eef1akmt2 Antibody

What is EEF1AKMT2 and what cellular function does it serve?

EEF1AKMT2 (also known as METTL10) is a protein-lysine methyltransferase that selectively catalyzes the trimethylation of eukaryotic elongation factor 1A (eEF1A) at Lysine-318 (K318) . This enzyme represents one of several methyltransferases that modify eEF1A, which is one of the most methylated eukaryotic proteins. eEF1A itself is an evolutionarily conserved and fundamental component of the translational machinery, delivering aminoacyl-tRNAs to the ribosome during the elongation step of mRNA translation . The methylation of eEF1A at various sites, including K318 by EEF1AKMT2, appears to regulate the rate and fidelity of mRNA translation elongation, thus affecting protein synthesis and gene expression .

What applications can EEF1AKMT2 antibodies be used for in research settings?

According to the literature, EEF1AKMT2 antibodies have been validated for several research applications:

• Western blot (WB) analysis: For detection of EEF1AKMT2 protein expression and studying methylation events

• Immunohistochemistry (IHC): For visualization of EEF1AKMT2 in tissue samples, including human prostate cancer and esophagus cancer tissues

• Enzyme-linked immunosorbent assay (ELISA): For quantitative detection of EEF1AKMT2

• Characterization of eEF1A methylation dynamics in cellular contexts

These applications enable researchers to investigate EEF1AKMT2 expression patterns and the methylation status of eEF1A across different experimental conditions and tissue types.

How can researchers validate the specificity of eEF1A methylation antibodies?

The validation of methylation-specific antibodies requires multiple complementary approaches:

• Peptide array analysis: Testing antibodies against dilution series of various methylated and unmethylated peptides to confirm specificity for the cognate methyl-epitope without cross-reactivity to other methylation sites or states .

• Knockdown experiments: Depleting the cognate methyltransferase (EEF1AKMT2) using CRISPR/Cas9 or RNAi systems and demonstrating corresponding reduction in the antibody signal by Western blot .

• Peptide competition assays: Including excess peptide corresponding to the epitope as a blocking peptide in IHC applications to confirm signal specificity .

• Cross-reactivity testing: Screening against non-eEF1A methylated histone and non-histone peptides to ensure antibodies do not detect other methylation events .

• Mass spectrometry correlation: Comparing antibody results with quantitative mass spectrometry data to validate methylation stoichiometry detection .

What are the optimal conditions for using EEF1AKMT2 antibodies in immunohistochemistry?

Based on the current literature, the following protocols have shown effective results when using EEF1AKMT2 antibodies for immunohistochemistry:

For optimal results, researchers should perform antibody titration experiments to determine the ideal concentration for their specific tissue samples and experimental conditions.

How should researchers interpret changes in eEF1A methylation levels across different tissues?

Interpretation of eEF1A methylation patterns requires careful consideration of multiple factors:

• Tissue-specific expression patterns: Research has shown that methylation stoichiometry may be modulated in a tissue-specific manner. For example, mouse skeletal muscle shows lower signals with eEF1A methylation antibodies compared to colon and pancreatic tissue .

• eEF1A isoform distribution: The predominant expression of eEF1A2 versus eEF1A1 in certain tissues (e.g., muscle) may affect methylation patterns and antibody detection . Some antibodies may have preference for eEF1A1 methylation compared to eEF1A2 .

• Total eEF1A levels: Lower intensity signals in certain tissues may result from lower total eEF1A levels rather than reduced methylation stoichiometry .

• Methylation crosstalk: Data suggest dynamic crosstalk between different eEF1A methylation sites. For example, knockdown of METTL10 (which depletes K318me3) can be accompanied by reduction in K79me3 levels, while depletion of N6AMT2 with loss of K79me3 may be associated with reduced levels of K36me3 and K165me2 .

• Physiological state: Methylation levels may decline in aged tissues, suggesting potential roles in aging biology .

How can EEF1AKMT2 antibodies be incorporated into studies of protein methylation dynamics?

Researchers can design several experimental approaches using EEF1AKMT2 antibodies to study methylation dynamics:

• Temporal analysis following stimulus: Monitor changes in eEF1A methylation levels in response to serum starvation and stimulation using Western blot with methyl-specific antibodies .

• Aging studies: Compare methylation levels between young and aged tissues to investigate the relationship between methylation and aging biology. Research has shown that several eEF1A methylation events decrease in aged muscle tissue .

• Enzyme knockdown/knockout experiments: Generate CRISPR/Cas9 knockdown of individual eEF1A methyltransferases to study the impact on cognate methylation events and potential crosstalk between different methylation sites .

• Cell type comparison: Analyze methylation stoichiometry across different cell types (cancer vs. normal, different tissues) to identify cell-specific methylation patterns .

• Inhibitor treatment: Test the effects of methyltransferase inhibitors on eEF1A methylation levels and corresponding cellular phenotypes.

What approaches can reveal the functional significance of eEF1A methylation by EEF1AKMT2?

