ACOT11 Antibody

What is ACOT11 and why is it important in cancer research?

ACOT11 encodes enzymes that hydrolyze fatty acyl-CoA esters into free fatty acids and CoA, playing a critical role in fatty acid metabolism . Beyond its metabolic functions, ACOT11 has demonstrated significant involvement in cancer progression. In lung squamous carcinoma (LUSC), high ACOT11 expression correlates with poor prognosis, while its knockdown inhibits cell proliferation, migration, and invasion both in vitro and in vivo . Conversely, in clear cell renal cell carcinoma (ccRCC), ACOT11 is significantly downregulated and shows potential as a diagnostic biomarker with exceptionally high specificity and sensitivity (AUC = 0.964) . These contrasting expression patterns highlight ACOT11's tissue-specific roles and importance in understanding cancer mechanisms.

What detection methods are most effective for ACOT11 expression analysis?

Multiple complementary techniques should be employed for comprehensive ACOT11 expression analysis:

• Western Blotting: Effective for quantitative protein expression analysis, particularly using antibodies targeting the C-terminal region (AA 549-575) of human ACOT11 .

• qRT-PCR: Essential for mRNA expression analysis, as demonstrated in studies comparing ACOT11 levels between cancer cell lines (786-O, 769-P, A549, NCI-H1975) and normal cell lines (HK-2) .

• Immunohistochemistry (IHC): Valuable for visualizing ACOT11 distribution in tissue samples, using either polyclonal or monoclonal antibodies depending on experimental needs .

• Flow Cytometry: Useful for analyzing ACOT11 expression at the cellular level, particularly when examining heterogeneous populations .

For maximum reliability, researchers should validate findings across multiple detection methods, as exemplified in ccRCC studies where ACOT11 downregulation was confirmed at both mRNA and protein levels through qRT-PCR and IHC .

How should researchers select appropriate ACOT11 antibodies for specific applications?

Selection criteria for ACOT11 antibodies should account for:

• Target epitope region: Different antibodies target distinct regions of ACOT11 (e.g., AA 1-110, AA 19-250, AA 504-553, AA 549-575) . For investigating protein interactions, choose antibodies targeting regions away from known binding interfaces.

• Host species compatibility: Consider the host species (rabbit, mouse) in relation to your experimental system to avoid cross-reactivity issues, especially for co-localization studies .

• Clonality requirements: Polyclonal antibodies offer broader epitope recognition but potential batch variability, while monoclonals provide consistency and specificity for defined epitopes .

• Application validation: Select antibodies specifically validated for your intended application (WB, IHC, FACS, ELISA). For example, the ACOT11 antibody targeting AA 549-575 is validated for ELISA, Western Blotting, Immunohistochemistry, and Flow Cytometry .

• Species reactivity: Verify cross-reactivity with your species of interest. Some ACOT11 antibodies react only with human samples, while others show broader reactivity across human, mouse, and rat tissues .

What are the standard protocols for ACOT11 Western blot analysis?

For optimal ACOT11 detection via Western blot:

• Sample preparation: Lyse cells in RIPA buffer supplemented with protease inhibitors. For tissue samples, homogenize in the same buffer at 4°C.

• Protein denaturation: Heat samples at 95°C for 5 minutes in Laemmli buffer with β-mercaptoethanol.

• Gel selection: Use 10% SDS-PAGE gels for effective resolution of ACOT11 (approximately 70 kDa).

• Transfer conditions: Transfer proteins to PVDF membranes at 100V for 90 minutes in cold transfer buffer.

• Blocking: Block membranes with 5% non-fat milk in TBST for 1 hour at room temperature.

• Primary antibody incubation: Dilute ACOT11 antibody (typically 1:1000) in blocking solution and incubate overnight at 4°C .

• Detection: Use appropriate HRP-conjugated secondary antibodies and ECL substrate for visualization.

When working with tissue samples, researchers should optimize protein extraction protocols, as ACOT11 detection sensitivity varies between antibodies targeting different epitopes .

How can researchers address discrepancies in ACOT11 expression between different cancer types?

The contrasting ACOT11 expression patterns between lung cancer (upregulated) and ccRCC (downregulated) present methodological challenges requiring careful consideration:

• Tissue-specific normalization: Always compare cancer tissues with matched normal tissues from the same origin. Studies show ACOT11 is upregulated in lung cancer but downregulated in ccRCC compared to their respective normal tissues .

