ALAAT1 Antibody

What is ALAAT1/ALT1 and what biological functions does it serve?

ALT1, also known as glutamic-pyruvate transaminase (GPT), is an enzyme primarily found in the liver, with smaller amounts present in muscles, kidneys, and other organs . It catalyzes the reversible transamination between alanine and 2-oxoglutarate to generate pyruvate and glutamate, playing a key role in the intermediary metabolism of glucose and amino acids . Functionally, ALT1 is involved in various cellular processes including breaking down food into energy . The enzyme has gained significant research attention as serum ALT levels serve as a routine biomarker for liver injury caused by drug toxicity, infection, alcohol consumption, and steatosis .

How does ALT1 antibody reactivity vary across species?

ALT1 antibodies show cross-reactivity across multiple species, which is important for comparative studies. Based on validated testing, certain ALT1 antibodies (like 67531-1-Ig) demonstrate reactivity with human, pig, rat, and mouse samples . This cross-reactivity allows researchers to design experiments across different model organisms while using the same detection methodology. When designing multi-species experiments, researchers should verify the specific reactivity profile of their chosen antibody and may need to optimize protocols for each species.

What are the key specifications for ALT1 antibody selection in research applications?

When selecting an ALT1 antibody for research, consider these critical specifications:

These parameters should align with your experimental design, target tissue/cells, and detection methods .

What are the optimal dilution ratios for different ALT1 antibody applications?

The optimal dilution ratios vary significantly by application type and specific antibody:

It is essential to titrate the antibody in your specific testing system to obtain optimal results, as sample type significantly influences optimal concentration . A titration experiment should include a range of dilutions to determine the concentration that provides the best signal-to-noise ratio.

How should researchers optimize immunohistochemistry protocols for ALT1 detection in tissue microarrays?

For optimal immunohistochemical detection of ALT1 in tissue microarrays, researchers should follow this methodological approach:

• Sample preparation: Deparaffinize with xylene, rehydrate using an ethanol gradient, and wash with double-distilled H₂O .

• Peroxidase quenching: Soak tissues in 3% H₂O₂ for 10 minutes .

• Blocking: Block with BSA for 30 minutes to reduce non-specific binding .

• Primary antibody incubation: Incubate tissue samples with ALT1 primary antibodies overnight at 4°C .

• Detection system: Use an EnVision+ System with HRP for visualization under 200x magnification .

This protocol has been successfully used to demonstrate higher expression of ALT1 in hepatocellular carcinoma (HCC) tissues compared to non-tumor adjacent tissues, with darker staining occurring in HCC samples .

What methodologies are effective for studying ALT1 protein interactions in hepatocellular carcinoma?

For investigating ALT1 protein interactions in HCC, co-immunoprecipitation followed by mass spectrometry has proven effective:

• Cell preparation: Cultivate HepG2 cells to approximately 80% confluence, then transfect with ALT1 knockdown siRNA or negative control siRNA .

• Cell lysis: Wash cells three times with PBS and lyse with 500μl-1ml cell lysate buffer .

• Immunoprecipitation: Add 0.2-2μg of primary antibody and incubate lysates overnight at 4°C with slow shaking .

• Bead binding: Add 40μl of resuspended Protein A+G Agarose and shake slowly at 4°C for 1-3 hours .

• Washing and elution: Centrifuge at 2500 rpm for 5 minutes, wash pellets 5 times with PBS, then resuspend in 20-40μl 1X SDS-PAGE electrophoresis loading buffer .

• Analysis: For proteomics analysis, precipitate proteins with acetone at -20°C overnight, then process according to iTRAQ protocol with appropriate labeling .

This approach has successfully identified 116 differentially expressed proteins in the ALT1 interaction network in HepG2 cells .

How can researchers effectively knockdown ALT1 expression to study its functional role in hepatocellular carcinoma?

For effective ALT1 knockdown studies:

• siRNA design: Target specific sequences within the ALT1 gene that have been validated to reduce expression effectively .

• Transfection optimization: Determine optimal transfection conditions for your specific cell line (e.g., HepG2 cells), including transfection reagent, cell density, and duration.

• Knockdown verification: Confirm ALT1 suppression using western blot analysis before proceeding with functional assays .

• Functional assessments:

  • Migration: Employ wound healing assays to assess cellular migration capacity

  • Invasion: Use transwell assays to measure invasion capabilities

  • Proliferation: Implement CCK-8 and colony formation assays to evaluate cellular proliferation

  • Apoptosis: Quantify apoptotic changes through flow cytometry

  • Cell cycle: Analyze cell cycle progression using flow cytometry to detect potential cell cycle arrest

Research has demonstrated that ALT1 knockdown significantly inhibits migration, invasion, and proliferation of HepG2 cells while promoting apoptosis, with cell cycle arrest occurring in G2 phase .

