AIFM3 Antibody

What is AIFM3 and what cellular functions does it regulate?

AIFM3 (also known as AIFL) is a mitochondrial protein that shares functional similarity with other apoptosis-inducing factor family members but possesses distinct characteristics. Research indicates AIFM3 induces apoptosis through a caspase-dependent pathway and reduces mitochondrial membrane potential . Unlike AIFM1, AIFM3 has unique functional properties and tissue distribution patterns. AIFM3 is ubiquitously expressed in various tissues and has been shown to be aberrantly upregulated in several cancer types . The protein has a molecular weight of approximately 66-68 kDa based on its amino acid sequence .

How is AIFM3 expression correlated with clinical outcomes in cancer patients?

AIFM3 overexpression correlates significantly with poorer clinical outcomes in several cancer types. In breast cancer, higher AIFM3 expression is associated with:

Multivariate analysis identified lymph node metastasis (P = 0.015) and TNM stage (P = 0.009/0.003) as independent factors associated with AIFM3 expression . This suggests AIFM3 may serve as a potential biomarker for predicting prognosis in breast cancer patients.

What critical considerations should researchers evaluate when selecting an AIFM3 antibody?

When selecting an AIFM3 antibody for research applications, consider:

• Validated Applications: Confirm the antibody has been validated for your specific application (WB, IHC-P, IF, ELISA)

• Species Reactivity: Verify reactivity with your experimental model (human, mouse, rat)

• Epitope Location: Consider antibodies raised against distinct epitopes for confirmation experiments

• Clonality: Polyclonal antibodies offer higher sensitivity while monoclonals provide higher specificity

• Predicted Band Sizes: AIFM3 antibodies may detect multiple bands (15, 19, 26, 39, 45, 59, 66, 68, 72, 76 kDa) depending on isoforms and processing

For comprehensive studies, researchers should validate antibody performance in their specific experimental conditions before proceeding with large-scale experiments.

What are the recommended protocols for validating AIFM3 antibody specificity?

A rigorous validation protocol for AIFM3 antibodies should include:

• Positive Control Tissues: Human brain tissue has shown consistent AIFM3 expression and can serve as a positive control

• Western Blot Analysis: Verify antibody detects bands of expected molecular weights (observed bands include 15, 26, 40, and 60 kDa)

• Knockdown/Knockout Validation: Use AIFM3-silenced cells (siRNA or CRISPR) to confirm signal specificity

• Peptide Blocking: Block antibody binding with the immunizing peptide to confirm specificity

• Cross-Reactivity Testing: Ensure the antibody doesn't cross-react with other AIF family members

For IHC applications, include appropriate negative controls by omitting primary antibody or using isotype controls to identify non-specific staining.

How can AIFM3 antibodies be optimized for immunohistochemical analysis of clinical specimens?

For optimal IHC results with AIFM3 antibodies:

• Antigen Retrieval: Heat-induced epitope retrieval in citrate buffer (pH 6.0) has shown good results

• Antibody Concentration: Begin with 2.5 μg/mL concentration for paraffin-embedded tissues and adjust as needed

• Incubation Conditions: Overnight incubation at 4°C generally yields optimal signal-to-noise ratio

• Detection System: HRP-conjugated secondary antibodies with DAB produce clear visualization

• Scoring System: Implement a standardized scoring system:

This scoring approach was successfully used in studies correlating AIFM3 expression with clinical outcomes in breast cancer patients .

What experimental approaches are effective for studying AIFM3's role in cancer metastasis?

To investigate AIFM3's contribution to metastasis:

• Cell Migration/Invasion Assays: AIFM3 gene silencing significantly decreased CCA cell migration/invasion (p<0.001)

• AIFM3-FMNL3 Interaction Studies: Bioinformatic analyses identified formin-like protein 3 (FMNL3) as an AIFM3 interaction partner involved in cell motility

• In vivo Metastasis Models: Examine AIFM3's role in lymph node metastasis, which shows significant correlation with AIFM3 expression (p=0.0009)

• Proteomic Analysis: Mass spectrometry identified 441 AIFM3-related proteins, providing a network for exploring metastatic mechanisms

Researchers should consider combined approaches using both in vitro and in vivo models to comprehensively assess AIFM3's metastatic functions.

How can researchers investigate the signaling pathways regulated by AIFM3?

Gene Set Enrichment Analysis (GSEA) has identified several signaling pathways potentially regulated by AIFM3:

• Estrogen Response Pathway: AIFM3 may influence early and late responses to estrogen

• Cancer Stemness Regulation: AIFM3 might decrease stem-like properties of breast cancer cells

• P53 Signaling: AIFM3 may participate in tumor cell survival, proliferation, and migration via P53 pathways

• Wnt/β-catenin Signaling: AIFM3 potentially influences this pathway known to regulate cancer progression

• Oxidative Phosphorylation: AIFM3 may participate in maintaining tumor cell energy metabolism

• DNA Repair Mechanisms: AIFM3 likely participates in reactive oxygen species pathways

To investigate these pathways:

• Use phospho-specific antibodies to monitor pathway activation

• Combine AIFM3 overexpression/knockdown with pathway inhibitors

• Perform co-immunoprecipitation to identify direct interaction partners

• Employ ChIP-seq to identify transcriptional targets

What methodological approaches can resolve contradictory findings when studying AIFM3?

When faced with contradictory findings regarding AIFM3 function:

• Examine Cell Type-Specific Effects: AIFM3 may have context-dependent functions across different cancer types

• Consider Protein Partners: AIFM3 interacts with proteins like PTPN12 and FMNL3, which may modulate its function

• Investigate Post-Translational Modifications: Different modifications might explain varying functions

• Multiple Antibody Validation: Use antibodies targeting different epitopes to confirm observations

• In vivo Verification: Validate key findings in animal models to resolve in vitro discrepancies

For comprehensive analysis, researchers should systematically document experimental conditions that influence AIFM3 behavior, including cell density, culture conditions, and experimental timepoints.

How can researchers address multiple band detection when using AIFM3 antibodies in Western blots?

AIFM3 antibodies frequently detect multiple bands in Western blot applications. To address this:

• Confirm Isoform Profile: AIFM3 has multiple predicted isoforms; observed bands (15, 19, 26, 39, 45, 59, 66, 68, 72, 76 kDa) may represent different splice variants

• Optimize Sample Preparation:

  • Use protease inhibitors to prevent degradation

  • Test different lysis buffers to improve extraction

  • Consider subcellular fractionation to enrich mitochondrial proteins

• Optimize Blocking Conditions: Test both BSA and milk-based blocking buffers

• Antibody Titration: Test multiple antibody concentrations (1-2 µg/mL recommended range)

• Positive Control Inclusion: Human brain tissue lysate consistently shows AIFM3 expression

When reporting results, document all detected bands and their relative intensities to provide comprehensive data.

What strategies can improve detection sensitivity in tissues with low AIFM3 expression?

For tissues with low AIFM3 expression levels:

• Signal Amplification Systems:

  • Tyramide signal amplification (TSA) can increase sensitivity 10-50 fold

  • Polymer-based detection systems enhance signal without increasing background

• Extended Primary Antibody Incubation: 48-72 hours at 4°C may improve signal

• Optimized Antigen Retrieval: Test both heat-mediated and enzymatic methods

• Concentration Adjustment: Up to 20 µg/mL antibody concentration may be required for low-expressing tissues

• Alternative Detection Methods: Consider more sensitive methods like RNAscope to detect AIFM3 mRNA when protein detection is challenging

Additionally, compare antibody performance across multiple vendors, as immunogens and production methods can significantly impact sensitivity.