Recombinant Human Fibroblast growth factor receptor 3 (FGFR3), partial (Active)

What is the structure of FGFR3 and how does it function in normal cells?

FGFR3 belongs to the fibroblast growth factor receptor family, which are type I transmembrane receptor tyrosine kinases with immunoglobulin-like domains. The receptor consists of an extracellular domain containing three Ig-like loops, a transmembrane domain, and an intracellular tyrosine kinase domain. In its normal state, FGFR3 binds to various FGF ligands, particularly FGF1, FGF2, FGF8, and FGF9 in the case of the FGFR3c splice variant . This binding triggers receptor dimerization, autophosphorylation, and activation of downstream signaling cascades.

The activation of FGFR3 leads to multiple signaling outputs primarily through the MAPK pathway and PLCγ signaling, with additional pathways including STAT activation and RSK2 . The cellular outcomes of FGFR3 signaling are highly context-dependent and can lead to proliferation, migration, or differentiation, depending on the cell type and specific conditions. In normal urothelial cells, both wildtype FGFR3 and its splice variants are expressed, with expression levels changing during differentiation and confluence .

How do FGFR3 isoforms differ and what are their functional implications?

FGFR3 exists in multiple isoforms resulting from alternative splicing. The two major splice variants are FGFR3b and FGFR3c, which differ in their third Ig-like domain, resulting in different ligand binding specificities. The FGFR3c variant is primarily activated by FGF1, FGF2, FGF8, and FGF9 .

Additionally, a truncated splice variant (Δ8-10) has been identified in normal urothelial cells. This variant lacks the region encoding the second part of the third Ig-like loop and the transmembrane domain. This isoform is glycosylated and secreted, capable of binding FGF1 and dimerizing. Interestingly, it can block the response to FGF1 in cells expressing full-length FGFR3, suggesting a negative regulatory role in normal urothelium . This variant is expressed at lower levels in tumor cell lines, potentially contributing to the dysregulation of FGFR3 signaling in cancer.

What experimental systems are available for studying recombinant FGFR3 function?

Several experimental approaches have been developed to study FGFR3 function:

• Micropatterned surfaces for live cell analysis: This technique allows for quantification of GRB2 recruitment to mature receptors at the plasma membrane, providing insights into signaling events specifically at the cell surface .

• Western blotting: This traditional method quantifies the activation of FGFRs by determining the phosphorylation state of tyrosines in adaptor protein docking sites, comparing the signal of the phosphorylated protein to the signal intensity of the pan-protein .

• Cell-based functional assays: These assays measure outcomes such as proliferation, migration, and differentiation in response to FGFR3 activation or inhibition.

• Molecular dynamics simulations: Computational approaches using software such as GROMACS to study FGFR3 structure, conformational changes, and interactions with ligands or inhibitors .

How can researchers accurately quantify FGFR3 signaling activity in different cellular compartments?

Quantifying FGFR3 signaling in different cellular compartments requires specialized approaches beyond traditional Western blotting, which typically measures bulk cellular responses. A micropatterning method has been developed to specifically report on signaling events at the plasma membrane, providing a more nuanced understanding of receptor activation .

This approach involves:

• Creating micropatterned surfaces that allow for precise spatial organization of cells

• Live cell imaging to monitor GRB2 recruitment to FGFR3 at the plasma membrane

• Quantitative analysis of these recruitment events as a measure of receptor activation

This method has revealed that FGFR3 activation at the cell surface can differ significantly from measurements in bulk cell extracts. For instance, some FGFR3 mutants (K650Q and K650E) demonstrated either unexpectedly high or low activation states compared to previous reports, likely because the micropatterning method specifically probes the receptor population at the plasma membrane rather than the entire cellular pool .

What are the molecular mechanisms underlying ligand-independent activation of FGFR3 mutants?

FGFR3 mutants often display increased basal (ligand-independent) receptor activity, which contributes to their pathological effects. Studies have examined various FGFR3 mutants to understand this phenomenon:

The mechanisms of ligand-independent activation may include:

• Altered receptor conformation that mimics the ligand-bound state

• Enhanced dimerization propensity in the absence of ligand

• Changes in interactions with regulatory proteins

• Altered subcellular localization affecting access to downstream signaling components

Understanding these mechanisms is crucial for developing targeted therapeutic approaches for conditions associated with FGFR3 mutations.

How do computational methods contribute to FGFR3 inhibitor discovery and optimization?

Advanced computational methods have become essential tools in the discovery and optimization of FGFR3 inhibitors:

• Structure relaxation through molecular dynamics (MD) simulations: Using the FGFR3 crystal structure (e.g., PDB code: 6LVM) as the starting point, MD simulations with packages like GROMACS can explore the receptor's conformational space and identify stable states for virtual screening. These simulations typically employ force fields such as AMBER 99SB-ILDN with explicit solvation .

• Free energy landscape analysis: By analyzing Gibbs free energy during simulations, researchers can identify energy basins and transition states, revealing the lowest energy conformations of FGFR3 after releasing bound inhibitors. This approach was used to determine that a 500 ns simulation successfully sampled FGFR3 after releasing Pyrimidine Derivative 37b .

