GPSM3 Antibody

What is GPSM3 and what are its key molecular interactions?

GPSM3 functions as a GDP dissociation inhibitor that binds inactive Gαi, preventing Gβγ association. Beyond this established role, research has revealed that GPSM3 also directly interacts with Gβ subunits, which provides new insights into G-protein signaling regulation. Yeast two-hybrid screens using full-length GPSM3 as bait have identified Gβ1 subunit as a novel interacting partner, while co-immunoprecipitation experiments have confirmed GPSM3's interaction with all four conventional Gβ subunits . This unique finding expands our understanding of GPSM3 beyond its traditional characterization as a Gαi·GDP-interacting protein via its GoLoco motifs.

When designing experiments to study GPSM3, consider its dual interactions with both Gα and Gβ subunits and how this may influence interpretation of results. Successful experimental approaches have included co-immunoprecipitation, BiFC overexpression, FRET analysis, and confocal microscopy for co-localization studies.

How should GPSM3 antibodies be selected for specific experimental applications?

Selection criteria should be based on:

• Target reactivity: Determine whether human, mouse, or rat GPSM3 detection is required for your experimental model. Commercial antibodies show various reactivity patterns, with some recognizing GPSM3 across species while others are species-specific .

• Application compatibility: Different experimental techniques require antibodies validated for specific applications. Consider whether your research requires antibodies optimized for:

  • Western blotting (WB)

  • Enzyme-linked immunosorbent assay (ELISA)

  • Immunohistochemistry (IHC)

  • Immunofluorescence (IF)

  • Immunoprecipitation (IP)

• Validation data: Prioritize antibodies with extensive validation. For example, antibody ABIN528490 has three validations for human GPSM3 in ELISA and WB applications, while ABIN6262066 has been validated for detection across human, mouse, and rat samples .

How can GPSM3 antibodies be optimized for co-immunoprecipitation experiments to study G-protein interactions?

When designing co-immunoprecipitation experiments to study GPSM3 interactions with G-proteins:

• Buffer consideration: Since GPSM3 interacts with both Gα and Gβ subunits, consider that these interactions may be differentially affected by buffer conditions. Previous successful co-immunoprecipitation experiments were performed without added nucleotide or aluminum tetrafluoride, avoiding forced active Gα nucleotide states that could release Gβ protein from intact heterotrimers .

• Antibody orientation: Both forward and reverse co-immunoprecipitation approaches have been validated. You can immunoprecipitate:

  • GPSM3 to detect co-precipitated G-protein subunits

  • G-protein subunits (Gβ or Gα) to detect co-precipitated GPSM3

• Endogenous vs. overexpression systems: While overexpression systems provide strong signals, endogenous detection has been successful in monocytic THP-1 cell lines using anti-GPSM3 monoclonal antibodies (such as clone 35.5.1) .

• Detection challenges: Pay particular attention to the detection of Gγ subunits due to their small molecular weight. Reciprocal co-immunoprecipitation experiments can help exclude technical issues in Gγ detection .

• Control experiments: Include appropriate controls to distinguish specific GPSM3-G protein interactions from non-specific binding. Controls should include isotype-matched irrelevant antibodies and lysates from cells where GPSM3 expression has been silenced.

What methodological approaches are effective for analyzing GPSM3's role in immune cell function?

GPSM3 is preferentially expressed in cells of hematopoietic origin, making it an important target for immunological research. Several methodological approaches have proven effective:

• Genetic manipulation in immune cell lines: THP-1 monocytic cells have been successfully transduced with control or GPSM3-silencing shRNA constructs (e.g., sh19 or sh20). Selection in puromycin (2.5 μg/ml) followed by immunoblot confirmation provides a reliable knockdown model .

• Flow cytometry approaches: For analyzing GPSM3's impact on immune cell subpopulations, consider co-staining with immune cell markers such as CD14 (for monocytes) alongside GPSM3 .

• Immune cell recruitment assays: Given GPSM3's role in immune function, chemotaxis assays using relevant chemokines (available from suppliers like PeproTech) can assess functional outcomes of GPSM3 manipulation .

• Bioinformatic immune cell infiltration analysis: Several computational methods have been used to assess GPSM3's relationship with immune cell infiltration:

  • ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data)

  • CIBERSORT (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts)

  • TIMER2.0 (Tumor Immune Estimation Resource)

When designing these experiments, consider appropriate controls and validation approaches for both antibody-based detection and functional outcomes.

