GALNT4 Antibody

What is GALNT4 and why is it important in research?

GALNT4 is a member of the UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase (GalNAc-T) family of enzymes that initiate mucin-type O-linked glycosylation in the Golgi apparatus. It's characterized by an N-terminal transmembrane domain, a stem region, a lumenal catalytic domain containing GT1 and Gal/GalNAc transferase motifs, and a C-terminal ricin/lectin-like domain . GALNT4 has been implicated in various disease processes, including cardiac hypertrophy and multiple cancer types, making it an important research target . Studies have shown that GALNT4 can directly bind to ASK1, inhibiting its dimerization and phosphorylation, which influences cardiac hypertrophy pathways .

What applications are GALNT4 antibodies typically used for?

GALNT4 antibodies are primarily used for:

• Western Blot (WB) analysis to detect GALNT4 protein expression (~66-67 kDa)

• Immunohistochemistry (IHC) to visualize GALNT4 in tissue sections

• ELISA for quantitative protein detection

• Flow Cytometry (FCM) for cellular analysis

The choice of application depends on your research question and experimental design. Western blot is commonly used to quantify expression levels, while IHC provides spatial information about GALNT4 distribution in tissues .

How do I choose the right GALNT4 antibody for my experiment?

When selecting a GALNT4 antibody, consider:

• Species reactivity: Ensure compatibility with your experimental model (human, mouse, rat)

• Application compatibility: Verify the antibody is validated for your intended application (WB, IHC, ELISA)

• Clonality: Polyclonal antibodies offer broader epitope recognition, while monoclonal antibodies provide higher specificity

• Immunogen information: Check if the antibody recognizes your region of interest (N-terminal, middle region, etc.)

• Validation data: Review published literature and manufacturer data showing successful applications

For example, the sheep polyclonal antibody from R&D Systems (AF7528) has been validated for Western blot in human lung carcinoma and hepatocellular carcinoma cell lines, as well as mouse liver tissue .

What controls should I include when using GALNT4 antibodies?

For rigorous experimental design with GALNT4 antibodies, include:

• Positive controls: Cell lines with known GALNT4 expression (A549, HepG2)

• Negative controls: GALNT4 knockout cells generated via CRISPR/Cas9

• Loading controls: GAPDH for normalization in Western blots

• Specificity controls: Pre-absorption with immunizing peptide or testing in GALNT4-KO models

• Cross-reactivity assessment: Test for reactivity with other GALNT family members (particularly important as there are multiple GALNTs with similar structures)

A well-validated example from the literature demonstrated GALNT4 antibody specificity by confirming absence of signals in GALNT4-KO mice generated using CRISPR/Cas9 targeting .

What is the optimal protocol for Western blot detection of GALNT4?

For optimal Western blot detection of GALNT4:

• Sample preparation: Lyse cells in buffer containing protease inhibitors

• Protein loading: Load 20-50 μg of total protein per lane

• Gel conditions: Run under reducing conditions using SDS-PAGE (8-10% gel)

• Transfer: Use PVDF membrane for optimal protein binding

• Blocking: 5% non-fat milk or BSA in TBST

• Primary antibody: Dilute according to manufacturer recommendation (typical range: 0.5-1 μg/mL)

• Secondary antibody: Use appropriate HRP-conjugated secondary (e.g., anti-sheep IgG for AF7528)

• Detection: Use enhanced chemiluminescence (ECL)

The expected molecular weight for GALNT4 is approximately 66-67 kDa . If you encounter non-specific bands, optimize antibody concentration or consider using different blocking reagents.

How can I troubleshoot weak or absent GALNT4 signal in Western blot?

