NR1H3 Antibody

What is NR1H3 and why is it significant in research?

NR1H3 (Nuclear Receptor Subfamily 1 Group H Member 3), also known as LXRα (Liver X Receptor alpha), is a nuclear receptor that functions as a key regulator of lipid metabolism, inflammatory responses, and macrophage function. It forms heterodimers with retinoid X receptor and regulates target genes containing LXR response elements .

Research significance:

• Regulates cholesterol and lipid homeostasis in various tissues

• Controls transcriptional programs involved in inflammation

• Associated with cancer prognosis in certain lymphomas

• Implicated in macrophage polarization toward pro-inflammatory phenotypes

• Genetic variants affect response to antihypertensive medications

Which tissues predominantly express NR1H3?

NR1H3 expression has been detected in multiple tissues with varying expression levels:

• High expression: Liver tissue (validated by immunohistochemistry and western blot)

• Moderate expression: Adipose tissue, skeletal muscle (including longissimus dorsi)

• Cell-specific expression:

  • Macrophages (particularly M0/M1 polarized macrophages)

  • THP-1 cells (human monocytic cell line)

  • HL-60 cells and COLO 320, as documented in western blot validations

How does NR1H3 expression differ between M1 and M2 macrophages?

NR1H3 shows distinctive expression patterns between macrophage phenotypes:

• M1 (pro-inflammatory) macrophages: Significantly higher NR1H3 expression (p-value = 0.001)

• M2 (anti-inflammatory) macrophages: Lower NR1H3 expression compared to M1

• Functional consequence: M1 macrophages show substantial upregulation of ABCA1, the main NR1H3 target gene

• Clinical correlation: Patients with low NR1H3 expression show a significant predominance of M2 macrophages, while high NR1H3 expression correlates with increased M0/M1 macrophage fraction

What are the optimal applications and dilutions for NR1H3 antibodies in different experimental contexts?

Based on validated protocols from multiple sources:

Methodological note: Antibody dilutions should be optimized for each experimental system. For IHC, antigen retrieval with TE buffer pH 9.0 is recommended, with citrate buffer pH 6.0 as an alternative .

How should NR1H3 gene expression be accurately quantified in tissue samples?

For reliable quantification of NR1H3 mRNA:

• Sample preparation:

  • Fresh or properly preserved tissue samples (liver, adipose, muscle)

  • RNA extraction using standard protocols maintaining RNA integrity

• RT-PCR protocol:

  • Use Bio-Rad CFX96 System or equivalent

  • Reaction mixture: 1.0 μL cDNA, 0.5 μL of forward/reverse primers (10.0 nmol/μL), 10.0 μL SYBR Green qPCR SuperMix

  • Thermal cycling: Initial denaturation (95°C for 3 min), 40 cycles of 95°C for 10s and 60°C for 20s, followed by melting curve analysis

• Data normalization:

  • Use GAPDH or other validated housekeeping genes

  • Calculate amplification efficiency from a standard curve (5-fold dilution series)

  • Perform technical triplicates to ensure reproducibility

How can I design experiments to investigate the role of NR1H3 in macrophage polarization?

Recommended experimental approach:

• Cell preparation:

  • Use THP-1 cells as a model system for human macrophages

  • Primary monocyte isolation from peripheral blood is an alternative for more physiologically relevant studies

• Polarization protocol:

  • M1 polarization: LPS (100 ng/mL) + IFN-γ (20 ng/mL) for 24-48 hours

  • M2 polarization: IL-4 (20 ng/mL) + IL-13 (20 ng/mL) for 24-48 hours

• Validation markers:

  • M1 markers: Monitor CXCL10 and IL-1β expression

  • M2 markers: Assess CD206 and CD163 expression

  • NR1H3 target: Measure ABCA1 expression as a functional readout

• Analytical methods:

  • qRT-PCR for gene expression analysis

  • Flow cytometry for cell surface marker quantification

  • Western blot for protein level confirmation

  • Functional assays (phagocytosis, cytokine secretion)

How should prognostic associations of NR1H3 expression in clinical samples be evaluated?

For robust prognostic evaluation:

What statistical approaches are appropriate when analyzing NR1H3 genetic variants in pharmacogenetic studies?

Based on validated approaches in hypertension studies :

• Association analysis:

  • Use generalized linear regression models with adjustment for relevant covariates (age, sex, BMI, baseline BP, smoking, alcohol intake)

  • Calculate means, mean differences, and 95% CIs between genotype groups

• Multiple testing correction:

  • Apply Benjamin & Hochberg method to control false discovery rate (FDR)

  • Consider adjusted p-values significant when FDR < 0.05

• Stratification analyses:

  • Stratify by sex to identify gender-specific effects

  • Analyze treatment subgroups separately (e.g., monotherapy vs. multitherapy)

• Advanced modeling:

  • Consider multiple linear regression to assess correlation coefficients

  • Apply linear mixed models to account for random effects (e.g., study center, entry time)

  • Perform epistasis analysis to explore gene-gene interactions

How can I integrate NR1H3 expression data with immune cell infiltration analyses in cancer research?

