Recombinant Human Tumor protein p53-inducible protein 11 (TP53I11)

What is TP53I11 and how is it related to the p53 pathway?

TP53I11 (Tumor Protein P53 Inducible Protein 11) is an early transcriptional target of the p53 tumor suppressor, first reported approximately two decades ago. It serves as a downstream effector in the p53 signaling network, which regulates critical cellular functions including cell cycle arrest, DNA repair, apoptosis, autophagy, and metabolism. The p53 protein functions primarily as a transcription factor and is widely regarded as the "guardian of the genome" due to its central role in maintaining genomic stability. When p53 is activated by cellular stress signals, it induces the expression of multiple target genes including TP53I11, which contributes to the tumor suppressive functions of the p53 pathway .

How does TP53I11 affect cellular metabolism?

TP53I11 significantly influences cellular metabolism through multiple mechanisms:

• Enhanced glycolysis: Overexpression of TP53I11 increases basal glycolysis, glycolytic capacity, and glycolytic reserve in multiple cell lines (MCF10A, MDA-MB-231, and MCF7).

• Oxidative phosphorylation regulation: Loss of TP53I11 decreases basal oxygen consumption rate (OCR) but increases maximal respiration and spare respiratory capacity, suggesting that cells shift toward OXPHOS when TP53I11 is depleted.

• AMPK pathway modulation: TP53I11 reduces AMPK activation, which disrupts metabolic flexibility and thereby impairs cell survival under stressful growth conditions such as ECM detachment and glucose starvation.

These metabolic effects have been demonstrated through precise Seahorse extracellular flux analysis measurements, showing that TP53I11 promotes a more glycolytic phenotype under normal conditions, which supports higher proliferation rates .

How does the loss of TP53I11 affect cancer cell survival under stress conditions?

The loss of TP53I11 confers survival advantages to cancer cells under various stress conditions through several mechanisms:

• Enhanced ECM-independent survival: Loss of TP53I11 promotes spheroid formation (both in size and number), increases survival, and reduces anoikis of detached MCF10A and MDA-MB-231 cells.

• Resistance to glucose starvation: TP53I11 knockdown improves cell survival under glucose starvation conditions.

• Increased AMPK activation: Loss of TP53I11 enhances AMPK activation in both ECM-detached and glucose-starved cells. AMPK serves as a key energy sensor that helps cells adapt to metabolic stress by preserving energy homeostasis.

• Metabolic flexibility: The increased AMPK activation following TP53I11 depletion appears to confer greater metabolic flexibility, allowing cancer cells to survive hostile microenvironments encountered during tumor progression and metastasis .

What experimental approaches are most effective for studying TP53I11's role in metabolic regulation?

To comprehensively investigate TP53I11's role in metabolic regulation, researchers should consider a multi-faceted experimental approach:

• Real-time metabolic flux analysis: Utilize Seahorse XF Analyzers to measure ECAR (extracellular acidification rate) and OCR (oxygen consumption rate) in cells with manipulated TP53I11 expression levels. This provides direct quantification of glycolytic parameters (basal glycolysis, glycolytic capacity, and glycolytic reserve) and mitochondrial respiration parameters (basal respiration, maximal respiration, and spare capacity).

• Metabolite profiling: Employ mass spectrometry-based metabolomics to quantify intracellular metabolites involved in glycolysis, TCA cycle, and related pathways.

• Signaling pathway analysis: Investigate AMPK activation status through western blotting for phosphorylated AMPK (Thr172) and its downstream targets after manipulating TP53I11 levels.

• Glucose uptake and lactate production assays: Measure the rates of glucose consumption and lactate production in cultures to assess glycolytic flux.

• Mitochondrial function assays: Assess mitochondrial membrane potential, mass, and reactive oxygen species production using fluorescent probes.

These techniques should be applied under both normal and stress conditions (e.g., glucose starvation, glutamine deprivation, hypoxia, matrix detachment) to fully understand TP53I11's context-dependent metabolic effects .

How can researchers effectively model the dual functions of TP53I11 in both promoting proliferation and suppressing metastasis?