What approaches can address non-specific binding when using ALT1 antibodies in complex tissue samples?

When confronting non-specific binding issues:

• Optimized blocking: Extend blocking time (up to 2 hours) with 5% BSA or 5% non-fat milk in PBS-T to reduce non-specific binding sites.

• Secondary antibody controls: Include controls omitting primary antibody to identify non-specific binding from secondary antibodies.

• Isotype controls: Use matched IgG isotype controls (e.g., Mouse IgG1 for the 67531-1-Ig antibody) at the same concentration as your primary antibody .

• Pre-absorption controls: Pre-incubate antibody with excess target antigen (ALT1 fusion protein) to verify signal specificity.

• Cross-adsorption: Consider antibodies that have been cross-adsorbed against common interfering proteins.

• Optimization of antibody concentration: Titrate antibody concentrations to find the optimal signal-to-noise ratio.

• Alternative detection systems: If background persists, consider alternative detection methods or more specific secondary antibodies.

How do researchers distinguish between ALT1 and ALT2 in experimental systems?

Distinguishing between ALT1 (cytosolic) and ALT2 (mitochondrial) requires careful experimental design:

• Antibody selection: Use antibodies specifically raised against unique epitopes of ALT1 that don't cross-react with ALT2 .

• Subcellular fractionation: Separate cytosolic and mitochondrial fractions before analysis to exploit the different cellular localizations.

• Molecular weight verification: Confirm the observed molecular weight matches ALT1 (approximately 52 kDa) rather than ALT2 .

• Gene-specific knockdown: Implement siRNA targeting specifically ALT1 or ALT2 sequences to confirm specificity of observed effects.

• Recombinant protein controls: Include purified recombinant ALT1 and ALT2 as controls in immunoblotting experiments.

• Enzymatic activity assays: Design assays that distinguish between the slightly different substrate preferences of the two isoforms.

How should researchers interpret changes in ALT1 expression patterns in relation to hepatocellular carcinoma progression?

When interpreting ALT1 expression data in HCC research:

• Quantitative analysis: Properly quantify IHC staining intensity and percentage of positive cells using established scoring systems to enable statistical comparison between HCC and non-tumor tissues .

• Clinical correlation: Correlate ALT1 expression levels with clinical parameters such as tumor stage, grade, and patient outcomes.

• Multimarker approach: Analyze ALT1 expression in conjunction with other markers like Ki67 and EP-CAM, as ALT1 knockdown has been shown to reduce their expression .

• Pathway analysis: Consider the relationship between ALT1 and the p53 signaling pathway, as research indicates ALT1 protein interaction network is associated with HepG2 cell behaviors via this pathway .

• EMT marker evaluation: Assess epithelial-mesenchymal transition (EMT)-associated markers and matrix metalloproteinases (MMPs) in relation to ALT1 expression, as suppression of ALT1 contributes to alterations in these markers .

Research has demonstrated significantly higher expression of ALT1 in HCC tissues compared to non-tumor adjacent tissues, suggesting its potential role in tumor progression .

What statistical approaches are most appropriate for analyzing ALT1 expression data across different experimental conditions?

For rigorous statistical analysis of ALT1 expression data:

• Normality testing: Begin with tests for normal distribution (Shapiro-Wilk or Kolmogorov-Smirnov) to determine appropriate parametric or non-parametric approaches.

• For two-group comparisons:

  • Parametric data: Use independent t-tests with appropriate correction for multiple comparisons

  • Non-parametric data: Apply Mann-Whitney U tests

• For multiple group comparisons:

  • Parametric data: Implement one-way ANOVA followed by post-hoc tests (Tukey, Bonferroni)

  • Non-parametric data: Use Kruskal-Wallis test followed by Dunn's test

• For paired samples (e.g., tumor vs. adjacent normal): Apply paired t-tests or Wilcoxon signed-rank tests based on data distribution.

• Correlation analysis: Use Pearson's (parametric) or Spearman's (non-parametric) correlation to assess relationships between ALT1 expression and other variables.

• Multivariate analysis: Consider linear regression or logistic regression models to identify independent factors associated with ALT1 expression.

• Survival analysis: Implement Kaplan-Meier curves with log-rank tests and Cox proportional hazards models to correlate ALT1 expression with patient outcomes.

How can researchers integrate ALT1 antibody-derived data with other omics analyses to understand ALT1's role in cellular pathways?

