At1g69460 Antibody

What is the At1g69460 gene and what is known about its function in Arabidopsis?

At1g69460 encodes a 184-amino acid protein in Arabidopsis thaliana, with the following sequence: MFLQSQKLWTMLLILAIWSPISHSLHFDLHSGRTKCIAEDIKSNSMTVGKYNIDNPHEGQALPQTHKISVKVTSNSGNNYHHAEQVDSGQFAFSAVEAGDYMACFTAVDHKPEVSLSIDFEWKTGVQSKSWANVAKKSQVEVMEFEVKSLLDTVNSIHEEMYYLRDRYFSFSIVWYYEDIVYMT . While the specific function remains under investigation, research suggests it may be involved in plant stress responses, particularly drought stress pathways, as indicated by transcriptome analysis studies of Arabidopsis under stress conditions . The protein contains domains that suggest involvement in cell wall interactions, which may explain its response to environmental stresses.

How does At1g69460 expression change under different stress conditions?

Transcriptome profiling studies have shown that At1g69460 expression is responsive to various abiotic stresses. According to the AtGenExpress global stress expression dataset, At1g69460 shows altered expression patterns under drought, salt, and osmotic stress conditions . In drought stress experiments specifically, the gene shows significant upregulation in certain tissues. Expression analysis from transcriptome studies shows that At1g69460 may be involved in stress-responsive pathways that affect cell wall integrity and modification . This pattern is consistent with other stress-responsive genes that contribute to plant adaptation under adverse environmental conditions.

What types of At1g69460 antibodies are currently available for research?

Current commercially available antibodies for At1g69460 include several mouse monoclonal antibodies targeting different regions of the protein:

Each antibody combination is designed from synthetic peptides representing different regions of the target protein and has been tested with ELISA titers of approximately 10,000, corresponding to detection sensitivity of about 1 ng of target protein in Western blot applications .

What are the recommended protocols for using At1g69460 antibodies in Western blot experiments?

For optimal Western blot results with At1g69460 antibodies:

• Sample preparation: Extract total protein from Arabidopsis tissues using a buffer containing 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1% Triton X-100, 0.5% sodium deoxycholate, and protease inhibitor cocktail.

• Protein separation: Use 12-15% SDS-PAGE gels for optimal resolution of the 184-amino acid protein.

• Transfer conditions: Transfer to PVDF membrane (preferred over nitrocellulose for plant proteins) at 100V for 1 hour in standard transfer buffer.

• Blocking: Block membranes with 5% non-fat dry milk in TBST for 1 hour at room temperature.

• Primary antibody incubation: Dilute antibody to 1 μg/ml in 1% BSA-TBST and incubate overnight at 4°C .

• Secondary antibody: Use anti-mouse IgG conjugated with horseradish peroxidase at 1:5000 dilution for 1 hour at room temperature.

• Detection: Visualize with chemiluminescent substrate like Super Signal West Dura or equivalent.

This protocol is adapted from studies using similar plant protein antibodies and optimized based on the characteristics of the At1g69460 protein.

How can I optimize immunofluorescence experiments using At1g69460 antibodies?

For successful immunofluorescence with At1g69460 antibodies in plant tissues:

• Tissue fixation: Fix tissue samples in 4% paraformaldehyde in PBS for 2 hours at room temperature.

• Permeabilization: After washing, treat with 0.1% Triton X-100 for 15 minutes to allow antibody penetration.

• Antigen retrieval: For better epitope accessibility, treat sections with 10 mM sodium citrate buffer (pH 6.0) at 95°C for 10 minutes.

• Blocking: Block with 5% normal goat serum and 2% BSA in PBS for 1 hour at room temperature.

• Primary antibody: Use the antibody at 2-5 μg/ml in blocking solution and incubate overnight at 4°C.

• Secondary antibody: Apply fluorophore-conjugated anti-mouse IgG (Alexa Fluor 488 or similar) at 1:200-1:500 dilution for 1 hour at room temperature.

• Counterstaining: DAPI (1 μg/ml) for nucleus visualization for 10 minutes.

• Controls: Always include a negative control (secondary antibody only) and, if possible, a peptide competition assay to confirm specificity.

This protocol is informed by standard practices in plant immunohistochemistry and adapted for the specific properties of At1g69460 antibodies.

What methods should be used to validate the specificity of At1g69460 antibodies?

To rigorously validate At1g69460 antibodies:

• Western blot analysis with recombinant At1g69460 protein and Arabidopsis total protein extract to confirm expected molecular weight (approximately 20 kDa calculated from the 184-amino acid sequence).

