EN1 Antibody

What is EN1 (Engrailed 1) and what cellular structures does it typically localize to?

EN1 (Engrailed 1) is a homeodomain-containing transcription factor that plays critical roles in development and cellular regulation. Immunofluorescence studies reveal that EN1 predominantly localizes to the nucleus, as evidenced by staining patterns observed in U251 cells. When cells are fixed with 4% paraformaldehyde and permeabilized with Triton X-100, EN1 immunoreactivity is significantly concentrated in nuclear regions rather than cytoplasmic compartments. This nuclear localization aligns with its function as a transcription factor that regulates gene expression . Researchers should note that proper fixation and permeabilization protocols are essential for accurately visualizing this localization pattern, as incomplete membrane permeabilization may result in false negative nuclear staining.

What are the recommended applications for EN1 antibodies in research?

Based on validated protocols, EN1 antibodies are recommended for multiple research applications including:

• Western Blotting (WB): Effective at dilutions of 1:1000 with detection using secondary antibodies such as goat anti-rabbit IgG H&L(HRP) at 1:5000-1:10000 dilution .

• Immunohistochemistry on Paraffin-embedded tissues (IHC-P): Successfully demonstrated on human lung carcinoma tissue samples using peroxidase-conjugated secondary antibodies followed by DAB staining .

• Immunofluorescence (IF): Validated protocols include fixation with 4% PFA (20 min), permeabilization with 0.1% Triton X-100 (10 min), and primary antibody incubation at 1:25 dilution for 1 hour at 37°C .

Each application requires specific optimization of antibody concentration, incubation conditions, and detection methods to achieve optimal signal-to-noise ratios.

How should EN1 antibodies be stored to maintain optimal activity?

EN1 antibodies require specific storage conditions to maintain their binding capacity and specificity over time. For short-term storage (up to several weeks), antibodies should be kept at 2-8°C to prevent degradation . For long-term storage and to maintain antibody functionality for extended periods, storage at -20°C is recommended . When stored properly at -20°C, EN1 antibodies typically remain stable for up to 12 months from the date of shipment . To minimize freeze-thaw cycles that can degrade antibody performance, it's advisable to prepare working aliquots before freezing. Additionally, when shipping EN1 antibodies between laboratories, blue ice conditions are recommended to maintain antibody integrity during transport .

What cell lines have been validated for EN1 expression and antibody testing?

Several cell lines have been validated for EN1 expression and can serve as positive controls for antibody testing:

These cell lines have demonstrated reliable EN1 expression patterns across multiple experiments, making them suitable positive controls for optimizing new protocols . When establishing a new experimental system, researchers should include at least one of these validated cell lines as a reference point.

What are the critical parameters for successful Western blot detection of EN1?

Successful Western blot detection of EN1 requires attention to several critical parameters:

• Sample preparation: Lysate concentration of 20-35 μg per lane has been validated for optimal detection .

• Antibody dilution: For primary antibody, a 1:1000 dilution of anti-EN1 antibody has been established as effective. For secondary detection, goat anti-rabbit IgG H&L(HRP) at 1:5000-1:10000 dilution provides good signal-to-noise ratio .

• Blocking conditions: 5% non-fat dry milk (NFDM) in TBST has been demonstrated to minimize background while preserving specific signal .

• Detection method: HRP-conjugated secondary antibodies with appropriate chemiluminescent substrates provide sensitive detection of EN1, which has a predicted band size of approximately 40 kDa .

• Positive controls: Including lysates from validated cell lines such as NCI-H460, 293, or SH-SY5Y helps confirm antibody performance and provides reference band intensity .

Troubleshooting approaches should focus on these parameters when unexpected results occur, with particular attention to protein extraction methods that preserve nuclear proteins.

How does EN1 expression change during viral infection, and what methodologies best capture these dynamics?

Research has demonstrated that host protein expression, including EN1-related proteins like α-enolase (ENO1), can undergo significant changes during viral infection. During Enterovirus A71 (EV-A71) infection, ENO1 protein expression increases in a dose-dependent manner, with changes observable at viral concentrations from 0.001 to 0.01 M.O.I. . Furthermore, temporal expression patterns show that ENO1 expression changes kinetically throughout the course of infection .

