CIR1 Antibody

What is CIR1 and what cellular functions does it regulate?

CIR1 (Corepressor Interacting with RBPJ 1) is a 52.3 kDa protein (450 amino acids) that functions primarily as a transcriptional corepressor. It modulates splice site selection during alternative splicing of pre-mRNAs and regulates transcription by acting as a corepressor for RBPJ. CIR1 recruits RBPJ to the Sin3-histone deacetylase complex (HDAC), which is essential for RBPJ-mediated repression of transcription .

The protein is localized in both the nucleus and cytoplasm, with high expression reported in the heart, brain, placenta, liver, skeletal muscle, and pancreas . Known synonyms include CBF1-interacting corepressor, corepressor interacting with RBPJ 1, CBF1 (RBPJ) interacting corepressor 1, recepin, and CIR .

In certain organisms like the fungal pathogen Cryptococcus neoformans, Cir1 (a GATA-type zinc-finger protein) plays a critical role in regulating iron uptake, iron homeostasis, and virulence factor expression .

Which CIR1 antibody types are most widely validated for research applications?

Based on current antibody validation databases, several CIR1 antibodies have demonstrated reliable performance across multiple applications. The following table summarizes top validated antibodies according to Antibodypedia:

Table 1: Top validated CIR1 antibodies and their applications (WB = Western Blot; ELISA = Enzyme-Linked Immunosorbent Assay; IHC = Immunohistochemistry)

Western Blot is one of the most commonly used applications for CIR1 antibodies, followed by ELISA .

What are the key considerations when designing Western blot experiments with CIR1 antibodies?

When designing Western blot experiments with CIR1 antibodies, researchers should consider:

• Sample preparation: Given CIR1's localization in both nuclear and cytoplasmic compartments, appropriate cellular fractionation may be necessary depending on your research question.

• Antibody dilution: Most manufacturers recommend a dilution of 1:1000 for Western blotting applications, but this should be optimized for each specific antibody .

• Molecular weight validation: The canonical form of human CIR1 has a predicted molecular weight of 52.3 kDa . Confirm this band appears at the expected size to verify specificity.

• Positive controls: Use cell lines known to express CIR1, such as HeLa, Jurkat, or 293T cells, which have been validated in Western blot applications with CIR1 antibodies .

• Loading controls: Include appropriate housekeeping protein controls (e.g., actin) to normalize expression levels, particularly when studying iron-dependent regulation of CIR1 levels .

As demonstrated in experimentally validated Western blots, CIR1 antibodies like ab113203 show clear detection at 0.1 μg/mL concentration in various cell lysates including HeLa (at both 50 μg and 15 μg loading), Jurkat (50 μg), and 293T (50 μg) .

How can I optimize immunoprecipitation protocols for CIR1?

For effective immunoprecipitation of CIR1:

• Antibody selection: Choose antibodies specifically validated for IP applications. For example, Bethyl Laboratories offers affinity-purified rabbit anti-CIR1 antibodies validated for both WB and IP .

• Lysate preparation: Use buffers that preserve protein-protein interactions if investigating CIR1's interactions with RBPJ or histone deacetylase complexes.

• Antibody amount: Typically, 1:50 dilution is recommended for immunoprecipitation applications , but this should be optimized for your specific experimental conditions.

• Cross-validation: Confirm IP results with Western blot using a different CIR1 antibody that recognizes a separate epitope.

• Controls: Include appropriate negative controls (non-specific IgG of the same species) and positive controls (input lysate).

For detecting immunoprecipitated CIR1, subsequent Western blot analysis using 1:1000 dilution of the detection antibody is typically effective .

How does iron availability influence CIR1 expression and stability, and how can this be studied with antibodies?

Research has revealed that iron availability significantly impacts CIR1 protein stability through post-translational mechanisms. In Cryptococcus neoformans, CIR1 abundance decreases upon iron deprivation, with this destabilization influenced by reducing conditions and inhibition of proteasome function .

To study iron-dependent regulation of CIR1:

• Experimental approach: Treat cells with varying iron concentrations (0-100 μM) and analyze CIR1 protein levels by Western blot. Studies have demonstrated that higher iron levels result in greater abundance of CIR1 relative to loading controls like actin .

• Proteasome involvement: To investigate if proteasomal degradation mediates CIR1 destabilization under low iron conditions, pre-treat cells with proteasome inhibitors (e.g., MG132) before iron deprivation.

• Redox sensitivity: Examine the influence of reducing agents on CIR1 stability in combination with altered iron levels.

• Transcriptional vs. post-translational regulation: Compare CIR1 mRNA levels (by RT-PCR or Northern blot) with protein levels. Research has shown that CIR1 transcript levels remain constant regardless of iron concentration, confirming post-transcriptional/post-translational regulation .

• Antibody considerations: When studying iron-dependent effects, ensure antibodies recognize epitopes outside potential iron-binding regions to avoid detection artifacts.

What approaches can be used to design antibodies targeting specific epitopes within CIR1?

Recent advances in computational antibody design offer powerful approaches for creating antibodies targeting specific CIR1 epitopes:

• Structure-guided design: Methods like AbDesign utilize a three-stage process: segmenting natural antibody backbones, docking against the target antigen, and optimizing sequences through Rosetta design calculations . This approach jointly optimizes antibody stability and binding energy.

