ACOT13 Antibody

What is ACOT13 and what are its cellular localizations?

ACOT13 (Acyl-CoA thioesterase 13), also known as THEM2 (Thioesterase superfamily member 2), is a protein that catalyzes the hydrolysis of acyl-CoA esters to free fatty acids and coenzyme A. It plays crucial roles in lipid biosynthesis, gene transcription, and signal transduction .

ACOT13 predominantly localizes to the mitochondrial matrix, as demonstrated through protease protection assays. When isolated mitochondria from liver, kidney, and heart tissues were exposed to proteases (trypsin or proteinase K), ACOT13 was only digested in the presence of Triton X-100, following the same pattern as matrix-localized PDH .

• In liver: Significant fractions appear in both mitochondria and cytoplasm

• In heart: Predominantly mitochondrial with minimal cytoplasmic presence

Proper subcellular fractionation experiments using differential centrifugation followed by immunoblotting are essential for accurately determining ACOT13 localization in your specific experimental system.

How should I validate the specificity of an ACOT13 antibody?

Antibody validation is critical for ensuring experimental reliability. For ACOT13 antibodies, implement the following validation strategy:

• Recombinant protein testing: Use purified recombinant ACOT13 as a positive control

• Knockout controls: Test the antibody on samples from ACOT13 knockout models

• Molecular weight verification: Confirm a single band at approximately 15-16 kDa

• Cross-validation: Compare results with multiple antibodies targeting different ACOT13 epitopes

Published validation data show that specific ACOT13 antibodies detect a single band at ~15 kDa in control samples, while this band is absent in knockout samples unless Western blot membranes are significantly overexposed .

Which experimental applications are suitable for ACOT13 antibodies?

ACOT13 antibodies have been successfully employed in multiple research applications:

Remember to optimize antibody concentrations for your specific experimental conditions and include appropriate controls for each application.

Which tissues are ideal positive controls for ACOT13 antibody validation?

Based on expression analysis across multiple tissues, these samples serve as robust positive controls:

For cell lines, validated positive controls include K562, Jurkat, and THP-1 cells, all showing specific ACOT13 expression that disappears in knockout lines .

How can I experimentally distinguish between mitochondrial and cytoplasmic ACOT13?

Distinguishing between mitochondrial and cytoplasmic ACOT13 requires careful experimental design:

• Differential centrifugation protocol:

  • Homogenize tissue in isotonic buffer (250 mM sucrose, 10 mM HEPES, 1 mM EDTA, pH 7.4)

  • Low-speed centrifugation (1,000 × g) to remove nuclei and debris

  • High-speed centrifugation (12,000 × g) to pellet mitochondria

  • Ultra-centrifugation of supernatant (100,000 × g) to separate microsomes from cytosol

• Fraction purity verification using established markers:

  • Outer mitochondrial membrane: Tom20

  • Inner mitochondrial membrane/intermembrane space: Tim23

  • Matrix: Pyruvate dehydrogenase (PDH)

  • Cytosol: GAPDH or other cytosolic markers

• Protease protection assays:

  • Expose isolated mitochondria to proteases (trypsin or proteinase K)

  • Test with and without detergents (Triton X-100 or increasing concentrations of digitonin)

  • Compare degradation patterns with known compartment markers

Research has shown that ACOT13 follows the pattern of matrix-localized proteins, being protected from proteases unless Triton X-100 is added to disrupt all mitochondrial membranes .

What approaches are effective for studying tissue-specific differences in ACOT13 expression?

To accurately analyze tissue-specific ACOT13 expression patterns:

• Normalization strategy:

  • Use multiple mitochondrial proteins (Complex I 37-kDa subunit, SDHA, Cytochrome c)

  • Calculate average normalized values to account for variability in individual markers

• Account for mitochondrial content differences:

  • Normalize to tissue-specific mitochondrial density

  • Use mitochondrial DNA quantification as an additional normalization method

• Complementary methodologies:

  • Protein expression (Western blotting)

  • mRNA expression (RT-qPCR)

  • Enzymatic activity assays (thioesterase activity with different acyl-CoA substrates)

  • Immunohistochemistry for spatial distribution

• Controls for cross-contamination:

  • Verify purity of mitochondrial fractions

  • Include cytosolic marker proteins in all analyses

How can I differentiate between ACOT13 and other mitochondrial ACOTs in experimental systems?