• Multiple reference genes: When performing qRT-PCR analysis, use multiple stable reference genes validated for each specific tissue type to ensure accurate normalization.

• Subcellular localization analysis: Conduct fractionation studies to determine if ACOT11's functional significance varies based on its subcellular distribution in different cancer types.

• Pathway context analysis: Integrate ACOT11 expression data with pathway analyses. For example, in ccRCC, ACOT11 expression correlates with oxidative phosphorylation (OXPHOS) and ferroptosis-related genes .

• Meta-analysis approach: When comparing across studies, implement systematic meta-analysis methodologies that account for technical variations between studies, as exemplified by the integrated analysis of TCGA and GEO databases in ACOT11 research .

This multi-faceted approach can help reconcile seemingly contradictory findings and reveal tissue-specific functions of ACOT11.

What are the optimal immunoprecipitation protocols for studying ACOT11 protein interactions?

For investigating ACOT11 protein-protein interactions:

• Epitope tagging: Express 3X Flag-ACOT11 in cell lines (e.g., A549) to facilitate efficient immunoprecipitation, as demonstrated in studies identifying the ACOT11-CSE1L interaction .

• Lysis conditions: Use mild non-denaturing lysis buffers (150 mM NaCl, 50 mM Tris-HCl pH 7.4, 1% NP-40, protease inhibitors) to preserve protein-protein interactions.

• Antibody selection: For endogenous ACOT11 immunoprecipitation, select antibodies targeting regions unlikely to be involved in protein interactions. For tagged ACOT11, use high-affinity anti-tag antibodies (e.g., anti-Flag) .

• Validation controls: Include isotype controls and lysates from ACOT11-knockdown cells to verify specificity of identified interactions.

• Crosslinking considerations: For transient interactions, implement reversible crosslinking with DSP (dithiobis(succinimidyl propionate)) prior to cell lysis.

• Reciprocal confirmation: Validate key interactions through reciprocal co-immunoprecipitation, as was performed to confirm the ACOT11-CSE1L interaction in lung cancer research .

These methodological refinements are crucial for accurately mapping the ACOT11 interactome, which has revealed 573 potential interacting proteins in previous studies .

How can researchers effectively validate ACOT11 knockdown models?

Rigorous validation of ACOT11 knockdown models is essential for experimental reliability:

• Multiple shRNA targets: Design and test multiple shRNA sequences targeting different regions of ACOT11 mRNA. The validated target sequence TTGTCTATGCAGACACCAT has shown efficacy in A549 cells .

• Quantification methods: Verify knockdown efficiency at both mRNA level (qRT-PCR) and protein level (Western blot), using appropriately validated primers and antibodies.

• Time-course analysis: Assess the stability of knockdown over time, particularly for long-term experiments such as colony formation assays (14 days) .

• Rescue experiments: Perform phenotype rescue experiments by reintroducing shRNA-resistant ACOT11 constructs to confirm specificity of observed effects.

• Functional validation: Confirm knockdown effects through functional assays relevant to ACOT11's role, such as fatty acid metabolism assays, proliferation, migration, and invasion assays .

• Off-target effect screening: Perform transcriptome analysis to identify potential off-target effects, particularly when using RNAi-based approaches.

When preparing stable ACOT11 knockdown cell lines, researchers should culture cells at 37°C in DMEM with 10% FBS and appropriate antibiotics in a 5% CO₂ humidified atmosphere, following protocols established for A549 and NCI-H1975 cell lines .

What strategies should be employed when using ACOT11 as a diagnostic biomarker?

To leverage ACOT11's diagnostic potential, especially for ccRCC where it demonstrates exceptional specificity (AUC = 0.964) :

• Sample standardization: Implement standardized tissue collection and processing protocols to minimize preanalytical variability. For ccRCC studies, use matched normal-tumor pairs when possible .

• Multi-marker panels: Integrate ACOT11 with other biomarkers. For ccRCC, considering ACOT8 alongside ACOT11 provides complementary diagnostic and prognostic information .

• Threshold optimization: Determine optimal ACOT11 expression cut-offs for diagnostic decisions through ROC curve analysis of large patient cohorts, as demonstrated in studies using TCGA data (n=598) .

