ENOPH1 Antibody

What is ENOPH1 and what are its known biological functions?

ENOPH1 (Enolase-phosphatase 1) is an enzyme involved in polyamine biosynthesis and cellular stress responses. It plays a crucial role in the cysteine/methionine metabolism pathway, particularly in methionine salvage. Recent studies have established ENOPH1's significance beyond basic metabolism, demonstrating its involvement in cancer progression mechanisms. In normal tissues, ENOPH1 is expressed at relatively low levels, while in malignant conditions, its expression is significantly upregulated. Research has shown that ENOPH1 is implicated in cellular processes related to proliferation, migration, and invasion, making it relevant to cancer biology and potential therapeutic interventions .

How does ENOPH1 expression vary across different cancer types?

ENOPH1 expression shows distinct patterns across various cancer types, with significant upregulation observed in glioma and hepatocellular carcinoma (HCC). In glioma, ENOPH1 levels are markedly increased compared to normal brain tissues, with expression positively correlating with pathological grade. Western blot, qPCR, and immunohistochemistry analyses of glioma tissues from 86 patients demonstrated this elevation pattern . Similarly, in HCC, ENOPH1 expression is significantly higher in tumor tissues compared to para-tumor tissues, with an average fold change of 5.91 in mRNA levels. This overexpression pattern is particularly pronounced in more aggressive, metastatic cell lines such as MHCC97H, HCCLM3, and HCCLM6 . The Cancer Genome Atlas (TCGA) data further confirms that ENOPH1 expression is significantly higher in advanced stage (T3/T4) tumors compared to early stage (T1/T2) tumors, suggesting its potential role in cancer progression .

What cellular pathways are affected by ENOPH1 activity?

ENOPH1 influences several key cellular pathways that contribute to cancer progression:

• AKT Signaling Pathway: ENOPH1 enhances AKT phosphorylation, as demonstrated in both overexpression and knockdown studies. This activation promotes cell migration and invasion in cancer cells. Treating ENOPH1-overexpressing cells with perifosine (an AKT inhibitor) reverses these effects, confirming the mechanistic connection .

• Cysteine/Methionine Metabolism: ENOPH1 is a key enzyme in this pathway, which is critical for maintaining methionine levels and S-adenosylmethionine (SAM) for various cellular methylation reactions. SAM treatment significantly impairs the migration and invasive abilities promoted by ENOPH1 upregulation .

• Protein Localization: ENOPH1 knockdown promotes its downstream protein, aci-reductone dioxygenase 1 (ADI1), to relocate from the nucleus to the cytoplasm in glioma cells, suggesting a role in regulating protein subcellular distribution .

• Matrix Metalloproteinase Regulation: ENOPH1 knockdown significantly downregulates MT1-MMP expression, a protein involved in extracellular matrix degradation and cancer invasion .

What are the most effective experimental approaches for detecting ENOPH1 in tissue samples?

For effective detection of ENOPH1 in tissue samples, researchers should consider multiple complementary approaches:

• Immunohistochemistry (IHC): This technique provides spatial information about ENOPH1 expression within tissue architecture. The standard protocol involves deparaffinization, rehydration, antigen retrieval in sodium citrate buffer (pH 6.0), and incubation with anti-ENOPH1 antibodies overnight at 4°C. Visualization can be achieved using an HRP-DAB detection kit. Scoring systems that consider both staining intensity (0-3 scale) and percentage of immunopositive cells (0-3 scale) allow for semi-quantitative analysis, with final scores categorizing ENOPH1 expression as low (0-3) or high (4-9) .

• Western Blotting: This provides quantitative assessment of ENOPH1 protein levels. Standard protocols apply, with commercially available antibodies such as rabbit polyclonal antibodies against ENOPH1. This method is particularly valuable for comparing expression levels across different tissue samples or experimental conditions .

• Quantitative PCR (qPCR): For mRNA expression analysis, qPCR offers high sensitivity. Studies have successfully used this approach to compare ENOPH1 mRNA levels between tumor and para-tumor tissues, showing significant differences (5.91-fold increase in HCC tissues) .

• Multiplex Approaches: For comprehensive analysis, researchers can employ cytometric bead arrays or sandwich ELISA using matched antibody pairs, such as 68179-1-PBS (capture) and 68179-2-PBS (detection) .

