Grem1 Antibody

What is the molecular structure and basic function of GREM1?

GREM1 is a 20.7 kDa protein that functions as a high-affinity antagonist of bone morphogenetic proteins (BMP)-2, -4, and -7. It belongs to the DAN family of BMP antagonists and is also known as DAND2, Drm, and Gremlin. Structurally, GREM1 forms dimers, with each monomer capable of binding BMP ligands to prevent their interaction with BMP receptors. The GREM1/Ab7326 Fab crystal structure revealed a gremlin-1 dimer with a Fab molecule bound to each monomer that blocked BMP binding . This antagonist activity allows GREM1 to modulate embryonic development, tissue homeostasis, and various pathological processes.

Which signaling pathways does GREM1 regulate?

• BMP antagonism: Directly binds to BMP-2, BMP-4, and BMP-7, preventing their interaction with receptors and subsequent SMAD1/5/8 phosphorylation and ID1 transcription

• VEGFR2 activation: Independent of its BMP-antagonizing function, GREM1 can directly bind and activate VEGFR2, leading to downstream MAPK signaling

• FAO enhancement: In intestinal fibrosis, GREM1 activates VEGFR2, which further activates downstream MAPK signaling, resulting in fatty acid oxidation (FAO) enhancement

• Potential STAT3 signaling: GREM1 was reported to activate STAT3 signaling in breast cancer cells, though this appears to be context-dependent

Understanding these diverse signaling mechanisms is essential for interpreting antibody-mediated inhibition results.

What are the typical expression patterns of GREM1 in normal versus diseased tissues?

GREM1 is significantly upregulated in multiple cancer types compared to adjacent normal tissues. For example:

• In bladder cancer (BC), GREM1 shows a log2FoldChange of 1.31 (P = 0.011) in tumor tissues compared to normal tissues

• Higher expression is also observed in breast invasive carcinoma, cholangiocarcinoma, and head and neck squamous cell carcinoma

• In lung adenocarcinoma (LUAD), GREM1 is significantly upregulated in tumor tissues

• During intestinal fibrosis, GREM1 protein levels are dramatically elevated in both murine and human fibrosed colon tissues

Immunohistochemistry staining for GREM1 typically shows stronger signals in diseased tissues, with staining intensity scores (0-3) and quantity scores (0-3) providing semi-quantitative representation of expression levels .

What are the main approaches to generate effective anti-GREM1 antibodies?

Development of functional anti-GREM1 antibodies that can block BMP binding has proven challenging. Several approaches have been employed:

• Phage display technology: The most successful approach, as demonstrated by ginisortamab development. This method involved using a large naïve single-chain variable fragment (scFv) library panned against both human and mouse recombinant GREM1 protein to identify cross-reactive clones . This approach was more successful than traditional immunization methods in generating blocking antibodies.

• Traditional immunization: While immunization of rats and rabbits successfully produced antibodies binding to GREM1, these antibodies often failed to restore BMP signaling in reporter assays .

• Epitope-specific targeting: Successful antibodies often target specific regions of GREM1 that are critical for BMP binding. The crystal structure of the GREM1/Ab7326 complex revealed that the antibody binds to regions of GREM1 that are involved in BMP interaction .

The cross-species reactivity is particularly important for translational research, requiring antibodies that bind both human and mouse/rat GREM1 for preclinical validation.

How can researchers validate the functional activity of anti-GREM1 antibodies?

Functional validation of anti-GREM1 antibodies should include multiple assays:

• BMP-dependent reporter assays: The most common approach uses HEK-ID1 luciferase reporter cells to measure restoration of BMP signaling. The protocol involves:

  • Plating reporter cells in assay media

  • Titrating antibody and pre-incubating with GREM1 protein

  • Adding BMP ligands (BMP-4/-7 heterodimer, BMP-4, or BMP-7)

  • Measuring luciferase signal after 24 hours

• Phospho-SMAD1/8 flow cytometry: Measures the direct downstream effect of BMP signaling:

  • Cells are treated with recombinant GREM1 with or without antibody

  • Fixed, permeabilized, and stained for phosphorylated SMAD1/8

  • Analyzed by flow cytometry to quantify signal restoration

• Western blot analysis: Probing for P-Smad1/5/8 to assess BMP pathway activation

• Cell-based functional assays: Testing effects on cell proliferation, migration, EMT markers, and other relevant functional readouts

The gold standard for validation is demonstrating that the antibody can block GREM1's antagonism of BMP signaling in relevant cellular contexts.

