Recombinant Pongo abelii Type-1 angiotensin II receptor-associated protein (AGTRAP)

What is the basic structure and function of Pongo abelii AGTRAP?

Pongo abelii (Sumatran Orangutan) AGTRAP is a member of the lysosome-associated membrane protein family. The recombinant protein typically consists of the expression region 527-637aa with a molecular weight of approximately 19.8 kDa when tagged . Functionally, AGTRAP serves as a specific binding modulator of the angiotensin II type 1 receptor, playing crucial roles in metabolic regulation and potentially immune response modulation .

The protein has been successfully expressed in yeast systems with N-terminal 10xHis-tags and C-terminal Myc-tags, which facilitate purification and detection in experimental applications . Its structural characteristics enable binding capacity that can be detected through functional ELISA, making it valuable for various biochemical and immunological studies .

How does Pongo abelii AGTRAP compare structurally to human AGTRAP?

While the search results don't provide direct comparative data between Pongo abelii and human AGTRAP, we can infer significant homology due to evolutionary proximity. Sumatran orangutans (Pongo abelii) share approximately 97% genetic similarity with humans, suggesting conservation of key protein domains. Researchers often use non-human primate proteins as models for human protein function when studying conserved physiological pathways.

The expressed region (527-637aa) of the recombinant Pongo abelii AGTRAP likely contains functionally important domains that are conserved across primates . When designing experiments, researchers should consider potential species-specific differences in post-translational modifications, though core functional domains related to angiotensin II receptor binding are likely preserved.

What are the optimal expression systems for producing recombinant Pongo abelii AGTRAP?

Based on available data, both yeast and E. coli expression systems have been successfully used for recombinant Pongo abelii AGTRAP production . The choice of expression system depends on experimental requirements:

E. coli Expression System:

• Advantages: Higher yield, cost-effective, rapid growth

• Considerations: May lack proper post-translational modifications

• Reported source for Pongo abelii AGTRAP with expression region 527-637aa

Yeast Expression System:

• Advantages: Better post-translational modifications than bacterial systems

• Considerations: Lower yield than E. coli, more complex cultivation

• Successfully used for expression with N-terminal His-tags

For functional studies requiring proper protein folding and post-translational modifications, yeast systems may be preferable. For structural studies requiring larger quantities, E. coli systems offer advantages. The reported purity of >85% as determined by SDS-PAGE suggests effective purification strategies have been established for both systems .

What purification techniques yield the highest purity for recombinant AGTRAP?

Affinity chromatography utilizing the N-terminal 10xHis-tag appears to be the primary purification method for recombinant Pongo abelii AGTRAP. This approach has yielded preparations with greater than 85% purity as determined by SDS-PAGE . For researchers seeking to optimize purification protocols, a multi-step approach is recommended:

• Initial Capture: Immobilized metal affinity chromatography (IMAC) utilizing the His-tag

• Intermediate Purification: Ion exchange chromatography to separate charged contaminants

• Polishing Step: Size exclusion chromatography to achieve >95% purity for sensitive applications

For applications requiring detection of interaction partners, the dual-tagging system (N-terminal His and C-terminal Myc) enables tandem affinity purification strategies to achieve higher purity and recover specific protein complexes .

How does AGTRAP expression change in metabolic disorders and what experimental approaches best capture these changes?

AGTRAP expression exhibits significant changes in metabolic disorders, with decreased expression observed in adipose tissues from both patients and mouse models with metabolic dysfunction . This altered expression appears to be tissue-specific and diet-responsive.

