MTMR3 Antibody, HRP conjugated

What is MTMR3 and what cellular functions does it regulate?

MTMR3 (Myotubularin-related protein 3) is a member of the myotubularin family with dual phosphatase activity (EC 3.1.3.48), functioning as a phosphatidylinositol-3-phosphate phosphatase (EC 3.1.3.64) and phosphatidylinositol-3,5-bisphosphate 3-phosphatase (EC 3.1.3.95). It contains a FYVE domain and is also known as FYVE domain-containing dual specificity protein phosphatase 1 (FYVE-DSP1) or zinc finger FYVE domain-containing protein 10 (ZFYVE10). MTMR3 plays a critical role in regulating autophagy initiation through modulation of local phosphatidylinositol 3-phosphate (PtdIns3P) levels. Research has demonstrated that MTMR3 modulates autophagosome formation, with knockdown enhancing autophagosome formation and overexpression reducing autophagic activity and creating smaller nascent autophagosomes .

What is the significance of MTMR3 in cancer research?

MTMR3 has emerged as a significant protein in cancer research, with differential expression and functional roles across various cancer types. In breast cancer, MTMR3 is significantly upregulated in tumor tissues compared to adjacent normal tissues, with particularly high expression in triple-negative breast cancer (TNBC) subtypes. Clinical studies have shown that MTMR3 expression positively correlates with disease recurrence. Experimental studies demonstrate that MTMR3 knockdown in MDA-MB-231 cells inhibits cell proliferation, induces cell cycle arrest, and activates autophagy, suggesting its protumorigenic function . Interestingly, MTMR3 appears to have dual functions depending on cancer type—it enhances oral cancer cell proliferation, migration, and invasion, while exogenous expression inhibits clonal growth in lung carcinoma cells .

What is an HRP-conjugated antibody and why is it useful for MTMR3 detection?

An HRP (horseradish peroxidase)-conjugated antibody has the enzyme HRP covalently attached to the antibody molecule, enabling direct detection through enzymatic reactions that produce colorimetric, chemiluminescent, or fluorescent signals. For MTMR3 detection, this conjugation eliminates the need for secondary antibody incubation, reducing assay time and potential cross-reactivity. The MTMR3 Antibody, HRP conjugated product is specifically developed for ELISA applications, with demonstrated reactivity against human MTMR3 protein . The conjugation typically aims for 2-4 HRP molecules per antibody for optimal signal-to-noise ratio . This direct detection system is particularly valuable for quantitative analysis of MTMR3 expression in clinical samples and experimental models where speed and specificity are critical.

What are the optimal storage conditions for maintaining MTMR3 Antibody, HRP conjugated activity?

For maximum retention of enzymatic activity and antibody specificity, MTMR3 Antibody, HRP conjugated should be stored at -20°C or -80°C immediately upon receipt. The antibody is supplied in a protective buffer containing 50% glycerol and 0.01M PBS (pH 7.4) with 0.03% Proclin 300 as a preservative . Repeated freeze-thaw cycles significantly reduce both antibody binding capacity and HRP enzymatic activity; therefore, aliquoting the antibody into single-use volumes before freezing is strongly recommended. For short-term storage (1-2 weeks), 4°C is acceptable, but prolonged storage at this temperature will result in gradual activity loss. Always centrifuge the antibody briefly before opening the vial to ensure all solution is collected at the bottom. When diluting for assays, use fresh, high-quality buffers free of contaminants that might interfere with HRP activity.

What protocol modifications are necessary when transitioning from unconjugated to HRP-conjugated MTMR3 antibodies in ELISA?

When transitioning from unconjugated to HRP-conjugated MTMR3 antibodies in ELISA, several key protocol modifications are required:

• Elimination of secondary antibody step: Remove the secondary antibody incubation entirely, as the HRP is directly conjugated to the primary antibody.

• Dilution optimization: HRP-conjugated antibodies typically require different dilutions than unconjugated antibodies. Perform a titration experiment (typical starting ranges: 1:500 to 1:5000) to determine optimal signal-to-noise ratio.

• Incubation time adjustment: Reduce primary antibody incubation time by 25-30% compared to two-step detection systems, as direct detection is more efficient.

• Buffer compatibility: Ensure all buffers are compatible with HRP activity. Avoid sodium azide and thimerosal in all steps as these preservatives inhibit HRP.

• Substrate selection: Select an appropriate HRP substrate (TMB, ABTS, or luminol-based substrates) based on desired sensitivity and detection method.

• Signal development monitoring: HRP-conjugated antibodies often develop signals more rapidly; establish a time course for optimal signal development.

• Blocking optimization: Reoptimize blocking conditions to prevent non-specific binding of the conjugated antibody.

A side-by-side comparison experiment with the unconjugated system is recommended to establish the correlation between results from both detection methods.