What is the crystallographic evidence for how anti-GREM1 antibodies recognize their target?

The crystal structure of the GREM1/Ab7326 antigen-binding fragment (Fab) complex provides key insights into antibody recognition:

• The structure revealed a GREM1 dimer with a Fab molecule bound to each monomer in a configuration that directly blocks BMP binding

• Significant flexibility was observed in the GREM1 protein within the complex structure, where several regions remained unresolved, including the finger loops and N-terminal 25 amino acids, highlighting the dynamic nature of GREM1

• The truncated form of GREM1 (A43–D160) was used for crystallography since attempts with full-length protein were unsuccessful

• The N-terminus of GREM1 undergoes significant conformational changes when BMP ligands bind, and this flexibility is necessary for function

This structural information is critical for designing next-generation antibodies with improved blocking activity.

How does GREM1 contribute to cancer progression and what are the implications for antibody therapy?

GREM1 promotes cancer progression through multiple mechanisms:

• Tumor cell proliferation and invasion:

  • In lung adenocarcinoma (LUAD), GREM1 overexpression significantly promotes cell proliferation, migration, and epithelial-mesenchymal transition (EMT)

  • In bladder cancer, high GREM1 expression correlates with higher tumor grade, advanced T stage, and lymph node metastasis

• Maintenance of cellular heterogeneity:

  • GREM1 is required to maintain cellular heterogeneity in tumors

  • EMT induced by GREM1 might contribute to aggressive clinical features

• Tumor microenvironment modulation:

  • GREM1 expression correlates with immune infiltration patterns

  • Significant positive correlation exists between GREM1 expression and immune, stromal, and ESTIMATE scores in LUAD

  • GREM1 affects infiltration of specific immune cells, including CD4 memory T cells, NK cells, monocytes, and macrophages

These findings suggest that anti-GREM1 antibodies could potentially inhibit tumor growth, reduce invasiveness, and modulate the tumor immune microenvironment, making them promising therapeutic candidates.

What is the evidence for GREM1's role in fibrotic diseases and potential therapeutic applications?

GREM1 plays significant roles in various fibrotic conditions:

These findings indicate that while GREM1 is a promising therapeutic target in some fibrotic conditions, its effectiveness may be tissue-specific.

How does GREM1 influence immune responses and what are the implications for immunotherapy?

GREM1 significantly impacts immune responses with important implications for immunotherapy:

• Immune checkpoint regulation:

  • In bladder cancer, many immune checkpoints (CD274, PDCD1, CTLA4) are differentially expressed between low- and high-GREM1 groups

  • GREM1 expression is significantly lower in patients who respond to immunotherapy than those who do not

• Prediction of immunotherapy response:

  • GREM1 expression was positively correlated with Tumor Immune Dysfunction and Exclusion (TIDE) scores

  • In bladder cancer, 61% of patients in the low-GREM1 group were predicted to respond to immunotherapy, compared to only 13.3% in the high-GREM1 group

• Immune cell infiltration patterns:

  • The low-GREM1 group had significantly higher T follicular helper cell and monocyte infiltration

  • In LUAD, GREM1 expression is associated with infiltration of multiple immune cell types, including CD4 memory T cells, NK cells, monocytes, and macrophages

These findings suggest that anti-GREM1 antibodies might enhance the efficacy of immune checkpoint inhibitors in combination therapy approaches.

What are the key considerations for designing experiments to evaluate anti-GREM1 antibody efficacy in different disease models?

