MAT1A Antibody

What is MAT1A and why is it important in research?

MAT1A is a gene that encodes methionine adenosyltransferase α1 (MATα1), an enzyme responsible for the biosynthesis of S-adenosylmethionine (SAMe), which serves as the principal methyl donor in cellular metabolism. While traditionally considered primarily a cytosolic protein in hepatocytes, recent research has revealed that MATα1 is also present in the mitochondrial matrix and functions as a transcriptional co-factor by interacting with various transcription factors . Its importance in research stems from its roles in liver function, metabolism, and unexpectedly, in cancer biology where its expression varies significantly across different cancer types .

What are the typical applications of MAT1A antibodies in basic research?

MAT1A antibodies are primarily used for:

• Protein detection: Western blotting, immunohistochemistry (IHC), and immunofluorescence to detect and quantify MAT1A expression in tissues and cell lines.

• Subcellular localization studies: Immunogold electron microscopy has revealed MATα1's presence in the mitochondrial matrix, challenging previous assumptions about its exclusively cytosolic localization .

• Protein-protein interaction studies: Co-immunoprecipitation (co-IP) experiments using MAT1A antibodies have identified numerous binding partners, especially mitochondrial proteins and transcription factors .

• Methylation studies: Investigating the role of MAT1A in methylation of target proteins such as CYP2E1, which affects protein stability and function .

How can I determine the specificity of a MAT1A antibody for my experiments?

To determine the specificity of a MAT1A antibody:

• Positive and negative controls: Use tissue samples or cell lines known to express (liver tissue) or not express (many extrahepatic tissues) MAT1A as controls .

• Knockout validation: When possible, use Mat1a knockout mouse tissues/cells as negative controls. Research has shown clear differences in protein detection between wild-type and Mat1a knockout samples in immunoprecipitation experiments .

• Competing peptide assay: Pre-incubate the antibody with the immunizing peptide before applying to your sample to confirm specificity.

• Multiple antibody verification: Use antibodies from different sources or raised against different epitopes to confirm results.

• Recombinant protein control: Test the antibody against purified recombinant MAT1A protein as a positive control.

What are the key considerations for immunohistochemical detection of MAT1A?

For effective immunohistochemical detection of MAT1A:

• Tissue fixation: Optimal fixation is crucial as overfixation can mask epitopes. Typically, 10% neutral buffered formalin for 24-48 hours provides good results for most MAT1A epitopes.

• Antigen retrieval: Heat-induced epitope retrieval (HIER) using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) is often necessary to unmask MAT1A epitopes.

• Background reduction: Since MAT1A is highly expressed in liver, endogenous biotin blocking and protein blocking steps are essential to reduce background.

• Positive controls: Include liver tissue sections as positive controls, as MAT1A is highly expressed in hepatocytes .

• Negative controls: Include tissues known not to express MAT1A such as normal bladder tissue , or use secondary antibody-only controls.

• Quantification methods: Consider digital image analysis to quantify MAT1A expression levels accurately, especially when comparing normal and pathological samples.

How can I distinguish between MAT1A and MAT2A in my research?

Distinguishing between MAT1A and MAT2A is critical in cancer research due to the "M1-M2 switch" phenomenon:

• Antibody selection: Use highly specific antibodies that target non-homologous regions between MAT1A and MAT2A. Verify cross-reactivity tests with recombinant proteins for both isoforms.

• Dual immunostaining: Perform dual immunofluorescence staining with differently labeled secondary antibodies to detect both proteins simultaneously and analyze their relative expression patterns.

• Gene expression analysis: Complement protein detection with mRNA analysis using isoform-specific primers in RT-qPCR or RNA-seq to quantify the MAT1A/MAT2A expression ratio.

• Functional assays: Measure SAMe levels and methylation activity in conjunction with antibody-based detection, as MAT1A typically produces higher levels of SAMe than MAT2A .

• Tissue-specific expression patterns: In liver cancer research, the M1-M2 switch (decreased MAT1A and increased MAT2A) is significant, while in bladder cancer, the expression is tipped in favor of M1, with different downstream outcomes .

What methods can be used to study MAT1A's mitochondrial localization and function?

To investigate MAT1A's recently discovered mitochondrial localization:

• Subcellular fractionation: Perform careful mitochondrial isolation followed by subfractionation to separate outer membrane, intermembrane space, inner membrane, and matrix components, then analyze MAT1A distribution by Western blotting .

• Immunogold electron microscopy: This high-resolution technique provides definitive evidence of MAT1A's presence in the mitochondrial matrix. Use gold-conjugated secondary antibodies with MAT1A primary antibodies on ultrathin sections .