To investigate the functional consequences of eEF1A methylation by EEF1AKMT2, researchers can employ these methodologies:

• Ribosome profiling: This technique can reveal how loss of methylation affects translation dynamics and alters translation rates of specific codons .

• Mass spectrometry (MS): Quantitative MS approaches can determine methylation stoichiometry and identify changes across conditions .

• Genetic perturbation: CRISPR/Cas9 knockout of EEF1AKMT2 followed by phenotypic assays can reveal cellular functions dependent on this methylation .

• Protein synthesis measurements: Pulse-labeling experiments with radioactive or non-radioactive amino acids can assess how methylation affects global protein synthesis rates .

• Structural biology approaches: Techniques like protein cross-linking combined with computational methods (e.g., AlphaFold) can reveal how methylation affects eEF1A structure and interactions with other translation factors .

What is the relationship between SETD2 and EEF1AKMT2 in kidney cancer pathogenesis?

Recent research has uncovered an intriguing relationship between SETD2 and EEF1AKMT2 in kidney cancer:

• Expression dependency: The expression of EEF1AKMT2 and EEF1AKMT3 is dependent on the SET-domain function of SETD2. Loss of SETD2 expression is associated with decreased expression of EEF1AKMT2 and EEF1AKMT3 .

• Clinical correlation: SETD2-mutated tumors with predicted loss of SET domain function show inferior progression-free survival and decreased expression of EEF1AKMT2 (p=0.03) .

• Translational dysregulation: Both SETD2-knockout cell lines and SETD2-mutated tumors show increased expression of proteins and genes associated with protein translation, suggesting that dysregulation of protein translation may be a component of the transformed phenotype .

• Mechanistic model: SETD2 may regulate protein translation through indirect modulation of eEF1A methylation. Loss of EEF1AKMT2 expression due to SETD2 mutation could lead to decreased eEF1A methylation at K318, affecting translation regulation .

• Prognostic significance: Low expression of EEF1AKMT2 is associated with poor survival in renal cell carcinoma patients, suggesting its potential utility as a prognostic marker .

How does eEF1A methylation affect codon-specific translation rates?

Research using ribosome profiling and other techniques has revealed that eEF1A methylation has specific effects on translation:

• Codon-specific modulation: Loss of METTL13, which methylates eEF1A at K55, alters translation dynamics and results in changed translation rates of specific codons rather than affecting global translation uniformly .

• Translatome reprogramming: Loss of methylation at K165 (catalyzed by EEF1AKMT3) results in global reprogramming of peptide synthesis, with upregulation in translation of some mRNA molecules and down-regulation of others .

• Translation machinery regulation: Pathway analysis of EEF1AKMT3-null cells reveals increased translation of mRNA molecules associated with protein translation components, similar to what is observed in SETD2 SET domain-deficient cells .

• Cancer relevance: Kras-driven cancers are reliant on the methylation of eEF1A1 by METTL13 at K55. Loss of this methyltransferase leads to decreased proliferation and viability of these malignancies due to decreased peptide synthesis .

How does eEF1A methylation change during aging, and what are the potential functional consequences?

Emerging evidence suggests important relationships between eEF1A methylation and aging:

• Declining methylation levels: Application of methyl-specific antibodies indicates that several eEF1A methylation events decrease in aged muscle tissue compared to young tissue .

• Tissue-specific patterns: The decline in methylation appears to be tissue-specific, with muscle tissue showing notable changes .

• Potential mechanistic connections: The decrease in eEF1A methylation could affect protein synthesis regulation, which is known to be altered during aging .

• Biological implications: As eEF1A methylation regulates translation efficiency and fidelity, reduced methylation might contribute to age-related decreases in protein quality control and altered gene expression programs .

• Research opportunities: The relationship between eEF1A methylation and aging represents an emerging area for investigation, potentially connecting translation regulation with broader aspects of aging biology .

How can researchers address discrepancies between antibody-based and mass spectrometry-based methylation detection?

When facing contradictory results between antibody-based methods and mass spectrometry (MS), consider these approaches:

• Antibody specificity verification: Re-validate antibody specificity using peptide arrays and blocking peptides. Some antibodies may recognize eEF1A1 better than eEF1A2, potentially explaining tissue-specific discrepancies .

• Sample preparation differences: MS typically requires extensive sample preparation that may affect methylation status. Ensure comparable extraction methods are used for both techniques .

• Methylation stoichiometry: MS can provide quantitative information about the percentage of proteins with a specific modification, while antibodies may have detection thresholds that miss low-abundance modifications .

• Enrichment strategies: For low-abundance methylation events, consider using cation exchange chromatography to enrich eEF1A before analysis, as described in published protocols .

• Complementary approaches: Use immunoprecipitation with tagged eEF1A proteins followed by MS analysis to combine the strengths of both methods .

What factors might affect the reproducibility of EEF1AKMT2 antibody experiments?

Several factors can influence experimental reproducibility when working with EEF1AKMT2 antibodies: