ACAT2 Antibody

What is ACAT2 and which cellular functions make it relevant for research?

ACAT2 (Acetyl-CoA acetyltransferase 2) is an enzyme involved in lipid metabolism that catalyzes the formation of cholesterol esters. It is primarily expressed in the liver and intestine where it plays a crucial role in lipoprotein synthesis. Recent research has expanded our understanding of ACAT2 beyond its metabolic functions, identifying its involvement in cancer progression, particularly in gastric cancer (GC) and epithelial ovarian cancer (EOC) .

ACAT2's relevance stems from its:

• Role in cholesterol metabolism and homeostasis

• Association with various cancers, where it may promote proliferation and metastasis

• Potential as a biomarker for cancer prognosis

• Involvement in chemotherapy resistance mechanisms

Which biological samples are most appropriate for ACAT2 antibody applications?

Based on validated research protocols, ACAT2 antibodies have demonstrated reactivity with:

Human samples:

• Gastric cancer tissues and paired adjacent non-tumor tissues

• Epithelial ovarian cancer specimens

• Liver tissue

• Lung tissue

• Placenta tissue

Cell lines:

• GC cell lines: HGC-27, NCI-N87, MKN45, BGC-823

• Ovarian cancer lines: A2780, A2780/DDP (cisplatin-resistant), OVCAR8, OVCAR8/DDP

• Hepatic lines: HepG2, Caco-2

• Other validated lines: K-562, MOLT-4, BT-474, MCF7, HeLa, PC-12

Animal models:

• Mouse liver tissue

• Rat liver tissue

When selecting samples, consider that ACAT2 expression varies significantly between tissues and can be altered in disease states.

What are the standard applications for ACAT2 antibodies in experimental research?

ACAT2 antibodies have been validated for multiple experimental applications with specific recommended dilutions:

Research indicates ACAT2 primarily localizes to the cytoplasm, with occasional nuclear staining observed in gastric cancer tissues .

What protocol modifications are necessary for optimal ACAT2 detection by immunohistochemistry?

For optimal ACAT2 detection in tissue samples by IHC, researchers should consider:

Tissue preparation:

• Paraffin-embedded tissues should be sectioned at 4 μm thickness

• Bake sections at 65°C for 2 hours

• Complete deparaffinization in xylene followed by rehydration through graded alcohol

Antigen retrieval options:

• Primary recommendation: TE buffer (pH 9.0) using pressure cooker

• Alternative method: Sodium citrate buffer (pH 6.0)

Blocking and antibody incubation:

• Block with 5% bovine serum albumin

• Incubate with anti-ACAT2 antibody (1:300 dilution) overnight at 4°C

• Secondary antibody incubation for 1 hour at room temperature

• Visualization using diaminobenzidine as chromogen

• Counterstain with hematoxylin

Scoring methodology:
For semi-quantitative analysis, evaluate five high-power fields randomly selected from each specimen:

• Calculate immune score = percentage of positive cells × staining intensity

• Percentage scoring: 0 (≤5%), 1 (6-25%), 2 (26-50%), 3 (51-75%), 4 (76-100%)

• Intensity scoring: 0 (negative), 1 (weak), 2 (moderate), 3 (strong)

• Final score ranges: 0-4 (low expression), 5-12 (high expression)

These parameters have been successfully utilized to correlate ACAT2 expression with clinical outcomes in cancer studies.

What approaches should be taken to quantify ACAT2 mRNA expression levels?

For reliable quantification of ACAT2 mRNA expression, researchers should:

• RNA extraction and quality control:

  • Extract total RNA using TRIzol reagent

  • Treat with RNase-free DNase to eliminate genomic DNA contamination

  • Verify RNA integrity using spectrophotometry (A260/A280 ratio) and gel electrophoresis

• Reverse transcription:

  • Use 1 μg of RNA for cDNA synthesis

  • Employ a reliable cDNA Synthesis Kit (e.g., Novozan as used in referenced studies)

• qRT-PCR setup:

  • Use a SYBR-based master mix for amplification

  • Recommended primers:

    • ACAT2 forward: 5'-GCCTTCCATTATGGGAATAGGA-3'

    • ACAT2 reverse: 5'-GACCTTCTCTGGGTTTAATCCA-3'

    • GAPDH forward: 5'-GGAGTCCACTGGCGTCTTCA-3'

    • GAPDH reverse: 5'-GTCATGAGTCCTTCCACGATACC-3'

• Data analysis:

  • Calculate relative expression using the 2^(-ΔΔCt) method

  • Use GAPDH as internal control

  • Include technical replicates (minimum triplicates) for each sample

  • Validate findings using independent biological replicates

This approach has been successfully used to demonstrate differential ACAT2 expression between chemosensitive and chemoresistant ovarian cancer cell lines.