When troubleshooting weak or absent GALNT4 signals:

• Check protein expression levels: Verify GALNT4 expression in your sample by RT-PCR

• Optimize protein extraction: GALNT4 is a transmembrane Golgi protein, so ensure your lysis buffer effectively solubilizes membrane proteins

• Adjust antibody concentration: Try a range of dilutions (1:200-1:1000 for WB)

• Extend incubation time: Consider overnight incubation at 4°C

• Use signal enhancement: Try more sensitive ECL substrates

• Check antibody quality: Antibodies may lose activity over time; use freshly prepared aliquots

• Verify transfer efficiency: Use Ponceau S staining to confirm protein transfer

If cell-specific expression is a concern, note that GALNT4 expression varies across cell types. In breast cancer cell lines, for example, luminal subtypes (MCF7, T47D) show higher expression than basal-like cells (MDA-MB series) .

What is the recommended protocol for immunohistochemical detection of GALNT4?

For IHC detection of GALNT4 in tissue sections:

• Fixation: 10% neutral-buffered formalin is standard

• Sectioning: 4-5 μm thickness

• Antigen retrieval: TE buffer (pH 9.0) is recommended, though citrate buffer (pH 6.0) may also work

• Blocking: 3-5% normal serum from the same species as secondary antibody

• Primary antibody: Dilute 1:20-1:200 depending on the antibody

• Secondary antibody: Use appropriate detection system (HRP/DAB)

• Counterstain: Hematoxylin for nuclear visualization

• Mounting: Use permanent mounting medium

GALNT4 has been successfully detected in human prostate cancer tissue using this approach . Sample-dependent optimization may be necessary for different tissue types.

How can I validate the specificity of GALNT4 antibody staining?

To validate GALNT4 antibody specificity:

• Genetic validation: Compare staining between wild-type and GALNT4-KO tissues/cells

• Peptide competition: Pre-incubate antibody with immunizing peptide to block specific binding

• Multiple antibodies: Use antibodies targeting different epitopes of GALNT4

• siRNA knockdown: Compare staining in cells with and without GALNT4 knockdown

• Correlation with mRNA: Verify that protein expression patterns correlate with mRNA expression

• Cross-reactivity testing: Test reactivity with recombinant GALNT family members (e.g., GALNT1, GALNT3)

In published studies, GALNT4 antibody specificity was confirmed by demonstrating reduced signals in GALNT4-KO mice and cells where the gene was targeted using CRISPR/Cas9 .

How can I study the functional role of GALNT4 in disease models?

To investigate GALNT4's functional role:

• Gene knockout/knockdown approaches:

  • CRISPR/Cas9 targeting with sgRNAs: 5'-GAATCCGGATGGCGGTGAGG TGG-3' and 5'-CTGTTAAAAACGCCAGCAGC AGG-3'

  • Adenovirus-mediated shRNA knockdown

• Overexpression studies:

  • Adenoviral GALNT4 (AdGALNT4) for cellular studies

  • AAV9-GALNT4 for in vivo cardiac studies (7.5 × 10¹¹ viral genomes)

• Enzymatic activity assessment:

  • Compare wild-type GALNT4 with catalytically inactive mutants (GALNT4-DN)

  • Measure O-glycosylation using VVA lectin detection

• Protein interaction studies:

  • Co-immunoprecipitation to identify GALNT4 binding partners

  • GST pull-down assays with purified proteins

In cardiac hypertrophy models, GALNT4-KO mice showed accelerated pathology after transverse aortic constriction (TAC), while AAV9-GALNT4 mice exhibited protection, revealing GALNT4's cardioprotective function .

How do you detect O-GalNAcylation mediated by GALNT4?

To detect GALNT4-mediated O-GalNAcylation:

• Lectin-based methods:

  • Western blotting with biotinylated Vicia villosa lectin (VVA) to detect Tn antigen

  • VVA-agarose pull-down assays to isolate O-GalNAcylated proteins

  • Flow cytometry with fluorescent-labeled VVA to quantify cell surface Tn antigen

• Mass spectrometry approaches:

  • Enrichment of GalNAc-modified peptides followed by LC-MS/MS

  • SimpleCell strategy: COSMC knockout to accumulate Tn antigen structures

• In vitro glycosylation assays:

  • Recombinant GALNT4 with synthetic peptide substrates

  • UDP-GalNAc incorporation assays

• Immunoprecipitation strategies:

  • Duplex immunoprecipitation with VVL-agarose beads and anti-HA magnetic beads

  • Treatment with neuraminidase to remove sialic acids and expose Tn structures

For example, researchers successfully demonstrated GALNT4-mediated O-GalNAcylation of TGF-β receptors by comparing wild-type versus GALNT4-KO cells using VVL-agarose pull-down .