Key Experimental Findings:

• Adipose tissues from patients and mice with metabolic disorders show decreased AGTRAP expression despite abundant expression in normal adipose tissue

• AGTRAP deficient (Agtrap−/−) mice develop systemic metabolic dysfunction under high-fat diet conditions

Recommended Experimental Approaches:

• Quantitative PCR: For accurate measurement of tissue-specific transcriptional changes

• Western Blotting: For protein-level validation using antibodies against AGTRAP (1:2000 dilution recommended)

• Immunohistochemistry: Using streptavidin-peroxidase method with anti-AGTRAP antibody (1:100 dilution)

• Tissue-Specific Conditional Knockouts: To distinguish primary from secondary effects

Researchers should consistently account for dietary status, age, and sex in experimental design, as these factors significantly impact AGTRAP expression patterns and metabolic phenotypes .

What mechanistic insights have been gained from AGTRAP knockout models in metabolic research?

AGTRAP knockout mouse models have provided valuable insights into the protein's role in metabolic regulation. Homozygous AGTRAP deficient (Agtrap−/−) mice display normal physiological phenotypes under standard diet conditions but develop significant metabolic dysfunction when challenged with a high-fat diet .

Key Phenotypic Observations in Agtrap−/− Mice Under High-Fat Diet:

• Increased accumulation of pad fat

• Development of hypertension

• Dyslipidemia

• Insulin resistance

• Adipose tissue inflammation

Mechanistic Insights:
The metabolic dysfunction in Agtrap−/− mice was reversed by subcutaneous transplantation of fat pads overexpressing AGTRAP from Agtrap transgenic mice, confirming a direct protective role of AGTRAP in adipose tissue metabolism . This suggests AGTRAP functions through local tissue-specific mechanisms rather than systemic effects.

For researchers designing AGTRAP knockout experiments, it's crucial to include both standard diet and high-fat diet conditions to observe the full phenotypic spectrum, as the protein's metabolic functions become particularly evident under metabolic stress conditions .

How does AGTRAP expression vary across different cancer types and what are the implications for research?

AGTRAP shows differential expression across various cancer types, with particularly notable upregulation in hepatocellular carcinoma (HCC). Pan-cancer analysis reveals that AGTRAP expression has significant prognostic implications in multiple cancers .

Expression Profile Across Cancer Types:

• Hepatocellular Carcinoma (HCC): Significantly higher expression in HCC cells and primary liver tissues compared to normal tissues

• Glioma, Liver Cancer, Kidney Chromophobe: High expression correlated with poor prognosis

Research Implications:

• Cancer-Specific Targeting: Different cancers may require different approaches to AGTRAP-based interventions

• Biomarker Development: AGTRAP expression may serve as a prognostic indicator in specific cancer types

• Mechanistic Heterogeneity: The role of AGTRAP likely varies across cancer types, necessitating tissue-specific research

Researchers should consider employing cancer cell line panels that represent diverse tissue origins when investigating AGTRAP's functions, as demonstrated by CCLE database analyses that showed varied expression across HCC cell lines .

What techniques are most effective for studying AGTRAP's role in tumor microenvironment and immune response?

Investigating AGTRAP's role in the tumor microenvironment (TME) and immune response requires specialized methodological approaches that capture both expression patterns and functional interactions.

Recommended Experimental Approaches:

• Immunohistochemistry (IHC):

  • Protocol: Streptavidin-peroxidase method using anti-AGTRAP antibody (1:100 dilution)

  • Scoring: Calculate IHC score as intensity (0-3) multiplied by percentage of positive cells (0-4)

• Protein-Protein Interaction Analysis:

  • STRING database analysis for interaction network mapping

  • Co-immunoprecipitation to validate direct interactions

  • Correlation analysis between AGTRAP and immune markers

• Gene Set Enrichment Analysis (GSEA):

  • Single-gene GSEA to identify enriched pathways across different cancers

  • Correlation with immune cell infiltration markers

• Immune Infiltration Analysis:

  • TIMER database queries to analyze correlations between AGTRAP expression and immune cell populations

  • Flow cytometry validation in tissue samples

Researchers should integrate these approaches with functional assays such as T-cell activation studies or macrophage polarization experiments to establish causative relationships beyond correlative data .