• Conformation-dependent sequence constraint strategy: This critical approach clusters natural Fv backbone conformations by similarity and computes position-specific scoring matrices (PSSMs) to constrain sequence optimization . This strategy addresses issues like unpaired charges and cavities in the antibody core.

• Segment boundaries optimization: Research shows that proper segmentation of antibody structures is crucial. For optimal results, each chain should be segmented into two parts: one encompassing CDRs 1 and 2 with supporting framework, and the other encompassing CDR 3 .

• Diffusion-based design: More recent approaches like RFdiffusion enable atomically accurate de novo design of antibodies. This technique combines computational protein design using fine-tuned diffusion networks with experimental screening methods like yeast display to generate antibodies with atomic-level precision in targeting specific epitopes .

• Deep learning methods: Models like IgDesign demonstrate improved performance in designing complementarity-determining regions (CDRs). This method predicts both CDR-H3 residue sequences and generates coordinates for entire antibody structures, achieving superior accuracy in both sequence metrics and docking performance .

When applying these approaches to CIR1, researchers should consider focusing on functional domains important for protein-protein interactions, such as regions mediating RBPJ binding.

What strategies should I use to validate the specificity of CIR1 antibodies?

Comprehensive validation of CIR1 antibodies should include:

• Genetic approaches:

  • Test antibody reactivity in CIR1 knockout/knockdown models (CRISPR-Cas9, siRNA).

  • Compare detection between samples with normal and reduced CIR1 expression.

• Peptide competition assays: Pre-incubate the antibody with excess synthetic peptide containing the target epitope before application to samples. Loss of signal confirms epitope specificity.

• Cross-validation with multiple antibodies: Use different antibodies targeting distinct CIR1 epitopes and compare detection patterns.

• Recombinant protein controls: Include purified recombinant CIR1 proteins as positive controls. Various sources (yeast, E. coli, baculovirus, mammalian cell) of recombinant CIR1 are available for human, mouse, rat, and chicken variants .

• Orthogonal detection methods: Correlate antibody-based detection with orthogonal techniques like mass spectrometry or RNA-seq.

• Expected expression patterns: Verify that detection aligns with known CIR1 expression patterns across tissues, with highest expression expected in heart, brain, placenta, liver, skeletal muscle, and pancreas .

How can I address non-specific binding issues when using CIR1 antibodies?

To troubleshoot and minimize non-specific binding:

• Blocking optimization: Test different blocking reagents (BSA, milk, serum) and concentrations to identify optimal conditions for your specific application.

• Antibody titration: Perform careful dilution series to determine the minimum antibody concentration needed for specific detection, reducing background.

• Washing stringency: Increase the number and duration of washes, and consider adding low concentrations of detergents (0.05-0.1% Tween-20) to reduce non-specific interactions.

• Sample preparation: Ensure complete cell lysis and proper denaturation for Western blot applications. For IF/IHC, optimize fixation and permeabilization protocols.

• Pre-adsorption: For tissues with high background, pre-adsorb the antibody with tissue homogenate from a species different from your experimental sample.

• Secondary antibody controls: Include controls omitting primary antibody to identify potential secondary antibody cross-reactivity.

• Antigen retrieval optimization: For IHC applications, test different antigen retrieval methods (heat-induced, enzymatic) to improve specific epitope accessibility.

How are computational approaches advancing antibody design for targets like CIR1?

Recent computational approaches are revolutionizing antibody design, with potential applications for CIR1 research:

• Deep learning architectures: Models like IgFormer enhance antibody-antigen binding interface representation by integrating personalized propagation with global attention mechanisms, allowing comprehensive capture of local chemical interactions and global conformational dependencies .

• Diffusion-based technologies: RFdiffusion enables atomically accurate antibody design, as demonstrated by cryo-EM validation confirming proper Ig fold, binding pose, and CDR loop conformations in designed antibodies .

• Inverse folding models: IgDesign has shown success in preserving binding to target antigens while designing heavy chain CDR3 (HCDR3) or all three heavy chain CDRs (HCDR123), outperforming HCDR3 sampling from training sets .

• Structure-guided approaches: AbDesign addresses challenges in designing irregular interactions and buried polar networks characteristic of antibody CDRs by recombining segments from different natural antibodies and jointly optimizing stability and binding energy .

• AI-powered design: Recent models from companies like Absci have demonstrated the ability to design novel HCDR3 and HCDR123 variants that bind specific targets with high affinity, showing sequence novelty compared to training data .

These approaches could be applied to design novel antibodies targeting specific functional domains or epitopes of CIR1, potentially creating research tools with enhanced specificity or therapeutic antibodies targeting CIR1-mediated pathways.

What are the challenges in studying post-translational modifications of CIR1 using antibodies?

Investigating post-translational modifications (PTMs) of CIR1 presents several challenges:

To address these challenges, researchers should consider combining antibody-based detection with mass spectrometry validation, and potentially develop targeted approaches based on CIR1's known regulatory mechanisms, particularly its iron-responsive degradation pathway .