AOX1C Antibody

What is AOX1 and why is it important in research?

AOX1 can refer to two distinct proteins depending on the research context: Alternative Oxidase 1 (involved in mitochondrial electron transport) or Aldehyde Oxidase 1 (involved in aldehyde metabolism). Alternative Oxidase 1a (AOX1a) is a mitochondrial protein that plays a crucial role in plant metabolism, particularly in seed viability during storage. Research has demonstrated that AOX1a deficiency significantly affects mitochondrial metabolism, with mutant plants displaying altered oxygen consumption rates with various substrates. For instance, OsAOX1a-RNAi plants show significantly reduced oxygen consumption rates with NADH (17.3 ± 1.9 nmolO₂·min⁻¹mg⁻¹ protein) compared to wild type plants (44.4 ± 3.2 nmolO₂·min⁻¹mg⁻¹ protein) . In the context of Aldehyde Oxidase 1, the protein is primarily expressed in liver, lung, and pancreatic tissues, making it relevant for metabolic and toxicological research .

What are the optimal dilutions for using AOX1 antibodies in different experimental applications?

Optimal dilution ratios vary significantly based on the specific application and the antibody itself. For Aldehyde oxidase antibody (such as the 19495-1-AP), a dilution of 1:20 is recommended for Western blot applications . When designing experiments with other AOX1 antibodies, researchers should consider:

• Western Blot: Generally, dilutions between 1:500 and 1:2000 are common starting points, but must be optimized

• Immunohistochemistry: Typically requires antigen retrieval with TE buffer pH 9.0 or citrate buffer pH 6.0

• Immunofluorescence: Cell-type dependent optimization is necessary

The specific antibody concentration should be determined through pilot experiments, as the optimal dilution will depend on tissue type, fixation method, and detection system.

What tissues are most appropriate for studying AOX1 expression?

Based on experimental data, AOX1 protein expression varies significantly by tissue type. For Aldehyde oxidase antibodies, positive Western blot detection has been confirmed in mouse liver tissue, mouse lung tissue, and mouse pancreas tissue . For immunoprecipitation experiments, mouse lung tissue has shown reliable results . In immunohistochemistry applications, human hepatocirrhosis tissue has been successfully used . Alternative Oxidase 1a (AOX1a) has been extensively studied in plant tissues, particularly in seeds where it plays a critical role in viability during storage .

When selecting appropriate tissues for your research, consider:

• The expression level of AOX1 in your tissue of interest

• The species-specific differences in expression patterns

• The potential for cross-reactivity with similar proteins

• The preservation method and tissue preparation protocol

How can I validate the specificity of an AOX1 antibody for my experiments?

Proper validation of antibody specificity is critical for ensuring reliable experimental results. For AOX1 antibodies, several validation methods can be employed:

• Compare protein detection in wild-type vs. knockout models: Studies with aox1a knockout mutants show that the AOX protein was only detected in wild-type samples using Western blot, confirming antibody specificity .

• Verify protein size: The expected protein size for Alternative Oxidase 1a is approximately 358 amino acids, while knockout mutants show truncated proteins (62 aa for a1.1 mutant, and 48 aa for a2.3 mutant) .

• Functional validation: Measure AOX capacity in samples. The aox1a mutant lines displayed significantly reduced AOX capacity compared to wild-type plants, providing functional confirmation of antibody specificity .

• Cross-tissue validation: Test antibody performance across different tissue types where the protein is known to be expressed or absent .

• Peptide competition assay: Pre-incubate the antibody with the immunizing peptide to confirm specific binding is blocked.

What are the key considerations when using AOX1 antibodies in co-immunoprecipitation experiments?

When designing co-immunoprecipitation (co-IP) experiments with AOX1 antibodies, researchers should consider several critical factors:

• Antibody characteristics: The binding affinity of the antibody to the AOX1 protein is crucial. For instance, research on antibody-oligonucleotide conjugates shows that antibodies with high binding affinity (e.g., in the picomolar range) perform better in targeted applications. One study demonstrated binding affinities of 42 pM for TfR1 antibodies .

• Buffer optimization: For successful co-IP of AOX1, careful optimization of lysis and binding buffers is essential. Mouse lung tissue has been successfully used for immunoprecipitation with Aldehyde oxidase antibodies .

