Recombinant Mouse Probable fructose-2,6-bisphosphatase TIGAR (Tigar)

What is the primary function of TIGAR in cellular metabolism?

TIGAR primarily functions as a regulator of reactive oxygen species (ROS) by modulating cellular antioxidant defense mechanisms. TIGAR acts as a fructose-2,6-bisphosphatase, decreasing levels of fructose-2,6-bisphosphate and shifting glucose metabolism from glycolysis to the pentose phosphate pathway, which generates NADPH for antioxidant defense. This activity helps maintain redox homeostasis within cells, particularly under stress conditions. In experimental models, TIGAR overexpression decreases oxidative stress as measured by markers such as malondialdehyde (MDA), a product of lipid peroxidation .

How does TIGAR expression affect ROS levels in mouse models?

TIGAR expression has an inverse relationship with ROS levels in mouse models. In pancreatic ductal adenocarcinoma (PDAC) models:

• TIGAR deficiency (knockout models): Increased ROS levels, enhanced epithelial-to-mesenchymal transition, and elevated ERK signaling

• TIGAR overexpression (transgenic models): Decreased oxidative stress, maintained epithelial phenotype (higher E-cadherin expression), reduced ERK activation, and increased expression of DUSP6 (a phosphatase that inactivates ERK)

This ROS regulation by TIGAR has stage-specific effects on tumor progression, supporting early tumor initiation while restricting metastatic capacity at later stages .

How can researchers generate mouse models with altered TIGAR expression?

Researchers can generate mouse models with altered TIGAR expression through several genetic engineering approaches:

• TIGAR knockout models: Using the Tigar^(fl/fl) strain crossed with appropriate Cre-expressing strains (such as Pdx1-Cre for pancreas-specific deletion). Complete knockout mice (Tigar^(-/-)) or conditional tissue-specific knockouts can be generated .

• TIGAR overexpression models: A targeting vector can be constructed to insert a lox-stop-lox Tigar cDNA at the mouse Hprt locus. The procedure involves:

  • Cloning mouse Tigar cDNA sequence using PCR

  • Inserting elements containing CAGSA-STOP-TIGARcDNA-pA into the Hprt-targeting vector

  • Transfecting mouse embryonic stem cells (mESCs) with the targeting vector

  • Selecting for cells with regained Hprt activity using HAT medium

  • Confirming correct insertion through long-range PCR

What phenotypes are observed in TIGAR-deficient mouse models?

TIGAR-deficient mouse models exhibit complex phenotypes that are context-dependent:

How does TIGAR modulate cancer cell-stromal cell interactions in the tumor microenvironment?

TIGAR modulates cancer cell-stromal cell interactions through ROS-dependent signaling mechanisms that affect cytokine production and subsequent stromal cell behavior:

• TIGAR deficiency in cancer cells leads to increased ROS, which promotes:

  • Production of cytokines that induce surrounding fibroblasts to adopt tumor-supportive phenotypes

  • Attraction of macrophages that support cancer cell dissemination and metastasis

  • Increase in cancer-associated fibroblast (CAF) markers (αSMA and FAP)

  • Enhanced collagen deposition and desmoplastic stroma formation

• Experimental evidence from co-culture systems:

  • Normal fibroblasts exposed to conditioned medium from TIGAR-deficient cancer cells (KFC-KO) show increased αSMA expression compared to controls

  • Treatment of KFC-KO cells with the antioxidant NAC diminishes their ability to reprogram fibroblasts, confirming the ROS-dependent mechanism

  • The presence of fibroblasts dramatically enhances the migratory capacity of TIGAR-deficient cancer cells compared to control cells

This bidirectional communication between cancer cells and stromal components represents a complex mechanism by which TIGAR-regulated ROS levels influence tumor progression beyond cell-autonomous effects.

What are the molecular mechanisms of TIGAR-mediated protein-protein interactions and signaling pathway regulation?