What controls should be included when using ENOPH1 antibodies in experimental procedures?

When working with ENOPH1 antibodies, proper controls are essential for result validation:

• Positive and Negative Tissue Controls:

  • Include known ENOPH1-expressing tissues (HCC or glioma samples with confirmed high expression)

  • Include normal tissue counterparts (normal liver or brain tissue) as negative or low-expression controls

  • Grade comparison controls (samples of different pathological grades) to demonstrate expression correlation with disease progression

• Genetic Manipulation Controls:

  • ENOPH1-overexpressing cells as positive controls

  • ENOPH1-knockdown cells (using validated siRNA or shRNA) as negative controls

  • Empty vector-transfected cells to control for transfection effects

• Loading and Technical Controls:

  • Housekeeping proteins (β-actin) for Western blot normalization

  • Standardized positive control lysates across different experimental batches

  • Secondary antibody-only controls to assess non-specific binding

  • Technical replicates to ensure reproducibility

• Antibody Validation Controls:

  • Pre-absorption controls using the immunizing peptide

  • Multiple antibodies targeting different epitopes of ENOPH1

  • Concentration gradients to determine optimal antibody dilutions

How can ENOPH1 function be studied through genetic manipulation approaches?

Genetic manipulation offers powerful insights into ENOPH1 function through several established approaches:

• RNA Interference:

  • Short hairpin RNA (shRNA): Studies have successfully employed shRNA to create stable ENOPH1 knockdown in cell lines like HCCLM3 and MHCC97L. This approach revealed that ENOPH1 downregulation severely impaired cell proliferation and significantly reduced cell migration and invasion .

  • Small interfering RNA (siRNA): Transient knockdown with synthesized specific siRNA in cancer cells has demonstrated similar effects on reducing cell proliferation and migration capabilities .

• Overexpression Systems:

  • Plasmid-based overexpression: Using commercially available ENOPH1-expressing plasmids transfected with Lipofectamine 3000 in low-expressing cell lines (Huh7 and PLC cells) significantly increased cell growth, migration, and invasiveness .

• Functional Assessment:

  • Cell proliferation: MTT assays and colony formation assays provide quantitative measures of how ENOPH1 modulation affects cell growth rates .

  • Migration assays: Wound-healing assays effectively demonstrate the impact of ENOPH1 expression on cell motility .

  • Invasion assays: Transwell-chamber and Matrigel invasion assays can quantify changes in invasive capacity following ENOPH1 manipulation .

• Pathway Analysis:

  • Western blotting for downstream effectors (e.g., phosphorylated AKT) following ENOPH1 modulation

  • Rescue experiments with pathway inhibitors (e.g., perifosine for AKT pathway) to confirm mechanistic relationships .

  • Metabolite supplementation (e.g., SAM) to investigate biochemical pathway involvement .

What molecular mechanisms underlie ENOPH1's role in cancer progression?

ENOPH1 promotes cancer progression through multiple interconnected molecular mechanisms:

• AKT Pathway Activation: ENOPH1 enhances phosphorylation of AKT, a central regulator of cell survival, proliferation, and metastasis. Reversed genetics analysis of ENOPH1-knockdown and ENOPH1-overexpressing cells revealed that the AKT pathway contained the maximum number of differentially expressed genes among the overlapped relevant pathways. Western blotting confirmed that upregulation of ENOPH1 enhanced AKT phosphorylation, while ENOPH1 knockdown inhibited it. Treatment with perifosine (an AKT inhibitor) reversed the increased migration and invasion in ENOPH1-overexpressing cells, confirming the mechanistic link .

• Regulation of Protein Localization: In glioma cells, ENOPH1 knockdown promotes the translocation of aci-reductone dioxygenase 1 (ADI1) from the nucleus to the cytoplasm. This redistribution may alter ADI1's function, affecting downstream cellular processes related to cancer progression .

• Extracellular Matrix Regulation: ENOPH1 knockdown significantly downregulates MT1-MMP expression in glioma cells. As a matrix metalloproteinase involved in extracellular matrix degradation, MT1-MMP is crucial for cancer cell invasion and metastasis. This suggests that ENOPH1 may enhance tumor invasiveness by promoting matrix degradation .