What are the critical considerations when designing experiments to study AGTRAP functional mechanisms?

When designing experiments to investigate AGTRAP's functional mechanisms, researchers should consider several critical factors:

Expression System Selection:

• For structural studies: E. coli expression systems yield higher protein quantities

• For functional studies: Yeast expression systems provide better post-translational modifications

• Tag placement can affect protein function; compare N-terminal vs. C-terminal tags

Knockout/Knockdown Approaches:

• Complete knockout mice (Agtrap−/−) show phenotypes primarily under metabolic stress

• Consider conditional and tissue-specific knockouts to isolate primary effects

• For cell culture: Compare transient vs. stable knockdown phenotypes

Physiological Relevance:

• Include dietary challenges (standard vs. high-fat diet) in metabolic studies

• For cancer studies: Use both in vitro and in vivo models to capture microenvironment effects

• Compare findings across species when possible to validate conserved mechanisms

Controls and Validation:

Researchers should design experiments that distinguish direct AGTRAP-mediated effects from secondary consequences by employing time-course studies and rescue experiments with wild-type AGTRAP .

How should researchers address contradictory findings in AGTRAP research literature?

Contradictory findings in AGTRAP research may stem from methodological differences, tissue-specific effects, or contextual factors. Researchers should employ the following strategies to address inconsistencies:

• Methodological Reconciliation:

  • Compare antibody sources and validation methods across studies

  • Assess differences in experimental conditions (cell types, animal strains, diets)

  • Evaluate quantification methods and statistical approaches

• Context-Dependent Analysis:

  • AGTRAP function may differ between normal physiology and disease states

  • High-fat diet induces phenotypes in knockout models not seen under standard diet

  • Cancer type specificity may explain varied prognostic associations

• Resolution Approaches:

  • Perform side-by-side comparisons using standardized protocols

  • Develop tissue-specific conditional models to isolate contradictory findings

  • Use multiple complementary techniques to validate key findings

• Meta-Analysis Strategy:

  • Pool data across multiple datasets as demonstrated in pan-cancer analyses

  • Examine AGTRAP expression across different databases (TIMER, GEPIA2, MERAV)

  • Stratify analyses by specific conditions to identify pattern-explaining variables

Researchers encountering contradictory results should consider publishing comprehensive methodological details and contributing to standardization efforts within the field.

What are the current hypotheses regarding AGTRAP's molecular mechanism in regulating metabolic homeostasis?

Current research suggests several potential molecular mechanisms through which AGTRAP regulates metabolic homeostasis:

Angiotensin Receptor Modulation Hypothesis:
As a specific binding modulator of the angiotensin II type 1 receptor, AGTRAP may attenuate angiotensin II-induced pro-inflammatory signaling in adipose tissue . This mechanism could explain why AGTRAP deficiency leads to enhanced adipose tissue inflammation under high-fat diet conditions.

Adipose Tissue Function Regulation:
AGTRAP appears to maintain healthy adipose tissue function, as evidenced by the rescue of metabolic dysfunction in Agtrap−/− mice through fat pad transplantation from AGTRAP-overexpressing donors . This suggests AGTRAP influences adipocyte differentiation, lipid storage, or adipokine secretion profiles.

Anti-Inflammatory Pathway:
The correlation between decreased AGTRAP expression and a shift toward pro-inflammatory phenotypes in adipose tissue suggests AGTRAP may regulate inflammatory signaling pathways . This could involve NF-κB pathway modulation or regulation of macrophage polarization in adipose tissue.

Metabolic Stress Response:
The emergence of phenotypes specifically under high-fat diet conditions indicates AGTRAP may function as a metabolic stress response protein, becoming particularly important during nutritional excess .

Advanced research should focus on elucidating the specific molecular interactions between AGTRAP and inflammatory signaling components in metabolically active tissues.

How might emerging technologies advance our understanding of AGTRAP's role in disease pathways?

