CPNE1 Antibody, HRP conjugated

What is CPNE1 and what are its key biological functions?

CPNE1 is a calcium-dependent membrane-binding protein involved in several crucial cellular processes. It contains two N-terminal type II C2 domains and an integrin A domain-like sequence in the C-terminus, but lacks signal sequences or transmembrane domains. CPNE1 functions in:

• Calcium-mediated intracellular processes

• TNF-alpha receptor signaling pathway in a calcium-dependent manner

• Neuronal progenitor cell differentiation via AKT-dependent signaling

• Membrane trafficking and protein recruitment to the cell membrane

• Regulation of NF-kappa-B transcriptional activity through endoprotease processing

CPNE1 is broadly distributed across tissues and primarily localizes to the cell membrane, cytoplasm, and nucleus, making it an important target for various research applications .

What are the structural and molecular characteristics of CPNE1 antibodies with HRP conjugation?

CPNE1 antibodies with HRP (horseradish peroxidase) conjugation are typically polyclonal antibodies derived from rabbits immunized with recombinant human CPNE1 protein. The antibodies are purified through antigen affinity chromatography and conjugated with HRP enzyme for direct detection without requiring secondary antibodies. The expected molecular weight for CPNE1 is approximately 59 kDa, though the observed weight in Western blotting is often around 65 kDa due to post-translational modifications. These antibodies are typically supplied in a buffer containing PBS (pH 7.4), stabilizers, and 50% glycerol .

What is the typical reactivity profile of commercially available CPNE1 antibodies?

Based on the available technical information, CPNE1 antibodies show varying reactivity profiles:

The amino acid sequence homology indicates high conservation between species, with interspecies antigen sequence showing 91% similarity between mouse and rat models, facilitating cross-species research applications .

What are the recommended protocols for using CPNE1 HRP-conjugated antibodies in immunohistochemistry?

For optimal IHC results with CPNE1 HRP-conjugated antibodies:

• Tissue preparation: Use 4-7μm thick sections of formalin-fixed, paraffin-embedded tissues

• Antigen retrieval: Perform heat-induced epitope retrieval in citrate buffer (pH 6.0)

• Blocking: Block with 5% normal serum in PBS for 1 hour at room temperature

• Primary antibody incubation: Apply CPNE1 HRP-conjugated antibody (optimally diluted as per manufacturer recommendation) and incubate overnight at 4°C

• Visualization: As the antibody is HRP-conjugated, directly apply diaminobenzidine (DAB) substrate

• Counterstaining: Use hematoxylin for nuclear counterstaining

• Controls: Include both positive controls (known CPNE1-expressing tissues like colorectal or breast cancer) and negative controls (omitting primary antibody)

Researchers have successfully used this approach to demonstrate that CPNE1 protein levels are significantly higher in colorectal cancer specimens compared to adjacent normal tissues .

How can I optimize Western blot protocols for detecting CPNE1 using HRP-conjugated antibodies?

For Western blot optimization with CPNE1 HRP-conjugated antibodies:

• Sample preparation: Extract total protein using RIPA buffer containing protease inhibitors

• Protein loading: Load 20-40μg of protein per lane on 10% SDS-PAGE gels

• Transfer: Use PVDF membrane and semi-dry transfer at 15V for 30-45 minutes

• Blocking: Block with 5% non-fat milk in TBST for 1 hour at room temperature

• Antibody incubation: Dilute CPNE1 HRP-conjugated antibody 1:500-1:2000 in blocking buffer and incubate overnight at 4°C

• Washing: Wash membrane with TBST (3×10 minutes)

• Detection: Apply ECL substrate directly (no secondary antibody needed)

• Expected results: Look for a band at approximately 65 kDa (observed molecular weight)

Note: The observed molecular weight (65 kDa) differs from the calculated weight (59 kDa) due to post-translational modifications. If multiple bands appear, validate specificity using CPNE1 knockdown or overexpression samples .

What approaches can be used to quantitatively measure CPNE1 expression levels in patient samples?

