B3GNT3 Antibody

What is B3GNT3 and what function does it serve in cellular processes?

B3GNT3 (beta-1,3-N-acetylglucosaminyltransferase 3) is a glycosyltransferase enzyme involved in the synthesis of poly-N-acetyllactosamine. It demonstrates activity for type 2 oligosaccharides and functions as a core1-1,3-N-acetylglucosaminyltransferase (Core1-beta3GlcNAcT) to form the 6-sulfo sialyl Lewis x on extended core1 O-glycans . This enzyme plays critical roles in multiple biological processes including cell adhesion, immune regulation, and cancer progression. The protein has a predicted molecular weight of approximately 43 kDa .

What are the standard methods for detecting B3GNT3 expression in tissue samples?

Detection of B3GNT3 in tissue samples typically employs these methodologies:

• Immunohistochemistry (IHC): Used to localize B3GNT3 in tissue sections, with immunoreactivity primarily detected in the cytoplasm. Published studies have employed various scoring systems based on staining intensity (weak, moderate, strong) .

• ELISA: Particularly useful for serum samples, as demonstrated in studies measuring B3GNT3 levels in patients with lung adenocarcinoma versus healthy controls .

• Western Blotting: Commercial antibodies for B3GNT3 (such as ab96267 and ab70156) are validated for western blot applications, typically using 10% SDS-PAGE with dilutions around 1/1000 .

• qRT-PCR: For quantifying B3GNT3 mRNA expression levels, as implemented in studies examining B3GNT3 expression in cancer cell lines .

How should researchers validate B3GNT3 antibody specificity for their experimental systems?

To ensure antibody specificity:

• Positive and negative controls: Include cell lines with known B3GNT3 expression levels. The 293T cell line has been validated for western blot applications with anti-B3GNT3 antibody .

• Knockdown validation: Implement siRNA or shRNA to create B3GNT3 knockdown controls and verify reduced antibody signal. This approach has been demonstrated in studies examining B3GNT3 function in esophageal and lung cancer cell lines .

• Western blot band verification: Confirm that detected bands match the predicted molecular weight of 43 kDa for B3GNT3 .

• Cross-reactivity assessment: Test antibody reactivity across multiple relevant human tissues and cell lines to establish specificity patterns.

How does B3GNT3 expression correlate with clinical outcomes across different cancer types?

B3GNT3 expression demonstrates consistent associations with clinical outcomes across multiple cancer types:

Notably, high B3GNT3 expression serves as an independent prognostic factor in multiple cancer types, with Kaplan-Meier analyses consistently showing shorter survival times in patients with elevated expression .

What methodological approaches can be used to study the relationship between B3GNT3 and immune cell infiltration in tumors?

Several complementary methodologies can be employed:

• Bioinformatic analysis:

  • TIMER (Tumor Immune Estimation Resource) tool can analyze correlations between B3GNT3 expression and immune infiltration levels, particularly CD4+ T cells, neutrophils, macrophages, and dendritic cells .

  • TISIDB (Tumor-Immune System Interaction Database) can examine correlations between B3GNT3 and various immune signatures, including immune checkpoint gene sets .

• Co-expression studies:

  • Immunohistochemical co-staining for B3GNT3 and immune cell markers (e.g., CD68 for macrophages) to assess spatial relationships .

  • Flow cytometry to quantify immune populations in relation to B3GNT3 expression levels.

• Functional validation:

  • B3GNT3 knockdown experiments followed by immune cell co-culture assays.

  • Analysis of cytokine production and immune checkpoint expression after B3GNT3 manipulation.

Research has identified negative correlations between B3GNT3 expression and immune infiltration levels in pancreatic cancer, particularly with macrophages (correlation coefficient = -0.366) . This suggests immunomodulatory functions that warrant further investigation.

What experimental designs are optimal for investigating how miR-149-5p regulates B3GNT3 expression in cancer development?

To rigorously investigate the miR-149-5p/B3GNT3 regulatory axis:

• Bioinformatic prediction and validation:

  • Utilize TargetScan to identify potential miR-149-5p binding sites in the B3GNT3 3'-UTR .

  • Perform luciferase reporter assays with wild-type and mutant B3GNT3 3'-UTR constructs to confirm direct targeting .

• Expression analysis:

  • Quantify miR-149-5p and B3GNT3 expression levels in matched cancer tissues and cell lines using qRT-PCR.

  • Assess correlation patterns between miR-149-5p and B3GNT3 across patient samples.

• Functional studies:

  • Transfect cancer cells with miR-149-5p mimics or inhibitors and measure changes in B3GNT3 expression using Western blot and qRT-PCR.