Modeling the seemingly contradictory functions of TP53I11 requires sophisticated experimental approaches:

• 3D culture systems: Employ Matrigel and suspension culture systems to mimic ECM-detached conditions. This allows assessment of TP53I11's effect on spheroid formation, architecture, and invasive capacity.

• Microenvironmental stress models: Design experiments that transition between normal and stress conditions (nutrient deprivation, hypoxia, matrix detachment) to observe how TP53I11 differentially regulates cellular responses.

• In vivo xenograft models with metabolic manipulation: Utilize xenograft models with manipulated TP53I11 expression while simultaneously monitoring or altering metabolic parameters through diet modifications or metabolic inhibitors.

• Spatiotemporal analysis of TP53I11 expression: Develop systems for inducible TP53I11 expression at different stages of tumor progression to determine stage-specific effects.

• Patient-derived organoids: Cultivate organoids from primary and metastatic tumor tissues to evaluate how TP53I11 expression correlates with metabolic profiles and invasion capacity.

This comprehensive approach allows researchers to capture the context-dependent functions of TP53I11 and understand how it promotes proliferation under normal conditions while suppressing metastatic potential under stress conditions .

What is the relationship between TP53I11 and AMPK signaling, and how can this be experimentally validated?

The relationship between TP53I11 and AMPK signaling represents a critical axis in metabolic regulation and stress response:

Experimental evidence indicates:

• Loss of TP53I11 increases AMPK activation in both detached and glucose-starved cells

• TP53I11 overexpression reduces AMPK activation under stress conditions

• This regulatory relationship impacts metabolic flexibility and cell survival under hostile environments

Experimental validation approaches:

• Pharmacological manipulation: Use AMPK activators (e.g., AICAR, metformin) and inhibitors (e.g., Compound C) in combination with TP53I11 modulation to determine if AMPK activation is sufficient to rescue phenotypes caused by TP53I11 overexpression.

• Genetic approaches: Perform simultaneous manipulation of TP53I11 and AMPK (using AMPK-α1/α2 siRNA or CRISPR knockout) to establish epistatic relationships.

• Biochemical interaction studies: Investigate whether TP53I11 directly interacts with AMPK or its upstream regulators (LKB1, CaMKK2) through co-immunoprecipitation and proximity ligation assays.

• Phosphorylation analysis: Quantify phosphorylation levels of AMPK and its downstream targets (ACC, ULK1) after TP53I11 manipulation under different conditions.

• Metabolic stress sensors: Monitor cellular ATP/AMP ratios and calcium levels to determine if TP53I11 affects the upstream signals that activate AMPK.

The data collected from these approaches can be organized in comparative tables showing AMPK activation metrics across different experimental conditions, providing a comprehensive view of how TP53I11 regulates this central metabolic sensor .

How does the p53 status of cancer cells influence the function and expression of TP53I11?

As a p53 transcriptional target, TP53I11's expression and function may be significantly impacted by the p53 status of cancer cells:

• Expression correlation analysis: In cells with wild-type p53, TP53I11 expression should increase following p53 activation by DNA damage or other stressors. This response would be absent or altered in p53-mutant or p53-null cells.

• Chromatin immunoprecipitation (ChIP): Perform ChIP assays to confirm direct binding of wild-type p53 to the TP53I11 promoter region, and investigate how common p53 mutations affect this binding.

• Rescue experiments: In p53-null cells, determine whether ectopic TP53I11 expression can rescue certain p53-dependent functions, particularly those related to metabolic regulation and anoikis resistance.

• Mutant p53 gain-of-function effects: Investigate whether certain p53 mutations can alter the regulation of TP53I11 expression or function through gain-of-function mechanisms.

• Clinical correlation studies: Analyze patient tumor samples to determine if TP53I11 expression levels correlate with p53 mutation status across different cancer types.

Understanding this relationship is crucial because p53 is the most frequently mutated gene in human cancers, and alterations in the p53 pathway may significantly impact downstream targets like TP53I11, potentially contributing to the metabolic reprogramming observed in many tumors .

What are the optimal experimental conditions for detecting TP53I11 protein and assessing its functional effects?

Detecting TP53I11 protein and assessing its functional effects requires careful optimization:

For protein detection:

• Antibody selection: Choose validated antibodies specifically tested for TP53I11 detection in western blotting, immunofluorescence, and immunohistochemistry applications.