• Immunoprecipitation followed by mass spectrometry to confirm the identity of the precipitated protein.

• Peptide competition assay: Pre-incubate antibody with excess synthetic peptide antigen before applying to Western blot or immunostaining to demonstrate signal reduction.

• Knockout/knockdown validation: Compare antibody reactivity in wild-type vs. At1g69460 knockout or RNAi knockdown lines, expecting reduced or absent signal in the mutant.

• Cross-reactivity testing: Test against closely related proteins to ensure specificity.

• Multiple antibody approach: Compare results using antibodies targeting different epitopes (N-terminus, C-terminus, and middle region) .

• Dot blot analysis using purified peptides corresponding to different regions of the protein to confirm epitope recognition .

This comprehensive validation strategy aligns with FDA guidance on antibody validation (search result ) and established practices in the plant research community.

How can At1g69460 antibodies be used to study protein-protein interactions in stress response pathways?

For studying At1g69460 protein interactions in stress response:

• Co-immunoprecipitation (Co-IP): Use At1g69460 antibodies to pull down the protein complex from stress-treated and control Arabidopsis tissues. Analyze precipitated proteins by mass spectrometry to identify interaction partners. This approach has successfully identified protein complexes in Arabidopsis stress responses .

• Proximity labeling: Combine At1g69460 antibodies with BioID or APEX2 proximity labeling systems to identify proteins in close proximity under different stress conditions.

• Förster Resonance Energy Transfer (FRET): Use fluorescently labeled At1g69460 antibodies to detect protein interactions in vivo through FRET microscopy.

• Bimolecular Fluorescence Complementation (BiFC): Though not antibody-based, can complement antibody studies to visualize protein interactions in planta.

• Chromatin Immunoprecipitation (ChIP): If At1g69460 has nuclear functions, ChIP with At1g69460 antibodies can identify DNA binding sites or chromatin associations.

Based on studies of other stress-responsive proteins in Arabidopsis, At1g69460 might interact with components of drought stress signaling pathways, potentially including transcription factors like RGL3 that respond to multiple stresses including drought, salt, and hormone treatments .

What approaches are effective for studying At1g69460 post-translational modifications using specific antibodies?

To investigate post-translational modifications (PTMs) of At1g69460:

• Modification-specific antibodies: Develop antibodies against predicted PTMs (phosphorylation, glycosylation, etc.) based on sequence analysis and molecular weight shifts observed in Western blots.

• Two-dimensional electrophoresis: Combine with Western blotting using At1g69460 antibodies to separate protein isoforms with different PTMs.

• Immunoprecipitation followed by mass spectrometry: Use At1g69460 antibodies to purify the protein from plants under different conditions, then analyze by MS to identify PTMs.

• Phosphatase treatment assay: Treat protein extracts with phosphatase before Western blotting with At1g69460 antibodies to identify phosphorylation-dependent mobility shifts.

• PTM-specific staining: Combine with immunoblotting, such as Pro-Q Diamond for phosphoproteins or periodic acid-Schiff for glycoproteins, followed by At1g69460 antibody detection.

• In vitro modification assays: Use purified At1g69460 (immunoprecipitated with antibodies) to test specific enzymes' ability to modify the protein.

Since many stress-responsive proteins in Arabidopsis undergo phosphorylation during signaling cascades, this is a likely PTM to investigate for At1g69460.

How can integrative multi-omics approaches incorporate At1g69460 antibodies for comprehensive stress response studies?

Integrating At1g69460 antibody-based techniques with multi-omics approaches:

• Immunoprecipitation coupled with RNA-seq (RIP-seq): If At1g69460 binds RNA, use antibodies to isolate RNA-protein complexes followed by sequencing to identify associated transcripts under stress conditions.

• Proteomics with selective enrichment: Use At1g69460 antibodies for targeted proteomics to track protein levels during stress, complementing global proteomics data.

• Spatial transcriptomics with immunofluorescence: Combine spatial transcriptomics with At1g69460 immunolocalization to correlate protein presence with local transcriptional changes.

• ChIP-seq integration: If At1g69460 is involved in transcriptional regulation, combine ChIP-seq data with transcriptome data to identify regulatory networks.

• Antibody-based affinity purification for interactome studies: Use At1g69460 antibodies to purify protein complexes for mass spectrometry, then integrate with transcriptome data to build functional networks.