TIGAR engages in multiple protein-protein interactions that regulate signaling pathways independent of its metabolic functions:

• TIGAR-TAK1-TRAF6 complex formation:

  • TIGAR directly interacts with both TAK1 and TRAF6, potentially forming a tripolymer complex

  • TAK1 (1-300) fragment interacts with TRAF6 (332-530) fragment, and both fragments are recognized by TIGAR

  • This interaction promotes the formation of the TAK1-TRAF6 complex and enhances TAK1 ubiquitination

• Critical binding regions and residues:

  • Computational modeling through molecular dynamics simulations identified that residues 146-161 of TIGAR are crucial for interaction with the ATP-binding domain of TAK1

  • Residues 152-161 form a loop structure that dynamically interacts with TAK1 through hydrophobic interactions and backbone hydrogen bonds

  • Residues 158-161 (particularly G159 and G161) are especially important, as mutations in this region (termed Mut3) significantly attenuate TIGAR-TAK1 binding and subsequent complex formation with TRAF6

• Functional significance:

  • The catalytically inactive mutant of TIGAR (TMU) maintains the ability to interact with TAK1 and TRAF6, indicating that these protein-protein interactions occur independently of TIGAR's enzymatic activity

  • Disruption of these interactions through targeted mutations affects downstream signaling events, suggesting potential therapeutic strategies

How does TIGAR expression dynamically change during tumor progression, and what are the implications for experimental design?

TIGAR expression shows dynamic changes during tumor progression with significant experimental implications:

• Temporal expression pattern:

  • Fluctuating TIGAR levels are observed during PDAC development in both mouse and human systems

  • Higher expression may be found in early premalignant lesions

  • Lower expression is often observed in invasive tumors, correlating with increased metastatic capacity

• Tissue-specific effects:

  • TIGAR modulation (either knockout or overexpression) affects metastasis to the lung but not to the liver in pancreatic cancer models

  • This suggests tissue-dependent mechanisms that researchers should consider when designing metastasis studies

• Experimental design considerations:

  • Timing of intervention: Different effects may be observed depending on when TIGAR expression is modulated (early vs. late in tumor development)

  • Model selection: The genetic background of the cancer model is crucial - effects of TIGAR loss or overexpression differ between KFC (driven by KRAS mutation and loss of p53) and KPC (driven by mutations in both KRAS and p53) models

  • ROS measurement: Incorporating multiple markers of oxidative stress (like MDA staining for lipid peroxidation) provides more robust assessment

  • Controls for compensatory mechanisms: Long-term TIGAR modulation may trigger adaptive responses that should be monitored

What experimental approaches can be used to investigate the differential effects of TIGAR on primary tumor growth versus metastatic capacity?

To investigate TIGAR's differential effects on primary tumor development versus metastasis, researchers can employ several complementary approaches:

• In vivo experimental design:

  • Generate complementary mouse models with either TIGAR knockout (KFC-KO) or overexpression (KPC-Tg)

  • Monitor tumor initiation, progression, and metastasis through:

    • Longitudinal imaging techniques (MRI, micro-CT)

    • Histological assessment at defined time points

    • Quantification of metastatic burden across multiple organs

    • Comparison of organ-specific metastasis patterns (lung vs. liver)

• Metastasis quantification methods:

  • Detailed histological assessment using pancreatic markers (like CK19) to identify metastatic foci in distant organs

  • Quantification metrics should include both:

    • Percentage of mice with metastases

    • Total number of organs containing metastases

    • Organ-specific metastatic burden

• Cell-based functional assays:

  • Derive cell lines from primary tumors with different TIGAR expression levels

  • Compare their phenotypes through:

    Assay Type	Parameters to Measure	Expected Results in TIGAR-deficient Cells
    Wound healing assay	Migration rate, wound closure time	Increased migration speed
    Transwell migration assay	Number of migrating cells	Higher number of migrating cells
    Epithelial-mesenchymal marker analysis	E-cadherin, vimentin, DUSP6 levels	Decreased E-cadherin, increased mesenchymal markers
    ERK signaling assessment	Phospho-ERK levels	Increased ERK phosphorylation
    Fibroblast co-culture assays	Cancer cell migration in presence of fibroblasts	Enhanced migration with fibroblast co-culture

• Molecular profiling:

  • Compare transcriptome profiles of primary tumors versus metastatic lesions to identify TIGAR-dependent gene expression changes

  • Analyze ROS-responsive signaling pathways and cytokine production patterns

How can researchers effectively manipulate TIGAR activity for experimental purposes beyond genetic approaches?

