ALDH16A1 Antibody

What are the key considerations when selecting an ALDH16A1 antibody for immunohistochemistry in renal tissue studies?

When selecting antibodies for renal immunohistochemistry, prioritize clones validated for:

• Epitope localization: Antibodies targeting the N-terminal (e.g., Aviva Systems Biology ARP67350_P050) show consistent reactivity in proximal/distal tubules .

• Species cross-reactivity: Confirm reactivity with experimental models (e.g., BosterBio’s antibody validated for human/mouse/rat) .

• Fixation compatibility: Pilot studies using paraffin-embedded vs. frozen sections are essential, as ALDH16A1’s conformational sensitivity varies by tissue processing .

Methodological recommendation:

• Perform antigen retrieval using pH 9.0 Tris-EDTA buffer for 20 min at 95°C.

• Include knockout tissue controls to confirm signal specificity .

• Quantify staining intensity across cortical vs. medullary regions using automated image analysis platforms.

How can researchers validate the specificity of ALDH16A1 antibodies in Western blot analysis?

Validation requires a multi-platform approach:

• Molecular weight verification: Compare observed bands (75 kDa canonical, 55 kDa isoform) against recombinant standards .

• Knockout validation: Use tissues from Aldh16a1 −/− mice as negative controls .

• Cross-reactivity testing: Validate against other ALDH isoforms (ALDH1A1, ALDH2, ALDH3A1) using overexpression lysates .

Table 1: Antibody Validation Parameters

What methodological approaches are recommended for determining ALDH16A1 protein localization in different tissue types?

A tiered localization protocol is advised:

• Subcellular fractionation: Confirm cytoplasmic vs. membrane association via differential centrifugation .

• Multi-resolution microscopy: Combine widefield (10x) surveys with super-resolution (STED) imaging of tubular structures .

• Co-localization metrics: Calculate Pearson coefficients for markers like aquaporin-1 (proximal tubules) or THP (distal tubules) .

Critical controls:

• Isotype-matched IgG at matching concentrations

• siRNA knockdown efficiency ≥70% for functional studies

How should researchers interpret RNA-seq data showing contradictory ALDH16A1 expression patterns in different knockout models?

Contradictions often arise from:

• Strain-specific compensation: C57BL/6N vs. BALB/c backgrounds show differential Abcc4 upregulation .

• Temporal effects: Collect data at multiple timepoints (e.g., 8/16/24 weeks post-knockout).

Analytical framework:

• Normalize counts using TMM-weighted log2(CPM).

• Conduct pathway overrepresentation analysis (ORA) with STRING-DB v12.0.

• Validate top hits (e.g., Slc16a9, Abcc4) via Nanostring nCounter .

Table 2: RNA-seq Parameters in ALDH16A1 Studies

What advanced techniques are required to resolve discrepancies in ALDH16A1 subcellular localization reports?

Implement multimodal imaging pipelines:

• Correlative light-electron microscopy (CLEM): Map antibody signals to ultrastructural features (e.g., ER membranes vs. cytoplasmic vesicles) .

• Fluorescence lifetime imaging (FLIM): Detect protein-protein interactions via FRET efficiency changes between ALDH16A1 and HPRT1 .

• Spatial transcriptomics: Combine Visium HD (10x Genomics) with IHC to link mRNA-protein expression gradients .

Case study: CLEM revealed ALDH16A1’s dual localization in S3 tubules – 68% cytoplasmic, 32% associated with mitochondrial outer membranes .

What computational strategies can predict functional consequences of ALDH16A1 genetic variants identified in association studies?

For missense variants (e.g., p.Pro527Arg):

• Molecular dynamics simulations:

  • Simulate mutant vs. wild-type structures for 100 ns using AMBER22.

  • Calculate RMSD/RMSF for interaction interfaces (HPRT1 binding cleft) .

• Docking analysis:

  • Use HADDOCK 2.4 to model ALDH16A1-HPRT1 complexes.

  • Compare binding energies (ΔG wild-type: −9.3 kcal/mol vs. mutant: −5.1 kcal/mol) .

Critical parameters:

• Solvation model: TIP3P explicit water

• Force field: ff19SB for proteins

• Cluster analysis with 2Å cutoff

How does the non-catalytic nature of ALDH16A1 influence experimental design for studying its metabolic regulatory functions?

Key design considerations:

• Interaction screening: Employ BioID2 proximity labeling to map interactomes in renal cells .

• Metabolomic integration: Pair LC-MS/MS data (e.g., uric acid, hypoxanthine) with co-immunoprecipitation results .

• Allosteric mutagenesis: Introduce mutations distant from catalytic pseudo-sites (e.g., D458A) to test scaffolding roles .

Example workflow:

• Generate tetracycline-inducible HEK293 lines expressing WT/P527R ALDH16A1.

• Perform timed 13C-glucose tracer studies with HPRT1 activity assays.

• Correlate metabolite fluxes (e.g., PRPP → IMP) with interaction kinetics via SPR .