• Control experiments: Include appropriate controls such as:

  • IgG isotype control antibody to identify non-specific binding

  • Input samples to verify protein presence

  • Known interacting partners as positive controls

  • Knockout or knockdown samples as negative controls

• Conjugation effects: Be aware that conjugation of molecules to antibodies can potentially affect binding. Research has shown that in some cases, "conjugation of an oligonucleotide had no impact on antibody binding to the target receptor" , but this should be verified for your specific antibody.

• Cross-linking considerations: In some cases, chemical cross-linking may be necessary to capture transient interactions, but this should be carefully validated.

How can I troubleshoot inconsistent results when using AOX1 antibodies across different tissue types?

Inconsistent results across tissue types is a common challenge when working with AOX1 antibodies. Several methodological approaches can help address this issue:

• Optimize tissue-specific extraction protocols: Different tissues require specific extraction conditions. For example, when working with AOX1 antibodies, successful protein detection has been achieved in diverse tissues including mouse liver, lung, and pancreas for Western blot applications .

• Validate antibody performance in each tissue type: The same antibody may perform differently across tissues. Systematic validation in each tissue type is recommended.

• Adjust detection methods: For tissue-specific optimization:

  • For immunohistochemistry with hepatic tissues, antigen retrieval with TE buffer pH 9.0 is recommended, though citrate buffer pH 6.0 may be used as an alternative

  • For immunofluorescence applications, cell type-specific protocols may be necessary (positive results have been reported in HepG2 cells for Aldehyde oxidase antibodies)

• Control for post-translational modifications: Different tissues may exhibit tissue-specific post-translational modifications of AOX1, affecting antibody recognition.

• Account for isoform expression: If multiple AOX1 isoforms exist, their expression patterns may vary by tissue. Research has shown that mutations in AOX1a can produce truncated proteins of different lengths (e.g., 62 aa vs. 48 aa compared to the wild-type 358 aa protein) , which may affect antibody recognition.

What approaches can be used to analyze AOX1 expression changes under different experimental conditions?

Analyzing AOX1 expression under various experimental conditions requires robust analytical approaches:

• Quantitative Western Blot analysis: For accurate quantification of AOX1 protein levels, normalize to appropriate loading controls and use replicate samples. Research has shown significant differences in AOX protein levels between wild-type and mutant plants .

• Functional assays: Complement protein level analysis with functional assays. For Alternative Oxidase, oxygen consumption rates provide a functional readout. Data shows:

These values represent the 100% germination rate condition .

• Time-course experiments: AOX1 expression can change over time, particularly during developmental processes. Research on fruit ripening shows that "metabolic consequences of the AOX1a mutation at early ripening stages were still modest," but later stages show clear separation between wild-type and mutant samples in metabolic profile analysis .

• Metabolic profiling: Changes in AOX1 activity can have widespread metabolic effects. Research has identified specific metabolites affected by AOX1a mutations, including "2-amino-adipic acid, beta-alanine, glucarate, quinate, Succ, Tyr, and Val" .

• Statistical analysis: Apply appropriate statistical methods to determine significant differences between experimental conditions.

How do I design experiments to distinguish between different AOX isoforms using antibodies?

Distinguishing between AOX isoforms requires careful experimental design:

• Epitope selection: Design or select antibodies that target unique regions of specific AOX isoforms. Computational approaches can help identify distinctive epitopes. Research on antibody design has shown that "identification of different binding modes, each associated with a particular ligand" can help distinguish between similar targets .

• Validation with knockout models: Use knockout or knockdown models of specific isoforms as controls. Research with aox1a knockout mutants demonstrated clear differences in protein detection between wild-type and mutant plants .

• Combined approaches: Employ multiple detection methods:

  • Western blot for molecular weight differentiation

  • Immunoprecipitation followed by mass spectrometry for definitive identification

  • RT-qPCR to correlate protein levels with isoform-specific mRNA expression

• Biophysics-informed modeling: Recent research suggests that "biophysics-informed modeling and extensive selection experiments" can be applied "for designing proteins with desired physical properties" , which could be adapted for developing isoform-specific antibodies.

• Cross-specificity testing: Test antibodies against recombinant versions of each isoform to establish specificity profiles.

What are the advantages and limitations of different detection methods when using AOX1 antibodies?