• Single-cell approaches: Combine single-cell transcriptomics with immunofluorescence to understand cell-specific roles of At1g69460.

• Systems biology modeling: Use antibody-validated protein data to constrain mathematical models of stress response pathways.

This integrative approach is modeled after successful studies in Arabidopsis that identified gene regulatory networks in stress responses, such as those described by Caldo et al. (2004) and DeCook et al. (2006) .

How should researchers address non-specific binding issues with At1g69460 antibodies?

To minimize non-specific binding:

• Optimization of blocking conditions:

  • Test different blocking agents (5% milk, 3-5% BSA, or commercial blocking buffers)

  • Extend blocking time to 2 hours or overnight at 4°C

  • Add 0.1-0.5% Tween-20 to reduce hydrophobic interactions

• Antibody dilution optimization:

  • Perform titration experiments (1:500 to 1:5000) to determine optimal concentration

  • Prepare antibodies in fresh blocking buffer with 0.05% Tween-20

• Sample preparation improvements:

  • Include additional washing steps with higher salt concentration (up to 500 mM NaCl)

  • Add 0.1% SDS to wash buffers for Western blots

  • Consider using gradient gels for better protein separation

• Cross-adsorption technique:

  • Pre-incubate antibodies with Arabidopsis protein extract from At1g69460 knockout/knockdown plants

  • Use this pre-adsorbed antibody for experiments to reduce non-specific binding

• Alternative detection systems:

  • Try different secondary antibodies or detection methods

  • Consider using monovalent Fab fragments instead of complete IgG

• Stringent washing protocols:

  • Increase number of washes (5-6 times, 10 minutes each)

  • Use both high-salt and low-salt wash buffers alternately

These approaches align with recommendations from immunogenicity testing guidance documents and proven practices in plant antibody research.

What are the most common pitfalls when using At1g69460 antibodies in different experimental contexts, and how can they be avoided?

Common pitfalls and solutions:

Additionally, when working with Arabidopsis tissues specifically, include controls to account for the presence of endogenous peroxidases and phosphatases that may interfere with detection systems.

How do experimental conditions affect epitope accessibility when using different At1g69460 antibody types?

Epitope accessibility issues and solutions for different At1g69460 antibody types:

N-terminal antibodies (X-C0Z267-N):

• May be affected by signal peptide cleavage in mature proteins

• Solution: Use multiple antibodies targeting different N-terminal regions

• For fixed tissues: Extend antigen retrieval time to 15-20 minutes

• For native conditions: Avoid detergents that may alter N-terminal conformation

C-terminal antibodies (X-C0Z267-C):

• May be affected by protein interactions or PTMs at the C-terminus

• Solution: Test multiple extraction buffers with different detergent concentrations

• For immunoprecipitation: Consider mild cross-linking to preserve interactions

• For Western blots: Compare reducing vs. non-reducing conditions

Middle region antibodies (X-C0Z267-M):

• Generally more reliable but may be affected by protein folding

• Solution: Include denaturants like urea in extraction buffers

• For immunofluorescence: Try different fixation methods (paraformaldehyde vs. methanol)

• For flow cytometry: Test permeabilization with saponin vs. Triton X-100

General considerations:

• Membrane proteins often require specialized extraction methods with appropriate detergents

• Heat-induced epitope retrieval may be necessary for formalin-fixed samples

• If the protein forms complexes, native conditions may mask epitopes

• Consider using peptide competition assays with each antibody type to confirm specificity

These recommendations are based on general principles of epitope accessibility in plant proteins and should be optimized for specific experimental conditions.

How can At1g69460 antibodies be used to investigate protein dynamics during drought stress response in Arabidopsis?

At1g69460 antibodies can be applied to track protein dynamics during drought stress through:

• Time-course protein expression analysis:

  • Collect Arabidopsis tissue samples at multiple timepoints during drought exposure (0h, 6h, 12h, 24h, 48h, 72h)

  • Perform Western blot analysis using At1g69460 antibodies to quantify protein abundance changes

  • Correlate protein expression with transcriptome data from the AtGenExpress drought stress dataset

• Cellular localization changes:

  • Use immunofluorescence microscopy with At1g69460 antibodies on fixed tissue sections

  • Compare subcellular localization between well-watered and drought-stressed plants

  • Combine with organelle markers to track potential translocation during stress

• Protein stability assessment:

  • Perform cycloheximide chase experiments with At1g69460 antibody detection

  • Compare protein half-life under normal and stress conditions

  • Use proteasome inhibitors to determine if drought affects protein degradation pathways