Researchers can manipulate TIGAR activity through several approaches beyond genetic modification:

• Pharmacological modulators:

  • Antioxidants (such as N-acetylcysteine/NAC) can be used to counteract the effects of TIGAR deficiency

  • Experiments show NAC treatment reduces the ability of TIGAR-deficient cancer cells to reprogram fibroblasts, confirming the ROS-dependent mechanism

• Structure-guided mutagenesis:

  • Create specific TIGAR mutants based on structural insights from computational models:

    • Mut1: Mutations in residues 146-151

    • Mut2: Mutations in residues 152-157

    • Mut3: Mutations in residues 158-161 (particularly G159W and G161W)

  • These mutants can selectively disrupt protein-protein interactions while maintaining enzymatic activity, allowing dissection of different TIGAR functions

• Domain-specific constructs:

  • The catalytically inactive mutant (TMU) retains the ability to form protein complexes

  • Truncated constructs can be designed to selectively preserve or eliminate specific interaction domains

  • These tools enable researchers to distinguish between TIGAR's metabolic and signaling functions

• Conditional expression systems:

  • Doxycycline-inducible systems allow temporal control of TIGAR expression

  • This approach is valuable for studying stage-specific requirements for TIGAR during tumor progression

• Cell-free systems for studying TIGAR interactions:

  • In vitro binding assays using purified recombinant proteins

  • Co-immunoprecipitation studies with tagged TIGAR variants

  • These approaches can identify direct binding partners and characterize interaction dynamics

What are the optimal methods for measuring ROS levels in TIGAR-modified experimental systems?

Optimal ROS measurement in TIGAR-modified systems requires multiple complementary approaches:

• Histological assessment of oxidative damage:

  • Malondialdehyde (MDA) staining for lipid peroxidation provides a reliable tissue-level assessment of oxidative stress

  • This method has successfully demonstrated decreased oxidative stress in TIGAR-overexpressing tumors (KPC-Tg) compared to controls

• Live-cell ROS indicators:

  • Fluorescent probes (CM-H2DCFDA, DHE, MitoSOX) for different ROS species

  • Each probe has specific selectivity for different reactive species (hydrogen peroxide, superoxide, peroxynitrite)

  • Multiple probes should be used to comprehensively assess the ROS profile

• Biochemical assays:

  • Glutathione (GSH/GSSG) ratio measurement

  • Activity assays for antioxidant enzymes (SOD, catalase, GPx)

  • Protein carbonylation detection

• Controls and validation:

  • Include positive controls (H2O2 treatment) and negative controls (antioxidant treatment)

  • Validate ROS involvement through rescue experiments with antioxidants (NAC has been successfully used to reverse phenotypes in TIGAR-deficient cells)

  • Consider the timing of measurements, as ROS levels can fluctuate rapidly

What are the critical quality control parameters for recombinant mouse TIGAR protein production and characterization?

Production of high-quality recombinant mouse TIGAR requires rigorous quality control at multiple stages:

• Expression and purification:

  • Expression system selection: Mammalian expression systems are preferred for proper folding and post-translational modifications

  • Purification strategy: Multi-step purification including affinity chromatography followed by size-exclusion chromatography

  • Purity assessment: >95% purity by SDS-PAGE and silver staining

• Structural validation:

  • Circular dichroism (CD) spectroscopy to confirm secondary structure

  • Thermal shift assays to assess protein stability

  • Limited proteolysis to verify proper folding

• Functional characterization:

  • Enzymatic activity: Measure fructose-2,6-bisphosphatase activity using enzyme-coupled assays

  • Binding assays: Verify interactions with known binding partners (TAK1, TRAF6)

  • ROS modulation: Confirm the ability to reduce ROS levels when added to cellular systems

• Quality control documentation:

  • Batch-to-batch consistency verification

  • Endotoxin testing (<1 EU/mg protein)

  • Mass spectrometry confirmation of protein identity and integrity

  • Stability assessment under various storage conditions

How can researchers address experimental contradictions in TIGAR function across different model systems?

