MTERF3 Antibody

What is MTERF3 and what are its primary functions in mitochondria?

MTERF3 (also known as MTERFD1) is a mitochondrial protein that serves as a negative regulator of mitochondrial DNA transcription. Its functions include:

• Binding to promoter DNA to regulate transcription initiation

• Maintaining normal mitochondrial transcription and translation processes

• Supporting proper assembly of mitochondrial respiratory complexes

• Maintaining 16S rRNA levels through regulation of 39S ribosomal subunit biogenesis

MTERF3 is essential for normal mitochondrial function, with its dysregulation potentially contributing to cellular pathologies. Research indicates that MTERF3 plays a modular role in mitochondrial ribosome biogenesis and protein synthesis, highlighting its importance in cellular energy metabolism.

What is the subcellular localization pattern of MTERF3 and how does this inform antibody-based detection?

MTERF3 demonstrates predominantly cytoplasmic localization, consistent with its mitochondrial function. Immunohistochemical studies reveal that MTERF3 appears as fine brown-yellow granules in the cytoplasm of breast cancer cells . This localization pattern is critical for researchers using MTERF3 antibodies, as they should expect:

• Cytoplasmic rather than nuclear staining in immunohistochemistry

• Punctate or granular staining pattern consistent with mitochondrial distribution

• Potential co-localization with mitochondrial markers when performing double-staining experiments

When validating MTERF3 antibodies, researchers should confirm this cytoplasmic staining pattern, as aberrant nuclear or membrane staining may indicate non-specific binding or cross-reactivity with other proteins.

What are the validated applications for MTERF3 antibodies in research settings?

MTERF3 antibodies have been successfully employed in several experimental applications:

• Western Blotting (WB): Effective for detecting MTERF3 protein in cell lysates from diverse cell lines including U-251 MG (human brain glioma), HEK-293T, and HeLa cells . Typical dilution ratios of 1/1000 have shown good results.

• Immunohistochemistry - Paraffin (IHC-P): Successfully used for detecting MTERF3 in formalin-fixed, paraffin-embedded tissues, particularly in breast cancer and hepatocellular carcinoma samples .

• Comparative Expression Analysis: MTERF3 antibodies have been used to compare expression levels across different cell types, including MCF7 (Luminal A), BT-474 (Luminal B), SKBR3 (HER2 overexpression), and MDA-MB-468 (Basal-like) breast cancer cells .

These validated applications provide a foundation for research into MTERF3's role in normal physiology and disease states, with the strongest evidence supporting its use in cancer research contexts.

How should researchers optimize MTERF3 antibody protocols for immunohistochemical detection?

Optimizing MTERF3 antibody protocols for immunohistochemistry requires attention to several methodological considerations:

• Fixation and Processing:

  • Standard formalin fixation with paraffin embedding (FFPE) has proven effective

  • Consistent fixation times are critical (typically 24-48 hours) to prevent overfixation, which can mask epitopes

  • Fresh-frozen tissues may provide alternative antigen preservation when FFPE results are suboptimal

• Antigen Retrieval:

  • Heat-induced epitope retrieval is typically necessary for FFPE tissues

  • Citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) should be empirically tested to determine optimal conditions

  • Pressure cooking or microwave-based retrieval methods both show efficacy

• Antibody Dilution and Incubation:

  • Titrate antibody concentration starting from manufacturer recommendations (e.g., 1/1000 dilution as used for Western blot applications)

  • Overnight incubation at 4°C often yields better signal-to-noise ratio than shorter incubations

  • Include both positive controls (breast cancer or HCC tissues) and negative controls (normal tissues or antibody omission controls)

• Detection Systems:

  • Polymer-based detection systems generally provide cleaner backgrounds than avidin-biotin systems

  • DAB (3,3'-diaminobenzidine) chromogen provides stable signal for long-term storage

  • Counterstaining with hematoxylin enhances visualization of tissue architecture

Following these methodological considerations will help researchers achieve consistent and specific MTERF3 staining patterns in tissue sections.

How is MTERF3 expression altered in human cancers, and what are the implications for research?