What are the known substrate specificities of GALNT4 and how are they determined?

GALNT4 substrate specificity characteristics:

• Known substrates:

  • TGF-β type I and II receptors (TβR I and TβR II) - O-GalNAcylation of extracellular domains

  • EGFR - modifies O-linked glycosylation affecting receptor activity

  • MUC1 - glycosylates the T⁸ residue in the immunogenic epitope PDTRP

  • ASK1 - direct binding inhibits N-terminal dimerization

• Determination methods:

  • In vitro glycosylation with recombinant GALNT4

  • Comparison of glycosylation patterns between wild-type and GALNT4-KO cells

  • SimpleCell strategy to accumulate and identify Tn-modified glycopeptides

• Specificity characteristics:

  • GALNT4 can modify sites that other GALNTs cannot (e.g., T⁸ in MUC1's PDTRP sequence)

  • Has distinct but overlapping substrate specificities with other family members

  • Classified as group IIa in the GALNT family classification system

GALNT4's ability to modify specific substrates that other GALNTs cannot highlights its unique role in the glycosylation machinery, with important implications for its function in disease contexts .

How does GALNT4 expression correlate with disease progression and prognosis?

GALNT4 expression patterns in disease:

• Breast cancer:

  • Upregulated in luminal breast tumors compared to normal breast tissues

  • No significant difference between basal-like subtypes and normal tissues

  • Higher expression correlates with better recurrence-free survival (RFS)

  • Expression levels higher in luminal subtypes (MCF7, T47D) than in basal-like cell lines (MDA-MB series)

• Liver cancer (HCC):

  • Loss of GALNT4 appears to promote malignant transformation

  • GALNT4 levels positively correlate with Tn antigen levels in HCC specimens

  • GALNT4 modulates EGFR activity via O-linked glycosylation

• Renal cancer:

  • GALNT4 expression is a positive prognostic factor in clear-cell renal cell carcinoma (ccRCC)

• Cardiac disease:

  • Upregulated in mouse models of cardiac hypertrophy and cardiomyocytes treated with phenylephrine or angiotensin II

  • Higher levels in blood of patients with acute coronary syndrome (ACS)

These variable expression patterns across different diseases highlight the context-dependent role of GALNT4, suggesting it may function as both a tumor suppressor and promoter depending on the tissue type and disease stage .

What experimental models are most appropriate for studying GALNT4 function?

Selecting appropriate experimental models for GALNT4 research:

• Cell line models:

  • A549 and HepG2 cells: Express detectable GALNT4 levels for antibody validation

  • Breast cancer cell lines: MCF7, T47D (high GALNT4) vs. MDA-MB series (low GALNT4)

  • HEK-293T cells: Used for overexpression studies

• Animal models:

  • GALNT4 knockout mice: Generated via CRISPR/Cas9 using specific sgRNAs

  • AAV9-GALNT4 mice: For cardiac-specific overexpression studies

  • Transverse aortic constriction (TAC) mouse model: For cardiac hypertrophy studies

• Specialized models:

  • SimpleCell strategy: COSMC knockout cells that accumulate Tn antigen structures

  • Adenoviral expression systems for gain/loss-of-function studies

Each model offers distinct advantages. Cell lines provide controlled environments for mechanistic studies, while mouse models enable investigation of physiological relevance. The SimpleCell strategy specifically enhances detection of O-GalNAc modifications by preventing further glycan elaboration .

How can I quantitatively assess GALNT4 expression and activity?