When designing experiments to evaluate anti-GREM1 antibody efficacy, researchers should consider:

• Selection of appropriate disease models:

  • Different disease models show variable GREM1 dependency (e.g., effective in intestinal fibrosis and pulmonary hypertension models but not in liver fibrosis)

  • Consider both genetic (GREM1 overexpression/knockdown) and pharmacological (antibody treatment) approaches

• Antibody characteristics:

  • Ensure antibody cross-reactivity if using mouse models (human antibodies may not recognize mouse GREM1)

  • Consider generating chimeric antibodies (e.g., Ab7326 mIgG1 with human variable regions and mouse constant regions) for rodent studies

  • Verify functional activity in relevant cell types before in vivo studies

• Endpoint measurements:

  • Include molecular (pSMAD1/8, ID1 expression), cellular (proliferation, migration), and physiological (organ function) readouts

  • For cancer models, assess both tumor growth and immune infiltration

  • For fibrosis models, quantify ECM deposition and fibroblast activation

• Combination strategies:

  • Test anti-GREM1 antibodies in combination with standard therapies (e.g., chemotherapy for cancer, anti-TNF-α for inflammatory conditions)

  • Evaluate synergy with immune checkpoint inhibitors in cancer models

How can researchers differentiate between BMP-dependent and BMP-independent effects of GREM1 when interpreting antibody inhibition data?

Differentiating between BMP-dependent and BMP-independent effects requires careful experimental design:

• Mechanistic validation approaches:

  • Compare anti-GREM1 antibody effects with direct BMP pathway activation (using recombinant BMPs)

  • Use pathway-specific inhibitors alongside anti-GREM1 antibodies (e.g., VEGFR2 inhibitors like SU5408)

  • Employ genetic approaches (e.g., SMAD knockdowns) to determine dependency on canonical BMP signaling

• Endpoint selection:

  • Include both BMP-specific readouts (pSMAD1/5/8, ID1 expression) and broader phenotypic endpoints

  • Monitor MAPK pathway activation to detect VEGFR2-dependent effects

  • Assess fatty acid oxidation to identify metabolic effects independent of canonical BMP signaling

• Context-dependent signaling:

  • Be aware that GREM1 can activate different signaling pathways in different cellular contexts (e.g., STAT3 in breast cancer but not in fibroblasts)

  • Use cell type-specific approaches to isolate effects in heterogeneous tissues

This comprehensive approach helps distinguish which therapeutic effects of anti-GREM1 antibodies depend on restored BMP signaling versus alternative mechanisms.

What are the technical challenges in measuring GREM1 protein expression and antibody binding in complex tissue samples?

Researchers face several technical challenges when measuring GREM1 expression and antibody binding:

• Antibody specificity and validation:

  • Commercial anti-GREM1 antibodies vary in specificity and performance across applications

  • Validate antibodies using positive and negative controls, including GREM1 knockout/knockdown samples

  • Consider using multiple antibodies targeting different epitopes to confirm findings

• Immunohistochemistry scoring system:

  • Semi-quantitative representation of GREM1 immunostaining requires standardized scoring

  • Most studies use a combined scoring system:

    • Staining intensity (0-3): 0 (unstained), 1 (light yellow/weak), 2 (brownish yellow/medium), 3 (brown/strong)

    • Quantity scores (0-3): Based on percentage of cells showing nuclear or cytoplasmic staining

• Cell type-specific expression:

  • GREM1 is often produced by stromal cells (e.g., fibroblasts) but affects epithelial or cancer cells

  • Single-cell approaches or co-localization studies may be needed to identify cell type-specific expression

  • In co-culture systems, determine which cell types produce versus respond to GREM1

• Secreted nature of GREM1:

  • As a secreted protein, tissue levels may not reflect local concentrations

  • Consider measuring both tissue-bound and soluble GREM1 in biological fluids

  • Proximity ligation assays can detect GREM1-BMP or GREM1-receptor complexes in tissues

What is the current status of anti-GREM1 antibodies in therapeutic development?