Research demonstrates significant alterations in MTERF3 expression across multiple cancer types, with important implications for research:

Breast Cancer Findings:

• MTERF3 is significantly upregulated in breast cancer tissues compared to non-cancerous breast tissues

• Positivity rate of 91.38% (53/58) in breast cancer specimens versus 32.76% (19/58) in non-cancerous tissues

• Higher expression across all breast cancer cell lines (MCF7, BT-474, SKBR3, MDA-MB-468) compared to non-cancerous MCF10A cells

• Expression varies by breast cancer subtype, with highest levels in Basal-like (MDA-MB-468) cells

Research Implications:

• MTERF3 antibodies serve as valuable tools for studying cancer-specific alterations in expression

• Differential expression across cancer subtypes suggests potential utility as a biomarker

• The consistent upregulation in multiple cancer types points to a fundamental role in cancer biology

These findings establish MTERF3 as an important research target for understanding mitochondrial dysfunction in cancer development and progression.

What are the correlations between MTERF3 expression and clinical parameters in breast cancer?

Analysis of the TCGA breast cancer dataset reveals several significant correlations between MTERF3 expression and clinical parameters, as summarized in the table below:

These correlations provide important insights for researchers designing studies with MTERF3 antibodies:

• Stratification by molecular subtype is essential when examining MTERF3 expression patterns

• ER and PR status should be considered as potential confounding variables

• Different histological types may require separate analysis approaches

• The lack of correlation with stage suggests MTERF3 alterations may be early events in carcinogenesis

Understanding these associations helps researchers contextualize MTERF3 antibody staining results within the broader clinical and molecular landscape of breast cancer.

How can researchers effectively use MTERF3 knockdown approaches to study its function?

Based on successful MTERF3 knockdown studies in cancer research, the following methodological approaches are recommended:

siRNA-Mediated Knockdown:

• Use sequence-specific siRNAs targeting MTERF3 (siMTERF3)

• Transfection using standard lipid-based reagents has proven effective in HCC cell lines

• Optimal analysis timepoint is typically 48-72 hours post-transfection

• Include non-targeting siRNA controls (siNC) to control for non-specific effects

• Validate knockdown efficiency via Western blot and qRT-PCR

Stable Knockdown Using Lentiviral shRNA:

• Establish stable MTERF3 knockdown cell lines using lentivirus expressing shMTERF3

• This approach is particularly valuable for:

  • Long-term experiments

  • In vivo xenograft studies

  • Studies requiring consistent knockdown across multiple experiments

• Regularly validate continued knockdown after multiple passages

Validation and Controls:

• Always confirm knockdown at both mRNA and protein levels

• Include cell viability assays to account for potential toxicity

• Consider using multiple knockdown sequences to control for off-target effects

• For cancer studies, confirm effects in multiple cell lines representing different subtypes

Functional Readouts Following Knockdown:

• Cell proliferation (cell counting, MTT/CCK-8 assays)

• Colony formation capacity

• Cell cycle distribution by flow cytometry

• Mitochondrial function parameters (membrane potential, ROS production)

• Activation of stress response pathways (p38 MAPK)

This comprehensive methodology enables researchers to establish causal relationships between MTERF3 expression and cellular phenotypes.

How should experimental designs address the relationship between MTERF3 and mitochondrial dysfunction?

To effectively investigate the relationship between MTERF3 and mitochondrial dysfunction, researchers should implement a systematic experimental design:

MTERF3 Expression Modulation:

• Establish paired experimental conditions: MTERF3 knockdown, overexpression, and controls

• Use multiple cell models to ensure findings are not cell-type specific

• Validate expression changes at both protein and mRNA levels using MTERF3 antibodies and qRT-PCR

Comprehensive Mitochondrial Function Assessment:

• Respiratory Function: Measure oxygen consumption rate (OCR) using Seahorse XF Analyzer

• Membrane Potential: Assess using JC-1 or TMRM dyes with flow cytometry or live-cell imaging

• ROS Production: Quantify using MitoSOX Red with correlation to MTERF3 expression levels

• ATP Production: Measure using luminescence-based ATP assays

• mtDNA Copy Number: Determine using qPCR comparing mitochondrial to nuclear DNA ratios

Mitochondrial Transcription Analysis:

• Measure expression of mitochondrially-encoded genes (e.g., MT-CO1, MT-ND1) by qRT-PCR

• Assess assembly of mitochondrial respiratory complexes by Blue Native PAGE

• Analyze mitochondrial ribosome assembly focusing on the 39S subunit

Mechanistic Pathway Investigation:

• Study ROS-dependent signaling pathways (particularly p38 MAPK)

• Perform time-course experiments to establish sequence of events

• Use pathway inhibitors (e.g., p38 MAPK inhibitors) to establish causality

Rescue Experiments:

• Attempt phenotype rescue with wild-type MTERF3 re-expression

• Use antioxidants to determine if effects are ROS-dependent

• Target downstream pathways to identify key mediators

This experimental framework allows researchers to establish the precise mechanisms by which MTERF3 dysregulation leads to mitochondrial dysfunction and subsequent cellular phenotypes.