Distinguishing between the five mitochondrial matrix-localized ACOTs (ACOT2, ACOT7, ACOT9, ACOT13, and ACOT15) requires:

• Antibody specificity verification:

  • Use knockout controls for each specific ACOT

  • Validate using recombinant proteins

• Molecular weight discrimination:

  • ACOT13: ~15 kDa

  • ACOT7: ~37 kDa

  • Other ACOTs have distinct molecular weights

• Tissue expression patterns:

  • ACOT2 and ACOT13: Heart, skeletal muscle, BAT, kidney

  • ACOT11: Predominantly in BAT

  • Use these tissue-specific patterns for comparative analysis

• Substrate specificity assays:

  • Different ACOTs have preferences for acyl-CoAs of varying chain lengths

  • Design enzymatic assays using specific substrates to distinguish activity

• Knockout/knockdown models:

  • Use genetic models with specific ACOT deletions

  • Analyze compensatory changes in other ACOTs when one is depleted

What methodological considerations are important when analyzing ACOT13 in disease contexts?

When studying ACOT13 in disease settings, consider these methodological approaches:

• Establishing causality versus correlation:

  • Time-course studies to determine if ACOT13 changes precede disease progression

  • Genetic manipulation (overexpression/knockdown) to assess direct effects

  • Patient sample analysis with proper controls matched for age, gender, and disease stage

• Disease-specific experimental design:

  • For cancer research: Compare different tumor stages and grades

    • In ovarian cancer, ACOT13 expression is higher in stages I-II than stages III-IV

  • For kidney disease: Analyze both cystic and normal tissues

    • ACOT13 levels are reduced in renal cystic tissues from ADPKD patients

• Pathway analysis approaches:

  • Gene Set Enrichment Analysis (GSEA) to identify affected pathways

    • In ADPKD, high ACOT13 expression correlates with inactivated PI3K-Akt and MAPK pathways

    • High ACOT13 expression correlates with activated PPAR signaling and fatty acid metabolism

• Integrated multi-omics approach:

  • Combine transcriptomics, proteomics, and metabolomics data

  • Analyze changes in related lipid metabolism pathways

How should I design experiments to evaluate the functional consequences of ACOT13 modulation?

To comprehensively investigate ACOT13 function:

• Genetic manipulation approaches:

  • Overexpression using vectors containing full-length ACOT13 cDNA

  • Knockdown using validated shRNA sequences (e.g., 5'-CGATATGAACATAACGTACAT-3')

  • CRISPR/Cas9-mediated knockout using validated guide RNAs

  • Include rescue experiments with wild-type ACOT13 to confirm specificity

• Functional readouts:

  • Cell proliferation: EdU incorporation, CCK-8 assays

  • Cell cycle analysis: Flow cytometry with propidium iodide staining

  • Apoptosis assessment: Annexin V/PI staining, caspase activation

  • Mitochondrial function: Membrane potential, ATP production, oxygen consumption

• Molecular analyses:

  • Expression of proliferation markers (Ki67, PCNA)

  • Apoptotic markers (cleaved caspase-3)

  • Signaling pathway components (PI3K-Akt, MAPK, PPAR)

Research has demonstrated that ACOT13 overexpression can:

• Reduce cell proliferation (~ 32% growth inhibition at 72h)

• Trigger G0/G1 cell cycle arrest

• Induce apoptosis (increasing from ~10% to ~32%)

• Decrease ATP production

• Induce loss of mitochondrial membrane potential

What approaches are recommended for investigating ACOT13's role in immune responses?

For exploring ACOT13-immune interactions, particularly in cancer research:

• Bioinformatic analyses:

  • Single-sample Gene Set Enrichment Analysis (ssGSEA)

  • ESTIMATE algorithm for tumor microenvironment assessment

  • CIBERSORT for immune cell composition analysis

• Correlation analyses with immune markers:

  • Immune checkpoint genes (PD-1, PD-L1, SIGLEC15)

  • Tumor Mutational Burden (TMB)

  • Stromal and immune scores

• Experimental validation:

  • Co-culture systems with immune and cancer cells

  • ACOT13 manipulation followed by immune function assessment

  • In vivo models with immune profiling

Research has identified significant correlations between ACOT13 expression and:

• Immune checkpoint SIGLEC15 (positive correlation)

• Tumor Mutational Burden (positive correlation)

• Specific immune cell infiltration (Th2, T helper, cytotoxic, and mast cells)

How can I address contradictory findings regarding ACOT13 expression in different experimental systems?

To reconcile conflicting data about ACOT13:

• Systematic analysis of methodological differences:

  • Antibody sources and validation methods

  • Sample preparation techniques

  • Detection methods and sensitivities

  • Normalization strategies

• Context-dependent considerations:

  • Tissue-specific expression patterns and functions

  • Disease stage-specific effects

  • Metabolic state influences on expression

  • Compensatory mechanisms by other ACOTs

• Comprehensive experimental design:

  • Use multiple antibodies and detection methods

  • Include diverse cellular models

  • Analyze both expression and function

  • Perform time-course studies

• Meta-analysis approach:

  • Integrate findings across multiple studies

  • Stratify results by experimental conditions

  • Identify consistent patterns despite methodological differences

For example, in ovarian cancer research, ACOT13 shows stage-dependent expression patterns (higher in early stages) and correlates with better prognosis, despite some studies reporting both increased and decreased expression in cancer tissues compared to normal samples .