The development of anti-GREM1 antibodies as therapeutics is still in early clinical phases:

• Ginisortamab (UCB6114):

  • First-in-class clinical anti-human gremlin-1 antibody

  • Currently in Phase 1/2 clinical trial (NCT04393298) for patients with advanced solid tumors

  • Fully human IgG4P antibody with the IgG4 heavy chain sequence modified in its hinge region to reduce chain exchange

• Preclinical development:

  • Ab7326 mIgG1: Mouse analog of ginisortamab used in preclinical models

  • VEGFR2 inhibitor SU5408 has been used to disrupt GREM1-VEGFR2 axis signaling as an alternative approach

  • Anti-GREM1 antibody has shown efficacy in pulmonary arterial hypertension models

• Combination approaches:

  • Blocking the GREM1-VEGFR2 axis combined with anti-TNF-α therapy provided benefits in mice with intestinal fibrosis

  • Potential for combination with immune checkpoint inhibitors based on immunomodulatory effects

The field is still evolving, with ongoing efforts to develop more specific neutralizing antibodies or peptides that block interactions between GREM1 and VEGFR2.

How does GREM1 expression correlate with treatment response in different disease contexts?

GREM1 expression shows significant correlations with treatment responses across multiple diseases:

• Cancer immunotherapy:

  • In bladder cancer, GREM1 expression was significantly lower in patients who responded to immunotherapy

  • TIDE scores (predicting immunotherapy response) were positively correlated with GREM1 expression

  • 61% of patients in the low-GREM1 group were predicted to respond to immunotherapy, compared to only 13.3% in the high-GREM1 group

• Chemotherapy sensitivity:

  • In bladder cancer, patients with high GREM1 expression showed increased sensitivity to several chemotherapeutic agents:

    • Cisplatin

    • Docetaxel

    • Gemcitabine

    • Vinblastine

• Disease-specific variations:

  • GREM1 upregulation in fibrotic diseases does not uniformly predict treatment response

  • Anti-GREM1 antibody was effective in pulmonary hypertension models but not in liver fibrosis models

This heterogeneous correlation suggests that GREM1 expression may serve as a biomarker for treatment selection in some, but not all, disease contexts.

What are the potential biomarkers to predict efficacy of anti-GREM1 antibody therapy?

Several potential biomarkers could predict anti-GREM1 antibody efficacy:

• BMP pathway activation status:

  • Baseline pSMAD1/5/8 levels may indicate BMP pathway suppression by GREM1

  • ID1 expression levels as a readout of BMP signaling activity

  • BMP receptor expression profiles to determine potential for pathway reactivation

• GREM1 expression patterns:

  • Tissue GREM1 levels by immunohistochemistry using standardized scoring systems

  • Circulating GREM1 levels in plasma or serum

  • Cell type-specific GREM1 expression (stromal vs. epithelial)

• Immune microenvironment characteristics:

  • Immune cell infiltration patterns, particularly T follicular helper cells and monocytes

  • Expression of immune checkpoints (CD274, PDCD1, CTLA4)

  • TIDE scores to predict immunotherapy response potential

• Disease-specific markers:

  • For fibrotic diseases: myofibroblast activation markers, ECM deposition

  • For cancer: tumor grade, metastasis status, stemness index (which negatively correlates with GREM1 expression)

Integration of these biomarkers into a comprehensive panel could help identify patients most likely to benefit from anti-GREM1 therapies.

What are the recommended protocols for validating anti-GREM1 antibodies for specific applications?