• Expression systems: When using recombinant TP53I11, consider both mammalian and bacterial expression systems. Mammalian expression (e.g., HEK293 cells) will ensure proper post-translational modifications, while bacterial systems may provide higher yields for structural studies.

• Sample preparation: For cell lysates, use RIPA buffer supplemented with protease inhibitors. For tissue samples, optimize fixation methods (4% paraformaldehyde for immunofluorescence, formalin for IHC) to preserve antigenicity.

For functional assays:

• Timing considerations: Assess proliferation effects at 24, 48, and 72 hours post-manipulation of TP53I11 levels.

• Detachment assays: For ECM-independent survival studies, use ultra-low attachment plates and polyHEMA-coated surfaces, with assessment points at 24, 48, and 72 hours.

• Metabolic stress induction: For glucose starvation experiments, gradually reduce glucose concentrations (e.g., step down from standard 25mM to 5mM, 1mM, and 0mM) to capture dose-dependent effects.

• Cell type considerations: Include both normal epithelial cells (MCF10A) and cancer cells with different p53 status (e.g., MCF7 [p53 wild-type] and MDA-MB-231 [p53 mutant]) to account for p53-dependent effects .

What experimental design strategies should be used to investigate TP53I11's role in the EMT process?

Investigating TP53I11's role in EMT requires a comprehensive experimental design:

• EMT induction models:

  • TGF-β treatment (1-10 ng/ml for 48-72 hours)

  • Hypoxia exposure (1% O₂ for 24-72 hours)

  • EGF stimulation (50-100 ng/ml)

  • Matrix detachment culture systems

• Molecular markers assessment:

  Epithelial Markers	Mesenchymal Markers	EMT Transcription Factors
  E-cadherin	N-cadherin	Snail
  Claudins	Vimentin	Slug
  Occludin	Fibronectin	Twist
  ZO-1	α-SMA	ZEB1/2

• Functional assays:

  • Migration assays (wound healing, transwell)

  • Invasion assays (Matrigel-coated transwell)

  • 3D morphogenesis in Matrigel (assess acinar structures and branching)

• Time-course analysis:

  • Monitor changes in TP53I11 expression during EMT progression

  • Perform TP53I11 overexpression or knockdown at different stages of EMT

• Reversibility assessment:

  • Determine if TP53I11 manipulation can reverse established EMT

  • Test if TP53I11 affects the mesenchymal-epithelial transition (MET)

• In vivo validation:

  • Orthotopic xenograft models with circulating tumor cell analysis

  • Assessment of metastatic capacity with manipulated TP53I11 levels

This multifaceted approach will provide comprehensive insights into how TP53I11 regulates the EMT process, which is crucial for understanding its role in suppressing metastasis .

What techniques are most suitable for studying the impact of TP53I11 on metabolic flexibility in cancer cells?

Studying TP53I11's impact on metabolic flexibility requires specialized techniques that can measure metabolic parameters under various conditions:

• Real-time metabolic flux analysis:

  • Seahorse XF analysis to measure the glycolytic switch and mitochondrial adaptability

  • Design assays that test metabolic parameters under sequential stress conditions

  • Measure the glycolytic stress response and mitochondrial stress response

• Substrate utilization profiling:

  • Use isotope-labeled glucose, glutamine, and fatty acids to track their metabolic fates

  • Measure consumption rates of different nutrients under normal and stress conditions

  • Analyze how TP53I11 affects preference for different energy substrates

• Metabolic inhibitor sensitivity:

  • Test sensitivity to glycolysis inhibitors (2-DG, 3-BP)

  • Assess response to OXPHOS inhibitors (oligomycin, rotenone)

  • Determine if TP53I11 alters dependency on specific metabolic pathways

• Nutrient rescue experiments:

  • Deprive cells of glucose and assess rescue by alternative substrates

  • Test if TP53I11-overexpressing cells show decreased ability to utilize alternative nutrients

• AMPK signaling dynamics:

  • Monitor AMPK activation kinetics in response to various metabolic stressors

  • Use AMPK activity reporters to track real-time changes in AMPK activity

  • Assess downstream AMPK targets to determine signaling output differences

• Metabolomics time-course:

  Condition	Time Points	Metabolites to Monitor
  Normal	0h, 6h, 24h	Glycolytic intermediates, TCA metabolites, ATP/ADP/AMP
  Low glucose	0h, 6h, 24h	Amino acids, fatty acid oxidation intermediates
  Detached	0h, 6h, 24h	Glutathione, ROS indicators, pentose phosphate pathway metabolites

These approaches collectively provide a comprehensive assessment of how TP53I11 influences metabolic flexibility, which is crucial for cancer cell survival under changing microenvironmental conditions .