• Active learning-based virtual screening: This approach combines physics-based methods with machine learning to efficiently screen large compound libraries. The process involves:

  • Generating a receptor grid from a prepared protein

  • Preparing compound libraries (e.g., natural compound libraries)

  • Initial docking using Glide SP

  • Training machine learning models on physics-based data

  • Iterative improvement through multiple training rounds

These computational approaches significantly enhance the efficiency of drug discovery efforts by prioritizing compounds with favorable binding profiles for experimental validation.

What are the optimal experimental conditions for analyzing FGFR3 activation in response to different ligands?

Analyzing FGFR3 activation requires careful consideration of experimental conditions:

• Ligand selection: FGFR3c is primarily activated by FGF1, FGF2, FGF8, and FGF9, with FGF1 and FGF2 showing differences in inducing kinase phosphorylation. FGF2 has been reported to have a stronger activating effect when added at saturating conditions .

• Heparin co-factor: Heparin or heparan sulfate proteoglycans are typically required as co-factors for optimal FGF binding and receptor activation. For example, the effective concentration (ED50) for FGF3 effect is 0.02-0.1 μg/mL in the presence of 1 μg/mL of heparin .

• Receptor isoform consideration: Experiments should specify which FGFR3 isoform is being studied (e.g., FGFR3b or FGFR3c) as they have different ligand binding profiles.

• Cell context: The outcome of FGFR3 signaling depends heavily on cell context, so the choice of cell system is crucial. Responses in NIH-3T3 cells may differ significantly from those in urothelial cells or other relevant cell types .

• Readout selection: Different readouts (phosphorylation of specific sites, GRB2 recruitment, downstream pathway activation) may give varying results and should be carefully chosen based on the specific research question.

How can researchers effectively study the interplay between FGFR3 signaling and immune checkpoint pathways?

Studying the interplay between FGFR3 signaling and immune checkpoint pathways requires integrated experimental and computational approaches:

• Mathematical modeling: Developing validated mathematical models of FGFR3-mediated tumor growth can help investigate the impact of combined therapies. Such models can be calibrated with experimental data before exploring survival benefits and optimal dosing schedules .

• Combined therapy experiments: Testing the effects of FGFR3 inhibitors in combination with immune checkpoint inhibitors (e.g., anti-PD-L1 therapy) under various conditions and sequences.

• Parameter space exploration: Identifying regions where each monotherapy can outperform the other, and determining optimal combination strategies .

Recent mathematical models have suggested that:

• FGFR3 mutation reduces the effectiveness of anti-PD-L1 therapy

• There are specific parameter regions where either anti-FGFR3 or anti-PD-L1 monotherapy can outperform the other

• Pretreatment with anti-PD-L1 therapy consistently results in greater tumor reduction, even when anti-FGFR3 therapy is the more effective monotherapy

These findings provide a rational framework for designing experimental studies and clinical trials of combination therapies targeting both FGFR3 and immune checkpoints.

What is the role of FGFR3 mutations in bladder cancer pathogenesis and progression?

FGFR3 plays a central role in bladder cancer pathogenesis:

• Prevalence of alterations: Over 80% of non-muscle-invasive bladder cancers (NMIBC) and approximately 40% of muscle-invasive bladder cancers (MIBC) have upregulated FGFR3 signaling. These frequencies may be even higher when considering alternative splicing, ligand expression, and regulatory mechanism changes .

• Types of alterations: FGFR3 alterations in bladder cancer include point mutations, overexpression of wildtype receptor, and FGFR3 gene fusions.

• Prognostic significance: FGFR3 mutation generally identifies patients with favorable NMIBC disease, though only grade and stage remain single independent predictors of outcome in multivariate analyses .

• Therapeutic implications: FGFR3 mutations rather than upregulated expression may represent better predictive biomarkers for FGFR inhibitor therapies. The FGFR inhibitor Erdafitinib has shown a 40% response rate in patients with FGFR3 point mutations or FGFR2/3 fusions, leading to FDA approval for locally advanced or metastatic bladder cancer .

• Resistance mechanisms: Despite encouraging response rates, approximately 60% of patients with FGFR3 alterations do not respond to FGFR inhibitors, suggesting the presence of escape mechanisms, development of stable resistance, loss of dependence on FGFR3 during tumor progression, or intratumor heterogeneity .

How can researchers distinguish between oncogenic and non-oncogenic FGFR3 mutations in experimental systems?

Distinguishing between oncogenic and non-oncogenic FGFR3 mutations requires multiple complementary approaches:

• Functional assays: Measuring basal and ligand-stimulated receptor activity using methods such as GRB2 recruitment in micropatterned cells. Oncogenic mutations typically show increased basal activity, though the response to ligand stimulation varies among mutants .

• Signaling pathway analysis: Examining downstream signaling pathway activation (MAPK, PLCγ, STAT) in response to different mutations. Oncogenic mutations often show altered patterns of pathway activation compared to wildtype FGFR3 .

• Cell transformation assays: Assessing the ability of mutant FGFR3 to transform NIH-3T3 or other relevant cell types. Oncogenic mutations generally promote anchorage-independent growth and other hallmarks of cellular transformation .

• In vivo models: Evaluating the tumorigenic potential of FGFR3 mutations in animal models, particularly in tissue-specific contexts relevant to human cancers.

• Computational prediction: Using in silico analysis methods such as CADD or SIFT scores, or merging information from multiple component methods with experimental data to predict the functional impact of mutations .

The integration of these approaches provides a more comprehensive understanding of the oncogenic potential of specific FGFR3 mutations, helping to prioritize therapeutic targets and stratify patients for clinical trials.