• Protein complex dynamics:

  • Use immunoprecipitation with At1g69460 antibodies followed by mass spectrometry

  • Compare interacting partners under well-watered vs. drought conditions

  • Validate key interactions with reciprocal co-immunoprecipitation

• PTM profiling during stress progression:

  • Analyze At1g69460 by 2D electrophoresis followed by Western blotting

  • Identify drought-induced PTMs by mass spectrometry after immunoprecipitation

  • Develop phospho-specific antibodies if phosphorylation sites are identified

This experimental framework follows approaches used in previous drought stress studies in Arabidopsis that revealed dynamic changes in protein expression, localization, and modifications .

What comparative approaches can be used with At1g69460 antibodies to study functional conservation across plant species?

To study cross-species functional conservation:

• Cross-reactivity testing and optimization:

  • Test At1g69460 antibodies against protein extracts from related species

  • Perform epitope sequence alignment across species to predict cross-reactivity

  • Optimize antibody concentration and conditions for each species

• Comparative expression analysis:

  • Analyze At1g69460 homologs across model plants (Arabidopsis, rice, maize) under identical stress conditions

  • Use Western blotting to compare protein abundance patterns

  • Normalize with evolutionary conserved proteins (e.g., actin, tubulin) for cross-species comparison

• Conservation of subcellular localization:

  • Perform immunofluorescence in multiple species to compare localization patterns

  • Co-localize with conserved organelle markers to verify similar targeting

• Conservation of protein interactions:

  • Conduct immunoprecipitation followed by mass spectrometry in different species

  • Identify conserved interaction partners using comparative proteomics

  • Validate key interactions in each species with candidate approach methods

• Functional complementation studies:

  • Express At1g69460 homologs from different species in Arabidopsis mutants

  • Use antibodies to confirm expression of the transgene

  • Correlate protein expression levels with phenotypic rescue

• Evolutionary proteomics approach:

  • Compare post-translational modifications across species using immunoprecipitation and mass spectrometry

  • Identify conserved regulatory sites and species-specific modifications

This approach builds on methods used in evolutionary studies of plant proteins and can reveal both conserved and divergent aspects of At1g69460 function across plant lineages.

How can At1g69460 antibodies contribute to understanding the crosstalk between drought stress and hormone signaling pathways?

To investigate stress-hormone crosstalk:

• Hormone treatment effects on At1g69460:

  • Treat Arabidopsis plants with different hormones (ABA, JA, GA, ethylene)

  • Quantify At1g69460 protein levels by Western blotting

  • Compare with transcriptome data showing hormone responsiveness of related genes

• Combined stress and hormone treatments:

  • Apply drought stress with/without hormone treatments or inhibitors

  • Use At1g69460 antibodies to track protein abundance changes

  • Correlate with physiological responses to identify functional relationships

• Protein interaction network under hormone influence:

  • Perform immunoprecipitation with At1g69460 antibodies after hormone treatments

  • Identify hormone-dependent interaction partners

  • Focus on known components of hormone signaling pathways, especially those related to DELLA proteins and RGL3

• Co-localization with hormone signaling components:

  • Use dual immunofluorescence with At1g69460 antibodies and antibodies against key hormone signaling proteins

  • Analyze co-localization patterns under different stress/hormone conditions

  • Focus on potential associations with nuclear-localized transcription factors

• Chromatin dynamics and transcriptional regulation:

  • If At1g69460 functions in transcriptional regulation, use ChIP with At1g69460 antibodies

  • Compare DNA binding patterns under different hormone treatments

  • Integrate with transcriptome data to build gene regulatory networks

Based on studies of RGL3 and other stress-responsive genes in Arabidopsis , At1g69460 may be involved in the crosstalk between drought stress and hormone signaling pathways, particularly abscisic acid, jasmonic acid, and gibberellin responses.

What statistical approaches are most appropriate for analyzing quantitative data from At1g69460 antibody-based experiments?

