ACSM3 Antibody

What is ACSM3 and what cellular functions does it regulate?

ACSM3, known as acyl-CoA synthetase medium-chain family member 3, is a protein originally identified as the SA hypertension-associated rat homolog (SAH). Research demonstrates it is expressed at significantly higher levels (10-fold greater) in the kidneys of spontaneously hypertensive rats compared to corresponding wild-type strains . The protein plays a role in lipid metabolism through mediating the activation of fatty acids.

In human tissues, ACSM3 has a calculated molecular weight of 49-66 kDa, though its observed molecular weight in experimental conditions typically ranges from 60-70 kDa . Beyond its metabolic functions, recent research has uncovered its potential role in cancer biology, particularly as a tumor suppressor in malignant melanoma and other cancers.

What applications can ACSM3 antibody be used for in experimental research?

ACSM3 antibody (such as the 10168-2-AP) has been validated for multiple experimental applications:

The antibody shows positive Western blot detection in mouse kidney tissue, HEK-293 cells, and rat kidney tissue. For immunohistochemistry, positive detection has been demonstrated in human colon cancer tissue and human prostate cancer tissue. Immunofluorescence applications have confirmed detection in HepG2 cells .

What are the optimal experimental conditions for ACSM3 antibody applications?

For optimal results with ACSM3 antibody, researchers should consider the following application-specific conditions:

Researchers should note that titration in each testing system is recommended to obtain optimal results, as sensitivity may be sample-dependent . For IHC applications, proper antigen retrieval is critical for consistent and specific staining.

What reactivity profile does ACSM3 antibody exhibit across species?

The ACSM3 antibody (10168-2-AP) has demonstrated specific reactivity patterns:

The antibody was developed using ACSM3 fusion protein Ag0224 as the immunogen, which provides the basis for its reactivity profile. For experimental planning involving other species, researchers should conduct preliminary validation tests to confirm cross-reactivity .

How does ACSM3 expression correlate with malignant melanoma prognosis and what mechanisms underlie this relationship?

Research has established a significant correlation between ACSM3 expression and malignant melanoma (MM) prognosis. Through comprehensive analysis of TCGA MM datasets, GEO datasets, and human protein atlas data, ACSM3 expression has been found significantly downregulated in MM tissue compared to normal skin tissue .

Lower expression of ACSM3 confers worsened prognosis in MM patients, with survival poorest in Asian MM patients . Interestingly, subgroup analysis showed:

• Significantly higher ACSM3 expression in metastatic MM compared to primary tumors

• No differential expression across clinical stages

• Lowest ACSM3 expression in Asian MM cases

• A trend toward higher ACSM3 expression in obese patients

Mechanistically, in vitro studies demonstrate that ACSM3 knockdown significantly increases proliferation while decreasing G1 phase population and apoptosis in MM cells. Conversely, ACSM3 overexpression decreases proliferation, increases G1 phase population, and promotes apoptosis in MM cells. These effects extend to cell migration, invasion, and colony formation capabilities . These findings strongly suggest ACSM3 functions as a tumor suppressor in MM, with its loss contributing to more aggressive tumor behavior.

What methodological considerations are important when using ACSM3 antibody for cancer immunity research?

When investigating ACSM3's role in cancer immunity using antibody-based approaches, several methodological considerations warrant attention:

• Tissue-specific expression analysis: ACSM3 shows variable expression across tissue types. When studying its role in tumor microenvironment, researchers should include appropriate positive controls (such as kidney tissue) and negative controls .

• Correlation with immune infiltrates: Research demonstrates ACSM3 expression correlates with immune cell infiltration patterns. When designing experiments:

  • Include markers for CD8+ T cells, macrophages, neutrophils and dendritic cells

  • Assess both primary and metastatic samples when available

  • Consider breakdown analysis of specific immune cell subtypes (CD8+ central memory and naïve cells, T regulatory cells, macrophage subtypes)

• Copy number variation assessment: Higher ACSM3 copy number has been associated with less CD8+ T cells, neutrophils and DCs infiltration. Including copy number analysis alongside expression studies provides more comprehensive insights .

• Multivariate analysis approach: When correlating ACSM3 expression with clinical outcomes, include race, tumor stage, and immune infiltrate density in multivariate Cox regression analyses, as these factors may interact with ACSM3's prognostic significance .

For comprehensive cancer immunity studies, combining ACSM3 antibody-based techniques with RNA-seq or proteomics approaches can provide more robust mechanistic insights.

How does ACSM3 interact with cellular signaling pathways in cancer and what experimental approaches best characterize these interactions?