To effectively capture these dynamics, researchers should:

• Implement time-course studies: Sample collection at multiple time points post-infection (h.p.i.) is crucial for understanding the temporal regulation of EN1 expression.

• Use quantitative Western blotting: Normalize EN1 protein expression to stable housekeeping proteins such as α-tubulin to accurately measure fold-changes .

• Combine with functional assays: Correlation of expression changes with viral titers provides insights into the functional significance of these changes.

• Consider overexpression and knockdown studies: These complementary approaches help establish causality between EN1 expression and viral replication efficiency .

These methodologies have successfully demonstrated that ENO1 overexpression significantly increases viral titers, while knockdown reduces viral replication—effects that can be rescued by ENO1 reintroduction .

What are the methodological considerations when using anti-EN1 antibodies for immunohistochemistry in tissue sections?

Immunohistochemical detection of EN1 in tissue sections requires careful attention to several methodological factors:

• Tissue preparation: Formalin fixation and paraffin embedding have been validated for preserving EN1 epitopes in tissue samples, including human lung carcinoma tissues .

• Antigen retrieval: Heat-induced epitope retrieval is typically necessary to expose antibody binding sites that may be masked by fixation-induced protein crosslinking.

• Antibody validation: The N-terminal targeting anti-EN1 antibodies have demonstrated specific staining in lung carcinoma tissues .

• Detection system: Peroxidase-conjugated secondary antibodies followed by DAB (3,3'-diaminobenzidine) staining provide a stable, permanent signal for long-term analysis .

• Controls: Both positive controls (tissues known to express EN1) and negative controls (primary antibody omission) should be included in every experiment.

Researchers should note that while immunohistochemistry provides valuable spatial information about EN1 expression, clinical relevance of staining patterns should be established through correlation with patient outcomes or disease parameters, as this aspect has not been thoroughly evaluated for all commercially available antibodies .

How can single-chain variable fragment (scFv) libraries be leveraged for studying EN1-related antibodies?

Single-chain variable fragment (scFv) libraries provide powerful platforms for studying antibody-antigen interactions relevant to EN1 research. Recent methodological advances include:

• Mammalian cell expression systems: Libraries expressed in mammalian cells can be directly screened for functional properties rather than just binding affinity .

• Phenotypic directed screening: This approach allows identification of antibodies with specific functional outcomes, such as inhibition of viral growth, beyond simple binding characteristics .

• AlphaSeq assay implementation: This technology enables quantitative measurement of binding interactions for tens of thousands of antibodies simultaneously, creating large datasets that can inform structure-function relationships .

• Chain orientation optimization: Testing both heavy-light (HL) and light-heavy (LH) chain orientations can significantly impact binding affinity, with some scFvs showing distinct preferences .

These approaches have successfully identified functional antibodies, including those targeting host proteins like ENO1 that can interfere with viral infections such as EV-A71 . For researchers implementing these methods, it's critical to validate binding affinities through complementary techniques like ELISA, where purified proteins (5 μg/mL) coated on 96-well plates can be used to confirm antibody specificities .

What are the most common sources of false positive and false negative results when using EN1 antibodies?

When working with EN1 antibodies, researchers should be aware of several potential sources of experimental artifacts:

Sources of false positive results:

• Insufficient blocking: Inadequate blocking can lead to non-specific binding of primary or secondary antibodies to the membrane or tissue, creating background signal.

• Cross-reactivity: Some antibodies may recognize structural similarities in unrelated proteins, particularly when using polyclonal antibodies.

• High antibody concentration: Excessive primary antibody can increase non-specific binding.

• Extended substrate incubation: Prolonged exposure to chemiluminescent or chromogenic substrates can create background signal.

Sources of false negative results:

• Insufficient antigen retrieval: For IHC applications, inadequate retrieval may prevent antibody access to epitopes.

• Protein degradation: Improper sample handling can lead to EN1 degradation before analysis.