What are the critical parameters for detecting ACAT2 protein by Western blotting?

For optimal detection of ACAT2 protein via Western blotting:

Sample preparation:

• Lyse cells/tissues with RIPA buffer containing 1 μM phenylmethanesulfonyl fluoride

• Maintain samples on ice during 30-minute lysis

• Centrifuge at 13,000 rpm for 30 minutes to clarify lysates

• Quantify protein concentration using bicinchoninic acid assay

Electrophoresis and transfer:

• Load equal amounts of protein per lane (20-50 μg typically sufficient)

• Separate using 10% SDS-PAGE

• Transfer to 0.45 μm PVDF membrane

Antibody incubation:

• Block with 5% defatted milk powder for 1 hour at room temperature

• Primary antibody: anti-ACAT2 (1:5000 dilution) incubated overnight at 4°C

• Wash 3 times with TBST

• Secondary antibody: HRP-conjugated anti-rabbit (1:3000) for 1 hour at room temperature

Detection and analysis:

• Visualize using ECL detection reagent

• Expected molecular weight: 40-42 kDa

• Include appropriate positive controls (HepG2, Caco-2 cells recommended)

• Use GAPDH (36 kDa) as loading control

This protocol has been validated to detect differential ACAT2 expression in multiple experimental contexts, including chemoresistant cancer cell models.

How does ACAT2 expression correlate with cancer progression and chemoresistance?

Research demonstrates complex relationships between ACAT2 expression and cancer progression:

In gastric cancer:

In epithelial ovarian cancer:

These findings suggest ACAT2 may serve as both a prognostic biomarker and potential therapeutic target in multiple cancer types, though its precise mechanisms may differ between cancer types.

What molecular mechanisms explain ACAT2's role in tumor progression?

Research has elucidated several mechanisms through which ACAT2 promotes cancer progression:

In gastric cancer:
ACAT2 operates through a molecular cascade involving:

• Upregulation of SETD7 (SET domain containing lysine methyltransferase 7)

• SETD7-mediated reduction of YAP1 (Yes-associated protein 1) ubiquitination

• Protection of YAP1 from proteasomal degradation

• Increased YAP1 protein levels activate YAP1/TAZ-TEAD1 signaling

• Enhanced cell proliferation, EMT, and metastatic capability

This mechanism was validated through:

• Direct correlation between ACAT2 and SETD7 expression (R² = 0.6215, p < 0.05)

• Functional studies showing ACAT2's pro-tumoral effects depend on SETD7

• Animal models confirming ACAT2's role in tumor growth and metastasis

In epithelial ovarian cancer:
Evidence suggests ACAT2 may interact with HSPA9 (70 kDa Heat Shock Protein member 9), which:

• Is overexpressed in platinum-resistant ovarian cancer

• Potentially acts through P53 signaling pathway to confer resistance

• Demonstrates direct interaction with ACAT2 in bioinformatics analyses

Additional research indicates epigenetic regulation:

• ACAT2 overexpression in chemoresistant ovarian cancer tissues may result from DNA hypomethylation

• This suggests methylation status of ACAT2 may influence its role in chemoresistance

In monocytic cells:

• C/EBP (CCAAT/enhancer-binding protein) transcription factors bind to specific elements in the ACAT2 promoter

• This binding drives low-level expression of ACAT2

• Knockdown of C/EBPα, C/EBPβ, or C/EBPε decreases ACAT2 expression

• ChIP assays confirm direct binding of these factors to promoter elements

These mechanistic insights provide potential therapeutic targets for modulating ACAT2 activity in disease contexts.

How should researchers reconcile contradictory findings about ACAT2's role in different cancer types?