• Live-cell imaging: Utilize fluorescently tagged MAT1A constructs with mitochondrial markers for real-time visualization of localization and dynamics.

• Protease protection assays: Determine the submitochondrial localization by treating isolated mitochondria with proteases in the presence or absence of membrane-permeabilizing agents.

• Functional assays: Measure mitochondrial membrane potential, reactive oxygen species (ROS) levels, and other mitochondrial functions in models with manipulated MAT1A expression .

• Mitochondrial methylation studies: Investigate the methylation status of mitochondrial proteins in the presence or absence of MAT1A using methylation-specific antibodies or mass spectrometry .

How can I effectively study MAT1A-mediated protein methylation?

MAT1A produces SAMe, which serves as a methyl donor for various methyltransferases. To study MAT1A-mediated protein methylation:

• Methylation-specific antibodies: Use antibodies that recognize methylated residues (mono-, di-, or tri-methylated lysine or arginine) to detect changes in methylation patterns of target proteins.

• IP-MS approach: Immunoprecipitate proteins of interest (such as CYP2E1) followed by mass spectrometry to identify specific methylation sites. This approach revealed R379 methylation of CYP2E1 mediated by MAT1A .

• Methylation ratio analysis: Calculate the ratio of methylated to total protein (as demonstrated for CYP2E1) in different conditions such as normal versus disease states .

• Site-directed mutagenesis: Create methylation-deficient mutants (e.g., R379A in CYP2E1) to study the functional consequences of specific methylation events .

• SAMe level manipulation: Combine antibody detection of methylated proteins with manipulations of cellular SAMe levels through MAT1A overexpression or silencing.

• Proteomics screening: Perform global methylproteome analysis using antibody-based enrichment of methylated proteins followed by mass spectrometry to identify novel MAT1A-dependent methylation targets.

What are the key considerations for studying MAT1A in cancer research?

When investigating MAT1A in cancer contexts:

• Tissue-specific expression patterns: Consider that MAT1A expression varies dramatically across tissues and cancer types. It shows reduced expression in hepatocellular carcinoma but increased expression in bladder cancer and NSCLC .

• Prognostic significance: Analyze correlation between MAT1A expression and patient survival data. In NSCLC, MAT1A serves as a prognostic marker for poor survival .

• Drug resistance mechanisms: Examine MAT1A's role in chemoresistance, as it has been shown to enhance cell survival during gemcitabine treatment in bladder cancer .

• Metabolic reprogramming: Investigate MAT1A's impact on cancer metabolism, particularly its role in activating glycolytic pathways in NSCLC through interaction with CCND1 .

• Expression switch phenomena: In liver cancer, assess the MAT1A to MAT2A switch, while in extrahepatic cancers like bladder cancer, examine aberrant MAT1A expression in tissues where it's normally absent .

• Protein interaction networks: Map MAT1A's interactome in cancer cells to understand its non-canonical functions beyond SAMe production, such as its interaction with CCND1 in NSCLC .

How can I design experiments to study the relationship between MAT1A and metabolic disorders?

To investigate MAT1A's role in metabolic regulation:

• Animal models: Utilize Mat1a knockout mice or liver-specific knockdown models using antisense oligonucleotides (ASOs) to study metabolic phenotypes, as these models have shown prevention and reversal of obesity and insulin resistance .

• Metabolic parameter assessment: Measure parameters such as body weight, food intake, glucose tolerance, and insulin sensitivity in models with altered MAT1A expression .

• Tissue-specific analysis: Examine MAT1A expression and its downstream effects in metabolically relevant tissues including liver, adipose tissue, and muscle.

• Mitochondrial function assessment: Since MAT1A affects mitochondrial membrane potential and ROS production, measure these parameters in hepatocytes from normal and Mat1a-deficient models .

• Metabolomic profiling: Perform comprehensive metabolomic analysis to identify metabolic pathways affected by MAT1A manipulation.

• Methylation status of metabolic enzymes: Investigate whether key metabolic enzymes undergo MAT1A-dependent methylation that affects their stability or activity.

Why might I observe inconsistent MAT1A antibody staining across different tissues?

Inconsistent MAT1A staining may occur for several reasons:

• Tissue-specific expression variations: MAT1A is highly expressed in liver, pancreas, skin, ovaries, and testis, but has low or no expression in many other tissues under normal conditions .

• Fixation differences: Different tissues may require optimized fixation protocols. Overfixation can mask epitopes while underfixation may lead to poor morphology.

