GLIPR2 Antibody

What is GLIPR2 and what cellular functions does it perform?

GLIPR2, also known as Golgi-associated plant pathogenesis-related protein 1 (GAPR1) or C9orf19, is a multifunctional Golgi membrane protein that has gained increasing attention in cancer biology research . This protein is implicated in several critical cellular processes, most notably in the regulation of autophagy, where it functions as a negative regulator that inhibits the class III phosphatidylinositol 3-kinase complex I . GLIPR2 has emerged as a potential tumor suppressor in various cancer types, with particularly strong evidence in lung adenocarcinoma (LUAD) . The protein has dual engagement in both normal cellular processes and cancer biology, making it an intriguing target for both basic research and translational applications .

What are the molecular characteristics of GLIPR2?

GLIPR2 exists in multiple forms with the following molecular characteristics:

Understanding these basic molecular characteristics is essential for proper experimental design and interpretation of results when working with GLIPR2 antibodies .

How is GLIPR2 expression regulated in normal versus cancer tissues?

GLIPR2 expression is significantly reduced in neoplastic tissues compared to its prevalence in healthy tissues, suggesting its potential role as a tumor suppressor . Regulation of GLIPR2 expression involves multiple mechanisms including:

• Copy number variations (CNV) which show discernible correlations with GLIPR2 expression within tumor tissues .

• DNA methylation patterns, particularly at CpG sites proximal to the promoters, which significantly impact gene expression .

• Post-treatment expression changes, such as increased GLIPR2 levels observed in lung adenocarcinoma following radiotherapy .

These regulatory patterns make GLIPR2 an interesting target for both diagnostic and therapeutic applications in cancer research.

What antibodies are commercially available for GLIPR2 detection?

Based on available research tools, here is information about a validated GLIPR2 antibody:

This antibody has been validated in various applications and tissue types, making it suitable for diverse research approaches in studying GLIPR2 .

What experimental conditions are recommended for GLIPR2 antibody applications?

The following table summarizes recommended working conditions for different applications:

*Note: For IHC applications, antigen retrieval with TE buffer pH 9.0 is suggested, with citrate buffer pH 6.0 as an alternative . Researchers should optimize these conditions for their specific experimental systems to obtain optimal results.

What methodologies are most effective for analyzing GLIPR2 expression in tumor samples?

For comprehensive analysis of GLIPR2 in tumor samples, researchers should consider a multi-omics approach:

• RNA-seq Analysis: Leveraging data from sources like TCGA and GTEx, researchers can analyze GLIPR2 transcript levels across different cancer types. For data processing, logarithmic base 2 conversion (log2(value+1)) is recommended to accommodate zero values .

• Protein Expression Analysis: Using resources like the Human Protein Atlas (HPA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases to examine protein-level expression . Immunohistochemistry with appropriate controls is crucial for validation.

• Methylation Analysis: Employing tools like UALCAN and MEXPRESS to examine the correlation between GLIPR2 expression and DNA methylation patterns, particularly at CpG sites near promoter regions .

• Quantitative PCR Validation: For targeted validation studies, researchers can use established primer sequences: forward primer 5′-GAAGATGGGCGTGGGGAAGG-3′ and reverse primer 5′-TTACTTCTTCGGCGGCAGGAC-3′, normalizing to GAPDH .

This integrated approach provides a more comprehensive understanding of GLIPR2 dysregulation in cancer than any single method alone.

How can researchers validate GLIPR2 as a biomarker in lung adenocarcinoma?

To validate GLIPR2 as a biomarker in lung adenocarcinoma (LUAD), researchers should implement a systematic approach:

This multifaceted validation approach strengthens the case for GLIPR2 as both a diagnostic and prognostic biomarker in LUAD.

What is the relationship between GLIPR2 and immune infiltration in the tumor microenvironment?

GLIPR2 shows pronounced associations with immune checkpoint genes and the relative abundance of immune cells in the neoplastic microenvironment across various cancer types, with lung adenocarcinoma (LUAD) being particularly prominent . To investigate this relationship:

• Stromal and Immune Score Analysis: Use the ESTIMATE algorithm through the "estimate" package (Version R4.2.1) to analyze disparities in stromal score and immune score .

• Correlation with Immunological Parameters: Analyze associations between GLIPR2 expression and tumor mutation burden (TMB) as well as homologous recombination deficiency (HRD) using platforms like the Sanger Box and Pearson's rank correlation test .

• Immune Cell Infiltration Assessment: Quantify tumor-infiltrating immune cells (TIICs) using multiple independent algorithms including CIBERSORT, MCP-counter, EPIC, quanTIseq, XCELL, and TIMER to avoid calculation discrepancies . These algorithms help determine the extent of infiltration by various immune cell types including B cells, CD4+ T memory cells, CD8+ T cells, NK cells, monocytes, macrophages, and neutrophils.

• Single-cell RNA Sequencing Integration: Combine bulk RNA-seq with single-cell analysis to elucidate relationships among cellular heterogeneity, immune infiltration, and GLIPR2 levels in different cancer types .

This comprehensive approach can help uncover GLIPR2's potential as an immunotherapeutic target, particularly in LUAD where its involvement with immune cell infiltration has been noted.

What experimental approaches are recommended for studying GLIPR2's tumor suppressor function?

Based on published methodologies, researchers investigating GLIPR2's tumor suppressor function should consider:

• Gene Overexpression Studies:

  • Construct plasmids for GLIPR2 overexpression using high-fidelity PCR amplification to obtain GLIPR2 cDNA, which can then be inserted into appropriate vectors (e.g., GV208 plasmid at the Age I site) .

  • Transform purified plasmids into competent cells for amplification, followed by plasmid extraction.

  • Transfect target cells (e.g., H1299 cells) at 60%-70% confluence using Lipofectamine 3000 or similar transfection reagents .

• Functional Assays:

  • Transwell assays to assess migration and invasion capabilities .

  • Wound healing analyses to evaluate cell motility .

  • Drug sensitivity testing to determine if GLIPR2 modulation affects treatment response .

• Expression Validation:

  • Confirm GLIPR2 expression changes using qRT-PCR with validated primers, normalizing to GAPDH or other appropriate housekeeping genes .

  • Verify protein expression changes via Western blotting using antibodies at 1:500-1:1000 dilution .

  • Assess subcellular localization through immunofluorescence (1:50-1:500 dilution) in appropriate cell lines like A431 .

These approaches collectively provide a robust methodology for characterizing GLIPR2's potential tumor suppressor functions in various cancer models.

How can researchers use artificial intelligence to enhance GLIPR2-related research?

While traditional experimental approaches remain crucial, emerging AI technologies can significantly enhance GLIPR2 research:

• Protein Structure Prediction: Tools like AF2Complex can predict protein-protein interactions involving GLIPR2, particularly with binding partners that may mediate its function in autophagy or tumor suppression . This approach combines deep learning with advanced sequencing techniques to predict protein interactions.

• Epitope Mapping: AI algorithms can identify potential epitopes on GLIPR2 that could be targeted for therapeutic antibody development, similar to techniques used for SARS-CoV-2 spike protein .

• Data Integration: Developing AI models that integrate multi-omics data (transcriptomics, proteomics, methylation) can help identify novel relationships between GLIPR2 expression and clinical outcomes across different cancer types .

• Biomarker Panel Development: AI can help identify optimal combinations of biomarkers that include GLIPR2 for improved cancer detection and prognosis prediction, particularly in lung adenocarcinoma where GLIPR2 shows strong biomarker potential .

Researchers can apply these AI-driven approaches to accelerate discovery while validating findings through traditional experimental methods.