For robust statistical analysis of At1g69460 antibody data:

• Western blot quantification:

  • Use biological replicates (n≥3) and technical replicates (n≥2)

  • Normalize to loading controls (GAPDH, actin, total protein)

  • Apply ANOVA with post-hoc tests (Tukey's HSD) for multiple condition comparisons

  • Use linear mixed-effects models for time-course experiments to account for repeated measures

• Immunofluorescence intensity analysis:

  • Collect data from multiple cells (n>30) across at least 3 biological replicates

  • Use normalized fluorescence intensity (background subtraction, normalization to reference channel)

  • Apply non-parametric tests (Mann-Whitney U) if normality cannot be assumed

  • For subcellular localization changes, use co-localization coefficients (Pearson's, Mander's)

• Protein interaction studies:

  • For co-immunoprecipitation, use spectral counting or intensity-based methods for semi-quantitative analysis

  • Apply Fisher's exact test to determine significant interactors

  • Use SAINT (Significance Analysis of INTeractome) algorithm for scoring interactions

• Multi-condition experiments:

  • Consider principal component analysis (PCA) to identify major sources of variation

  • For time-series data, apply functional data analysis techniques

  • Calculate false discovery rates for multiple testing correction

• Integration with other -omics data:

  • Use correlation analyses (Pearson, Spearman) to link protein levels with transcript abundance

  • Apply network-based statistics for pathway analysis

  • Consider Bayesian approaches for integrating multiple data types

These statistical approaches align with best practices described for plant molecular biology studies and should be selected based on experimental design, data distribution, and specific research questions.

How can researchers effectively integrate and visualize At1g69460 antibody-derived data with transcriptomic datasets for comprehensive pathway analysis?

For integrated data visualization and analysis:

• Correlation analysis visualization:

  • Create scatter plots of At1g69460 protein levels vs. transcript levels across conditions

  • Generate heatmaps clustering samples by both protein and transcript patterns

  • Calculate and visualize Pearson or Spearman correlation coefficients

• Time-course integration:

  • Plot protein and transcript levels on the same graph with dual y-axes

  • Create phase-plane plots to visualize trajectory of protein vs. mRNA changes

  • Implement time-delay correlation analysis to identify regulatory relationships

• Pathway mapping approaches:

  • Overlay protein expression data on known stress-response pathways

  • Use Cytoscape or similar tools to visualize protein-protein interaction networks

  • Implement pathway enrichment visualization with tools like GSEA or MapMan

• Multi-level data visualization:

  • Develop Sankey diagrams to show flow of information from gene to transcript to protein

  • Create multi-panel visualizations showing protein localization alongside expression data

  • Implement 3D visualizations combining spatial, temporal, and quantitative dimensions

• Interactive visualization tools:

  • Develop R Shiny applications for exploring relationships across datasets

  • Implement clickable network graphs for drilling down into specific interactions

  • Create condition-comparison tools to visualize differential responses

• Integration with public datasets:

  • Compare At1g69460 protein data with publicly available stress transcriptome datasets like AtGenExpress

  • Visualize alongside other stress-responsive proteins identified in previous studies

  • Create comparative visualizations across multiple plant species

These approaches build on visualization methods demonstrated in plant transcriptomics studies and modern data integration techniques to provide comprehensive views of At1g69460 function in stress response pathways.

What criteria should be applied to interpret conflicting results between protein abundance (antibody-based) and transcript levels of At1g69460?

When facing protein-transcript discrepancies:

• Temporal dynamics assessment:

  • Evaluate time-course data to identify potential time lags between transcription and translation

  • Consider protein half-life vs. mRNA stability

  • Look for evidence of delayed protein accumulation following transcriptional peaks

• Post-transcriptional regulation:

  • Investigate potential miRNA regulation of At1g69460 transcripts

  • Consider alternative splicing that might affect antibody detection

  • Examine 5' and 3' UTR features that might influence translation efficiency

• Post-translational regulation mechanisms:

  • Assess protein stability under different conditions

  • Investigate PTM patterns that might affect antibody recognition

  • Consider compartmentalization that could affect protein extraction efficiency

• Technical validation:

  • Verify antibody specificity under each experimental condition

  • Use multiple antibodies targeting different epitopes

  • Perform spike-in controls to verify quantification accuracy

  • Test different protein extraction methods to ensure complete protein recovery

• Biological validation approaches:

  • Use transgenic lines with tagged At1g69460 for independent verification

  • Employ direct measurement of protein synthesis rates (e.g., puromycin incorporation)

  • Conduct polysome profiling to assess translation efficiency

• Integration of multiple data types:

  • Consider proteome-wide patterns to identify global vs. gene-specific discrepancies

  • Analyze related genes in the same pathway for consistent or discordant patterns

  • Examine protein-protein interactions that might affect stability or localization

This analytical framework draws on approaches used in integrative studies of gene expression and protein abundance in plants , providing a systematic way to resolve apparent contradictions between transcript and protein data.