How can I determine the optimal working dilution for MTMR3 Antibody, HRP conjugated for my specific experimental system?

To determine the optimal working dilution of MTMR3 Antibody, HRP conjugated for your specific experimental system, implement a systematic titration approach:

• Prepare a dilution series: Create a 2-fold or 3-fold serial dilution series of the antibody starting from 1:100 to 1:12,800 (e.g., 1:100, 1:300, 1:900, 1:2,700, 1:8,100).

• Test against known positive and negative controls: Use cell lines with confirmed MTMR3 expression (e.g., MDA-MB-231 cells which express high levels of MTMR3) and controls with knocked-down MTMR3 expression or cell lines known not to express MTMR3 .

• Analyze signal-to-noise ratio: Calculate the ratio between specific signal (positive control) and background signal (negative control) for each dilution.

• Create a quantitative assessment table:

• Select optimal dilution: Choose the dilution that provides the highest signal-to-noise ratio with acceptable coefficient of variation (CV) between replicates (typically <10%).

• Verify with sample titration: Once the optimal antibody dilution is determined, perform a sample titration to confirm linearity of detection across your expected range of MTMR3 concentrations.

This methodical approach ensures reliable quantitative analysis while conserving valuable antibody reagent.

How can MTMR3 Antibody, HRP conjugated be utilized to investigate autophagy regulation in cancer cells?

MTMR3 Antibody, HRP conjugated can be strategically employed to investigate autophagy regulation in cancer cells through several advanced approaches:

• Quantitative autophagy correlation studies: Implement sandwich ELISA to simultaneously measure MTMR3 protein levels and autophagy markers (LC3-II, p62) across cancer cell lines with varying autophagy states. Research has established that MTMR3 knockdown significantly upregulates the LC3-II/LC3-I ratio and downregulates p62 in MDA-MB-231 cells, indicating enhanced autophagy activation .

• Autophagy modulation response: Use the antibody to quantify changes in MTMR3 expression following treatment with autophagy inducers (rapamycin, starvation) or inhibitors (chloroquine, bafilomycin A1). Correlate these changes with functional autophagy readouts.

• Phosphoinositide metabolism analysis: Combine MTMR3 protein detection with parallel assays measuring PtdIns3P levels, as MTMR3 modulates local PtdIns3P concentrations critical for autophagosome formation.

• Dynamic expression profiling: Track MTMR3 expression changes during autophagy flux using time-course experiments. Implement pulsed ELISA approaches to detect rapid expression changes in response to autophagy triggers.

• Chemoresistance correlation: Quantify MTMR3 expression in chemosensitive versus chemoresistant cancer cells and correlate with autophagy markers to investigate whether MTMR3-mediated autophagy regulation contributes to therapy resistance.

This antibody allows precise quantification of MTMR3 protein expression changes in response to experimental manipulations, facilitating mechanistic understanding of MTMR3's role in cancer-related autophagy.

What are the methodological considerations when using MTMR3 Antibody, HRP conjugated for prognostic studies in triple-negative breast cancer?

When leveraging MTMR3 Antibody, HRP conjugated for prognostic studies in triple-negative breast cancer (TNBC), several methodological considerations are critical:

• Sample selection and standardization: Establish strict inclusion criteria for TNBC cases based on molecular classification (ER−, PR−, HER2−). Ensure tissue processing protocols are standardized across all samples to minimize technical variability.

• Quantitative scoring system development: Implement a reproducible quantitative scoring system similar to that used in previous studies where MTMR3 expression score >2 (on a standardized scale) was associated with increased recurrence risk . Document your scoring methodology thoroughly for reproducibility.

• Statistical power calculation: Determine appropriate sample size for meaningful prognostic correlation by performing power calculations based on expected effect sizes. Research indicates MTMR3 has a moderate effect on relapse prediction (OR=0.412, 95% CI=0.158–1.075, P=0.070) .

• Multivariate analysis protocol: Design a comprehensive multivariate analysis protocol that includes established prognostic factors (clinical stage, ER status) alongside MTMR3 expression. This is critical as univariate analysis showed MTMR3 expression (P=0.038), ER status (P=0.031), and clinical stage (P=0.004) were all associated with disease recurrence .

• Correlation with molecular subtypes: Implement methods to correlate MTMR3 expression with TNBC molecular subtypes (basal-like, immunomodulatory, mesenchymal, etc.) using parallel gene expression analysis. Research has shown significant positive correlation between MTMR3 expression and TNBC subtype (r=0.209, P=0.022) .

• Follow-up time standardization: Establish adequate follow-up periods (minimum 5 years) based on TNBC recurrence patterns, as shorter follow-up may miss late recurrences.

These methodological considerations ensure robust prognostic data generation and interpretation in TNBC studies.