Different detection methods offer distinct advantages and limitations for AOX1 research:

• Western Blot:

  • Advantages: Provides molecular weight information; semi-quantitative; widely accessible

  • Limitations: May not detect low abundance proteins; potential cross-reactivity

  • Application notes: For Aldehyde oxidase antibody, a 1:20 dilution is recommended

• Immunohistochemistry (IHC):

  • Advantages: Preserves tissue architecture; localizes protein in cellular context

  • Limitations: Requires optimization of antigen retrieval; potential background issues

  • Application notes: For human hepatocirrhosis tissue, antigen retrieval with TE buffer pH 9.0 or citrate buffer pH 6.0 is recommended

• Immunofluorescence (IF):

  • Advantages: High sensitivity; allows co-localization studies; quantifiable

  • Limitations: Potential autofluorescence; photobleaching

  • Application notes: Positive results have been reported in HepG2 cells for Aldehyde oxidase antibodies

• Immunoprecipitation (IP):

  • Advantages: Enriches target protein; can identify interaction partners

  • Limitations: May disrupt weak interactions; potential antibody interference

  • Application notes: Successful IP has been reported using mouse lung tissue for Aldehyde oxidase antibodies

• ELISA:

  • Advantages: Highly quantitative; high throughput; sensitive

  • Limitations: May not detect all protein conformations; requires antibody pairs

  • Application notes: Requires validation for specific AOX1 research applications

How can I incorporate AOX1 antibodies into multi-omics research approaches?

Integrating AOX1 antibody-based methods into multi-omics research requires strategic experimental design:

• Proteomics integration:

  • Use AOX1 antibodies for immunoprecipitation followed by mass spectrometry to identify interaction partners

  • Combine with whole proteome analysis to understand system-wide effects of AOX1 modulation

• Metabolomics correlation:

  • Research has shown that AOX1a mutations lead to significant metabolic changes. Hierarchical clustering analysis of metabolite data revealed "two major clusters separated the metabolites displaying increases along ripening from those displaying decreases and/or minor changes"

  • Specific metabolites affected by AOX1a mutations include "2-amino-adipic acid, beta-alanine, glucarate, quinate, Succ, Tyr, and Val"

• Transcriptomics correlation:

  • Compare protein levels detected by AOX1 antibodies with mRNA expression data

  • Assess post-transcriptional regulation by identifying discordances between protein and mRNA levels

• Functional assays:

  • Correlate AOX1 protein levels with functional readouts such as oxygen consumption rates

  • Integrate with phenotypic data, such as developmental timing differences observed in aox1a mutants

• Systems biology approaches:

  • Use computational models to integrate multi-omics data and predict system-wide effects of AOX1 modulation

  • Design validation experiments using AOX1 antibodies to test model predictions

How do I correctly analyze and interpret Western blot results using AOX1 antibodies?

Proper analysis of Western blot results is crucial for accurate interpretation:

• Molecular weight verification:

  • For Alternative Oxidase 1a, the expected full-length protein is approximately 358 amino acids, while mutant truncated versions may be significantly smaller (e.g., 62 aa or 48 aa)

  • Confirm that observed bands match expected molecular weights

• Quantification approach:

  • Use digital image analysis software for densitometry

  • Normalize to appropriate loading controls

  • Include technical and biological replicates

• Sensitivity considerations:

  • AOX1 expression can vary significantly between tissues and conditions

  • Lower protein abundance may require longer exposure times or more sensitive detection methods

• Control interpretation:

  • Include positive controls (tissues known to express AOX1)

  • Use negative controls such as knockout models where available

  • Research has shown clear differences in AOX protein detection between wild-type and aox1a mutant plants

• Troubleshooting unexpected results:

  • Multiple bands may indicate degradation, isoforms, or post-translational modifications

  • Absence of signal may require optimization of extraction conditions or antibody concentration

What statistical approaches are recommended for analyzing quantitative data from AOX1 immunoassays?