Several complementary approaches can quantitatively assess CPNE1 expression:

• Quantitative RT-PCR:

  • Extract RNA using TRIzol reagent

  • Perform reverse transcription with oligo(dT) primers

  • Use SYBR Green-based qPCR with CPNE1-specific primers

  • Normalize to appropriate housekeeping genes (GAPDH, β-actin)

• Immunohistochemical (IHC) scoring:

  • Calculate H-score = Σ(intensity score × percentage of positive cells)

  • Intensity scored as: 0 (negative), 1 (weak), 2 (moderate), 3 (strong)

  • Consider positive when >10% of cells show staining

  • Categorize as "high" or "low" expression based on median scores

• ELISA using matched antibody pairs:

  • Sensitivity range: 3-100 ng/ml for human CPNE1

  • Capture with rabbit polyclonal anti-CPNE1

  • Detect with mouse polyclonal anti-CPNE1

These approaches have been successfully employed to demonstrate that CPNE1 expression is significantly upregulated in colorectal and triple-negative breast cancer tissues compared to adjacent normal tissues .

How does CPNE1 expression affect cancer prognosis and what methodologies best demonstrate this relationship?

CPNE1 expression strongly correlates with poor prognosis across multiple cancer types. To investigate this relationship:

• Kaplan-Meier survival analysis: Researchers have demonstrated that high CPNE1 expression negatively correlates with survival rates in colorectal cancer patients (p<0.001)

• Clinicopathological correlation: Studies show CPNE1 expression significantly associates with:

  • Tumor size (p=0.0282)

  • Differentiation grade (p=0.0035)

  • Metastatic status (p=0.011)

  • WHO tumor grade (p=0.031)

• Multivariate Cox regression analysis: When adjusting for confounding factors, CPNE1 serves as an independent prognostic factor

To establish these correlations, researchers performed tissue microarray analysis with CPNE1 antibody staining, followed by comprehensive statistical analysis incorporating clinical follow-up data. Additionally, validation through The Cancer Genome Atlas (TCGA) and GEO datasets confirmed that CPNE1 mRNA is markedly elevated in tumor tissues compared to non-tumor tissues .

What is the relationship between CPNE1 and AKT signaling pathway in cancer progression?

CPNE1 functions as a critical regulator of the AKT signaling pathway in cancer:

• Mechanism of action: CPNE1 promotes AKT phosphorylation (activation), which subsequently:

  • Upregulates glucose transporter 1 (GLUT1) and hexokinase 2 (HK2)

  • Enhances aerobic glycolysis (Warburg effect)

  • Increases mitochondrial respiration

  • Inhibits apoptosis via downregulation of cleaved Caspase-3

• Experimental evidence:

  • Western blot analysis shows CPNE1 knockdown decreases p-AKT levels

  • AKT inhibitors block CPNE1-mediated effects on cancer cell proliferation

  • Inactivation of AKT signaling inhibits tumorigenesis and radioresistance mediated by CPNE1

• Functional significance:

  • This signaling axis promotes cancer cell survival, proliferation, and therapy resistance

  • Targeting CPNE1-AKT pathway sensitizes cancer cells to radiation and chemotherapy

The relationship has been extensively demonstrated through knockdown and overexpression studies, pharmacological inhibition of AKT, and in vivo xenograft models, suggesting that targeted CPNE1 expression may be a promising strategy to sensitize cancer cells toward therapy .

How can CPNE1 expression be manipulated in experimental models to study its function in cancer?

Several approaches have been validated for modulating CPNE1 expression:

• RNA interference (siRNA/shRNA):

  • Design CPNE1-specific siRNA sequences using online software (e.g., Invitrogen)

  • Anneal oligonucleotides and ligate into pLKO.1 plasmid vector

  • Transfect plasmids into HEK293-T cells to produce lentiviruses

  • Infect target cancer cells with lentiviruses carrying CPNE1-specific siRNA

  • Validation of knockdown efficiency: >70% reduction at mRNA and protein levels

• Lentiviral overexpression:

  • Clone full-length CPNE1 cDNA into lentiviral expression vectors

  • Produce lentiviruses in packaging cells

  • Transduce target cells and select with appropriate antibiotics

  • Confirm overexpression through qRT-PCR and Western blot

• In vivo models:

  • Xenograft models: Inject CPNE1-modified cancer cells subcutaneously into immunodeficient mice

  • Patient-derived xenograft (PDX) models: Separate tumors based on CPNE1 expression levels

  • Drug sensitivity testing: Administer chemotherapeutic agents (e.g., oxaliplatin at 5 mg/kg/d)

These approaches have demonstrated that CPNE1 knockdown inhibits tumor growth and promotes apoptosis, while enhancing sensitivity to radiation and chemotherapy .