  • Perform rescue experiments where B3GNT3 is overexpressed in miR-149-5p-overexpressing cells to determine if B3GNT3 restoration reverses miR-149-5p-mediated effects.

• Phenotypic assays:

  • Assess cell proliferation, migration, and invasion in cells with altered miR-149-5p/B3GNT3 expression using CCK-8, wound healing, and transwell assays .

  • Evaluate in vivo tumor formation using xenograft models with miR-149-5p overexpression or B3GNT3 knockdown .

Previous research has validated that miR-149-5p negatively regulates B3GNT3 expression by directly targeting its 3'-UTR, and overexpression of miR-149-5p can antagonize the tumorigenic effects of B3GNT3 in lung cancer cells .

How can researchers effectively study the role of B3GNT3 in cancer cell invasion and metastasis?

To comprehensively investigate B3GNT3's role in invasion and metastasis:

• In vitro invasion assays:

  • Perform Transwell invasion assays with Matrigel coating using B3GNT3-knockdown or B3GNT3-overexpressing cancer cells .

  • Analyze changes in cell morphology and cytoskeletal organization using immunofluorescence staining.

• Migration analysis:

  • Conduct wound healing/scratch assays to measure collective cell migration.

  • Employ live-cell imaging to track individual cell movement patterns and dynamics.

• Molecular mechanism investigation:

  • Analyze expression of epithelial-mesenchymal transition (EMT) markers following B3GNT3 manipulation.

  • Investigate changes in cell adhesion molecules and matrix metalloproteinases.

  • Examine the potential interaction between B3GNT3 and PD-L1, as this has been implicated in triple-negative breast cancer .

• In vivo metastasis models:

  • Utilize tail vein injection or orthotopic implantation models with B3GNT3-modified cells to assess metastatic potential.

  • Employ bioluminescence imaging to track metastatic spread longitudinally.

Research has demonstrated that knockdown of B3GNT3 significantly suppresses lung cancer cell growth and invasion in vitro, and inhibits tumor development in xenograft models . Additionally, in esophageal cancer cells, B3GNT3 silencing reduced growth rate and Ki-67 expression levels .

What methodological challenges exist when studying B3GNT3's role in glycosylation pathways related to cancer progression?

Several technical challenges require specific methodological approaches:

• Glycan structure analysis:

  • Mass spectrometry techniques (MALDI-TOF, LC-MS/MS) for detailed glycan profiling.

  • Lectin microarrays to identify differential glycosylation patterns in B3GNT3-modified cells.

  • Specialized glycan labeling strategies to track altered O-glycan structures.

• Functional redundancy assessment:

  • Multi-gene knockdown approaches to address potential compensatory mechanisms from other glycosyltransferases.

  • CRISPR-Cas9 gene editing to create complete B3GNT3 knockout models.

• Specific glycosylation target identification:

  • Immunoprecipitation followed by glycoproteomic analysis to identify proteins specifically modified by B3GNT3.

  • Focused analysis on PD-L1 glycosylation, as B3GNT3 has been shown to be essential for epidermal growth factor-induced communication between PD-1 and PD-L1 in triple-negative breast cancer .

• Microenvironmental context:

  • 3D organoid cultures to better recapitulate physiological glycosylation patterns.

  • Co-culture systems to assess how B3GNT3-mediated glycosylation affects tumor-stromal interactions.

Current research indicates that B3GNT3 plays dual roles in cancer, with both tumor-promoting effects in various epithelial cancers and potentially tumor-suppressive effects in neuroblastoma , highlighting the complexity of glycosylation biology in different tumor contexts.

How do tissue-specific differences in B3GNT3 expression impact experimental design when using B3GNT3 antibodies?

When designing experiments across different tissue types:

• Baseline expression profiling:

  • Perform comprehensive analysis of B3GNT3 expression across normal tissues using resources like The Cancer Genome Atlas (TCGA) database .

  • Select appropriate positive and negative control tissues for antibody validation.

• Tissue-specific optimization:

  • Adjust antibody concentrations and incubation times based on known expression levels (e.g., higher dilutions for tissues with elevated B3GNT3 expression).

  • Optimize antigen retrieval methods specifically for each tissue type being analyzed.

• Context-specific interpretation:

  • Consider the biological context of each tissue when interpreting B3GNT3 staining patterns.

  • In lung adenocarcinoma, B3GNT3 expression correlates with clinical stage progression .

  • In pancreatic cancer, B3GNT3 expression shows negative correlation with immune cell infiltration .