• Descriptive statistics:

  • Report mean ± standard deviation or standard error

  • Include sample size and number of replicates

  • Research on oxygen consumption rates reports values as "44.4 ± 3.2 nmolO₂·min⁻¹mg⁻¹ Protein" for wild-type and "17.3 ± 1.9 nmolO₂·min⁻¹mg⁻¹ Protein" for OsAOX1a-RNAi samples with NADH substrate

• Inferential statistics:

  • For comparing two groups (e.g., wild-type vs. mutant), use t-tests or non-parametric alternatives

  • For multiple groups or conditions, use ANOVA with appropriate post-hoc tests

  • Research on developmental timing reported "both aox1a mutant lines displayed a slight delay on the first fruit appearance as compared to WT plants (Fig. 4A, P=0.049 for a1.1 and P=0.051 for a2.3)"

• Multiple testing correction:

  • When analyzing multiple parameters, apply appropriate corrections (e.g., Bonferroni, FDR)

  • Consider using multivariate approaches for complex datasets

• Regression analysis:

  • For dose-response or time-course experiments, use appropriate regression models

  • Report R² values and confidence intervals

• Visualization approaches:

  • Use box plots, scatter plots, or bar graphs with error bars

  • Consider hierarchical clustering for complex datasets, as used in metabolite analysis of AOX1a mutants

How can I reconcile conflicting results between different detection methods using AOX1 antibodies?

Addressing conflicting results requires systematic troubleshooting:

• Method-specific limitations assessment:

  • Western blot may detect denatured epitopes not accessible in native conditions

  • Immunohistochemistry may be affected by fixation and antigen retrieval methods

  • Different methods vary in sensitivity and specificity

• Antibody characteristics evaluation:

  • Monoclonal and polyclonal antibodies may recognize different epitopes

  • Examine if antibodies were raised against different regions of the protein

  • Consider if antibodies recognize different post-translational modifications

• Experimental validation approaches:

  • Use knockout models to confirm specificity across methods

  • Research with aox1a knockout mutants demonstrated absence of protein detection, confirming antibody specificity

  • Test recombinant proteins to establish detection limits for each method

• Integrated data analysis:

  • Combine results from multiple detection methods for a more complete picture

  • Use orthogonal methods (e.g., mass spectrometry) to validate antibody-based findings

  • Consider computational approaches to integrate data from different methods

• Reporting guidelines:

  • Clearly report all experimental conditions for each method

  • Discuss potential reasons for discrepancies

  • Present conflicting results transparently rather than selectively reporting

How can antibody-oligonucleotide conjugate technology be applied to AOX1 research?

Antibody-oligonucleotide conjugates (AOCs) represent an innovative approach for targeted delivery that could be applied to AOX1 research:

• Targeted delivery applications:

  • AOCs can achieve ">15-fold higher concentration to muscle tissue than unconjugated siRNA"

  • This technology could be used to deliver AOX1-targeting oligonucleotides to specific tissues

• Tissue-specific modulation:

  • Research shows that "αTfR1 AOCs achieved a > 15-fold higher concentration to muscle tissue than unconjugated siRNA" and "A single dose of an αTfR1 conjugated to an siRNA against... mRNA reduction in skeletal muscle was >75-fold less than in systemic tissues"

  • This suggests potential for tissue-specific modulation of AOX1 expression

• Cross-species translation:

  • AOC technology has shown efficacy "across various oligonucleotide modalities" and "PKPD properties translated to higher species"

  • This suggests potential for translational AOX1 research across model organisms

• Conjugation considerations:

  • When designing AOCs, it's important to note that "conjugation of an oligonucleotide had no impact on antibody binding to the target receptor"

  • This suggests that AOX1 antibodies could maintain specificity when conjugated to oligonucleotides

• Therapeutic implications:

  • For diseases involving AOX1 dysregulation, AOC technology could provide "promise for a new class of oligonucleotide therapeutics"

What computational approaches can assist in designing highly specific AOX1 antibodies?

Computational methods offer powerful tools for designing specific AOX1 antibodies:

• Biophysics-informed modeling:

  • Research has demonstrated that "biophysics-informed modeling and extensive selection experiments" can be applied to antibody design

  • These approaches could help design antibodies specific to particular AOX1 isoforms or regions

• Specificity profiling:

  • Computational approaches can "demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles"

  • This could be used to create antibodies "either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands"

• Mode identification:

  • The "identification of different binding modes, each associated with a particular ligand" can help distinguish between similar targets

  • This approach could be valuable for distinguishing between closely related AOX isoforms

• Machine learning integration:

  • Recent advances combine experimental data with machine learning to predict antibody-antigen interactions

  • These approaches could optimize AOX1 antibody design before experimental validation

• Epitope mapping:

  • Computational epitope mapping can identify unique regions for targeting specific AOX1 variants

  • This is particularly valuable for distinguishing between closely related isoforms