How can MTMR3 Antibody, HRP conjugated be incorporated into multiplex detection systems to study MTMR3's relationship with other signaling proteins?

Incorporating MTMR3 Antibody, HRP conjugated into multiplex detection systems requires sophisticated methodological approaches:

• Sequential multiplex ELISA development: Develop a sequential detection protocol where the HRP-conjugated MTMR3 antibody is used first, followed by signal development and quenching before detecting additional proteins. This prevents cross-reactivity between detection systems.

• Antibody labeling differentiation: For simultaneous detection, convert a portion of the HRP-conjugated MTMR3 antibody to a different reporter system using antibody fragmentation and relabeling techniques. Ensure the MTMR3 epitope recognition (aa 652-899) remains intact during this conversion.

• Spatial multiplexing implementation: Utilize spatial multiplexing platforms where physically separated detection zones contain different capture antibodies, allowing the same HRP-conjugated MTMR3 antibody to be used across the platform without cross-reactivity concerns.

• Signal unmixing protocols: When using substrates with different spectral properties, develop robust signal unmixing algorithms to distinguish between different HRP-generated signals. This is essential when studying MTMR3's relationship with autophagy proteins like LC3 and p62 .

• Internal normalization system: Implement an internal normalization system using house-keeping proteins detected in parallel to ensure quantitative comparisons between MTMR3 and other signaling proteins are valid across different samples.

• Bead-based multiplex adaptation: Adapt the HRP-conjugated MTMR3 antibody for bead-based multiplex systems by optimizing buffer conditions to maintain HRP activity while preventing non-specific binding to beads coated with other target-specific antibodies.

These methodological approaches enable researchers to study MTMR3's relationships with autophagy regulators, cell cycle proteins, and other phosphoinositide-modulating enzymes within the same experimental system.

What strategies can address weak or inconsistent signals when using MTMR3 Antibody, HRP conjugated in ELISA?

When encountering weak or inconsistent signals with MTMR3 Antibody, HRP conjugated in ELISA, implement these systematic troubleshooting strategies:

• Antibody activity verification: Confirm HRP enzymatic activity using a direct enzyme activity assay before attempting complex experiments. The HRP-conjugated antibody should exhibit >200 units/mg protein activity for optimal performance.

• Temperature optimization: MTMR3 epitope recognition may be temperature-sensitive; conduct parallel experiments at room temperature and 37°C to determine optimal binding conditions.

• Incubation time extension: Increase primary antibody incubation time from standard 1-2 hours to overnight at 4°C to enhance binding equilibrium, particularly for samples with low MTMR3 expression.

• Signal amplification implementation: Introduce tyramide signal amplification (TSA) or other HRP signal enhancement systems to boost detection sensitivity by 10-100 fold.

• Blocking reagent optimization: Test multiple blocking agents (BSA, casein, commercial blocking buffers) to identify optimal blocking conditions that prevent non-specific binding while maintaining specific MTMR3 detection.

• Sample pretreatment protocol: Develop optimal sample extraction and pretreatment protocols specific for MTMR3 detection. Consider testing native versus denatured sample preparation methods.

• Systematic optimization matrix: Implement a grid optimization approach:

Methodically testing each combination will identify optimal conditions for consistent, specific MTMR3 detection.

How can researchers distinguish between true MTMR3 signal and potential cross-reactivity with other myotubularin family members?

Distinguishing between true MTMR3 signal and potential cross-reactivity with other myotubularin family members requires implementation of rigorous validation strategies:

• Epitope specificity analysis: The MTMR3 Antibody, HRP conjugated is raised against a recombinant human MTMR3 protein fragment (amino acids 652-899) . Perform a comparative sequence analysis of this epitope region across all myotubularin family members (MTMR1-MTMR13) to identify potential homology that might lead to cross-reactivity.

• Knockout/knockdown validation: Implement MTMR3 knockdown systems using the validated shRNA sequence (5′-CCAGTCGAGTATGCAAGTCTTGGTACCAAGACTTGCATACTCGACTGG-3′) in positive control cells. A true MTMR3-specific antibody will show significant signal reduction in knockdown models.

• Competitive binding assay development: Develop competitive binding assays using recombinant MTMR3 and closely related family members (particularly MTMR4 and MTMR1 which share highest homology). Pre-incubation with specific recombinant proteins will selectively block binding if cross-reactivity exists.

• Western blot molecular weight verification: Although the antibody is optimized for ELISA, perform parallel Western blot analysis using unconjugated MTMR3 antibody to verify the molecular weight of detected proteins (MTMR3: ~127 kDa versus other family members with distinct molecular weights).