Emerging technologies offer unprecedented opportunities to deepen our understanding of AGTRAP's role in disease pathways:

Single-Cell Technologies:
Single-cell RNA sequencing can reveal cell type-specific expression patterns of AGTRAP within heterogeneous tissues like tumors or adipose tissue. This approach could identify specific cell populations where AGTRAP expression changes most dramatically during disease progression.

Spatial Transcriptomics:
Techniques that preserve spatial information while measuring gene expression could reveal localization patterns of AGTRAP in tissue microenvironments, potentially identifying spatial correlations with specific cellular populations or pathological features.

CRISPR-Based Functional Genomics:
CRISPR interference or activation screens targeting AGTRAP and its interaction partners could systematically map functional pathways in relevant cellular models of metabolic disease or cancer.

Proteomics Approaches:

• Proximity labeling techniques (BioID, APEX) to identify AGTRAP's protein interaction network in living cells

• Phosphoproteomics to identify signaling changes downstream of AGTRAP manipulation

• Interactome studies under different metabolic conditions to capture context-dependent interactions

In Vivo Imaging:
Development of AGTRAP-targeted imaging probes could enable tracking of expression changes in live animal models during disease progression, potentially correlating with metabolic parameters or tumor development.

Researchers seeking to apply these technologies should consider integrated multi-omics approaches that combine multiple data types to build comprehensive models of AGTRAP function in disease contexts.

What statistical approaches are most appropriate for analyzing AGTRAP expression data across different experimental contexts?

Proper statistical analysis of AGTRAP expression data requires careful consideration of experimental design and data characteristics:

For Differential Expression Analysis:

• Parametric tests (t-test, ANOVA) for normally distributed data with equal variances

• Non-parametric alternatives (Mann-Whitney, Kruskal-Wallis) for non-normal distributions

• Paired analyses for matched samples (e.g., tumor vs. adjacent normal tissue)

• Multiple testing correction (Benjamini-Hochberg) for genome-wide analyses

For Correlation Studies:

• Pearson correlation for linear relationships between AGTRAP and other continuous variables

• Spearman correlation for non-linear relationships or when data contains outliers

• Partial correlation to control for confounding variables

For Survival Analysis:

• Kaplan-Meier curves with log-rank tests to compare survival between high and low AGTRAP expression groups

• Cox proportional hazards models for multivariate analysis including clinical covariates

For Complex Data Integration:

• GSEA for pathway enrichment analysis based on AGTRAP expression

• Hierarchical clustering to identify expression patterns across sample groups

• Principal component analysis (PCA) to reduce dimensionality in multi-parameter datasets

Researchers should clearly report all statistical parameters, including sample sizes, measures of center, dispersion, precision, and exact p-values to facilitate reproducibility and meta-analysis.

How should researchers interpret AGTRAP expression changes in the context of complex disease networks?

Interpreting AGTRAP expression changes requires consideration of biological context and network effects:

Contextual Interpretation Framework:

• Tissue/Cell Type Context: Expression changes may have different implications in different tissues (e.g., adipose tissue vs. liver)

• Disease Stage Context: Early vs. late-stage expression patterns may reflect different biological processes

• Pathway Context: Interpret changes in relation to known interacting pathways (angiotensin signaling, metabolic pathways)

• Network Context: Consider AGTRAP as part of protein-protein interaction networks

Biological Significance Assessment:

• Distinguish statistical significance from biological relevance

• Consider effect size in addition to p-values

• Validate key findings across independent datasets and experimental models

Multi-Level Data Integration:

• Correlate expression changes with functional outcomes (e.g., metabolic parameters, tumor growth)

• Integrate transcriptomic, proteomic, and phenotypic data

• Consider epigenetic regulation mechanisms that may explain expression changes

Researchers should avoid over-interpretation of correlative findings without functional validation and should explicitly discuss limitations and alternative explanations when presenting results.