OSBPL3 Antibody

What is OSBPL3 and why is it significant in cancer research?

OSBPL3 (Oxysterol-binding protein-like 3) belongs to the oxysterol-binding protein family and plays a crucial role in intracellular lipid transport and metabolism. Its significance in cancer research stems from its upregulation in multiple malignancies, including liver hepatocellular carcinoma (LIHC), gastric cancer, colorectal adenocarcinoma, osteosarcoma, and testicular cancer . Research indicates that OSBPL3 functions as a promoter of cancer cell proliferation, invasion, and migration through modulation of the R-Ras/Akt signaling pathway . In LIHC specifically, overexpressed OSBPL3 correlates with higher tumor grades, more advanced stages, and poor clinical outcomes, suggesting its potential as both a biomarker and therapeutic target .

What are the validated applications for OSBPL3 antibodies in research?

Current research shows OSBPL3 antibodies are validated primarily for Western blot (WB), immunohistochemistry (IHC), and ELISA applications . For Western blot, recommended dilutions range from 1:200-1:2000 or 1:1000-1:4000 depending on the specific antibody . For immunohistochemistry, optimal dilutions typically fall between 1:50-1:500 . Positive controls for Western blot validation include human cell lines (Jurkat, HeLa, HepG2, DU145, HCT 116, K-562) and animal tissues (mouse heart, lung, brain, and rat kidney) .

What is the cellular localization of OSBPL3 and how does this affect experimental design?

OSBPL3 exhibits complex subcellular localization patterns that researchers must consider when designing experiments. The protein has been detected in the cytosol, filopodium tip, nuclear membrane, perinuclear endoplasmic reticulum, and plasma membrane . This diverse localization reflects OSBPL3's multifunctional role in lipid transport, signaling, and cell adhesion. For immunofluorescence studies, researchers should anticipate this distribution pattern and consider dual staining with organelle markers. The observed molecular weight in experimental settings typically ranges between 95-110 kDa, despite a calculated molecular weight of 101 kDa, suggesting potential post-translational modifications that may affect detection .

How should OSBPL3 knockdown experiments be designed for cancer studies?

Based on published methodologies, effective OSBPL3 knockdown experiments should incorporate both transient and stable approaches depending on experimental duration . For transient knockdown, researchers have successfully employed siRNAs (e.g., siOSBPL3 #1 and siOSBPL3 #2) using Lipofectamine RNAiMAX transfection . For longer-term studies, including xenograft models, stable knockdown using shRNA constructs is recommended . This approach utilizes plasmids such as pcDNA6.2-GW/EmGFP-OSBPL3-shRNA vectors, with subsequent selection using blasticidin (6 μg/mL) followed by GFP sorting via FACS . Functional validation of knockdown should assess both mRNA levels (via RT-qPCR) and protein levels (via Western blot), with primers such as:

• OSBPL3 forward: 5′-TTGGTGTGTCCCAAAAATTGGT-3′

• OSBPL3 reverse: 5′-TCCTGGGTGTAATTCATCTCCC-3′

For phenotypic validation, incorporate cell proliferation assays (MTT), colony formation assays, and analyze downstream pathway components (particularly pAkt and R-Ras activity) .

What methodologies are most effective for analyzing OSBPL3's role in tumor immune infiltration?

Comprehensive analysis of OSBPL3's relationship with tumor immune infiltration requires multiple computational and experimental approaches . Research indicates that OSBPL3 expression correlates significantly with immune cell infiltration in LIHC, with the following correlation coefficients:

• B cells (cor = 0.378, p = 3.70e-13)

• CD8+ T cells (cor = 0.313, p = 3.44e-09)

• CD4+ T cells (cor = 0.543, p = 9.58e-28)

• Macrophages (cor = 0.553, p = 1.00e-28)

• Neutrophils (cor = 0.451, p = 1.91e-18)

• Dendritic cells (cor = 0.432, p = 6.58e-17)

To replicate such analyses, researchers should utilize databases like TIMER2 with the "Immune-Gene" module and analyze immune scores using R software with "Estimations" and Spearman's analysis . Multiple algorithms including TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER, and EPIC should be employed for comprehensive immune infiltration estimation . Additionally, researchers should investigate the impact of OSBPL3 copy number variations on immune cell infiltration, as significant correlations have been observed with infiltration of B cells, CD4+ T cells, neutrophils, and dendritic cells .

What parameters are critical when optimizing immunohistochemistry protocols for OSBPL3 detection?

Successful immunohistochemical detection of OSBPL3 depends on several critical parameters that must be optimized . Antigen retrieval methods significantly impact staining quality, with recommended approaches including TE buffer at pH 9.0 or alternatively citrate buffer at pH 6.0 . For challenging samples, pressure cooker-based antigen retrieval may yield superior results .

