GOT1 Antibody Pair

What is GOT1 and why is it important in cellular metabolism?

GOT1 (glutamic-oxaloacetate transaminase 1, also known as aspartate aminotransferase) catalyzes the reversible reaction of L-aspartate and alpha-ketoglutarate into oxaloacetate and L-glutamate. This enzyme plays a crucial role in carbon and nitrogen metabolism . GOT1 is particularly important for:

• Supporting serine biosynthesis and purine nucleotide production

• Facilitating the transfer of reducing equivalents between cellular compartments via the malate-aspartate shuttle

• Potentially controlling intracellular levels of reactive oxygen species (ROS) through NADPH synthesis

• Posttranslationally regulating HIF1α expression in cytotoxic T lymphocytes

Recent studies have demonstrated that GOT1 is upregulated in effector CD8+ T cells and plays a role in T cell proliferation under serine-restricted conditions, highlighting its importance in immune cell function .

What are GOT1 antibody pairs and how do they differ from single antibodies?

GOT1 antibody pairs consist of two different antibodies designed to recognize distinct epitopes on the GOT1 protein. Unlike single antibodies used in applications like Western blotting or immunohistochemistry, antibody pairs are specifically developed for sandwich-based detection methods such as ELISA. In a typical configuration:

• The capture antibody is immobilized on a solid surface to bind and capture GOT1 from samples

• The detection antibody (often biotin-conjugated) binds to a different epitope on GOT1, enabling specific detection

This dual-antibody approach significantly enhances specificity and sensitivity compared to single-antibody detection methods, allowing for accurate quantification of GOT1 in complex biological samples.

How should I select the appropriate GOT1 antibody pair for my research?

Selection of GOT1 antibody pairs should be guided by several critical factors:

For GOT1 research, polyclonal antibody pairs often provide better epitope coverage, but monoclonal pairs may offer greater consistency between lots. Review published literature using similar experimental systems to identify validated antibody combinations for your specific research question .

What is the optimal protocol for developing a sandwich ELISA using GOT1 antibody pairs?

An optimized sandwich ELISA protocol for GOT1 detection typically follows these methodological steps:

• Plate coating: Dilute capture antibody (typically 1-10 μg/ml) in coating buffer (carbonate/bicarbonate pH 9.6) and coat microplate wells overnight at 4°C

• Blocking: Block remaining binding sites with 1-5% BSA or animal serum in PBS for 1-2 hours at room temperature

• Sample addition: Add diluted samples and standards in assay buffer, incubate 2 hours at room temperature

• Detection antibody: Add biotinylated detection antibody (typically 0.1-1 μg/ml), incubate 1-2 hours at room temperature

• Signal development: Add streptavidin-HRP (1:5000-1:20000), followed by TMB substrate

• Termination and reading: Stop reaction with H₂SO₄ and read absorbance at 450 nm

Critical optimization parameters include:

• Antibody concentrations (titration experiments recommended)

• Sample dilutions (to ensure measurements fall within the linear range)

• Incubation times and temperatures

• Washing procedures (typically 3-5 washes with PBS-T between steps)

Each parameter should be systematically optimized to achieve maximum sensitivity while maintaining low background signal.

How can GOT1 antibody pairs be applied to study metabolic reprogramming in cancer cells?

GOT1 antibody pairs are valuable tools for investigating metabolic reprogramming in cancer research:

• Quantitative expression analysis: Use ELISA-based quantification to measure GOT1 protein levels across cancer cell lines, tumor specimens, and non-transformed controls. This approach has revealed that GOT1 is upregulated in certain cancer types, including pancreatic ductal adenocarcinoma .

• Response to metabolic stress: Develop immunoassays to monitor GOT1 expression changes following metabolic perturbations such as:

  • Glucose deprivation

  • Glutamine restriction

  • Hypoxic conditions

  • Treatment with metabolic inhibitors

• Correlation with clinical outcomes: Quantify GOT1 in patient samples and correlate with survival data. High GOT1 expression has been linked to poor survival in thyroid and breast carcinoma and in lung adenocarcinoma .

• Metabolic pathway analysis: Combine GOT1 quantification with metabolomic approaches to study the relationship between GOT1 levels and critical metabolites such as aspartate, glutamate, and NAD+/NADH ratios .

A comprehensive approach would involve measuring GOT1 expression together with related metabolic enzymes (e.g., GLS1, GLUD1) to construct a metabolic profile characteristic of specific cancer subtypes .

What considerations are important when using GOT1 antibody pairs for studying immune cell populations?