How can I address discrepancies between expected and observed molecular weights when detecting CPNE1?

Discrepancies between calculated (59 kDa) and observed (65 kDa) molecular weights of CPNE1 are common and may result from:

• Post-translational modifications:

  • Phosphorylation: CPNE1 contains multiple potential phosphorylation sites

  • Glycosylation: Potential N-linked glycosylation sites may increase apparent molecular weight

  • Ubiquitination: Can lead to multiple higher molecular weight bands

• Isoform variation:

  • Alternative splicing yields multiple transcript variants encoding different proteins

  • Validate specific isoforms using isoform-specific primers in RT-PCR

• Troubleshooting approach:

  • Perform dephosphorylation assays using phosphatases

  • Use deglycosylation enzymes (PNGase F) to remove N-linked glycans

  • Include positive controls with recombinant CPNE1 protein

  • Validate antibody specificity using CPNE1 knockdown samples

Remember that protein mobility in SDS-PAGE is affected by multiple factors besides molecular weight, including protein conformation and charge distribution .

What methodological approaches can address the challenge of studying CPNE1's calcium-dependent functions?

To effectively investigate CPNE1's calcium-dependent functions:

• Calcium manipulation strategies:

  • Chelators: Use EGTA (extracellular) or BAPTA-AM (intracellular) at 1-5 mM

  • Ionophores: A23187 or ionomycin (1-10 μM) to increase intracellular calcium

  • Calcium-free media with controlled calcium reintroduction

• Binding assays:

  • Liposome binding assays with varying calcium concentrations

  • Surface plasmon resonance to measure calcium-dependent interactions

  • Pull-down assays with recombinant CPNE1 under different calcium conditions

• Mutational analysis:

  • Generate C2 domain mutants with altered calcium-binding properties

  • Compare wild-type and mutant CPNE1 in membrane translocation assays

  • Assess calcium-dependent protein interactions through co-immunoprecipitation

• Live-cell imaging:

  • Use fluorescently tagged CPNE1 to monitor translocation upon calcium flux

  • Combine with calcium indicators (Fluo-4, Fura-2) for simultaneous monitoring

  • FRET-based approaches to detect conformational changes in response to calcium

These approaches help distinguish between calcium-dependent and calcium-independent functions of CPNE1 in cellular processes .

How can I develop a reliable sandwich ELISA for quantifying CPNE1 in complex biological samples?

To establish a robust sandwich ELISA for CPNE1 quantification:

• Antibody pair selection:

  • Use capture antibody: rabbit polyclonal anti-CPNE1 (affinity-purified)

  • Use detection antibody: mouse polyclonal anti-CPNE1

  • Ensure antibodies recognize different, non-overlapping epitopes

• Protocol optimization:

  • Coating concentration: Titrate capture antibody (1-10 μg/mL)

  • Blocking: 1-5% BSA or non-fat milk in PBS

  • Sample dilution: Optimize based on expected CPNE1 concentration

  • Detection antibody concentration: Titrate for optimal signal-to-noise ratio

  • Substrate incubation time: Optimize for sensitivity without background

• Validation parameters:

  • Sensitivity: Establish lower limit of detection (typically 3 ng/mL)

  • Dynamic range: 3-100 ng/mL for human CPNE1

  • Specificity: Test with recombinant CPNE1 and related proteins

  • Precision: Intra- and inter-assay CV <15%

  • Recovery: Spike-and-recovery in relevant biological matrices

• Standardization:

  • Use purified recombinant CPNE1 (such as H00008904-P01) as a standard

  • Prepare standard curve in the same matrix as samples

  • Include quality control samples in each assay

This approach provides sensitivity from 3 ng/ml to 100 ng/ml, suitable for detecting CPNE1 in serum, plasma, or cell/tissue lysates .