How can researchers investigate the relationship between MTERF3 and cell cycle regulation?

Research in HCC has revealed that MTERF3 knockdown induces S-G2/M cell cycle arrest . To thoroughly investigate this relationship, researchers should implement the following advanced methodological approach:

Comprehensive Cell Cycle Analysis:

• Flow Cytometry with PI Staining: Quantify cell distribution across G0/G1, S, and G2/M phases

• BrdU/EdU Incorporation: Measure active DNA synthesis in S-phase

• Cell Cycle Markers by Western Blot:

  • Cyclins (A, B, D, E)

  • Cyclin-dependent kinases (CDK1, 2, 4, 6)

  • Inhibitors (p21, p27)

  • Phospho-Rb status

Cell Synchronization Approaches:

• Use serum starvation to synchronize cells in G0/G1

• Apply double thymidine block for S-phase synchronization

• Employ nocodazole treatment for G2/M arrest

• Following synchronization, modulate MTERF3 expression and track cell cycle progression

Temporal Analysis:

• Perform time-course experiments following MTERF3 modulation

• Determine whether cell cycle effects are immediate or delayed

• Correlate cell cycle changes with mitochondrial dysfunction parameters

Mechanistic Investigations:

• Checkpoint Activation: Assess DNA damage response markers (γH2AX, p-ATM, p-CHK1/2)

• ROS Dependency: Use antioxidants to determine if cell cycle arrest is ROS-mediated

• p38 MAPK Pathway: Apply p38 inhibitors to establish pathway dependency

• Mitochondrial Function: Correlate mitochondrial parameters with cell cycle effects

Cell Type-Specific Considerations:

• Compare effects across cancer subtypes (e.g., different breast cancer molecular subtypes)

• Determine whether effects differ between cancer and non-cancer cells

• For breast cancer research, consider the influence of hormone receptor status

This methodological framework enables researchers to establish the precise mechanisms linking MTERF3, mitochondrial function, and cell cycle regulation, providing insights into potential therapeutic interventions.

How can researchers address inconsistent staining patterns with MTERF3 antibodies?

When encountering variable or inconsistent staining with MTERF3 antibodies, researchers should implement this systematic troubleshooting approach:

Antibody Validation and Selection:

• Verify antibody specificity using positive controls (breast cancer or HCC tissues)

• Include MTERF3 knockdown samples as negative controls when possible

• Consider testing multiple antibodies targeting different MTERF3 epitopes

• For IHC applications, determine if the antibody targets an epitope susceptible to fixation-induced masking

Sample Processing Optimization:

• Standardize fixation protocols (duration, fixative composition)

• Compare multiple antigen retrieval methods:

  • Heat-induced epitope retrieval using citrate buffer (pH 6.0)

  • Heat-induced epitope retrieval using EDTA buffer (pH 9.0)

  • Enzymatic retrieval using proteinase K

• Ensure consistent section thickness (4-5μm optimal for most IHC applications)

Protocol Refinement:

• Titrate primary antibody concentration across a wide range

• Test different incubation conditions (overnight at 4°C vs. 1-2 hours at room temperature)

• Optimize blocking conditions to reduce background (BSA, normal serum, commercial blockers)

• Compare different detection systems (ABC vs. polymer-based)

Biological Considerations:

• Be aware that MTERF3 expression varies significantly across breast cancer subtypes

• Account for potential differences between cancer and normal tissues

• Consider that mitochondrial content varies between tissues and cell types

Quantification and Interpretation:

• Develop standardized scoring criteria

• Use digital image analysis when possible for objective quantification

• Employ multiple independent observers for manual scoring

• Correlate IHC findings with other methods (Western blot, qRT-PCR)

Careful attention to these methodological details will help researchers achieve consistent, specific, and interpretable results with MTERF3 antibodies.

How should researchers interpret differences between MTERF3 mRNA and protein expression data?