When applying GOT1 antibody pairs to study immune cells, researchers should consider:

• Cell-specific expression patterns: GOT1 expression varies across immune cell populations, with higher expression observed in effector CD8+ T cells compared to memory CD8+ T cells .

• Activation-dependent regulation: GOT1 is upregulated during T cell activation, so experimental designs should account for activation state and timepoints of analysis.

• Metabolic context sensitivity: GOT1 function in T cells is particularly important under serine-restricted conditions, suggesting experimental designs should consider nutrient availability .

• Sample preparation protocols:

  • For flow cytometry applications: Optimize fixation and permeabilization conditions to maintain epitope accessibility

  • For cell lysates: Use appropriate lysis buffers that preserve GOT1 structure while efficiently extracting the protein

• Multiplex analysis strategies: Consider combining GOT1 detection with other markers of T cell differentiation (e.g., CD62L, CD44) or metabolic state (e.g., Glut1, HIF1α) .

When studying GOT1 in CD8+ T cells, it's crucial to account for the cell's differentiation state, as GOT1 has been shown to promote effector differentiation while its deletion enhances memory T cell generation .

How can I distinguish between cytosolic GOT1 and mitochondrial GOT2 when using antibody-based detection methods?

Distinguishing between the cytosolic (GOT1) and mitochondrial (GOT2) isoforms is essential for accurate interpretation of experimental results:

• Epitope selection: Choose antibody pairs validated specifically against unique sequences of GOT1 not present in GOT2. Review sequence alignments to identify isoform-specific regions.

• Validation controls: Include:

  • Recombinant GOT1 and GOT2 proteins as positive controls

  • Lysates from cells with GOT1 or GOT2 knockdown/knockout as negative controls

  • Cross-reactivity testing against both isoforms

• Expression pattern analysis: GOT1 and GOT2 show distinct expression patterns across tissues and cell types. For instance, in T cells, GOT1 is highly expressed in effector CD8+ T cells while GLUD1 (which works with GOT2) is preferentially expressed in memory CD8+ T cells .

What are the common sources of inconsistent results when using GOT1 antibody pairs, and how can they be resolved?

Inconsistent results with GOT1 antibody pairs can stem from several sources:

For optimal reproducibility, researchers should:

• Maintain consistent sample preparation protocols

• Include appropriate positive and negative controls

• Consider using GOT1 knockout/knockdown samples for validation

• Verify antibody specificity through Western blotting before using in quantitative assays

How can GOT1 antibody pairs be utilized to investigate the relationship between GOT1 activity and ferroptosis in cancer?

Recent research has revealed a complex relationship between GOT1 and ferroptosis, a form of regulated cell death characterized by iron-dependent lipid peroxidation. GOT1 antibody pairs can be employed to investigate this relationship through several advanced approaches:

• Correlation studies: Quantify GOT1 protein levels in cancer cells before and after treatment with ferroptosis inducers (e.g., RSL3, erastin) to establish expression patterns associated with ferroptosis sensitivity.

• Mechanistic investigations: Combine GOT1 quantification with:

  • Measurements of lipid peroxidation (C11-BODIPY probe)

  • Glutathione depletion assays

  • Analysis of other ferroptosis-related proteins (GPX4, SLC7A11)

• Genetic manipulation models:

  • In GOT1 knockdown/knockout models, quantify remaining GOT1 levels using antibody pairs

  • In inducible systems (e.g., doxycycline-inducible shRNA), monitor GOT1 suppression kinetics

  • Correlate GOT1 levels with ferroptosis sensitivity

Research indicates that GOT1 inhibition promotes pancreatic cancer cell death by ferroptosis, and combining GOT1 knockdown with GPX4 inhibitors (e.g., RSL3) significantly potentiates ferroptotic cell death. This suggests GOT1 plays a protective role against ferroptosis in certain cancer contexts .

Specifically, GOT1 inhibition has been observed to:

• Increase lipid peroxidation as measured by C11-BODIPY

• Enhance cytotoxicity when combined with GPX4 inhibitors

• Lead to a mixture of ferroptotic cells (showing cell blistering morphology) and growth inhibition

What strategies can be employed to study the regulation of GOT1 activity beyond protein expression levels?