• Methionine Metabolism: As a component of the cysteine/methionine pathway, ENOPH1 may influence cellular methylation status and polyamine biosynthesis. Treatment with S-adenosylmethionine (SAM) significantly impairs the migration and invasive abilities promoted by ENOPH1 upregulation, indicating a metabolic component to ENOPH1's oncogenic effects .

How does ENOPH1 expression correlate with cancer metastasis in experimental models?

ENOPH1 shows a strong positive correlation with metastatic potential across multiple experimental systems:

• Cell Line Studies: In HCC cell lines with increasing metastatic capabilities (MHCC97L, MHCC97H, HCCLM3, and HCCLM6), ENOPH1 expression progressively increases at both mRNA and protein levels. qPCR analysis confirmed that ENOPH1 expression was significantly upregulated in highly metastatic HCC cells compared to less metastatic counterparts. Western blot analysis across seven HCC cell lines showed that ENOPH1 was consistently upregulated in cells with higher metastatic potential .

• Functional Studies: Manipulation of ENOPH1 expression directly impacts metastatic behaviors:

  • ENOPH1 overexpression significantly enhances cell migration (wound-healing assays) and invasion (Transwell-chamber and Matrigel invasion assays)

  • ENOPH1 knockdown severely impairs these metastatic capabilities

  • These effects are mediated primarily through the AKT pathway, as demonstrated by rescue experiments with the AKT inhibitor perifosine .

• In Vivo Metastasis Model: An intrahepatic tumor xenograft mouse model provided compelling evidence for ENOPH1's role in metastasis. ENOPH1-overexpressing Huh7 cells injected orthotopically into mouse liver induced significantly more intrahepatic micro-metastases compared to control cells. Hematoxylin-eosin staining confirmed more extensive tumor foci in mice injected with ENOPH1-overexpressing cells .

What is the prognostic significance of ENOPH1 expression in clinical cancer samples?

Clinical studies demonstrate that ENOPH1 expression has significant prognostic value in cancer patients:

What is the optimal experimental design to investigate ENOPH1's role in tumor progression?

An optimal experimental design to investigate ENOPH1's role in tumor progression should incorporate multiple complementary approaches:

• Cell Culture Models:

  • Compare multiple cell lines with varying endogenous ENOPH1 expression (e.g., HCCLM3, MHCC97H vs. Huh7, PLC)

  • Create stable knockdown and overexpression models using lentiviral vectors for consistent expression

  • Include rescue experiments with wild-type ENOPH1 re-expression in knockdown cells

  • Implement domain-specific mutations to identify critical functional regions of ENOPH1

• Functional Assays:

  • Proliferation: MTT assays, colony formation, and cell cycle analysis

  • Migration: Wound-healing assays with time-lapse imaging

  • Invasion: Transwell and Matrigel invasion assays

  • Pathway activation: Western blotting for phosphorylated AKT and downstream effectors

  • Metabolic profiling: Analysis of methionine cycle metabolites and polyamines

• In Vivo Models:

  • Orthotopic implantation (as demonstrated in the intrahepatic xenograft model)

  • Metastasis quantification through histological examination and imaging

  • Treatment arms with pathway inhibitors (e.g., AKT inhibitors like perifosine)

  • Longitudinal monitoring of tumor progression and metastatic spread

• Patient-Derived Models:

  • PDX models from tumors with varying ENOPH1 expression levels

  • Primary cell cultures from patient tumors for functional validation

  • Correlation of experimental findings with patient outcomes

• Pathway Analysis:

  • Pathway inhibition studies (e.g., using perifosine for AKT pathway)

  • Metabolite supplementation (e.g., SAM) to investigate biochemical mechanisms

  • Transcriptome analysis after ENOPH1 modulation to identify affected gene networks

How can researchers effectively compare ENOPH1 function across different cancer types?

