GNA13 Antibody

What is GNA13 and why is it an important research target?

GNA13 (guanine nucleotide binding protein, alpha 13) is a 44 kDa G-protein with critical roles in cellular signal transduction pathways. Research has demonstrated its involvement in multiple oncogenic processes, making it a significant target for cancer research. GNA13 has been shown to promote tumor growth and progression in several cancer types, including gastric cancer where it mediates cell proliferation and tumorigenicity both in vitro and in vivo . Additionally, GNA13 has been implicated in angiogenesis promotion in colorectal cancer and serves as a prognostic marker in follicular lymphoma . As a G-protein, GNA13 functions as a molecular switch in signal transduction pathways, particularly those involved in cell proliferation, migration, and invasion – key processes in cancer development and progression.

How do I select the appropriate GNA13 antibody for my experimental applications?

When selecting a GNA13 antibody, researchers should consider several criteria: the specific application (Western blot, immunohistochemistry, immunofluorescence), species reactivity, clone type, and validation data. Based on the search results, researchers have multiple validated options. For example, the 67188-1-Ig monoclonal antibody shows reactivity with human, mouse, rat, and pig samples and is validated for Western blot (1:1000-1:6000 dilution), immunohistochemistry (1:400-1:1600 dilution), and immunofluorescence (1:400-1:1600 dilution) . Alternatively, the EPR5436 clone (ab128900) is a rabbit monoclonal with similar applications and species reactivity . Researchers should examine validation data, including knockout controls as shown for ab128900 in HeLa cells, to ensure specificity. When working with novel tissue types or experimental conditions, antibody titration is recommended to determine optimal working concentrations for your specific experimental system.

What are the recommended protocols for GNA13 detection in Western blot applications?

For optimal Western blot detection of GNA13, researchers should follow validated protocols based on the specific antibody being used. From the search results, the recommended dilution ranges from 1:1000 to 1:6000 for 67188-1-Ig and 1:1000 to 1:5000 for ab128900 . When working with human samples, several cell lines have been successfully used as positive controls, including HEK-293, Y79, HepG2, and PC-12 cells . For animal studies, brain tissue from mouse, rat, and pig has demonstrated reliable GNA13 detection . The expected molecular weight of GNA13 is approximately 44 kDa, though it may appear at 40-45 kDa depending on the experimental system. For blocking, 5% non-fat dry milk in TBST has been validated with multiple antibodies . When troubleshooting, researchers should consider using knockout controls, as demonstrated with the ab128900 antibody in HeLa cell lysates, which provides confirmation of antibody specificity . Additionally, proper sample preparation is crucial, with samples typically prepared under reducing conditions at 20 μg protein per lane for optimal results.

How can I optimize immunohistochemistry protocols for GNA13 detection in different tissue types?

Optimizing immunohistochemistry (IHC) protocols for GNA13 detection requires careful consideration of antigen retrieval methods, antibody concentration, and detection systems. Based on the search results, heat-mediated antigen retrieval is essential, with two effective buffer systems reported: citrate buffer (pH 6.0) and EDTA buffer (pH 9.0) . The optimal antibody dilution ranges from 1:250 to 1:1600 depending on the specific antibody and tissue type . For the EPR5436 clone (ab128900), a concentration of 1.2 μg/ml (approximately 1:1000 dilution) has been validated across multiple tissue types including human bladder carcinoma and rodent stomach tissues . The immunohistochemical staining pattern for GNA13 typically shows cytoplasmic and membranous distribution in epithelial cells, with increased intensity in tumor tissues compared to normal tissues . When developing a new IHC protocol, researchers should include appropriate positive controls (such as bladder carcinoma or kidney tissue) and negative controls (primary antibody omission). For challenging tissue types, researchers may need to test both retrieval methods, as some tissues respond better to citrate while others to EDTA-based retrieval, as noted in the validation data for 67188-1-Ig antibody .

What are the validated approaches for GNA13 knockdown and overexpression studies?

For functional studies investigating GNA13's role in cellular processes, both knockdown and overexpression approaches have been validated in the literature. For overexpression, GNA13 cDNA subcloned into pcDNA3.1 vector with transfection using Lipofectamine 2000 has been successfully employed . Following transfection, stable cell lines can be selected using G418 (5 μg/mL) over approximately two weeks . For knockdown studies, shRNA approaches targeting GNA13 have been validated, with commercial shRNA plasmids available from sources such as Santa Cruz Biotechnology . Stable knockdown cell lines can be selected using puromycin (3 μg/mL) for approximately two weeks . The efficacy of both knockdown and overexpression should be validated using Western blot with anti-GNA13 antibodies, with expected changes in the 44 kDa band intensity. These genetic manipulation approaches have successfully demonstrated GNA13's role in promoting cell proliferation, G1/S cell cycle transition, and tumor formation in gastric cancer models , as well as its function in angiogenesis in colorectal cancer . When designing these experiments, researchers should include appropriate vector-only controls and validate multiple clones to account for potential insertion site effects.