What technical challenges might arise when using ACOT13 antibodies in mitochondrial research?

Common technical challenges and solutions include:

• Mitochondrial fraction purity:

  • Problem: Cytoplasmic contamination can confound results

  • Solution: Use multiple centrifugation steps and verify with compartment-specific markers

  • Validation: Immunoblot for markers of different cellular compartments

• Antibody specificity concerns:

  • Problem: Cross-reactivity with other ACOTs or non-specific binding

  • Solution: Use knockout controls and multiple antibodies targeting different epitopes

  • Validation: Include recombinant ACOT13 as positive control

• Tissue-specific expression variations:

  • Problem: Expression levels differ significantly between tissues

  • Solution: Adjust loading amounts and exposure times for different tissues

  • Optimization: Determine linear range of detection for each tissue type

• Submitochondrial localization:

  • Problem: Difficult to distinguish matrix vs. membrane association

  • Solution: Combine protease protection assays with detergent treatments

  • Analysis: Compare degradation patterns with known compartment markers

• Post-translational modifications:

  • Problem: May affect antibody recognition

  • Solution: Use multiple antibodies targeting different regions

  • Approach: Consider phospho-specific antibodies if phosphorylation sites are known

How can ACOT13 antibodies be utilized in cancer research?

ACOT13 antibodies offer valuable applications in cancer research:

• Prognostic marker assessment:

  • Immunohistochemistry on tissue microarrays

  • Correlation with patient survival data

  • Analysis across cancer stages and grades

Research findings demonstrate that low ACOT13 expression correlates with:

• Tumor microenvironment studies:

  • Co-staining with immune cell markers

  • Analysis of stromal vs. tumor cell expression

  • Correlation with tumor-infiltrating immune cells

• Therapy response prediction:

  • ACOT13 expression analysis in responders vs. non-responders

  • Correlation with chemotherapy sensitivity (e.g., cisplatin IC50)

  • Association with immune checkpoint inhibitor efficacy

Patients with low ACOT13 expression have shown:

• Higher cisplatin IC50 scores (indicating resistance)

• Potentially reduced benefit from immunotherapy

• Mechanistic investigations:

  • Pathway analysis in ACOT13-manipulated cancer cells

  • Lipid metabolism alterations in tumor vs. normal tissues

  • Mitochondrial function assessment in cancer progression

What experimental approaches are effective for studying ACOT13 in metabolic and kidney diseases?

For metabolic and kidney disease research:

• Tissue-specific analysis:

  • Compare ACOT13 expression between normal and diseased tissues

  • Analyze correlation with disease progression markers

  • Investigate metabolic pathway alterations

• Cell culture models:

  • Use disease-relevant cell lines (e.g., WT9-12 cells for ADPKD)

  • Manipulate ACOT13 expression via overexpression or knockdown

  • Assess functional consequences on:

    • Cell proliferation (EdU staining)

    • Cell cycle (flow cytometry)

    • Apoptosis (Annexin V/PI staining)

    • Mitochondrial function (membrane potential, ATP production)

• Signaling pathway analysis:

  • Evaluate the impact on key pathways:

    • PI3K-Akt signaling

    • MAPK pathway

    • PPAR signaling

    • Fatty acid metabolism

Research in ADPKD has shown that ACOT13 overexpression:

• Reduces cell proliferation

• Triggers cell cycle arrest at G0/G1 phase

• Induces mitochondrial-related apoptosis

• Decreases ATP production

• Causes loss of mitochondrial membrane potential

How should I interpret changes in ACOT13 expression across different disease stages?

Proper interpretation of ACOT13 expression changes requires:

• Stage-specific analysis:

  • In ovarian cancer: Higher expression in stages I-II than stages III-IV

  • Consider disease progression timeline and metabolic alterations

• Multi-parameter assessment:

  • Correlate expression with clinical outcomes

  • Analyze association with molecular subtypes

  • Consider metabolic status of tissues

• Functional context:

  • Determine if expression changes affect enzymatic activity

  • Assess impact on relevant signaling pathways

  • Evaluate consequences for cellular metabolism

• Causal relationship determination:

  • Is altered ACOT13 expression a cause or consequence of disease?

  • Use in vitro manipulation to establish direct effects

  • Consider feedback mechanisms in disease progression

• Therapeutic implications:

  • Does ACOT13 expression predict treatment response?

  • Could targeting ACOT13 have therapeutic potential?

  • Analyze relationship with drug resistance mechanisms

For example, in ovarian cancer, lower ACOT13 expression correlates with advanced stages and poorer prognosis, suggesting a potential tumor-suppressive role or association with metabolic changes that impact disease progression .