For comprehensive integrative analysis:

• Complementary methodologies:

  • Digital quantification: Use CIBERSORTx for inference of immune cell fractions from bulk tumor gene expression

  • Direct quantification: Perform immunohistochemistry for CD68 (macrophage marker) on tissue microarrays

• Correlation analysis:

  • Apply Spearman's correlation to assess relationships between NR1H3 expression and immune cell markers

  • Use TIMER2 and TISIDB databases to examine correlations with specific immune cell populations

• Visualization approaches:

  • Generate scatter plots of NR1H3 vs. immune markers

  • Create box plots comparing immune cell distributions between NR1H3-high vs. NR1H3-low groups

• Functional interpretation:

  • Perform Gene Ontology analysis on NR1H3-associated genes

  • Use Gene Set Enrichment Analysis to identify biological processes linked to NR1H3 expression

  • Connect findings to cholesterol metabolism, inflammation, and innate immunity pathways

What are common issues with NR1H3 antibodies in western blotting and how can they be resolved?

Common challenges and solutions:

• Multiple bands or unexpected molecular weight:

  • Expected molecular weight: 45-50 kDa

  • Alternative isoforms: NR1H3 exists in isoforms of 51 kDa and 43-45 kDa

  • Solution: Validate with positive controls (HL-60 cells, COLO 320 cells, liver tissue)

• Weak signal issues:

  • Optimize antibody concentration: Test dilution series (1:2000 to 1:10000)

  • Increase protein loading: 20-40 μg of total protein recommended

  • Enhance detection: Consider using enhanced chemiluminescence substrates

• Background or non-specific binding:

  • Optimize blocking: 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature

  • Increase washing steps: 3-5 washes with TBST, 5-10 minutes each

  • Try alternative antibody: Compare results with antibodies targeting different epitopes

What factors affect reproducibility in NR1H3 immunohistochemistry experiments?

Critical factors for consistent IHC results:

• Tissue processing and fixation:

  • Fixation time: Overfixation may mask epitopes

  • Proper paraffin embedding and sectioning thickness (4-5 μm optimal)

• Antigen retrieval methods:

  • Recommended primary approach: TE buffer pH 9.0

  • Alternative approach: Citrate buffer pH 6.0

  • Heat-induced epitope retrieval parameters: Optimize time and temperature

• Antibody validation:

  • Positive control tissues: Rat liver tissue shows consistent positive staining

  • Negative controls: Omit primary antibody or use isotype controls

  • Scoring system: Use established scoring method (intensity × percentage of positive cells)

• Quantification approach:

  • Standardize image acquisition parameters

  • Use digital image analysis software for objective quantification

  • Blind scoring by multiple observers to reduce bias

How can I design functional studies to investigate the impact of NR1H3 genetic variants?

Based on the rs11039149A>G variant study :

• Promoter activity assessment:

  • Construct dual-luciferase reporter plasmids containing wild-type and variant promoter sequences

  • Transfect into relevant cell types (e.g., vascular smooth muscle cells for hypertension studies)

  • Measure relative luciferase activity to quantify transcriptional differences

• Transcription factor binding analysis:

  • Perform electrophoretic mobility shift assays (EMSAs) to analyze binding affinity differences

  • Use ChIP assays to confirm binding of transcription factors (e.g., FOXC1) to the promoter region

  • Quantify differential binding between wild-type and variant sequences

• Functional readouts:

  • Measure target gene expression (e.g., ABCA1) in cells with different genotypes

  • Assess cellular phenotypes relevant to the condition (e.g., calcium handling in vascular cells)

  • Monitor physiological responses in genotyped primary cells

What approaches can be used to study NR1H3's role in tumor-associated macrophages in the cancer microenvironment?

Comprehensive investigation approach:

• Patient-derived samples:

  • Collect tumor tissues and paired normal tissues

  • Isolate tumor-associated macrophages by flow cytometry or laser capture microdissection

  • Perform single-cell RNA sequencing to define macrophage populations

• Co-culture systems:

  • Establish cancer cell and macrophage co-cultures

  • Manipulate NR1H3 levels using siRNA, CRISPR, or pharmacological modulators

  • Assess:

    • Macrophage polarization markers (M1: CXCL10, IL-1β; M2: CD206, CD163)

    • Cancer cell proliferation, invasion, and drug resistance

    • Cytokine profiles in the co-culture medium

• In vivo models:

  • Generate macrophage-specific NR1H3 knockout mice

  • Implant syngeneic tumors and analyze:

    • Tumor growth and metastasis

    • Macrophage infiltration and phenotype

    • Response to immunotherapies or targeted treatments

How can NR1H3 antibodies be applied in pharmacogenetic research for personalized medicine approaches?

Implementation strategies:

• Patient stratification protocols:

  • Genotype patients for NR1H3 variants (e.g., rs11039149)

  • Measure NR1H3 protein levels in accessible tissues or circulating cells

  • Correlate with treatment response (e.g., blood pressure control with CCB monotherapy)

• Experimental validation:

  • Develop cell-based assays to test drug responses based on NR1H3 genotype/expression

  • Use patient-derived cells to confirm genotype-dependent drug responses

  • Design prospective clinical trials to validate predictive markers

• Molecular monitoring:

  • Track NR1H3 target gene expression (e.g., ABCA1) as a pharmacodynamic marker

  • Develop protocols for tissue-specific or liquid biopsy-based biomarker assessment

  • Correlate with clinical outcomes to refine predictive algorithms

TABLE: NR1H3 Variant Associations with Antihypertensive Response