What signaling pathways does GNA13 regulate in cancer progression?

GNA13 regulates multiple signaling cascades critical for cancer progression, primarily functioning through PI3K/AKT and MAPK/ERK signaling pathways. Research in gastric cancer has demonstrated that GNA13 promotes G1/S cell cycle transition through several mechanisms: upregulation of c-Myc transcriptional activity, suppression of FOXO1 activity, enhanced AKT and ERK activity, upregulation of cyclin D1, and downregulation of CDK inhibitors p21Cip1 and p27Kip1 . This coordinated signaling activity results in accelerated cell proliferation and tumorigenicity both in vitro and in vivo. Additionally, in colorectal cancer, GNA13 has been shown to promote tumor growth and angiogenesis, suggesting involvement in VEGF-related signaling pathways . These findings indicate that GNA13 functions as an oncogenic signal transducer, integrating multiple pathways that drive cancer cell proliferation, survival, and angiogenesis. When investigating these pathways, researchers should consider downstream effector proteins (AKT, ERK, c-Myc, cyclin D1, p21, p27) as experimental readouts to assess GNA13 activity and inhibition in their experimental models.

How can GNA13 expression be effectively quantified in tumor samples for research purposes?

Effective quantification of GNA13 expression in tumor samples can be achieved through multiple complementary approaches. Immunohistochemistry (IHC) has been extensively validated for GNA13 quantification in formalin-fixed paraffin-embedded (FFPE) clinical samples. For standardized scoring, researchers have successfully employed the X-tile program to determine optimal cutoff values, dividing samples into high and low expression groups . This approach demonstrated significance in both training (p<0.01) and validation cohorts (p<0.01) . For precise quantification, qPCR analysis has been validated for GNA13 mRNA expression assessment in fresh tissue samples, showing concordance with protein expression patterns . Western blot analysis provides another quantitative approach, with validated protocols for protein extraction from both fresh tissues and cell lines . When analyzing tissue microarrays, a double-blind experimental design with independent scoring by multiple individuals enhances reliability . For optimal results in clinical correlation studies, researchers should consider employing multiple detection methods (IHC, qPCR, Western blot) when sample type permits, as this approach has successfully established GNA13 as both a biomarker and functional driver in gastric cancer, colorectal cancer, and follicular lymphoma .

What are the technical challenges in distinguishing GNA13 from other G-protein family members?

Distinguishing GNA13 from other closely related G-protein family members presents significant technical challenges due to sequence homology and shared functional domains. G-proteins, particularly alpha subunits, share structural similarities that can lead to antibody cross-reactivity. Researchers should select antibodies specifically validated against potential cross-reactive G-protein family members. The monoclonal antibodies described in the search results (67188-1-Ig and ab128900) have demonstrated specificity for GNA13 . Notably, the validation of ab128900 using GNA13 knockout cell lines provides strong evidence for specificity, as demonstrated by the complete loss of the 45 kDa band in knockout HeLa cells while maintaining GAPDH detection at 37 kDa . For researchers developing new detection methods, specificity testing should include both positive controls (known GNA13-expressing cells like HEK-293, Y79, HepG2) and negative controls (ideally knockout lines) . When performing functional studies, researchers should consider the potential for compensatory upregulation of other G-protein family members following GNA13 manipulation, which may confound interpretation of phenotypic effects. RNA-based detection methods (RT-PCR, RNA-seq) with carefully designed primers can offer higher specificity than protein-based methods in some experimental contexts.

How can contradictory data regarding GNA13 function in different cancer models be reconciled?

Reconciling contradictory data on GNA13 function across different cancer models requires careful consideration of cellular context, experimental methodology, and technical variables. While the search results predominantly show GNA13 as oncogenic in gastric cancer, colorectal cancer, and follicular lymphoma , researchers should recognize that G-protein signaling is highly context-dependent. When encountering contradictory findings, researchers should first examine methodological differences in GNA13 detection, scoring criteria (for IHC studies), and functional assay conditions. For example, the cutoff values used to define "high" versus "low" expression can significantly impact clinical correlations, as demonstrated by the use of X-tile program to determine statistically optimal cutpoints . Additionally, researchers should consider cancer-specific molecular contexts, as GNA13 may interact with different downstream effectors depending on the mutational landscape and expression profiles of other signaling components. To address contradictions methodologically, researchers should: (1) employ multiple independent GNA13 antibodies to verify expression patterns, (2) validate functional findings using both overexpression and knockdown approaches, (3) test findings across multiple cell lines representing the same cancer type, and (4) correlate in vitro findings with clinical data whenever possible. This comprehensive approach has successfully established GNA13's role in gastric cancer progression through multiple complementary methods .

What are the validated approaches for studying GNA13 interactions with downstream effector proteins?