Discrepancies between MTERF3 mRNA and protein expression are not uncommon and require careful methodological consideration:

Technical Validation Approaches:

• Confirm primer specificity and efficiency for mRNA detection

• Validate antibody specificity using knockdown controls

• Ensure protein loading is properly normalized using housekeeping proteins

• Use multiple methods to measure both mRNA (qRT-PCR, RNA-seq) and protein (Western blot, IHC)

Biological Explanations to Consider:

• Post-transcriptional Regulation: miRNAs may regulate MTERF3 mRNA stability or translation

• Protein Stability: Differences in protein turnover may explain discrepancies

• Post-translational Modifications: These might affect antibody recognition

• Temporal Dynamics: mRNA and protein expression may have different temporal patterns

Analytical Considerations:

• In breast cancer studies, both MTERF3 mRNA and protein levels were found to be upregulated , suggesting correlation in this context

• Expression correlations may be tissue or disease-specific

• Quantitative comparisons require appropriate normalization strategies

Experimental Approaches to Resolve Discrepancies:

• Perform time-course analyses to detect temporal relationships

• Use protein synthesis inhibitors (cycloheximide) to assess protein stability

• Examine polysome profiles to assess translational efficiency

• Consider subcellular localization that might affect protein detection

Reporting and Interpretation Guidelines:

• Report discrepancies transparently rather than selectively reporting concordant data

• Consider discrepancies as potential biological insights rather than technical failures

• Correlate both mRNA and protein measurements with functional outcomes to determine which better predicts biological effects

Understanding the relationship between MTERF3 mRNA and protein expression can provide valuable insights into its regulation in normal physiology and disease states.

What are the most promising research directions for MTERF3 antibody applications?

Based on current research findings, several high-priority research directions emerge for MTERF3 antibody applications:

Cancer Biomarker Development:

• Further investigation of MTERF3 as a prognostic marker in HCC, where high expression correlates with poor survival

• Expansion of studies to additional cancer types beyond breast cancer and HCC

• Development of standardized IHC scoring systems for clinical application

• Correlation with treatment response and resistance mechanisms

Mechanistic Studies:

• Detailed investigation of how MTERF3 regulates mitochondrial transcription in cancer cells

• Exploration of the ROS-p38 MAPK axis identified in HCC

• Investigation of MTERF3's role in mitochondrial ribosome assembly across cell types

• Understanding how MTERF3 dysregulation impacts cellular metabolism

Therapeutic Target Validation:

• Use of MTERF3 antibodies to monitor expression changes in response to potential therapeutic interventions

• Correlation of MTERF3 levels with sensitivity to mitochondrial-targeting compounds

• Development of companion diagnostics for stratifying patients for mitochondrial-targeted therapies

Technical Advances:

• Development of phospho-specific MTERF3 antibodies to study post-translational regulation

• Creation of antibodies specific to potential MTERF3 isoforms

• Implementation of multiplexed IHC to study MTERF3 in the context of other mitochondrial proteins

• Adaptation of MTERF3 antibodies for live-cell imaging applications

These research directions will expand our understanding of MTERF3's role in normal physiology and disease, potentially opening new therapeutic avenues for mitochondrial dysfunction in cancer.

How can researchers integrate MTERF3 studies into broader mitochondrial research?

Integrating MTERF3 research into the broader context of mitochondrial biology requires multidisciplinary approaches:

Systems Biology Integration:

• Correlate MTERF3 expression with other mitochondrial transcription factors (TFAM, TFB1M, TFB2M)

• Study interactions between MTERF3 and the entire mitochondrial transcription machinery

• Examine relationships between MTERF3 levels and mitochondrial network dynamics

• Analyze how MTERF3 fits into broader metabolic regulatory networks

Disease-Specific Context:

• In cancer research, correlate MTERF3 with known oncogenic pathways that affect mitochondria

• For breast cancer, consider molecular subtype-specific effects given the differential expression across subtypes

• In HCC, further explore the ROS-p38 MAPK axis identified as a downstream mechanism

• Investigate potential roles in neurodegenerative disorders where mitochondrial dysfunction is implicated

Technical Integration Strategies:

• Combine MTERF3 antibody-based approaches with metabolomic analyses

• Implement multi-omics approaches (proteomics, transcriptomics, metabolomics)

• Use MTERF3 antibodies in combination with mitochondrial functional assays

• Develop multiplexed imaging approaches to study MTERF3 alongside other mitochondrial proteins

Therapeutic Context:

• Evaluate MTERF3 expression changes in response to mitochondrially-targeted therapies

• Assess whether MTERF3 levels predict sensitivity to metabolic inhibitors

• Consider MTERF3 modulation as a potential approach to sensitize cells to existing therapies

By positioning MTERF3 within the broader landscape of mitochondrial biology and pathology, researchers can gain deeper insights into its functional significance and potential as a therapeutic target.