• Methodological specificity enhancement: Increase assay stringency by:

  • Implementing high-salt wash buffers (150-300 mM NaCl)

  • Including mild detergents (0.05-0.1% Tween-20) in wash buffers

  • Optimizing antibody dilution to favor high-affinity specific binding

• Recombinant protein standard curve: Establish a standard curve using purified recombinant MTMR3 protein to confirm detection linearity and compare binding characteristics with other family members.

These methodological approaches will provide comprehensive validation of MTMR3 antibody specificity and reliable distinction from other myotubularin family members.

How should researchers design experiments to study the contradictory roles of MTMR3 across different cancer types?

To effectively investigate MTMR3's contradictory roles across different cancer types, researchers should implement a comprehensive experimental design strategy:

• Multi-cancer cell line panel characterization: Establish a diverse panel of cancer cell lines representing cancers where MTMR3 shows contradictory roles (breast, oral, lung, and colon cancers). Quantify baseline MTMR3 expression using the HRP-conjugated antibody to categorize high and low expressors within each cancer type.

• Uniform MTMR3 modulation methodology: Implement identical MTMR3 knockdown and overexpression systems across all cell lines using the validated shRNA targeting sequence (5′-CCAGTCGAGTATGCAAGTCTTGGTACCAAGACTTGCATACTCGACTGG-3′) and full-length MTMR3 cDNA, respectively.

• Standardized phenotypic assay battery: Apply a consistent battery of functional assays across all models:

  • Proliferation: MTT assay and colony formation

  • Cell cycle analysis: Flow cytometry with PI staining

  • Autophagy assessment: LC3-II/LC3-I ratio and p62 levels by western blotting

  • Migration/invasion: Transwell assays

  • Apoptosis: Annexin V/PI staining

• Context-dependent signaling analysis: Design experiments to identify tissue-specific signaling partners by:

  • Implementing identical immunoprecipitation protocols across cell types

  • Performing phosphoproteomic analysis after MTMR3 modulation

  • Quantifying PtdIns3P levels in different cellular compartments

• 3D culture systems implementation: Move beyond 2D cultures to 3D organoid models that better recapitulate tissue microenvironments, potentially explaining context-dependent functions.

• Unified in vivo validation approach: Design xenograft studies with standardized protocols across multiple cancer types, using identical MTMR3 knockdown/overexpression systems.

This methodological framework allows systematic comparison of MTMR3 functions across cancer types, potentially revealing tissue-specific molecular contexts that explain its contradictory roles.

What experimental controls are essential when establishing correlations between MTMR3 expression and autophagy in cancer cells?

When establishing correlations between MTMR3 expression and autophagy in cancer cells, implementing rigorous experimental controls is essential:

• MTMR3 expression manipulation controls:

  • Include both shRNA-mediated knockdown (using validated 5′-CCAGTCGAGTATGCAAGTCTTGGTACCAAGACTTGCATACTCGACTGG-3′ sequence) and scrambled negative control (5′-TTCTCCGAACGTGTCACGTCTCGAGACGTGACACGTTCGGAGAA-3′)

  • Include unmanipulated parental cells (Con group) as baseline control

  • Validate knockdown efficiency using both protein quantification and functional phosphatase activity assays

• Autophagy pathway manipulation controls:

  • Positive autophagy induction control: Starvation (EBSS medium) and rapamycin treatment

  • Negative autophagy control: 3-methyladenine (PI3K inhibitor) treatment

  • Autophagy flux controls: Bafilomycin A1 or chloroquine treatment at standardized time points

• Time-course controls: Implement synchronized time-course experiments measuring MTMR3 expression and autophagy markers (LC3-II/LC3-I ratio, p62 levels) at consistent intervals (0, 2, 6, 12, 24, 48 hours) after manipulation.

• Cell type controls: Include multiple cell lines with different baseline autophagy states:

  • High basal autophagy: MDA-MB-231 (TNBC cell line)

  • Low basal autophagy: MCF-7 (non-TNBC breast cancer)

  • Normal cell control: MCF-10A (non-transformed mammary epithelial)

• Technical methodology controls:

  • Use multiple autophagy detection methods (LC3 puncta formation by immunofluorescence, LC3-II/LC3-I ratio by western blot, autophagic vesicle quantification by electron microscopy)

  • Include all antibody controls (isotype control, secondary-only control)

  • Implement internal loading controls (β-actin, GAPDH) for all protein quantification

This comprehensive control framework ensures that observed correlations between MTMR3 expression and autophagy represent true biological relationships rather than experimental artifacts.

How can researchers integrate MTMR3 expression data with clinical outcomes to develop prognostic models for breast cancer?

To develop robust prognostic models integrating MTMR3 expression data with clinical outcomes in breast cancer, researchers should implement the following methodological framework:

This comprehensive methodological approach enables development of clinically relevant prognostic models that properly contextualize MTMR3's prognostic significance within the broader clinicopathological and molecular landscape of breast cancer.