The literature reveals apparently contradictory findings regarding ACAT2's role in cancer progression that require careful interpretation:

Observed contradictions:

Reconciliation approaches:

• Tissue-specific functions:

  • ACAT2's normal physiological role differs between tissues (primarily expressed in liver and intestine)

  • Cancer type-specific microenvironments may alter ACAT2's function

  • Consider analyzing tissue-specific interaction partners

• Methodological considerations:

  • Compare antibody specificity across studies (epitope differences)

  • Evaluate scoring systems used to classify "high" versus "low" expression

  • Assess whether studies examined mRNA or protein levels (may not correlate)

• Molecular context analysis:

  • Investigate how ACAT2 interacts with other cancer-relevant pathways in different tissues

  • In gastric cancer, ACAT2 operates through SETD7/YAP1 pathway

  • In ovarian cancer, potential interaction with HSPA9/p53 pathway

  • Different downstream effectors may explain opposite effects

• Patient cohort considerations:

  • Analyze treatment history differences between study populations

  • Consider genetic background variations

  • Evaluate whether studies controlled for confounding clinicopathological factors

• Integrated multi-omics approach:

  • Combine transcriptomics, proteomics, and metabolomics data

  • Evaluate ACAT2's relationship to lipid metabolism status across cancer types

  • Consider epigenetic regulation (e.g., methylation status) in different cancers

Researchers should explicitly acknowledge these contradictions when designing studies and interpreting results, using multiple experimental approaches to validate findings.

What are common difficulties when detecting ACAT2 in tissue samples and how can they be addressed?

Researchers frequently encounter challenges when detecting ACAT2 in tissue samples that can be systematically addressed:

Challenge: Weak or absent signal
Solutions:

• Optimize antigen retrieval: Compare TE buffer (pH 9.0) versus citrate buffer (pH 6.0)

• Increase antibody concentration: Start with 1:20 dilution for IHC

• Extend primary antibody incubation: Overnight at 4°C typically yields better results

• Use signal amplification systems: Consider biotin-streptavidin systems if direct detection is insufficient

• Confirm tissue viability: Process samples within appropriate timeframe to prevent protein degradation

Challenge: High background staining
Solutions:

• Increase blocking stringency: Use 5% BSA or 10% normal serum from the species of secondary antibody

• Additional blocking step: Include 0.3% H₂O₂ treatment to block endogenous peroxidase

• Optimize washing: Extend wash steps (3× 5 minutes with TBST)

• Reduce secondary antibody concentration: Dilute to 1:500-1:1000

• Include negative controls: Omit primary antibody to identify non-specific binding

Challenge: Discrepancies between IHC and Western blot results
Solutions:

• Confirm antibody specificity: Use positive and negative control tissues

• Consider fixation differences: Compare FFPE versus frozen sections

• Evaluate epitope accessibility: Some antibodies perform better in denatured (WB) versus native (IHC) conditions

• Quantify with multiple methods: Validate IHC findings with qRT-PCR or Western blot

• Consider subcellular localization: ACAT2 shows both cytoplasmic and occasional nuclear staining

Challenge: Inconsistent staining patterns
Solutions:

• Standardize tissue processing protocols

• Implement automated staining platforms if available

• Use multi-tissue arrays for simultaneous processing

• Include internal control tissues on each slide

• Process all comparative samples in a single batch

These approaches have been validated in studies examining ACAT2 expression across multiple tissue types and disease states.

How can researchers validate ACAT2 antibody specificity for their experimental system?

Thorough validation of ACAT2 antibody specificity is essential for generating reliable research data:

Essential validation strategies:

• Positive and negative control samples:

  • Positive controls: HepG2, Caco-2, BT-474, mouse liver (known to express ACAT2)

  • Negative controls: Use tissues/cells with minimal ACAT2 expression or ACAT2 knockout models

  • Include isotype controls to detect non-specific binding

• Genetic manipulation validation:

  • siRNA/shRNA knockdown: Confirm signal reduction correlates with ACAT2 suppression

  • Overexpression systems: Verify increased signal with ACAT2 upregulation

  • CRISPR/Cas9 knockout: Demonstrate complete signal loss in knockout cells

• Multi-method concordance:

  • Compare protein detection by Western blot, IHC, and IF

  • Verify mRNA expression correlates with protein levels

  • Confirm expected molecular weight (40-42 kDa) in Western blot

• Peptide competition assay:

  • Pre-incubate antibody with immunizing peptide

  • Demonstrate specific signal blocking

  • Include gradient of blocking peptide concentrations

• Cross-reactivity assessment:

  • Test antibody against related family members (e.g., ACAT1)

  • Evaluate species cross-reactivity if using non-human models

  • Consider potential post-translational modifications

• Lot-to-lot consistency:

  • Compare performance of different antibody lots

  • Maintain reference samples for inter-experimental comparisons

  • Document exact conditions that yield optimal results

Implementation of these validation approaches increases confidence in experimental findings and facilitates reproducibility across research groups.