• Antigen retrieval optimization: Each tissue may require different antigen retrieval methods (heat vs. enzymatic) and buffers (citrate vs. EDTA) for optimal epitope exposure.

• Antibody concentration titration: Optimal antibody concentration may vary between tissues due to differences in target abundance and background interference.

• Disease state influence: Pathological conditions can dramatically alter MAT1A expression, as seen in alcoholic liver disease (reduced expression) or certain cancers (aberrant expression) .

• Subcellular localization variations: MAT1A localizes to both cytosolic and mitochondrial compartments, and this distribution may vary by tissue or cellular state .

How can I optimize co-immunoprecipitation experiments with MAT1A antibodies?

For successful MAT1A co-immunoprecipitation:

• Lysis buffer selection: Use buffers that maintain protein-protein interactions while effectively lysing cells. For mitochondrial proteins, consider specialized buffers that preserve mitochondrial associations.

• Antibody binding conditions: Optimize temperature (4°C is typically recommended) and duration (overnight incubation often yields better results) for antibody binding.

• Pre-clearing samples: Pre-clear lysates with protein A/G beads to reduce non-specific binding.

• Crosslinking consideration: For transient interactions, consider using chemical crosslinkers before lysis to stabilize protein complexes.

• Negative controls: Always include IgG control and, when possible, samples from MAT1A knockout or knockdown models .

• Washing stringency: Balance between removing non-specific binding (higher stringency) and preserving true interactions (lower stringency).

• Validation strategies: Confirm interactions through reciprocal co-IP (using antibodies against the interacting partner) and validate with recombinant proteins when possible .

What approaches can address contradictory results between MAT1A antibody-based detection and functional studies?

When facing contradictions between antibody detection and functional outcomes:

• Antibody validation: Verify antibody specificity using multiple approaches including western blots with positive and negative controls, especially Mat1a knockout tissues .

• Isoform consideration: Ensure your antibody can distinguish between MAT1A and the highly homologous MAT2A, as their functions differ significantly .

• Post-translational modifications: Consider that modifications may affect antibody recognition without altering protein levels. Check for phosphorylation, methylation, or other modifications.

• Protein stability vs. activity: MAT1A protein may be present but functionally impaired. Measure enzymatic activity (SAMe production) alongside protein detection.

• Subcellular localization: Confirm the subcellular distribution of MAT1A as it affects function. Use fractionation followed by western blotting or immunofluorescence .

• Alternative measurement approaches: Complement antibody detection with mRNA quantification, activity assays, or functional readouts like mitochondrial membrane potential .

• Context-dependent effects: Consider that MAT1A's function may vary dramatically between tissue types and disease states .

How can MAT1A antibodies be utilized in studying mitochondrial dysfunction in liver disease?

MAT1A antibodies can be powerful tools for investigating mitochondrial dysfunction in liver conditions:

• Dual immunostaining: Combine MAT1A antibodies with markers of mitochondrial stress or function (e.g., TOM20, COX IV) to visualize correlations between MAT1A localization and mitochondrial health.

• Alcoholic liver disease studies: Examine the relationship between reduced MAT1A levels, altered CYP2E1 methylation ratio, and mitochondrial dysfunction in alcoholic liver disease samples .

• Mitochondrial subcompartment analysis: Use immunogold electron microscopy with MAT1A antibodies to track changes in submitochondrial localization during disease progression .

• Protein-protein interaction mapping: Perform co-IP with MAT1A antibodies followed by mass spectrometry to identify disease-specific changes in the mitochondrial interactome of MAT1A .

• Functional correlation studies: Combine antibody-based detection of mitochondrial MAT1A with assays for mitochondrial membrane potential, ROS production, and oxidative phosphorylation to establish functional relationships .

• Therapeutic intervention assessment: Use MAT1A antibodies to track mitochondrial localization changes in response to treatments aimed at improving mitochondrial function in liver disease.

What methodologies can be employed to study MAT1A's role in cancer drug resistance?

To investigate MAT1A's emerging role in cancer drug resistance:

• Expression profiling: Compare MAT1A expression in treatment-naïve versus drug-resistant tumors using carefully validated antibodies for IHC or western blotting .

• Temporal dynamics analysis: Track MAT1A expression changes during drug treatment using time course experiments to identify transient upregulation that may be missed in endpoint analyses .

• Patient-derived xenograft models: Use PDX models to study MAT1A expression changes in response to chemotherapy, as demonstrated in bladder cancer studies with gemcitabine .