Studying GNA13 interactions with downstream effector proteins requires specialized techniques that have been validated in published research. Co-immunoprecipitation (CoIP) has been successfully employed with the 67188-1-Ig antibody, allowing researchers to pull down GNA13 protein complexes and identify interacting partners . For investigating the activation state of downstream signaling pathways, Western blot analysis of phosphorylated effector proteins has proven effective, particularly for AKT and ERK pathways in gastric cancer models . Researchers have successfully demonstrated GNA13's impact on c-Myc and FOXO1 transcriptional activity, suggesting that chromatin immunoprecipitation (ChIP) assays and reporter gene assays are valuable approaches for studying transcriptional regulation downstream of GNA13 . For comprehensive pathway analysis, researchers have effectively combined overexpression and knockdown approaches to demonstrate causality in GNA13-mediated regulation of cyclin D1, p21Cip1, and p27Kip1 expression . When designing interaction studies, researchers should consider both direct binding partners and secondary effectors that may be regulated through intermediate signaling components. Validation with multiple experimental approaches is recommended, as exemplified by studies that confirmed GNA13's oncogenic functions through complementary in vitro and in vivo models .

How can GNA13 expression analysis be incorporated into cancer biomarker panels?

Incorporating GNA13 expression analysis into cancer biomarker panels requires standardized assessment methods and clear cutoff criteria. Based on the search results, immunohistochemistry (IHC) offers the most clinically applicable approach for FFPE tissue samples routinely collected in pathology workflows. For standardization, researchers have successfully employed the X-tile program to determine statistically optimal cutoff scores for defining high versus low GNA13 expression . This approach demonstrated significance in both training and validation cohorts of gastric cancer patients . For follicular lymphoma, GNA13 positivity (observed in 15.6% of cases) significantly correlated with early disease progression and poorer survival outcomes . When developing a biomarker panel, researchers should consider combining GNA13 with established prognostic factors such as the Follicular Lymphoma International Prognostic Index . For research applications transitioning toward clinical implementation, tissue microarray analysis with double-blind scoring by multiple pathologists enhances reliability . The search results validate antibody dilutions of 1:400-1:1600 for IHC applications , providing a starting point for protocol standardization. Researchers developing GNA13-based biomarker panels should validate their protocols across multiple cancer types and correlate findings with established clinicopathological parameters to determine the additional prognostic value that GNA13 assessment contributes.

What is the evidence for GNA13 as a potential therapeutic target in cancer?

Multiple lines of evidence support GNA13 as a promising therapeutic target in cancer, particularly in gastric cancer where detailed mechanistic studies have been conducted. In vitro and in vivo experiments have demonstrated that knockdown of GNA13 effectively suppresses the proliferation and tumorigenicity of gastric cancer cells, while overexpression promotes these oncogenic phenotypes . Mechanistically, GNA13 promotes cancer progression through multiple pathways amenable to therapeutic intervention, including PI3K/AKT and MAPK/ERK signaling, c-Myc transcriptional activity, and cell cycle regulation through cyclin D1, p21Cip1, and p27Kip1 . In colorectal cancer, GNA13 has been implicated in tumor growth and angiogenesis, suggesting potential for combination therapies targeting both cancer cells and tumor vasculature . Furthermore, clinical correlations in follicular lymphoma demonstrate that GNA13-positive cases have poorer outcomes, indicating a patient population that might benefit from GNA13-targeted therapeutics . When designing drug discovery programs targeting GNA13, researchers should consider both direct inhibition strategies and approaches targeting downstream effectors like AKT or ERK. The successful application of shRNA knockdown in multiple experimental systems provides proof-of-concept for RNA-based therapeutic approaches , while the identification of GNA13's role in multiple signaling pathways offers opportunities for synthetic lethality approaches combining GNA13 inhibition with existing targeted therapies.

What are the key considerations for developing GNA13-based diagnostic assays for clinical research?

Developing GNA13-based diagnostic assays for clinical research requires careful optimization of detection methods, validation across diverse patient cohorts, and clear interpretation guidelines. Based on the search results, immunohistochemistry (IHC) represents the most clinically applicable approach, with validated protocols using both citrate (pH 6.0) and EDTA (pH 9.0) antigen retrieval methods . When transitioning from research to clinical application, researchers should conduct comprehensive antibody validation including positive and negative tissue controls, and ideally knockout controls as demonstrated for ab128900 . Standardized scoring systems should be implemented, with the X-tile program providing an evidence-based approach for determining optimal cutoff values . Multicenter validation is essential, as demonstrated by studies that confirmed GNA13's prognostic significance in independent patient cohorts . Researchers developing diagnostic assays should ensure adequate representation of diverse patient populations and disease subtypes, particularly given GNA13's variable expression across cancer types. For example, in follicular lymphoma, only 15.6% of cases were GNA13-positive, but this subgroup showed significantly poorer outcomes . Quality control measures should include both technical controls (antibody validation) and biological controls (correlation with established biomarkers and clinicopathological parameters) to ensure reliable performance across testing sites and patient populations.