For quantitative assessment of GALNT4:

• Expression quantification:

  • qRT-PCR using validated primers (see example from )

  • Western blot with densitometry (normalize to GAPDH)

  • Tissue microarrays with IHC scoring

  • Analysis of public datasets (GEPIA, Kaplan-Meier plotter)

• Activity assessment:

  • VVA lectin binding to detect Tn antigen structures

  • Flow cytometry quantification of cell surface Tn antigen

  • Enzyme activity assays with synthetic peptide substrates

  • In vitro glycosylation assays with UDP-GalNAc

• Functional readouts:

  • Phosphorylation status of downstream targets (e.g., ASK1, JNK, p38)

  • Receptor endocytosis rates (e.g., for EGFR)

  • Phenotypic changes (cell size for hypertrophy models)

For example, researchers successfully quantified GALNT4's impact on cardiomyocyte hypertrophy by measuring cell cross-sectional area (CSA) and expression of hypertrophic markers (ANP, BNP) following GALNT4 manipulation .

What are the key considerations when interpreting GALNT4 antibody results in cancer studies?

Critical considerations for cancer studies using GALNT4 antibodies:

• Context-dependent expression:

  • GALNT4 shows variable expression patterns across cancer types

  • Expression may differ between subtypes of the same cancer (e.g., luminal vs. basal breast cancer)

  • May function as tumor suppressor or promoter depending on context

• Technical considerations:

  • Antibody specificity verification is crucial (cross-reactivity with other GALNTs)

  • Sample preparation can affect glycoprotein detection (fixation impacts carbohydrate epitopes)

  • Expression in surrounding stroma vs. tumor cells must be distinguished in tissue samples

• Functional implications:

  • Consider both catalytic and non-catalytic functions of GALNT4

  • Compare wild-type with enzymatically inactive mutants

  • Evaluate impact on specific substrates relevant to cancer biology (EGFR, TGF-β receptors)

• Prognostic value:

  • Correlate with clinical outcomes (recurrence-free survival)

  • Consider multivariate analysis with other prognostic factors

  • Validate findings across multiple cohorts

Studies demonstrate that high GALNT4 expression correlates with better prognosis in breast cancer but may have different implications in other cancer types, highlighting the importance of cancer-specific interpretation .

What are the most effective strategies for studying GALNT4 interaction with signaling pathways?

To investigate GALNT4's role in signaling pathways:

• Protein-protein interaction studies:

  • Co-immunoprecipitation to identify binding partners

  • GST pull-down assays to confirm direct interactions

  • Proximity ligation assay for visualizing interactions in situ

• Signal transduction analysis:

  • Phosphorylation status of pathway components (e.g., ASK1, JNK, p38)

  • Activity of downstream transcription factors

  • Reporter gene assays for pathway activation

• Glycosylation-dependent mechanisms:

  • Compare effects of wild-type vs. enzymatically inactive GALNT4

  • Identify specific glycosylation sites using mass spectrometry

  • Mutate potential glycosylation sites on target proteins

• Inhibitor studies:

  • Pathway-specific inhibitors (e.g., ASK1 inhibitor)

  • Glycosylation inhibitors

  • Domain-specific blocking antibodies

For example, researchers demonstrated that GALNT4 inhibits ASK1 signaling by preventing N-terminal dimerization, verified through a combination of co-immunoprecipitation, GST pull-down assays, and pathway inhibitor studies .

What are the limitations of current GALNT4 antibodies and how might they be addressed?

Current limitations and potential solutions:

• Cross-reactivity concerns:

  • High homology between GALNT family members (20 in humans)

  • Solution: Epitope mapping and selection of unique regions for immunization

  • Validation using multiple approaches (GALNT4-KO models, peptide competition)

• Application-specific performance:

  • Antibodies optimized for WB may not work well for IHC or IP

  • Solution: Application-specific validation and potentially different antibodies for different techniques

• Species limitations:

  • Some antibodies show restricted species reactivity

  • Solution: Design conserved epitope-targeted antibodies or species-specific antibodies as needed

• Glycoform detection:

  • Current antibodies detect protein backbone but not specific glycoforms

  • Solution: Development of glycoform-specific antibodies or complementary lectin-based approaches

Future approaches might include developing recombinant antibodies with higher specificity, monoclonal antibodies targeting GALNT4-specific epitopes, and antibodies capable of distinguishing between active and inactive GALNT4 conformations.