How should experimental designs account for CPNE1's role in both tumorigenesis and therapy resistance?

When designing experiments to investigate CPNE1's dual role:

• Sequential experimental approach:

  • First establish baseline tumorigenesis parameters (proliferation, colony formation, migration, invasion)

  • Then investigate therapy response under identical genetic modifications

  • Compare effects to determine if mechanisms are shared or distinct

• Comprehensive in vivo models:

  • Xenograft tumors with CPNE1 modulation

  • Treatment arms: control, radiation, chemotherapy, combination

  • Endpoints: tumor volume, apoptotic index, molecular markers

  • For example: CPNE1(L) and CPNE1(H) tumors treated with oxaliplatin (5 mg/kg/d for 21 days)

• Mechanistic dissection:

  • Parallel analysis of proliferation pathways and stress response pathways

  • Temporal analysis: acute vs. chronic effects of CPNE1 modulation

  • Pathway-specific inhibitors to identify key nodes (e.g., AKT inhibitors)

• Patient-derived models:

  • Stratify patient samples by CPNE1 expression

  • Test therapy responses in patient-derived organoids or xenografts

  • Correlate with clinical outcomes data

This approach has revealed that CPNE1 promotes tumorigenesis through AKT-GLUT1/HK2 pathway activation while simultaneously conferring resistance to therapies through modulation of apoptotic pathways .

What is the recommended methodology for investigating CPNE1's impact on cancer metabolism?

A comprehensive approach to studying CPNE1's metabolic effects includes:

• Glycolysis assessment:

  • Extracellular Acidification Rate (ECAR) using Seahorse XF analyzer

  • Measure key parameters: glycolytic flux, glycolytic capacity

  • Compare siCPNE1 vs. control and oeCPNE1 vs. vector cells

• Mitochondrial respiration analysis:

  • Oxygen Consumption Rate (OCR) using Seahorse XF analyzer

  • Measure basal respiration, ATP-linked respiration, maximal respiration

  • Use modulators: oligomycin, FCCP, antimycin A/rotenone

• Metabolite profiling:

  • Glucose uptake using 2-NBDG fluorescent glucose analog

  • Lactate production using colorimetric/fluorometric assays

  • ATP levels using luminescence-based assays

  • Intracellular metabolites by mass spectrometry

• Molecular pathway analysis:

  • Key metabolic proteins: GLUT1, HK2, PKM2, LDHA

  • Signaling nodes: p-AKT, p-mTOR, HIF-1α

  • Gene expression changes using qRT-PCR arrays for metabolic genes

Studies using this methodology have revealed that CPNE1 significantly enhances glycolytic flux and glycolytic capacity, while simultaneously increasing basal respiration, ATP-linked respiration, and maximal respiration in cancer cells .

How should researchers interpret contradictory findings regarding CPNE1 function across different cancer types or experimental systems?

When faced with contradictory findings regarding CPNE1:

• Context-dependent factors to consider:

  • Cancer type-specific effects (breast vs. colorectal vs. others)

  • Genetic background (p53 status, PTEN status, etc.)

  • Experimental systems (2D culture vs. 3D models vs. in vivo)

  • Acute vs. chronic CPNE1 modulation

• Technical considerations:

  • Antibody specificity and isoform detection

  • Extent of knockdown or overexpression

  • Off-target effects of siRNA/shRNA

  • Assay sensitivity and dynamic range

• Integrative analysis approach:

  • Meta-analysis of multiple studies

  • Cross-validation in multiple cell lines

  • Parallel investigation of multiple endpoints

  • Patient data correlation to determine clinical relevance

• Resolution strategies:

  • Use genetic rescue experiments to confirm specificity

  • Generate isogenic cell lines with CRISPR/Cas9 CPNE1 knockout

  • Employ domain-specific mutations to identify critical functional regions

  • Conduct time-course experiments to capture temporal dynamics

While CPNE1 generally promotes cancer progression across multiple cancer types, the downstream effectors and relative importance of different pathways may vary by context, explaining apparent contradictions in research findings .