When selecting primary antibodies, those validated specifically for IHC applications should be prioritized, with recommended dilutions ranging from 1:50-1:500 . The avidin-biotin-peroxidase method (such as LSAB2 kit) has been successfully employed for detection , with hematoxylin counterstaining to visualize tissue architecture.

Control selection is crucial and should include:

• Positive tissue controls (human colon cancer tissue is recommended)

• Paired normal/tumor tissue samples for comparison

• Negative controls (primary antibody omission)

Image analysis should assess not only presence/absence of staining but also subcellular localization patterns and intensity differences between normal and malignant cells .

How should researchers interpret OSBPL3 expression data in relation to clinicopathological features?

Interpretation of OSBPL3 expression requires consideration of multiple clinicopathological parameters and survival metrics . Current research demonstrates that OSBPL3 expression increases with tumor progression, showing significant correlation with tumor grade (grade 3 vs. grade 1, P < 0.001; grade 2 vs. grade 1, P < 0.05) and stage (stage 3 vs. stage 1, P < 0.05) . Additionally, OSBPL3 expression is significantly higher in patients with TP53 mutations compared to those without (P < 0.01) .

For survival analysis, researchers should perform Kaplan-Meier analyses comparing high versus low OSBPL3 expression groups across multiple survival metrics. In LIHC, patients with high OSBPL3 expression demonstrate:

When interpreting these associations, researchers should consider potential confounding factors and validate findings across independent cohorts and multiple cancer types to establish broader oncogenic patterns.

What bioinformatics approaches should be used to identify and validate OSBPL3-related genes?

Effective identification of OSBPL3-related genes requires integration of multiple bioinformatics approaches . Researchers should begin with differential expression analysis comparing high versus low OSBPL3-expressing tumors using databases like TCGA through platforms such as UALCAN . For protein-protein interaction (PPI) network construction, utilize the STRING database with parameters including:

• Organism: "Homo sapiens"

• Minimum required interaction score: "low confidence (0.150)"

• Maximum interactors: "no more than 50 interactors" in first shell

• Active interaction sources: "experiments"

For identifying correlated genes, employ GEPIA2 with the "Similar Gene Detection" module followed by Pearson correlation analysis . Gene set enrichment analysis should incorporate both KEGG pathways and Gene Ontology (GO) terms using platforms like DAVID .

In LIHC, this approach identified six key hub genes (ANLN, CEP55, DEPDC1B, ECT2, IQGAP1, and KIF23) that were significantly upregulated and associated with poor prognosis . Pathway enrichment analysis revealed OSBPL3-related gene enrichment in protein binding, mitotic cytokinesis, inorganic anion transport, and I-kappaB kinase/NF-kappaB signaling .

How can researchers distinguish between correlation and causation when studying OSBPL3 in cancer?

Distinguishing correlation from causation in OSBPL3 research requires a multi-layered experimental approach that goes beyond observational data . While bioinformatic analyses establish strong correlations between OSBPL3 expression and cancer progression, functional studies are essential to demonstrate causation .

To establish causative relationships, researchers should:

• Perform both gain-of-function and loss-of-function experiments:

  • The documented impact of OSBPL3 knockdown on reducing cell proliferation, colony formation, and tumor growth in xenograft models provides strong evidence for causation

  • Complementary overexpression studies should be conducted to confirm bidirectional effects

• Delineate molecular mechanisms:

  • Demonstrate direct activation of downstream pathways (e.g., R-Ras/Akt signaling)

  • Utilize rescue experiments where phenotypes from OSBPL3 knockdown are reversed by reintroducing downstream effectors

• Establish temporal relationships:

  • Show that OSBPL3 expression changes precede phenotypic alterations

  • Utilize inducible systems to demonstrate temporal control

• Address potential confounders:

  • Control for variables like TP53 mutation status, which has been shown to correlate with OSBPL3 expression

Research has established causation in gastric cancer, where OSBPL3 knockdown reduced proliferation, colony formation, and tumor growth through demonstrated downregulation of R-Ras/Akt signaling , providing a methodological template for other cancer types.

How can researchers resolve inconsistent OSBPL3 antibody detection in Western blot experiments?

Inconsistent OSBPL3 detection in Western blot applications can be addressed through systematic optimization of multiple parameters . The observed molecular weight variation (95-110 kDa versus calculated 101 kDa) can complicate band identification . To resolve detection issues:

• Sample preparation optimization:

  • Use fresh samples with comprehensive protease inhibitor cocktails

  • Consider phosphatase inhibitors, as OSBPL3 is subject to phosphorylation

  • Optimize protein extraction method for membrane-associated proteins

• Antibody selection and validation:

  • Test multiple antibodies targeting different OSBPL3 epitopes

  • Validate specificity using OSBPL3 knockdown/knockout controls

  • Use recommended positive controls: Jurkat, HeLa, HepG2, DU145, K-562, or HCT 116 cells

• Protocol optimization:

  • Adjust antibody dilution within recommended ranges (1:200-1:2000 or 1:1000-1:4000)

  • Optimize blocking conditions to reduce background

  • Extend incubation times for primary antibody (overnight at 4°C)

  • Consider stronger detection methods for low abundance samples

• Address post-translational modifications:

  • Use phosphorylation-specific antibodies if investigating activated OSBPL3

  • Consider deglycosylation treatments if glycosylation affects detection

What strategies can resolve challenges in reproducing published OSBPL3 functional studies?