• Validation cohorts: Validate diagnostic performance in independent patient cohorts across different clinical settings and demographic backgrounds.

• Complementary techniques: Confirm ACOT11 expression changes through multiple techniques (qRT-PCR, IHC, Western blot) before clinical application .

• Clinical correlation: Correlate ACOT11 expression with clinical parameters (TNM staging, histological grade) to refine its diagnostic utility for specific patient subgroups .

This comprehensive approach maximizes the translational value of ACOT11 as a biomarker while addressing potential limitations in diverse clinical settings.

How should researchers design experiments to investigate ACOT11's role in cancer metabolism?

Given ACOT11's fundamental role in fatty acid metabolism and its dysregulation in cancer:

• Metabolic profiling: Implement untargeted lipidomics and targeted fatty acid profiling to identify specific metabolic pathways affected by ACOT11 modulation.

• Enzymatic activity assays: Measure thioesterase activity directly using fluorescent or radioactive acyl-CoA substrates in ACOT11-modulated cells.

• Metabolic flux analysis: Use isotope-labeled fatty acids to trace metabolic pathway alterations following ACOT11 knockdown or overexpression.

• Energy phenotyping: Employ Seahorse extracellular flux analysis to measure changes in oxidative phosphorylation and glycolysis in response to ACOT11 modulation, particularly relevant given ACOT11's association with OXPHOS in ccRCC .

• Contextual investigations: Design experiments that account for tissue-specific metabolic environments, as ACOT11's role differs between lung cancer and ccRCC .

• Interaction networks: Investigate ACOT11's interaction with metabolism-regulating proteins identified through immunoprecipitation-mass spectrometry, which has revealed 573 potential ACOT11-interacting proteins .

This integrated approach will provide mechanistic insights into how ACOT11's metabolic functions contribute to its context-dependent roles in cancer development and progression.

What statistical approaches are recommended for analyzing ACOT11 expression data in clinical samples?

For robust statistical analysis of ACOT11 expression:

• Paired analysis: Use paired t-tests for matched normal-tumor samples to control for inter-individual variation, as demonstrated in studies of ACOT11 in ccRCC .

• Clustering methods: Apply K-means clustering (K=2) to categorize patients based on ACOT11 expression levels, as utilized in TCGA data analysis .

• Survival analysis: Implement Kaplan-Meier curves with log-rank tests to evaluate the prognostic significance of ACOT11 expression, particularly important in lung cancer where high ACOT11 expression correlates with poor prognosis .

• Multivariate modeling: Apply Cox regression analysis to assess whether ACOT11 expression is an independent prognostic factor when accounting for clinical variables like tumor stage and grade .

• ROC curve analysis: Calculate Area Under the Curve (AUC) to quantify ACOT11's diagnostic value, critical for its application as a biomarker (demonstrated AUC = 0.964 for ccRCC) .

• Multiple testing correction: Apply appropriate corrections (e.g., Benjamini-Hochberg) when performing genome-wide analyses to minimize false discovery rates.

These statistical approaches ensure reliable interpretation of ACOT11 expression data across diverse experimental and clinical contexts.

How can researchers reconcile contradictory findings regarding ACOT11's role in different cancers?

To address the apparently contradictory roles of ACOT11 in different cancer types:

• Tissue context framework: Analyze ACOT11 within tissue-specific regulatory networks. The opposing expression patterns in lung cancer (upregulated) versus ccRCC (downregulated) suggest tissue-dependent functions .

• Pathway integration: Perform comprehensive pathway analysis to identify cancer-specific signaling contexts. In ccRCC, ACOT11 is associated with OXPHOS and ferroptosis pathways, which may differ in lung cancer .

• Temporal dynamics: Consider disease progression stages when interpreting ACOT11 expression data. In some cancers, a gene's role may evolve during disease progression .

• Molecular subtyping: Stratify analysis by molecular subtypes within each cancer type to identify subtype-specific patterns.

• Functional interrogation: Design parallel functional studies across different cancer models using identical methodologies to directly compare ACOT11's effects.

• Meta-analysis methods: Implement formal meta-analysis approaches that account for between-study heterogeneity when integrating findings across multiple cancer types.

This systematic approach can transform apparently contradictory findings into a nuanced understanding of ACOT11's context-dependent functions in cancer biology.