  • In esophageal cancer, high B3GNT3 expression is associated with poorer tissue differentiation .

• Specialized protocols:

  • For lung tissue, compare expression across adenocarcinoma, squamous cell carcinoma, and normal adjacent tissue .

  • For digestive system cancers, consider potential differences in B3GNT3 localization and expression patterns .

What are the optimal protocols for analyzing B3GNT3 expression in tumor versus adjacent normal tissues?

For rigorous comparative analysis:

• Sample collection and processing:

  • Collect matched tumor and adjacent normal tissues (at least 2 cm from tumor margin).

  • Use consistent fixation protocols (e.g., 10% neutral buffered formalin for 24 hours) to ensure comparable antigen preservation.

• IHC methodology:

  • Implement standardized scoring systems based on staining intensity and percentage of positive cells.

  • Reported systems include:

    • Weak (30%), moderate (45%), and strong (25%) staining categories

    • Four-tier system (negative, weak, moderate, strong) with percentage quantification

• Quantification approaches:

  • Digital image analysis with tissue segmentation for objective quantification.

  • Blinded assessment by multiple pathologists to reduce scoring bias.

• Controls and normalization:

  • Include internal positive controls (tissues known to express B3GNT3).

  • Normalize expression against housekeeping genes when using qRT-PCR.

• Multi-modal validation:

  • Verify IHC findings with orthogonal methods such as qRT-PCR and Western blotting.

  • For serum studies, employ ELISA with carefully matched patient-control cohorts .

Research has demonstrated that B3GNT3 expression is significantly higher in tumor tissues compared to adjacent normal tissues across multiple cancer types, with particularly well-documented differences in lung adenocarcinoma and esophageal cancer .

What methodological approaches can be used to evaluate B3GNT3 as a therapeutic target in cancer?

To assess B3GNT3's potential as a therapeutic target:

• Target validation strategies:

  • Implement inducible shRNA or CRISPR-Cas9 systems to modulate B3GNT3 expression in established tumors.

  • Assess changes in tumor growth, metastasis, and immune infiltration following B3GNT3 inhibition.

• Small molecule screening:

  • Develop high-throughput assays to identify inhibitors of B3GNT3 enzymatic activity.

  • Test candidate compounds in cell-based glycosylation assays to confirm target engagement.

• Combination therapy assessment:

  • Evaluate B3GNT3 inhibition in combination with immune checkpoint inhibitors, particularly given its relationship with PD-L1 .

  • Investigate synergistic effects with conventional chemotherapeutics.

• Biomarker development:

  • Establish standardized protocols for measuring serum B3GNT3 levels as a clinical biomarker.

  • Develop companion diagnostics to identify patients most likely to benefit from B3GNT3-targeted therapies.

Research has demonstrated that inhibiting B3GNT3 while simultaneously inhibiting PD-L1 significantly increases apoptosis and inhibits proliferation in lung adenocarcinoma models . Additionally, overexpression of miR-149-5p, which negatively regulates B3GNT3, antagonizes tumorigenic effects in vitro , suggesting multiple therapeutic intervention points.

How can researchers effectively design experiments to investigate the relationship between B3GNT3 and immune checkpoint pathways?

For comprehensive analysis of B3GNT3-immune checkpoint interactions:

• Co-expression and co-localization studies:

  • Perform multiplex immunofluorescence for B3GNT3 and immune checkpoint proteins (PD-1, PD-L1, CTLA-4).

  • Analyze spatial relationships and potential co-regulation in tissue microarrays.

• Glycosylation analysis of checkpoint proteins:

  • Employ immunoprecipitation followed by glycoproteomic analysis to identify B3GNT3-dependent glycan modifications on immune checkpoint proteins.

  • Utilize site-directed mutagenesis to eliminate specific glycosylation sites and assess functional consequences.

• Functional interaction assays:

  • Implement B3GNT3 knockdown/overexpression followed by immune checkpoint blockade to assess combinatorial effects.

  • Conduct T-cell killing assays to determine how B3GNT3 modulation affects tumor cell susceptibility to immune attack.

• Mechanistic pathway analysis:

  • Investigate downstream signaling events following B3GNT3-mediated modifications of immune checkpoint proteins.

  • Employ phospho-proteomics to map altered signaling networks.

Research has established that B3GNT3 is essential for epidermal growth factor-induced communication between PD-1 and PD-L1 in triple-negative breast cancer . The TISIDB analysis has also revealed correlations between B3GNT3 expression and immune checkpoint markers such as ICOS and NECTIN2 (CD112) , suggesting broad implications for immunotherapy response.