Addressing experimental contradictions in TIGAR function requires systematic investigation of context-dependent factors:

• Context-specific variables to consider:

  • Genetic background differences: Effects of TIGAR modulation differ between models with p53 deletion (KFC) versus p53 mutation (KPC)

  • Tissue specificity: TIGAR modulation affects lung metastasis but not liver metastasis, indicating tissue-dependent mechanisms

  • Temporal dynamics: TIGAR has different effects at early versus late stages of tumor development

• Experimental approaches to resolve contradictions:

  • Parallel testing in multiple model systems under identical conditions

  • Stage-specific interventions using inducible systems

  • Comprehensive phenotyping across multiple parameters rather than focusing on single readouts

  • Detailed documentation of experimental conditions that might influence outcomes

• Data integration strategies:

  • Meta-analysis of published data with careful attention to methodological differences

  • Development of computational models that incorporate context-dependent variables

  • Collaborative cross-laboratory validation studies

• Specific case study: ROS paradox in cancer biology

  • TIGAR deficiency (and resulting increased ROS) has opposite effects on tumor initiation versus progression:

    • Delays premalignant lesion development

    • Enhances metastatic capacity

  • This contradiction can be resolved by understanding that ROS requirements differ during tumor evolution, with TIGAR's dynamic expression reflecting these changing needs

What is the optimal experimental design for evaluating TIGAR's interactions with binding partners in vitro and in vivo?

Optimal experimental design for studying TIGAR interactions combines multiple complementary approaches:

• In vitro interaction studies:

  • Co-immunoprecipitation (Co-IP): Has successfully demonstrated TIGAR's interactions with TAK1 and TRAF6

  • Pull-down assays with recombinant proteins: Can confirm direct interactions

  • Truncation constructs: TAK1 (1-300) and TRAF6 (332-530) fragments have been identified as interacting with TIGAR

  • Surface plasmon resonance (SPR): For quantitative binding kinetics measurement

  • Isothermal titration calorimetry (ITC): For thermodynamic characterization of interactions

• Structural analysis:

  • Computational modeling: Molecular dynamics simulations identified key interaction residues (146-161 of TIGAR) with the ATP-binding domain of TAK1

  • Protein docking and MM/GBSA refinement: Generated initial dimer complex models

  • Mutation validation: Mut3 targeting residues 158-161 attenuated binding, confirming computational predictions

• Cellular validation:

  • Proximity ligation assay (PLA): For detecting protein interactions in situ

  • Fluorescence resonance energy transfer (FRET): For real-time interaction monitoring

  • Bimolecular fluorescence complementation (BiFC): For visualizing interactions in live cells

• Functional outcome assessment:

  • Ubiquitination assays: TIGAR promotes TAK1 ubiquitination

  • Complex formation analysis: TIGAR enhances TAK1-TRAF6 complex formation

  • Downstream signaling effects: Measuring activation of pathways affected by these interactions

What pitfalls should researchers be aware of when interpreting TIGAR phenotypes in cancer models?

Researchers should consider several potential pitfalls when interpreting TIGAR phenotypes in cancer models:

• Compensatory mechanisms:

  • Long-term TIGAR knockout or overexpression may trigger adaptive responses in redox homeostasis pathways

  • Other fructose-2,6-bisphosphatases or ROS regulators might be upregulated

  • Consider using acute, inducible systems to minimize compensation

• Heterogeneous tumor composition:

  • Effects observed in whole tumors may reflect changes in cellular composition rather than cell-autonomous effects

  • Single-cell approaches or cell type-specific analyses should complement bulk tumor studies

  • The interaction between cancer cells and stromal components (like fibroblasts and macrophages) significantly contributes to TIGAR phenotypes

• Stage-dependent effects:

  • TIGAR has opposing effects on tumor initiation versus progression

  • Sampling time points are critical - phenotypes at early versus late stages may contradict each other

  • Longitudinal studies are preferable to single time point analyses

• Tissue-specific effects:

  • TIGAR modulation affects metastasis to the lung but not to the liver in pancreatic cancer models

  • Results from one organ system should not be generalized without verification

  • The specific microenvironment of each tissue may interact differently with TIGAR-modulated cancer cells

• Genetic background considerations:

  • TIGAR phenotypes differ between mouse models with p53 deletion (KFC) versus p53 mutation (KPC)

  • The predictive value for human cancers may vary depending on the specific mutations present

  • Validation across multiple genetic backgrounds is recommended

What are the most promising therapeutic strategies targeting TIGAR in cancer and other diseases?

Several promising therapeutic strategies targeting TIGAR are emerging from current research:

• Targeting TIGAR-protein interactions:

  • Small molecule inhibitors disrupting TIGAR-TAK1-TRAF6 complex formation

  • Peptide-based inhibitors mimicking the critical interaction regions (residues 158-161) identified through computational modeling

  • These approaches could potentially separate TIGAR's metabolic functions from its signaling roles

• Context-specific TIGAR modulation:

  • Stage-specific interventions: Enhancing TIGAR in late-stage disease to reduce metastatic capacity

  • Tissue-specific delivery: Targeting lung-specific metastasis pathways where TIGAR shows the strongest effects

  • Combination with existing therapies to enhance efficacy or reduce resistance

• Targeting downstream effectors:

  • ERK pathway inhibitors could potentially mimic the effects of TIGAR overexpression

  • Modulating cytokine production to alter the tumor microenvironment

  • Counteracting fibroblast reprogramming signals induced by TIGAR-deficient cancer cells

• Biomarker development:

  • TIGAR expression levels as predictive biomarkers for metastatic potential

  • ROS signatures that correlate with TIGAR activity could guide personalized treatment approaches

  • Monitoring dynamic changes in TIGAR during disease progression

How does TIGAR function differ between mouse models and human disease conditions?

Understanding the translational implications of mouse TIGAR research requires careful consideration of species differences:

• Expression pattern comparisons:

  • Similar fluctuations in TIGAR levels during PDAC development have been observed in both mouse and human systems

  • Lower expression in invasive tumors correlates with increased metastatic capacity in both species

• Important species-specific considerations:

  • Mouse models typically have more homogeneous genetic backgrounds than human tumors

  • Tumor evolution timelines differ significantly between mouse models and human disease

  • The tumor microenvironment composition and immune infiltration patterns show species-specific characteristics

• Validation strategies for human relevance:

  • Correlation studies between mouse findings and human patient samples

  • TIGAR expression analysis in patient-derived xenografts

  • In vitro studies using human primary cells alongside mouse models

What novel technologies could advance our understanding of TIGAR's spatiotemporal regulation in complex biological systems?

Emerging technologies offer new opportunities to elucidate TIGAR's complex roles:

• Advanced imaging approaches:

  • Intravital microscopy to monitor TIGAR activity in living tissues

  • CRISPR-based fluorescent tagging of endogenous TIGAR for real-time visualization

  • Spatial transcriptomics to map TIGAR expression patterns within heterogeneous tumors

• Single-cell analysis:

  • Single-cell RNA sequencing to identify cell type-specific responses to TIGAR modulation

  • Mass cytometry for simultaneous measurement of multiple proteins in the TIGAR pathway

  • Single-cell metabolomics to assess metabolic consequences of TIGAR activity

• Optogenetic and chemogenetic tools:

  • Light-activated or drug-inducible TIGAR variants for precise spatiotemporal control

  • Rapid perturbation systems to distinguish direct from adaptive responses

  • Subcellular targeting to assess compartment-specific TIGAR functions

• Integrative multi-omics approaches:

  • Combined analysis of transcriptome, proteome, and metabolome data

  • Systems biology modeling of TIGAR's role in redox homeostasis networks

  • Machine learning algorithms to identify patterns across complex datasets