AHP1 Antibody

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Description

Research Findings on AHP1 Antibody Applications

AHP1 antibodies are pivotal in studying oxidative stress pathways and protein interactions:

  • Redox-Dependent Urmylation: Anti-AHP1 antibodies identified urmylated AHP1 in S. cerevisiae under non-reducing conditions, revealing a ~72 kDa oxidized dimer conjugated to Urm1 .

  • Oxidative Stress Sensitivity: Strains lacking AHP1 show heightened sensitivity to lipid peroxidation (e.g., methyl linolenic acid) but not to simple hydroperoxides like tBOOH (tert-butyl hydroperoxide) .

  • Genetic Interactions: AHP1 deletion synergizes with GPX3 (glutathione peroxidase) disruption, exacerbating tBOOH sensitivity, highlighting its role in peroxide detoxification .

Table 2: Key Studies Using AHP1 Antibodies

Study FocusMethodologyKey OutcomeReference
Urmylation of AHP1Electrophoretic mobility shift assays (EMSA)Identified Urm1- AHP1 conjugates under oxidative stress
Peroxidase ActivityNADPH oxidation assaysConfirmed Cys-31/Cys-62 as essential for tBOOH reduction
Genetic KnockoutsPhenotypic screeningAHP1Δ strains showed 4-fold increased sensitivity to lipid peroxidation

AHP1 Antibody Validation and Specificity

Rigorous validation ensures reliable detection of AHP1 in diverse contexts:

  • Epitope Mapping: Antibodies targeting linear epitopes (e.g., residues 58–95) show high specificity in immunoblotting .

  • Redox-Sensitive Detection: Non-reducing SDS-PAGE distinguishes oxidized (disulfide-linked) AHP1 dimers from reduced monomers .

  • Cross-Reactivity: Anti-AHP1 antibodies do not cross-react with related peroxiredoxins (e.g., Tsa1) in yeast lysates .

Clinical and Biotechnological Implications

While AHP1 research is primarily foundational, insights inform broader applications:

  • Antibody Engineering: Lessons from AHP1’s redox-regulated urmylation guide therapeutic antibody design, such as optimizing stability under oxidative conditions .

  • Disease Models: AHP1 homologs in humans (e.g., PRDX6) are implicated in cancer and neurodegenerative diseases, making AHP1 antibodies relevant for translational studies .

Challenges and Future Directions

  • Antibody Validation: As highlighted in studies like , inconsistent antibody performance in immunohistochemistry (IHC) underscores the need for multi-method validation.

  • Functional Redundancy: AHP1’s overlap with other peroxiredoxins (e.g., Tsa1) complicates phenotype interpretation in knockout models .

Product Specs

Buffer
Preservative: 0.03% ProClin 300; Constituents: 50% Glycerol, 0.01M PBS, pH 7.4
Form
Liquid
Lead Time
14-16 weeks (Made-to-order)
Synonyms
AHP1 antibody; ATHP3 antibody; At3g21510 antibody; MIL23.8 antibody; Histidine-containing phosphotransfer protein 1 antibody
Target Names
AHP1
Uniprot No.

Target Background

Function
AHP1 antibodies target proteins that function as two-component phosphorelay mediators. These mediators act as a bridge between cytokinin sensor histidine kinases and response regulators (B-type ARRs), playing a crucial role in propagating cytokinin signal transduction via the multistep His-to-Asp phosphorelay.
Gene References Into Functions
  • Studies have shown that mutating specific interface residues of ARR4 either abolishes or destabilizes its interaction with AHP1. Specifically, D45A and Y96A mutations weakened the AHP1 interaction and resulted in weaker rescue of root elongation in hextuple mutants. PMID: 25643735
  • The first crystal structure of a plant histidine kinase receiver domain (RD), AHK5(RD), complexed with its cognate phosphotransfer protein, AHP1, has been determined. PMID: 23132142
  • Evidence demonstrates an interaction between ETR1 and AHP1. PMID: 21912672
  • Fluorescence polarization studies confirm a specific interaction between ETR1 and the histidine-containing phosphotransfer protein AHP1, supporting the involvement of a phosphorelay module in ethylene signaling. PMID: 18384742
Database Links

KEGG: ath:AT3G21510

STRING: 3702.AT3G21510.1

UniGene: At.24157

Subcellular Location
Cytoplasm, cytosol. Nucleus.
Tissue Specificity
Strongly expressed in roots.

Q&A

Alpha-1 antitrypsin (A1AT) is a serine protease inhibitor with emerging roles in immunomodulation and metabolic regulation, showing therapeutic potential in chronic inflammatory diseases and diabetes management through mechanisms like cytokine regulation and anti-apoptotic effects . Below is a structured FAQ addressing key research considerations for AHP1 antibody studies in plant biology, synthesized from peer-reviewed methodologies and technical specifications.

Advanced Research Challenges

How to resolve contradictory results in cytokinin signaling studies using AHP1 antibodies?

  • Experimental Design:

    • Compare diurnal sampling times (AHP1 expression fluctuates circadianly)

    • Use 3+ biological replicates to address natural variability

  • Data Interpretation:

    • Apply Shapiro-Wilk normality test (α=0.05) before selecting parametric tests

    • For non-normal distributions, use finite mixture models to identify latent populations

What methods validate antibody specificity against homologous AHP proteins?

  • BLAST Analysis:

    • Target peptide (N-terminal 14aa): 100% homology in Brassica spp., 80-99% in Solanaceae

  • Cross-Reactivity Testing:

    • Test on ahp1/ahp2/ahp3 Arabidopsis T-DNA mutants

  • Functional Assays:

    • Measure phosphotransfer activity in recombinant AHP1 vs AHP2 (Δactivity <15% indicates cross-reactivity)

Methodological Considerations Table

ChallengeSolutionKey Parameters
Low SignalIncrease antigen retrieval time20 min citrate buffer (pH 6.0) at 95°C
Background NoiseAlternative blocking: 3% BSA + 0.1% Tween-20Reduce non-specific binding by 40%
Quantitative AnalysisChemiluminescence with CCD calibrationLinear detection range: 2.5-25 ng recombinant protein

Statistical Analysis Guidance

For proteomic studies:

  • Use Spearman’s correlation to address antibody data non-linearity (average ρ=0.312 in multiplex assays)

  • Apply Benjamini-Hochberg FDR correction (5% threshold) to mitigate false positives in multi-antibody panels

  • Super-Learner classifiers improve predictive power (AUC=0.801 vs 0.713 in single-analyte models)

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