How can GPSM3 antibodies be utilized to investigate its role in autoimmune diseases?

GPSM3 polymorphisms have been inversely associated with four systemic autoimmune diseases, including rheumatoid arthritis, making it an important research target . Researchers can apply several methodological approaches:

• Genotype-phenotype correlation studies:

  • Use GPSM3 antibodies to quantify protein expression levels in samples with known GPSM3 genetic variants

  • Compare GPSM3 expression between control subjects and patients with autoimmune conditions

  • Correlate GPSM3 expression levels with disease activity markers

• Signaling pathway analysis:

  • Investigate how GPSM3 affects G-protein signaling in immune cells isolated from autoimmune disease patients

  • Analyze downstream effects on chemokine receptor signaling, which influences immune cell recruitment

  • Utilize phospho-specific antibodies alongside GPSM3 antibodies to map signaling cascades

• Ex vivo functional studies:

  • Isolate primary immune cells from peripheral blood

  • Analyze GPSM3 expression by immunoblotting or flow cytometry

  • Correlate expression with functional outcomes such as chemotaxis, cytokine production, or activation status

• Therapeutic intervention models:

  • Monitor changes in GPSM3 expression following immunomodulatory treatments

  • Investigate whether GPSM3 levels could serve as biomarkers for treatment response

These approaches require validated GPSM3 antibodies suitable for the specific application and appropriate controls to account for variations in sample processing and cell types.

What approaches should researchers use when investigating GPSM3 in cancer, particularly in gliomas?

Recent research has identified GPSM3 as a potential prognostic biomarker in low-grade gliomas (LGG). When investigating GPSM3 in cancer contexts:

• Expression analysis methodologies:

  • RT-qPCR for mRNA quantification in clinical samples

  • Immunohistochemistry using validated anti-GPSM3 antibodies on tissue microarrays

  • Western blotting for protein expression in tumor lysates

  • Analysis of public datasets (TCGA, CGGA) for correlation with clinical parameters

• Prognostic value assessment:

  • Multivariate Cox regression models have demonstrated GPSM3 expression as an independent prognostic factor in LGG patients

  • Kaplan-Meier survival analysis stratified by GPSM3 expression levels

  • Integration with other clinical parameters (age, tumor grade, molecular markers)

• Tumor microenvironment characterization:

  • Correlation of GPSM3 expression with:

    • Immune score (positive correlation)

    • Stromal scores (positive correlation)

    • ESTIMATE scores (positive correlation)

    • Tumor purity (negative correlation)

  • Analysis of immune cell infiltration patterns in GPSM3-high versus GPSM3-low tumors

• Immune checkpoint correlation:

  • GPSM3 expression exhibits significant correlations with immune checkpoint-related genes, especially:

    • PD-1 (Programmed cell death protein 1)

    • PD-L1 (Programmed death-ligand 1)

    • PD-L2 (Programmed death-ligand 2)

    • CTLA4 (Cytotoxic T-lymphocyte-associated protein 4)

    • TIM3 (T-cell immunoglobulin and mucin-domain containing-3)

What are common technical challenges when working with GPSM3 antibodies and how can they be addressed?

Several technical challenges may arise when working with GPSM3 antibodies:

• Specificity concerns:

  • Challenge: Cross-reactivity with other G-protein signaling modulators (GPSM1, GPSM2) or related proteins

  • Solution: Validate antibody specificity using knockout/knockdown controls; GPSM3-silenced THP-1 cells (using constructs like sh19 or sh20) serve as excellent negative controls

• Subcellular localization detection:

  • Challenge: GPSM3 exhibits both plasma membrane and juxtanuclear (Golgi proximal) localization patterns

  • Solution: Optimize fixation protocols; paraformaldehyde fixation followed by mild permeabilization has been successful for visualizing both pools

• Co-immunoprecipitation difficulties:

  • Challenge: Transient or weak interactions between GPSM3 and G-protein subunits

  • Solution: Consider crosslinking approaches or optimize lysis conditions; avoid nucleotides or aluminum tetrafluoride that might disrupt G-protein interactions

• Detection of endogenous GPSM3:

  • Challenge: Low expression levels in non-hematopoietic cells

  • Solution: Focus on cells with known expression (THP-1, primary monocytes); consider enrichment steps before detection; use monoclonal antibodies with demonstrated sensitivity (e.g., clone 35.5.1)

• Preservation of functional epitopes:

  • Challenge: Some fixation methods may destroy antibody epitopes

  • Solution: Compare multiple fixation protocols; consider native conditions for functional studies

How should researchers interpret contradictory results in GPSM3 signaling experiments?