ACSM3 appears to intersect with multiple signaling pathways in cancer, particularly those involving immune regulation and MAPK signaling. To effectively characterize these interactions, researchers should consider:

• Pathway enrichment analysis: Genes expressed in significant correlation with ACSM3 in MM show enrichment in immune-regulatory pathways. Both standard enrichment analysis and network-based enrichment analysis reveal significant associations .

• BRAF signaling interaction: ACSM3 overexpression shows synergistic effects with BRAF inhibitor PLX-4720 in MM cells. This suggests intersection with the MAPK pathway, critical in melanoma biology .

• mRNA stability analysis: Evidence suggests IGF2BP2 may downregulate ACSM3 expression by reducing ACSM3 mRNA stability. RNA immunoprecipitation assays can confirm such interactions, while actinomycin D treatment followed by RT-qPCR can evaluate mRNA stabilization dynamics .

• Combined pathway inhibition experiments: When investigating ACSM3's role in signaling, combining ACSM3 overexpression or knockdown with specific pathway inhibitors (e.g., BRAF inhibitors like PLX-4720) provides insights into synergistic relationships. Both in vitro proliferation assays and in vivo xenograft models have demonstrated the value of this approach .

For reliable results, researchers should implement positive and negative controls in signaling studies and consider time-course experiments to capture dynamic interactions between ACSM3 and various signaling components.

What is the relationship between ACSM3 expression and immune checkpoint molecules in cancer research?

Intriguingly, despite ACSM3's apparent role in cancer immunity, research indicates that ACSM3 expression shows no significant correlation with major immune checkpoint molecules:

• PD-1, PD-L1, and PD-L2 expression: Analysis reveals no correlation between ACSM3 expression and these key immune checkpoint molecules . This suggests ACSM3 likely influences tumor immunity through mechanisms independent of these established checkpoints.

• T cell signature correlations: While ACSM3 expression correlates with signatures of effector T cells and regulatory T cells (Tregs), it shows no association with memory T cell signatures . This differential correlation pattern provides insights into how ACSM3 might modulate specific immune compartments.

• Immune exclusion mechanism: Rather than functioning through checkpoint inhibition, evidence suggests ACSM3 primarily affects immune infiltrate exclusion in MM. Lower ACSM3 expression correlates with decreased CD8+, macrophage, and dendritic cell infiltration .

When investigating these relationships, researchers should employ multiplexed immunohistochemistry or cytometry approaches to simultaneously assess ACSM3 expression alongside immune checkpoint molecules and immune cell markers within the same tumor microenvironment. Single-cell RNA sequencing offers another powerful approach to delineate cell-specific relationships between ACSM3 and immune regulatory networks.

How can ACSM3 overexpression be effectively combined with other therapeutic approaches in experimental cancer models?

Preclinical research demonstrates promising results when combining ACSM3 overexpression with other therapeutic approaches, particularly BRAF inhibition in melanoma models. Key methodological considerations include:

• In vitro combination approaches:

  • Cell proliferation assays show synergistic inhibition when combining ACSM3 overexpression with PLX-4720 (BRAF inhibitor) compared to either monotherapy .

  • Researchers should establish dose-response relationships for both interventions individually before testing combinations.

• Xenograft model implementation:

  • MM xenograft murine models demonstrate significantly decreased tumor growth with combination therapy of ACSM3 overexpression and PLX-4720 .

  • Toxicity monitoring showed no significant adverse effects across treatment and control groups, supporting the safety profile of this approach .

• Immune checkpoint inhibitor combinations:

  • Although not yet fully tested, researchers have proposed modeling combination of ACSM3 inhibition and immune checkpoint inhibitors as a potential therapeutic approach .

  • This stems from ACSM3's apparent role in immune exclusion rather than checkpoint regulation.

• Therapeutic resistance considerations:

  • When designing combination studies, researchers should include long-term experiments to assess potential development of resistance.

  • Sequential versus concurrent administration protocols should be compared to identify optimal therapeutic scheduling.

The synergistic effects observed with BRAF inhibitors suggest ACSM3-based approaches might effectively complement existing targeted therapies, particularly in cancers where MAPK pathway activation drives disease progression.

How should researchers address variable ACSM3 antibody detection across different tissue types?

When encountering variable detection of ACSM3 across tissue types, researchers should consider:

• Optimization of antigen retrieval protocols: For IHC applications, the choice between TE buffer (pH 9.0) and citrate buffer (pH 6.0) can significantly impact staining quality. Systematic comparison of both methods with appropriate controls is recommended .

• Dilution range adjustment: The recommended dilution ranges (1:1000-1:4000 for WB, 1:50-1:500 for IHC/IF) provide starting points, but optimization for specific tissue types is essential . Serial dilution experiments with both positive controls (kidney tissue) and test tissues can help identify optimal conditions.

