EFNA3 Antibody

What is EFNA3 and what are its primary cellular functions?

EFNA3 is a member of the ephrin family that interacts with Eph receptors, a group of tyrosine kinase receptors. It functions as a cell surface GPI-bound ligand for Eph receptors, which are crucial for migration, repulsion, and adhesion during neuronal, vascular, and epithelial development . EFNA3 binds promiscuously to Eph receptors on adjacent cells, leading to contact-dependent bidirectional signaling. The downstream pathway from the receptor is called forward signaling, while the pathway from the ephrin ligand is termed reverse signaling .

EFNA3 is associated with multiple signaling pathways involved in cell growth and tumor cell metastasis. Research has demonstrated its role in:

• Cell-cell communication through Eph receptor interactions

• Regulation of axonal orientation and synaptic development

• Cell adhesion and movement

• Ion channel activity-related pathways

How should researchers interpret EFNA3 expression patterns across different tissue types?

Researchers should consider several factors when analyzing EFNA3 expression:

• Normal vs. Cancer Tissues: EFNA3 is significantly upregulated in various cancer types compared to normal tissues. For example, in bladder cancer studies, EFNA3 protein expression was detected in 57.4% (282/491) of bladder urothelial carcinoma samples but only 31.3% (25/80) of normal bladder tissues .

• Expression Variation: The absolute expression levels of EFNA3 vary widely between RNA species. The canonical coding isoform often shows lower expression compared to the combined expression of all isoforms, suggesting that under normoxic conditions, the transcription of EFNA3 long non-coding RNAs (lncRNAs) predominates .

• Hypoxia-Induced Changes: Research has shown that hypoxia leads to Ephrin-A3 protein accumulation via HIF-mediated transcriptional mechanisms. While the canonical EFNA3 mRNA is barely induced under hypoxia, there is robust upregulation of other EFNA3 transcripts .

What detection methods are most effective for EFNA3 research?

Based on current research protocols, the following methods have proven effective for EFNA3 detection:

For IHC protocols, researchers commonly use a scoring system where the proportional score is multiplied by the staining intensity score to generate a final IHC score (ranging from 0-300). High EFNA3 expression is typically defined as having an IHC score >10 .

How does EFNA3 expression correlate with clinical outcomes across different cancer types?

EFNA3 expression has demonstrated significant prognostic value in multiple cancer types:

Hepatocellular Carcinoma (HCC):

Researchers should consider using EFNA3 as a biomarker in their cancer studies, particularly for these cancer types.

What signaling pathways interact with EFNA3, and how can researchers investigate these networks?

EFNA3 is involved in multiple signaling pathways that can be investigated through various approaches:

Key Signaling Pathways Associated with EFNA3:

• Ras signaling pathway

• Rap1 signaling pathway

• PI3K-Akt signaling pathway

• mTOR signaling pathway

• MAPK signaling pathway

Protein Interaction Partners:
EFNA3 interacts with several protein partners that researchers should consider examining:

Research Methods for Pathway Investigation:

• Protein-Protein Interaction (PPI) Analysis: Use tools like STRING (http://string-db.org/) to analyze interaction networks .

• Gene Set Enrichment Analysis (GSEA): Divide gene expression profiles into high and low EFNA3 expression groups and identify enriched pathways (significant when FDR <0.25 and P.adjust <0.05) .

• Weighted Co-expression Network Analysis (WGCNA): Extract genes (approximately 4000 by variance) to construct WGCNA using the "WGCNA" package. Convert adjacency matrix into topological overlap matrix (TOM) when power of β equals 3 (R² = 0.868) .

What are the best practices for validating EFNA3 antibody specificity in experimental protocols?

Researchers should implement the following validation steps to ensure EFNA3 antibody specificity:

Recommended Validation Protocol:

• Positive and Negative Controls: Include tissues/cells known to express high EFNA3 levels (e.g., bladder cancer or lung adenocarcinoma tissues) and those with minimal expression.

• Western Blot Validation:

  • Use 12% SDS-PAGE for optimal protein separation

  • Follow dilution protocols (e.g., 1/500 dilution for antibodies like ab153706)

  • Verify band size against expected molecular weight

  • Include siRNA knockdown controls to confirm specificity

• Immunohistochemistry Controls:

  • Include antigen retrieval steps (e.g., immersion in antigen retrieval buffer and boiling at 120°C in a pressure cooker for 3 min)

  • Analyze staining patterns in comparison to known expression patterns

  • Perform blocking experiments to confirm specificity

• Cross-Reactivity Assessment: Test antibody against recombinant proteins of related Ephrin family members to ensure specificity.

• Multiple Antibody Validation: When possible, compare results using antibodies from different sources or that recognize different epitopes of EFNA3.

How does EFNA3 influence the tumor immune microenvironment, and what methodologies can assess this relationship?