To effectively compare ENOPH1 function across different cancer types, researchers should implement a systematic cross-cancer analysis approach:

• Standardized Expression Analysis:

  • Use consistent antibodies and protocols across cancer types

  • Implement tissue microarrays containing multiple cancer types

  • Analyze public databases (TCGA, GEO) for ENOPH1 expression patterns across cancer types

  • Normalize expression relative to matched normal tissues for each cancer type

• Comparative Functional Studies:

  • Select representative cell lines from each cancer type with similar characteristics

  • Apply identical ENOPH1 modulation techniques (same vectors, knockdown efficiency)

  • Perform parallel functional assays under standardized conditions

  • Utilize isogenic cell line panels where possible to minimize genetic background variation

• Pathway Analysis:

  • Compare AKT pathway activation across cancer types following ENOPH1 modulation

  • Investigate cancer-specific downstream effectors

  • Identify common vs. cancer-specific interaction partners through co-immunoprecipitation

  • Use pharmacological inhibitors to determine pathway dependencies in each cancer type

• In Vivo Comparative Models:

  • Develop parallel orthotopic models for each cancer type

  • Compare metastatic patterns and organotropism

  • Evaluate therapeutic responses to pathway inhibitors across cancer types

• Clinical Correlation:

  • Create a multi-cancer tissue microarray for ENOPH1 staining

  • Correlate ENOPH1 expression with clinicopathological features across cancer types

  • Compare prognostic significance of ENOPH1 in different cancer types

  • Identify cancer-specific vs. universal aspects of ENOPH1 biology

What experimental approaches can elucidate the relationship between ENOPH1 and the AKT pathway?

To elucidate the relationship between ENOPH1 and the AKT pathway, researchers should employ multiple complementary approaches:

• Temporal Activation Analysis:

  • Time-course experiments following ENOPH1 modulation

  • Western blotting for phosphorylated AKT at multiple time points

  • Determination of whether AKT activation is an immediate or delayed response

  • Analysis of upstream AKT regulators (PI3K, PTEN) after ENOPH1 manipulation

• Pharmacological Intervention:

  • AKT pathway inhibitors at different levels:

    • Perifosine (AKT inhibitor) - Already demonstrated to reverse ENOPH1-induced migration and invasion

    • PI3K inhibitors (upstream of AKT)

    • mTOR inhibitors (downstream of AKT)

  • Dose-response studies to determine sensitivity shifts with ENOPH1 modulation

  • Rescue experiments combining ENOPH1 overexpression with pathway inhibition

• Genetic Manipulation:

  • Co-transfection experiments with ENOPH1 and dominant-negative AKT

  • Double knockdown of ENOPH1 and AKT components

  • Overexpression of constitutively active AKT in ENOPH1-knockdown cells

  • CRISPR-Cas9 editing of specific AKT pathway components

• Protein-Protein Interaction Studies:

  • Co-immunoprecipitation to identify direct interactions between ENOPH1 and AKT pathway components

  • Proximity ligation assays to visualize interactions in situ

  • Domain mapping to identify interaction interfaces

  • Subcellular fractionation to determine compartment-specific interactions

• Mechanistic Analysis:

  • Investigation of ENOPH1's effect on AKT phosphatases

  • Analysis of potential metabolic mechanisms connecting ENOPH1 to AKT activation

  • Examination of methylation-dependent regulation of AKT pathway components

  • Characterization of ENOPH1's enzymatic activity in relation to AKT activation

How should researchers interpret contradictory results when studying ENOPH1 across different experimental models?

When encountering contradictory results in ENOPH1 research across experimental models, researchers should implement a systematic approach to reconciliation:

• Model-Dependent Effects Assessment:

  • Compare cancer type differences (glioma vs. HCC vs. other cancers)

  • Evaluate cell line genetic backgrounds (p53 status, PTEN status, etc.)

  • Consider in vitro vs. in vivo model discrepancies

  • Assess the impact of the tumor microenvironment in different models

• Technical Variation Analysis:

  • Examine differences in knockdown/overexpression efficiency

  • Compare antibody specificity and epitope recognition

  • Evaluate assay sensitivity and dynamic range differences

  • Consider timing variations in experimental endpoints

• Context-Dependent Function Exploration:

  • Investigate whether ENOPH1 has dual functions depending on cellular context

  • Examine potential compensatory mechanisms in different models

  • Consider post-translational modifications affecting ENOPH1 function

  • Analyze the methionine pathway status across models

• Reconciliation Approaches:

  • Perform side-by-side comparisons using standardized protocols

  • Develop comprehensive dose-response and time-course experiments

  • Use multiple methodological approaches to confirm key findings

  • Implement genetic rescue experiments with wild-type ENOPH1

• Data Integration Framework:

  • Create an integrative model that accounts for context-dependent effects

  • Identify core conserved functions versus variable context-dependent ones

  • Use computational modeling to predict conditions where contradictions occur

  • Develop testable hypotheses that explain apparent contradictions

What are the key technical challenges in ENOPH1 antibody-based experiments and their solutions?