• Signal amplification strategies: For tissues with low ACSM3 expression, consider signal amplification techniques such as tyramide signal amplification or polymer-based detection systems to enhance sensitivity without increasing background.

• Protein extraction modifications: For Western blotting applications, optimization of protein extraction conditions (detergent types, buffer compositions) may improve detection from challenging tissue types. ACSM3's observed molecular weight (60-70 kDa) may also vary slightly across tissues .

• Validation with alternative methods: When antibody-based detection yields inconsistent results, validation with orthogonal methods (RT-qPCR, mass spectrometry) can help confirm expression patterns and troubleshoot antibody-specific issues.

Researchers should document all optimization steps to establish reproducible protocols for their specific experimental systems.

What controls should be implemented when studying ACSM3 in cancer immunity research?

Robust control implementation is critical for reliable ACSM3 research in cancer immunity contexts:

• Positive tissue controls: Include kidney tissue (mouse, rat or human) which demonstrates consistently high ACSM3 expression . For cell line work, HEK-293 cells and HepG2 cells serve as validated positive controls for Western blot and immunofluorescence, respectively .

• Genetic manipulation controls: When performing ACSM3 knockdown or overexpression:

  • Include appropriate vector-only controls

  • Validate expression changes at both mRNA and protein levels

  • Consider rescue experiments to confirm specificity of observed phenotypes

• Immune infiltrate assessment controls:

  • Include established markers for specific immune cell populations

  • Consider spatial distribution analysis alongside quantitative assessment

  • Incorporate multiplexed approaches to simultaneously assess multiple parameters

• Treatment combination controls:

  • Include single-agent controls alongside combination approaches

  • Implement vehicle controls matched to all treatment conditions

  • Consider dose-response relationships to identify potential antagonistic effects

By implementing these systematic controls, researchers can increase confidence in results attributing specific phenotypes to ACSM3 modulation rather than experimental artifacts or off-target effects.

How does ACSM3 expression vary across different cancer types and what methodological approaches best capture this heterogeneity?

While significant research has focused on ACSM3 in malignant melanoma, emerging evidence suggests relevance across multiple cancer types. To effectively investigate this heterogeneity:

• Pan-cancer database analysis: Leverage TCGA, GEO, and other resources to compare ACSM3 expression across cancer types, considering both primary and metastatic samples . This approach has already revealed significant downregulation in MM compared to normal skin.

• Single-cell resolution studies: Bulk tissue analysis may mask cellular heterogeneity. Single-cell RNA sequencing can reveal cell-specific expression patterns and potential associations with cellular states or lineages within the tumor microenvironment.

• Spatial transcriptomics approaches: Technologies combining histological information with gene expression data can reveal spatial distribution of ACSM3 expression within tumors, potentially identifying expression patterns associated with invasive fronts or hypoxic regions.

• Epigenetic regulation assessment: Investigate whether differential ACSM3 expression across cancer types relates to epigenetic modifications through:

  • DNA methylation analysis of ACSM3 promoter regions

  • Histone modification profiling

  • Chromatin accessibility studies

• Ethnic and demographic correlations: Research has identified lowest ACSM3 expression in Asian MM cases and a trend toward higher expression in obese patients . Expanded demographic analysis across cancer types might reveal additional clinically relevant patterns.

These approaches can provide a more comprehensive understanding of how ACSM3 functions across diverse cancer contexts, potentially revealing new therapeutic opportunities.

What are the implications of ACSM3's role in immune exclusion for combination immunotherapy approaches?

ACSM3's apparent involvement in immune exclusion rather than checkpoint regulation presents unique opportunities for immunotherapy advancement:

• Dual targeting strategies: Since ACSM3 expression shows no correlation with PD-1, PD-L1, or PD-L2 , combining ACSM3-targeted approaches with checkpoint inhibitors might address complementary immune evasion mechanisms.

• Immune infiltrate modulation: Research should explore whether ACSM3 modulation can enhance immune cell infiltration in "cold" tumors, potentially rendering them more responsive to existing immunotherapies. This involves:

  • Assessing changes in chemokine profiles following ACSM3 modulation

  • Characterizing alterations in endothelial activation and adhesion molecule expression

  • Quantifying changes in immunosuppressive myeloid populations

• Predictive biomarker development: ACSM3 expression levels might serve as predictive biomarkers for immunotherapy response. Retrospective analysis of samples from immunotherapy trials, correlating ACSM3 expression with clinical outcomes, could validate this hypothesis.

• Therapeutic sequencing optimization: Determining whether ACSM3 modulation should precede, accompany, or follow checkpoint inhibition requires systematic investigation in preclinical models before clinical translation.

The emerging understanding of ACSM3's immune regulatory functions opens new avenues for overcoming immunotherapy resistance, particularly in tumors characterized by immune exclusion rather than exhaustion.