Research has revealed significant correlations between EFNA3 and immune components:

EFNA3 and Immune Cell Infiltration:
EFNA3 expression shows positive correlations with various immune cell populations:

• B cells infiltration (r=0.137, P=1.06e-03)

• CD4+ T cells infiltration (r=0.18, P=8.20e-04)

• CD8+ T cells infiltration (r=0.153, P=4.60e-03)

• Dendritic Cells infiltration (r=0.25, P=3.03e-06)

• Neutrophils infiltration (r=0.258, P=1.23e-06)

EFNA3 and Immune Checkpoint Molecules:
EFNA3 expression positively correlates with immune checkpoint molecules:

• PDCD1 (PD-1)

• CTLA4

Methodological Approaches for Investigation:

• Immune Cell Correlation Analysis:

  • Use TIMER (http://timer.cistrome.org) to investigate relationships between gene expression and immune cell infiltration

  • Analyze correlations with surface markers of specific immune cell populations

• Immune Subtype Analysis:

  • Categorize tumors into immune subtypes (C1-C6)

  • Compare EFNA3 expression across subtypes (e.g., EFNA1, EFNA3, and EFNA4 show highest expression in C1 subtype)

• Immune Checkpoint Correlation:

  • Perform correlation analysis between EFNA3 and immune checkpoint molecules

  • Consider co-immunoprecipitation to verify physical interactions

• Single-Cell RNA-Seq Approach:

  • Analyze EFNA3 expression in distinct cell populations within the tumor microenvironment

  • Map receptor-ligand interactions between tumor and immune cells

What technical considerations should researchers address when investigating contradictory findings in EFNA3 expression studies?

When faced with contradictory results regarding EFNA3 expression or function, researchers should consider:

Sources of Variability to Address:

• Transcript Isoform Specificity:

  • EFNA3 locus encodes multiple RNA species with varying expression levels

  • The canonical coding isoform may be low compared to total expression

  • Use specific primers/probes that distinguish between isoforms

• Hypoxia Effects:

  • EFNA3 protein accumulation under hypoxia occurs through mechanisms involving lncRNAs rather than canonical mRNA induction

  • The canonical EFNA3 isoform is barely induced by hypoxia, while lncRNAs show robust upregulation

  • Consider oxygen conditions during experiments

• Cancer Type Specificity:

  • EFNA3 effects differ across cancer types

  • For example, EFNA3 correlates negatively with immune cells in most cancers except UVM

  • EFNA5 shows positive correlation with immune cells in COAD, KIRC, LIHC, PRAD, READ, and THCA, but negative correlation in LUAD, MESO, TGCT, THYM, and UCEC

• Methodological Reconciliation Protocol:

  • Perform meta-analysis with strict inclusion criteria

  • Use multiple detection methods (protein level, mRNA level)

  • Control for tumor heterogeneity with microdissection

  • Account for patient demographics and treatment history

What are the latest methodological approaches for studying EFNA3 post-translational modifications and their impact on protein function?

While the search results don't directly address EFNA3 post-translational modifications, researchers can apply these state-of-the-art approaches to investigate this aspect:

Advanced Methodological Approaches:

• Mass Spectrometry-Based Techniques:

  • Employ tandem mass spectrometry (MS/MS) to identify and quantify post-translational modifications

  • Use stable isotope labeling by amino acids in cell culture (SILAC) to compare modification patterns between conditions

  • Apply selected reaction monitoring (SRM) for targeted analysis of specific modifications

• Site-Directed Mutagenesis:

  • Generate EFNA3 mutants with altered potential modification sites

  • Compare functional outcomes in cellular assays

  • Use in conjunction with molecular dynamics simulations to predict effects on protein structure

• Proximity Ligation Assays:

  • Detect protein-protein interactions that depend on specific modifications

  • Visualize interactions in their cellular context

  • Combine with super-resolution microscopy for detailed localization studies

• Integration with Signaling Pathway Analysis:

  • Since EFNA3 is involved in Ras, PI3K-Akt, and mTOR signaling pathways, researchers should analyze how post-translational modifications affect these pathways

  • Use pathway inhibitors to determine if modifications are dependent on specific signaling events

How can researchers investigate the functional relationship between EFNA3, non-coding RNAs, and protein translation?

Research has revealed that EFNA3 lncRNAs play important roles in regulating Ephrin-A3 protein levels:

Key Findings on EFNA3 and Non-coding RNAs:

• EFNA3 locus encodes both canonical mRNA and long non-coding RNAs (NC1, NC2)

• Under hypoxia, the canonical EFNA3 mRNA is barely induced while lncRNAs show robust upregulation

• Overexpression of lncRNAs, particularly short isoforms (NC1s and NC2s), causes EFNA3 protein accumulation without affecting mRNA levels

• miR-210, which is induced by hypoxia, prevents the translation of several mRNAs including EFNA3

Methodological Approaches:

• lncRNA-Protein Relationship Analysis:

  • Use TaqMan probes specific to different EFNA3 transcripts to quantify expression

  • Employ lentiviral infection with dose-dependent approaches to assess effect on protein levels

  • Analyze protein accumulation via western blot after lncRNA overexpression

• miRNA Regulation Investigations:

  • Test the hypothesis that EFNA3 lncRNAs increase EFNA3 mRNA translation by depleting miR-210

  • Interfere with miRNA processing machinery (e.g., by knocking down DGCR8)

  • Perform reporter assays with the EFNA3 3'-UTR with and without lncRNA overexpression

• Translation Efficiency Assessment:

  • Use polysome profiling to measure translation efficiency of EFNA3 mRNA

  • Perform ribosome footprinting to map translation at nucleotide resolution

  • Apply SILAC or pulsed SILAC to quantify newly synthesized protein

• Integrated Approach:

  • Combine RNA-seq, CLIP-seq, and proteomics to build comprehensive models

  • Use CRISPR-Cas9 to delete specific lncRNAs and assess effects on EFNA3 protein expression

  • Apply computational approaches to predict RNA-RNA interactions