How does TP53I11 expression correlate with clinical outcomes in different cancer types?

Understanding the clinical relevance of TP53I11 requires comprehensive analysis of expression patterns and their correlation with patient outcomes:

This comprehensive clinical correlation analysis will help determine if TP53I11 has potential as a prognostic biomarker and if its metabolic regulatory functions observed in laboratory studies translate to clinical relevance .

What strategies could be developed to therapeutically target the TP53I11 pathway in cancer treatment?

Based on TP53I11's dual role in metabolism and metastasis suppression, several therapeutic strategies could be developed:

• TP53I11 restoration approaches:

  • For tumors with low TP53I11 expression: develop viral vectors or nanoparticle delivery systems for TP53I11 gene therapy

  • Small molecules that enhance TP53I11 expression in p53 wild-type tumors

  • Epigenetic modulators to reverse potential silencing of TP53I11

• Metabolic vulnerability targeting:

  • Combine TP53I11 restoration with metabolic inhibitors targeting glycolysis or OXPHOS based on the tumor's metabolic profile

  • Exploit the reduced metabolic flexibility conferred by TP53I11 overexpression using nutrient restriction strategies

• AMPK modulation approaches:

  • In tumors with high TP53I11: combine with AMPK activators to counter the TP53I11-mediated AMPK suppression

  • In tumors with low TP53I11: combine AMPK inhibitors with stress-inducing therapies

• Context-dependent combination strategies:

  Tumor Context	Primary Strategy	Combination Approach
  Primary tumor growth	Enhance TP53I11 to exploit its anti-metastatic potential	Combine with conventional chemotherapy
  Metastatic disease	Target metabolic flexibility	Combine TP53I11 modulation with metabolic inhibitors
  p53 mutant tumors	Restore TP53I11 independent of p53	Combine with mutant p53 reactivators

• Biomarker-guided approaches:

  • Develop diagnostics to identify tumors likely to respond to TP53I11-targeted therapies

  • Monitor metabolic adaptation during treatment to guide therapeutic adjustments

These potential therapeutic strategies highlight the importance of understanding TP53I11's context-dependent functions and suggest that targeting its pathway may offer new approaches for inhibiting both tumor growth and metastasis .

How can researchers develop in vivo models to validate TP53I11's role in tumor progression and metastasis?

Developing robust in vivo models is crucial for validating TP53I11's functions observed in vitro:

• Genetically engineered mouse models (GEMMs):

  • Develop TP53I11 knockout mice for tumor initiation studies

  • Create conditional TP53I11 expression models to study stage-specific effects

  • Generate models with TP53I11 mutations found in human cancers

• Xenograft models with temporal control:

  • Establish inducible TP53I11 expression systems in cancer cell lines

  • Implant cells orthotopically and modulate TP53I11 expression at different stages

  • Use primary patient-derived xenografts (PDXs) with TP53I11 manipulation

• Metastasis-specific models:

  • Tail vein injection models to assess circulating tumor cell survival and colonization

  • Intracardiac injection to study multi-organ metastasis

  • Spontaneous metastasis models from primary tumors

• Metabolic perturbation models:

  • Dietary interventions (ketogenic diet, caloric restriction)

  • Metabolic inhibitor treatment combined with TP53I11 modulation

  • Glucose/insulin manipulation to test metabolic flexibility

• Non-invasive monitoring approaches:

  • Bioluminescence imaging for tumor progression

  • PET imaging with metabolic tracers to assess glycolysis and OXPHOS in vivo

  • Circulating tumor DNA analysis for monitoring tumor evolution

• Ex vivo analysis protocols:

  Analysis	Technique	Key Parameters
  Metabolism	Metabolomics from tumor tissue	Glycolytic intermediates, TCA cycle metabolites
  Signaling	Phospho-proteomics	AMPK pathway activation, downstream targets
  Metastasis	Immunohistochemistry	EMT markers, invasion indicators
  Microenvironment	Single-cell RNA-seq	Stromal interactions, immune infiltration

These comprehensive in vivo approaches will provide crucial validation of TP53I11's roles and help translate laboratory findings toward potential clinical applications .