• Cell cycle analysis: Correlate MAT1A expression with cell cycle status to investigate its role in establishing a less proliferative, drug-resistant state .

• RNA sequencing integration: Combine antibody-based protein detection with RNA-seq to identify transcriptional networks associated with MAT1A-mediated drug resistance .

• Methylation status assessment: Examine how MAT1A-dependent methylation of key proteins affects drug resistance pathways.

• Biomarker validation studies: Evaluate MAT1A as a predictive biomarker for drug response, particularly in cancers where it is not normally expressed, such as bladder cancer .

How can I design experiments to investigate the MAT1A-CCND1-glycolysis axis in cancer?

Based on the recently identified MAT1A-CCND1-glycolysis connection in NSCLC , consider:

• Interaction verification: Use co-IP with MAT1A antibodies to confirm the interaction with CCND1 in your specific cancer model, followed by western blotting or mass spectrometry .

• Ubiquitination assays: Investigate how MAT1A affects CCND1 protein stability by examining ubiquitination levels after immunoprecipitation in the presence or absence of MAT1A .

• Glycolytic function measurement: Assess glycolytic parameters (glucose consumption, lactate production, extracellular acidification rate) in response to MAT1A manipulation .

• Rescue experiments: After MAT1A knockdown, perform rescue experiments with exogenous CCND1 to determine whether CCND1 is the primary mediator of MAT1A's effects on glycolysis .

• In vivo validation: Use xenograft models with MAT1A knockdown to confirm the relationship between MAT1A expression, CCND1 stability, glycolytic activity, and tumor growth .

• SKP2-dependent degradation: Investigate the role of SKP2 in mediating CCND1 degradation upon MAT1A depletion using SKP2 inhibitors or knockdown approaches .

• Metabolic flux analysis: Perform 13C-glucose tracing experiments to quantitatively assess how MAT1A alterations affect carbon flux through glycolysis and connected pathways.

What are the methodological considerations for studying MAT1A in the context of obesity and metabolic disorders?

When investigating MAT1A's role in metabolic regulation:

• Targeted knockdown approaches: Use liver-specific antisense oligonucleotides (ASOs) rather than systemic knockout to specifically assess hepatic MAT1A's effects on whole-body metabolism .

• Diet protocols: Apply standardized diet interventions (60% fat calories high-fat diet for 10 weeks) to establish obesity models before MAT1A manipulation .

• Comprehensive metabolic phenotyping: Measure body weight, food intake, glucose tolerance, and insulin sensitivity at regular intervals throughout the study .

• Genetic model selection: Consider both diet-induced obesity models and genetic obesity models (ob/ob mice) to determine whether MAT1A's effects are consistent across different obesity etiologies .

• Downstream mediator analysis: Investigate the role of FGF21 and other hepatokines that might mediate the systemic effects of hepatic MAT1A manipulation .

• Mitochondrial function assessment: Since MAT1A affects mitochondrial ROS and membrane potential, include these measurements in metabolic tissues to connect molecular and physiological observations .

• Translation to human samples: Compare MAT1A expression and localization patterns between rodent models and human samples from patients with obesity or metabolic syndrome.

How should I interpret discrepancies in MAT1A expression between different detection methods?

When facing inconsistencies between different detection approaches:

What statistical approaches are recommended for analyzing MAT1A expression data in patient samples?

For rigorous analysis of MAT1A expression in clinical samples:

• Sample size determination: Perform power analysis to determine adequate sample sizes for detecting clinically meaningful differences in MAT1A expression.

• Normalization strategies:

  • For IHC: Use digital pathology with standardized scoring systems (H-score, Allred score)

  • For Western blotting: Normalize to multiple housekeeping proteins

  • For qPCR: Select stable reference genes validated for your specific tissue type

• Expression ratio analyses: Consider calculating ratios such as:

  • MAT1A/MAT2A expression ratio in liver cancer studies

  • Methylated CYP2E1/total CYP2E1 ratio in alcoholic liver disease

• Correlation with clinical parameters: Use appropriate tests to correlate MAT1A expression with:

  • Survival outcomes: Kaplan-Meier and Cox regression analyses

  • Disease stage: Chi-square or Fisher's exact test

  • Continuous variables: Spearman or Pearson correlation

• Multivariate analyses: Include MAT1A expression in multivariate models to determine its independent prognostic value while controlling for established factors.

• Subgroup analyses: Perform stratified analyses based on relevant clinical or molecular features to identify patient populations where MAT1A expression has particular significance.

• Meta-analysis approaches: When multiple datasets are available, consider meta-analysis techniques to increase statistical power and generalizability.