For integrative analysis of ALT1 within multiomics frameworks:

• Transcriptomics integration:

  • Correlate protein expression data from ALT1 antibody experiments with mRNA expression data

  • Identify transcription factors potentially regulating ALT1 expression through promoter analysis

• Proteomics correlation:

  • Compare ALT1 antibody-derived data with mass spectrometry-based proteomic profiles

  • Analyze ALT1 interactome data to identify protein interaction networks

• Metabolomics connection:

  • Correlate ALT1 expression/activity with metabolites involved in alanine and pyruvate metabolism

  • Investigate the relationship between ALT1 levels and glucose metabolism intermediates

• Functional genomics:

  • Integrate ALT1 knockdown phenotypic data with pathway analysis tools

  • Use GSEA (Gene Set Enrichment Analysis) to identify pathways affected by ALT1 modulation

• Systems biology approach:

  • Construct network models incorporating ALT1 and its interacting partners

  • Identify regulatory motifs and feedback loops involving ALT1

• Clinical data integration:

  • Correlate experimental ALT1 findings with clinical parameters and outcomes

  • Develop predictive models incorporating ALT1 expression with other biomarkers

Research has demonstrated that ALT1 knockdown affects multiple cellular pathways, including p53 signaling, proliferation, apoptosis, and epithelial-mesenchymal transition .

What emerging technologies could enhance the sensitivity and specificity of ALT1 detection in research applications?

Emerging technologies with potential to advance ALT1 detection include:

• Single-cell proteomics: Applying mass cytometry (CyTOF) or single-cell western blotting to detect ALT1 expression at the individual cell level, revealing heterogeneity within tissues.

• Proximity ligation assays (PLA): Implementing this technique to visualize and quantify ALT1 protein interactions with higher sensitivity and spatial resolution than traditional co-immunoprecipitation.

• CRISPR-based tagging: Using CRISPR-Cas9 to introduce endogenous tags to ALT1, enabling real-time tracking in living cells without antibody-related artifacts.

• Super-resolution microscopy: Applying techniques like STORM or PALM with ALT1 antibodies to visualize subcellular localization with nanometer precision.

• Tissue-clearing techniques: Combining ALT1 antibodies with CLARITY or iDISCO+ for three-dimensional visualization of ALT1 distribution in intact tissues.

• Aptamer-based detection: Developing highly specific aptamers against ALT1 as alternatives to traditional antibodies for detection applications.

• Microfluidic immunoassays: Implementing chip-based platforms for automated, high-throughput ALT1 detection with minimal sample consumption.

What are the most promising research avenues for understanding ALT1's role in liver cancer therapeutic development?

Promising research directions for ALT1 in liver cancer therapeutics include:

• Targeting ALT1-associated metabolic pathways: Investigating how ALT1's role in alanine metabolism contributes to cancer cell survival and identifying potential metabolic vulnerabilities.

• ALT1 and cancer immunotherapy: Exploring how ALT1 expression affects tumor microenvironment and immune cell infiltration, with potential implications for immunotherapy efficacy.

• ALT1 as a biomarker for treatment response: Investigating whether changes in ALT1 expression or activity could predict response to conventional therapies.

• Structure-based drug design: Using structural information about ALT1 to design small molecule inhibitors that could modulate its activity in cancer cells.

• ALT1 in combination therapies: Studying whether ALT1 modulation could sensitize resistant tumor cells to existing chemotherapeutics.

• miRNA regulation of ALT1: Investigating microRNAs that regulate ALT1 expression as potential therapeutic targets.

• ALT1 and cancer stem cells: Exploring the relationship between ALT1 and cancer stem cell maintenance, as research has shown ALT1 knockdown affects proliferation, migration, and invasion of HepG2 cells .

• P53 pathway modulation: Further investigating the interaction between ALT1 and the p53 signaling pathway as a potential therapeutic target, given research indicating ALT1 regulates biological behaviors through this pathway .

How might ALT1 function in non-hepatic tissues impact experimental design considerations?

While ALT1 is predominantly expressed in liver, its presence in other tissues requires careful experimental design:

• Tissue-specific expression profiling: Establish baseline ALT1 expression levels across tissues of interest using tissue microarrays and appropriate controls.

• Cell type heterogeneity: Consider that even within non-hepatic tissues, ALT1 may be expressed differently across cell types, requiring single-cell approaches or cell sorting.

• Conditional knockout models: Develop tissue-specific conditional knockout models to distinguish hepatic from non-hepatic ALT1 functions.

• Alternative splicing consideration: Investigate potential tissue-specific ALT1 isoforms that might have different antibody epitopes or functional roles.

• Compensatory mechanisms: Assess whether other transaminases compensate for ALT1 in non-hepatic tissues, potentially masking phenotypes in knockdown experiments.

• Metabolism differences: Account for tissue-specific metabolic contexts when interpreting ALT1 function, as its role in alanine-glucose metabolism may vary by tissue energy requirements.

• Species differences: Consider that the relative importance of ALT1 in non-hepatic tissues may vary across experimental species, affecting translation of findings .

• Pathological contexts: Evaluate whether disease states alter ALT1 expression in non-hepatic tissues, potentially revealing new functions beyond normal physiology.