What are the critical considerations for ACOT11 immunohistochemistry protocol optimization?

For reliable ACOT11 immunohistochemistry:

• Antibody validation: Verify antibody specificity using positive and negative controls, including ACOT11 knockdown tissues. Studies have successfully used rabbit anti-human ACOT11 antibodies at 1:50 dilution .

• Antigen retrieval methods: Compare heat-induced epitope retrieval methods (citrate buffer pH 6.0 vs. EDTA buffer pH 9.0) to determine optimal conditions for ACOT11 epitope accessibility.

• Signal amplification: For low-abundance ACOT11 detection, implement tyramide signal amplification systems to enhance sensitivity without increasing background.

• Scoring standardization: Adopt standardized scoring systems evaluated by multiple independent pathologists blinded to clinical data, as implemented in published ACOT11 studies .

• Multiplex approaches: Consider multiplex IHC to co-localize ACOT11 with interacting partners (e.g., CSE1L) or pathway components to gain functional insights .

• Digital pathology: Utilize software like Nano Zoomer Digital Pathology View 1.6 for quantitative analysis of ACOT11 staining, reducing subjectivity in assessment .

These optimizations ensure reliable ACOT11 detection and interpretation in diverse tissue samples, critical for translational applications.

What controls are essential for validating ACOT11 antibody specificity?

Comprehensive antibody validation requires multiple controls:

• Genetic controls: Include samples from ACOT11 knockdown or knockout models alongside wild-type samples to confirm specificity .

• Peptide competition: Pre-incubate antibody with immunizing peptide prior to sample application to demonstrate binding specificity.

• Multiple antibody validation: Compare staining patterns using antibodies targeting different ACOT11 epitopes (e.g., N-terminal AA 1-110 vs. C-terminal AA 549-575) .

• Cross-species validation: Verify consistent detection patterns across species with high ACOT11 sequence homology, accounting for species-specific epitope differences .

• Recombinant protein standards: Include purified recombinant ACOT11 as positive controls for Western blot applications.

• Non-specific binding assessment: Evaluate secondary antibody-only controls and isotype controls to identify potential non-specific binding.

These validation steps establish confidence in experimental findings and should be documented in publications to enhance reproducibility.

How might ACOT11 serve as a therapeutic target in cancer treatment strategies?

Based on current understanding of ACOT11's role in cancer:

• Differential targeting strategies: Design context-specific approaches acknowledging ACOT11's contrasting roles in different cancers—potential inhibition for lung cancer where it promotes tumor growth versus potential activation for ccRCC where it is downregulated .

• Metabolic intervention: Target ACOT11-dependent metabolic vulnerabilities, particularly in lung cancer where ACOT11 knockdown affects multiple signaling pathways .

• Protein-protein interaction disruption: Develop small molecules disrupting specific ACOT11 interactions, such as the ACOT11-CSE1L interaction identified in lung cancer .

• Combination approaches: Design combination therapies targeting ACOT11 alongside related metabolic enzymes to prevent compensatory mechanisms.

• Biomarker-guided therapy: Utilize ACOT11 expression as a stratification biomarker to identify patients likely to respond to specific therapeutic approaches, particularly in lung squamous carcinoma where high ACOT11 expression correlates with poor prognosis .

These therapeutic strategies represent promising avenues for translating ACOT11 research into clinical applications.

What are the promising directions for studying ACOT11's role in non-cancerous pathologies?

Beyond cancer, ACOT11's fundamental role in metabolism suggests potential involvement in:

• Metabolic disorders: Investigate ACOT11's function in obesity, diabetes, and fatty liver disease, given its role in fatty acid metabolism.

• Inflammatory conditions: Explore connections between ACOT11-mediated lipid metabolism and inflammatory signaling pathways.

• Neurodegenerative diseases: Examine ACOT11's potential contribution to brain lipid homeostasis and its implications for conditions like Alzheimer's disease.

• Developmental biology: Study ACOT11's role in embryonic development and tissue differentiation, particularly in metabolically active tissues.

• Aging research: Investigate age-related changes in ACOT11 expression and activity and their relationship to metabolic dysfunction in aging.

These emerging research directions extend ACOT11's significance beyond cancer biology and may reveal novel therapeutic applications.