While GOT1 antibody pairs primarily quantify protein expression, comprehensive investigation of GOT1 regulation requires multi-faceted approaches:

• Post-translational modifications: Develop or utilize antibodies specific to modified forms of GOT1:

  • Phosphorylation sites

  • Acetylation status

  • Ubiquitination

• Protein-protein interactions: Implement co-immunoprecipitation protocols using GOT1 antibodies to isolate protein complexes, followed by:

  • Mass spectrometry to identify interacting partners

  • Western blot analysis for specific suspected interactors

  • Proximity ligation assays for in situ interaction detection

• Subcellular localization dynamics: Investigate GOT1 trafficking between cellular compartments using:

  • Immunofluorescence with GOT1-specific antibodies

  • Subcellular fractionation coupled with quantitative immunoassays

  • Live-cell imaging with fluorescently tagged GOT1

• Metabolic flux analysis: Correlate GOT1 protein levels with:

  • [U-¹³C]glucose or [U-¹³C]glutamine tracing studies

  • Measurement of aspartate/glutamate ratios

  • NADH/NAD+ redox state analysis

Research has shown that GOT1 activity can influence HIF1α levels through post-translational regulation, potentially by regulating α-ketoglutarate levels that affect prolyl hydroxylase activity. This mechanism appears to be independent of GOT1 protein levels, highlighting the importance of studying both expression and functional regulation .

How can GOT1 antibody pairs contribute to understanding the role of GOT1 in CD8+ T cell differentiation and function?

GOT1 antibody pairs offer sophisticated approaches to investigate the complex role of GOT1 in T cell biology:

• Temporal expression profiling: Track GOT1 protein levels during T cell activation and differentiation:

  • Naïve → Effector → Memory transition

  • Correlation with activation markers (CD25, CD69)

  • Analysis of different memory subsets (TCM, TEM, TSCM)

• Single-cell analysis: Implement flow cytometry or mass cytometry (CyTOF) with GOT1 antibodies to:

  • Identify GOT1-high and GOT1-low populations

  • Correlate GOT1 expression with functional markers

  • Examine heterogeneity within seemingly uniform populations

• Functional correlation studies: Quantify GOT1 in relation to:

  • Cytokine production (IFNγ, TNFα)

  • Cytotoxic molecule expression (perforin, granzymes)

  • Proliferative capacity (Ki67, CFSE dilution)

  • Nutrient consumption rates

• Metabolic context dependency: Measure GOT1 across varied nutrient conditions:

  • Serine availability (crucial for GOT1 function)

  • Glucose concentration

  • Glutamine levels

  • Oxygen tension

Research has demonstrated that GOT1 is upregulated in effector CD8+ T cells and essential for their proliferation under serine-free conditions. Mechanistically, GOT1 enhances proliferation by maintaining intracellular redox balance and supporting serine-mediated purine nucleotide biosynthesis. Additionally, GOT1 promotes glycolytic programming and cytotoxic function via posttranslational regulation of HIF protein levels. Conversely, genetic deletion of GOT1 promotes the generation of memory CD8+ T cells, suggesting a regulatory role in T cell fate decisions .

This dual role makes GOT1 a promising target for immunotherapeutic approaches aiming to modulate the balance between effector and memory T cell responses.

How can GOT1 antibody pairs be utilized to investigate the role of GOT1 in brown adipose tissue thermogenesis?

Recent research has identified GOT1 as a cold-inducible gene in brown adipose tissue (BAT) with roles in activating the malate-aspartate shuttle (MAS) and thermogenesis. GOT1 antibody pairs can be employed to explore this emerging area through:

• Expression analysis in thermogenic tissues:

  • Quantify GOT1 protein levels in BAT versus white adipose tissue

  • Monitor expression changes during cold exposure or β-adrenergic stimulation

  • Compare expression across different adipose depots

• Correlation with thermogenic markers:

  • UCP1 (uncoupling protein 1)

  • PGC-1α (peroxisome proliferator-activated receptor gamma coactivator 1-alpha)

  • Thermogenic genes (Cidea, Dio2, Elovl3)

• Mechanistic investigations:

  • Relationship between GOT1 levels and NADH/NAD+ ratios

  • Correlation with mitochondrial respiration parameters

  • Association with glucose uptake and metabolism

Research has shown that GOT1 is upregulated in BAT via a βAR-cAMP-PKA-PGC-1α/NT-PGC-1α signaling axis during cold exposure. Overexpression of GOT1 in BAT enhances cold tolerance, increases fat oxidation and glucose uptake, and activates the malate-aspartate shuttle. This activation leads to decreased cytosolic NADH levels and increased mitochondrial respiration, supporting enhanced thermogenesis .

These findings establish GOT1 as a potential therapeutic target for metabolic disorders, making accurate quantification of GOT1 protein levels in adipose tissues critically important for future research in this area.