• Ineffective transfer: For Western blotting, incomplete protein transfer to membranes may result in weak or absent signals.

• Inappropriate fixation: Overfixation can mask epitopes, while underfixation may result in poor tissue morphology and protein retention.

Quality control measures should include appropriate positive controls (such as U251, NCI-H460, 293, or SH-SY5Y cell lines) and careful optimization of experimental conditions for each new lot of antibody.

How should researchers validate the specificity of new EN1 antibody lots?

Rigorous validation of EN1 antibody specificity is essential for reliable research results. A comprehensive validation protocol should include:

• Western blot analysis: Testing on lysates from multiple cell lines known to express EN1 (such as NCI-H460, 293, SH-SY5Y) should show bands at the expected molecular weight (approximately 40 kDa) .

• Immunofluorescence patterns: Nuclear localization in U251 cells provides a characteristic pattern that can be used to confirm antibody specificity .

• Positive tissue controls: Human lung carcinoma tissue has been validated for IHC applications of EN1 antibodies and should show consistent staining patterns between antibody lots .

• Peptide competition assays: Pre-incubation of the antibody with the immunizing peptide should abolish specific signals in all applications.

• Knockout/knockdown controls: When available, EN1 knockout or knockdown samples provide the gold standard for antibody specificity verification.

For polyclonal antibodies like the rabbit anti-human EN1 antibody, lot-to-lot variation is possible since these are generated from different immunized animals. Therefore, each new lot should undergo comprehensive validation across multiple applications before use in critical experiments.

What role do EN1 antibodies play in understanding auto-immune responses in viral infections?

Recent research has uncovered intriguing connections between auto-antibodies targeting host proteins and viral infections. A novel approach identified that antibodies targeting α-enolase (ENO1), which has functional relationships with the EN1 pathway, can exhibit antiviral properties against Enterovirus A71 (EV-A71) . This discovery emerged through phenotypic directed screening of a human single chain variable fragment (scFv) library.

Key methodological findings include:

• The prevalence of anti-ENO1 antibodies was found to be higher in patients with mild EV-A71 cases compared to severe cases, suggesting a potential protective role .

• Administration of human IgG1 antibodies targeting ENO1 increased survival rates in mouse models challenged with lethal doses of virus .

• These antibodies significantly decreased viral RNA levels and neural pathology in brain tissue of treated animals .

The discovery process involved a strategic methodology where scFv libraries were expressed in mammalian cells and screened by infection with lethal doses of EV-A71, allowing for functional selection rather than mere binding affinity . This approach represents a paradigm shift in antibody discovery, focusing on phenotypic outcomes rather than target-based selection.

These findings suggest that EN1-related research may have implications beyond traditional applications, potentially informing novel therapeutic strategies against viral infections through modulation of host protein interactions.

How can machine learning approaches enhance EN1 antibody research and development?

The integration of machine learning approaches with large-scale antibody datasets presents transformative opportunities for EN1 antibody research:

• Leveraging comprehensive binding datasets: Recent advances have created datasets containing quantitative binding scores for over 100,000 antibodies, providing unprecedented training data for machine learning models. While not specifically focused on EN1, these methodologies are applicable to EN1 antibody development .

• In silico antibody design: Starting from seed sequences identified through traditional methods like phage display, computational approaches can systematically introduce mutations (k=1, k=2, k=3) throughout complementary-determining regions (CDRs) to generate diverse antibody candidates with potentially improved properties .

• Affinity prediction models: Machine learning models trained on large datasets can predict binding affinities ranging from picomolar to millimolar, potentially reducing wet-lab screening efforts in EN1 antibody optimization .

• Orientation optimization: Computational prediction of optimal chain orientations (heavy-light vs. light-heavy) can enhance binding properties, as demonstrated in scFv design studies where orientation changes could significantly impact affinity .

For researchers looking to implement these approaches, it's important to note that successful machine learning applications require careful validation through experimental testing of computational predictions. The combination of in silico design followed by targeted experimental validation represents a powerful hybrid approach to accelerate EN1 antibody development.