What expression systems and purification strategies yield optimal recombinant human TP53I11 protein for research applications?

Producing high-quality recombinant human TP53I11 requires careful consideration of expression systems and purification strategies:

• Expression systems comparison:

  System	Advantages	Considerations
  E. coli	High yield, cost-effective	Potential folding issues, lacks PTMs
  Mammalian (HEK293, CHO)	Proper folding, native PTMs	Lower yield, higher cost
  Insect cells (Sf9, Hi5)	High yield, some PTMs	Intermediate complexity
  Cell-free systems	Rapid production, avoids toxicity	Limited scale, higher cost

• Optimal construct design:

  • Include appropriate tags (His6, GST, MBP) based on downstream applications

  • Consider fusion protein approaches if solubility is an issue

  • Include precision protease cleavage sites for tag removal

  • Codon optimization for the chosen expression system

• Purification strategy:

  • Initial capture: Affinity chromatography (IMAC for His-tagged proteins)

  • Intermediate purification: Ion exchange chromatography

  • Polishing: Size exclusion chromatography to ensure monodispersity

  • Consider on-column refolding protocols if inclusion bodies form

• Quality control assessments:

  • SDS-PAGE and western blotting for purity and identity verification

  • Mass spectrometry for molecular weight confirmation and PTM analysis

  • Circular dichroism for secondary structure assessment

  • Dynamic light scattering for aggregation analysis

  • Functional assays to confirm biological activity

• Storage stability optimization:

  • Buffer screening to identify optimal pH and salt conditions

  • Addition of stabilizing agents (glycerol, reducing agents)

  • Lyophilization protocols for long-term storage

  • Aliquoting strategy to avoid freeze-thaw cycles

These considerations ensure that the recombinant TP53I11 used in research applications retains native structure and function, which is crucial for obtaining reliable and reproducible results .

What are the key considerations for designing functional assays using recombinant TP53I11 protein?

Designing robust functional assays using recombinant TP53I11 requires careful consideration of protein characteristics and experimental conditions:

• Protein preparation considerations:

  • Verify protein activity before experiments using established functional readouts

  • Determine the optimal working concentration range (typically 10-100 ng/ml for cellular assays)

  • Assess stability under experimental conditions (temperature, time, media composition)

  • Use appropriate negative controls (heat-inactivated protein, irrelevant protein of similar size)

• Cellular uptake and localization assays:

  • Fluorescently label TP53I11 to track cellular uptake and distribution

  • Perform time-course studies to determine optimal treatment duration

  • Assess potential receptor-mediated uptake mechanisms

  • Determine intracellular half-life of the recombinant protein

• Metabolic effect assessment:

  • Measure glycolytic parameters after protein treatment using Seahorse analysis

  • Assess AMPK pathway activation/inhibition through phosphorylation status

  • Monitor cellular ATP levels and energy charge

  • Determine if exogenous TP53I11 recapitulates the effects of endogenous expression

• Cell-free biochemical assays:

  • Develop binding assays to identify potential interaction partners

  • Assess potential enzymatic activities (kinase, phosphatase, etc.)

  • Investigate structural transitions under different conditions

  • Perform thermal shift assays to evaluate stabilizing conditions or binding interactions

• Validation strategies:

  Assay Type	Validation Approach	Controls
  Cellular uptake	Compare with tagged vs. untagged protein	Fluorescent protein alone
  Metabolic effects	Compare with overexpression studies	Mutant TP53I11 versions
  Signaling activation	Dose-response relationships	Pathway inhibitor co-treatment
  Transcriptional effects	Compare with gene expression data	RNA isolation timing optimization

These considerations ensure that functional assays using recombinant TP53I11 yield reliable and physiologically relevant results, facilitating the translation of findings from recombinant protein studies to endogenous protein functions .