What are the critical considerations when designing experiments to investigate the relationship between GOT1 and redox homeostasis?

Investigating GOT1's role in redox homeostasis requires careful experimental design:

• Redox-sensitive detection methods:

  • Ensure sample preparation preserves redox state (rapid processing, reducing agents when appropriate)

  • Consider redox proteomics approaches to detect oxidative modifications of GOT1

  • Use appropriate controls for oxidizing/reducing conditions

• Simultaneous assessment of redox parameters:

  Redox Parameter	Measurement Method	Relationship to GOT1
  NADH/NAD+ ratio	Enzymatic cycling assays or fluorescent sensors	GOT1 activity influences NADH levels
  Glutathione (GSH/GSSG)	HPLC or fluorometric assays	GOT1 may impact GSH synthesis through amino acid metabolism
  ROS levels	DCF-DA, MitoSOX, CellROX dyes	GOT1 can control ROS through NADPH synthesis
  Lipid peroxidation	C11-BODIPY, TBARS assay	GOT1 inhibition increases lipid peroxidation in some cancer cells

• Perturbation strategies:

  • Use GOT1 inhibitors (e.g., aminooxyacetate, AOA)

  • Implement genetic approaches (CRISPR/Cas9, RNAi)

  • Combine with redox-modulating compounds (H₂O₂, N-acetylcysteine, BSO)

• Context-dependency considerations:

  • Cell type specificity (cancer vs. normal cells)

  • Nutrient availability (glucose, glutamine, serine)

  • Oxygen tension (normoxia vs. hypoxia)

  • Proliferation status

Research has shown that GOT1 inhibition in pancreatic cancer cells leads to accumulation of NADH and a decreased NADH/NAD+ ratio under nutrient depletion conditions . In CD8+ T cells, GOT1 maintains intracellular redox balance to support proliferation . These context-specific roles highlight the importance of comprehensive experimental designs when investigating GOT1's contribution to redox homeostasis.

What are the emerging techniques that could enhance the utility of GOT1 antibody pairs in research?

Several cutting-edge technologies promise to expand applications of GOT1 antibody pairs:

• Single-cell proteomics:

  • Mass cytometry (CyTOF) for simultaneous detection of GOT1 and dozens of other proteins

  • Microfluidic-based single-cell Western blotting

  • Single-cell resolution ELISA platforms

• Spatial proteomics:

  • Multiplexed immunofluorescence to map GOT1 expression in tissue contexts

  • Imaging mass cytometry for subcellular localization

  • Digital spatial profiling for quantitative spatial analysis

• Proximity labeling approaches:

  • BioID or APEX2-based methods to identify proteins in proximity to GOT1

  • Combining with mass spectrometry for comprehensive interactome mapping

• In situ activity sensors:

  • Development of antibody-based FRET sensors for GOT1 activity

  • Proximity ligation assays to detect specific GOT1 complexes

  • Antibody-oligonucleotide conjugates for highly multiplexed detection

• Advanced immunoassay formats:

  • Ultrasensitive digital ELISA (Simoa) for detecting GOT1 at femtomolar concentrations

  • Microfluidic immunoassays for minimal sample consumption

  • Automated multiplexed platforms for high-throughput analysis

These technologies will enable researchers to address complex questions about GOT1 biology with unprecedented resolution, sensitivity, and throughput, potentially revealing new insights into its diverse roles in cellular metabolism, immune function, and disease processes.

How might GOT1 antibody pairs contribute to translational research and potential therapeutic applications?

GOT1 antibody pairs have significant potential for translational applications:

• Biomarker development:

  • Development of clinical assays to measure GOT1 in patient samples

  • Correlation of GOT1 levels with disease progression in cancer

  • Use as a companion diagnostic for GOT1-targeting therapies

• Therapeutic monitoring:

  • Quantify GOT1 expression/activity in response to metabolic inhibitors

  • Monitor effects of immunotherapies on T cell GOT1 levels

  • Assess pharmacodynamic responses to GOT1-targeting drugs

• Patient stratification:

  • Identify high-GOT1 tumors that might be susceptible to metabolic interventions

  • Group patients based on GOT1 expression patterns

  • Correlate GOT1 levels with response to existing therapies

• Novel therapeutic approaches:

  • Development of antibody-drug conjugates targeting GOT1-expressing cells

  • Design of bispecific antibodies linking GOT1-expressing cells to immune effectors

  • Creation of chimeric antigen receptor (CAR) T cells targeting GOT1-high cancer cells