Reproduction challenges in OSBPL3 functional studies typically stem from methodology variations, cell type differences, and incomplete protocol reporting . To improve reproducibility:

• Cell line considerations:

  • Match cell lines used in original studies (MKN45 and MKN74 for gastric cancer )

  • Verify OSBPL3 baseline expression levels before knockdown experiments

  • Consider intrinsic differences in OSBPL3 dependency across cancer subtypes

• Knockdown efficiency optimization:

  • Test multiple siRNA/shRNA sequences (at least 2-3 as in published work)

  • Validate knockdown at both mRNA and protein levels

  • Monitor knockdown stability over experimental timeline

• Assay standardization:

  • For proliferation assays, maintain consistent seeding densities and assessment timepoints (6 days post-transfection showed significant differences)

  • For colony formation, standardize incubation period and colony counting criteria

  • For xenograft models, match cell numbers (5.0 × 10^6 cells/mL), injection volume (200 μL), and assessment timeline (28 days)

• Signaling pathway validation:

  • Include positive controls for pathway activation

  • Use multiple readouts for R-Ras/Akt pathway activity

  • Consider pathway differences between cancer types

• Methodological transparency:

  • Request detailed protocols from original authors

  • Report all experimental parameters in your own publications

What therapeutic strategies could target OSBPL3 in cancer treatment?

Based on current understanding of OSBPL3's mechanisms, several therapeutic strategies hold promise :

• Direct OSBPL3 inhibition approaches:

  • Small molecule inhibitors targeting the lipid-binding domain

  • Peptide inhibitors disrupting OSBPL3-VAPA interaction, which has been shown to stimulate R-Ras signaling

  • Antisense oligonucleotides or siRNA-based therapeutics for OSBPL3 knockdown

• Targeting downstream pathways:

  • Combination with existing PI3K/Akt inhibitors, as OSBPL3 activates Akt signaling

  • R-Ras pathway inhibitors, particularly in cancers with high OSBPL3 expression

  • NF-κB pathway modulators, as OSBPL3-related genes enrich in I-kappaB kinase/NF-kappaB signaling

• Immunotherapy combinations:

  • Given OSBPL3's positive correlation with immune cell infiltration , combining OSBPL3 inhibition with immune checkpoint inhibitors may enhance efficacy

  • Targeting OSBPL3 in tumors with low immune infiltration might increase immunogenicity

• Biomarker-driven patient selection:

  • Stratify patients based on OSBPL3 expression levels

  • Consider TP53 mutation status, which correlates with OSBPL3 upregulation

  • Target advanced-stage tumors where OSBPL3 expression is highest

How might research into OSBPL3's role in lipid metabolism inform cancer biology?

OSBPL3's primary function in lipid transport and sensing presents unique opportunities to explore cancer metabolism from a lipid-centric perspective :

• Membrane dynamics and signaling:

  • Investigate how OSBPL3-mediated lipid transport affects membrane composition in cancer cells

  • Explore its impact on lipid raft formation and receptor clustering

  • Study how these changes influence receptor tyrosine kinase signaling

• Metabolic reprogramming:

  • Examine OSBPL3's role in facilitating lipid uptake and utilization in cancer cells

  • Investigate connections between OSBPL3 and fatty acid synthesis pathways

  • Explore how OSBPL3 might contribute to lipid droplet formation in aggressive cancers

• Organelle communication:

  • Study OSBPL3's function at membrane contact sites between organelles

  • Investigate how disruption of these contacts affects cancer cell metabolism

  • Explore connections between endoplasmic reticulum stress and OSBPL3 function

• Lipid signaling molecules:

  • Analyze OSBPL3's role in regulating bioactive lipid mediators

  • Investigate connections to sphingolipid metabolism and ceramide-mediated apoptosis

  • Study potential interactions with sterol regulatory element-binding proteins (SREBPs)

• Drug resistance mechanisms:

  • Explore whether OSBPL3-mediated lipid transport contributes to drug efflux or sequestration

  • Investigate if OSBPL3 inhibition could sensitize cancer cells to standard chemotherapies

Understanding these aspects could reveal novel vulnerabilities in cancer cells and establish OSBPL3 as a prototype for targeting lipid metabolism in cancer therapy.