How can ACAT2 antibody-based techniques be integrated with other methodologies to understand its role in chemoresistance?

Researchers exploring ACAT2's role in chemoresistance should consider integrated multi-technique approaches:

Comprehensive experimental framework:

• Antibody-based detection combined with functional assays:

  • Correlate ACAT2 levels (by IHC/WB) with standardized chemosensitivity assays

  • Measure IC50 values (e.g., cisplatin) in matched ACAT2-high versus ACAT2-low cells

  • Combine ACAT2 knockdown/overexpression with drug response profiling

• Integrate proteomic analyses:

  • Use ACAT2 antibodies for co-immunoprecipitation followed by mass spectrometry

  • Identify ACAT2 interaction partners in chemoresistant versus sensitive models

  • Validate key protein-protein interactions (e.g., ACAT2-HSPA9) through proximity ligation assays

• Combine with epigenetic profiling:

  • Correlate ACAT2 antibody staining with DNA methylation status

  • Implement ChIP-seq to identify transcription factors regulating ACAT2

  • Evaluate histone modifications at ACAT2 promoter in resistant versus sensitive cells

• Incorporate metabolomics:

  • Compare lipid profiles between ACAT2-high and ACAT2-low tumors

  • Assess cholesterol ester levels in relation to ACAT2 expression and drug response

  • Examine metabolic adaptations in chemoresistant cells with altered ACAT2 expression

• In vivo validation approaches:

  • Use ACAT2 antibodies for patient-derived xenograft characterization

  • Implement tissue clearing techniques with ACAT2 immunofluorescence for 3D visualization

  • Combine with in vivo drug response monitoring

• Translational applications:

  • Develop ACAT2 IHC protocols suitable for clinical diagnostic laboratories

  • Create standardized scoring systems for patient stratification

  • Correlate ACAT2 levels with treatment outcomes in prospective clinical trials

This integrated approach has been partially validated in studies showing ACAT2 upregulation correlates with cisplatin resistance in ovarian cancer cell lines and patient samples.

What considerations should guide researchers studying ACAT2's dual role in metabolism and cancer biology?

ACAT2's involvement in both metabolic processes and cancer progression requires careful experimental design:

Key research considerations:

• Contextual expression analysis:

  • Compare ACAT2 levels between normal metabolic tissues and cancer samples

  • Evaluate expression in matched primary tumors and metastases

  • Assess correlations with markers of metabolic dysregulation

• Functional domain-specific approaches:

  • Use domain-specific antibodies to distinguish ACAT2's catalytic versus non-catalytic functions

  • Implement mutational analyses to separate metabolic from signaling roles

  • Consider how post-translational modifications affect each function

• Microenvironment influences:

  • Examine how nutrient availability affects ACAT2 expression and function

  • Study ACAT2's role under hypoxic versus normoxic conditions

  • Investigate how lipid composition of tumor microenvironment influences ACAT2 activity

• Integration with cancer-specific pathways:

  • Explore interactions between ACAT2 and established oncogenic pathways

  • In gastric cancer: Connection to YAP1/TAZ-TEAD1 signaling

  • In ovarian cancer: Potential interaction with HSPA9/P53 pathway

  • Consider cell cycle regulation through p21/CDKN1A

• Therapeutic implications:

  • Evaluate whether metabolic ACAT2 inhibitors affect cancer progression

  • Assess potential for combination therapies targeting both functions

  • Consider cancer-specific delivery of ACAT2-targeting agents

• Model system selection:

  • Choose models that recapitulate both metabolic and oncogenic contexts

  • Consider 3D organoid cultures that maintain metabolic gradients

  • Implement diet-controlled animal models to assess metabolic influences

These considerations help distinguish between ACAT2's canonical roles in cholesterol metabolism and its emerging functions in cancer biology.