally occurring cancer mutations with engineered mutations

• Cell-based functional assays:

  • Reconstitute Pbrm1-deficient cells with wild-type or mutant Pbrm1

  • Assess effects on:

    • Global gene expression (RNA-seq)

    • Chromatin accessibility (ATAC-seq)

    • IFNγ signaling (pSTAT1 levels, IFNγ target gene expression)

    • Cell proliferation, migration, and invasion

• Chromatin remodeling activity assessment:

  • In vitro nucleosome remodeling assays with reconstituted PBAF complexes containing wild-type or mutant Pbrm1

  • Measure changes in chromatin accessibility, nucleosome positioning, and DNA exposure

• Protein-protein interaction analysis:

  • Immunoprecipitation followed by mass spectrometry to identify differential interactors

  • Proximity labeling techniques (BioID, APEX) to map protein interaction networks in living cells

  • Yeast two-hybrid screening to identify affected protein interactions

• In vivo modeling:

  • Generate knock-in mouse models with specific Pbrm1 mutations

  • Compare phenotypes with complete knockout models to identify mutation-specific effects

  • Assess tumor development, immune infiltration, and response to therapy

How can researchers investigate the cooperation between Pbrm1 and other tumor suppressors in renal cell carcinoma development?

Investigating the cooperation between Pbrm1 and other tumor suppressors in renal cell carcinoma requires systematic approaches:

• Combinatorial genetic modeling:

  • Generate mouse models with combined deletion of Pbrm1 and other tumor suppressors (Vhl, Bap1, Tsc1)

  • Compare single knockout versus double knockout phenotypes for:

    • Tumor incidence and latency

    • Tumor grade and histology

    • Molecular characteristics and pathway activation

    • Immune microenvironment

• Molecular pathway analysis:

  • Perform comprehensive multi-omic profiling (genomics, transcriptomics, proteomics, metabolomics)

  • Identify convergent and divergent pathway alterations

  • Map chromatin landscape changes using ChIP-seq and ATAC-seq

  • Analyze effects on specific pathways (e.g., mTORC1, which is differentially activated based on which tumor suppressors are lost)

• Cell-of-origin studies:

  • Use different Cre-drivers to target distinct cell populations

  • Employ lineage tracing to identify cells of origin for different genetic combinations

  • Current evidence points to the parietal cell of the Bowman capsule as a potential cell of origin for ccRCC

• Therapeutic response assessment:

  • Compare response to targeted therapies (mTOR inhibitors, anti-angiogenics)

  • Evaluate immunotherapy efficacy across different genetic backgrounds

  • Identify synthetic lethal interactions that could be therapeutically exploited

• Clinical correlation:

  • Analyze patient cohorts with different combinations of tumor suppressor mutations

  • Correlate genetic alterations with clinical outcomes and treatment responses

  • Develop predictive models for patient stratification