When faced with contradictory results in GPSM3 experiments:

• Consider GPSM3's dual binding partners:

  • GPSM3 interacts with both Gαi (via GoLoco motifs) and Gβ subunits

  • These interactions may produce seemingly contradictory effects depending on experimental context

  • For example, GPSM3 inhibits both GPCR-mediated signaling and direct Gβγ-stimulated PLCβ2 activation

• Cell type-specific effects:

  • GPSM3 is predominantly expressed in hematopoietic cells

  • Effects observed in overexpression systems (HEK293, COS-7) may differ from endogenous systems (THP-1)

  • Always consider the cellular context when interpreting results

• Subcellular localization variations:

  • GPSM3 functions at both plasma membrane and intracellular locations

  • Evaluate whether contradictory results might reflect different subcellular pools

  • Confirm localization through microscopy alongside functional studies

• Experimental approach differences:

  • Results from overexpression versus knockdown approaches may differ

  • Recombinant versus endogenous protein interactions may show different properties

  • In vitro versus cellular assays may not align perfectly

• Statistical analysis considerations:

  • Ensure appropriate statistical tests for your experimental design

  • For clinical correlations, univariate and multivariate analyses should both be considered

  • Account for potential confounding variables in disease studies

How is GPSM3 being explored as a therapeutic target in autoimmune and cancer contexts?

GPSM3 represents an intriguing target for therapeutic development based on several lines of evidence:

• Autoimmune disease applications:

  • Genetic evidence: GPSM3 polymorphisms are inversely associated with autoimmune diseases including rheumatoid arthritis

  • Mechanistic rationale: GPSM3 affects G-protein signaling in immune cells, potentially modulating chemokine receptor function

  • Therapeutic approaches being explored:

    • Small molecule modulators of GPSM3-G protein interactions

    • Targeting GPSM3 expression in specific immune cell populations

    • Exploiting GPSM3's role as a biomarker for patient stratification

• Cancer immunotherapy relevance:

  • GPSM3 expression correlates with immune checkpoint genes (PD-1, PD-L1, PD-L2, CTLA4, TIM3)

  • GPSM3-high tumors show distinct immune infiltration patterns (higher regulatory T cells, neutrophils, M2 macrophages)

  • Potential applications:

    • Companion diagnostic for immune checkpoint inhibitor therapy

    • Combinatorial target with established immunotherapies

    • Biomarker for predicting immunotherapy response

• Technical considerations for therapeutic development:

  • Target validation approaches using GPSM3 antibodies:

    • Immunohistochemistry for patient stratification

    • Flow cytometry for monitoring immune populations

    • Proximity-based assays to screen for interaction disruptors

Researchers exploring therapeutic applications should consider both direct GPSM3 targeting and its utility as a biomarker for existing therapeutic approaches.

What are the key methodological considerations for studying GPSM3 epigenetic regulation?

Evidence suggests that GPSM3 expression is regulated by epigenetic mechanisms, with DNA methylation negatively correlating with GPSM3 expression in low-grade gliomas . Key methodological considerations include:

• DNA methylation analysis approaches:

  • Bisulfite sequencing of GPSM3 promoter regions

  • Methylation-specific PCR for targeted analysis

  • Correlation analysis between methylation arrays and expression data

  • Integration with public methylation datasets (TCGA, CGGA)

• Experimental manipulation of methylation status:

  • Treatment with demethylating agents (5-azacytidine, decitabine)

  • Monitoring GPSM3 expression changes via RT-qPCR and Western blot

  • Specific antibodies for detecting GPSM3 protein following epigenetic manipulation

• Chromatin immunoprecipitation (ChIP) applications:

  • Analysis of histone modifications at the GPSM3 locus

  • Investigation of transcription factor binding affected by methylation

  • Integration with GPSM3 expression data

• Cell type-specific considerations:

  • Compare methylation patterns across relevant cell types (immune cells, tumor cells)

  • Analyze correlation between cell-specific methylation and GPSM3 function

  • Consider developmental changes in methylation patterns

These approaches require careful experimental design, including appropriate controls and validation of findings across multiple techniques and cell systems.