Validation protocols should be application-specific:

Western Blot Validation:

• Sample preparation:

  • Include positive controls (tissues with known high GREM1 expression)

  • Include negative controls (GREM1 knockdown samples)

  • Prepare both tissue and cell lysates appropriately

• Protocol specifics:

  • Use appropriate antibody concentrations (typically 1:1000 dilution)

  • Include loading controls (e.g., GAPDH)

  • Detect with appropriate secondary antibodies (e.g., IR 680 and IR 800)

  • Perform densitometric analysis using appropriate scanners (e.g., Li-Cor Odyssey)

Immunohistochemistry Validation:

• Sample preparation:

  • Use properly fixed tissues (formalin-fixed, paraffin-embedded)

  • Include both normal and diseased tissues

• Protocol specifics:

  • Antigen retrieval: Usually citrate buffer pH 6.0

  • Primary antibody incubation: 1.5 hours at room temperature

  • Secondary antibody: HRP-linked, 30 min incubation

  • Development: Diaminobenzidine with hematoxylin counterstain

  • Scoring: Use standardized intensity (0-3) and quantity (0-3) scoring systems

Functional Validation:

• BMP reporter assay:

  • Use HEK-ID1 luciferase reporter cells

  • Pre-incubate antibody with GREM1 protein (45 min, 37°C)

  • Add BMP ligands and incubate (24h)

  • Detect luciferase signal using Steady Glo or Bright-Glo

What experimental designs are most appropriate for studying GREM1 function in different cellular contexts?

Research questions dictate experimental design choices:

For Cancer Research:

• Cell models:

  • Cancer cell lines with variable GREM1 expression

  • Co-culture systems with fibroblasts (which often produce GREM1) and cancer cells

  • 3D organoid cultures to assess invasion and metastatic potential

• Functional assays:

  • Proliferation, migration, invasion assays

  • EMT marker assessment (E-cadherin, vimentin, etc.)

  • pSMAD1/8 phospho-flow assays to assess BMP pathway activity

For Fibrosis Research:

• Cell models:

  • Primary fibroblasts or stellate cells

  • Co-culture with epithelial cells

  • Ex vivo tissue explants

• Functional assays:

  • Fibroblast activation (α-SMA expression)

  • ECM production (collagen expression)

  • Contractility assays

  • Fatty acid oxidation measurements

For Immune Function Research:

• Cell models:

  • Immune cell isolation and culture

  • Co-culture with tumor cells or fibroblasts

  • Patient-derived samples

• Functional assays:

  • Immune cell infiltration analysis using CIBERSORT or MCP counter algorithms

  • Flow cytometry for immune cell activation markers

  • Cytokine/chemokine production assays

What are the recommended gene expression analysis methods for studying GREM1 and related pathway components?

Multiple approaches are recommended for comprehensive gene expression analysis:

RT-qPCR Analysis:

• Validated primers and probes:

  • Human GREM1: Hs01879841_s1

  • Mouse Grem1: Mm00488615_s1

  • Human BMPR2: Hs00176148_m1

  • Mouse Id1: Mm00775963_g1

  • Mouse Pai1: Mm00435860_m1

  • Housekeeping genes: GAPDH (Human: 4333764F, Mouse: Mm99999915_g1)

• Protocol considerations:

  • Use validated reference genes for normalization

  • Include both BMP pathway components and GREM1-responsive genes

  • Analyze data using the comparative Ct (2^-ΔΔCt) method

Pathway Analysis:

• Gene Ontology (GO) enrichment analysis:

  • Selection criteria: P value < 0.05 and Q < 0.05

  • Examine molecular function (MF), biological process (BP), and cellular component (CC)

• KEGG pathway analysis:

  • Selection criteria: P value < 0.05 and Q < 0.05

• Gene Set Enrichment Analysis (GSEA):

  • Focus on REACTOME pathways

  • Selection criteria: P value < 0.05 and FDR < 25%

• Protein-protein interaction networks:

  • Use tools like GeneMANIA (www.genemania.org)[1]

Immune-Related Analysis:

• Immune cell infiltration:

  • CIBERSORT algorithm to compare immune cell contents

  • MCP counter algorithm to examine associations between GREM1 and immune cells

• Stemness and treatment response:

  • mRNA expression-based stemness index (mRNAsi)

  • Tumor Immune Dysfunction and Exclusion (TIDE) algorithm for immunotherapy response prediction