ENOPH1 antibody-based experiments present several technical challenges that researchers should address:

• Antibody Specificity Issues:

  • Challenge: Cross-reactivity with related proteins or non-specific binding

  • Solution: Validate using ENOPH1-knockdown controls; use multiple antibodies targeting different epitopes; perform pre-absorption controls with recombinant ENOPH1 protein

• Variability Across Applications:

  • Challenge: Antibodies optimized for Western blotting may underperform in IHC

  • Solution: Validate each antibody for specific applications; use application-specific optimization protocols; consider conjugation-ready antibody formats for specialized applications like cytometric bead arrays

• Signal-to-Noise Ratio:

  • Challenge: High background obscuring specific ENOPH1 signal

  • Solution: Optimize blocking conditions (duration, BSA concentration); use monoclonal antibodies for higher specificity; increase washing steps and duration; employ biotin-streptavidin amplification for weak signals

• Epitope Accessibility:

  • Challenge: Fixation methods may mask ENOPH1 epitopes in tissue samples

  • Solution: Compare multiple antigen retrieval methods (heat-induced vs. enzymatic); optimize retrieval buffer composition (citrate vs. EDTA); test different fixation protocols when possible

• Quantification Challenges:

  • Challenge: Semi-quantitative nature of IHC scoring systems

  • Solution: Implement digital image analysis; use standardized scoring criteria combining intensity and percentage of positive cells; include internal calibration standards

• Reproducibility Issues:

  • Challenge: Batch-to-batch variability in antibody performance

  • Solution: Purchase larger lots when possible; validate each new lot against previous standards; include consistent positive and negative controls; consider monoclonal antibodies for greater consistency

How can researchers distinguish between ENOPH1's direct effects and secondary consequences in cancer progression?

Distinguishing between ENOPH1's direct effects and secondary consequences requires a multi-faceted experimental approach:

• Temporal Analysis:

  • Implement time-course experiments following ENOPH1 modulation

  • Monitor immediate (0-24 hours) versus delayed (24-72+ hours) changes

  • Use inducible expression/knockdown systems for precise temporal control

  • Establish a temporal sequence of events following ENOPH1 manipulation

• Pathway Dissection:

  • Apply specific inhibitors at different points in hypothesized pathways

  • Use genetic manipulation of downstream effectors

  • Perform epistasis experiments combining ENOPH1 modulation with effector modulation

  • Analyze pathway component phosphorylation/activation states over time

• Direct Biochemical Assessment:

  • Characterize ENOPH1's enzymatic activity in the methionine salvage pathway

  • Measure metabolic changes directly resulting from ENOPH1 activity

  • Assess S-adenosylmethionine (SAM) levels and methylation patterns

  • Perform in vitro enzymatic assays with purified components

• Protein Interaction Studies:

  • Identify direct binding partners through co-immunoprecipitation

  • Use yeast two-hybrid or proximity labeling approaches

  • Perform domain mapping to identify interaction interfaces

  • Apply FRET or BRET techniques to confirm direct interactions in living cells

• Rescue Experiments:

  • Express enzyme-dead ENOPH1 mutants to separate enzymatic from scaffolding functions

  • Add metabolic pathway intermediates to bypass ENOPH1's enzymatic function

  • Restore expression of specific downstream effectors in ENOPH1-knockdown cells

  • Use parallel pathway activators to determine specificity of ENOPH1 effects

• Systems-Level Analysis:

  • Apply network analysis to transcriptomic data following ENOPH1 modulation

  • Identify directly regulated gene sets versus secondary response clusters

  • Use causal network inference algorithms to establish direct versus indirect relationships

  • Integrate multi-omics data to build comprehensive influence models