How can emerging technologies enhance GALNT4 research?

Emerging technologies with potential for GALNT4 research:

• CRISPR-based approaches:

  • Base editors for introducing specific mutations without double-strand breaks

  • CRISPRi/CRISPRa for precise modulation of GALNT4 expression

  • CRISPR screens to identify GALNT4 substrates or regulators

• Advanced imaging techniques:

  • Super-resolution microscopy for subcellular localization

  • Live-cell imaging of GALNT4 trafficking and activity

  • FRET/BRET for monitoring protein-protein interactions

• Glycoproteomics technologies:

  • Enrichment strategies for O-GalNAc-modified peptides

  • Targeted glycoproteomics for specific substrates

  • Ion mobility mass spectrometry for improved glycopeptide analysis

• Computational approaches:

  • AI-based prediction of O-glycosylation sites

  • Molecular dynamics simulations of GALNT4-substrate interactions

  • Systems biology modeling of glycosylation networks

• Antibody engineering:

  • De novo antibody design as described in recent research

  • Development of recombinant antibody fragments with enhanced specificity

  • Bispecific antibodies targeting GALNT4 and its substrates simultaneously

These technologies could overcome current limitations in studying GALNT4's diverse functions across cellular contexts and disease states.

What are the most promising therapeutic applications targeting GALNT4?

Potential therapeutic approaches involving GALNT4:

• Cardiac disease applications:

  • GALNT4 activation/overexpression for protection against cardiac hypertrophy

  • Targeting the GALNT4-ASK1 interaction to prevent pathological remodeling

  • AAV-based gene therapy to increase cardiac GALNT4 expression

• Cancer applications:

  • Context-dependent approaches based on cancer type and stage

  • GALNT4 restoration in cancers where it functions as a tumor suppressor

  • Inhibition of specific GALNT4-substrate interactions in cancers where it promotes progression

  • Combination with conventional therapies based on glycosylation patterns

• Potential therapeutic modalities:

  • Small molecule modulators of GALNT4 activity

  • Peptide-based inhibitors of protein-protein interactions

  • AAV-mediated gene therapy for tissue-specific expression

  • RNA-based therapeutics (siRNA, antisense oligonucleotides)

The demonstrated role of GALNT4 in protecting against cardiac hypertrophy by inhibiting ASK1 signaling represents one of the most promising therapeutic applications, potentially offering a new strategy for heart failure prevention .

How can researchers better correlate GALNT4 expression, activity, and clinical outcomes?

Strategies for improved clinical correlation:

• Comprehensive biospecimen analysis:

  • Paired analysis of protein expression, glycosylation status, and mRNA levels

  • Multi-omics approaches integrating transcriptomics, proteomics, and glycomics

  • Spatial analysis in tissue samples (spatial transcriptomics, multiplexed IHC)

• Clinical data integration:

  • Detailed patient phenotyping and longitudinal follow-up

  • Multi-parameter correlation with disease progression and treatment response

  • Machine learning approaches to identify patterns in complex datasets

• Functional validation:

  • Patient-derived models (organoids, xenografts)

  • Genetic manipulation in relevant disease models

  • Correlation of glycosylation changes with functional outcomes

• Standardized assessment methods:

  • Validated antibodies and protocols for consistent detection

  • Quantitative scoring systems for tissue analysis

  • Reference standards for comparing results across studies

For example, researchers have begun integrating GALNT4 expression data with recurrence-free survival information from breast cancer patients using tools like GEPIA and Kaplan-Meier plotter, revealing significant positive correlations between high GALNT4 expression and improved outcomes .